diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index c3b180d..33e1e11 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -26,7 +26,7 @@ RUN \ USER vscode ENV PATH=/home/vscode/.local/bin:$PATH WORKDIR /home/vscode -COPY --chown=vscode:nogroup src/orcabridge/requirements.txt /tmp/requirements.txt +COPY --chown=vscode:nogroup src/orcapod/requirements.txt /tmp/requirements.txt RUN \ # python setup curl -LsSf https://astral.sh/uv/install.sh | sh && \ diff --git a/.gitignore b/.gitignore index a5279a0..76b840b 100644 --- a/.gitignore +++ b/.gitignore @@ -5,9 +5,22 @@ notebooks/**/*.parquet notebooks/**/*.pkl notebooks/**/*.db +# Ignore npy and npz data files by default +*.np[yz] -# Ignore any notebook that starts with an underscore +# Ignore common data types by default +*.csv +*.parquet +*.xls +*.xlsx +*.txt + +# Ignore profiler output +*.prof + +# Ignore any notebook that starts with an underscore or test notebooks/**/_*.ipynb +notebooks/**/test*.ipynb # Ignore vscode settings .vscode/ @@ -198,3 +211,6 @@ cython_debug/ dj_*_conf.json # directory excluded from source control e.g. trash, scratch work, etc. .untracked +# pixi environments +.pixi/* +!.pixi/config.toml diff --git a/misc/demo_redis_mocking.py b/misc/demo_redis_mocking.py index cc18dcb..2fd1f92 100644 --- a/misc/demo_redis_mocking.py +++ b/misc/demo_redis_mocking.py @@ -72,10 +72,10 @@ def demonstrate_redis_mocking(): # Patch the Redis availability and exceptions with ( - patch("orcabridge.hashing.string_cachers.REDIS_AVAILABLE", True), - patch("orcabridge.hashing.string_cachers.redis.RedisError", MockRedisError), + patch("orcapod.hashing.string_cachers.REDIS_AVAILABLE", True), + patch("orcapod.hashing.string_cachers.redis.RedisError", MockRedisError), patch( - "orcabridge.hashing.string_cachers.redis.ConnectionError", + "orcapod.hashing.string_cachers.redis.ConnectionError", MockConnectionError, ), ): diff --git a/notebooks/01_orcapod_core_concepts copy.ipynb b/notebooks/old_tutorials/01_orcapod_core_concepts copy.ipynb similarity index 100% rename from notebooks/01_orcapod_core_concepts copy.ipynb rename to notebooks/old_tutorials/01_orcapod_core_concepts copy.ipynb diff --git a/notebooks/02_orcapod_basic_usage copy.ipynb b/notebooks/old_tutorials/02_orcapod_basic_usage copy.ipynb similarity index 100% rename from notebooks/02_orcapod_basic_usage copy.ipynb rename to notebooks/old_tutorials/02_orcapod_basic_usage copy.ipynb diff --git a/notebooks/02_orcapod_basic_usage.ipynb b/notebooks/old_tutorials/02_orcapod_basic_usage.ipynb similarity index 100% rename from notebooks/02_orcapod_basic_usage.ipynb rename to notebooks/old_tutorials/02_orcapod_basic_usage.ipynb diff --git a/notebooks/03_orcacapod_qol_features.ipynb b/notebooks/old_tutorials/03_orcacapod_qol_features.ipynb similarity index 100% rename from notebooks/03_orcacapod_qol_features.ipynb rename to notebooks/old_tutorials/03_orcacapod_qol_features.ipynb diff --git a/notebooks/04_orcapod_tracker.ipynb b/notebooks/old_tutorials/04_orcapod_tracker.ipynb similarity index 100% rename from notebooks/04_orcapod_tracker.ipynb rename to notebooks/old_tutorials/04_orcapod_tracker.ipynb diff --git a/notebooks/05_orcabridge_dj_integration.ipynb b/notebooks/old_tutorials/05_orcabridge_dj_integration.ipynb similarity index 100% rename from notebooks/05_orcabridge_dj_integration.ipynb rename to notebooks/old_tutorials/05_orcabridge_dj_integration.ipynb diff --git a/notebooks/tutorials/01_introduction_to_orcapod.ipynb b/notebooks/tutorials/01_introduction_to_orcapod.ipynb new file mode 100644 index 0000000..ef11dc8 --- /dev/null +++ b/notebooks/tutorials/01_introduction_to_orcapod.ipynb @@ -0,0 +1,2141 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "b6454c12", + "metadata": {}, + "outputs": [], + "source": [ + "import orcapod as op\n", + "import shutil" + ] + }, + { + "cell_type": "markdown", + "id": "14852fb6", + "metadata": {}, + "source": [ + "We will also make heavy use of PyArrow:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e6a9e8b6", + "metadata": {}, + "outputs": [], + "source": [ + "import pyarrow as pa" + ] + }, + { + "cell_type": "markdown", + "id": "f0157ee4", + "metadata": {}, + "source": [ + "### Preparing the environment" + ] + }, + { + "cell_type": "markdown", + "id": "4773c9d7", + "metadata": {}, + "source": [ + "In this notebook, we will create a local directory called `pipeline_data` and store results in there. To make sure we get reproducibile results, we start by making sure that this directory does not exist locally." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "420477e8", + "metadata": {}, + "outputs": [], + "source": [ + "shutil.rmtree(\"./pipeline_data\", ignore_errors=True)" + ] + }, + { + "cell_type": "markdown", + "id": "64f69204", + "metadata": {}, + "source": [ + "### Creating streams" + ] + }, + { + "cell_type": "markdown", + "id": "c86895f3", + "metadata": {}, + "source": [ + "At the moment, there is only one way to create stream and that is by wrapping a PyArrow table." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dab6bf9c", + "metadata": {}, + "outputs": [], + "source": [ + "table = pa.Table.from_pydict(\n", + " {\n", + " \"a\": [1, 2, 3],\n", + " \"b\": [\"x\", \"y\", \"z\"],\n", + " \"c\": [True, False, True],\n", + " \"d\": [1.1, 2.2, 3.3],\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c2ac8f32", + "metadata": {}, + "source": [ + "Use `op.streams.TableStream` to turn table into a stream. You will also have to specify which columns are the tags." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cd0394d8", + "metadata": {}, + "outputs": [], + "source": [ + "stream = op.streams.TableStream(table, tag_columns=[\"a\", \"b\"])" + ] + }, + { + "cell_type": "markdown", + "id": "93ac78cc", + "metadata": {}, + "source": [ + "### Working with streams" + ] + }, + { + "cell_type": "markdown", + "id": "08a854e7", + "metadata": {}, + "source": [ + "Once you have a stream, you can iterate through tag, packet pair:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2d4a0812", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tag: {'a': 1, 'b': 'x'}, Packet: {'c': True, 'd': 1.1}\n", + "Tag: {'a': 2, 'b': 'y'}, Packet: {'c': False, 'd': 2.2}\n", + "Tag: {'a': 3, 'b': 'z'}, Packet: {'c': True, 'd': 3.3}\n" + ] + } + ], + "source": [ + "for tag, packet in stream:\n", + " print(f\"Tag: {tag}, Packet: {packet}\")" + ] + }, + { + "cell_type": "markdown", + "id": "41c7876b", + "metadata": {}, + "source": [ + "You can also get all tag packet pairs as a list of tuples by calling `.flow()`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "79e67bfc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[({'a': 1, 'b': 'x'}, {'c': True, 'd': 1.1}),\n", + " ({'a': 2, 'b': 'y'}, {'c': False, 'd': 2.2}),\n", + " ({'a': 3, 'b': 'z'}, {'c': True, 'd': 3.3})]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream.flow()" + ] + }, + { + "cell_type": "markdown", + "id": "20fa500e", + "metadata": {}, + "source": [ + "Every stream can be converted into a Polars dataframe with `as_df()` method" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "52baee9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (3, 6)\n", + "┌─────┬─────┬───────┬─────┬───────────┬───────────┐\n", + "│ a ┆ b ┆ c ┆ d ┆ _source_c ┆ _source_d │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ bool ┆ f64 ┆ str ┆ str │\n", + "╞═════╪═════╪═══════╪═════╪═══════════╪═══════════╡\n", + "│ 1 ┆ x ┆ true ┆ 1.1 ┆ null ┆ null │\n", + "│ 2 ┆ y ┆ false ┆ 2.2 ┆ null ┆ null │\n", + "│ 3 ┆ z ┆ true ┆ 3.3 ┆ null ┆ null │\n", + "└─────┴─────┴───────┴─────┴───────────┴───────────┘" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream.as_df(include_source=True)" + ] + }, + { + "cell_type": "markdown", + "id": "83ec7b19", + "metadata": {}, + "source": [ + "`include_content_hash` will compute `content_hash` for each packet and include it as `_content_hash` column" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "001b2a9c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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abcd_content_hash
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2"y"false2.2"arrow_v0.1:083f8c4d8a4c7608af3…
3"z"true3.3"arrow_v0.1:d4a11ad88c1d27eba1c…
" + ], + "text/plain": [ + "shape: (3, 5)\n", + "┌─────┬─────┬───────┬─────┬─────────────────────────────────┐\n", + "│ a ┆ b ┆ c ┆ d ┆ _content_hash │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ bool ┆ f64 ┆ str │\n", + "╞═════╪═════╪═══════╪═════╪═════════════════════════════════╡\n", + "│ 1 ┆ x ┆ true ┆ 1.1 ┆ arrow_v0.1:dbd5a1efe0a1a306cc2… │\n", + "│ 2 ┆ y ┆ false ┆ 2.2 ┆ arrow_v0.1:083f8c4d8a4c7608af3… │\n", + "│ 3 ┆ z ┆ true ┆ 3.3 ┆ arrow_v0.1:d4a11ad88c1d27eba1c… │\n", + "└─────┴─────┴───────┴─────┴─────────────────────────────────┘" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream.as_df(include_content_hash=True)" + ] + }, + { + "cell_type": "markdown", + "id": "58d74238", + "metadata": {}, + "source": [ + "Alternatively, you can pass in a custom column name to use for the content hash column" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d3b9e394", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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abcdmy_hash_values
i64strboolf64str
1"x"true1.1"arrow_v0.1:dbd5a1efe0a1a306cc2…
2"y"false2.2"arrow_v0.1:083f8c4d8a4c7608af3…
3"z"true3.3"arrow_v0.1:d4a11ad88c1d27eba1c…
" + ], + "text/plain": [ + "shape: (3, 5)\n", + "┌─────┬─────┬───────┬─────┬─────────────────────────────────┐\n", + "│ a ┆ b ┆ c ┆ d ┆ my_hash_values │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ bool ┆ f64 ┆ str │\n", + "╞═════╪═════╪═══════╪═════╪═════════════════════════════════╡\n", + "│ 1 ┆ x ┆ true ┆ 1.1 ┆ arrow_v0.1:dbd5a1efe0a1a306cc2… │\n", + "│ 2 ┆ y ┆ false ┆ 2.2 ┆ arrow_v0.1:083f8c4d8a4c7608af3… │\n", + "│ 3 ┆ z ┆ true ┆ 3.3 ┆ arrow_v0.1:d4a11ad88c1d27eba1c… │\n", + "└─────┴─────┴───────┴─────┴─────────────────────────────────┘" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream.as_df(include_content_hash=\"my_hash_values\")" + ] + }, + { + "cell_type": "markdown", + "id": "b7012c5a", + "metadata": {}, + "source": [ + "Finally, `include_data_context` adds data context column as `_context_key` which captures information about the OrcaPod version, hasher version etc that were used when generting that packet." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "92cbfa50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 5)
abcd_context_key
i64strboolf64str
1"x"true1.1"std:v0.1:default"
2"y"false2.2"std:v0.1:default"
3"z"true3.3"std:v0.1:default"
" + ], + "text/plain": [ + "shape: (3, 5)\n", + "┌─────┬─────┬───────┬─────┬──────────────────┐\n", + "│ a ┆ b ┆ c ┆ d ┆ _context_key │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ bool ┆ f64 ┆ str │\n", + "╞═════╪═════╪═══════╪═════╪══════════════════╡\n", + "│ 1 ┆ x ┆ true ┆ 1.1 ┆ std:v0.1:default │\n", + "│ 2 ┆ y ┆ false ┆ 2.2 ┆ std:v0.1:default │\n", + "│ 3 ┆ z ┆ true ┆ 3.3 ┆ std:v0.1:default │\n", + "└─────┴─────┴───────┴─────┴──────────────────┘" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream.as_df(include_data_context=True)" + ] + }, + { + "cell_type": "markdown", + "id": "574a2031", + "metadata": {}, + "source": [ + "If preferred you can view any stream as Arrow table by calling `as_table` method. It takes the same set of arguments as `as_df`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bf6acd59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "a: int64\n", + "b: string\n", + "c: bool\n", + "d: double\n", + "_context_key: large_string\n", + "----\n", + "a: [[1,2,3]]\n", + "b: [[\"x\",\"y\",\"z\"]]\n", + "c: [[true,false,true]]\n", + "d: [[1.1,2.2,3.3]]\n", + "_context_key: [[\"std:v0.1:default\",\"std:v0.1:default\",\"std:v0.1:default\"]]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream.as_table(include_data_context=True)" + ] + }, + { + "cell_type": "markdown", + "id": "7ce05b68", + "metadata": {}, + "source": [ + "### Tags and Packets" + ] + }, + { + "cell_type": "markdown", + "id": "20783626", + "metadata": {}, + "source": [ + "The tags and packets returned by the streams can be thought of as special dictionary." + ] + }, + { + "cell_type": "markdown", + "id": "995161fc", + "metadata": {}, + "source": [ + "Let's work again with the stream we created earlier with the following content" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "68bff9fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (3, 4)
abcd
i64strboolf64
1"x"true1.1
2"y"false2.2
3"z"true3.3
" + ], + "text/plain": [ + "shape: (3, 4)\n", + "┌─────┬─────┬───────┬─────┐\n", + "│ a ┆ b ┆ c ┆ d │\n", + "│ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ bool ┆ f64 │\n", + "╞═════╪═════╪═══════╪═════╡\n", + "│ 1 ┆ x ┆ true ┆ 1.1 │\n", + "│ 2 ┆ y ┆ false ┆ 2.2 │\n", + "│ 3 ┆ z ┆ true ┆ 3.3 │\n", + "└─────┴─────┴───────┴─────┘" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream.as_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c78096a7", + "metadata": {}, + "outputs": [], + "source": [ + "all_tags_and_packets = stream.flow()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6f8a2f0b", + "metadata": {}, + "outputs": [], + "source": [ + "tag, packet = all_tags_and_packets[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e1ac13b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 1, 'b': 'x'}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tag" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "263fa1c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'c': True, 'd': 1.1}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet" + ] + }, + { + "cell_type": "markdown", + "id": "17be117a", + "metadata": {}, + "source": [ + "The element of tag/packet can be accessed just like dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "42158816", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tag[\"a\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6a792175", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tag[\"b\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a28f2051", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet[\"c\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "981e6c44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.1" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet[\"d\"]" + ] + }, + { + "cell_type": "markdown", + "id": "c992134a", + "metadata": {}, + "source": [ + "They have a few methods that will provide additional insights:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "56423d2c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'c': bool, 'd': float}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Returns typespec (dictionary of key to type)\n", + "packet.types()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d5e02f81", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('c', 'd')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# entry names as strings\n", + "packet.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "fd70ee75", + "metadata": {}, + "source": [ + "They can also be converted to an Arrow table by calling `as_table`" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b1b18ee4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "c: bool\n", + "d: double\n", + "----\n", + "c: [[true]]\n", + "d: [[1.1]]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet.as_table()" + ] + }, + { + "cell_type": "markdown", + "id": "f4e4a38f", + "metadata": {}, + "source": [ + "And schema is conveniently available as:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3aa4020e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "c: bool\n", + "d: double" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet.arrow_schema()" + ] + }, + { + "cell_type": "markdown", + "id": "37ad91d0", + "metadata": {}, + "source": [ + "You can also get a plain dictionary from tag/packet with `as_dict`" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bea6c771", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a': 1, 'b': 'x'}" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tag.as_dict()" + ] + }, + { + "cell_type": "markdown", + "id": "4fadd572", + "metadata": {}, + "source": [ + "Packet contains some additional data such as `source_info`" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "92f00feb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'c': None, 'd': None}" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet.source_info()" + ] + }, + { + "cell_type": "markdown", + "id": "1d755600", + "metadata": {}, + "source": [ + "These additional data can be included when converting to dict or table" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "bba2bc5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'c': True, 'd': 1.1, '_source_c': None, '_source_d': None}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet.as_dict(include_source=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "bd09d9d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyarrow.Table\n", + "c: bool\n", + "d: double\n", + "_source_c: large_string\n", + "_source_d: large_string\n", + "----\n", + "c: [[true]]\n", + "d: [[1.1]]\n", + "_source_c: [[null]]\n", + "_source_d: [[null]]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "packet.as_table(include_source=True)" + ] + }, + { + "cell_type": "markdown", + "id": "98ab6fc7", + "metadata": {}, + "source": [ + "The hash of tag/packet can be computed with `content_hash()` method. The result will be cached so that it won't be computed again unnecessarily." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "03219fd3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ContentHash(method='arrow_v0.1', digest=b'n\\x11C\\x89ms\\xd3pux\\x11\\xb5,\\xee\\xea\\x8d\\x1dEl\\xd3\\x02\\x06Ao\\xbf\\x81uN\\x1c\\xeaUh')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tag.content_hash()" + ] + }, + { + "cell_type": "markdown", + "id": "ce0ab6c6", + "metadata": {}, + "source": [ + "## Working with operators" + ] + }, + { + "cell_type": "markdown", + "id": "6a9dd928", + "metadata": {}, + "source": [ + "We start getting into orcapod computation when we use operators. At the time of the writing, only `Join` operator is implemented fully but more are to come very shortly." + ] + }, + { + "cell_type": "markdown", + "id": "7ef99b67", + "metadata": {}, + "source": [ + "Let's prepare two streams:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "11ee5130", + "metadata": {}, + "outputs": [], + "source": [ + "table1 = pa.Table.from_pydict(\n", + " {\n", + " \"id\": [0, 1, 4],\n", + " \"a\": [1, 2, 3],\n", + " \"b\": [\"x\", \"y\", \"z\"],\n", + " }\n", + ")\n", + "\n", + "table2 = pa.Table.from_pydict(\n", + " {\n", + " \"id\": [0, 1, 2],\n", + " \"c\": [True, False, True],\n", + " \"d\": [1.1, 2.2, 3.3],\n", + " }\n", + ")\n", + "\n", + "stream1 = op.streams.TableStream(table1, tag_columns=[\"id\"])\n", + "stream2 = op.streams.TableStream(table2, tag_columns=[\"id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "73b75816", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idab
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" + ], + "text/plain": [ + "shape: (3, 3)\n", + "┌─────┬─────┬─────┐\n", + "│ id ┆ a ┆ b │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ str │\n", + "╞═════╪═════╪═════╡\n", + "│ 0 ┆ 1 ┆ x │\n", + "│ 1 ┆ 2 ┆ y │\n", + "│ 4 ┆ 3 ┆ z │\n", + "└─────┴─────┴─────┘" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream1.as_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "519754a0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idcd
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" + ], + "text/plain": [ + "shape: (3, 3)\n", + "┌─────┬───────┬─────┐\n", + "│ id ┆ c ┆ d │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ bool ┆ f64 │\n", + "╞═════╪═══════╪═════╡\n", + "│ 0 ┆ true ┆ 1.1 │\n", + "│ 1 ┆ false ┆ 2.2 │\n", + "│ 2 ┆ true ┆ 3.3 │\n", + "└─────┴───────┴─────┘" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream2.as_df()" + ] + }, + { + "cell_type": "markdown", + "id": "6f87fcf3", + "metadata": {}, + "source": [ + "We now join the two streams by instantiating the Join operator and then passing in the two streams:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "8299d4b1", + "metadata": {}, + "outputs": [], + "source": [ + "join = op.operators.Join()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "dfc7ee9f", + "metadata": {}, + "outputs": [], + "source": [ + "joined_stream = join(stream1, stream2)" + ] + }, + { + "cell_type": "markdown", + "id": "4f24a492", + "metadata": {}, + "source": [ + "Calling an operator on stream(s) immediately performs checks to make sure that the input streams are comaptible with the operator but otherwise it does NOT trigger any computation. Computation occurs only when you try to **access the output stream's content via iteration, flow, or through conversion to table**." + ] + }, + { + "cell_type": "markdown", + "id": "095856e3", + "metadata": {}, + "source": [ + "The output of the computation is automatically cached so that as long as you access the same output stream, you won't be triggering unnecessary recomputation!" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "48ef0a8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idabcd
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12"y"false2.2
" + ], + "text/plain": [ + "shape: (2, 5)\n", + "┌─────┬─────┬─────┬───────┬─────┐\n", + "│ id ┆ a ┆ b ┆ c ┆ d │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ str ┆ bool ┆ f64 │\n", + "╞═════╪═════╪═════╪═══════╪═════╡\n", + "│ 0 ┆ 1 ┆ x ┆ true ┆ 1.1 │\n", + "│ 1 ┆ 2 ┆ y ┆ false ┆ 2.2 │\n", + "└─────┴─────┴─────┴───────┴─────┘" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "joined_stream.as_df()" + ] + }, + { + "cell_type": "markdown", + "id": "b979bc35", + "metadata": {}, + "source": [ + "### [NEW] Convenience methods" + ] + }, + { + "cell_type": "markdown", + "id": "67c8c1e0", + "metadata": {}, + "source": [ + "In fact, streams comes with methods to conveniently perform common operators" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "fbc58246", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idab
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" + ], + "text/plain": [ + "shape: (2, 3)\n", + "┌─────┬─────┬─────┐\n", + "│ id ┆ a ┆ b │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ str │\n", + "╞═════╪═════╪═════╡\n", + "│ 0 ┆ 1 ┆ x │\n", + "│ 1 ┆ 2 ┆ y │\n", + "└─────┴─────┴─────┘" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream1.semi_join(stream2).as_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5be42490", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ida_mappedb
i64i64str
01"x"
12"y"
43"z"
" + ], + "text/plain": [ + "shape: (3, 3)\n", + "┌─────┬──────────┬─────┐\n", + "│ id ┆ a_mapped ┆ b │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ str │\n", + "╞═════╪══════════╪═════╡\n", + "│ 0 ┆ 1 ┆ x │\n", + "│ 1 ┆ 2 ┆ y │\n", + "│ 4 ┆ 3 ┆ z │\n", + "└─────┴──────────┴─────┘" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream1.map_packets({\"a\": \"a_mapped\"}).as_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "c9c98304", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nameab
i64i64str
01"x"
12"y"
43"z"
" + ], + "text/plain": [ + "shape: (3, 3)\n", + "┌──────┬─────┬─────┐\n", + "│ name ┆ a ┆ b │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ str │\n", + "╞══════╪═════╪═════╡\n", + "│ 0 ┆ 1 ┆ x │\n", + "│ 1 ┆ 2 ┆ y │\n", + "│ 4 ┆ 3 ┆ z │\n", + "└──────┴─────┴─────┘" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stream1.map_tags({\"id\": \"name\"}).as_df()" + ] + }, + { + "cell_type": "markdown", + "id": "5869a1da", + "metadata": {}, + "source": [ + "## Working with Function Pods" + ] + }, + { + "cell_type": "markdown", + "id": "e4b7991a", + "metadata": {}, + "source": [ + "Now we have explored the basics of streams, tags, packets, and operators (i.e. Join), it's time to explore the meat of `orcapod` -- `FunctionPod`s! Let's start by defining a very simple function pod that takes in two numbers and return the sum." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "35423d9a", + "metadata": {}, + "outputs": [], + "source": [ + "@op.function_pod(output_keys=[\"sum\"])\n", + "def add_numbers(a: int, b: int) -> int:\n", + " \"\"\"A simple function pod that adds two numbers.\"\"\"\n", + " return a + b" + ] + }, + { + "cell_type": "markdown", + "id": "f737eeac", + "metadata": {}, + "source": [ + "You'll notice that, aside from the `op.function_pod` decorator, this is nothing but an ordinary Python function with type hints! The type hints are crucial however, as this will be used by `orcapod` system to validate the input streams into your pods and to be able to predict if the output of your pod can be fed into another operator/pod without an issue." + ] + }, + { + "cell_type": "markdown", + "id": "caf23360", + "metadata": {}, + "source": [ + "Once you have function pod defined, you can already use it on streams just like operators. Let's prepare a stream that has entries for `a` and `b` and then feed them into the function pod." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "119d33a3", + "metadata": {}, + "outputs": [], + "source": [ + "input_table = pa.Table.from_pydict(\n", + " {\n", + " \"id\": [0, 1, 2, 3, 4],\n", + " \"a\": [1, 2, 3, 4, 5],\n", + " \"b\": [10, 20, 30, 40, 50],\n", + " }\n", + ")\n", + "\n", + "input_stream = op.streams.TableStream(input_table, tag_columns=[\"id\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e3b60eca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idab
i64i64i64
0110
1220
2330
3440
4550
" + ], + "text/plain": [ + "shape: (5, 3)\n", + "┌─────┬─────┬─────┐\n", + "│ id ┆ a ┆ b │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ i64 ┆ i64 │\n", + "╞═════╪═════╪═════╡\n", + "│ 0 ┆ 1 ┆ 10 │\n", + "│ 1 ┆ 2 ┆ 20 │\n", + "│ 2 ┆ 3 ┆ 30 │\n", + "│ 3 ┆ 4 ┆ 40 │\n", + "│ 4 ┆ 5 ┆ 50 │\n", + "└─────┴─────┴─────┘" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_stream.as_df()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "2b3b42ff", + "metadata": {}, + "outputs": [], + "source": [ + "# run the stream through the function pod!\n", + "output_stream = add_numbers(input_stream)" + ] + }, + { + "cell_type": "markdown", + "id": "5b5beae2", + "metadata": {}, + "source": [ + "And that's it! Believe it or not, that is all it takes to set up the computation. The actual computation will be triggered the first time you access the content of the output stream." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "ff05a8fc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KernelStream(kernel=FunctionPod:add_numbers(a: int, b: int)-> , upstreams=(TableStream(table=['id', 'a', 'b'], tag_columns=('id',)),))" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_stream" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "35107c18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idsum
i64i64
011
122
233
344
455
" + ], + "text/plain": [ + "shape: (5, 2)\n", + "┌─────┬─────┐\n", + "│ id ┆ sum │\n", + "│ --- ┆ --- │\n", + "│ i64 ┆ i64 │\n", + "╞═════╪═════╡\n", + "│ 0 ┆ 11 │\n", + "│ 1 ┆ 22 │\n", + "│ 2 ┆ 33 │\n", + "│ 3 ┆ 44 │\n", + "│ 4 ┆ 55 │\n", + "└─────┴─────┘" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_stream.as_df() # this triggers the computation!" + ] + }, + { + "cell_type": "markdown", + "id": "ab5d3ef0", + "metadata": {}, + "source": [ + "If you prefer, you can explicitly trigger the comptuation by calling `run` method. In a future tutorial, we will see how we can combine `run` with execution engine to achieve asynchronous computations!" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "6431180f", + "metadata": {}, + "outputs": [], + "source": [ + "output_stream.run()" + ] + }, + { + "cell_type": "markdown", + "id": "4d8d7f23", + "metadata": {}, + "source": [ + "### [NEW] Pods with structured inputs and outputs" + ] + }, + { + "cell_type": "markdown", + "id": "5f7d21cc", + "metadata": {}, + "source": [ + "You can now use more complex structured data types like lists and dictionaries in your input and output for a pod" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "9b7fcbbf", + "metadata": {}, + "outputs": [], + "source": [ + "@op.function_pod(output_keys=[\"stats\"])\n", + "def compute_stats(a: int, b: int) -> dict[str, int]:\n", + " \"\"\"Compute various statistics.\"\"\"\n", + " return {\"sum\": a + b, \"difference\": a - b, \"product\": a * b}\n", + "\n", + "\n", + "@op.function_pod(output_keys=[\"message\"])\n", + "def build_message(stats: dict[str, int]) -> str:\n", + " \"\"\"Build a message from the stats.\"\"\"\n", + " return f\"Hi! The sum was {stats['sum']}, the difference was {stats['difference']}, and the product was {stats['product']}.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "3e496c59", + "metadata": {}, + "outputs": [], + "source": [ + "stats_output = compute_stats(input_stream)\n", + "messages = build_message(stats_output)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "23c0fa92", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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4"Hi! The sum was 55, the differ…
" + ], + "text/plain": [ + "shape: (5, 2)\n", + "┌─────┬─────────────────────────────────┐\n", + "│ id ┆ message │\n", + "│ --- ┆ --- │\n", + "│ i64 ┆ str │\n", + "╞═════╪═════════════════════════════════╡\n", + "│ 0 ┆ Hi! The sum was 11, the differ… │\n", + "│ 1 ┆ Hi! The sum was 22, the differ… │\n", + "│ 2 ┆ Hi! The sum was 33, the differ… │\n", + "│ 3 ┆ Hi! The sum was 44, the differ… │\n", + "│ 4 ┆ Hi! The sum was 55, the differ… │\n", + "└─────┴─────────────────────────────────┘" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages.as_df()" + ] + }, + { + "cell_type": "markdown", + "id": "04b0a24e", + "metadata": {}, + "source": [ + "## Chaining operators and pods into a pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "21fa1e75", + "metadata": {}, + "source": [ + "Now that we have seen how to define and run pods, it's time to put them together into a concrete pipeline. To do so, we will construct a `Pipeline` instance. When doing so, we have to pass in a place to save data to, so we will also prepare a data store." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "cb4bc91a", + "metadata": {}, + "outputs": [], + "source": [ + "database = op.databases.DeltaTableDatabase(base_path=\"./pipeline_data\")\n", + "\n", + "pipeline = op.Pipeline(name=\"my_pipeline\", pipeline_database=database)" + ] + }, + { + "cell_type": "markdown", + "id": "ef281a1e", + "metadata": {}, + "source": [ + "Once we have the pipeline ready, we can define the pipeline by simply running & chaining operators and pods **inside the pipeline context**. Typically, you'd want to define your function pods before hand:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "f371822b", + "metadata": {}, + "outputs": [], + "source": [ + "@op.function_pod(output_keys=[\"sum\"])\n", + "def add_numbers(a: int, b: int) -> int:\n", + " \"\"\"A simple function pod that adds two numbers.\"\"\"\n", + " return a + b\n", + "\n", + "\n", + "@op.function_pod(output_keys=[\"product\"])\n", + "def multiply_numbers(a: int, b: int) -> int:\n", + " \"\"\"A simple function pod that multiplies two numbers.\"\"\"\n", + " return a * b\n", + "\n", + "\n", + "@op.function_pod(output_keys=[\"result\"])\n", + "def combine_results(sum: int, product: int) -> str:\n", + " \"\"\"A simple function pod that combines results.\"\"\"\n", + " return f\"Sum: {sum}, Product: {product}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "e132fc93", + "metadata": {}, + "outputs": [], + "source": [ + "# now defien the pipeline\n", + "with pipeline:\n", + " sum_results = add_numbers(input_stream)\n", + " product_results = multiply_numbers(input_stream)\n", + " final_results = combine_results(sum_results, product_results)" + ] + }, + { + "cell_type": "markdown", + "id": "dad175c6", + "metadata": {}, + "source": [ + "You can access individual elements of the pipeline as an attribute. By default, the attribute is named after the operator/pod name." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "cca9e0d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PodNode(pod=FunctionPod:add_numbers)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.add_numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "08add7d9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'TableStream': KernelNode(kernel=StreamSource),\n", + " 'multiply_numbers': PodNode(pod=FunctionPod:multiply_numbers),\n", + " 'add_numbers': PodNode(pod=FunctionPod:add_numbers),\n", + " 'Join': KernelNode(kernel=Join()),\n", + " 'combine_results': PodNode(pod=FunctionPod:combine_results)}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.nodes" + ] + }, + { + "cell_type": "markdown", + "id": "5f33f5a9", + "metadata": {}, + "source": [ + "Notice that elements of the pipeline is wrapped in a `Node`, being either `PodNode` or `KernelNode`." + ] + }, + { + "cell_type": "markdown", + "id": "2b6bc8df", + "metadata": {}, + "source": [ + "You can fetch results of the pipeline through these nodes. For example, you can access the saved results of the pipeline as Polars dataframe by access the `df` attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "21086f72", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (5, 2)\n", + "┌─────┬─────┐\n", + "│ id ┆ sum │\n", + "│ --- ┆ --- │\n", + "│ i64 ┆ i64 │\n", + "╞═════╪═════╡\n", + "│ 0 ┆ 11 │\n", + "│ 1 ┆ 22 │\n", + "│ 2 ┆ 33 │\n", + "│ 3 ┆ 44 │\n", + "│ 4 ┆ 55 │\n", + "└─────┴─────┘" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.add_numbers.as_df()" + ] + }, + { + "cell_type": "markdown", + "id": "43931402", + "metadata": {}, + "source": [ + "You now have the computations saved at each node!" + ] + }, + { + "cell_type": "markdown", + "id": "82312bda", + "metadata": {}, + "source": [ + "### Labeling nodes in the pipeline" + ] + }, + { + "cell_type": "markdown", + "id": "1f0a8f8f", + "metadata": {}, + "source": [ + "When constructing the pipeline, each invocation of the operator/pod results in a new node getting added, with the name of the node defaulting to the name of the operator/pod. If you use the same pod multiple times, then the nodes will be given names of form `{pod_name}_0`, `{pod_name}_1`, and so on.\n", + "\n", + "While this is helpful default behavior, you'd likely want to explicitly name each node so you can more easily understand what you are accessing within the pipeline. To achieve this, you can explicitly label each invocation with `label=` argument in the call." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "37e65e33", + "metadata": {}, + "outputs": [], + "source": [ + "database = op.databases.DeltaTableDatabase(base_path=\"./pipeline_data\")\n", + "\n", + "pipeline2 = op.Pipeline(name=\"my_pipeline\", pipeline_database=database)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "3bad8332", + "metadata": {}, + "outputs": [], + "source": [ + "# now defien the pipeline\n", + "with pipeline2:\n", + " sum_results = add_numbers(input_stream, label=\"my_summation\")\n", + " product_results = multiply_numbers(input_stream, label=\"my_product\")\n", + " final_results = combine_results(\n", + " sum_results, product_results, label=\"my_final_result\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "8f146ae7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0"Sum: 11, Product: 10"
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" + ], + "text/plain": [ + "shape: (5, 2)\n", + "┌─────┬───────────────────────┐\n", + "│ id ┆ result │\n", + "│ --- ┆ --- │\n", + "│ i64 ┆ str │\n", + "╞═════╪═══════════════════════╡\n", + "│ 0 ┆ Sum: 11, Product: 10 │\n", + "│ 1 ┆ Sum: 22, Product: 40 │\n", + "│ 2 ┆ Sum: 33, Product: 90 │\n", + "│ 3 ┆ Sum: 44, Product: 160 │\n", + "│ 4 ┆ Sum: 55, Product: 250 │\n", + "└─────┴───────────────────────┘" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline2.my_final_result.as_df()" + ] + }, + { + "cell_type": "markdown", + "id": "5380dad8", + "metadata": {}, + "source": [ + "Notice that despite just freshly creating the pipeline, each node already had results filled in! This is because the results from the previous pipeline with the same name (`my_pipeline`) was smartly fetched back. Critically, this was done only because Orcapod noticed that you had an identical pipeline with the same inputs and same operators/pods so that you can reuse the result as is. Should the structure of pipeline been different, the wront results would not be loaded." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/tutorials/02_parallel_execution_on_ray.ipynb b/notebooks/tutorials/02_parallel_execution_on_ray.ipynb new file mode 100644 index 0000000..d6ff7d5 --- /dev/null +++ b/notebooks/tutorials/02_parallel_execution_on_ray.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0c2dfaec", + "metadata": {}, + "outputs": [], + "source": [ + "from orcapod.execution_engines import RayEngine\n", + "import orcapod as op\n", + "import pyarrow as pa" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9e1f338b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-08-10 23:27:14,560\tINFO client_builder.py:242 -- Passing the following kwargs to ray.init() on the server: log_to_driver\n", + "SIGTERM handler is not set because current thread is not the main thread.\n", + "2025-08-10 23:27:17,455\tWARNING utils.py:1280 -- Python patch version mismatch: The cluster was started with:\n", + " Ray: 2.48.0\n", + " Python: 3.13.5\n", + "This process on Ray Client was started with:\n", + " Ray: 2.48.0\n", + " Python: 3.13.3\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[36m(autoscaler +28s)\u001b[0m Tip: use `ray status` to view detailed cluster status. To disable these messages, set RAY_SCHEDULER_EVENTS=0.\n", + "\u001b[36m(autoscaler +28s)\u001b[0m Adding 5 node(s) of type workergroup.\n", + "\u001b[36m(autoscaler +28s)\u001b[0m Resized to 6 CPUs, 5 GPUs.\n", + "\u001b[36m(autoscaler +28s)\u001b[0m No available node types can fulfill resource requests {'CPU': 1.0}*44. Add suitable node types to this cluster to resolve this issue.\n", + "\u001b[36m(autoscaler +34s)\u001b[0m No available node types can fulfill resource requests {'CPU': 1.0}*22. Add suitable node types to this cluster to resolve this issue.\n", + "\u001b[36m(autoscaler +49s)\u001b[0m No available node types can fulfill resource requests {'CPU': 1.0}*11. Add suitable node types to this cluster to resolve this issue.\n", + "\u001b[36m(autoscaler +55s)\u001b[0m No available node types can fulfill resource requests {'CPU': 1.0}*22. Add suitable node types to this cluster to resolve this issue.\n", + "\u001b[36m(autoscaler +1m0s)\u001b[0m No available node types can fulfill resource requests {'CPU': 1.0}*27. Add suitable node types to this cluster to resolve this issue.\n", + "\u001b[36m(autoscaler +1m10s)\u001b[0m No available node types can fulfill resource requests {'CPU': 1.0}*6. Add suitable node types to this cluster to resolve this issue.\n" + ] + } + ], + "source": [ + "ray_engine = RayEngine(\n", + " \"ray://raycluster-op-test-kuberay-head-svc.ray.svc.cluster.local:10001\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "64a4e7d9", + "metadata": {}, + "outputs": [], + "source": [ + "input_stream = op.streams.TableStream(\n", + " pa.Table.from_pylist([{\"id\": i, \"x\": i * 2, \"y\": i * 3} for i in range(50)]),\n", + " tag_columns=[\"id\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "63c692df", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "\n", + "@op.function_pod(\"sum\")\n", + "def add_numbers(x: int, y: int) -> int:\n", + " \"\"\"\n", + " A simple function that adds two numbers.\n", + " \"\"\"\n", + " import time\n", + "\n", + " time.sleep(0.2)\n", + " return x + y" + ] + }, + { + "cell_type": "markdown", + "id": "52c36a2a", + "metadata": {}, + "source": [ + "First run synchronously" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4e12bab5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (50, 2)\n", + "┌─────┬─────┐\n", + "│ id ┆ sum │\n", + "│ --- ┆ --- │\n", + "│ i64 ┆ i64 │\n", + "╞═════╪═════╡\n", + "│ 0 ┆ 0 │\n", + "│ 1 ┆ 5 │\n", + "│ 2 ┆ 10 │\n", + "│ 3 ┆ 15 │\n", + "│ 4 ┆ 20 │\n", + "│ … ┆ … │\n", + "│ 45 ┆ 225 │\n", + "│ 46 ┆ 230 │\n", + "│ 47 ┆ 235 │\n", + "│ 48 ┆ 240 │\n", + "│ 49 ┆ 245 │\n", + "└─────┴─────┘" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.add_numbers.as_df()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index ca1c20c..5a23cad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "orcapod" -description = "simple yet powerful pipeline library for building reproducible scientific pipeline" +description = "Intuitive and powerful library for highly reproducible scientific data pipeline" dynamic = ["version"] dependencies = [ "xxhash", @@ -14,11 +14,14 @@ dependencies = [ "pandas>=2.2.3", "pyyaml>=6.0.2", "pyarrow>=20.0.0", - "polars>=1.30.0", + "polars>=1.31.0", "beartype>=0.21.0", + "deltalake>=1.0.2", + "selection-pipeline", + "graphviz>=0.21", ] readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.11.0" license = { text = "MIT License" } classifiers = [ "Programming Language :: Python :: 3", @@ -31,6 +34,8 @@ Homepage = "https://github.com/walkerlab/orcapod-python" [project.optional-dependencies] redis = ["redis>=6.2.0"] +ray = ["ray[default]==2.48.0", "ipywidgets>=8.1.7"] +all = ["orcapod[redis]", "orcapod[ray]"] [tool.setuptools.packages.find] @@ -41,13 +46,36 @@ version_file = "src/orcapod/_version.py" [dependency-groups] dev = [ + "adlfs>=2024.12.0", + "boto3>=1.39.11", + "datajoint>=0.14.6", "deltalake>=1.0.2", + "gcsfs>=2025.7.0", "httpie>=3.2.4", + "hydra-core>=1.3.2", + "imageio>=2.37.0", "ipykernel>=6.29.5", + "ipywidgets>=8.1.7", + "jsonschema>=4.25.0", + "minio>=7.2.16", + "mkdocs>=1.6.1", + "mkdocs-material>=9.6.18", + "mkdocstrings[python]>=0.30.0", + "pdoc>=15.0.4", + "pyarrow-stubs>=20.0.0.20250716", + "pygraphviz>=1.14", "pyiceberg>=0.9.1", + "pyright>=1.1.404", "pytest>=8.3.5", "pytest-cov>=6.1.1", + "ray[default]==2.48.0", "redis>=6.2.0", "ruff>=0.11.11", + "s3fs>=2025.7.0", + "sphinx>=8.2.3", "tqdm>=4.67.1", ] + + +[tool.uv.sources] +selection-pipeline = { git = "https://github.com/enigma-brain/selection_pipeline" } #, rev = "6218290" } diff --git a/src/orcapod/__init__.py b/src/orcapod/__init__.py index a84492a..f1ebd4b 100644 --- a/src/orcapod/__init__.py +++ b/src/orcapod/__init__.py @@ -1,34 +1,28 @@ -from .core import operators, sources, streams -from .core.streams import SyncStreamFromLists, SyncStreamFromGenerator -from . import hashing, pod, store -from .core.operators import Join, MapPackets, MapTags, packet, tag -from .pod import FunctionPod, function_pod -from .core.sources import GlobSource -from .store import DirDataStore, SafeDirDataStore -from .core.tracker import GraphTracker +from .config import DEFAULT_CONFIG, Config +from .core import DEFAULT_TRACKER_MANAGER +from .core.pods import function_pod, FunctionPod, CachedPod +from .core import streams +from .core import operators +from .core import sources +from .core.sources import DataFrameSource +from . import databases +from .pipeline import Pipeline -DEFAULT_TRACKER = GraphTracker() -DEFAULT_TRACKER.activate() +no_tracking = DEFAULT_TRACKER_MANAGER.no_tracking __all__ = [ - "hashing", - "store", - "pod", - "operators", + "DEFAULT_CONFIG", + "Config", + "DEFAULT_TRACKER_MANAGER", + "no_tracking", + "function_pod", + "FunctionPod", + "CachedPod", "streams", + "databases", "sources", - "MapTags", - "MapPackets", - "Join", - "tag", - "packet", - "FunctionPod", - "function_pod", - "GlobSource", - "DirDataStore", - "SafeDirDataStore", - "DEFAULT_TRACKER", - "SyncStreamFromLists", - "SyncStreamFromGenerator", + "DataFrameSource", + "operators", + "Pipeline", ] diff --git a/src/orcapod/config.py b/src/orcapod/config.py new file mode 100644 index 0000000..f36a514 --- /dev/null +++ b/src/orcapod/config.py @@ -0,0 +1,36 @@ +# config.py +from dataclasses import dataclass, replace +from typing import Self + + +@dataclass(frozen=True) +class Config: + """Immutable configuration object.""" + + system_tag_hash_n_char: int = 12 + schema_hash_n_char: int = 12 + path_hash_n_char: int = 20 + + def with_updates(self, **kwargs) -> Self: + """Create a new Config instance with updated values.""" + return replace(self, **kwargs) + + def merge(self, other: "Config") -> "Config": + """Merge with another config, other takes precedence.""" + if not isinstance(other, Config): + raise TypeError("Can only merge with another Config instance") + + # Get all non-default values from other + defaults = Config() + updates = {} + for field_name in self.__dataclass_fields__: + other_value = getattr(other, field_name) + default_value = getattr(defaults, field_name) + if other_value != default_value: + updates[field_name] = other_value + + return self.with_updates(**updates) + + +# Module-level default config - created at import time +DEFAULT_CONFIG = Config() diff --git a/src/orcapod/contexts/__init__.py b/src/orcapod/contexts/__init__.py new file mode 100644 index 0000000..116dbbb --- /dev/null +++ b/src/orcapod/contexts/__init__.py @@ -0,0 +1,243 @@ +""" +OrcaPod Data Context System + +This package manages versioned data contexts that define how data +should be interpreted and processed throughout the OrcaPod system. + +A DataContext contains: +- Semantic type registry for handling structured data types +- Arrow hasher for hashing Arrow tables +- Object hasher for general object hashing +- Versioning information for reproducibility + +Example usage: + # Get default context + context = resolve_context() + + # Get specific version + context = resolve_context("v0.1") + + # Use context components + registry = context.semantic_type_registry + hasher = context.arrow_hasher + + # List available contexts + versions = get_available_contexts() +""" + +from .core import DataContext, ContextValidationError, ContextResolutionError +from .registry import JSONDataContextRegistry +from typing import Any +from orcapod.protocols import hashing_protocols as hp, semantic_types_protocols as sp + +# Global registry instance (lazily initialized) +_registry: JSONDataContextRegistry | None = None + + +def _get_registry() -> JSONDataContextRegistry: + """Get the global context registry, initializing if needed.""" + global _registry + if _registry is None: + _registry = JSONDataContextRegistry() + return _registry + + +def get_default_context_key() -> str: + return get_default_context().context_key + + +def resolve_context(context_info: str | DataContext | None = None) -> DataContext: + """ + Resolve context information to a DataContext instance. + + Args: + context_info: One of: + - None: Use default context + - str: Version string ("v0.1") or full key ("std:v0.1:default") + - DataContext: Return as-is + + Returns: + DataContext instance + + Raises: + ContextResolutionError: If context cannot be resolved + + Examples: + >>> context = resolve_context() # Default + >>> context = resolve_context("v0.1") # Specific version + >>> context = resolve_context("std:v0.1:default") # Full key + >>> context = resolve_context("latest") # Latest version + """ + # If already a DataContext, return as-is + if isinstance(context_info, DataContext): + return context_info + + # Use registry to resolve string/None to DataContext + registry = _get_registry() + return registry.get_context(context_info) + + +def get_available_contexts() -> list[str]: + """ + Get list of all available context versions. + + Returns: + Sorted list of version strings + + Example: + >>> get_available_contexts() + ['v0.1', 'v0.1-fast', 'v0.2'] + """ + registry = _get_registry() + return registry.get_available_versions() + + +def get_context_info(version: str) -> dict[str, Any]: + """ + Get metadata about a specific context version. + + Args: + version: Context version string + + Returns: + Dictionary with context metadata + + Example: + >>> info = get_context_info("v0.1") + >>> print(info['description']) + 'Initial stable release with basic Path semantic type support' + """ + registry = _get_registry() + return registry.get_context_info(version) + + +def set_default_context_version(version: str) -> None: + """ + Set the default context version globally. + + Args: + version: Version string to set as default + + Raises: + ContextResolutionError: If version doesn't exist + """ + registry = _get_registry() + registry.set_default_version(version) + + +def validate_all_contexts() -> dict[str, str | None]: + """ + Validate that all available contexts can be instantiated. + + Returns: + Dict mapping version -> error message (None if valid) + + Example: + >>> results = validate_all_contexts() + >>> for version, error in results.items(): + ... if error: + ... print(f"{version}: {error}") + ... else: + ... print(f"{version}: OK") + """ + registry = _get_registry() + return registry.validate_all_contexts() + + +def reload_contexts() -> None: + """ + Reload context specifications from disk. + + Useful during development or when context files have been updated. + Clears all cached contexts and reloads from JSON files. + """ + registry = _get_registry() + registry.reload_contexts() + + +def get_default_context() -> DataContext: + """ + Get the default data context. + + Returns: + DataContext instance for the default version + """ + return resolve_context() + + +def get_default_object_hasher() -> hp.ObjectHasher: + """ + Get the default object hasher. + + Returns: + ObjectHasher instance for the default context + """ + return get_default_context().object_hasher + + +def get_default_arrow_hasher() -> hp.ArrowHasher: + """ + Get the default arrow hasher. + + Returns: + ArrowHasher instance for the default context + """ + return get_default_context().arrow_hasher + + +def get_default_type_converter() -> "sp.TypeConverter": + """ + Get the default type converter. + + Returns: + UniversalTypeConverter instance for the default context + """ + return get_default_context().type_converter + + +# Convenience function for creating custom registries +def create_registry( + contexts_dir: str | None = None, + schema_file: str | None = None, + default_version: str = "v0.1", +) -> JSONDataContextRegistry: + """ + Create a custom context registry. + + Useful for testing or when you need to use a different set of contexts. + + Args: + contexts_dir: Directory containing context JSON files + schema_file: JSON schema file for validation + default_version: Default version to use + + Returns: + JSONDataContextRegistry instance + + Example: + >>> # Create registry for testing + >>> test_registry = create_registry("/path/to/test/contexts") + >>> test_context = test_registry.get_context("test") + """ + return JSONDataContextRegistry(contexts_dir, schema_file, default_version) + + +# Public API +__all__ = [ + # Core types + "DataContext", + "ContextValidationError", + "ContextResolutionError", + # Main functions + "resolve_context", + "get_available_contexts", + "get_context_info", + "get_default_context", + # Management functions + "set_default_context_version", + "validate_all_contexts", + "reload_contexts", + # Advanced usage + "create_registry", + "JSONDataContextRegistry", +] diff --git a/src/orcapod/contexts/core.py b/src/orcapod/contexts/core.py new file mode 100644 index 0000000..f1b35d3 --- /dev/null +++ b/src/orcapod/contexts/core.py @@ -0,0 +1,48 @@ +""" +Core data structures and exceptions for the OrcaPod context system. + +This module defines the basic types and exceptions used throughout +the context management system. +""" + +from dataclasses import dataclass + +from orcapod.protocols import hashing_protocols as hp, semantic_types_protocols as sp + + +@dataclass +class DataContext: + """ + Data context containing all versioned components needed for data interpretation. + + A DataContext represents a specific version of the OrcaPod system configuration, + including semantic type registries, hashers, and other components that affect + how data is processed and interpreted. + + Attributes: + context_key: Unique identifier (e.g., "std:v0.1:default") + version: Version string (e.g., "v0.1") + description: Human-readable description of this context + semantic_type_registry: Registry of semantic type converters + arrow_hasher: Arrow table hasher for this context + object_hasher: General object hasher for this context + """ + + context_key: str + version: str + description: str + type_converter: sp.TypeConverter + arrow_hasher: hp.ArrowHasher + object_hasher: hp.ObjectHasher + + +class ContextValidationError(Exception): + """Raised when context validation fails.""" + + pass + + +class ContextResolutionError(Exception): + """Raised when context cannot be resolved.""" + + pass diff --git a/src/orcapod/contexts/data/schemas/context_schema.json b/src/orcapod/contexts/data/schemas/context_schema.json new file mode 100644 index 0000000..0485d51 --- /dev/null +++ b/src/orcapod/contexts/data/schemas/context_schema.json @@ -0,0 +1,210 @@ +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "$id": "https://orcapod.dev/schemas/data-context.json", + "$comment": "Located at: src/orcapod/contexts/data/schemas/context_schema.json", + "title": "OrcaPod Data Context Specification", + "description": "Schema for OrcaPod data context JSON files", + "type": "object", + "required": [ + "context_key", + "version", + "semantic_registry", + "type_converter", + "arrow_hasher", + "object_hasher" + ], + "properties": { + "context_key": { + "type": "string", + "pattern": "^[a-zA-Z0-9_-]+:v[0-9]+\\.[0-9]+:[a-zA-Z0-9_-]+$", + "description": "Unique identifier for this context (e.g., 'std:v0.1:default')", + "examples": [ + "std:v0.1:default", + "std:v0.2:fast", + "custom:v1.0:production" + ] + }, + "version": { + "type": "string", + "pattern": "^v[0-9]+\\.[0-9]+$", + "description": "Version identifier matching the filename", + "examples": [ + "v0.1", + "v1.0", + "v2.3" + ] + }, + "description": { + "type": "string", + "description": "Human-readable description of this context version", + "examples": [ + "Initial stable release with basic Path semantic type support", + "Enhanced version with timestamp support and improved hashing" + ] + }, + "semantic_registry": { + "$ref": "#/$defs/objectspec", + "description": "ObjectSpec for the semantic registry" + }, + "type_converter": { + "$ref": "#/$defs/objectspec", + "description": "ObjectSpec for the python-arrow type converter" + }, + "arrow_hasher": { + "$ref": "#/$defs/objectspec", + "description": "ObjectSpec for the Arrow hasher component" + }, + "object_hasher": { + "$ref": "#/$defs/objectspec", + "description": "ObjectSpec for the object hasher component" + }, + "metadata": { + "type": "object", + "description": "Optional metadata about this context", + "properties": { + "created_date": { + "type": "string", + "format": "date", + "description": "Date this context was created" + }, + "author": { + "type": "string", + "description": "Author of this context specification" + }, + "changelog": { + "type": "array", + "items": { + "type": "string" + }, + "description": "List of changes in this version" + }, + "compatibility": { + "type": "object", + "properties": { + "backwards_compatible_with": { + "type": "array", + "items": { + "type": "string" + }, + "description": "List of versions this context can read data from" + }, + "breaking_changes": { + "type": "array", + "items": { + "type": "string" + }, + "description": "List of breaking changes from previous versions" + } + } + } + } + } + }, + "additionalProperties": false, + "$defs": { + "objectspec": { + "description": "ObjectSpec pattern used throughout OrcaPod", + "oneOf": [ + { + "type": "object", + "required": [ + "_class" + ], + "properties": { + "_class": { + "type": "string", + "pattern": "^[a-zA-Z_][a-zA-Z0-9_.]*\\.[a-zA-Z_][a-zA-Z0-9_]*$", + "description": "Fully qualified class name", + "examples": [ + "orcapod.types.semantic_types.SemanticTypeRegistry", + "orcapod.hashing.arrow_hashers.SemanticArrowHasher" + ] + }, + "_config": { + "type": "object", + "description": "Configuration parameters for the class", + "additionalProperties": true + } + }, + "additionalProperties": false + }, + { + "type": "array", + "description": "Array of object specifications", + "items": { + "$ref": "#/$defs/objectspec" + } + }, + { + "type": [ + "string", + "number", + "boolean", + "null" + ], + "description": "Primitive values" + } + ] + } + }, + "examples": [ + { + "context_key": "std:v0.1:default", + "version": "v0.1", + "description": "Initial stable release with basic Path semantic type support", + "semantic_type_registry": { + "_class": "orcapod.types.semantic_types.SemanticTypeRegistry", + "_config": { + "converters": [ + { + "_class": "orcapod.types.semantic_types.PathStructConverter", + "_config": {} + } + ] + } + }, + "arrow_hasher": { + "_class": "orcapod.hashing.arrow_hashers.SemanticArrowHasher", + "_config": { + "hasher_id": "arrow_v0.1", + "hash_algorithm": "sha256", + "serialization_method": "logical", + "semantic_type_hashers": { + "path": { + "_class": "orcapod.hashing.semantic_type_hashers.PathHasher", + "_config": { + "file_hasher": { + "_class": "orcapod.hashing.file_hashers.BasicFileHasher", + "_config": { + "algorithm": "sha256" + } + } + } + } + } + } + }, + "object_hasher": { + "_class": "orcapod.hashing.object_hashers.BasicObjectHasher", + "_config": { + "hasher_id": "object_v0.1", + "function_info_extractor": { + "_class": "orcapod.hashing.function_info_extractors.FunctionSignatureExtractor", + "_config": { + "include_module": true, + "include_defaults": true + } + } + } + }, + "metadata": { + "created_date": "2025-08-01", + "author": "OrcaPod Team", + "changelog": [ + "Initial release with semantic type registry", + "Basic Arrow and object hashing capabilities" + ] + } + } + ] +} \ No newline at end of file diff --git a/src/orcapod/contexts/data/v0.1.json b/src/orcapod/contexts/data/v0.1.json new file mode 100644 index 0000000..9f1708e --- /dev/null +++ b/src/orcapod/contexts/data/v0.1.json @@ -0,0 +1,58 @@ +{ + "context_key": "std:v0.1:default", + "version": "v0.1", + "description": "Initial stable release with basic Path semantic type support", + "semantic_registry": { + "_class": "orcapod.semantic_types.semantic_registry.SemanticTypeRegistry", + "_config": { + "converters": { + "path": { + "_class": "orcapod.semantic_types.semantic_struct_converters.PathStructConverter", + "_config": {} + } + } + } + }, + "arrow_hasher": { + "_class": "orcapod.hashing.arrow_hashers.SemanticArrowHasher", + "_config": { + "hasher_id": "arrow_v0.1", + "hash_algorithm": "sha256", + "chunk_size": 8192, + "serialization_method": "logical", + "semantic_registry": { + "_ref": "semantic_registry" + } + } + }, + "type_converter": { + "_class": "orcapod.semantic_types.universal_converter.UniversalTypeConverter", + "_config": { + "semantic_registry": { + "_ref": "semantic_registry" + } + } + }, + "object_hasher": { + "_class": "orcapod.hashing.object_hashers.BasicObjectHasher", + "_config": { + "hasher_id": "object_v0.1", + "function_info_extractor": { + "_class": "orcapod.hashing.function_info_extractors.FunctionSignatureExtractor", + "_config": { + "include_module": true, + "include_defaults": true + } + } + } + }, + "metadata": { + "created_date": "2025-08-01", + "author": "OrcaPod Core Team", + "changelog": [ + "Initial release with Path semantic type support", + "Basic SHA-256 hashing for files and objects", + "Arrow logical serialization method" + ] + } +} \ No newline at end of file diff --git a/src/orcapod/contexts/registry.py b/src/orcapod/contexts/registry.py new file mode 100644 index 0000000..e3f0989 --- /dev/null +++ b/src/orcapod/contexts/registry.py @@ -0,0 +1,350 @@ +""" +JSON-based Context Registry implementation. + +This module contains the core registry that loads and manages +data contexts from JSON files with validation and caching. +""" + +import json + + +from pathlib import Path +from typing import Any +import logging +from orcapod.utils.object_spec import parse_objectspec +from .core import DataContext, ContextValidationError, ContextResolutionError + +logger = logging.getLogger(__name__) + +try: + import jsonschema +except ImportError: + jsonschema = None + logger.info("jsonschema not available, skipping schema validation") + + +class JSONDataContextRegistry: + """ + Registry that loads data contexts from JSON files with validation. + + Features: + - Loads context specs from JSON files in a directory + - Validates JSON structure against schema + - Lazy loading with caching + - Robust error handling and logging + - Context string resolution (e.g., "v0.1", "std:v0.1:default") + """ + + def __init__( + self, + contexts_dir: Path | str | None = None, + schema_file: Path | str | None = None, + default_version: str = "v0.1", + ): + """ + Initialize the context registry. + + Args: + contexts_dir: Directory containing JSON context files + schema_file: JSON schema file for validation (optional) + default_version: Default context version to use + """ + # Set up paths + if contexts_dir is None: + contexts_dir = self._get_default_contexts_dir() + self.contexts_dir = Path(contexts_dir) + + if schema_file is None: + schema_file = self.contexts_dir / "schemas" / "context_schema.json" + self.schema_file = Path(schema_file) if schema_file else None + + # Internal state + self._specs: dict[str, dict[str, Any]] = {} + self._contexts: dict[str, DataContext] = {} + self._schema: dict[str, Any] | None = None + self._default_version = default_version + + # Load everything on initialization + self._load_schema() + self._load_all_specs() + logger.info(f"Loaded {len(self._specs)} context specifications") + + def _get_default_contexts_dir(self) -> Path: + """Get the default contexts directory from package data.""" + try: + # Python 3.9+ preferred method + import importlib.resources as resources + + contexts_path = resources.files("orcapod.contexts") / "data" + return Path(str(contexts_path)) + except (ImportError, AttributeError): + # Fallback for older Python versions + return Path(__file__).parent / "data" + + def _load_schema(self) -> None: + """Load JSON schema for validation if available.""" + if self.schema_file and self.schema_file.exists(): + try: + with open(self.schema_file, "r") as f: + self._schema = json.load(f) + logger.info(f"Loaded validation schema from {self.schema_file}") + except Exception as e: + logger.warning(f"Failed to load schema from {self.schema_file}: {e}") + self._schema = None + else: + logger.info("No validation schema specified or found") + self._schema = None + + def _load_all_specs(self) -> None: + """Load all JSON context specifications from the contexts directory.""" + if not self.contexts_dir.exists(): + raise ContextValidationError( + f"Contexts directory not found: {self.contexts_dir}" + ) + + json_files = list(self.contexts_dir.glob("*.json")) + if not json_files: + raise ContextValidationError( + f"No JSON context files found in {self.contexts_dir}" + ) + + for json_file in json_files: + try: + self._load_spec_file(json_file) + except Exception as e: + logger.error(f"Failed to load context spec from {json_file}: {e}") + raise ContextValidationError(f"Invalid context file {json_file}: {e}") + + def _load_spec_file(self, json_file: Path) -> None: + """Load and validate a single context specification file.""" + version = json_file.stem # e.g., "v0.1" from "v0.1.json" + + # Load JSON + with open(json_file, "r") as f: + spec = json.load(f) + + # Validate basic structure + if not isinstance(spec, dict): + raise ContextValidationError("Context spec must be a JSON object") + + # Check version consistency + spec_version = spec.get("version") + if spec_version != version: + raise ContextValidationError( + f"Version mismatch in {json_file}: filename suggests '{version}' " + f"but spec contains '{spec_version}'" + ) + + # TODO: clean this up -- sounds redundant to the validation performed by schema check + # Validate required fields + required_fields = [ + "context_key", + "version", + "type_converter", + "arrow_hasher", + "object_hasher", + ] + missing_fields = [field for field in required_fields if field not in spec] + if missing_fields: + raise ContextValidationError(f"Missing required fields: {missing_fields}") + + # Validate against JSON schema if available + if self._schema and jsonschema is not None: + try: + jsonschema.validate(spec, self._schema) + except jsonschema.ValidationError as e: + raise ContextValidationError(f"Schema validation failed: {e.message}") + + # Store the validated spec + self._specs[version] = spec + logger.debug(f"Loaded context spec: {version} -> {spec.get('context_key')}") + + def get_available_versions(self) -> list[str]: + """Get all available context versions, sorted.""" + return sorted(self._specs.keys()) + + def get_context_info(self, version: str) -> dict[str, Any]: + """Get context metadata without creating the full context.""" + if version not in self._specs: + available = ", ".join(self.get_available_versions()) + raise ContextResolutionError( + f"Unknown context version '{version}'. Available: {available}" + ) + + spec = self._specs[version] + return { + "version": spec["version"], + "context_key": spec["context_key"], + "description": spec.get("description", "No description provided"), + "file_path": self.contexts_dir / f"{version}.json", + } + + def resolve_context_string(self, context_string: str | None) -> str: + """ + Resolve context string to a version identifier. + + Supports various formats: + - None -> default version + - "v0.1" -> "v0.1" + - "std:v0.1:default" -> "v0.1" (extract version from full key) + - "latest" -> highest version number + """ + if context_string is None: + return self._default_version + + # Handle special cases + if context_string == "latest": + versions = self.get_available_versions() + return versions[-1] if versions else self._default_version + + # If it looks like a simple version (v0.1), use directly + if context_string.startswith("v") and ":" not in context_string: + return context_string + + # If it looks like a full context key (std:v0.1:default), extract version + if ":" in context_string: + parts = context_string.split(":") + if len(parts) >= 2 and parts[1].startswith("v"): + return parts[1] # Extract version part + + # Fallback: treat as version string + return context_string + + def get_context(self, context_string: str | None = None) -> DataContext: + """ + Get DataContext instance, creating it lazily if needed. + + Args: + context_string: Version string, full context key, or None for default + + Returns: + DataContext instance + + Raises: + ContextResolutionError: If context cannot be resolved or created + """ + try: + # Resolve to version + version = self.resolve_context_string(context_string) + + # Return cached context if available + if version in self._contexts: + logger.debug(f"Returning cached context for version {version}") + return self._contexts[version] + + # Validate version exists + if version not in self._specs: + available = ", ".join(self.get_available_versions()) + raise ContextResolutionError( + f"Unknown context version '{version}' (resolved from '{context_string}'). " + f"Available: {available}" + ) + + # Create context from spec + logger.info(f"Creating new context for version {version}") + spec = self._specs[version] + context = self._create_context_from_spec(spec) + + # Cache and return + self._contexts[version] = context + return context + + except Exception as e: + if isinstance(e, (ContextResolutionError, ContextValidationError)): + raise + else: + raise ContextResolutionError( + f"Failed to resolve context '{context_string}': {e}" + ) + + def _create_context_from_spec(self, spec: dict[str, Any]) -> DataContext: + """Create DataContext instance from validated specification.""" + try: + # Parse each component using ObjectSpec + context_key = spec["context_key"] + version = spec["version"] + description = spec.get("description", "") + ref_lut = {} + + logger.debug(f"Creating type converter for {version}") + ref_lut["semantic_registry"] = parse_objectspec( + spec["semantic_registry"], + ref_lut=ref_lut, + ) + + logger.debug(f"Creating type converter for {version}") + ref_lut["type_converter"] = parse_objectspec( + spec["type_converter"], ref_lut=ref_lut + ) + + logger.debug(f"Creating arrow hasher for {version}") + ref_lut["arrow_hasher"] = parse_objectspec( + spec["arrow_hasher"], ref_lut=ref_lut + ) + + logger.debug(f"Creating object hasher for {version}") + ref_lut["object_hasher"] = parse_objectspec( + spec["object_hasher"], ref_lut=ref_lut + ) + + return DataContext( + context_key=context_key, + version=version, + description=description, + type_converter=ref_lut["type_converter"], + arrow_hasher=ref_lut["arrow_hasher"], + object_hasher=ref_lut["object_hasher"], + ) + + except Exception as e: + raise ContextValidationError( + f"Failed to create context from spec: {e}" + ) from e + + def set_default_version(self, version: str) -> None: + """Set the default context version.""" + if version not in self._specs: + available = ", ".join(self.get_available_versions()) + raise ContextResolutionError( + f"Cannot set default to unknown version '{version}'. Available: {available}" + ) + + old_default = self._default_version + self._default_version = version + logger.info(f"Changed default context version from {old_default} to {version}") + + def reload_contexts(self) -> None: + """Reload all context specifications from disk.""" + logger.info("Reloading context specifications from disk") + + # Clear caches + self._specs.clear() + self._contexts.clear() + + # Reload + self._load_schema() + self._load_all_specs() + + logger.info(f"Reloaded {len(self._specs)} context specifications") + + def validate_all_contexts(self) -> dict[str, str | None]: + """ + Validate that all context specifications can be instantiated. + + Returns: + Dict mapping version -> error message (None if valid) + """ + results = {} + + for version in self.get_available_versions(): + try: + # Try to create the context (don't cache it) + spec = self._specs[version] + self._create_context_from_spec(spec) + results[version] = None # Success + logger.debug(f"Context {version} validates successfully") + except Exception as e: + results[version] = str(e) + logger.error(f"Context {version} validation failed: {e}") + + return results diff --git a/src/orcapod/core/__init__.py b/src/orcapod/core/__init__.py index e69de29..24f5aab 100644 --- a/src/orcapod/core/__init__.py +++ b/src/orcapod/core/__init__.py @@ -0,0 +1,7 @@ +from .trackers import DEFAULT_TRACKER_MANAGER +from .system_constants import constants + +__all__ = [ + "DEFAULT_TRACKER_MANAGER", + "constants", +] diff --git a/src/orcapod/core/arrow_data_utils.py b/src/orcapod/core/arrow_data_utils.py new file mode 100644 index 0000000..7194208 --- /dev/null +++ b/src/orcapod/core/arrow_data_utils.py @@ -0,0 +1,115 @@ +# Collection of functions to work with Arrow table data that underlies streams and/or datagrams +from orcapod.utils.lazy_module import LazyModule +from typing import TYPE_CHECKING +from orcapod.core.system_constants import constants +from collections.abc import Collection + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +def drop_columns_with_prefix( + table: "pa.Table", + prefix: str | tuple[str, ...], + exclude_columns: Collection[str] = (), +) -> "pa.Table": + """Drop columns with a specific prefix from an Arrow table.""" + columns_to_drop = [ + col + for col in table.column_names + if col.startswith(prefix) and col not in exclude_columns + ] + return table.drop(columns=columns_to_drop) + + +def drop_system_columns( + table: "pa.Table", + system_column_prefix: tuple[str, ...] = ( + constants.META_PREFIX, + constants.DATAGRAM_PREFIX, + ), +) -> "pa.Table": + return drop_columns_with_prefix(table, system_column_prefix) + + +def get_system_columns(table: "pa.Table") -> "pa.Table": + """Get system columns from an Arrow table.""" + return table.select( + [ + col + for col in table.column_names + if col.startswith(constants.SYSTEM_TAG_PREFIX) + ] + ) + + +def add_system_tag_column( + table: "pa.Table", + system_tag_column_name: str, + system_tag_values: str | Collection[str], +) -> "pa.Table": + """Add a system tags column to an Arrow table.""" + if not table.column_names: + raise ValueError("Table is empty") + if isinstance(system_tag_values, str): + system_tag_values = [system_tag_values] * table.num_rows + else: + system_tag_values = list(system_tag_values) + if len(system_tag_values) != table.num_rows: + raise ValueError( + "Length of system_tag_values must match number of rows in the table." + ) + if not system_tag_column_name.startswith(constants.SYSTEM_TAG_PREFIX): + system_tag_column_name = ( + f"{constants.SYSTEM_TAG_PREFIX}{system_tag_column_name}" + ) + tags_column = pa.array(system_tag_values, type=pa.large_string()) + return table.append_column(system_tag_column_name, tags_column) + + +def append_to_system_tags(table: "pa.Table", value: str) -> "pa.Table": + """Append a value to the system tags column in an Arrow table.""" + if not table.column_names: + raise ValueError("Table is empty") + + column_name_map = { + c: f"{c}:{value}" if c.startswith(constants.SYSTEM_TAG_PREFIX) else c + for c in table.column_names + } + return table.rename_columns(column_name_map) + + +def add_source_info( + table: "pa.Table", + source_info: str | Collection[str] | None, + exclude_prefixes: Collection[str] = ( + constants.META_PREFIX, + constants.DATAGRAM_PREFIX, + ), + exclude_columns: Collection[str] = (), +) -> "pa.Table": + """Add source information to an Arrow table.""" + # Create a new column with the source information + if source_info is None or isinstance(source_info, str): + source_column = [source_info] * table.num_rows + elif isinstance(source_info, Collection): + if len(source_info) != table.num_rows: + raise ValueError( + "Length of source_info collection must match number of rows in the table." + ) + source_column = source_info + + # identify columns for which source columns should be created + + for col in table.column_names: + if col.startswith(tuple(exclude_prefixes)) or col in exclude_columns: + continue + source_column = pa.array( + [f"{source_val}::{col}" for source_val in source_column], + type=pa.large_string(), + ) + table = table.append_column(f"{constants.SOURCE_PREFIX}{col}", source_column) + + return table diff --git a/src/orcapod/core/base.py b/src/orcapod/core/base.py index 0b1ed63..828c371 100644 --- a/src/orcapod/core/base.py +++ b/src/orcapod/core/base.py @@ -1,528 +1,161 @@ -# Collection of base classes for operations and streams in the orcabridge framework. -import threading -from abc import ABC, abstractmethod -from collections.abc import Callable, Collection, Iterator -from typing import Any, TypeVar, Hashable - - -from orcapod.hashing import HashableMixin -from orcapod.types import Packet, Tag, TypeSpec -from orcapod.utils.stream_utils import get_typespec - import logging +from abc import ABC +from typing import Any +from orcapod import DEFAULT_CONFIG, contexts +from orcapod.config import Config +from orcapod.protocols import hashing_protocols as hp logger = logging.getLogger(__name__) -class Kernel(ABC, HashableMixin): - """ - Kernel defines the fundamental unit of computation that can be performed on zero, one or more streams of data. - It is the base class for all computations and transformations that can be performed on a collection of streams - (including an empty collection). - A kernel is defined as a callable that takes a (possibly empty) collection of streams as the input - and returns a new stream as output (note that output stream is always singular). - Each "invocation" of the kernel on a collection of streams is assigned a unique ID. - The corresponding invocation information is stored as Invocation object and attached to the output stream - for computational graph tracking. - """ - - def __init__(self, label: str | None = None, **kwargs) -> None: - super().__init__(**kwargs) +class LablableBase: + def __init__(self, label: str | None = None, **kwargs): self._label = label + super().__init__(**kwargs) @property - def label(self) -> str: + def has_assigned_label(self) -> bool: """ - Returns a human-readable label for this kernel. - Default implementation returns the provided label or class name if no label was provided. - """ - if self._label: - return self._label - return self.__class__.__name__ - - @label.setter - def label(self, label: str) -> None: - self._label = label - - def __call__(self, *streams: "SyncStream", **kwargs) -> "SyncStream": - # Special handling of Source: trigger call on source if passed as stream - normalized_streams = [ - stream() if isinstance(stream, Source) else stream for stream in streams - ] + Check if the label is explicitly set for this object. - output_stream = self.forward(*normalized_streams, **kwargs) - # create an invocation instance - invocation = Invocation(self, normalized_streams) - # label the output_stream with the invocation that produced the stream - output_stream.invocation = invocation - - # register the invocation to all active trackers - active_trackers = Tracker.get_active_trackers() - for tracker in active_trackers: - tracker.record(invocation) - - return output_stream - - @abstractmethod - def forward(self, *streams: "SyncStream") -> "SyncStream": - """ - Trigger the main computation of the kernel on a collection of streams. - This method is called when the kernel is invoked with a collection of streams. - Subclasses should override this method to provide the kernel with its unique behavior + Returns: + bool: True if the label is explicitly set, False otherwise. """ + return self._label is not None - def __repr__(self): - return self.__class__.__name__ - - def __str__(self): - if self._label is not None: - return f"{self.__class__.__name__}({self._label})" - return self.__class__.__name__ - - def identity_structure(self, *streams: "SyncStream") -> Any: - # Default implementation of identity_structure for the kernel only - # concerns the kernel class and the streams if present. Subclasses of - # Kernels should override this method to provide a more meaningful - # representation of the kernel. Note that kernel must provide the notion - # of identity under possibly two distinct contexts: - # 1) identity of the kernel in itself when invoked without any stream - # 2) identity of the specific invocation of the kernel with a collection of streams - # While the latter technically corresponds to the identity of the invocation and not - # the kernel, only kernel can provide meaningful information as to the uniqueness of - # the invocation as only kernel would know if / how the input stream(s) alter the identity - # of the invocation. For example, if the kernel corresponds to an commutative computation - # and therefore kernel K(x, y) == K(y, x), then the identity structure must reflect the - # equivalence of the two by returning the same identity structure for both invocations. - # This can be achieved, for example, by returning a set over the streams instead of a tuple. - logger.warning( - f"Identity structure not implemented for {self.__class__.__name__}" - ) - return (self.__class__.__name__,) + tuple(streams) - - def keys( - self, *streams: "SyncStream", trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Returns the keys of the kernel output. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - If trigger_run is False (default), the keys are returned only if it is feasible to do so without triggering - the chain of computations. If trigger_run is True, underlying computation may get triggered if doing so - would allow for the keys to be determined. Returns None for either part of the keys cannot be inferred. - - This should be overridden by the subclass if subclass can provide smarter inference based on the specific - implementation of the subclass and input streams. + @property + def label(self) -> str: """ - if not trigger_run: - return None, None + Get the label of this object. - # resolve to actually executing the stream to fetch the first element - tag, packet = next(iter(self(*streams))) - return tuple(tag.keys()), tuple(packet.keys()) - - def types( - self, *streams: "SyncStream", trigger_run: bool = False - ) -> tuple[TypeSpec | None, TypeSpec | None]: - """ - Returns the tag and packet typespec of the kernel output. - Each typespec consists of mapping from field name to Python type. - If trigger_run is False (default), the typespec info is returned only if it is feasible to do so without triggering - the chain of computation. If trigger_run is True, underlying computation may get triggered if doing so - would allow for the typespec to be determined. Returns None for either part of the typespec cannot be inferred. + Returns: + str | None: The label of the object, or None if not set. """ - if not trigger_run: - return None, None - - tag, packet = next(iter(self(*streams))) - return get_typespec(tag), get_typespec(packet) + return self._label or self.computed_label() or self.__class__.__name__ - def claims_unique_tags( - self, *streams: "SyncStream", trigger_run: bool = False - ) -> bool | None: - """ - Returns True if the kernel claims that it has unique tags, False otherwise. - False indicates that it can be inferred that the kernel does not have unique tags - based on the input streams and the kernel's implementation. None indicates that - whether it is unique or not cannot be determined with certainty. - If trigger_run is True, the kernel may trigger the computation to verify - the uniqueness of tags. If trigger_run is False, the kernel will return - None if it cannot determine the uniqueness of tags without triggering the computation. - This method is useful for checking if the kernel can be used as a source - for other kernels that require unique tags. - Subclasses should override this method if it can provide reasonable check/guarantee - of unique tags. The default implementation returns False, meaning that the kernel - does not claim to have unique tags, even if turns out to be unique. + @label.setter + def label(self, label: str | None) -> None: """ - return None + Set the label of this object. + Args: + label (str | None): The label to set for this object. + """ + self._label = label -class Tracker(ABC): - """ - A tracker is a class that can track the invocations of kernels. Only "active" trackers - participate in tracking and its `record` method gets called on each invocation of a kernel. - Multiple trackers can be active at any time. - """ - - _local = threading.local() - - @classmethod - def get_active_trackers(cls) -> list["Tracker"]: - if hasattr(cls._local, "active_trackers"): - return cls._local.active_trackers - return [] - - def __init__(self): - self.active = False - - def activate(self) -> None: + def computed_label(self) -> str | None: """ - Activate the tracker. This is a no-op if the tracker is already active. + Compute a label for this object based on its content. If label is not explicitly set for this object + and computed_label returns a valid value, it will be used as label of this object. """ - if not self.active: - if not hasattr(self._local, "active_trackers"): - self._local.active_trackers = [] - self._local.active_trackers.append(self) - self.active = True - - def deactivate(self) -> None: - # Remove this tracker from active trackers - if hasattr(self._local, "active_trackers") and self.active: - if self in self._local.active_trackers: - self._local.active_trackers.remove(self) - self.active = False - - def __enter__(self): - self.activate() - return self - - def __exit__(self, exc_type, exc_val, ext_tb): - self.deactivate() - - @abstractmethod - def record(self, invocation: "Invocation") -> None: ... + return None -# This is NOT an abstract class, but rather a concrete class that -# represents an invocation of a kernel on a collection of streams. -class Invocation(HashableMixin): - """ - This class represents an invocation of a kernel on a collection of streams. - It contains the kernel and the streams that were used in the invocation. - Note that the collection of streams may be empty, in which case the invocation - likely corresponds to a source kernel. - """ - +class ContextAwareConfigurableBase(ABC): def __init__( self, - kernel: Kernel, - # TODO: technically this should be Stream to stay consistent with Stream interface. Update to Stream when AsyncStream is implemented - streams: Collection["SyncStream"], - ) -> None: - self.kernel = kernel - self.streams = streams - - def __hash__(self) -> int: - return super().__hash__() - - def __repr__(self) -> str: - return f"Invocation(kernel={self.kernel}, streams={self.streams})" - - def __str__(self) -> str: - return f"Invocation[ID:{self.__hash__()}]({self.kernel}, {self.streams})" - - def __eq__(self, other: Any) -> bool: - if not isinstance(other, Invocation): - return False - return hash(self) == hash(other) - - def __lt__(self, other: Any) -> bool: - if not isinstance(other, Invocation): - return NotImplemented - - if self.kernel == other.kernel: - return hash(self) < hash(other) - # otherwise, order by the kernel - return hash(self.kernel) < hash(other.kernel) - - # Pass-through implementations: these methods are implemented by "passing-through" the methods logic, - # simply invoking the corresopnding methods on the underlying kernel with the input streams - - def claims_unique_tags(self, trigger_run: bool = True) -> bool | None: - """ - Returns True if the invocation has unique tags, False otherwise. - This method is useful for checking if the invocation can be used as a source - for other kernels that require unique tags. None is returned if the - uniqueness of tags cannot be determined. - Note that uniqueness is best thought of as a "claim" by the kernel - that it has unique tags. The actual uniqueness can only be verified - by iterating over the streams and checking the tags. - """ - return self.kernel.claims_unique_tags(*self.streams, trigger_run=trigger_run) + data_context: str | contexts.DataContext | None = None, + orcapod_config: Config | None = None, + **kwargs, + ): + super().__init__(**kwargs) + if orcapod_config is None: + orcapod_config = DEFAULT_CONFIG + self._orcapod_config = orcapod_config + self._data_context = contexts.resolve_context(data_context) - def keys(self) -> tuple[Collection[str] | None, Collection[str] | None]: - return self.kernel.keys(*self.streams) + @property + def orcapod_config(self) -> Config: + return self._orcapod_config - def types(self) -> tuple[TypeSpec | None, TypeSpec | None]: - return self.kernel.types(*self.streams) + @property + def data_context(self) -> contexts.DataContext: + return self._data_context - def identity_structure(self) -> int: - # Identity of an invocation is entirely determined by the - # the kernel's identity structure upon invocation - return self.kernel.identity_structure(*self.streams) + @property + def data_context_key(self) -> str: + """Return the data context key.""" + return self._data_context.context_key -class Stream(ABC, HashableMixin): +class ContentIdentifiableBase(ContextAwareConfigurableBase): """ - A stream is a collection of tagged-packets that are generated by an operation. - The stream is iterable and can be used to access the packets in the stream. - - A stream has property `invocation` that is an instance of Invocation that generated the stream. - This may be None if the stream is not generated by a kernel (i.e. directly instantiated by a user). + Base class for content-identifiable objects. + This class provides a way to define objects that can be uniquely identified + based on their content rather than their identity in memory. Specifically, the identity of the + object is determined by the structure returned by the `identity_structure` method. + The hash of the object is computed based on the `identity_structure` using the provided `ObjectHasher`, + which defaults to the one returned by `get_default_object_hasher`. + Two content-identifiable objects are considered equal if their `identity_structure` returns the same value. """ - def __init__(self, label: str | None = None, **kwargs) -> None: - super().__init__(**kwargs) - self._invocation: Invocation | None = None - self._label = label - - @property - def label(self) -> str: + def __init__(self, **kwargs) -> None: """ - Returns a human-readable label for this stream. - If no label is provided and the stream is generated by an operation, - the label of the operation is used. - Otherwise, the class name is used as the label. - """ - if self._label is None: - if self.invocation is not None: - # use the invocation operation label - return self.invocation.kernel.label - else: - return self.__class__.__name__ - return self._label + Initialize the ContentHashable with an optional ObjectHasher. - @label.setter - def label(self, label: str) -> None: - """ - Sets a human-readable label for this stream. + Args: + identity_structure_hasher (ObjectHasher | None): An instance of ObjectHasher to use for hashing. """ - if not isinstance(label, str): - raise TypeError("label must be a string") - self._label = label - - @property - def invocation(self) -> Invocation | None: - return self._invocation - - @invocation.setter - def invocation(self, value: Invocation) -> None: - if not isinstance(value, Invocation): - raise TypeError("invocation field must be an instance of Invocation") - self._invocation = value - - @abstractmethod - def __iter__(self) -> Iterator[tuple[Tag, Packet]]: - raise NotImplementedError("Subclasses must implement __iter__ method") + super().__init__(**kwargs) + self._cached_content_hash: hp.ContentHash | None = None + self._cached_int_hash: int | None = None - def flow(self) -> Collection[tuple[Tag, Packet]]: - """ - Flow everything through the stream, returning the entire collection of - (Tag, Packet) as a collection. This will tigger any upstream computation of the stream. + def identity_structure(self) -> Any: """ - return list(self) + Return a structure that represents the identity of this object. - # --------------------- Recursive methods --------------------------- - # These methods form a step in the multi-class recursive invocation that follows the pattern of - # Stream -> Invocation -> Kernel -> Stream ... -> Invocation -> Kernel - # Most of the method logic would be found in Kernel's implementation of the method with - # Stream and Invocation simply serving as recursive steps + Override this method in your subclass to provide a stable representation + of your object's content. The structure should contain all fields that + determine the object's identity. - def identity_structure(self) -> Any: - """ - Identity structure of a stream is deferred to the identity structure - of the associated invocation, if present. - A bare stream without invocation has no well-defined identity structure. - Specialized stream subclasses should override this method to provide more meaningful identity structure + Returns: + Any: A structure representing this object's content, or None to use default hash """ - if self.invocation is not None: - return self.invocation.identity_structure() - return super().identity_structure() + raise NotImplementedError("Subclasses must implement identity_structure") - def keys( - self, *, trigger_run=False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Returns the keys of the stream. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - The keys are returned on based-effort basis, and this invocation may trigger the - upstream computation of the stream. - Furthermore, the keys are not guaranteed to be identical across all packets in the stream. - This method is useful for inferring the keys of the stream without having to iterate - over the entire stream. - """ - if self.invocation is not None: - # if the stream is generated by an operation, use the keys from the invocation - tag_keys, packet_keys = self.invocation.keys() - if tag_keys is not None and packet_keys is not None: - return tag_keys, packet_keys - # otherwise, use the keys from the first packet in the stream - # note that this may be computationally expensive - tag, packet = next(iter(self)) - return list(tag.keys()), list(packet.keys()) - - def types(self, *, trigger_run=False) -> tuple[TypeSpec | None, TypeSpec | None]: - """ - Returns the keys of the stream. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - The keys are returned on based-effort basis, and this invocation may trigger the - upstream computation of the stream. - Furthermore, the keys are not guaranteed to be identical across all packets in the stream. - This method is useful for inferring the keys of the stream without having to iterate - over the entire stream. + def content_hash(self) -> hp.ContentHash: """ - tag_types, packet_types = None, None - if self.invocation is not None: - # if the stream is generated by an operation, use the keys from the invocation - tag_types, packet_types = self.invocation.types() - if not trigger_run or (tag_types is not None and packet_types is not None): - return tag_types, packet_types - # otherwise, use the keys from the first packet in the stream - # note that this may be computationally expensive - tag, packet = next(iter(self)) - return tag_types or get_typespec(tag), packet_types or get_typespec(packet) - - def claims_unique_tags(self, *, trigger_run=False) -> bool | None: - """ - Returns True if the stream has unique tags, False otherwise. - This method is useful for checking if the stream can be used as a source - for other operations that require unique tags. None is returned if the - uniqueness of tags cannot be determined. - If the stream is generated by an operation, the invocation is consulted for - the information about unique tags. - """ - if self.invocation is not None: - return self.invocation.claims_unique_tags(trigger_run=trigger_run) - return None - - -class SyncStream(Stream): - """ - A stream that will complete in a fixed amount of time. - It is suitable for synchronous operations that - will have to wait for the stream to finish before proceeding. - """ + Compute a hash based on the content of this object. - def head(self, n: int = 5) -> None: + Returns: + bytes: A byte representation of the hash based on the content. + If no identity structure is provided, return None. """ - Print the first n elements of the stream. - This method is useful for previewing the stream - without having to iterate over the entire stream. - If n is <= 0, the entire stream is printed. - """ - for idx, (tag, packet) in enumerate(self): - if n > 0 and idx >= n: - break - print(f"Tag: {tag}, Packet: {packet}") + if self._cached_content_hash is None: + structure = self.identity_structure() + # processed_structure = process_structure(structure) + self._cached_content_hash = self.data_context.object_hasher.hash_object( + structure + ) + return self._cached_content_hash - def __len__(self) -> int: - """ - Returns the number of packets in the stream. - Note that this method may trigger the upstream computation of the stream. - This method is not guaranteed to be efficient and should be used with caution. + def __hash__(self) -> int: """ - return sum(1 for _ in self) + Hash implementation that uses the identity structure if provided, + otherwise falls back to the superclass's hash method. - def __rshift__( - self, transformer: dict | Callable[["SyncStream"], "SyncStream"] - ) -> "SyncStream": + Returns: + int: A hash value based on either content or identity """ - Returns a new stream that is the result of applying the mapping to the stream. - The mapping is applied to each packet in the stream and the resulting packets - are returned in a new stream. - """ - # TODO: remove just in time import - from .operators import MapPackets - - if isinstance(transformer, dict): - return MapPackets(transformer)(self) - elif isinstance(transformer, Callable): - return transformer(self) - - # Otherwise, do not know how to handle the transformer - raise TypeError( - "transformer must be a dictionary or a callable that takes a SyncStream" - ) + # Get the identity structure + return self.content_hash().to_int() - def __mul__(self, other: "SyncStream") -> "SyncStream": + def __eq__(self, other: object) -> bool: """ - Returns a new stream that is the result joining with the other stream - """ - # TODO: remove just in time import - from .operators import Join + Equality check that compares the identity structures of two objects. - if not isinstance(other, SyncStream): - raise TypeError("other must be a SyncStream") - return Join()(self, other) + Args: + other (object): The object to compare against. - def claims_unique_tags(self, *, trigger_run=False) -> bool | None: - """ - For synchronous streams, if the stream is generated by an operation, the invocation - is consulted first to see if the uniqueness of tags can be determined without iterating over the stream. - If uniqueness cannot be determined from the invocation and if trigger_run is True, uniqueness is checked - by iterating over all elements and verifying uniqueness. - Consequently, this may trigger upstream computations and can be expensive. - If trigger_run is False, the method will return None if the uniqueness cannot be determined. - Since this consults the invocation, the resulting value is ultimately a claim and not a guarantee - of uniqueness. If guarantee of uniquess is required, then use has_unique_tags method + Returns: + bool: True if both objects have the same identity structure, False otherwise. """ - result = super().claims_unique_tags(trigger_run=trigger_run) - if not trigger_run or result is not None: - return result - - # If the uniqueness cannot be determined from the invocation, iterate over the stream - unique_tags = set() - for tag, _ in self: - if tag in unique_tags: - return False - unique_tags.add(tag) - return True - - -class Operator(Kernel): - """ - A Mapper is an operation that does NOT generate new file content. - It is used to control the flow of data in the pipeline without modifying or creating data content. - """ - + if not isinstance(other, ContentIdentifiableBase): + return NotImplemented -class Source(Kernel, SyncStream): - """ - A base class for all sources in the system. A source can be seen as a special - type of kernel that takes no input and produces a stream of packets. - For convenience, the source itself can act as a stream and thus can be used - as an input to other kernels directly. - However, note that a source is still best thought of as a kernel that - produces a stream of packets, rather than a stream itself. On almost all occasions, - a source acts as a kernel. - """ + return self.identity_structure() == other.identity_structure() - def __init__(self, label: str | None = None, **kwargs) -> None: - super().__init__(label=label, **kwargs) - self._invocation = None - def __iter__(self) -> Iterator[tuple[Tag, Packet]]: - """ - Simple iter method that allows for Source object to act as a stream. - """ - yield from self() - - # TODO: consider adding stream-like behavior for determining keys and types - def keys( - self, *streams: "SyncStream", trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - return Kernel.keys(self, *streams, trigger_run=trigger_run) - - def types( - self, *streams: "SyncStream", trigger_run: bool = False - ) -> tuple[TypeSpec | None, TypeSpec | None]: - return Kernel.types(self, *streams, trigger_run=trigger_run) +class LabeledContentIdentifiableBase(ContentIdentifiableBase, LablableBase): + pass diff --git a/src/orcapod/core/datagrams/__init__.py b/src/orcapod/core/datagrams/__init__.py new file mode 100644 index 0000000..0c255e3 --- /dev/null +++ b/src/orcapod/core/datagrams/__init__.py @@ -0,0 +1,13 @@ +from .arrow_datagram import ArrowDatagram +from .arrow_tag_packet import ArrowTag, ArrowPacket +from .dict_datagram import DictDatagram +from .dict_tag_packet import DictTag, DictPacket + +__all__ = [ + "ArrowDatagram", + "ArrowTag", + "ArrowPacket", + "DictDatagram", + "DictTag", + "DictPacket", +] diff --git a/src/orcapod/core/datagrams/arrow_datagram.py b/src/orcapod/core/datagrams/arrow_datagram.py new file mode 100644 index 0000000..52c046e --- /dev/null +++ b/src/orcapod/core/datagrams/arrow_datagram.py @@ -0,0 +1,842 @@ +import logging +from collections.abc import Collection, Iterator, Mapping +from typing import Self, TYPE_CHECKING + + +from orcapod import contexts +from orcapod.core.datagrams.base import BaseDatagram +from orcapod.core.system_constants import constants +from orcapod.types import DataValue, PythonSchema +from orcapod.protocols.hashing_protocols import ContentHash +from orcapod.utils import arrow_utils +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + +logger = logging.getLogger(__name__) +DEBUG = False + + +class ArrowDatagram(BaseDatagram): + """ + Immutable datagram implementation using PyArrow Table as storage backend. + + This implementation provides high-performance columnar data operations while + maintaining the datagram interface. It efficiently handles type conversions, + semantic processing, and interoperability with Arrow-based tools. + + The underlying table is split into separate components: + - Data table: Primary business data columns + - Meta table: Internal system metadata with {orcapod.META_PREFIX} ('__') prefixes + - Context table: Data context information with {orcapod.CONTEXT_KEY} + + Future Packet subclass will also handle: + - Source info: Data provenance with {orcapod.SOURCE_PREFIX} ('_source_') prefixes + + When exposing to external tools, semantic types are encoded as + `_{semantic_type}_` prefixes (_path_config_file, _id_user_name). + + All operations return new instances, preserving immutability. + + Example: + >>> table = pa.Table.from_pydict({ + ... "user_id": [123], + ... "name": ["Alice"], + ... "__pipeline_version": ["v2.1.0"], + ... "{orcapod.CONTEXT_KEY}": ["financial_v1"] + ... }) + >>> datagram = ArrowDatagram(table) + >>> updated = datagram.update(name="Alice Smith") + """ + + def __init__( + self, + table: "pa.Table", + meta_info: Mapping[str, DataValue] | None = None, + data_context: str | contexts.DataContext | None = None, + ) -> None: + """ + Initialize ArrowDatagram from PyArrow Table. + + Args: + table: PyArrow Table containing the data. Must have exactly one row. + semantic_converter: Optional converter for semantic type handling. + If None, will be created based on the data context and table schema. + data_context: Context key string or DataContext object. + If None and table contains context column, will extract from table. + + Raises: + ValueError: If table doesn't contain exactly one row. + + Note: + The input table is automatically split into data, meta, and context + components based on column naming conventions. + """ + # Validate table has exactly one row for datagram + if len(table) != 1: + raise ValueError( + "Table must contain exactly one row to be a valid datagram." + ) + + # normalize the table to large data types (for Polars compatibility) + table = arrow_utils.normalize_table_to_large_types(table) + + # Split table into data, meta, and context components + context_columns = ( + [constants.CONTEXT_KEY] + if constants.CONTEXT_KEY in table.column_names + else [] + ) + + # Extract context table from passed in table if present + if constants.CONTEXT_KEY in table.column_names and data_context is None: + context_table = table.select([constants.CONTEXT_KEY]) + data_context = context_table[constants.CONTEXT_KEY].to_pylist()[0] + + # Initialize base class with data context + super().__init__(data_context) + + meta_columns = [ + col for col in table.column_names if col.startswith(constants.META_PREFIX) + ] + # Split table into components + self._data_table = table.drop_columns(context_columns + meta_columns) + self._meta_table = table.select(meta_columns) if meta_columns else None + + if len(self._data_table.column_names) == 0: + raise ValueError("Data table must contain at least one data column.") + + # process supplemented meta info if provided + if meta_info is not None: + # make sure it has the expected prefixes + meta_info = { + ( + f"{constants.META_PREFIX}{k}" + if not k.startswith(constants.META_PREFIX) + else k + ): v + for k, v in meta_info.items() + } + new_meta_table = ( + self._data_context.type_converter.python_dicts_to_arrow_table( + [meta_info], + ) + ) + + if self._meta_table is None: + self._meta_table = new_meta_table + else: + # drop any column that will be overwritten by the new meta table + keep_meta_columns = [ + c + for c in self._meta_table.column_names + if c not in new_meta_table.column_names + ] + self._meta_table = arrow_utils.hstack_tables( + self._meta_table.select(keep_meta_columns), new_meta_table + ) + + # Create data context table + data_context_schema = pa.schema({constants.CONTEXT_KEY: pa.large_string()}) + self._data_context_table = pa.Table.from_pylist( + [{constants.CONTEXT_KEY: self._data_context.context_key}], + schema=data_context_schema, + ) + + # Initialize caches + self._cached_python_schema: PythonSchema | None = None + self._cached_python_dict: dict[str, DataValue] | None = None + self._cached_meta_python_schema: PythonSchema | None = None + self._cached_content_hash: ContentHash | None = None + + # 1. Core Properties (Identity & Structure) + @property + def meta_columns(self) -> tuple[str, ...]: + """Return tuple of meta column names.""" + if self._meta_table is None: + return () + return tuple(self._meta_table.column_names) + + # 2. Dict-like Interface (Data Access) + def __getitem__(self, key: str) -> DataValue: + """Get data column value by key.""" + if key not in self._data_table.column_names: + raise KeyError(f"Data column '{key}' not found") + + return self._data_table[key].to_pylist()[0] + + def __contains__(self, key: str) -> bool: + """Check if data column exists.""" + return key in self._data_table.column_names + + def __iter__(self) -> Iterator[str]: + """Iterate over data column names.""" + return iter(self._data_table.column_names) + + def get(self, key: str, default: DataValue = None) -> DataValue: + """Get data column value with default.""" + if key in self._data_table.column_names: + return self.as_dict()[key] + return default + + # 3. Structural Information + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> tuple[str, ...]: + """Return tuple of column names.""" + # Start with data columns + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + + result_keys = list(self._data_table.column_names) + + # Add context if requested + if include_context: + result_keys.append(constants.CONTEXT_KEY) + + # Add meta columns if requested + if include_meta_columns: + if include_meta_columns is True: + result_keys.extend(self.meta_columns) + elif isinstance(include_meta_columns, Collection): + # Filter meta columns by prefix matching + filtered_meta_cols = [ + col + for col in self.meta_columns + if any(col.startswith(prefix) for prefix in include_meta_columns) + ] + result_keys.extend(filtered_meta_cols) + + return tuple(result_keys) + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> PythonSchema: + """ + Return Python schema for the datagram. + + Args: + include_meta_columns: Whether to include meta column types. + - True: include all meta column types + - Collection[str]: include meta column types matching these prefixes + - False: exclude meta column types + include_context: Whether to include context type + + Returns: + Python schema + """ + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + + # Get data schema (cached) + if self._cached_python_schema is None: + self._cached_python_schema = ( + self._data_context.type_converter.arrow_schema_to_python_schema( + self._data_table.schema + ) + ) + + schema = dict(self._cached_python_schema) + + # Add context if requested + if include_context: + schema[constants.CONTEXT_KEY] = str + + # Add meta schema if requested + if include_meta_columns and self._meta_table is not None: + if self._cached_meta_python_schema is None: + self._cached_meta_python_schema = ( + self._data_context.type_converter.arrow_schema_to_python_schema( + self._meta_table.schema + ) + ) + meta_schema = dict(self._cached_meta_python_schema) + if include_meta_columns is True: + schema.update(meta_schema) + elif isinstance(include_meta_columns, Collection): + filtered_meta_schema = { + k: v + for k, v in meta_schema.items() + if any(k.startswith(prefix) for prefix in include_meta_columns) + } + schema.update(filtered_meta_schema) + + return schema + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Schema": + """ + Return the PyArrow schema for this datagram. + + Args: + include_meta_columns: Whether to include meta columns in the schema. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include context column in the schema + + Returns: + PyArrow schema representing the datagram's structure + """ + # order matters + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + + all_schemas = [self._data_table.schema] + + # Add context schema if requested + if include_context: + # TODO: reassess the efficiency of this approach + all_schemas.append(self._data_context_table.schema) + + # Add meta schema if requested + if include_meta_columns and self._meta_table is not None: + if include_meta_columns is True: + meta_schema = self._meta_table.schema + elif isinstance(include_meta_columns, Collection): + # Filter meta schema by prefix matching + matched_fields = [ + field + for field in self._meta_table.schema + if any( + field.name.startswith(prefix) for prefix in include_meta_columns + ) + ] + if matched_fields: + meta_schema = pa.schema(matched_fields) + else: + meta_schema = None + else: + meta_schema = None + + if meta_schema is not None: + all_schemas.append(meta_schema) + + return arrow_utils.join_arrow_schemas(*all_schemas) + + def content_hash(self) -> ContentHash: + """ + Calculate and return content hash of the datagram. + Only includes data columns, not meta columns or context. + + Returns: + Hash string of the datagram content + """ + if self._cached_content_hash is None: + self._cached_content_hash = self._data_context.arrow_hasher.hash_table( + self._data_table, + ) + return self._cached_content_hash + + # 4. Format Conversions (Export) + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> dict[str, DataValue]: + """ + Return dictionary representation of the datagram. + + Args: + include_meta_columns: Whether to include meta columns. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include context key + + Returns: + Dictionary representation + """ + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + + # Get data dict (cached) + if self._cached_python_dict is None: + self._cached_python_dict = ( + self._data_context.type_converter.arrow_table_to_python_dicts( + self._data_table + )[0] + ) + + result_dict = dict(self._cached_python_dict) + + # Add context if requested + if include_context: + result_dict[constants.CONTEXT_KEY] = self._data_context.context_key + + # Add meta data if requested + if include_meta_columns and self._meta_table is not None: + if include_meta_columns is True: + meta_dict = self._meta_table.to_pylist()[0] + elif isinstance(include_meta_columns, Collection): + meta_dict = self._meta_table.to_pylist()[0] + # Include only meta columns matching prefixes + meta_dict = { + k: v + for k, v in meta_dict.items() + if any(k.startswith(prefix) for prefix in include_meta_columns) + } + if meta_dict is not None: + result_dict.update(meta_dict) + + return result_dict + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Table": + """ + Convert the datagram to an Arrow table. + + Args: + include_meta_columns: Whether to include meta columns. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include the context column + + Returns: + Arrow table representation + """ + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + + all_tables = [self._data_table] + + # Add context if requested + if include_context: + all_tables.append(self._data_context_table) + + # Add meta columns if requested + if include_meta_columns and self._meta_table is not None: + meta_table = None + if include_meta_columns is True: + meta_table = self._meta_table + elif isinstance(include_meta_columns, Collection): + # Filter meta columns by prefix matching + # ensure all given prefixes start with the meta prefix + prefixes = ( + f"{constants.META_PREFIX}{prefix}" + if not prefix.startswith(constants.META_PREFIX) + else prefix + for prefix in include_meta_columns + ) + + matched_cols = [ + col + for col in self._meta_table.column_names + if any(col.startswith(prefix) for prefix in prefixes) + ] + if matched_cols: + meta_table = self._meta_table.select(matched_cols) + else: + meta_table = None + + if meta_table is not None: + all_tables.append(meta_table) + + return arrow_utils.hstack_tables(*all_tables) + + def as_arrow_compatible_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> dict[str, DataValue]: + """ + Return dictionary representation compatible with Arrow. + + Args: + include_meta_columns: Whether to include meta columns. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include context key + + Returns: + Dictionary representation compatible with Arrow + """ + return self.as_table( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ).to_pylist()[0] + + # 5. Meta Column Operations + def get_meta_value(self, key: str, default: DataValue = None) -> DataValue: + """ + Get a meta column value. + + Args: + key: Meta column key (with or without {orcapod.META_PREFIX} ('__') prefix) + default: Default value if not found + + Returns: + Meta column value + """ + if self._meta_table is None: + return default + + # Handle both prefixed and unprefixed keys + if not key.startswith(constants.META_PREFIX): + key = constants.META_PREFIX + key + + if key not in self._meta_table.column_names: + return default + + return self._meta_table[key].to_pylist()[0] + + def with_meta_columns(self, **meta_updates: DataValue) -> Self: + """ + Create a new ArrowDatagram with updated meta columns. + Maintains immutability by returning a new instance. + + Args: + **meta_updates: Meta column updates (keys will be prefixed with {orcapod.META_PREFIX} ('__') if needed) + + Returns: + New ArrowDatagram instance + """ + # Prefix the keys and prepare updates + prefixed_updates = {} + for k, v in meta_updates.items(): + if not k.startswith(constants.META_PREFIX): + k = constants.META_PREFIX + k + prefixed_updates[k] = v + + new_datagram = self.copy(include_cache=False) + + # Start with existing meta data + meta_dict = {} + if self._meta_table is not None: + meta_dict = self._meta_table.to_pylist()[0] + + # Apply updates + meta_dict.update(prefixed_updates) + + # TODO: properly handle case where meta data is None (it'll get inferred as NoneType) + + # Create new meta table + new_datagram._meta_table = ( + self._data_context.type_converter.python_dicts_to_arrow_table([meta_dict]) + if meta_dict + else None + ) + return new_datagram + + def drop_meta_columns(self, *keys: str, ignore_missing: bool = False) -> Self: + """ + Create a new ArrowDatagram with specified meta columns dropped. + Maintains immutability by returning a new instance. + + Args: + *keys: Meta column keys to drop (with or without {orcapod.META_PREFIX} ('__') prefix) + + Returns: + New ArrowDatagram instance without specified meta columns + """ + if self._meta_table is None: + return self # No meta columns to drop + + # Normalize keys to have prefixes + prefixed_keys = set() + for key in keys: + if not key.startswith(constants.META_PREFIX): + key = constants.META_PREFIX + key + prefixed_keys.add(key) + + missing_keys = prefixed_keys - set(self._meta_table.column_names) + if missing_keys and not ignore_missing: + raise KeyError( + f"Following meta columns do not exist and cannot be dropped: {sorted(missing_keys)}" + ) + + # Only drop columns that actually exist + existing_keys = prefixed_keys - missing_keys + + new_datagram = self.copy(include_cache=False) + if existing_keys: # Only drop if there are existing columns to drop + new_datagram._meta_table = self._meta_table.drop_columns( + list(existing_keys) + ) + + return new_datagram + + # 6. Data Column Operations + def select(self, *column_names: str) -> Self: + """ + Create a new ArrowDatagram with only specified data columns. + Maintains immutability by returning a new instance. + + Args: + *column_names: Data column names to keep + + Returns: + New ArrowDatagram instance with only specified data columns + """ + # Validate columns exist + missing_cols = set(column_names) - set(self._data_table.column_names) + if missing_cols: + raise ValueError(f"Columns not found: {missing_cols}") + + new_datagram = self.copy(include_cache=False) + new_datagram._data_table = new_datagram._data_table.select(column_names) + + return new_datagram + + def drop(self, *column_names: str, ignore_missing: bool = False) -> Self: + """ + Create a new ArrowDatagram with specified data columns dropped. + Maintains immutability by returning a new instance. + + Args: + *column_names: Data column names to drop + + Returns: + New ArrowDatagram instance without specified data columns + """ + + # Filter out specified data columns + missing = set(column_names) - set(self._data_table.column_names) + if missing and not ignore_missing: + raise KeyError( + f"Following columns do not exist and cannot be dropped: {sorted(missing)}" + ) + # Only keep columns that actually exist + existing_columns = tuple( + c for c in column_names if c in self._data_table.column_names + ) + + new_datagram = self.copy(include_cache=False) + if existing_columns: # Only drop if there are existing columns to drop + new_datagram._data_table = self._data_table.drop_columns( + list(existing_columns) + ) + # TODO: consider dropping extra semantic columns if they are no longer needed + return new_datagram + + def rename(self, column_mapping: Mapping[str, str]) -> Self: + """ + Create a new ArrowDatagram with data columns renamed. + Maintains immutability by returning a new instance. + + Args: + column_mapping: Mapping from old column names to new column names + + Returns: + New ArrowDatagram instance with renamed data columns + """ + # Create new schema with renamed fields, preserving original types + + if not column_mapping: + return self + + new_names = [column_mapping.get(k, k) for k in self._data_table.column_names] + + new_datagram = self.copy(include_cache=False) + new_datagram._data_table = new_datagram._data_table.rename_columns(new_names) + + return new_datagram + + def update(self, **updates: DataValue) -> Self: + """ + Create a new ArrowDatagram with specific column values updated. + + Args: + **updates: Column names and their new values + + Returns: + New ArrowDatagram instance with updated values + + Raises: + KeyError: If any specified column doesn't exist + + Example: + # Convert relative path to absolute path + updated = datagram.update(file_path="/absolute/path/to/file.txt") + + # Update multiple values + updated = datagram.update(status="processed", file_path="/new/path") + """ + # Only update if there are columns to update + if not updates: + return self + + # Validate all columns exist + missing_cols = set(updates.keys()) - set(self._data_table.column_names) + if missing_cols: + raise KeyError( + f"Only existing columns can be updated. Following columns were not found: {sorted(missing_cols)}" + ) + + new_datagram = self.copy(include_cache=False) + + # use existing schema + sub_schema = arrow_utils.schema_select( + new_datagram._data_table.schema, list(updates.keys()) + ) + + update_table = self._data_context.type_converter.python_dicts_to_arrow_table( + [updates], arrow_schema=sub_schema + ) + + new_datagram._data_table = arrow_utils.hstack_tables( + self._data_table.drop_columns(list(updates.keys())), update_table + ).select(self._data_table.column_names) # adjsut the order to match original + + return new_datagram + + def with_columns( + self, + column_types: Mapping[str, type] | None = None, + **updates: DataValue, + ) -> Self: + """ + Create a new ArrowDatagram with new data columns added. + Maintains immutability by returning a new instance. + + Args: + column_updates: New data columns as a mapping + column_types: Optional type specifications for new columns + **kwargs: New data columns as keyword arguments + + Returns: + New ArrowDatagram instance with new data columns added + + Raises: + ValueError: If any column already exists (use update() instead) + """ + # Combine explicit updates with kwargs + + if not updates: + return self + + # Error if any of the columns already exists + existing_overlaps = set(updates.keys()) & set(self._data_table.column_names) + if existing_overlaps: + raise ValueError( + f"Columns already exist: {sorted(existing_overlaps)}. " + f"Use update() to modify existing columns." + ) + + # create a copy and perform in-place updates + new_datagram = self.copy() + + # TODO: consider simplifying this conversion logic + + # TODO: cleanup the handling of typespec python schema and various conversion points + new_data_table = self._data_context.type_converter.python_dicts_to_arrow_table( + [updates], python_schema=dict(column_types) if column_types else None + ) + + # perform in-place update + new_datagram._data_table = arrow_utils.hstack_tables( + new_datagram._data_table, new_data_table + ) + + return new_datagram + + # 7. Context Operations + def with_context_key(self, new_context_key: str) -> Self: + """ + Create a new ArrowDatagram with a different data context key. + Maintains immutability by returning a new instance. + + Args: + new_context_key: New data context key string + + Returns: + New ArrowDatagram instance with new context + """ + # TODO: consider if there is a more efficient way to handle context + # Combine all tables for reconstruction + + new_datagram = self.copy(include_cache=False) + new_datagram._data_context = contexts.resolve_context(new_context_key) + return new_datagram + + # 8. Utility Operations + def copy(self, include_cache: bool = True) -> Self: + """Return a copy of the datagram.""" + new_datagram = super().copy() + + new_datagram._data_table = self._data_table + new_datagram._meta_table = self._meta_table + new_datagram._data_context = self._data_context + + if include_cache: + new_datagram._cached_python_schema = self._cached_python_schema + new_datagram._cached_python_dict = self._cached_python_dict + new_datagram._cached_content_hash = self._cached_content_hash + new_datagram._cached_meta_python_schema = self._cached_meta_python_schema + else: + new_datagram._cached_python_schema = None + new_datagram._cached_python_dict = None + new_datagram._cached_content_hash = None + new_datagram._cached_meta_python_schema = None + + return new_datagram + + # 9. String Representations + def __str__(self) -> str: + """ + Return user-friendly string representation. + + Shows the datagram as a simple dictionary for user-facing output, + messages, and logging. Only includes data columns for clean output. + + Returns: + Dictionary-style string representation of data columns only. + + Example: + >>> str(datagram) + "{'user_id': 123, 'name': 'Alice'}" + >>> print(datagram) + {'user_id': 123, 'name': 'Alice'} + """ + return str(self.as_dict()) + + def __repr__(self) -> str: + """ + Return detailed string representation for debugging. + + Shows the datagram type and comprehensive information including + data columns, meta columns count, and context for debugging purposes. + + Returns: + Detailed representation with type and metadata information. + + Example: + >>> repr(datagram) + "ArrowDatagram(data={'user_id': 123, 'name': 'Alice'}, meta_columns=2, context='std:v1.0.0:abc123')" + """ + if DEBUG: + data_dict = self.as_dict() + meta_count = len(self.meta_columns) + context_key = self.data_context_key + + return ( + f"{self.__class__.__name__}(" + f"data={data_dict}, " + f"meta_columns={meta_count}, " + f"context='{context_key}'" + f")" + ) + else: + return str(self.as_dict()) diff --git a/src/orcapod/core/datagrams/arrow_tag_packet.py b/src/orcapod/core/datagrams/arrow_tag_packet.py new file mode 100644 index 0000000..24d2185 --- /dev/null +++ b/src/orcapod/core/datagrams/arrow_tag_packet.py @@ -0,0 +1,554 @@ +import logging +from collections.abc import Collection, Mapping +from typing import Self, TYPE_CHECKING + + +from orcapod.core.system_constants import constants +from orcapod import contexts +from orcapod.semantic_types import infer_python_schema_from_pylist_data + +from orcapod.types import DataValue, PythonSchema +from orcapod.utils import arrow_utils + +from orcapod.core.datagrams.arrow_datagram import ArrowDatagram +from orcapod.utils.lazy_module import LazyModule + +logger = logging.getLogger(__name__) + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class ArrowTag(ArrowDatagram): + """ + A tag implementation using Arrow table backend. + + Represents a single-row Arrow table that can be converted to Python + dictionary representation while caching computed values for efficiency. + + Initialize with an Arrow table. + + Args: + table: Single-row Arrow table representing the tag + + Raises: + ValueError: If table doesn't contain exactly one row + """ + + def __init__( + self, + table: "pa.Table", + system_tags: Mapping[str, DataValue] | None = None, + data_context: str | contexts.DataContext | None = None, + ) -> None: + if len(table) != 1: + raise ValueError( + "ArrowTag should only contain a single row, " + "as it represents a single tag." + ) + super().__init__( + table=table, + data_context=data_context, + ) + extracted_system_tag_columns = [ + c + for c in self._data_table.column_names + if c.startswith(constants.SYSTEM_TAG_PREFIX) + ] + self._system_tags_dict: dict[str, DataValue] = ( + self._data_context.type_converter.arrow_table_to_python_dicts( + self._data_table.select(extracted_system_tag_columns) + )[0] + ) + self._system_tags_dict.update(system_tags or {}) + self._system_tags_python_schema = infer_python_schema_from_pylist_data( + [self._system_tags_dict] + ) + self._system_tags_table = ( + self._data_context.type_converter.python_dicts_to_arrow_table( + [self._system_tags_dict], python_schema=self._system_tags_python_schema + ) + ) + + self._data_table = self._data_table.drop_columns(extracted_system_tag_columns) + + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> tuple[str, ...]: + keys = super().keys( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + keys += tuple(self._system_tags_dict.keys()) + return keys + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> PythonSchema: + """Return copy of the Python schema.""" + schema = super().types( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + schema.update(self._system_tags_python_schema) + return schema + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> "pa.Schema": + """ + Return the PyArrow schema for this datagram. + + Args: + include_data_context: Whether to include data context column in the schema + include_source: Whether to include source info columns in the schema + + Returns: + PyArrow schema representing the datagram's structure + """ + schema = super().arrow_schema( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + return arrow_utils.join_arrow_schemas( + schema, self._system_tags_table.schema + ) + return schema + + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> dict[str, DataValue]: + """ + Convert to dictionary representation. + + Args: + include_source: Whether to include source info fields + + Returns: + Dictionary representation of the packet + """ + return_dict = super().as_dict( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + return_dict.update(self._system_tags_dict) + return return_dict + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> "pa.Table": + table = super().as_table( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if ( + include_all_info or include_system_tags + ) and self._system_tags_table.num_columns > 0: + # add system_tags only if there are actual system tag columns + table = arrow_utils.hstack_tables(table, self._system_tags_table) + return table + + def as_datagram( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_system_tags: bool = False, + ) -> ArrowDatagram: + table = self.as_table( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_system_tags=include_system_tags, + ) + return ArrowDatagram( + table, + data_context=self._data_context, + ) + + def system_tags(self) -> dict[str, DataValue | None]: + """ + Return system tags for all keys. + + Returns: + Copy of the dictionary mapping field names to their source info + """ + return self._system_tags_dict.copy() + + # 8. Utility Operations + def copy(self, include_cache: bool = True) -> Self: + """Return a copy of the datagram.""" + new_tag = super().copy(include_cache=include_cache) + + new_tag._system_tags_dict = self._system_tags_dict.copy() + new_tag._system_tags_python_schema = self._system_tags_python_schema.copy() + new_tag._system_tags_table = self._system_tags_table + + return new_tag + + +class ArrowPacket(ArrowDatagram): + """ + Arrow table-based packet implementation with comprehensive features. + + A packet implementation that uses Arrow tables as the primary storage format, + providing efficient memory usage and columnar data operations while supporting + source information tracking and content hashing. + + + Initialize ArrowPacket with Arrow table and configuration. + + Args: + table: Single-row Arrow table representing the packet + source_info: Optional source information mapping + semantic_converter: Optional semantic converter + semantic_type_registry: Registry for semantic types + finger_print: Optional fingerprint for tracking + arrow_hasher: Optional Arrow hasher + post_hash_callback: Optional callback after hash calculation + skip_source_info_extraction: Whether to skip source info processing + + Raises: + ValueError: If table doesn't contain exactly one row + """ + + def __init__( + self, + table: "pa.Table | pa.RecordBatch", + meta_info: Mapping[str, DataValue] | None = None, + source_info: Mapping[str, str | None] | None = None, + data_context: str | contexts.DataContext | None = None, + ) -> None: + if len(table) != 1: + raise ValueError( + "ArrowPacket should only contain a single row, " + "as it represents a single packet." + ) + if source_info is None: + source_info = {} + else: + # normalize by removing any existing prefixes + source_info = { + ( + k.removeprefix(constants.SOURCE_PREFIX) + if k.startswith(constants.SOURCE_PREFIX) + else k + ): v + for k, v in source_info.items() + } + + # normalize the table to ensure it has the expected source_info columns + # TODO: use simpler function to ensure source_info columns + data_table, prefixed_tables = arrow_utils.prepare_prefixed_columns( + table, + {constants.SOURCE_PREFIX: source_info}, + exclude_columns=[constants.CONTEXT_KEY], + exclude_prefixes=[constants.META_PREFIX], + ) + + super().__init__( + data_table, + meta_info=meta_info, + data_context=data_context, + ) + self._source_info_table = prefixed_tables[constants.SOURCE_PREFIX] + + self._cached_source_info: dict[str, str | None] | None = None + self._cached_python_schema: PythonSchema | None = None + + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> tuple[str, ...]: + keys = super().keys( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + keys += tuple(f"{constants.SOURCE_PREFIX}{k}" for k in self.keys()) + return keys + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> PythonSchema: + """Return copy of the Python schema.""" + schema = super().types( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + for key in self.keys(): + schema[f"{constants.SOURCE_PREFIX}{key}"] = str + return schema + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> "pa.Schema": + """ + Return the PyArrow schema for this datagram. + + Args: + include_data_context: Whether to include data context column in the schema + include_source: Whether to include source info columns in the schema + + Returns: + PyArrow schema representing the datagram's structure + """ + schema = super().arrow_schema( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + return arrow_utils.join_arrow_schemas( + schema, self._source_info_table.schema + ) + return schema + + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> dict[str, DataValue]: + """ + Convert to dictionary representation. + + Args: + include_source: Whether to include source info fields + + Returns: + Dictionary representation of the packet + """ + return_dict = super().as_dict( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + return_dict.update( + { + f"{constants.SOURCE_PREFIX}{k}": v + for k, v in self.source_info().items() + } + ) + return return_dict + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> "pa.Table": + table = super().as_table( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + # add source_info only if there are columns and the table has meaningful data + if ( + self._source_info_table.num_columns > 0 + and self._source_info_table.num_rows > 0 + ): + table = arrow_utils.hstack_tables(table, self._source_info_table) + return table + + def as_datagram( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_source: bool = False, + ) -> ArrowDatagram: + table = self.as_table( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_source=include_source, + ) + return ArrowDatagram( + table, + data_context=self._data_context, + ) + + def source_info(self) -> dict[str, str | None]: + """ + Return source information for all keys. + + Returns: + Copy of the dictionary mapping field names to their source info + """ + if self._cached_source_info is None: + self._cached_source_info = { + k.removeprefix(constants.SOURCE_PREFIX): v + for k, v in self._source_info_table.to_pylist()[0].items() + } + return self._cached_source_info.copy() + + def with_source_info(self, **source_info: str | None) -> Self: + """ + Create a copy of the packet with updated source information. + + Args: + source_info: New source information mapping + + Returns: + New ArrowPacket instance with updated source info + """ + new_packet = self.copy(include_cache=False) + + existing_source_info_with_prefix = self._source_info_table.to_pylist()[0] + for key, value in source_info.items(): + if not key.startswith(constants.SOURCE_PREFIX): + # Ensure the key is prefixed correctly + key = f"{constants.SOURCE_PREFIX}{key}" + if key in existing_source_info_with_prefix: + existing_source_info_with_prefix[key] = value + + new_packet._source_info_table = pa.Table.from_pylist( + [existing_source_info_with_prefix] + ) + return new_packet + + def rename(self, column_mapping: Mapping[str, str]) -> Self: + """ + Create a new ArrowDatagram with data columns renamed. + Maintains immutability by returning a new instance. + + Args: + column_mapping: Mapping from old column names to new column names + + Returns: + New ArrowDatagram instance with renamed data columns + """ + # Create new schema with renamed fields, preserving original types + + if not column_mapping: + return self + + new_names = [column_mapping.get(k, k) for k in self._data_table.column_names] + + new_source_info_names = [ + f"{constants.SOURCE_PREFIX}{column_mapping.get(k.removeprefix(constants.SOURCE_PREFIX), k.removeprefix(constants.SOURCE_PREFIX))}" + for k in self._source_info_table.column_names + ] + + new_datagram = self.copy(include_cache=False) + new_datagram._data_table = new_datagram._data_table.rename_columns(new_names) + new_datagram._source_info_table = ( + new_datagram._source_info_table.rename_columns(new_source_info_names) + ) + + return new_datagram + + def with_columns( + self, + column_types: Mapping[str, type] | None = None, + **updates: DataValue, + ) -> Self: + """ + Create a new ArrowPacket with new data columns added. + Maintains immutability by returning a new instance. + Also adds corresponding empty source info columns for new columns. + + Args: + column_types: Optional type specifications for new columns + **updates: New data columns as keyword arguments + + Returns: + New ArrowPacket instance with new data columns and corresponding source info columns + + Raises: + ValueError: If any column already exists (use update() instead) + """ + if not updates: + return self + + # First call parent method to add the data columns + new_packet = super().with_columns(column_types=column_types, **updates) + + # Now add corresponding empty source info columns for the new columns + source_info_updates = {} + for column_name in updates.keys(): + source_key = f"{constants.SOURCE_PREFIX}{column_name}" + source_info_updates[source_key] = None # Empty source info + + # Add new source info columns to the source info table + if source_info_updates: + # Get existing source info + schema = new_packet._source_info_table.schema + existing_source_info = new_packet._source_info_table.to_pylist()[0] + + # Add the new empty source info columns + existing_source_info.update(source_info_updates) + schema_columns = list(schema) + schema_columns.extend( + [ + pa.field(name, pa.large_string()) + for name in source_info_updates.keys() + ] + ) + new_schema = pa.schema(schema_columns) + + # Update the source info table + new_packet._source_info_table = pa.Table.from_pylist( + [existing_source_info], new_schema + ) + + return new_packet + + # 8. Utility Operations + def copy(self, include_cache: bool = True) -> Self: + """Return a copy of the datagram.""" + new_packet = super().copy(include_cache=include_cache) + new_packet._source_info_table = self._source_info_table + + if include_cache: + new_packet._cached_source_info = self._cached_source_info + else: + new_packet._cached_source_info = None + + return new_packet diff --git a/src/orcapod/core/datagrams/base.py b/src/orcapod/core/datagrams/base.py new file mode 100644 index 0000000..ec68860 --- /dev/null +++ b/src/orcapod/core/datagrams/base.py @@ -0,0 +1,283 @@ +""" +Data structures and utilities for working with datagrams in OrcaPod. + +This module provides classes and functions for handling packet-like data structures +that can represent data in various formats (Python dicts, Arrow tables, etc.) while +maintaining type information, source metadata, and semantic type conversion capability. + +Key classes: +- SemanticConverter: Converts between different data representations. Intended for internal use. +- DictDatagram: Immutable dict-based data structure +- PythonDictPacket: Python dict-based packet with source info +- ArrowPacket: Arrow table-based packet implementation +- PythonDictTag/ArrowTag: Tag implementations for data identification + +The module also provides utilities for schema validation, table operations, +and type conversions between semantic stores, Python stores, and Arrow tables. +""" + +import logging +from abc import abstractmethod +from collections.abc import Collection, Iterator, Mapping +from typing import Self, TypeAlias, TYPE_CHECKING +from orcapod import contexts +from orcapod.core.base import ContentIdentifiableBase +from orcapod.protocols.hashing_protocols import ContentHash + +from orcapod.utils.lazy_module import LazyModule +from orcapod.types import DataValue, PythonSchema + +logger = logging.getLogger(__name__) + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + +# A conveniece packet-like type that defines a value that can be +# converted to a packet. It's broader than Packet and a simple mapping +# from string keys to DataValue (e.g., int, float, str) can be regarded +# as PacketLike, allowing for more flexible interfaces. +# Anything that requires Packet-like data but without the strict features +# of a Packet should accept PacketLike. +# One should be careful when using PacketLike as a return type as it does not +# enforce the typespec or source_info, which are important for packet integrity. +PacketLike: TypeAlias = Mapping[str, DataValue] + +PythonStore: TypeAlias = Mapping[str, DataValue] + + +class ImmutableDict(Mapping[str, DataValue]): + """ + An immutable dictionary-like container for DataValues. + + Provides a read-only view of a dictionary mapping strings to DataValues, + implementing the Mapping protocol for compatibility with dict-like operations. + + Initialize with data from a mapping. + Args: + data: Source mapping to copy data from + """ + + def __init__(self, data: Mapping[str, DataValue]): + self._data = dict(data) + + def __getitem__(self, key: str) -> DataValue: + return self._data[key] + + def __iter__(self): + return iter(self._data) + + def __len__(self) -> int: + return len(self._data) + + def __repr__(self) -> str: + return self._data.__repr__() + + def __str__(self) -> str: + return self._data.__str__() + + def __or__(self, other: Mapping[str, DataValue]) -> Self: + """ + Create a new ImmutableDict by merging with another mapping. + + Args: + other: Another mapping to merge with + + Returns: + A new ImmutableDict containing the combined data + """ + return self.__class__(self._data | dict(other)) + + +def contains_prefix_from(column: str, prefixes: Collection[str]) -> bool: + """ + Check if a column name matches any of the given prefixes. + + Args: + column: Column name to check + prefixes: Collection of prefixes to match against + + Returns: + True if the column starts with any of the prefixes, False otherwise + """ + for prefix in prefixes: + if column.startswith(prefix): + return True + return False + + +class BaseDatagram(ContentIdentifiableBase): + """ + Abstract base class for immutable datagram implementations. + + Provides shared functionality and enforces consistent interface across + different storage backends (dict, Arrow table, etc.). Concrete subclasses + must implement the abstract methods to handle their specific storage format. + + The base class only manages the data context key string - how that key + is interpreted and used is left to concrete implementations. + """ + + def __init__(self, data_context: contexts.DataContext | str | None = None) -> None: + """ + Initialize base datagram with data context. + + Args: + data_context: Context for semantic interpretation. Can be a string key + or a DataContext object, or None for default. + """ + self._data_context = contexts.resolve_context(data_context) + self._converter = self._data_context.type_converter + + # 1. Core Properties (Identity & Structure) + @property + def data_context_key(self) -> str: + """Return the data context key.""" + return self._data_context.context_key + + @property + @abstractmethod + def meta_columns(self) -> tuple[str, ...]: + """Return tuple of meta column names.""" + ... + + # TODO: add meta info + + # 2. Dict-like Interface (Data Access) + @abstractmethod + def __getitem__(self, key: str) -> DataValue: + """Get data column value by key.""" + ... + + @abstractmethod + def __contains__(self, key: str) -> bool: + """Check if data column exists.""" + ... + + @abstractmethod + def __iter__(self) -> Iterator[str]: + """Iterate over data column names.""" + ... + + @abstractmethod + def get(self, key: str, default: DataValue = None) -> DataValue: + """Get data column value with default.""" + ... + + # 3. Structural Information + @abstractmethod + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> tuple[str, ...]: + """Return tuple of column names.""" + ... + + @abstractmethod + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> PythonSchema: + """Return type specification for the datagram.""" + ... + + @abstractmethod + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Schema": + """Return the PyArrow schema for this datagram.""" + ... + + @abstractmethod + def content_hash(self) -> ContentHash: + """Calculate and return content hash of the datagram.""" + ... + + # 4. Format Conversions (Export) + @abstractmethod + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> dict[str, DataValue]: + """Return dictionary representation of the datagram.""" + ... + + @abstractmethod + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Table": + """Convert the datagram to an Arrow table.""" + ... + + # 5. Meta Column Operations + @abstractmethod + def get_meta_value(self, key: str, default: DataValue = None) -> DataValue: + """Get a meta column value.""" + ... + + @abstractmethod + def with_meta_columns(self, **updates: DataValue) -> Self: + """Create new datagram with updated meta columns.""" + ... + + @abstractmethod + def drop_meta_columns(self, *keys: str) -> Self: + """Create new datagram with specified meta columns removed.""" + ... + + # 6. Data Column Operations + @abstractmethod + def select(self, *column_names: str) -> Self: + """Create new datagram with only specified data columns.""" + ... + + @abstractmethod + def drop(self, *column_names: str) -> Self: + """Create new datagram with specified data columns removed.""" + ... + + @abstractmethod + def rename(self, column_mapping: Mapping[str, str]) -> Self: + """Create new datagram with data columns renamed.""" + ... + + @abstractmethod + def update(self, **updates: DataValue) -> Self: + """Create new datagram with existing column values updated.""" + ... + + @abstractmethod + def with_columns( + self, + column_types: Mapping[str, type] | None = None, + **updates: DataValue, + ) -> Self: + """Create new datagram with additional data columns.""" + ... + + # 7. Context Operations + def with_context_key(self, new_context_key: str) -> Self: + """Create new datagram with different data context.""" + new_datagram = self.copy(include_cache=False) + new_datagram._data_context = contexts.resolve_context(new_context_key) + return new_datagram + + # 8. Utility Operations + def copy(self, include_cache: bool = True) -> Self: + """Create a shallow copy of the datagram.""" + new_datagram = object.__new__(self.__class__) + new_datagram._data_context = self._data_context + return new_datagram diff --git a/src/orcapod/core/datagrams/dict_datagram.py b/src/orcapod/core/datagrams/dict_datagram.py new file mode 100644 index 0000000..642a5b2 --- /dev/null +++ b/src/orcapod/core/datagrams/dict_datagram.py @@ -0,0 +1,836 @@ +import logging +from collections.abc import Collection, Iterator, Mapping +from typing import Self, cast, TYPE_CHECKING + +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.system_constants import constants +from orcapod import contexts +from orcapod.core.datagrams.base import BaseDatagram +from orcapod.semantic_types import infer_python_schema_from_pylist_data +from orcapod.types import DataValue, PythonSchema, PythonSchemaLike +from orcapod.utils import arrow_utils +from orcapod.protocols.hashing_protocols import ContentHash + +logger = logging.getLogger(__name__) + +# FIXME: make this configurable! +DEBUG = False + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class DictDatagram(BaseDatagram): + """ + Immutable datagram implementation using dictionary as storage backend. + + This implementation uses composition (not inheritance from Mapping) to maintain + control over the interface while leveraging dictionary efficiency for data access. + Provides clean separation between data, meta, and context components. + + The underlying data is split into separate components: + - Data dict: Primary business data columns + - Meta dict: Internal system metadata with {orcapod.META_PREFIX} ('__') prefixes + - Context: Data context information with {orcapod.CONTEXT_KEY} + + Future Packet subclass will also handle: + - Source info: Data provenance with {orcapod.SOURCE_PREFIX} ('_source_') prefixes + + When exposing to external tools, semantic types are encoded as + `_{semantic_type}_` prefixes (_path_config_file, _id_user_name). + + All operations return new instances, preserving immutability. + + Example: + >>> data = {{ + ... "user_id": 123, + ... "name": "Alice", + ... "__pipeline_version": "v2.1.0", + ... "{orcapod.CONTEXT_KEY}": "financial_v1" + ... }} + >>> datagram = DictDatagram(data) + >>> updated = datagram.update(name="Alice Smith") + """ + + def __init__( + self, + data: Mapping[str, DataValue], + python_schema: PythonSchemaLike | None = None, + meta_info: Mapping[str, DataValue] | None = None, + data_context: str | contexts.DataContext | None = None, + ) -> None: + """ + Initialize DictDatagram from dictionary data. + + Args: + data: Source data mapping containing all column data. + typespec: Optional type specification for fields. + semantic_converter: Optional converter for semantic type handling. + If None, will be created based on data context and inferred types. + data_context: Data context for semantic type resolution. + If None and data contains context column, will extract from data. + + Note: + The input data is automatically split into data, meta, and context + components based on column naming conventions. + """ + # Parse through data and extract different column types + data_columns = {} + meta_columns = {} + extracted_context = None + + for k, v in data.items(): + if k == constants.CONTEXT_KEY: + # Extract data context but keep it separate from meta data + if data_context is None: + extracted_context = v + # Don't store context in meta_data - it's managed separately + elif k.startswith(constants.META_PREFIX): + # Double underscore = meta metadata + meta_columns[k] = v + else: + # Everything else = user data (including _source_ and semantic types) + data_columns[k] = v + + # Initialize base class with data context + final_context = data_context or cast(str, extracted_context) + super().__init__(final_context) + + # Store data and meta components separately (immutable) + self._data = dict(data_columns) + if meta_info is not None: + meta_columns.update(meta_info) + self._meta_data = meta_columns + + # Combine provided typespec info with inferred typespec from content + # If the column value is None and no type spec is provided, defaults to str. + inferred_schema = infer_python_schema_from_pylist_data( + [self._data], default_type=str + ) + + self._data_python_schema = ( + {k: python_schema.get(k, v) for k, v in inferred_schema.items()} + if python_schema + else inferred_schema + ) + + # Create schema for meta data + inferred_meta_schema = infer_python_schema_from_pylist_data( + [self._meta_data], default_type=str + ) + self._meta_python_schema = ( + {k: python_schema.get(k, v) for k, v in inferred_meta_schema.items()} + if python_schema + else inferred_meta_schema + ) + + # Initialize caches + self._cached_data_table: pa.Table | None = None + self._cached_meta_table: pa.Table | None = None + self._cached_content_hash: ContentHash | None = None + self._cached_data_arrow_schema: pa.Schema | None = None + self._cached_meta_arrow_schema: pa.Schema | None = None + + def _get_total_dict(self) -> dict[str, DataValue]: + """ + Return the total dictionary representation including meta and context. + + This is used for content hashing and exporting to Arrow. + """ + total_dict = dict(self._data) + total_dict.update(self._meta_data) + total_dict[constants.CONTEXT_KEY] = self._data_context + return total_dict + + # 1. Core Properties (Identity & Structure) + @property + def meta_columns(self) -> tuple[str, ...]: + """Return tuple of meta column names.""" + return tuple(self._meta_data.keys()) + + def get_meta_info(self) -> dict[str, DataValue]: + """ + Get meta column information. + + Returns: + Dictionary of meta column names and their values. + """ + return dict(self._meta_data) + + # 2. Dict-like Interface (Data Access) + def __getitem__(self, key: str) -> DataValue: + """Get data column value by key.""" + if key not in self._data: + raise KeyError(f"Data column '{key}' not found") + return self._data[key] + + def __contains__(self, key: str) -> bool: + """Check if data column exists.""" + return key in self._data + + def __iter__(self) -> Iterator[str]: + """Iterate over data column names.""" + return iter(self._data) + + def get(self, key: str, default: DataValue = None) -> DataValue: + """Get data column value with default.""" + return self._data.get(key, default) + + # 3. Structural Information + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> tuple[str, ...]: + """Return tuple of column names.""" + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + # Start with data columns + result_keys = list(self._data.keys()) + + # Add context if requested + if include_context: + result_keys.append(constants.CONTEXT_KEY) + + # Add meta columns if requested + if include_meta_columns: + if include_meta_columns is True: + result_keys.extend(self.meta_columns) + elif isinstance(include_meta_columns, Collection): + # Filter meta columns by prefix matching + filtered_meta_cols = [ + col + for col in self.meta_columns + if any(col.startswith(prefix) for prefix in include_meta_columns) + ] + result_keys.extend(filtered_meta_cols) + + return tuple(result_keys) + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> PythonSchema: + """ + Return Python schema for the datagram. + + Args: + include_meta_columns: Whether to include meta column types. + - True: include all meta column types + - Collection[str]: include meta column types matching these prefixes + - False: exclude meta column types + include_context: Whether to include context type + + Returns: + Python schema + """ + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + + # Start with data schema + schema = dict(self._data_python_schema) + + # Add context if requested + if include_context: + schema[constants.CONTEXT_KEY] = str + + # Add meta schema if requested + if include_meta_columns and self._meta_data: + if include_meta_columns is True: + schema.update(self._meta_python_schema) + elif isinstance(include_meta_columns, Collection): + filtered_meta_schema = { + k: v + for k, v in self._meta_python_schema.items() + if any(k.startswith(prefix) for prefix in include_meta_columns) + } + schema.update(filtered_meta_schema) + + return schema + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Schema": + """ + Return the PyArrow schema for this datagram. + + Args: + include_meta_columns: Whether to include meta columns in the schema. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include context column in the schema + + Returns: + PyArrow schema representing the datagram's structure + """ + include_meta_columns = include_all_info or include_meta_columns + include_context = include_all_info or include_context + + # Build data schema (cached) + if self._cached_data_arrow_schema is None: + self._cached_data_arrow_schema = ( + self._data_context.type_converter.python_schema_to_arrow_schema( + self._data_python_schema + ) + ) + + all_schemas = [self._cached_data_arrow_schema] + + # Add context schema if requested + if include_context: + context_schema = self._converter.python_schema_to_arrow_schema( + {constants.CONTEXT_KEY: str} + ) + all_schemas.append(context_schema) + + # Add meta schema if requested + if include_meta_columns and self._meta_data: + if include_meta_columns is True: + meta_schema = self._get_meta_arrow_schema() + elif isinstance(include_meta_columns, Collection): + # Filter meta schema by prefix matching + meta_schema = ( + arrow_utils.select_schema_columns_with_prefixes( + self._get_meta_arrow_schema(), + include_meta_columns, + ) + or None + ) + else: + meta_schema = None + + if meta_schema is not None: + all_schemas.append(meta_schema) + + return arrow_utils.join_arrow_schemas(*all_schemas) + + def content_hash(self) -> ContentHash: + """ + Calculate and return content hash of the datagram. + Only includes data columns, not meta columns or context. + + Returns: + Hash string of the datagram content + """ + if self._cached_content_hash is None: + self._cached_content_hash = self._data_context.arrow_hasher.hash_table( + self.as_table(include_meta_columns=False, include_context=False), + ) + return self._cached_content_hash + + # 4. Format Conversions (Export) + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> dict[str, DataValue]: + """ + Return dictionary representation of the datagram. + + Args: + include_meta_columns: Whether to include meta columns. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include context key + + Returns: + Dictionary representation + """ + include_context = include_all_info or include_context + include_meta_columns = include_all_info or include_meta_columns + + result_dict = dict(self._data) # Start with user data + + # Add context if requested + if include_context: + result_dict[constants.CONTEXT_KEY] = self._data_context.context_key + + # Add meta columns if requested + if include_meta_columns and self._meta_data: + if include_meta_columns is True: + # Include all meta columns + result_dict.update(self._meta_data) + elif isinstance(include_meta_columns, Collection): + # Include only meta columns matching prefixes + filtered_meta_data = { + k: v + for k, v in self._meta_data.items() + if any(k.startswith(prefix) for prefix in include_meta_columns) + } + result_dict.update(filtered_meta_data) + + return result_dict + + def as_arrow_compatible_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> dict[str, DataValue]: + """ + Return dictionary representation compatible with Arrow. + + Args: + include_meta_columns: Whether to include meta columns. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include context key + + Returns: + Dictionary representation compatible with Arrow + """ + # FIXME: this is a super inefficient implementation! + python_dict = self.as_dict( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + python_schema = self.types( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + + return self._data_context.type_converter.python_dicts_to_struct_dicts( + [python_dict], python_schema=python_schema + )[0] + + def _get_meta_arrow_table(self) -> "pa.Table": + if self._cached_meta_table is None: + arrow_schema = self._get_meta_arrow_schema() + self._cached_meta_table = pa.Table.from_pylist( + [self._meta_data], + schema=arrow_schema, + ) + assert self._cached_meta_table is not None, ( + "Meta Arrow table should be initialized by now" + ) + return self._cached_meta_table + + def _get_meta_arrow_schema(self) -> "pa.Schema": + if self._cached_meta_arrow_schema is None: + self._cached_meta_arrow_schema = ( + self._data_context.type_converter.python_schema_to_arrow_schema( + self._meta_python_schema + ) + ) + + assert self._cached_meta_arrow_schema is not None, ( + "Meta Arrow schema should be initialized by now" + ) + return self._cached_meta_arrow_schema + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Table": + """ + Convert the datagram to an Arrow table. + + Args: + include_meta_columns: Whether to include meta columns. + - True: include all meta columns + - Collection[str]: include meta columns matching these prefixes + - False: exclude meta columns + include_context: Whether to include the context column + + Returns: + Arrow table representation + """ + include_context = include_all_info or include_context + include_meta_columns = include_all_info or include_meta_columns + + # Build data table (cached) + if self._cached_data_table is None: + self._cached_data_table = ( + self._data_context.type_converter.python_dicts_to_arrow_table( + [self._data], + self._data_python_schema, + ) + ) + assert self._cached_data_table is not None, ( + "Data Arrow table should be initialized by now" + ) + result_table = self._cached_data_table + + # Add context if requested + # TODO: consider using type converter for consistency + if include_context: + result_table = result_table.append_column( + constants.CONTEXT_KEY, + pa.array([self._data_context.context_key], type=pa.large_string()), + ) + + # Add meta columns if requested + meta_table = None + if include_meta_columns and self._meta_data: + meta_table = self._get_meta_arrow_table() + # Select appropriate meta columns + if isinstance(include_meta_columns, Collection): + # Filter meta columns by prefix matching + meta_table = arrow_utils.select_table_columns_with_prefixes( + meta_table, include_meta_columns + ) + + # Combine tables if we have meta columns to add + if meta_table: + result_table = arrow_utils.hstack_tables(result_table, meta_table) + + return result_table + + # 5. Meta Column Operations + def get_meta_value(self, key: str, default: DataValue = None) -> DataValue: + """ + Get meta column value with optional default. + + Args: + key: Meta column key (with or without {orcapod.META_PREFIX} ('__') prefix). + default: Value to return if meta column doesn't exist. + + Returns: + Meta column value if exists, otherwise the default value. + """ + # Handle both prefixed and unprefixed keys + if not key.startswith(constants.META_PREFIX): + key = constants.META_PREFIX + key + + return self._meta_data.get(key, default) + + def with_meta_columns(self, **meta_updates: DataValue) -> Self: + """ + Create a new DictDatagram with updated meta columns. + Maintains immutability by returning a new instance. + + Args: + **meta_updates: Meta column updates (keys will be prefixed with {orcapod.META_PREFIX} ('__') if needed) + + Returns: + New DictDatagram instance + """ + # Prefix the keys and prepare updates + prefixed_updates = {} + for k, v in meta_updates.items(): + if not k.startswith(constants.META_PREFIX): + k = constants.META_PREFIX + k + prefixed_updates[k] = v + + # Start with existing meta data + new_meta_data = dict(self._meta_data) + new_meta_data.update(prefixed_updates) + + # Reconstruct full data dict for new instance + full_data = dict(self._data) # User data + full_data.update(new_meta_data) # Meta data + + return self.__class__( + data=full_data, + data_context=self._data_context, + ) + + def drop_meta_columns(self, *keys: str, ignore_missing: bool = False) -> Self: + """ + Create a new DictDatagram with specified meta columns dropped. + Maintains immutability by returning a new instance. + + Args: + *keys: Meta column keys to drop (with or without {orcapod.META_PREFIX} ('__') prefix) + ignore_missing: If True, ignore missing meta columns without raising an error. + + Raises: + KeyError: If any specified meta column to drop doesn't exist and ignore_missing=False. + + Returns: + New DictDatagram instance without specified meta columns + """ + # Normalize keys to have prefixes + prefixed_keys = set() + for key in keys: + if not key.startswith(constants.META_PREFIX): + key = constants.META_PREFIX + key + prefixed_keys.add(key) + + missing_keys = prefixed_keys - set(self._meta_data.keys()) + if missing_keys and not ignore_missing: + raise KeyError( + f"Following meta columns do not exist and cannot be dropped: {sorted(missing_keys)}" + ) + + # Filter out specified meta columns + new_meta_data = { + k: v for k, v in self._meta_data.items() if k not in prefixed_keys + } + + # Reconstruct full data dict for new instance + full_data = dict(self._data) # User data + full_data.update(new_meta_data) # Filtered meta data + + return self.__class__( + data=full_data, + data_context=self._data_context, + ) + + # 6. Data Column Operations + def select(self, *column_names: str) -> Self: + """ + Create a new DictDatagram with only specified data columns. + Maintains immutability by returning a new instance. + + Args: + *column_names: Data column names to keep + + Returns: + New DictDatagram instance with only specified data columns + """ + # Validate columns exist + missing_cols = set(column_names) - set(self._data.keys()) + if missing_cols: + raise KeyError(f"Columns not found: {missing_cols}") + + # Keep only specified data columns + new_data = {k: v for k, v in self._data.items() if k in column_names} + + # Reconstruct full data dict for new instance + full_data = new_data # Selected user data + full_data.update(self._meta_data) # Keep existing meta data + + return self.__class__( + data=full_data, + data_context=self._data_context, + ) + + def drop(self, *column_names: str, ignore_missing: bool = False) -> Self: + """ + Create a new DictDatagram with specified data columns dropped. + Maintains immutability by returning a new instance. + + Args: + *column_names: Data column names to drop + + Returns: + New DictDatagram instance without specified data columns + """ + # Filter out specified data columns + missing = set(column_names) - set(self._data.keys()) + if missing and not ignore_missing: + raise KeyError( + f"Following columns do not exist and cannot be dropped: {sorted(missing)}" + ) + + new_data = {k: v for k, v in self._data.items() if k not in column_names} + + if not new_data: + raise ValueError("Cannot drop all data columns") + + new_datagram = self.copy(include_cache=False) + new_datagram._data = new_data + return new_datagram + + def rename(self, column_mapping: Mapping[str, str]) -> Self: + """ + Create a new DictDatagram with data columns renamed. + Maintains immutability by returning a new instance. + + Args: + column_mapping: Mapping from old column names to new column names + + Returns: + New DictDatagram instance with renamed data columns + """ + # Rename data columns according to mapping, preserving original types + new_data = {} + for old_name, value in self._data.items(): + new_name = column_mapping.get(old_name, old_name) + new_data[new_name] = value + + # Handle python_schema updates for renamed columns + new_python_schema = None + if self._data_python_schema: + existing_python_schema = dict(self._data_python_schema) + + # Rename types according to column mapping + renamed_python_schema = {} + for old_name, old_type in existing_python_schema.items(): + new_name = column_mapping.get(old_name, old_name) + renamed_python_schema[new_name] = old_type + + new_python_schema = renamed_python_schema + + # Reconstruct full data dict for new instance + full_data = new_data # Renamed user data + full_data.update(self._meta_data) # Keep existing meta data + + return self.__class__( + data=full_data, + python_schema=new_python_schema, + data_context=self._data_context, + ) + + def update(self, **updates: DataValue) -> Self: + """ + Create a new DictDatagram with existing column values updated. + Maintains immutability by returning a new instance if any values are changed. + + Args: + **updates: Column names and their new values (columns must exist) + + Returns: + New DictDatagram instance with updated values + + Raises: + KeyError: If any column doesn't exist (use with_columns() to add new columns) + """ + if not updates: + return self + + # Error if any column doesn't exist + missing_columns = set(updates.keys()) - set(self._data.keys()) + if missing_columns: + raise KeyError( + f"Columns not found: {sorted(missing_columns)}. " + f"Use with_columns() to add new columns." + ) + + # Update existing columns + new_data = dict(self._data) + new_data.update(updates) + + new_datagram = self.copy(include_cache=False) + new_datagram._data = new_data + return new_datagram + + def with_columns( + self, + column_types: Mapping[str, type] | None = None, + **updates: DataValue, + ) -> Self: + """ + Create a new DictDatagram with new data columns added. + Maintains immutability by returning a new instance. + + Args: + column_updates: New data columns as a mapping + column_types: Optional type specifications for new columns + **kwargs: New data columns as keyword arguments + + Returns: + New DictDatagram instance with new data columns added + + Raises: + ValueError: If any column already exists (use update() instead) + """ + # Combine explicit updates with kwargs + + if not updates: + return self + + # Error if any column already exists + existing_overlaps = set(updates.keys()) & set(self._data.keys()) + if existing_overlaps: + raise ValueError( + f"Columns already exist: {sorted(existing_overlaps)}. " + f"Use update() to modify existing columns." + ) + + # Update user data with new columns + new_data = dict(self._data) + new_data.update(updates) + + # Create updated python schema - handle None values by defaulting to str + python_schema = self.types() + if column_types is not None: + python_schema.update(column_types) + + new_python_schema = infer_python_schema_from_pylist_data([new_data]) + new_python_schema = { + k: python_schema.get(k, v) for k, v in new_python_schema.items() + } + + new_datagram = self.copy(include_cache=False) + new_datagram._data = new_data + new_datagram._data_python_schema = new_python_schema + + return new_datagram + + # 8. Utility Operations + def copy(self, include_cache: bool = True) -> Self: + """ + Create a shallow copy of the datagram. + + Returns a new datagram instance with the same data and cached values. + This is more efficient than reconstructing from scratch when you need + an identical datagram instance. + + Returns: + New DictDatagram instance with copied data and caches. + """ + new_datagram = super().copy() + new_datagram._data = self._data.copy() + new_datagram._meta_data = self._meta_data.copy() + new_datagram._data_python_schema = self._data_python_schema.copy() + new_datagram._meta_python_schema = self._meta_python_schema.copy() + + if include_cache: + new_datagram._cached_data_table = self._cached_data_table + new_datagram._cached_meta_table = self._cached_meta_table + new_datagram._cached_content_hash = self._cached_content_hash + new_datagram._cached_data_arrow_schema = self._cached_data_arrow_schema + new_datagram._cached_meta_arrow_schema = self._cached_meta_arrow_schema + else: + new_datagram._cached_data_table = None + new_datagram._cached_meta_table = None + new_datagram._cached_content_hash = None + new_datagram._cached_data_arrow_schema = None + new_datagram._cached_meta_arrow_schema = None + + return new_datagram + + # 9. String Representations + def __str__(self) -> str: + """ + Return user-friendly string representation. + + Shows the datagram as a simple dictionary for user-facing output, + messages, and logging. Only includes data columns for clean output. + + Returns: + Dictionary-style string representation of data columns only. + """ + return str(self._data) + + def __repr__(self) -> str: + """ + Return detailed string representation for debugging. + + Shows the datagram type and comprehensive information including + data columns, meta columns count, and context for debugging purposes. + + Returns: + Detailed representation with type and metadata information. + """ + if DEBUG: + meta_count = len(self.meta_columns) + context_key = self.data_context_key + + return ( + f"{self.__class__.__name__}(" + f"data={self._data}, " + f"meta_columns={meta_count}, " + f"context='{context_key}'" + f")" + ) + else: + return str(self._data) diff --git a/src/orcapod/core/datagrams/dict_tag_packet.py b/src/orcapod/core/datagrams/dict_tag_packet.py new file mode 100644 index 0000000..11e6d66 --- /dev/null +++ b/src/orcapod/core/datagrams/dict_tag_packet.py @@ -0,0 +1,547 @@ +import logging +from collections.abc import Collection, Mapping +from typing import Self, TYPE_CHECKING + + +from orcapod.core.system_constants import constants +from orcapod import contexts +from orcapod.core.datagrams.dict_datagram import DictDatagram +from orcapod.utils import arrow_utils +from orcapod.semantic_types import infer_python_schema_from_pylist_data +from orcapod.types import DataValue, PythonSchema, PythonSchemaLike +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + +logger = logging.getLogger(__name__) + + +class DictTag(DictDatagram): + """ + A simple tag implementation using Python dictionary. + + Represents a tag (metadata) as a dictionary that can be converted + to different representations like Arrow tables. + """ + + def __init__( + self, + data: Mapping[str, DataValue], + system_tags: Mapping[str, DataValue] | None = None, + meta_info: Mapping[str, DataValue] | None = None, + python_schema: dict[str, type] | None = None, + data_context: str | contexts.DataContext | None = None, + ) -> None: + """ + Initialize the tag with data. + + Args: + data: Dictionary containing tag data + """ + # normalize the data content and remove any source info keys + data_only = { + k: v + for k, v in data.items() + if not k.startswith(constants.SYSTEM_TAG_PREFIX) + } + extracted_system_tags = { + k: v for k, v in data.items() if k.startswith(constants.SYSTEM_TAG_PREFIX) + } + + super().__init__( + data_only, + python_schema=python_schema, + meta_info=meta_info, + data_context=data_context, + ) + + self._system_tags = {**extracted_system_tags, **(system_tags or {})} + self._system_tags_python_schema: PythonSchema = ( + infer_python_schema_from_pylist_data([self._system_tags]) + ) + self._cached_system_tags_table: pa.Table | None = None + self._cached_system_tags_schema: pa.Schema | None = None + + def _get_total_dict(self) -> dict[str, DataValue]: + """Return the total dictionary representation including system tags.""" + total_dict = super()._get_total_dict() + total_dict.update(self._system_tags) + return total_dict + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> "pa.Table": + """Convert the packet to an Arrow table.""" + table = super().as_table( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + + if include_all_info or include_system_tags: + # Only create and stack system tags table if there are actually system tags + if self._system_tags: # Check if system tags dict is not empty + if self._cached_system_tags_table is None: + self._cached_system_tags_table = ( + self._data_context.type_converter.python_dicts_to_arrow_table( + [self._system_tags], + python_schema=self._system_tags_python_schema, + ) + ) + table = arrow_utils.hstack_tables(table, self._cached_system_tags_table) + return table + + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> dict[str, DataValue]: + """ + Return dictionary representation. + + Args: + include_source: Whether to include source info fields + + Returns: + Dictionary representation of the packet + """ + dict_copy = super().as_dict( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + dict_copy.update(self._system_tags) + return dict_copy + + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> tuple[str, ...]: + """Return keys of the Python schema.""" + keys = super().keys( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + keys += tuple(self._system_tags.keys()) + return keys + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> PythonSchema: + """Return copy of the Python schema.""" + schema = super().types( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + schema.update(self._system_tags_python_schema) + return schema + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> "pa.Schema": + """ + Return the PyArrow schema for this datagram. + + Args: + include_data_context: Whether to include data context column in the schema + include_source: Whether to include source info columns in the schema + + Returns: + PyArrow schema representing the datagram's structure + """ + schema = super().arrow_schema( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_system_tags: + if self._cached_system_tags_schema is None: + self._cached_system_tags_schema = ( + self._data_context.type_converter.python_schema_to_arrow_schema( + self._system_tags_python_schema + ) + ) + return arrow_utils.join_arrow_schemas( + schema, self._cached_system_tags_schema + ) + return schema + + def as_datagram( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_system_tags: bool = False, + ) -> DictDatagram: + """ + Convert the packet to a DictDatagram. + + Args: + include_source: Whether to include source info fields + + Returns: + DictDatagram representation of the packet + """ + + data = self.as_dict( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_system_tags=include_system_tags, + ) + python_schema = self.types( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_system_tags=include_system_tags, + ) + return DictDatagram( + data, + python_schema=python_schema, + data_context=self._data_context, + ) + + def system_tags(self) -> dict[str, DataValue]: + """ + Return source information for all keys. + + Returns: + Dictionary mapping field names to their source info + """ + return dict(self._system_tags) + + def copy(self, include_cache: bool = True) -> Self: + """Return a shallow copy of the packet.""" + instance = super().copy(include_cache=include_cache) + instance._system_tags = self._system_tags.copy() + if include_cache: + instance._cached_system_tags_table = self._cached_system_tags_table + instance._cached_system_tags_schema = self._cached_system_tags_schema + + else: + instance._cached_system_tags_table = None + instance._cached_system_tags_schema = None + + return instance + + +class DictPacket(DictDatagram): + """ + Enhanced packet implementation with source information support. + + Extends DictDatagram to include source information tracking and + enhanced table conversion capabilities that can include or exclude + source metadata. + + Initialize packet with data and optional source information. + + Args: + data: Primary data content + source_info: Optional mapping of field names to source information + typespec: Optional type specification + semantic_converter: Optional semantic converter + semantic_type_registry: Registry for semantic types. Defaults to system default registry. + arrow_hasher: Optional Arrow hasher. Defaults to system default arrow hasher. + """ + + def __init__( + self, + data: Mapping[str, DataValue], + meta_info: Mapping[str, DataValue] | None = None, + source_info: Mapping[str, str | None] | None = None, + python_schema: PythonSchemaLike | None = None, + data_context: str | contexts.DataContext | None = None, + ) -> None: + # normalize the data content and remove any source info keys + data_only = { + k: v for k, v in data.items() if not k.startswith(constants.SOURCE_PREFIX) + } + contained_source_info = { + k.removeprefix(constants.SOURCE_PREFIX): v + for k, v in data.items() + if k.startswith(constants.SOURCE_PREFIX) + } + + super().__init__( + data_only, + python_schema=python_schema, + meta_info=meta_info, + data_context=data_context, + ) + + self._source_info = {**contained_source_info, **(source_info or {})} + self._cached_source_info_table: pa.Table | None = None + self._cached_source_info_schema: pa.Schema | None = None + + @property + def _source_info_arrow_schema(self) -> "pa.Schema": + if self._cached_source_info_schema is None: + self._cached_source_info_schema = ( + self._converter.python_schema_to_arrow_schema( + self._source_info_python_schema + ) + ) + + return self._cached_source_info_schema + + @property + def _source_info_python_schema(self) -> dict[str, type]: + """Return the Python schema for source info.""" + return {f"{constants.SOURCE_PREFIX}{k}": str for k in self.keys()} + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> "pa.Table": + """Convert the packet to an Arrow table.""" + table = super().as_table( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + if self._cached_source_info_table is None: + source_info_data = { + f"{constants.SOURCE_PREFIX}{k}": v + for k, v in self.source_info().items() + } + self._cached_source_info_table = pa.Table.from_pylist( + [source_info_data], schema=self._source_info_arrow_schema + ) + assert self._cached_source_info_table is not None, ( + "Cached source info table should not be None" + ) + # subselect the corresponding _source_info as the columns present in the data table + source_info_table = self._cached_source_info_table.select( + [ + f"{constants.SOURCE_PREFIX}{k}" + for k in table.column_names + if k in self.keys() + ] + ) + table = arrow_utils.hstack_tables(table, source_info_table) + return table + + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> dict[str, DataValue]: + """ + Return dictionary representation. + + Args: + include_source: Whether to include source info fields + + Returns: + Dictionary representation of the packet + """ + dict_copy = super().as_dict( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + for key, value in self.source_info().items(): + dict_copy[f"{constants.SOURCE_PREFIX}{key}"] = value + return dict_copy + + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> tuple[str, ...]: + """Return keys of the Python schema.""" + keys = super().keys( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + keys += tuple(f"{constants.SOURCE_PREFIX}{key}" for key in super().keys()) + return keys + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> PythonSchema: + """Return copy of the Python schema.""" + schema = super().types( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + for key in self.keys(): + schema[f"{constants.SOURCE_PREFIX}{key}"] = str + return schema + + def rename(self, column_mapping: Mapping[str, str]) -> Self: + """ + Create a new DictDatagram with data columns renamed. + Maintains immutability by returning a new instance. + + Args: + column_mapping: Mapping from old column names to new column names + + Returns: + New DictDatagram instance with renamed data columns + """ + # Rename data columns according to mapping, preserving original types + + new_data = {column_mapping.get(k, k): v for k, v in self._data.items()} + + new_source_info = { + column_mapping.get(k, k): v for k, v in self._source_info.items() + } + + # Handle python_schema updates for renamed columns + new_python_schema = { + column_mapping.get(k, k): v for k, v in self._data_python_schema.items() + } + + return self.__class__( + data=new_data, + meta_info=self._meta_data, + source_info=new_source_info, + python_schema=new_python_schema, + data_context=self._data_context, + ) + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> "pa.Schema": + """ + Return the PyArrow schema for this datagram. + + Args: + include_data_context: Whether to include data context column in the schema + include_source: Whether to include source info columns in the schema + + Returns: + PyArrow schema representing the datagram's structure + """ + schema = super().arrow_schema( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_context=include_context, + ) + if include_all_info or include_source: + return arrow_utils.join_arrow_schemas( + schema, self._source_info_arrow_schema + ) + return schema + + def as_datagram( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_source: bool = False, + ) -> DictDatagram: + """ + Convert the packet to a DictDatagram. + + Args: + include_source: Whether to include source info fields + + Returns: + DictDatagram representation of the packet + """ + + data = self.as_dict( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_source=include_source, + ) + python_schema = self.types( + include_all_info=include_all_info, + include_meta_columns=include_meta_columns, + include_source=include_source, + ) + return DictDatagram( + data, + python_schema=python_schema, + data_context=self._data_context, + ) + + def source_info(self) -> dict[str, str | None]: + """ + Return source information for all keys. + + Returns: + Dictionary mapping field names to their source info + """ + return {key: self._source_info.get(key, None) for key in self.keys()} + + def with_source_info(self, **source_info: str | None) -> Self: + """ + Create a new packet with updated source information. + + Args: + **kwargs: Key-value pairs to update source information + + Returns: + New DictPacket instance with updated source info + """ + current_source_info = self._source_info.copy() + + for key, value in source_info.items(): + # Remove prefix if it exists, since _source_info stores unprefixed keys + if key.startswith(constants.SOURCE_PREFIX): + key = key.removeprefix(constants.SOURCE_PREFIX) + current_source_info[key] = value + + new_packet = self.copy(include_cache=False) + new_packet._source_info = current_source_info + + return new_packet + + def copy(self, include_cache: bool = True) -> Self: + """Return a shallow copy of the packet.""" + instance = super().copy(include_cache=include_cache) + instance._source_info = self._source_info.copy() + if include_cache: + instance._cached_source_info_table = self._cached_source_info_table + instance._cached_source_info_schema = self._cached_source_info_schema + + else: + instance._cached_source_info_table = None + instance._cached_source_info_schema = None + + return instance diff --git a/src/orcapod/core/kernels.py b/src/orcapod/core/kernels.py new file mode 100644 index 0000000..52e1f8c --- /dev/null +++ b/src/orcapod/core/kernels.py @@ -0,0 +1,241 @@ +from abc import abstractmethod +from collections.abc import Collection +from datetime import datetime, timezone +from typing import Any +from orcapod.protocols import core_protocols as cp +import logging +from orcapod.core.streams import KernelStream +from orcapod.core.base import LabeledContentIdentifiableBase +from orcapod.core.trackers import DEFAULT_TRACKER_MANAGER +from orcapod.types import PythonSchema + +logger = logging.getLogger(__name__) + + +class TrackedKernelBase(LabeledContentIdentifiableBase): + """ + Kernel defines the fundamental unit of computation that can be performed on zero, one or more streams of data. + It is the base class for all computations and transformations that can be performed on a collection of streams + (including an empty collection). + A kernel is defined as a callable that takes a (possibly empty) collection of streams as the input + and returns a new stream as output (note that output stream is always singular). + Each "invocation" of the kernel on a collection of streams is assigned a unique ID. + The corresponding invocation information is stored as Invocation object and attached to the output stream + for computational graph tracking. + """ + + def __init__( + self, + label: str | None = None, + skip_tracking: bool = False, + tracker_manager: cp.TrackerManager | None = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self._label = label + + self._skip_tracking = skip_tracking + self._tracker_manager = tracker_manager or DEFAULT_TRACKER_MANAGER + self._last_modified = None + self._kernel_hash = None + self._set_modified_time() + + @property + def reference(self) -> tuple[str, ...]: + """ + Returns a unique identifier for the kernel. + This is used to identify the kernel in the computational graph. + """ + return ( + f"{self.__class__.__name__}", + self.content_hash().to_hex(), + ) + + @property + def last_modified(self) -> datetime | None: + """ + When the kernel was last modified. For most kernels, this is the timestamp + of the kernel creation. + """ + return self._last_modified + + # TODO: reconsider making this a public method + def _set_modified_time( + self, timestamp: datetime | None = None, invalidate: bool = False + ) -> None: + """ + Sets the last modified time of the kernel. + If `invalidate` is True, it resets the last modified time to None to indicate unstable state that'd signal downstream + to recompute when using the kernel. Othewrise, sets the last modified time to the current time or to the provided timestamp. + """ + if invalidate: + self._last_modified = None + return + + if timestamp is not None: + self._last_modified = timestamp + else: + self._last_modified = datetime.now(timezone.utc) + + @abstractmethod + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Return the output types of the kernel given the input streams. + """ + ... + + def output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + processed_streams = self.pre_kernel_processing(*streams) + self.validate_inputs(*processed_streams) + return self.kernel_output_types( + *processed_streams, include_system_tags=include_system_tags + ) + + @abstractmethod + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + """ + Identity structure for this kernel. Input stream(s), if present, have already been preprocessed + and validated. + """ + ... + + def identity_structure(self, streams: Collection[cp.Stream] | None = None) -> Any: + """ + Default implementation of identity_structure for the kernel only + concerns the kernel class and the streams if present. Subclasses of + Kernels should override this method to provide a more meaningful + representation of the kernel. Note that kernel must provide the notion + of identity under possibly two distinct contexts: + 1) identity of the kernel in itself when invoked without any stream + 2) identity of the specific invocation of the kernel with a collection of streams + While the latter technically corresponds to the identity of the invocation and not + the kernel, only kernel can provide meaningful information as to the uniqueness of + the invocation as only kernel would know if / how the input stream(s) alter the identity + of the invocation. For example, if the kernel corresponds to an commutative computation + and therefore kernel K(x, y) == K(y, x), then the identity structure must reflect the + equivalence of the two by returning the same identity structure for both invocations. + This can be achieved, for example, by returning a set over the streams instead of a tuple. + """ + if streams is not None: + streams = self.pre_kernel_processing(*streams) + self.validate_inputs(*streams) + return self.kernel_identity_structure(streams) + + @abstractmethod + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Trigger the main computation of the kernel on a collection of streams. + This method is called when the kernel is invoked with a collection of streams. + Subclasses should override this method to provide the kernel with its unique behavior + """ + + def pre_kernel_processing(self, *streams: cp.Stream) -> tuple[cp.Stream, ...]: + """ + Pre-processing step that can be overridden by subclasses to perform any necessary pre-processing + on the input streams before the main computation. This is useful if you need to modify the input streams + or perform any other operations before the main computation. Critically, any Kernel/Pod invocations in the + pre-processing step will be tracked outside of the computation in the kernel. + Default implementation is a no-op, returning the input streams unchanged. + """ + return streams + + @abstractmethod + def validate_inputs(self, *streams: cp.Stream) -> None: + """ + Validate the input streams before the main computation but after the pre-kernel processing + """ + ... + + def prepare_output_stream( + self, *streams: cp.Stream, label: str | None = None + ) -> KernelStream: + """ + Prepare the output stream for the kernel invocation. + This method is called after the main computation is performed. + It creates a KernelStream with the provided streams and label. + """ + return KernelStream(source=self, upstreams=streams, label=label) + + def track_invocation(self, *streams: cp.Stream, label: str | None = None) -> None: + """ + Track the invocation of the kernel with the provided streams. + This is a convenience method that calls record_kernel_invocation. + """ + if not self._skip_tracking and self._tracker_manager is not None: + self._tracker_manager.record_kernel_invocation(self, streams, label=label) + + def __call__( + self, *streams: cp.Stream, label: str | None = None, **kwargs + ) -> KernelStream: + processed_streams = self.pre_kernel_processing(*streams) + self.validate_inputs(*processed_streams) + output_stream = self.prepare_output_stream(*processed_streams, label=label) + self.track_invocation(*processed_streams, label=label) + return output_stream + + def __repr__(self): + return self.__class__.__name__ + + def __str__(self): + if self._label is not None: + return f"{self.__class__.__name__}({self._label})" + return self.__class__.__name__ + + +class WrappedKernel(TrackedKernelBase): + """ + A wrapper for a kernels useful when you want to use an existing kernel + but need to provide some extra functionality. + + Default implementation provides a simple passthrough to the wrapped kernel. + If you want to provide a custom behavior, be sure to override the methods + that you want to change. Note that the wrapped kernel must implement the + `Kernel` protocol. Refer to `orcapod.protocols.data_protocols.Kernel` for more details. + """ + + def __init__(self, kernel: cp.Kernel, **kwargs) -> None: + # TODO: handle fixed input stream already set on the kernel + super().__init__(**kwargs) + self.kernel = kernel + + def computed_label(self) -> str | None: + """ + Compute a label for this kernel based on its content. + If label is not explicitly set for this kernel and computed_label returns a valid value, + it will be used as label of this kernel. + """ + return self.kernel.label + + @property + def reference(self) -> tuple[str, ...]: + return self.kernel.reference + + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + return self.kernel.output_types( + *streams, include_system_tags=include_system_tags + ) + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + return self.kernel.identity_structure(streams) + + def validate_inputs(self, *streams: cp.Stream) -> None: + return self.kernel.validate_inputs(*streams) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + return self.kernel.forward(*streams) + + def __repr__(self): + return f"WrappedKernel({self.kernel!r})" + + def __str__(self): + return f"WrappedKernel:{self.kernel!s}" diff --git a/src/orcapod/core/operators.py b/src/orcapod/core/operators.py deleted file mode 100644 index 093167b..0000000 --- a/src/orcapod/core/operators.py +++ /dev/null @@ -1,837 +0,0 @@ -from collections import defaultdict -from collections.abc import Callable, Collection, Iterator -from itertools import chain -from typing import Any - - -from orcapod.core.base import Operator, SyncStream -from orcapod.hashing import function_content_hash, hash_function -from orcapod.core.streams import SyncStreamFromGenerator -from orcapod.utils.stream_utils import ( - batch_packet, - batch_tags, - check_packet_compatibility, - join_tags, - fill_missing, - merge_typespecs, -) - -from orcapod.types import Packet, Tag, TypeSpec - - -class Repeat(Operator): - """ - A Mapper that repeats the packets in the stream a specified number of times. - The repeat count is the number of times to repeat each packet. - """ - - def __init__(self, repeat_count: int) -> None: - super().__init__() - if not isinstance(repeat_count, int): - raise TypeError("repeat_count must be an integer") - if repeat_count < 0: - raise ValueError("repeat_count must be non-negative") - self.repeat_count = repeat_count - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("Repeat operation requires exactly one stream") - - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - for tag, packet in stream: - for _ in range(self.repeat_count): - yield tag, packet - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return f"Repeat(count={self.repeat_count})" - - def identity_structure(self, *streams) -> tuple[str, int, set[SyncStream]]: - # Join does not depend on the order of the streams -- convert it onto a set - return (self.__class__.__name__, self.repeat_count, set(streams)) - - def keys( - self, *streams: SyncStream, trigger_run=False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Repeat does not alter the keys of the stream. - """ - if len(streams) != 1: - raise ValueError("Repeat operation requires exactly one stream") - - stream = streams[0] - return stream.keys(trigger_run=trigger_run) - - def types( - self, *streams: SyncStream, trigger_run=False - ) -> tuple[TypeSpec | None, TypeSpec | None]: - """ - Repeat does not alter the types of the stream. - """ - if len(streams) != 1: - raise ValueError("Repeat operation requires exactly one stream") - - stream = streams[0] - return stream.types(trigger_run=trigger_run) - - def claims_unique_tags( - self, *streams: SyncStream, trigger_run: bool = False - ) -> bool | None: - if len(streams) != 1: - raise ValueError( - "Repeat operation only supports operating on a single input stream" - ) - - # Repeat's uniquness is true only if (1) input stream has unique tags and (2) repeat count is 1 - return self.repeat_count == 1 and streams[0].claims_unique_tags( - trigger_run=trigger_run - ) - - -class Merge(Operator): - def forward(self, *streams: SyncStream) -> SyncStream: - tag_keys, packet_keys = self.keys(*streams) - - def generator() -> Iterator[tuple[Tag, Packet]]: - for tag, packet in chain(*streams): - # fill missing keys with None - tag = fill_missing(tag, tag_keys) - packet = fill_missing(packet, packet_keys) - yield tag, packet - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return "Merge()" - - def identity_structure(self, *streams): - # Merge does not depend on the order of the streams -- convert it onto a set - return (self.__class__.__name__, set(streams)) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Merge does not alter the keys of the stream. - """ - if len(streams) < 2: - raise ValueError("Merge operation requires at least two streams") - - merged_tag_keys = set() - merged_packet_keys = set() - - for stream in streams: - tag_keys, packet_keys = stream.keys(trigger_run=trigger_run) - if tag_keys is not None: - merged_tag_keys.update(set(tag_keys)) - if packet_keys is not None: - merged_packet_keys.update(set(packet_keys)) - - return list(merged_tag_keys), list(merged_packet_keys) - - def types( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[TypeSpec | None, TypeSpec | None]: - """ - Merge does not alter the types of the stream. - """ - if len(streams) < 2: - raise ValueError("Merge operation requires at least two streams") - - merged_tag_types: TypeSpec | None = {} - merged_packet_types: TypeSpec | None = {} - - for stream in streams: - if merged_tag_types is None and merged_packet_types is None: - break - tag_types, packet_types = stream.types(trigger_run=trigger_run) - if merged_tag_types is not None and tag_types is not None: - merged_tag_types.update(tag_types) - else: - merged_tag_types = None - if merged_tag_types is not None and packet_types is not None: - merged_packet_types.update(packet_types) - else: - merged_tag_types = None - - return merged_tag_types, merged_packet_types - - def claims_unique_tags( - self, *streams: SyncStream, trigger_run: bool = True - ) -> bool | None: - """ - Merge operation can only claim unique tags if all input streams have unique tags AND - the tag keys are not identical across all streams. - """ - # TODO: update implementation - if len(streams) < 2: - raise ValueError("Merge operation requires at least two streams") - # Check if all streams have unique tags - unique_tags = all( - stream.claims_unique_tags(trigger_run=trigger_run) for stream in streams - ) - if not unique_tags: - return False - # check that all streams' tag keys are not identical - tag_key_pool = set() - for stream in streams: - tag_keys, packet_keys = stream.keys() - # TODO: re-evaluate the implication of having empty tag keys in uniqueness guarantee - if tag_keys is None or set(tag_keys) in tag_key_pool: - return False - tag_key_pool.add(frozenset(tag_keys)) - - return True - - -class Join(Operator): - def identity_structure(self, *streams): - # Join does not depend on the order of the streams -- convert it onto a set - return (self.__class__.__name__, set(streams)) - - def types( - self, *streams: SyncStream, trigger_run=False - ) -> tuple[TypeSpec | None, TypeSpec | None]: - """ - Returns the types of the operation. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - The keys are returned if it is feasible to do so, otherwise a tuple - (None, None) is returned to signify that the keys are not known. - """ - if len(streams) != 2: - raise ValueError("Join operation requires exactly two streams") - - left_stream, right_stream = streams - left_tag_types, left_packet_types = left_stream.types(trigger_run=False) - right_tag_types, right_packet_types = right_stream.types(trigger_run=False) - - # TODO: do error handling when merge fails - joined_tag_types = merge_typespecs(left_tag_types, right_tag_types) - joined_packet_types = merge_typespecs(left_packet_types, right_packet_types) - - return joined_tag_types, joined_packet_types - - def forward(self, *streams: SyncStream) -> SyncStream: - """ - Joins two streams together based on their tags. - The resulting stream will contain all the tags from both streams. - """ - if len(streams) != 2: - raise ValueError("Join operation requires exactly two streams") - - left_stream, right_stream = streams - - def generator() -> Iterator[tuple[Tag, Packet]]: - for left_tag, left_packet in left_stream: - for right_tag, right_packet in right_stream: - if (joined_tag := join_tags(left_tag, right_tag)) is not None: - if not check_packet_compatibility(left_packet, right_packet): - raise ValueError( - f"Packets are not compatible: {left_packet} and {right_packet}" - ) - yield joined_tag, {**left_packet, **right_packet} - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return "Join()" - - -class FirstMatch(Operator): - def forward(self, *streams: SyncStream) -> SyncStream: - """ - Joins two streams together based on their tags, returning at most one match for each tag. - The resulting stream will contain all the tags from both streams. - """ - if len(streams) != 2: - raise ValueError("MatchUpToN operation requires exactly two streams") - - left_stream, right_stream = streams - - # get all elements from both streams - outer_stream = list(left_stream) - inner_stream = list(right_stream) - - # take the longer one as the outer stream - if len(outer_stream) < len(inner_stream): - # swap the stream - outer_stream, inner_stream = inner_stream, outer_stream - - # only finds up to one possible match for each packet - def generator(): - for outer_tag, outer_packet in outer_stream: - for idx, (inner_tag, inner_packet) in enumerate(inner_stream): - if (joined_tag := join_tags(outer_tag, inner_tag)) is not None: - if not check_packet_compatibility(outer_packet, inner_packet): - raise ValueError( - f"Packets are not compatible: {outer_packet} and {inner_packet}" - ) - # match is found - remove the packet from the inner stream - inner_stream.pop(idx) - yield joined_tag, {**outer_packet, **inner_packet} - # if enough matches found, move onto the next outer stream packet - break - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return "MatchUpToN()" - - def identity_structure(self, *streams: SyncStream) -> tuple[str, set[SyncStream]]: - # Join does not depend on the order of the streams -- convert it onto a set - return (self.__class__.__name__, set(streams)) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Returns the keys of the operation. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - The keys are returned if it is feasible to do so, otherwise a tuple - (None, None) is returned to signify that the keys are not known. - """ - if len(streams) != 2: - raise ValueError("FirstMatch operation requires exactly two streams") - - left_stream, right_stream = streams - left_tag_keys, left_packet_keys = left_stream.keys(trigger_run=trigger_run) - right_tag_keys, right_packet_keys = right_stream.keys(trigger_run=trigger_run) - - # if any of the components return None -> resolve to default operation - if ( - left_tag_keys is None - or right_tag_keys is None - or left_packet_keys is None - or right_packet_keys is None - ): - return super().keys(*streams, trigger_run=trigger_run) - - joined_tag_keys = list(set(left_tag_keys) | set(right_tag_keys)) - joined_packet_keys = list(set(left_packet_keys) | set(right_packet_keys)) - - return joined_tag_keys, joined_packet_keys - - def types( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[TypeSpec | None, TypeSpec | None]: - """ - Returns the typespecs of tag and packet. - """ - if len(streams) != 2: - raise ValueError("FirstMatch operation requires exactly two streams") - - left_stream, right_stream = streams - left_tag_types, left_packet_types = left_stream.types(trigger_run=trigger_run) - right_tag_types, right_packet_types = right_stream.types( - trigger_run=trigger_run - ) - - # if any of the components return None -> resolve to default operation - if ( - left_tag_types is None - or right_tag_types is None - or left_packet_types is None - or right_packet_types is None - ): - return super().types(*streams, trigger_run=trigger_run) - - joined_tag_types = merge_typespecs(left_tag_types, right_tag_types) - joined_packet_types = merge_typespecs(left_packet_types, right_packet_types) - - return joined_tag_types, joined_packet_types - - -class MapPackets(Operator): - """ - A Mapper that maps the keys of the packet in the stream to new keys. - The mapping is done using a dictionary that maps old keys to new keys. - If a key is not in the mapping, it will be dropped from the element unless - drop_unmapped=False, in which case unmapped keys will be retained. - """ - - def __init__(self, key_map: dict[str, str], drop_unmapped: bool = True) -> None: - super().__init__() - self.key_map = key_map - self.drop_unmapped = drop_unmapped - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("MapPackets operation requires exactly one stream") - - stream = streams[0] - - def generator(): - for tag, packet in stream: - if self.drop_unmapped: - packet = { - v: packet[k] for k, v in self.key_map.items() if k in packet - } - else: - packet = {self.key_map.get(k, k): v for k, v in packet.items()} - yield tag, packet - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - map_repr = ", ".join([f"{k} ⇒ {v}" for k, v in self.key_map.items()]) - return f"packets({map_repr})" - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - self.key_map, - self.drop_unmapped, - ) + tuple(streams) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Returns the keys of the operation. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - The keys are inferred based on the first (tag, packet) pair in the stream. - """ - if len(streams) != 1: - raise ValueError("MapPackets operation requires exactly one stream") - - stream = streams[0] - tag_keys, packet_keys = stream.keys(trigger_run=trigger_run) - if tag_keys is None or packet_keys is None: - return super().keys(trigger_run=trigger_run) - - if self.drop_unmapped: - # If drop_unmapped is True, we only keep the keys that are in the mapping - mapped_packet_keys = [ - self.key_map[k] for k in packet_keys if k in self.key_map - ] - else: - mapped_packet_keys = [self.key_map.get(k, k) for k in packet_keys] - - return tag_keys, mapped_packet_keys - - def types( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[TypeSpec | None, TypeSpec | None]: - """ - Returns the types of the operation. - The first list contains the types of the tags, and the second list contains the types of the packets. - The types are inferred based on the first (tag, packet) pair in the stream. - """ - if len(streams) != 1: - raise ValueError("MapPackets operation requires exactly one stream") - - stream = streams[0] - tag_types, packet_types = stream.types(trigger_run=trigger_run) - if tag_types is None or packet_types is None: - return super().types(trigger_run=trigger_run) - - if self.drop_unmapped: - # If drop_unmapped is True, we only keep the keys that are in the mapping - mapped_packet_types = { - self.key_map[k]: v for k, v in packet_types.items() if k in self.key_map - } - else: - mapped_packet_types = { - self.key_map.get(k, k): v for k, v in packet_types.items() - } - - return tag_types, mapped_packet_types - - -class DefaultTag(Operator): - """ - A Mapper that adds a default tag to the packets in the stream. - The default tag is added to all packets in the stream. If the - tag already contains the same key, it will not be overwritten. - """ - - def __init__(self, default_tag: Tag) -> None: - super().__init__() - self.default_tag = default_tag - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("DefaultTag operation requires exactly one stream") - - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - for tag, packet in stream: - yield {**self.default_tag, **tag}, packet - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return f"DefaultTag({self.default_tag})" - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Returns the keys of the operation. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - The keys are inferred based on the first (tag, packet) pair in the stream. - """ - if len(streams) != 1: - raise ValueError("DefaultTag operation requires exactly one stream") - - stream = streams[0] - tag_keys, packet_keys = stream.keys(trigger_run=trigger_run) - if tag_keys is None or packet_keys is None: - return super().keys(trigger_run=trigger_run) - tag_keys = list(set(tag_keys) | set(self.default_tag.keys())) - return tag_keys, packet_keys - - -class MapTags(Operator): - """ - A Mapper that maps the tags of the packet in the stream to new tags. Packet remains unchanged. - The mapping is done using a dictionary that maps old tags to new tags. - If a tag is not in the mapping, it will be dropped from the element unless - drop_unmapped=False, in which case unmapped tags will be retained. - """ - - def __init__(self, key_map: dict[str, str], drop_unmapped: bool = True) -> None: - super().__init__() - self.key_map = key_map - self.drop_unmapped = drop_unmapped - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("MapTags operation requires exactly one stream") - - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - for tag, packet in stream: - if self.drop_unmapped: - tag = {v: tag[k] for k, v in self.key_map.items() if k in tag} - else: - tag = {self.key_map.get(k, k): v for k, v in tag.items()} - yield tag, packet - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - map_repr = ", ".join([f"{k} ⇒ {v}" for k, v in self.key_map.items()]) - return f"tags({map_repr})" - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - self.key_map, - self.drop_unmapped, - ) + tuple(streams) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Returns the keys of the operation. - The first list contains the keys of the tags, and the second list contains the keys of the packets. - The keys are inferred based on the first (tag, packet) pair in the stream. - """ - if len(streams) != 1: - raise ValueError("MapTags operation requires exactly one stream") - - stream = streams[0] - tag_keys, packet_keys = stream.keys(trigger_run=trigger_run) - if tag_keys is None or packet_keys is None: - return super().keys(trigger_run=trigger_run) - - if self.drop_unmapped: - # If drop_unmapped is True, we only keep the keys that are in the mapping - mapped_tag_keys = [self.key_map[k] for k in tag_keys if k in self.key_map] - else: - mapped_tag_keys = [self.key_map.get(k, k) for k in tag_keys] - - return mapped_tag_keys, packet_keys - - -class Filter(Operator): - """ - A Mapper that filters the packets in the stream based on a predicate function. - Predicate function should take two arguments: the tag and the packet, both as dictionaries. - The predicate function should return True for packets that should be kept and False for packets that should be dropped. - """ - - def __init__(self, predicate: Callable[[Tag, Packet], bool]): - super().__init__() - self.predicate = predicate - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("Filter operation requires exactly one stream") - - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - for tag, packet in stream: - if self.predicate(tag, packet): - yield tag, packet - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return f"Filter({self.predicate})" - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - function_content_hash(self.predicate), - ) + tuple(streams) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Filter does not alter the keys of the stream. - """ - if len(streams) != 1: - raise ValueError("Filter operation requires exactly one stream") - - stream = streams[0] - return stream.keys(trigger_run=trigger_run) - - -class Transform(Operator): - """ - A Mapper that transforms the packets in the stream based on a transformation function. - The transformation function should take two arguments: the tag and the packet, both as dictionaries. - The transformation function should return a tuple of (new_tag, new_packet). - """ - - def __init__(self, transform: Callable[[Tag, Packet], tuple[Tag, Packet]]): - super().__init__() - self.transform = transform - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("Transform operation requires exactly one stream") - - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - for tag, packet in stream: - yield self.transform(tag, packet) - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return f"Transform({self.transform})" - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - hash_function(self.transform), - ) + tuple(streams) - - -class Batch(Operator): - """ - A Mapper that batches the packets in the stream based on a batch size. - The batch size is the number of packets to include in each batch. - If the final batch is smaller than the batch size, it will be dropped unless drop_last=False. - """ - - def __init__( - self, - batch_size: int, - tag_processor: None | Callable[[Collection[Tag]], Tag] = None, - drop_last: bool = True, - ): - super().__init__() - self.batch_size = batch_size - if tag_processor is None: - tag_processor = batch_tags # noqa: E731 - - self.tag_processor = tag_processor - self.drop_last = drop_last - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("Batch operation requires exactly one stream") - - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - batch_tags: list[Tag] = [] - batch_packets: list[Packet] = [] - for tag, packet in stream: - batch_tags.append(tag) - batch_packets.append(packet) - if len(batch_tags) == self.batch_size: - yield self.tag_processor(batch_tags), batch_packet(batch_packets) - batch_tags = [] - batch_packets = [] - if batch_tags and not self.drop_last: - yield self.tag_processor(batch_tags), batch_packet(batch_packets) - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return f"Batch(size={self.batch_size}, drop_last={self.drop_last})" - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - self.batch_size, - hash_function( - self.tag_processor, - function_hash_mode="name", - ), - self.drop_last, - ) + tuple(streams) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Batch does not alter the keys of the stream. - """ - if len(streams) != 1: - raise ValueError("Batch operation requires exactly one stream") - - stream = streams[0] - return stream.keys(trigger_run=trigger_run) - - -class GroupBy(Operator): - def __init__( - self, - group_keys: Collection[str] | None = None, - reduce_keys: bool = False, - selection_function: Callable[[Collection[tuple[Tag, Packet]]], Collection[bool]] - | None = None, - ) -> None: - super().__init__() - self.group_keys = group_keys - self.reduce_keys = reduce_keys - self.selection_function = selection_function - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 1: - raise ValueError("GroupBy operation requires exactly one stream") - - stream = streams[0] - stream_keys, packet_keys = stream.keys() - stream_keys = stream_keys or [] - packet_keys = packet_keys or [] - group_keys = self.group_keys if self.group_keys is not None else stream_keys - - def generator() -> Iterator[tuple[Tag, Packet]]: - # step through all packets in the stream and group them by the specified keys - grouped_packets: dict[tuple, list[tuple[Tag, Packet]]] = defaultdict(list) - for tag, packet in stream: - key = tuple(tag.get(key, None) for key in group_keys) - grouped_packets[key].append((tag, packet)) - - for key, packets in grouped_packets.items(): - if self.selection_function is not None: - # apply the selection function to the grouped packets - selected_packets = self.selection_function(packets) - packets = [ - p for p, selected in zip(packets, selected_packets) if selected - ] - - if not packets: - continue - - # create a new tag that combines the group keys - # if reduce_keys is True, we only keep the group keys as a singular value - new_tag: Tag = {} - if self.reduce_keys: - new_tag = {k: key[i] for i, k in enumerate(group_keys)} - remaining_keys = set(stream_keys) - set(group_keys) - else: - remaining_keys = set(stream_keys) | set(group_keys) - # for remaining keys return list of tag values - for k in remaining_keys: - if k not in new_tag: - new_tag[k] = [t.get(k, None) for t, _ in packets] - # combine all packets into a single packet - combined_packet: Packet = { - k: [p.get(k, None) for _, p in packets] for k in packet_keys - } - yield new_tag, combined_packet - - return SyncStreamFromGenerator(generator) - - def identity_structure(self, *streams: SyncStream) -> Any: - struct = (self.__class__.__name__, self.group_keys, self.reduce_keys) - if self.selection_function is not None: - struct += (hash_function(self.selection_function),) - return struct + tuple(streams) - - -class CacheStream(Operator): - """ - A Mapper that caches the packets in the stream, thus avoiding upstream recomputation. - The cache is filled the first time the stream is iterated over. - For the next iterations, the cached packets are returned. - Call `clear_cache()` to clear the cache. - """ - - def __init__(self) -> None: - super().__init__() - self.cache: list[tuple[Tag, Packet]] = [] - self.is_cached = False - - def forward(self, *streams: SyncStream) -> SyncStream: - if not self.is_cached and len(streams) != 1: - raise ValueError("CacheStream operation requires exactly one stream") - - def generator() -> Iterator[tuple[Tag, Packet]]: - if not self.is_cached: - for tag, packet in streams[0]: - self.cache.append((tag, packet)) - yield tag, packet - self.is_cached = True - else: - for tag, packet in self.cache: - yield tag, packet - - return SyncStreamFromGenerator(generator) - - def clear_cache(self) -> None: - """ - Clear the cache. - """ - self.cache = [] - self.is_cached = False - - def __repr__(self) -> str: - return f"CacheStream(active:{self.is_cached})" - - def identity_structure(self, *streams): - # treat every CacheStream as a different stream - return None - - -def tag( - mapping: dict[str, str], drop_unmapped: bool = True -) -> Callable[[SyncStream], SyncStream]: - def transformer(stream: SyncStream) -> SyncStream: - """ - Transform the stream by renaming the keys in the tag. - The mapping is a dictionary that maps the old keys to the new keys. - """ - return MapTags(mapping, drop_unmapped=drop_unmapped)(stream) - - return transformer - - -def packet( - mapping: dict[str, str], drop_unmapped: bool = True -) -> Callable[[SyncStream], SyncStream]: - def transformer(stream: SyncStream) -> SyncStream: - """ - Transform the stream by renaming the keys in the packet. - The mapping is a dictionary that maps the old keys to the new keys. - """ - return MapPackets(mapping, drop_unmapped=drop_unmapped)(stream) - - return transformer diff --git a/src/orcapod/core/operators/__init__.py b/src/orcapod/core/operators/__init__.py new file mode 100644 index 0000000..b1f0544 --- /dev/null +++ b/src/orcapod/core/operators/__init__.py @@ -0,0 +1,24 @@ +from .join import Join +from .semijoin import SemiJoin +from .mappers import MapTags, MapPackets +from .batch import Batch +from .column_selection import ( + SelectTagColumns, + SelectPacketColumns, + DropTagColumns, + DropPacketColumns, +) +from .filters import PolarsFilter + +__all__ = [ + "Join", + "SemiJoin", + "MapTags", + "MapPackets", + "Batch", + "SelectTagColumns", + "SelectPacketColumns", + "DropTagColumns", + "DropPacketColumns", + "PolarsFilter", +] diff --git a/src/orcapod/core/operators/base.py b/src/orcapod/core/operators/base.py new file mode 100644 index 0000000..b87748c --- /dev/null +++ b/src/orcapod/core/operators/base.py @@ -0,0 +1,290 @@ +from orcapod.core.kernels import TrackedKernelBase +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from abc import abstractmethod +from typing import Any +from collections.abc import Collection + + +class Operator(TrackedKernelBase): + """ + Base class for all operators. + Operators are a special type of kernel that can be used to perform operations on streams. + + They are defined as a callable that takes a (possibly empty) collection of streams as the input + and returns a new stream as output (note that output stream is always singular). + """ + + +class UnaryOperator(Operator): + """ + Base class for all operators. + """ + + def check_unary_input( + self, + streams: Collection[cp.Stream], + ) -> None: + """ + Check that the inputs to the unary operator are valid. + """ + if len(streams) != 1: + raise ValueError("UnaryOperator requires exactly one input stream.") + + def validate_inputs(self, *streams: cp.Stream) -> None: + self.check_unary_input(streams) + stream = streams[0] + return self.op_validate_inputs(stream) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Forward method for unary operators. + It expects exactly one stream as input. + """ + stream = streams[0] + return self.op_forward(stream) + + # TODO: complete substream implementation + # Substream implementation pending + # stream = streams[0] + # # visit each substream + # output_substreams = [] + # for substream_id in stream.substream_identities: + # substream = stream.get_substream(substream_id) + # output_substreams.append(self.op_forward(substream)) + + # # at the moment only single output substream is supported + # if len(output_substreams) != 1: + # raise NotImplementedError( + # "Support for multiple output substreams is not implemented yet." + # ) + # return output_substreams[0] + + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + stream = streams[0] + return self.op_output_types(stream, include_system_tags=include_system_tags) + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + """ + Return a structure that represents the identity of this operator. + This is used to ensure that the operator can be uniquely identified in the computational graph. + """ + if streams is not None: + stream = list(streams)[0] + return self.op_identity_structure(stream) + return self.op_identity_structure() + + @abstractmethod + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + ... + + @abstractmethod + def op_forward(self, stream: cp.Stream) -> cp.Stream: + """ + This method should be implemented by subclasses to define the specific behavior of the binary operator. + It takes two streams as input and returns a new stream as output. + """ + ... + + @abstractmethod + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + This method should be implemented by subclasses to return the typespecs of the input and output streams. + It takes two streams as input and returns a tuple of typespecs. + """ + ... + + @abstractmethod + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + """ + This method should be implemented by subclasses to return a structure that represents the identity of the operator. + It takes two streams as input and returns a tuple containing the operator name and a set of streams. + """ + ... + + +class BinaryOperator(Operator): + """ + Base class for all operators. + """ + + def check_binary_inputs( + self, + streams: Collection[cp.Stream], + ) -> None: + """ + Check that the inputs to the binary operator are valid. + This method is called before the forward method to ensure that the inputs are valid. + """ + if len(streams) != 2: + raise ValueError("BinaryOperator requires exactly two input streams.") + + def validate_inputs(self, *streams: cp.Stream) -> None: + self.check_binary_inputs(streams) + left_stream, right_stream = streams + return self.op_validate_inputs(left_stream, right_stream) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Forward method for binary operators. + It expects exactly two streams as input. + """ + left_stream, right_stream = streams + return self.op_forward(left_stream, right_stream) + + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + left_stream, right_stream = streams + return self.op_output_types( + left_stream, right_stream, include_system_tags=include_system_tags + ) + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + """ + Return a structure that represents the identity of this operator. + This is used to ensure that the operator can be uniquely identified in the computational graph. + """ + if streams is not None: + left_stream, right_stream = streams + self.op_identity_structure(left_stream, right_stream) + return self.op_identity_structure() + + @abstractmethod + def op_validate_inputs( + self, left_stream: cp.Stream, right_stream: cp.Stream + ) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + ... + + @abstractmethod + def op_forward(self, left_stream: cp.Stream, right_stream: cp.Stream) -> cp.Stream: + """ + This method should be implemented by subclasses to define the specific behavior of the binary operator. + It takes two streams as input and returns a new stream as output. + """ + ... + + @abstractmethod + def op_output_types( + self, + left_stream: cp.Stream, + right_stream: cp.Stream, + include_system_tags: bool = False, + ) -> tuple[PythonSchema, PythonSchema]: + """ + This method should be implemented by subclasses to return the typespecs of the input and output streams. + It takes two streams as input and returns a tuple of typespecs. + """ + ... + + @abstractmethod + def op_identity_structure( + self, + left_stream: cp.Stream | None = None, + right_stream: cp.Stream | None = None, + ) -> Any: + """ + This method should be implemented by subclasses to return a structure that represents the identity of the operator. + It takes two streams as input and returns a tuple containing the operator name and a set of streams. + """ + ... + + +class NonZeroInputOperator(Operator): + """ + Operators that work with at least one input stream. + This is useful for operators that can take a variable number of (but at least one ) input streams, + such as joins, unions, etc. + """ + + def verify_non_zero_input( + self, + streams: Collection[cp.Stream], + ) -> None: + """ + Check that the inputs to the variable inputs operator are valid. + This method is called before the forward method to ensure that the inputs are valid. + """ + if len(streams) == 0: + raise ValueError( + f"Operator {self.__class__.__name__} requires at least one input stream." + ) + + def validate_inputs(self, *streams: cp.Stream) -> None: + self.verify_non_zero_input(streams) + return self.op_validate_inputs(*streams) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Forward method for variable inputs operators. + It expects at least one stream as input. + """ + return self.op_forward(*streams) + + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + return self.op_output_types(*streams, include_system_tags=include_system_tags) + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + """ + Return a structure that represents the identity of this operator. + This is used to ensure that the operator can be uniquely identified in the computational graph. + """ + return self.op_identity_structure(streams) + + @abstractmethod + def op_validate_inputs(self, *streams: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + ... + + @abstractmethod + def op_forward(self, *streams: cp.Stream) -> cp.Stream: + """ + This method should be implemented by subclasses to define the specific behavior of the non-zero input operator. + It takes variable number of streams as input and returns a new stream as output. + """ + ... + + @abstractmethod + def op_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + This method should be implemented by subclasses to return the typespecs of the input and output streams. + It takes at least one stream as input and returns a tuple of typespecs. + """ + ... + + @abstractmethod + def op_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + """ + This method should be implemented by subclasses to return a structure that represents the identity of the operator. + It takes zero or more streams as input and returns a tuple containing the operator name and a set of streams. + If zero, it should return identity of the operator itself. + If one or more, it should return a identity structure approrpiate for the operator invoked on the given streams. + """ + ... diff --git a/src/orcapod/core/operators/batch.py b/src/orcapod/core/operators/batch.py new file mode 100644 index 0000000..be48b3c --- /dev/null +++ b/src/orcapod/core/operators/batch.py @@ -0,0 +1,106 @@ +from orcapod.core.operators.base import UnaryOperator +from collections.abc import Collection +from orcapod.protocols import core_protocols as cp +from typing import Any, TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.streams import TableStream + +if TYPE_CHECKING: + import pyarrow as pa + import polars as pl +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + +from orcapod.types import PythonSchema + + +class Batch(UnaryOperator): + """ + Base class for all operators. + """ + + def __init__(self, batch_size: int = 0, drop_partial_batch: bool = False, **kwargs): + if batch_size < 0: + raise ValueError("Batch size must be non-negative.") + + super().__init__(**kwargs) + + self.batch_size = batch_size + self.drop_partial_batch = drop_partial_batch + + def check_unary_input( + self, + streams: Collection[cp.Stream], + ) -> None: + """ + Check that the inputs to the unary operator are valid. + """ + if len(streams) != 1: + raise ValueError("UnaryOperator requires exactly one input stream.") + + def validate_inputs(self, *streams: cp.Stream) -> None: + self.check_unary_input(streams) + stream = streams[0] + return self.op_validate_inputs(stream) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + return None + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + """ + This method should be implemented by subclasses to define the specific behavior of the binary operator. + It takes two streams as input and returns a new stream as output. + """ + table = stream.as_table(include_source=True, include_system_tags=True) + + tag_columns, packet_columns = stream.keys() + + data_list = table.to_pylist() + + batched_data = [] + + next_batch = {} + + i = 0 + for entry in data_list: + i += 1 + for c in entry: + next_batch.setdefault(c, []).append(entry[c]) + + if self.batch_size > 0 and i >= self.batch_size: + batched_data.append(next_batch) + next_batch = {} + i = 0 + + if i > 0 and not self.drop_partial_batch: + batched_data.append(next_batch) + + batched_table = pa.Table.from_pylist(batched_data) + return TableStream(batched_table, tag_columns=tag_columns) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + This method should be implemented by subclasses to return the typespecs of the input and output streams. + It takes two streams as input and returns a tuple of typespecs. + """ + tag_types, packet_types = stream.types(include_system_tags=include_system_tags) + batched_tag_types = {k: list[v] for k, v in tag_types.items()} + batched_packet_types = {k: list[v] for k, v in packet_types.items()} + + # TODO: check if this is really necessary + return PythonSchema(batched_tag_types), PythonSchema(batched_packet_types) + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + (self.__class__.__name__, self.batch_size, self.drop_partial_batch) + + (stream,) + if stream is not None + else () + ) diff --git a/src/orcapod/core/operators/column_selection.py b/src/orcapod/core/operators/column_selection.py new file mode 100644 index 0000000..4140db8 --- /dev/null +++ b/src/orcapod/core/operators/column_selection.py @@ -0,0 +1,399 @@ +from orcapod.protocols import core_protocols as cp +from orcapod.core.streams import TableStream +from orcapod.types import PythonSchema +from typing import Any, TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule +from collections.abc import Collection, Mapping +from orcapod.errors import InputValidationError +from orcapod.core.system_constants import constants +from orcapod.core.operators.base import UnaryOperator +import logging + + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + +logger = logging.getLogger(__name__) + + +class SelectTagColumns(UnaryOperator): + """ + Operator that selects specified columns from a stream. + """ + + def __init__(self, columns: str | Collection[str], strict: bool = True, **kwargs): + if isinstance(columns, str): + columns = [columns] + self.columns = columns + self.strict = strict + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + tags_to_drop = [c for c in tag_columns if c not in self.columns] + new_tag_columns = [c for c in tag_columns if c not in tags_to_drop] + + if len(new_tag_columns) == len(tag_columns): + logger.info("All tag columns are selected. Returning stream unaltered.") + return stream + + table = stream.as_table( + include_source=True, include_system_tags=True, sort_by_tags=False + ) + + modified_table = table.drop_columns(list(tags_to_drop)) + + return TableStream( + modified_table, + tag_columns=new_tag_columns, + source=self, + upstreams=(stream,), + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # TODO: remove redundant logic + tag_columns, packet_columns = stream.keys() + columns_to_select = self.columns + missing_columns = set(columns_to_select) - set(tag_columns) + if missing_columns and self.strict: + raise InputValidationError( + f"Missing tag columns: {missing_columns}. Make sure all specified columns to select are present or use strict=False to ignore missing columns" + ) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_schema, packet_schema = stream.types( + include_system_tags=include_system_tags + ) + tag_columns, _ = stream.keys() + tags_to_drop = [tc for tc in tag_columns if tc not in self.columns] + + # this ensures all system tag columns are preserved + new_tag_schema = {k: v for k, v in tag_schema.items() if k not in tags_to_drop} + + return new_tag_schema, packet_schema + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.columns, + self.strict, + ) + ((stream,) if stream is not None else ()) + + +class SelectPacketColumns(UnaryOperator): + """ + Operator that selects specified columns from a stream. + """ + + def __init__(self, columns: str | Collection[str], strict: bool = True, **kwargs): + if isinstance(columns, str): + columns = [columns] + self.columns = columns + self.strict = strict + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + packet_columns_to_drop = [c for c in packet_columns if c not in self.columns] + new_packet_columns = [ + c for c in packet_columns if c not in packet_columns_to_drop + ] + + if len(new_packet_columns) == len(packet_columns): + logger.info("All packet columns are selected. Returning stream unaltered.") + return stream + + table = stream.as_table( + include_source=True, include_system_tags=True, sort_by_tags=False + ) + # make sure to drop associated source fields + associated_source_fields = [ + f"{constants.SOURCE_PREFIX}{c}" for c in packet_columns_to_drop + ] + packet_columns_to_drop.extend(associated_source_fields) + + modified_table = table.drop_columns(packet_columns_to_drop) + + return TableStream( + modified_table, + tag_columns=tag_columns, + source=self, + upstreams=(stream,), + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # TODO: remove redundant logic + tag_columns, packet_columns = stream.keys() + columns_to_select = self.columns + missing_columns = set(columns_to_select) - set(packet_columns) + if missing_columns and self.strict: + raise InputValidationError( + f"Missing packet columns: {missing_columns}. Make sure all specified columns to select are present or use strict=False to ignore missing columns" + ) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_schema, packet_schema = stream.types( + include_system_tags=include_system_tags + ) + _, packet_columns = stream.keys() + packets_to_drop = [pc for pc in packet_columns if pc not in self.columns] + + # this ensures all system tag columns are preserved + new_packet_schema = { + k: v for k, v in packet_schema.items() if k not in packets_to_drop + } + + return tag_schema, new_packet_schema + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.columns, + self.strict, + ) + ((stream,) if stream is not None else ()) + + +class DropTagColumns(UnaryOperator): + """ + Operator that drops specified columns from a stream. + """ + + def __init__(self, columns: str | Collection[str], strict: bool = True, **kwargs): + if isinstance(columns, str): + columns = [columns] + self.columns = columns + self.strict = strict + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + columns_to_drop = self.columns + if not self.strict: + columns_to_drop = [c for c in columns_to_drop if c in tag_columns] + + new_tag_columns = [c for c in tag_columns if c not in columns_to_drop] + + if len(columns_to_drop) == 0: + logger.info("No tag columns to drop. Returning stream unaltered.") + return stream + + table = stream.as_table( + include_source=True, include_system_tags=True, sort_by_tags=False + ) + + modified_table = table.drop_columns(list(columns_to_drop)) + + return TableStream( + modified_table, + tag_columns=new_tag_columns, + source=self, + upstreams=(stream,), + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # TODO: remove redundant logic + tag_columns, packet_columns = stream.keys() + columns_to_drop = self.columns + missing_columns = set(columns_to_drop) - set(tag_columns) + if missing_columns and self.strict: + raise InputValidationError( + f"Missing tag columns: {missing_columns}. Make sure all specified columns to drop are present or use strict=False to ignore missing columns" + ) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_schema, packet_schema = stream.types( + include_system_tags=include_system_tags + ) + tag_columns, _ = stream.keys() + new_tag_columns = [c for c in tag_columns if c not in self.columns] + + new_tag_schema = {k: v for k, v in tag_schema.items() if k in new_tag_columns} + + return new_tag_schema, packet_schema + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.columns, + self.strict, + ) + ((stream,) if stream is not None else ()) + + +class DropPacketColumns(UnaryOperator): + """ + Operator that drops specified columns from a stream. + """ + + def __init__(self, columns: str | Collection[str], strict: bool = True, **kwargs): + if isinstance(columns, str): + columns = [columns] + self.columns = columns + self.strict = strict + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + columns_to_drop = list(self.columns) + if not self.strict: + columns_to_drop = [c for c in columns_to_drop if c in packet_columns] + + if len(columns_to_drop) == 0: + logger.info("No packet columns to drop. Returning stream unaltered.") + return stream + + # make sure all associated source columns are dropped too + associated_source_columns = [ + f"{constants.SOURCE_PREFIX}{c}" for c in columns_to_drop + ] + columns_to_drop.extend(associated_source_columns) + + table = stream.as_table( + include_source=True, include_system_tags=True, sort_by_tags=False + ) + + modified_table = table.drop_columns(columns_to_drop) + + return TableStream( + modified_table, + tag_columns=tag_columns, + source=self, + upstreams=(stream,), + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # TODO: remove redundant logic + _, packet_columns = stream.keys() + missing_columns = set(self.columns) - set(packet_columns) + if missing_columns and self.strict: + raise InputValidationError( + f"Missing packet columns: {missing_columns}. Make sure all specified columns to drop are present or use strict=False to ignore missing columns" + ) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_schema, packet_schema = stream.types( + include_system_tags=include_system_tags + ) + new_packet_schema = { + k: v for k, v in packet_schema.items() if k not in self.columns + } + + return tag_schema, new_packet_schema + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.columns, + self.strict, + ) + ((stream,) if stream is not None else ()) + + +class MapTags(UnaryOperator): + """ + Operator that maps tags in a stream using a user-defined function. + The function is applied to each tag in the stream, and the resulting tags + are returned as a new stream. + """ + + def __init__( + self, name_map: Mapping[str, str], drop_unmapped: bool = False, **kwargs + ): + self.name_map = dict(name_map) + self.drop_unmapped = drop_unmapped + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + missing_tags = set(tag_columns) - set(self.name_map.keys()) + + if not any(n in tag_columns for n in self.name_map): + # nothing to rename in the tags, return stream as is + return stream + + table = stream.as_table(include_source=True, include_system_tags=True) + + name_map = { + tc: self.name_map.get(tc, tc) for tc in tag_columns + } # rename the tag as necessary + new_tag_columns = [name_map[tc] for tc in tag_columns] + for c in packet_columns: + name_map[c] = c # no renaming on packet columns + + renamed_table = table.rename_columns(name_map) + + if missing_tags and self.drop_unmapped: + # drop any tags that are not in the name map + renamed_table = renamed_table.drop_columns(list(missing_tags)) + + return TableStream( + renamed_table, tag_columns=new_tag_columns, source=self, upstreams=(stream,) + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # verify that renamed value does NOT collide with other columns + tag_columns, packet_columns = stream.keys() + relevant_source = [] + relevant_target = [] + for source, target in self.name_map.items(): + if source in tag_columns: + relevant_source.append(source) + relevant_target.append(target) + remaining_tag_columns = set(tag_columns) - set(relevant_source) + overlapping_tag_columns = remaining_tag_columns.intersection(relevant_target) + overlapping_packet_columns = set(packet_columns).intersection(relevant_target) + + if overlapping_tag_columns or overlapping_packet_columns: + message = f"Renaming {self.name_map} would cause collisions with existing columns: " + if overlapping_tag_columns: + message += f"overlapping tag columns: {overlapping_tag_columns}." + if overlapping_packet_columns: + message += f"overlapping packet columns: {overlapping_packet_columns}." + raise InputValidationError(message) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_typespec, packet_typespec = stream.types( + include_system_tags=include_system_tags + ) + + # Create new packet typespec with renamed keys + new_tag_typespec = {self.name_map.get(k, k): v for k, v in tag_typespec.items()} + + return new_tag_typespec, packet_typespec + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.name_map, + self.drop_unmapped, + ) + ((stream,) if stream is not None else ()) diff --git a/src/orcapod/core/operators/filters.py b/src/orcapod/core/operators/filters.py new file mode 100644 index 0000000..2edf4f7 --- /dev/null +++ b/src/orcapod/core/operators/filters.py @@ -0,0 +1,166 @@ +from orcapod.protocols import core_protocols as cp +from orcapod.core.streams import TableStream +from orcapod.types import PythonSchema +from typing import Any, TYPE_CHECKING, TypeAlias +from orcapod.utils.lazy_module import LazyModule +from collections.abc import Collection, Mapping +from orcapod.errors import InputValidationError +from orcapod.core.system_constants import constants +from orcapod.core.operators.base import UnaryOperator +import logging +from collections.abc import Iterable + + +if TYPE_CHECKING: + import pyarrow as pa + import polars as pl + import polars._typing as pl_type + import numpy as np +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + pl_type = LazyModule("polars._typing") + +logger = logging.getLogger(__name__) + +polars_predicate: TypeAlias = "pl_type.IntoExprColumn| Iterable[pl_type.IntoExprColumn]| bool| list[bool]| np.ndarray[Any, Any]" + + +class PolarsFilter(UnaryOperator): + """ + Operator that applies Polars filtering to a stream + """ + + def __init__( + self, + predicates: Collection[ + "pl_type.IntoExprColumn| Iterable[pl_type.IntoExprColumn]| bool| list[bool]| np.ndarray[Any, Any]" + ] = (), + constraints: Mapping[str, Any] | None = None, + **kwargs, + ): + self.predicates = predicates + self.constraints = constraints if constraints is not None else {} + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + if len(self.predicates) == 0 and len(self.constraints) == 0: + logger.info( + "No predicates or constraints specified. Returning stream unaltered." + ) + return stream + + # TODO: improve efficiency here... + table = stream.as_table( + include_source=True, include_system_tags=True, sort_by_tags=False + ) + df = pl.DataFrame(table) + filtered_table = df.filter(*self.predicates, **self.constraints).to_arrow() + + return TableStream( + filtered_table, + tag_columns=stream.tag_keys(), + source=self, + upstreams=(stream,), + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + + # Any valid stream would work + return + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + # data types are not modified + return stream.types(include_system_tags=include_system_tags) + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.predicates, + self.constraints, + ) + ((stream,) if stream is not None else ()) + + +class SelectPacketColumns(UnaryOperator): + """ + Operator that selects specified columns from a stream. + """ + + def __init__(self, columns: str | Collection[str], strict: bool = True, **kwargs): + if isinstance(columns, str): + columns = [columns] + self.columns = columns + self.strict = strict + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + packet_columns_to_drop = [c for c in packet_columns if c not in self.columns] + new_packet_columns = [ + c for c in packet_columns if c not in packet_columns_to_drop + ] + + if len(new_packet_columns) == len(packet_columns): + logger.info("All packet columns are selected. Returning stream unaltered.") + return stream + + table = stream.as_table( + include_source=True, include_system_tags=True, sort_by_tags=False + ) + # make sure to drop associated source fields + associated_source_fields = [ + f"{constants.SOURCE_PREFIX}{c}" for c in packet_columns_to_drop + ] + packet_columns_to_drop.extend(associated_source_fields) + + modified_table = table.drop_columns(packet_columns_to_drop) + + return TableStream( + modified_table, + tag_columns=tag_columns, + source=self, + upstreams=(stream,), + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # TODO: remove redundant logic + tag_columns, packet_columns = stream.keys() + columns_to_select = self.columns + missing_columns = set(columns_to_select) - set(packet_columns) + if missing_columns and self.strict: + raise InputValidationError( + f"Missing packet columns: {missing_columns}. Make sure all specified columns to select are present or use strict=False to ignore missing columns" + ) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_schema, packet_schema = stream.types( + include_system_tags=include_system_tags + ) + _, packet_columns = stream.keys() + packets_to_drop = [pc for pc in packet_columns if pc not in self.columns] + + # this ensures all system tag columns are preserved + new_packet_schema = { + k: v for k, v in packet_schema.items() if k not in packets_to_drop + } + + return tag_schema, new_packet_schema + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.columns, + self.strict, + ) + ((stream,) if stream is not None else ()) diff --git a/src/orcapod/core/operators/join.py b/src/orcapod/core/operators/join.py new file mode 100644 index 0000000..04c65ee --- /dev/null +++ b/src/orcapod/core/operators/join.py @@ -0,0 +1,141 @@ +from orcapod.protocols import core_protocols as cp +from orcapod.core.streams import TableStream +from orcapod.types import PythonSchema +from orcapod.utils import types_utils +from typing import Any, TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule +from collections.abc import Collection +from orcapod.errors import InputValidationError +from orcapod.core.operators.base import NonZeroInputOperator +from orcapod.core import arrow_data_utils + +if TYPE_CHECKING: + import pyarrow as pa + import polars as pl +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + + +class Join(NonZeroInputOperator): + @property + def kernel_id(self) -> tuple[str, ...]: + """ + Returns a unique identifier for the kernel. + This is used to identify the kernel in the computational graph. + """ + return (f"{self.__class__.__name__}",) + + def op_validate_inputs(self, *streams: cp.Stream) -> None: + try: + self.op_output_types(*streams) + except Exception as e: + # raise InputValidationError(f"Input streams are not compatible: {e}") from e + raise e + + def order_input_streams(self, *streams: cp.Stream) -> list[cp.Stream]: + # order the streams based on their hashes to offer deterministic operation + return sorted(streams, key=lambda s: s.content_hash().to_hex()) + + def op_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + if len(streams) == 1: + # If only one stream is provided, return its typespecs + return streams[0].types(include_system_tags=include_system_tags) + + # output type computation does NOT require consistent ordering of streams + + # TODO: consider performing the check always with system tags on + stream = streams[0] + tag_typespec, packet_typespec = stream.types( + include_system_tags=include_system_tags + ) + for other_stream in streams[1:]: + other_tag_typespec, other_packet_typespec = other_stream.types( + include_system_tags=include_system_tags + ) + tag_typespec = types_utils.union_typespecs(tag_typespec, other_tag_typespec) + intersection_packet_typespec = types_utils.intersection_typespecs( + packet_typespec, other_packet_typespec + ) + packet_typespec = types_utils.union_typespecs( + packet_typespec, other_packet_typespec + ) + if intersection_packet_typespec: + raise InputValidationError( + f"Packets should not have overlapping keys, but {packet_typespec.keys()} found in {stream} and {other_stream}." + ) + + return tag_typespec, packet_typespec + + def op_forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Joins two streams together based on their tags. + The resulting stream will contain all the tags from both streams. + """ + if len(streams) == 1: + return streams[0] + + COMMON_JOIN_KEY = "_common" + + stream = streams[0] + + tag_keys, _ = [set(k) for k in stream.keys()] + table = stream.as_table(include_source=True, include_system_tags=True) + # trick to get cartesian product + table = table.add_column(0, COMMON_JOIN_KEY, pa.array([0] * len(table))) + table = arrow_data_utils.append_to_system_tags( + table, + stream.content_hash().to_hex(self.orcapod_config.system_tag_hash_n_char), + ) + + for next_stream in streams[1:]: + next_tag_keys, _ = next_stream.keys() + next_table = next_stream.as_table( + include_source=True, include_system_tags=True + ) + next_table = arrow_data_utils.append_to_system_tags( + next_table, + next_stream.content_hash().to_hex( + char_count=self.orcapod_config.system_tag_hash_n_char + ), + ) + # trick to ensure that there will always be at least one shared key + # this ensure that no overlap in keys lead to full caretesian product + next_table = next_table.add_column( + 0, COMMON_JOIN_KEY, pa.array([0] * len(next_table)) + ) + common_tag_keys = tag_keys.intersection(next_tag_keys) + common_tag_keys.add(COMMON_JOIN_KEY) + + table = ( + pl.DataFrame(table) + .join(pl.DataFrame(next_table), on=list(common_tag_keys), how="inner") + .to_arrow() + ) + + tag_keys.update(next_tag_keys) + + # reorder columns to bring tag columns to the front + # TODO: come up with a better algorithm + table = table.drop(COMMON_JOIN_KEY) + reordered_columns = [col for col in table.column_names if col in tag_keys] + reordered_columns += [col for col in table.column_names if col not in tag_keys] + + return TableStream( + table.select(reordered_columns), + tag_columns=tuple(tag_keys), + source=self, + upstreams=streams, + ) + + def op_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + return ( + (self.__class__.__name__,) + (set(streams),) if streams is not None else () + ) + + def __repr__(self) -> str: + return "Join()" diff --git a/src/orcapod/core/operators/mappers.py b/src/orcapod/core/operators/mappers.py new file mode 100644 index 0000000..5500e1b --- /dev/null +++ b/src/orcapod/core/operators/mappers.py @@ -0,0 +1,216 @@ +from orcapod.protocols import core_protocols as cp +from orcapod.core.streams import TableStream +from orcapod.types import PythonSchema +from typing import Any, TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule +from collections.abc import Mapping +from orcapod.errors import InputValidationError +from orcapod.core.system_constants import constants +from orcapod.core.operators.base import UnaryOperator + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class MapPackets(UnaryOperator): + """ + Operator that maps packets in a stream using a user-defined function. + The function is applied to each packet in the stream, and the resulting packets + are returned as a new stream. + """ + + def __init__( + self, name_map: Mapping[str, str], drop_unmapped: bool = False, **kwargs + ): + self.name_map = dict(name_map) + self.drop_unmapped = drop_unmapped + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + unmapped_columns = set(packet_columns) - set(self.name_map.keys()) + + if not any(n in packet_columns for n in self.name_map): + # nothing to rename in the packet, return stream as is + return stream + + table = stream.as_table( + include_source=True, include_system_tags=True, sort_by_tags=False + ) + + name_map = { + c: c + for c in table.column_names + if c not in packet_columns and not c.startswith("") + } + name_map = { + tc: tc + for tc in table.column_names + if tc not in packet_columns and not tc.startswith(constants.SOURCE_PREFIX) + } # no renaming on tag columns + for c in packet_columns: + if c in self.name_map: + name_map[c] = self.name_map[c] + name_map[f"{constants.SOURCE_PREFIX}{c}"] = ( + f"{constants.SOURCE_PREFIX}{self.name_map[c]}" + ) + else: + name_map[c] = c + + renamed_table = table.rename_columns(name_map) + + if self.drop_unmapped and unmapped_columns: + renamed_table = renamed_table.drop_columns(list(unmapped_columns)) + + return TableStream( + renamed_table, tag_columns=tag_columns, source=self, upstreams=(stream,) + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # verify that renamed value does NOT collide with other columns + tag_columns, packet_columns = stream.keys() + relevant_source = [] + relevant_target = [] + for source, target in self.name_map.items(): + if source in packet_columns: + relevant_source.append(source) + relevant_target.append(target) + remaining_packet_columns = set(packet_columns) - set(relevant_source) + overlapping_packet_columns = remaining_packet_columns.intersection( + relevant_target + ) + overlapping_tag_columns = set(tag_columns).intersection(relevant_target) + + if overlapping_packet_columns or overlapping_tag_columns: + message = f"Renaming {self.name_map} would cause collisions with existing columns: " + if overlapping_packet_columns: + message += f"overlapping packet columns: {overlapping_packet_columns}, " + if overlapping_tag_columns: + message += f"overlapping tag columns: {overlapping_tag_columns}." + raise InputValidationError(message) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_typespec, packet_typespec = stream.types( + include_system_tags=include_system_tags + ) + + # Create new packet typespec with renamed keys + new_packet_typespec = { + self.name_map.get(k, k): v + for k, v in packet_typespec.items() + if k in self.name_map or not self.drop_unmapped + } + + return tag_typespec, new_packet_typespec + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.name_map, + self.drop_unmapped, + ) + ((stream,) if stream is not None else ()) + + +class MapTags(UnaryOperator): + """ + Operator that maps tags in a stream using a user-defined function. + The function is applied to each tag in the stream, and the resulting tags + are returned as a new stream. + """ + + def __init__( + self, name_map: Mapping[str, str], drop_unmapped: bool = False, **kwargs + ): + self.name_map = dict(name_map) + self.drop_unmapped = drop_unmapped + super().__init__(**kwargs) + + def op_forward(self, stream: cp.Stream) -> cp.Stream: + tag_columns, packet_columns = stream.keys() + missing_tags = set(tag_columns) - set(self.name_map.keys()) + + if not any(n in tag_columns for n in self.name_map): + # nothing to rename in the tags, return stream as is + return stream + + table = stream.as_table(include_source=True, include_system_tags=True) + + name_map = { + tc: self.name_map.get(tc, tc) + for tc in tag_columns + if tc in self.name_map or not self.drop_unmapped + } # rename the tag as necessary + new_tag_columns = list(name_map.values()) + for c in packet_columns: + name_map[c] = c # no renaming on packet columns + + renamed_table = table.rename_columns(name_map) + + if missing_tags and self.drop_unmapped: + # drop any tags that are not in the name map + renamed_table = renamed_table.drop_columns(list(missing_tags)) + + return TableStream( + renamed_table, tag_columns=new_tag_columns, source=self, upstreams=(stream,) + ) + + def op_validate_inputs(self, stream: cp.Stream) -> None: + """ + This method should be implemented by subclasses to validate the inputs to the operator. + It takes two streams as input and raises an error if the inputs are not valid. + """ + # verify that renamed value does NOT collide with other columns + tag_columns, packet_columns = stream.keys() + relevant_source = [] + relevant_target = [] + for source, target in self.name_map.items(): + if source in tag_columns: + relevant_source.append(source) + relevant_target.append(target) + remaining_tag_columns = set(tag_columns) - set(relevant_source) + overlapping_tag_columns = remaining_tag_columns.intersection(relevant_target) + overlapping_packet_columns = set(packet_columns).intersection(relevant_target) + + if overlapping_tag_columns or overlapping_packet_columns: + message = f"Renaming {self.name_map} would cause collisions with existing columns: " + if overlapping_tag_columns: + message += f"overlapping tag columns: {overlapping_tag_columns}." + if overlapping_packet_columns: + message += f"overlapping packet columns: {overlapping_packet_columns}." + raise InputValidationError(message) + + def op_output_types( + self, stream: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_typespec, packet_typespec = stream.types( + include_system_tags=include_system_tags + ) + + # Create new packet typespec with renamed keys + new_tag_typespec = {self.name_map.get(k, k): v for k, v in tag_typespec.items()} + + # Create new packet typespec with renamed keys + new_tag_typespec = { + self.name_map.get(k, k): v + for k, v in tag_typespec.items() + if k in self.name_map or not self.drop_unmapped + } + + return new_tag_typespec, packet_typespec + + return new_tag_typespec, packet_typespec + + def op_identity_structure(self, stream: cp.Stream | None = None) -> Any: + return ( + self.__class__.__name__, + self.name_map, + self.drop_unmapped, + ) + ((stream,) if stream is not None else ()) diff --git a/src/orcapod/core/operators/semijoin.py b/src/orcapod/core/operators/semijoin.py new file mode 100644 index 0000000..6cdff4c --- /dev/null +++ b/src/orcapod/core/operators/semijoin.py @@ -0,0 +1,140 @@ +from orcapod.protocols import core_protocols as cp +from orcapod.core.streams import TableStream +from orcapod.utils import types_utils +from orcapod.types import PythonSchema +from typing import Any, TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule +from orcapod.errors import InputValidationError +from orcapod.core.operators.base import BinaryOperator + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class SemiJoin(BinaryOperator): + """ + Binary operator that performs a semi-join between two streams. + + A semi-join returns only the entries from the left stream that have + matching entries in the right stream, based on equality of values + in overlapping columns (columns with the same name and compatible types). + + If there are no overlapping columns between the streams, the entire + left stream is returned unchanged. + + The output stream preserves the schema of the left stream exactly. + """ + + @property + def kernel_id(self) -> tuple[str, ...]: + """ + Returns a unique identifier for the kernel. + This is used to identify the kernel in the computational graph. + """ + return (f"{self.__class__.__name__}",) + + def op_identity_structure( + self, + left_stream: cp.Stream | None = None, + right_stream: cp.Stream | None = None, + ) -> Any: + """ + Return a structure that represents the identity of this operator. + Unlike Join, SemiJoin depends on the order of streams (left vs right). + """ + id_struct = (self.__class__.__name__,) + if left_stream is not None and right_stream is not None: + # Order matters for semi-join: (left_stream, right_stream) + id_struct += (left_stream, right_stream) + return id_struct + + def op_forward(self, left_stream: cp.Stream, right_stream: cp.Stream) -> cp.Stream: + """ + Performs a semi-join between left and right streams. + Returns entries from left stream that have matching entries in right stream. + """ + left_tag_typespec, left_packet_typespec = left_stream.types() + right_tag_typespec, right_packet_typespec = right_stream.types() + + # Find overlapping columns across all columns (tags + packets) + left_all_typespec = types_utils.union_typespecs( + left_tag_typespec, left_packet_typespec + ) + right_all_typespec = types_utils.union_typespecs( + right_tag_typespec, right_packet_typespec + ) + + common_keys = tuple( + types_utils.intersection_typespecs( + left_all_typespec, right_all_typespec + ).keys() + ) + + # If no overlapping columns, return the left stream unmodified + if not common_keys: + return left_stream + + # include source info for left stream + left_table = left_stream.as_table(include_source=True) + + # Get the right table for matching + right_table = right_stream.as_table() + + # Perform left semi join using PyArrow's built-in functionality + semi_joined_table = left_table.join( + right_table, + keys=list(common_keys), + join_type="left semi", + ) + + return TableStream( + semi_joined_table, + tag_columns=tuple(left_tag_typespec.keys()), + source=self, + upstreams=(left_stream, right_stream), + ) + + def op_output_types( + self, + left_stream: cp.Stream, + right_stream: cp.Stream, + include_system_tags: bool = False, + ) -> tuple[PythonSchema, PythonSchema]: + """ + Returns the output types for the semi-join operation. + The output preserves the exact schema of the left stream. + """ + # Semi-join preserves the left stream's schema exactly + return left_stream.types(include_system_tags=include_system_tags) + + def op_validate_inputs( + self, left_stream: cp.Stream, right_stream: cp.Stream + ) -> None: + """ + Validates that the input streams are compatible for semi-join. + Checks that overlapping columns have compatible types. + """ + try: + left_tag_typespec, left_packet_typespec = left_stream.types() + right_tag_typespec, right_packet_typespec = right_stream.types() + + # Check that overlapping columns have compatible types across all columns + left_all_typespec = types_utils.union_typespecs( + left_tag_typespec, left_packet_typespec + ) + right_all_typespec = types_utils.union_typespecs( + right_tag_typespec, right_packet_typespec + ) + + # intersection_typespecs will raise an error if types are incompatible + types_utils.intersection_typespecs(left_all_typespec, right_all_typespec) + + except Exception as e: + raise InputValidationError( + f"Input streams are not compatible for semi-join: {e}" + ) from e + + def __repr__(self) -> str: + return "SemiJoin()" diff --git a/src/orcapod/core/pods.py b/src/orcapod/core/pods.py new file mode 100644 index 0000000..d9855d9 --- /dev/null +++ b/src/orcapod/core/pods.py @@ -0,0 +1,881 @@ +import hashlib +import logging +import sys +from abc import abstractmethod +from collections.abc import Callable, Collection, Iterable, Sequence +from datetime import datetime, timezone +from typing import TYPE_CHECKING, Any, Literal + +from orcapod import contexts +from orcapod.core.datagrams import ( + ArrowPacket, + DictPacket, +) +from orcapod.core.kernels import KernelStream, TrackedKernelBase +from orcapod.core.operators import Join +from orcapod.core.streams import CachedPodStream, LazyPodResultStream +from orcapod.core.system_constants import constants +from orcapod.hashing.hash_utils import get_function_components, get_function_signature +from orcapod.protocols import core_protocols as cp +from orcapod.protocols import hashing_protocols as hp +from orcapod.protocols.database_protocols import ArrowDatabase +from orcapod.types import DataValue, PythonSchema, PythonSchemaLike +from orcapod.utils import types_utils +from orcapod.utils.lazy_module import LazyModule + + +# TODO: extract default char count as config +def combine_hashes( + *hashes: str, + order: bool = False, + prefix_hasher_id: bool = False, + hex_char_count: int | None = 20, +) -> str: + """Combine hashes into a single hash string.""" + + # Sort for deterministic order regardless of input order + if order: + prepared_hashes = sorted(hashes) + else: + prepared_hashes = list(hashes) + combined = "".join(prepared_hashes) + combined_hash = hashlib.sha256(combined.encode()).hexdigest() + if hex_char_count is not None: + combined_hash = combined_hash[:hex_char_count] + if prefix_hasher_id: + return "sha256@" + combined_hash + return combined_hash + + +if TYPE_CHECKING: + import pyarrow as pa + import pyarrow.compute as pc +else: + pa = LazyModule("pyarrow") + pc = LazyModule("pyarrow.compute") + +logger = logging.getLogger(__name__) + +error_handling_options = Literal["raise", "ignore", "warn"] + + +class ActivatablePodBase(TrackedKernelBase): + """ + FunctionPod is a specialized kernel that encapsulates a function to be executed on data streams. + It allows for the execution of a function with a specific label and can be tracked by the system. + """ + + @abstractmethod + def input_packet_types(self) -> PythonSchema: + """ + Return the input typespec for the pod. This is used to validate the input streams. + """ + ... + + @abstractmethod + def output_packet_types(self) -> PythonSchema: + """ + Return the output typespec for the pod. This is used to validate the output streams. + """ + ... + + @property + def version(self) -> str: + return self._version + + @abstractmethod + def get_record_id(self, packet: cp.Packet, execution_engine_hash: str) -> str: + """ + Return the record ID for the input packet. This is used to identify the pod in the system. + """ + ... + + @property + @abstractmethod + def tiered_pod_id(self) -> dict[str, str]: + """ + Return the tiered pod ID for the pod. This is used to identify the pod in a tiered architecture. + """ + ... + + def __init__( + self, + error_handling: error_handling_options = "raise", + label: str | None = None, + version: str = "v0.0", + **kwargs, + ) -> None: + super().__init__(label=label, **kwargs) + self._active = True + self.error_handling = error_handling + self._version = version + import re + + match = re.match(r"\D.*(\d+)", version) + major_version = 0 + if match: + major_version = int(match.group(1)) + else: + raise ValueError( + f"Version string {version} does not contain a valid version number" + ) + + self._major_version = major_version + + @property + def major_version(self) -> int: + return self._major_version + + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Return the input and output typespecs for the pod. + This is used to validate the input and output streams. + """ + tag_typespec, _ = streams[0].types(include_system_tags=include_system_tags) + return tag_typespec, self.output_packet_types() + + def is_active(self) -> bool: + """ + Check if the pod is active. If not, it will not process any packets. + """ + return self._active + + def set_active(self, active: bool) -> None: + """ + Set the active state of the pod. If set to False, the pod will not process any packets. + """ + self._active = active + + @staticmethod + def _join_streams(*streams: cp.Stream) -> cp.Stream: + if not streams: + raise ValueError("No streams provided for joining") + # Join the streams using a suitable join strategy + if len(streams) == 1: + return streams[0] + + joined_stream = streams[0] + for next_stream in streams[1:]: + joined_stream = Join()(joined_stream, next_stream) + return joined_stream + + def pre_kernel_processing(self, *streams: cp.Stream) -> tuple[cp.Stream, ...]: + """ + Prepare the incoming streams for execution in the pod. At least one stream must be present. + If more than one stream is present, the join of the provided streams will be returned. + """ + # if multiple streams are provided, join them + # otherwise, return as is + if len(streams) <= 1: + return streams + + output_stream = self._join_streams(*streams) + return (output_stream,) + + def validate_inputs(self, *streams: cp.Stream) -> None: + if len(streams) != 1: + raise ValueError( + f"{self.__class__.__name__} expects exactly one input stream, got {len(streams)}" + ) + input_stream = streams[0] + _, incoming_packet_types = input_stream.types() + if not types_utils.check_typespec_compatibility( + incoming_packet_types, self.input_packet_types() + ): + # TODO: use custom exception type for better error handling + raise ValueError( + f"Incoming packet data type {incoming_packet_types} from {input_stream} is not compatible with expected input typespec {self.input_packet_types()}" + ) + + def prepare_output_stream( + self, *streams: cp.Stream, label: str | None = None + ) -> KernelStream: + return KernelStream(source=self, upstreams=streams, label=label) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + assert len(streams) == 1, "PodBase.forward expects exactly one input stream" + return LazyPodResultStream(pod=self, prepared_stream=streams[0]) + + @abstractmethod + def call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + ) -> tuple[cp.Tag, cp.Packet | None]: ... + + @abstractmethod + async def async_call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + ) -> tuple[cp.Tag, cp.Packet | None]: ... + + def track_invocation(self, *streams: cp.Stream, label: str | None = None) -> None: + if not self._skip_tracking and self._tracker_manager is not None: + self._tracker_manager.record_pod_invocation(self, streams, label=label) + + +def function_pod( + output_keys: str | Collection[str] | None = None, + function_name: str | None = None, + version: str = "v0.0", + label: str | None = None, + **kwargs, +) -> Callable[..., "FunctionPod"]: + """ + Decorator that wraps a function in a FunctionPod instance. + + Args: + output_keys: Keys for the function output(s) + function_name: Name of the function pod; if None, defaults to the function name + **kwargs: Additional keyword arguments to pass to the FunctionPod constructor. Please refer to the FunctionPod documentation for details. + + Returns: + FunctionPod instance wrapping the decorated function + """ + + def decorator(func) -> FunctionPod: + if func.__name__ == "": + raise ValueError("Lambda functions cannot be used with function_pod") + + if not hasattr(func, "__module__") or func.__module__ is None: + raise ValueError( + f"Function {func.__name__} must be defined at module level" + ) + + # Store the original function in the module for pickling purposes + # and make sure to change the name of the function + module = sys.modules[func.__module__] + base_function_name = func.__name__ + new_function_name = f"_original_{func.__name__}" + setattr(module, new_function_name, func) + # rename the function to be consistent and make it pickleable + setattr(func, "__name__", new_function_name) + setattr(func, "__qualname__", new_function_name) + + # Create a simple typed function pod + pod = FunctionPod( + function=func, + output_keys=output_keys, + function_name=function_name or base_function_name, + version=version, + label=label, + **kwargs, + ) + return pod + + return decorator + + +class FunctionPod(ActivatablePodBase): + def __init__( + self, + function: cp.PodFunction, + output_keys: str | Collection[str] | None = None, + function_name=None, + version: str = "v0.0", + input_python_schema: PythonSchemaLike | None = None, + output_python_schema: PythonSchemaLike | Sequence[type] | None = None, + label: str | None = None, + function_info_extractor: hp.FunctionInfoExtractor | None = None, + **kwargs, + ) -> None: + self.function = function + + if output_keys is None: + output_keys = [] + if isinstance(output_keys, str): + output_keys = [output_keys] + self.output_keys = output_keys + if function_name is None: + if hasattr(self.function, "__name__"): + function_name = getattr(self.function, "__name__") + else: + raise ValueError( + "function_name must be provided if function has no __name__ attribute" + ) + self.function_name = function_name + # extract the first full index (potentially with leading 0) in the version string + if not isinstance(version, str): + raise TypeError(f"Version must be a string, got {type(version)}") + + super().__init__(label=label or self.function_name, version=version, **kwargs) + + # extract input and output types from the function signature + input_packet_types, output_packet_types = ( + types_utils.extract_function_typespecs( + self.function, + self.output_keys, + input_typespec=input_python_schema, + output_typespec=output_python_schema, + ) + ) + self._input_packet_schema = dict(input_packet_types) + self._output_packet_schema = dict(output_packet_types) + # TODO: add output packet converter for speed up + + self._function_info_extractor = function_info_extractor + object_hasher = self.data_context.object_hasher + # TODO: fix and replace with object_hasher protocol specific methods + self._function_signature_hash = object_hasher.hash_object( + get_function_signature(self.function) + ).to_string() + self._function_content_hash = object_hasher.hash_object( + get_function_components(self.function) + ).to_string() + + self._output_packet_type_hash = object_hasher.hash_object( + self.output_packet_types() + ).to_string() + + self._total_pod_id_hash = object_hasher.hash_object( + self.tiered_pod_id + ).to_string() + + @property + def tiered_pod_id(self) -> dict[str, str]: + return { + "version": self.version, + "signature": self._function_signature_hash, + "content": self._function_content_hash, + } + + @property + def reference(self) -> tuple[str, ...]: + return ( + self.function_name, + self._output_packet_type_hash, + "v" + str(self.major_version), + ) + + def get_record_id( + self, + packet: cp.Packet, + execution_engine_hash: str, + ) -> str: + return combine_hashes( + str(packet.content_hash()), + self._total_pod_id_hash, + execution_engine_hash, + prefix_hasher_id=True, + ) + + def input_packet_types(self) -> PythonSchema: + """ + Return the input typespec for the function pod. + This is used to validate the input streams. + """ + return self._input_packet_schema.copy() + + def output_packet_types(self) -> PythonSchema: + """ + Return the output typespec for the function pod. + This is used to validate the output streams. + """ + return self._output_packet_schema.copy() + + def __repr__(self) -> str: + return f"FunctionPod:{self.function_name}" + + def __str__(self) -> str: + include_module = self.function.__module__ != "__main__" + func_sig = get_function_signature( + self.function, + name_override=self.function_name, + include_module=include_module, + ) + return f"FunctionPod:{func_sig}" + + def call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + ) -> tuple[cp.Tag, DictPacket | None]: + if not self.is_active(): + logger.info( + f"Pod is not active: skipping computation on input packet {packet}" + ) + return tag, None + + execution_engine_hash = execution_engine.name if execution_engine else "default" + + # any kernel/pod invocation happening inside the function will NOT be tracked + if not isinstance(packet, dict): + input_dict = packet.as_dict(include_source=False) + else: + input_dict = packet + + with self._tracker_manager.no_tracking(): + if execution_engine is not None: + # use the provided execution engine to run the function + values = execution_engine.submit_sync(self.function, **input_dict) + else: + values = self.function(**input_dict) + + output_data = self.process_function_output(values) + + # TODO: extract out this function + def combine(*components: tuple[str, ...]) -> str: + inner_parsed = [":".join(component) for component in components] + return "::".join(inner_parsed) + + if record_id is None: + # if record_id is not provided, generate it from the packet + record_id = self.get_record_id(packet, execution_engine_hash) + source_info = { + k: combine(self.reference, (record_id,), (k,)) for k in output_data + } + + output_packet = DictPacket( + output_data, + source_info=source_info, + python_schema=self.output_packet_types(), + data_context=self.data_context, + ) + return tag, output_packet + + async def async_call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + ) -> tuple[cp.Tag, cp.Packet | None]: + """ + Asynchronous call to the function pod. This is a placeholder for future implementation. + Currently, it behaves like the synchronous call. + """ + if not self.is_active(): + logger.info( + f"Pod is not active: skipping computation on input packet {packet}" + ) + return tag, None + + execution_engine_hash = execution_engine.name if execution_engine else "default" + + # any kernel/pod invocation happening inside the function will NOT be tracked + # with self._tracker_manager.no_tracking(): + # FIXME: figure out how to properly make context manager work with async/await + # any kernel/pod invocation happening inside the function will NOT be tracked + if not isinstance(packet, dict): + input_dict = packet.as_dict(include_source=False) + else: + input_dict = packet + if execution_engine is not None: + # use the provided execution engine to run the function + values = await execution_engine.submit_async(self.function, **input_dict) + else: + values = self.function(**input_dict) + + output_data = self.process_function_output(values) + + # TODO: extract out this function + def combine(*components: tuple[str, ...]) -> str: + inner_parsed = [":".join(component) for component in components] + return "::".join(inner_parsed) + + if record_id is None: + # if record_id is not provided, generate it from the packet + record_id = self.get_record_id(packet, execution_engine_hash) + source_info = { + k: combine(self.reference, (record_id,), (k,)) for k in output_data + } + + output_packet = DictPacket( + output_data, + source_info=source_info, + python_schema=self.output_packet_types(), + data_context=self.data_context, + ) + return tag, output_packet + + def process_function_output(self, values: Any) -> dict[str, DataValue]: + output_values = [] + if len(self.output_keys) == 0: + output_values = [] + elif len(self.output_keys) == 1: + output_values = [values] # type: ignore + elif isinstance(values, Iterable): + output_values = list(values) # type: ignore + elif len(self.output_keys) > 1: + raise ValueError( + "Values returned by function must be a pathlike or a sequence of pathlikes" + ) + + if len(output_values) != len(self.output_keys): + raise ValueError( + f"Number of output keys {len(self.output_keys)}:{self.output_keys} does not match number of values returned by function {len(output_values)}" + ) + + return {k: v for k, v in zip(self.output_keys, output_values)} + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + id_struct = (self.__class__.__name__,) + self.reference + # if streams are provided, perform pre-processing step, validate, and add the + # resulting single stream to the identity structure + if streams is not None and len(streams) != 0: + id_struct += tuple(streams) + + return id_struct + + +class WrappedPod(ActivatablePodBase): + """ + A wrapper for an existing pod, allowing for additional functionality or modifications without changing the original pod. + This class is meant to serve as a base class for other pods that need to wrap existing pods. + Note that only the call logic is pass through to the wrapped pod, but the forward logic is not. + """ + + def __init__( + self, + pod: cp.Pod, + label: str | None = None, + data_context: str | contexts.DataContext | None = None, + **kwargs, + ) -> None: + # if data_context is not explicitly given, use that of the contained pod + if data_context is None: + data_context = pod.data_context_key + super().__init__( + label=label, + data_context=data_context, + **kwargs, + ) + self.pod = pod + + @property + def reference(self) -> tuple[str, ...]: + """ + Return the pod ID, which is the function name of the wrapped pod. + This is used to identify the pod in the system. + """ + return self.pod.reference + + def get_record_id(self, packet: cp.Packet, execution_engine_hash: str) -> str: + return self.pod.get_record_id(packet, execution_engine_hash) + + @property + def tiered_pod_id(self) -> dict[str, str]: + """ + Return the tiered pod ID for the wrapped pod. This is used to identify the pod in a tiered architecture. + """ + return self.pod.tiered_pod_id + + def computed_label(self) -> str | None: + return self.pod.label + + def input_packet_types(self) -> PythonSchema: + """ + Return the input typespec for the stored pod. + This is used to validate the input streams. + """ + return self.pod.input_packet_types() + + def output_packet_types(self) -> PythonSchema: + """ + Return the output typespec for the stored pod. + This is used to validate the output streams. + """ + return self.pod.output_packet_types() + + def validate_inputs(self, *streams: cp.Stream) -> None: + self.pod.validate_inputs(*streams) + + def call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + ) -> tuple[cp.Tag, cp.Packet | None]: + return self.pod.call( + tag, packet, record_id=record_id, execution_engine=execution_engine + ) + + async def async_call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + ) -> tuple[cp.Tag, cp.Packet | None]: + return await self.pod.async_call( + tag, packet, record_id=record_id, execution_engine=execution_engine + ) + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + return self.pod.identity_structure(streams) + + def __repr__(self) -> str: + return f"WrappedPod({self.pod!r})" + + def __str__(self) -> str: + return f"WrappedPod:{self.pod!s}" + + +class CachedPod(WrappedPod): + """ + A pod that caches the results of the wrapped pod. + This is useful for pods that are expensive to compute and can benefit from caching. + """ + + # name of the column in the tag store that contains the packet hash + DATA_RETRIEVED_FLAG = f"{constants.META_PREFIX}data_retrieved" + + def __init__( + self, + pod: cp.Pod, + result_database: ArrowDatabase, + record_path_prefix: tuple[str, ...] = (), + match_tier: str | None = None, + retrieval_mode: Literal["latest", "most_specific"] = "latest", + **kwargs, + ): + super().__init__(pod, **kwargs) + self.record_path_prefix = record_path_prefix + self.result_database = result_database + self.match_tier = match_tier + self.retrieval_mode = retrieval_mode + + @property + def version(self) -> str: + return self.pod.version + + @property + def record_path(self) -> tuple[str, ...]: + """ + Return the path to the record in the result store. + This is used to store the results of the pod. + """ + return self.record_path_prefix + self.reference + + def call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + skip_cache_lookup: bool = False, + skip_cache_insert: bool = False, + ) -> tuple[cp.Tag, cp.Packet | None]: + # TODO: consider logic for overwriting existing records + execution_engine_hash = execution_engine.name if execution_engine else "default" + if record_id is None: + record_id = self.get_record_id( + packet, execution_engine_hash=execution_engine_hash + ) + output_packet = None + if not skip_cache_lookup: + output_packet = self.get_cached_output_for_packet(packet) + if output_packet is None: + tag, output_packet = super().call( + tag, packet, record_id=record_id, execution_engine=execution_engine + ) + if output_packet is not None and not skip_cache_insert: + self.record_packet(packet, output_packet, record_id=record_id) + + return tag, output_packet + + async def async_call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + skip_cache_lookup: bool = False, + skip_cache_insert: bool = False, + ) -> tuple[cp.Tag, cp.Packet | None]: + # TODO: consider logic for overwriting existing records + execution_engine_hash = execution_engine.name if execution_engine else "default" + + if record_id is None: + record_id = self.get_record_id( + packet, execution_engine_hash=execution_engine_hash + ) + output_packet = None + if not skip_cache_lookup: + output_packet = self.get_cached_output_for_packet(packet) + if output_packet is None: + tag, output_packet = await super().async_call( + tag, packet, record_id=record_id, execution_engine=execution_engine + ) + if output_packet is not None and not skip_cache_insert: + self.record_packet( + packet, + output_packet, + record_id=record_id, + execution_engine=execution_engine, + ) + + return tag, output_packet + + def forward(self, *streams: cp.Stream) -> cp.Stream: + assert len(streams) == 1, "PodBase.forward expects exactly one input stream" + return CachedPodStream(pod=self, input_stream=streams[0]) + + def record_packet( + self, + input_packet: cp.Packet, + output_packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + skip_duplicates: bool = False, + ) -> cp.Packet: + """ + Record the output packet against the input packet in the result store. + """ + data_table = output_packet.as_table(include_context=True, include_source=True) + + for i, (k, v) in enumerate(self.tiered_pod_id.items()): + # add the tiered pod ID to the data table + data_table = data_table.add_column( + i, + f"{constants.POD_ID_PREFIX}{k}", + pa.array([v], type=pa.large_string()), + ) + + # add the input packet hash as a column + data_table = data_table.add_column( + 0, + constants.INPUT_PACKET_HASH, + pa.array([str(input_packet.content_hash())], type=pa.large_string()), + ) + # add execution engine information + execution_engine_hash = execution_engine.name if execution_engine else "default" + data_table = data_table.append_column( + constants.EXECUTION_ENGINE, + pa.array([execution_engine_hash], type=pa.large_string()), + ) + + timestamp = datetime.now(timezone.utc) + data_table = data_table.append_column( + constants.POD_TIMESTAMP, + pa.array([timestamp], type=pa.timestamp("us", tz="UTC")), + ) + + if record_id is None: + record_id = self.get_record_id( + input_packet, execution_engine_hash=execution_engine_hash + ) + + self.result_database.add_record( + self.record_path, + record_id, + data_table, + skip_duplicates=skip_duplicates, + ) + # if result_flag is None: + # # TODO: do more specific error handling + # raise ValueError( + # f"Failed to record packet {input_packet} in result store {self.result_store}" + # ) + # # TODO: make store return retrieved table + return output_packet + + def get_cached_output_for_packet(self, input_packet: cp.Packet) -> cp.Packet | None: + """ + Retrieve the output packet from the result store based on the input packet. + If more than one output packet is found, conflict resolution strategy + will be applied. + If the output packet is not found, return None. + """ + # result_table = self.result_store.get_record_by_id( + # self.record_path, + # self.get_entry_hash(input_packet), + # ) + + # get all records with matching the input packet hash + # TODO: add match based on match_tier if specified + constraints = {constants.INPUT_PACKET_HASH: str(input_packet.content_hash())} + if self.match_tier is not None: + constraints[f"{constants.POD_ID_PREFIX}{self.match_tier}"] = ( + self.pod.tiered_pod_id[self.match_tier] + ) + + result_table = self.result_database.get_records_with_column_value( + self.record_path, + constraints, + ) + if result_table is None or result_table.num_rows == 0: + return None + + if result_table.num_rows > 1: + logger.info( + f"Performing conflict resolution for multiple records for {input_packet.content_hash().display_name()}" + ) + if self.retrieval_mode == "latest": + result_table = result_table.sort_by( + self.DATA_RETRIEVED_FLAG, ascending=False + ).take([0]) + elif self.retrieval_mode == "most_specific": + # match by the most specific pod ID + # trying next level if not found + for k, v in reversed(self.tiered_pod_id.items()): + search_result = result_table.filter( + pc.field(f"{constants.POD_ID_PREFIX}{k}") == v + ) + if search_result.num_rows > 0: + result_table = search_result.take([0]) + break + if result_table.num_rows > 1: + logger.warning( + f"No matching record found for {input_packet.content_hash().display_name()} with tiered pod ID {self.tiered_pod_id}" + ) + result_table = result_table.sort_by( + self.DATA_RETRIEVED_FLAG, ascending=False + ).take([0]) + + else: + raise ValueError( + f"Unknown retrieval mode: {self.retrieval_mode}. Supported modes are 'latest' and 'most_specific'." + ) + + pod_id_columns = [ + f"{constants.POD_ID_PREFIX}{k}" for k in self.tiered_pod_id.keys() + ] + result_table = result_table.drop_columns(pod_id_columns) + result_table = result_table.drop_columns(constants.INPUT_PACKET_HASH) + + # note that data context will be loaded from the result store + return ArrowPacket( + result_table, + meta_info={self.DATA_RETRIEVED_FLAG: str(datetime.now(timezone.utc))}, + ) + + def get_all_cached_outputs( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + """ + Get all records from the result store for this pod. + If include_system_columns is True, include system columns in the result. + """ + record_id_column = ( + constants.PACKET_RECORD_ID if include_system_columns else None + ) + result_table = self.result_database.get_all_records( + self.record_path, record_id_column=record_id_column + ) + if result_table is None or result_table.num_rows == 0: + return None + + if not include_system_columns: + # remove input packet hash and tiered pod ID columns + pod_id_columns = [ + f"{constants.POD_ID_PREFIX}{k}" for k in self.tiered_pod_id.keys() + ] + result_table = result_table.drop_columns(pod_id_columns) + result_table = result_table.drop_columns(constants.INPUT_PACKET_HASH) + + return result_table diff --git a/src/orcapod/core/polars_data_utils.py b/src/orcapod/core/polars_data_utils.py new file mode 100644 index 0000000..7757a1d --- /dev/null +++ b/src/orcapod/core/polars_data_utils.py @@ -0,0 +1,122 @@ +# Collection of functions to work with Arrow table data that underlies streams and/or datagrams +from orcapod.utils.lazy_module import LazyModule +from typing import TYPE_CHECKING +from orcapod.core.system_constants import constants +from collections.abc import Collection + +if TYPE_CHECKING: + import polars as pl +else: + pl = LazyModule("polars") + + +def drop_columns_with_prefix( + df: "pl.DataFrame", + prefix: str | tuple[str, ...], + exclude_columns: Collection[str] = (), +) -> "pl.DataFrame": + """Drop columns with a specific prefix from a Polars DataFrame.""" + columns_to_drop = [ + col + for col in df.columns + if col.startswith(prefix) and col not in exclude_columns + ] + return df.drop(*columns_to_drop) + + +def drop_system_columns( + df: "pl.DataFrame", + system_column_prefix: tuple[str, ...] = ( + constants.META_PREFIX, + constants.DATAGRAM_PREFIX, + ), +) -> "pl.DataFrame": + return drop_columns_with_prefix(df, system_column_prefix) + + +def get_system_columns( + df: "pl.DataFrame", + system_column_prefix: tuple[str, ...] = ( + constants.META_PREFIX, + constants.DATAGRAM_PREFIX, + ), +) -> "pl.DataFrame": + """Get system columns from a Polars DataFrame.""" + return df.select( + [col for col in df.columns if col.startswith(system_column_prefix)] + ) + + +def add_system_tag_column( + df: "pl.DataFrame", + system_tag_column_name: str, + system_tag_values: str | Collection[str], +) -> "pl.DataFrame": + """Add a system tags column to a Polars DataFrame.""" + if df.is_empty(): + raise ValueError("DataFrame is empty") + if isinstance(system_tag_values, str): + system_tag_values = [system_tag_values] * df.height + else: + system_tag_values = list(system_tag_values) + if len(system_tag_values) != df.height: + raise ValueError( + "Length of system_tag_values must match number of rows in the DataFrame." + ) + if not system_tag_column_name.startswith(constants.SYSTEM_TAG_PREFIX): + system_tag_column_name = ( + f"{constants.SYSTEM_TAG_PREFIX}{system_tag_column_name}" + ) + tags_column = pl.Series( + system_tag_column_name, system_tag_values, dtype=pl.String() + ) + return df.with_columns(tags_column) + + +def append_to_system_tags(df: "pl.DataFrame", value: str) -> "pl.DataFrame": + """Append a value to the system tags column in an Arrow table.""" + if df.is_empty(): + raise ValueError("Table is empty") + + df.rename + column_name_map = { + c: f"{c}:{value}" + for c in df.columns + if c.startswith(constants.SYSTEM_TAG_PREFIX) + } + return df.rename(column_name_map) + + +def add_source_info( + df: "pl.DataFrame", + source_info: str | Collection[str] | None, + exclude_prefixes: Collection[str] = ( + constants.META_PREFIX, + constants.DATAGRAM_PREFIX, + ), + exclude_columns: Collection[str] = (), +) -> "pl.DataFrame": + """Add source information to an Arrow table.""" + # Create a new column with the source information + if source_info is None or isinstance(source_info, str): + source_column = [source_info] * df.height + elif isinstance(source_info, Collection): + if len(source_info) != df.height: + raise ValueError( + "Length of source_info collection must match number of rows in the table." + ) + source_column = source_info + + # identify columns for which source columns should be created + + for col in df.columns: + if col.startswith(tuple(exclude_prefixes)) or col in exclude_columns: + continue + source_column = pl.Series( + f"{constants.SOURCE_PREFIX}{col}", + [f"{source_val}::{col}" for source_val in source_column], + dtype=pl.String(), + ) + df = df.with_columns(source_column) + + return df diff --git a/src/orcapod/core/sources.py b/src/orcapod/core/sources.py deleted file mode 100644 index 33df20d..0000000 --- a/src/orcapod/core/sources.py +++ /dev/null @@ -1,141 +0,0 @@ -from collections.abc import Callable, Collection, Iterator -from os import PathLike -from pathlib import Path -from typing import Any, Literal - -from orcapod.core.base import Source -from orcapod.hashing import hash_function -from orcapod.core.streams import SyncStream, SyncStreamFromGenerator -from orcapod.types import Packet, Tag - - -class GlobSource(Source): - """ - A stream source that sources files from a directory matching a glob pattern. - - For each matching file, yields a tuple containing: - - A tag generated either by the provided tag_function or defaulting to the file's stem name - - A packet containing the file path under the provided name key - - Parameters - ---------- - name : str - The key name under which the file path will be stored in the packet - file_path : PathLike - The directory path to search for files - pattern : str, default='*' - The glob pattern to match files against - tag_key : Optional[Union[str, Callable[[PathLike], Tag]]], default=None - Optional function to generate a tag from a file path. If None, uses the file's - stem name (without extension) in a dict with key 'file_name'. If only string is - provided, it will be used as the key for the tag. If a callable is provided, it - should accept a file path and return a dictionary of tags. - - Examples - -------- - >>> # Match all .txt files in data_dir, using filename as tag - >>> glob_source = GlobSource('txt_file', 'data_dir', '*.txt') - >>> # Match all files but use custom tag function - >>> glob_source = GlobSource('file', 'data_dir', '*', - ... lambda f: {'date': Path(f).stem[:8]}) - """ - - @staticmethod - def default_tag_function(f: PathLike) -> Tag: - return {"file_name": Path(f).stem} # noqa: E731 - - def __init__( - self, - name: str, - file_path: PathLike, - pattern: str = "*", - absolute_path: bool = False, - label: str | None = None, - tag_function: Callable[[PathLike], Tag] | None = None, - tag_function_hash_mode: Literal["content", "signature", "name"] = "name", - expected_tag_keys: Collection[str] | None = None, - **kwargs, - ) -> None: - super().__init__(label=label, **kwargs) - self.name = name - file_path = Path(file_path) - if absolute_path: - file_path = file_path.resolve() - self.file_path = file_path - self.pattern = pattern - self.expected_tag_keys = expected_tag_keys - if tag_function is None: - tag_function = self.__class__.default_tag_function - self.tag_function: Callable[[PathLike], Tag] = tag_function - self.tag_function_hash_mode = tag_function_hash_mode - - def forward(self, *streams: SyncStream) -> SyncStream: - if len(streams) != 0: - raise ValueError( - "GlobSource does not support forwarding streams. " - "It generates its own stream from the file system." - ) - - def generator() -> Iterator[tuple[Tag, Packet]]: - for file in Path(self.file_path).glob(self.pattern): - yield self.tag_function(file), {self.name: str(file)} - - return SyncStreamFromGenerator(generator) - - def __repr__(self) -> str: - return f"GlobSource({str(Path(self.file_path) / self.pattern)}) ⇒ {self.name}" - - def identity_structure(self, *streams) -> Any: - hash_function_kwargs = {} - if self.tag_function_hash_mode == "content": - # if using content hash, exclude few - hash_function_kwargs = { - "include_name": False, - "include_module": False, - "include_declaration": False, - } - - tag_function_hash = hash_function( - self.tag_function, - function_hash_mode=self.tag_function_hash_mode, # type: ignore - hash_kwargs=hash_function_kwargs, - ) - return ( - self.__class__.__name__, - self.name, - str(self.file_path), - self.pattern, - tag_function_hash, - ) + tuple(streams) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - """ - Returns the keys of the stream. The keys are the names of the packets - in the stream. The keys are used to identify the packets in the stream. - If expected_keys are provided, they will be used instead of the default keys. - """ - if len(streams) != 0: - raise ValueError( - "GlobSource does not support forwarding streams. " - "It generates its own stream from the file system." - ) - - if self.expected_tag_keys is not None: - return tuple(self.expected_tag_keys), (self.name,) - return super().keys(trigger_run=trigger_run) - - def claims_unique_tags( - self, *streams: "SyncStream", trigger_run: bool = True - ) -> bool | None: - if len(streams) != 0: - raise ValueError( - "GlobSource does not support forwarding streams. " - "It generates its own stream from the file system." - ) - # Claim uniqueness only if the default tag function is used - if self.tag_function == self.__class__.default_tag_function: - return True - # Otherwise, delegate to the base class - return super().claims_unique_tags(trigger_run=trigger_run) diff --git a/src/orcapod/core/sources/__init__.py b/src/orcapod/core/sources/__init__.py new file mode 100644 index 0000000..6bc4cf3 --- /dev/null +++ b/src/orcapod/core/sources/__init__.py @@ -0,0 +1,16 @@ +from .base import SourceBase +from .arrow_table_source import ArrowTableSource +from .delta_table_source import DeltaTableSource +from .dict_source import DictSource +from .data_frame_source import DataFrameSource +from .source_registry import SourceRegistry, GLOBAL_SOURCE_REGISTRY + +__all__ = [ + "SourceBase", + "DataFrameSource", + "ArrowTableSource", + "DeltaTableSource", + "DictSource", + "SourceRegistry", + "GLOBAL_SOURCE_REGISTRY", +] diff --git a/src/orcapod/core/sources/arrow_table_source.py b/src/orcapod/core/sources/arrow_table_source.py new file mode 100644 index 0000000..7d3c789 --- /dev/null +++ b/src/orcapod/core/sources/arrow_table_source.py @@ -0,0 +1,132 @@ +from collections.abc import Collection +from typing import TYPE_CHECKING, Any + + +from orcapod.core.streams import TableStream +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.system_constants import constants +from orcapod.core import arrow_data_utils +from orcapod.core.sources.source_registry import GLOBAL_SOURCE_REGISTRY, SourceRegistry + +from orcapod.core.sources.base import SourceBase + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class ArrowTableSource(SourceBase): + """Construct source from a collection of dictionaries""" + + SOURCE_ID = "arrow" + + def __init__( + self, + arrow_table: "pa.Table", + tag_columns: Collection[str] = (), + source_name: str | None = None, + source_registry: SourceRegistry | None = None, + auto_register: bool = True, + preserve_system_columns: bool = False, + **kwargs, + ): + super().__init__(**kwargs) + + # clean the table, dropping any system columns + # TODO: consider special treatment of system columns if provided + if not preserve_system_columns: + arrow_table = arrow_data_utils.drop_system_columns(arrow_table) + + non_system_columns = arrow_data_utils.drop_system_columns(arrow_table) + tag_schema = non_system_columns.select(tag_columns).schema + # FIXME: ensure tag_columns are found among non system columns + packet_schema = non_system_columns.drop(list(tag_columns)).schema + + tag_python_schema = ( + self.data_context.type_converter.arrow_schema_to_python_schema(tag_schema) + ) + packet_python_schema = ( + self.data_context.type_converter.arrow_schema_to_python_schema( + packet_schema + ) + ) + + schema_hash = self.data_context.object_hasher.hash_object( + (tag_python_schema, packet_python_schema) + ).to_hex(char_count=self.orcapod_config.schema_hash_n_char) + + self.tag_columns = [ + col for col in tag_columns if col in arrow_table.column_names + ] + + self.table_hash = self.data_context.arrow_hasher.hash_table(arrow_table) + + if source_name is None: + # TODO: determine appropriate config name + source_name = self.content_hash().to_hex( + char_count=self.orcapod_config.path_hash_n_char + ) + + self._source_name = source_name + + row_index = list(range(arrow_table.num_rows)) + + source_info = [ + f"{self.source_id}{constants.BLOCK_SEPARATOR}row_{i}" for i in row_index + ] + + # add source info + arrow_table = arrow_data_utils.add_source_info( + arrow_table, source_info, exclude_columns=tag_columns + ) + + arrow_table = arrow_data_utils.add_system_tag_column( + arrow_table, f"source{constants.FIELD_SEPARATOR}{schema_hash}", source_info + ) + + self._table = arrow_table + + self._table_stream = TableStream( + table=self._table, + tag_columns=self.tag_columns, + source=self, + upstreams=(), + ) + + # Auto-register with global registry + if auto_register: + registry = source_registry or GLOBAL_SOURCE_REGISTRY + registry.register(self.source_id, self) + + @property + def reference(self) -> tuple[str, ...]: + return ("arrow_table", f"source_{self._source_name}") + + @property + def table(self) -> "pa.Table": + return self._table + + def source_identity_structure(self) -> Any: + return (self.__class__.__name__, self.tag_columns, self.table_hash) + + def get_all_records( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + return self().as_table(include_source=include_system_columns) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Load data from file and return a static stream. + + This is called by forward() and creates a fresh snapshot each time. + """ + return self._table_stream + + def source_output_types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Return tag and packet types based on provided typespecs.""" + return self._table_stream.types(include_system_tags=include_system_tags) diff --git a/src/orcapod/core/sources/base.py b/src/orcapod/core/sources/base.py new file mode 100644 index 0000000..8197640 --- /dev/null +++ b/src/orcapod/core/sources/base.py @@ -0,0 +1,450 @@ +from abc import abstractmethod +from ast import Not +from collections.abc import Collection, Iterator +from typing import TYPE_CHECKING, Any + + +from orcapod.core.kernels import TrackedKernelBase +from orcapod.core.streams import ( + KernelStream, + StatefulStreamBase, +) +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class InvocationBase(TrackedKernelBase, StatefulStreamBase): + def __init__(self, **kwargs): + super().__init__(**kwargs) + # Cache the KernelStream for reuse across all stream method calls + self._cached_kernel_stream: KernelStream | None = None + + def computed_label(self) -> str | None: + return None + + @abstractmethod + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: ... + + # Redefine the reference to ensure subclass would provide a concrete implementation + @property + @abstractmethod + def reference(self) -> tuple[str, ...]: + """Return the unique identifier for the kernel.""" + ... + + # =========================== Kernel Methods =========================== + + # The following are inherited from TrackedKernelBase as abstract methods. + # @abstractmethod + # def forward(self, *streams: dp.Stream) -> dp.Stream: + # """ + # Pure computation: return a static snapshot of the data. + + # This is the core method that subclasses must implement. + # Each call should return a fresh stream representing the current state of the data. + # This is what KernelStream calls when it needs to refresh its data. + # """ + # ... + + # @abstractmethod + # def kernel_output_types(self, *streams: dp.Stream) -> tuple[TypeSpec, TypeSpec]: + # """Return the tag and packet types this source produces.""" + # ... + + # @abstractmethod + # def kernel_identity_structure( + # self, streams: Collection[dp.Stream] | None = None + # ) -> dp.Any: ... + + def prepare_output_stream( + self, *streams: cp.Stream, label: str | None = None + ) -> KernelStream: + if self._cached_kernel_stream is None: + self._cached_kernel_stream = super().prepare_output_stream( + *streams, label=label + ) + return self._cached_kernel_stream + + def track_invocation(self, *streams: cp.Stream, label: str | None = None) -> None: + raise NotImplementedError("Behavior for track invocation is not determined") + + # ==================== Stream Protocol (Delegation) ==================== + + @property + def source(self) -> cp.Kernel | None: + """Sources are their own source.""" + return self + + # @property + # def upstreams(self) -> tuple[cp.Stream, ...]: ... + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """Delegate to the cached KernelStream.""" + return self().keys(include_system_tags=include_system_tags) + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Delegate to the cached KernelStream.""" + return self().types(include_system_tags=include_system_tags) + + @property + def last_modified(self): + """Delegate to the cached KernelStream.""" + return self().last_modified + + @property + def is_current(self) -> bool: + """Delegate to the cached KernelStream.""" + return self().is_current + + def __iter__(self) -> Iterator[tuple[cp.Tag, cp.Packet]]: + """ + Iterate over the cached KernelStream. + + This allows direct iteration over the source as if it were a stream. + """ + return self().iter_packets() + + def iter_packets( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + """Delegate to the cached KernelStream.""" + return self().iter_packets(execution_engine=execution_engine) + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + """Delegate to the cached KernelStream.""" + return self().as_table( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + + def flow( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> Collection[tuple[cp.Tag, cp.Packet]]: + """Delegate to the cached KernelStream.""" + return self().flow(execution_engine=execution_engine) + + def run(self, execution_engine: cp.ExecutionEngine | None = None) -> None: + """ + Run the source node, executing the contained source. + + This is a no-op for sources since they are not executed like pods. + """ + self().run(execution_engine=execution_engine) + + async def run_async( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> None: + """ + Run the source node asynchronously, executing the contained source. + + This is a no-op for sources since they are not executed like pods. + """ + await self().run_async(execution_engine=execution_engine) + + # ==================== LiveStream Protocol (Delegation) ==================== + + def refresh(self, force: bool = False) -> bool: + """Delegate to the cached KernelStream.""" + return self().refresh(force=force) + + def invalidate(self) -> None: + """Delegate to the cached KernelStream.""" + return self().invalidate() + + +class SourceBase(TrackedKernelBase, StatefulStreamBase): + """ + Base class for sources that act as both Kernels and LiveStreams. + + Design Philosophy: + 1. Source is fundamentally a Kernel (data loader) + 2. forward() returns static snapshots as a stream (pure computation) + 3. __call__() returns a cached KernelStream (live, tracked) + 4. All stream methods delegate to the cached KernelStream + + This ensures that direct source iteration and source() iteration + are identical and both benefit from KernelStream's lifecycle management. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + # Cache the KernelStream for reuse across all stream method calls + self._cached_kernel_stream: KernelStream | None = None + self._schema_hash: str | None = None + + # reset, so that computed label won't be used from StatefulStreamBase + def computed_label(self) -> str | None: + return None + + def schema_hash(self) -> str: + if self._schema_hash is None: + self._schema_hash = self.data_context.object_hasher.hash_object( + (self.tag_types(), self.packet_types()) + ).to_hex(self.orcapod_config.schema_hash_n_char) + return self._schema_hash + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + if streams is not None: + # when checked for invocation id, act as a source + # and just return the output packet types + # _, packet_types = self.stream.types() + # return packet_types + return self.schema_hash() + # otherwise, return the identity structure of the stream + return self.source_identity_structure() + + @property + def source_id(self) -> str: + return ":".join(self.reference) + + # Redefine the reference to ensure subclass would provide a concrete implementation + @property + @abstractmethod + def reference(self) -> tuple[str, ...]: + """Return the unique identifier for the kernel.""" + ... + + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + return self.source_output_types(include_system_tags=include_system_tags) + + @abstractmethod + def source_identity_structure(self) -> Any: ... + + @abstractmethod + def source_output_types(self, include_system_tags: bool = False) -> Any: ... + + # =========================== Kernel Methods =========================== + + # The following are inherited from TrackedKernelBase as abstract methods. + # @abstractmethod + # def forward(self, *streams: dp.Stream) -> dp.Stream: + # """ + # Pure computation: return a static snapshot of the data. + + # This is the core method that subclasses must implement. + # Each call should return a fresh stream representing the current state of the data. + # This is what KernelStream calls when it needs to refresh its data. + # """ + # ... + + # @abstractmethod + # def kernel_output_types(self, *streams: dp.Stream) -> tuple[TypeSpec, TypeSpec]: + # """Return the tag and packet types this source produces.""" + # ... + + # @abstractmethod + # def kernel_identity_structure( + # self, streams: Collection[dp.Stream] | None = None + # ) -> dp.Any: ... + + def validate_inputs(self, *streams: cp.Stream) -> None: + """Sources take no input streams.""" + if len(streams) > 0: + raise ValueError( + f"{self.__class__.__name__} is a source and takes no input streams" + ) + + def prepare_output_stream( + self, *streams: cp.Stream, label: str | None = None + ) -> KernelStream: + if self._cached_kernel_stream is None: + self._cached_kernel_stream = super().prepare_output_stream( + *streams, label=label + ) + return self._cached_kernel_stream + + def track_invocation(self, *streams: cp.Stream, label: str | None = None) -> None: + if not self._skip_tracking and self._tracker_manager is not None: + self._tracker_manager.record_source_invocation(self, label=label) + + # ==================== Stream Protocol (Delegation) ==================== + + @property + def source(self) -> cp.Kernel | None: + """Sources are their own source.""" + return self + + @property + def upstreams(self) -> tuple[cp.Stream, ...]: + """Sources have no upstream dependencies.""" + return () + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """Delegate to the cached KernelStream.""" + return self().keys(include_system_tags=include_system_tags) + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Delegate to the cached KernelStream.""" + return self().types(include_system_tags=include_system_tags) + + @property + def last_modified(self): + """Delegate to the cached KernelStream.""" + return self().last_modified + + @property + def is_current(self) -> bool: + """Delegate to the cached KernelStream.""" + return self().is_current + + def __iter__(self) -> Iterator[tuple[cp.Tag, cp.Packet]]: + """ + Iterate over the cached KernelStream. + + This allows direct iteration over the source as if it were a stream. + """ + return self().iter_packets() + + def iter_packets( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + """Delegate to the cached KernelStream.""" + return self().iter_packets(execution_engine=execution_engine) + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + """Delegate to the cached KernelStream.""" + return self().as_table( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + + def flow( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> Collection[tuple[cp.Tag, cp.Packet]]: + """Delegate to the cached KernelStream.""" + return self().flow(execution_engine=execution_engine) + + def run(self, execution_engine: cp.ExecutionEngine | None = None) -> None: + """ + Run the source node, executing the contained source. + + This is a no-op for sources since they are not executed like pods. + """ + self().run(execution_engine=execution_engine) + + async def run_async( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> None: + """ + Run the source node asynchronously, executing the contained source. + + This is a no-op for sources since they are not executed like pods. + """ + await self().run_async(execution_engine=execution_engine) + + # ==================== LiveStream Protocol (Delegation) ==================== + + def refresh(self, force: bool = False) -> bool: + """Delegate to the cached KernelStream.""" + return self().refresh(force=force) + + def invalidate(self) -> None: + """Delegate to the cached KernelStream.""" + return self().invalidate() + + # ==================== Source Protocol ==================== + + def reset_cache(self) -> None: + """ + Clear the cached KernelStream, forcing a fresh one on next access. + + Useful when the underlying data source has fundamentally changed + (e.g., file path changed, database connection reset). + """ + if self._cached_kernel_stream is not None: + self._cached_kernel_stream.invalidate() + self._cached_kernel_stream = None + + +class StreamSource(SourceBase): + def __init__(self, stream: cp.Stream, label: str | None = None, **kwargs) -> None: + """ + A placeholder source based on stream + This is used to represent a kernel that has no computation. + """ + label = label or stream.label + self.stream = stream + super().__init__(label=label, **kwargs) + + def source_output_types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Returns the types of the tag and packet columns in the stream. + This is useful for accessing the types of the columns in the stream. + """ + return self.stream.types(include_system_tags=include_system_tags) + + @property + def reference(self) -> tuple[str, ...]: + return ("stream", self.stream.content_hash().to_string()) + + def forward(self, *args: Any, **kwargs: Any) -> cp.Stream: + """ + Forward the stream through the stub kernel. + This is a no-op and simply returns the stream. + """ + return self.stream + + def source_identity_structure(self) -> Any: + return self.stream.identity_structure() + + # def __hash__(self) -> int: + # # TODO: resolve the logic around identity structure on a stream / stub kernel + # """ + # Hash the StubKernel based on its label and stream. + # This is used to uniquely identify the StubKernel in the tracker. + # """ + # identity_structure = self.identity_structure() + # if identity_structure is None: + # return hash(self.stream) + # return identity_structure + + +# ==================== Example Implementation ==================== diff --git a/src/orcapod/core/sources/csv_source.py b/src/orcapod/core/sources/csv_source.py new file mode 100644 index 0000000..cafc6c7 --- /dev/null +++ b/src/orcapod/core/sources/csv_source.py @@ -0,0 +1,66 @@ +from typing import TYPE_CHECKING, Any + + +from orcapod.core.streams import ( + TableStream, +) +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pandas as pd + import polars as pl + import pyarrow as pa +else: + pl = LazyModule("polars") + pd = LazyModule("pandas") + pa = LazyModule("pyarrow") + +from orcapod.core.sources.base import SourceBase + + +class CSVSource(SourceBase): + """Loads data from a CSV file.""" + + def __init__( + self, + file_path: str, + tag_columns: list[str] | None = None, + source_id: str | None = None, + **kwargs, + ): + super().__init__(**kwargs) + self.file_path = file_path + self.tag_columns = tag_columns or [] + if source_id is None: + source_id = self.file_path + + def source_identity_structure(self) -> Any: + return (self.__class__.__name__, self.source_id, tuple(self.tag_columns)) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Load data from file and return a static stream. + + This is called by forward() and creates a fresh snapshot each time. + """ + import pyarrow.csv as csv + + # Load current state of the file + table = csv.read_csv(self.file_path) + + return TableStream( + table=table, + tag_columns=self.tag_columns, + source=self, + upstreams=(), + ) + + def source_output_types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Infer types from the file (could be cached).""" + # For demonstration - in practice you might cache this + sample_stream = self.forward() + return sample_stream.types(include_system_tags=include_system_tags) diff --git a/src/orcapod/core/sources/data_frame_source.py b/src/orcapod/core/sources/data_frame_source.py new file mode 100644 index 0000000..2fb4a78 --- /dev/null +++ b/src/orcapod/core/sources/data_frame_source.py @@ -0,0 +1,153 @@ +from collections.abc import Collection +from typing import TYPE_CHECKING, Any + +from orcapod.core.streams import TableStream +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.system_constants import constants +from orcapod.core import polars_data_utils +from orcapod.core.sources.source_registry import GLOBAL_SOURCE_REGISTRY, SourceRegistry +import logging +from orcapod.core.sources.base import SourceBase + +if TYPE_CHECKING: + import pyarrow as pa + import polars as pl + from polars._typing import FrameInitTypes +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + + +logger = logging.getLogger(__name__) + + +class DataFrameSource(SourceBase): + """Construct source from a dataframe and any Polars dataframe compatible data structure""" + + SOURCE_ID = "polars" + + def __init__( + self, + data: "FrameInitTypes", + tag_columns: str | Collection[str] = (), + source_name: str | None = None, + source_registry: SourceRegistry | None = None, + auto_register: bool = True, + preserve_system_columns: bool = False, + **kwargs, + ): + super().__init__(**kwargs) + + # clean the table, dropping any system columns + # Initialize polars dataframe + # TODO: work with LazyFrame + df = pl.DataFrame(data) + + object_columns = [c for c in df.columns if df[c].dtype == pl.Object] + if len(object_columns) > 0: + logger.info( + f"Converting {len(object_columns)}object columns to Arrow format" + ) + sub_table = self.data_context.type_converter.python_dicts_to_arrow_table( + df.select(object_columns).to_dicts() + ) + df = df.with_columns([pl.from_arrow(c) for c in sub_table]) + + if isinstance(tag_columns, str): + tag_columns = [tag_columns] + + if not preserve_system_columns: + df = polars_data_utils.drop_system_columns(df) + + non_system_columns = polars_data_utils.drop_system_columns(df) + missing_columns = set(tag_columns) - set(non_system_columns.columns) + if missing_columns: + raise ValueError( + f"Following tag columns not found in data: {missing_columns}" + ) + tag_schema = non_system_columns.select(tag_columns).to_arrow().schema + packet_schema = non_system_columns.drop(list(tag_columns)).to_arrow().schema + self.tag_columns = tag_columns + + tag_python_schema = ( + self.data_context.type_converter.arrow_schema_to_python_schema(tag_schema) + ) + packet_python_schema = ( + self.data_context.type_converter.arrow_schema_to_python_schema( + packet_schema + ) + ) + schema_hash = self.data_context.object_hasher.hash_object( + (tag_python_schema, packet_python_schema) + ).to_hex(char_count=self.orcapod_config.schema_hash_n_char) + + self.table_hash = self.data_context.arrow_hasher.hash_table(df.to_arrow()) + + if source_name is None: + # TODO: determine appropriate config name + source_name = self.content_hash().to_hex( + char_count=self.orcapod_config.path_hash_n_char + ) + + self._source_name = source_name + + row_index = list(range(df.height)) + + source_info = [ + f"{self.source_id}{constants.BLOCK_SEPARATOR}row_{i}" for i in row_index + ] + + # add source info + df = polars_data_utils.add_source_info( + df, source_info, exclude_columns=tag_columns + ) + + df = polars_data_utils.add_system_tag_column( + df, f"source{constants.FIELD_SEPARATOR}{schema_hash}", source_info + ) + + self._df = df + + self._table_stream = TableStream( + table=self._df.to_arrow(), + tag_columns=self.tag_columns, + source=self, + upstreams=(), + ) + + # Auto-register with global registry + if auto_register: + registry = source_registry or GLOBAL_SOURCE_REGISTRY + registry.register(self.source_id, self) + + @property + def reference(self) -> tuple[str, ...]: + return ("data_frame", f"source_{self._source_name}") + + @property + def df(self) -> "pl.DataFrame": + return self._df + + def source_identity_structure(self) -> Any: + return (self.__class__.__name__, self.tag_columns, self.table_hash) + + def get_all_records( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + return self().as_table(include_source=include_system_columns) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Load data from file and return a static stream. + + This is called by forward() and creates a fresh snapshot each time. + """ + return self._table_stream + + def source_output_types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Return tag and packet types based on provided typespecs.""" + return self._table_stream.types(include_system_tags=include_system_tags) diff --git a/src/orcapod/core/sources/delta_table_source.py b/src/orcapod/core/sources/delta_table_source.py new file mode 100644 index 0000000..4249793 --- /dev/null +++ b/src/orcapod/core/sources/delta_table_source.py @@ -0,0 +1,198 @@ +from collections.abc import Collection +from typing import TYPE_CHECKING, Any + + +from orcapod.core.streams import TableStream +from orcapod.protocols import core_protocols as cp +from orcapod.types import PathLike, PythonSchema +from orcapod.utils.lazy_module import LazyModule +from pathlib import Path + + +from orcapod.core.sources.base import SourceBase +from orcapod.core.sources.source_registry import GLOBAL_SOURCE_REGISTRY, SourceRegistry +from deltalake import DeltaTable +from deltalake.exceptions import TableNotFoundError + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class DeltaTableSource(SourceBase): + """Source that generates streams from a Delta table.""" + + def __init__( + self, + delta_table_path: PathLike, + tag_columns: Collection[str] = (), + source_name: str | None = None, + source_registry: SourceRegistry | None = None, + auto_register: bool = True, + **kwargs, + ): + """ + Initialize DeltaTableSource with a Delta table. + + Args: + delta_table_path: Path to the Delta table + source_name: Name for this source (auto-generated if None) + tag_columns: Column names to use as tags vs packet data + source_registry: Registry to register with (uses global if None) + auto_register: Whether to auto-register this source + """ + super().__init__(**kwargs) + + # Normalize path + self._delta_table_path = Path(delta_table_path).resolve() + + # Try to open the Delta table + try: + self._delta_table = DeltaTable(str(self._delta_table_path)) + except TableNotFoundError: + raise ValueError(f"Delta table not found at {self._delta_table_path}") + + # Generate source name if not provided + if source_name is None: + source_name = self._delta_table_path.name + + self._source_name = source_name + self._tag_columns = tuple(tag_columns) + + # Auto-register with global registry + if auto_register: + registry = source_registry or GLOBAL_SOURCE_REGISTRY + registry.register(self.source_id, self) + + @property + def reference(self) -> tuple[str, ...]: + """Reference tuple for this source.""" + return ("delta_table", self._source_name) + + def source_identity_structure(self) -> Any: + """ + Identity structure for this source - includes path and modification info. + This changes when the underlying Delta table changes. + """ + # Get Delta table version for change detection + table_version = self._delta_table.version() + + return { + "class": self.__class__.__name__, + "path": str(self._delta_table_path), + "version": table_version, + "tag_columns": self._tag_columns, + } + + def validate_inputs(self, *streams: cp.Stream) -> None: + """Delta table sources don't take input streams.""" + if len(streams) > 0: + raise ValueError( + f"DeltaTableSource doesn't accept input streams, got {len(streams)}" + ) + + def source_output_types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Return tag and packet types based on Delta table schema.""" + # Create a sample stream to get types + return self.forward().types(include_system_tags=include_system_tags) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Generate stream from Delta table data. + + Returns: + TableStream containing all data from the Delta table + """ + + # Refresh table to get latest data + self._refresh_table() + + # Load table data + table_data = self._delta_table.to_pyarrow_dataset( + as_large_types=True + ).to_table() + + return TableStream( + table=table_data, + tag_columns=self._tag_columns, + source=self, + ) + + def _refresh_table(self) -> None: + """Refresh the Delta table to get latest version.""" + try: + # Create fresh Delta table instance to get latest data + self._delta_table = DeltaTable(str(self._delta_table_path)) + except Exception as e: + # If refresh fails, log but continue with existing table + import logging + + logger = logging.getLogger(__name__) + logger.warning( + f"Failed to refresh Delta table {self._delta_table_path}: {e}" + ) + + def get_table_info(self) -> dict[str, Any]: + """Get metadata about the Delta table.""" + self._refresh_table() + + schema = self._delta_table.schema() + history = self._delta_table.history() + + return { + "path": str(self._delta_table_path), + "version": self._delta_table.version(), + "schema": schema, + "num_files": len(self._delta_table.files()), + "tag_columns": self._tag_columns, + "latest_commit": history[0] if history else None, + } + + def resolve_field(self, collection_id: str, record_id: str, field_name: str) -> Any: + """ + Resolve a specific field value from source field reference. + + For Delta table sources: + - collection_id: Not used (single table) + - record_id: Row identifier (implementation dependent) + - field_name: Column name + """ + # This is a basic implementation - you might want to add more sophisticated + # record identification based on your needs + + # For now, assume record_id is a row index + try: + row_index = int(record_id) + table_data = self._delta_table.to_pyarrow_dataset( + as_large_types=True + ).to_table() + + if row_index >= table_data.num_rows: + raise ValueError( + f"Record ID {record_id} out of range (table has {table_data.num_rows} rows)" + ) + + if field_name not in table_data.column_names: + raise ValueError( + f"Field '{field_name}' not found in table columns: {table_data.column_names}" + ) + + return table_data[field_name][row_index].as_py() + + except ValueError as e: + if "invalid literal for int()" in str(e): + raise ValueError( + f"Record ID must be numeric for DeltaTableSource, got: {record_id}" + ) + raise + + def __repr__(self) -> str: + return ( + f"DeltaTableSource(path={self._delta_table_path}, name={self._source_name})" + ) + + def __str__(self) -> str: + return f"DeltaTableSource:{self._source_name}" diff --git a/src/orcapod/core/sources/dict_source.py b/src/orcapod/core/sources/dict_source.py new file mode 100644 index 0000000..d291b3f --- /dev/null +++ b/src/orcapod/core/sources/dict_source.py @@ -0,0 +1,113 @@ +from collections.abc import Collection, Mapping +from typing import TYPE_CHECKING, Any + + +from orcapod.protocols import core_protocols as cp +from orcapod.types import DataValue, PythonSchema, PythonSchemaLike +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.system_constants import constants +from orcapod.core.sources.arrow_table_source import ArrowTableSource + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + +from orcapod.core.sources.base import SourceBase + + +def add_source_field( + record: dict[str, DataValue], source_info: str +) -> dict[str, DataValue]: + """Add source information to a record.""" + # for all "regular" fields, add source info + for key in record.keys(): + if not key.startswith(constants.META_PREFIX) and not key.startswith( + constants.DATAGRAM_PREFIX + ): + record[f"{constants.SOURCE_PREFIX}{key}"] = f"{source_info}:{key}" + return record + + +def split_fields_with_prefixes( + record, prefixes: Collection[str] +) -> tuple[dict[str, DataValue], dict[str, DataValue]]: + """Split fields in a record into two dictionaries based on prefixes.""" + matching = {} + non_matching = {} + for key, value in record.items(): + if any(key.startswith(prefix) for prefix in prefixes): + matching[key] = value + else: + non_matching[key] = value + return matching, non_matching + + +def split_system_columns( + data: list[dict[str, DataValue]], +) -> tuple[list[dict[str, DataValue]], list[dict[str, DataValue]]]: + system_columns: list[dict[str, DataValue]] = [] + non_system_columns: list[dict[str, DataValue]] = [] + for record in data: + sys_cols, non_sys_cols = split_fields_with_prefixes( + record, [constants.META_PREFIX, constants.DATAGRAM_PREFIX] + ) + system_columns.append(sys_cols) + non_system_columns.append(non_sys_cols) + return system_columns, non_system_columns + + +class DictSource(SourceBase): + """Construct source from a collection of dictionaries""" + + def __init__( + self, + data: Collection[Mapping[str, DataValue]], + tag_columns: Collection[str] = (), + system_tag_columns: Collection[str] = (), + source_name: str | None = None, + data_schema: PythonSchemaLike | None = None, + **kwargs, + ): + super().__init__(**kwargs) + arrow_table = self.data_context.type_converter.python_dicts_to_arrow_table( + [dict(e) for e in data], python_schema=data_schema + ) + self._table_source = ArrowTableSource( + arrow_table, + tag_columns=tag_columns, + source_name=source_name, + system_tag_columns=system_tag_columns, + ) + + @property + def reference(self) -> tuple[str, ...]: + # TODO: provide more thorough implementation + return ("dict",) + self._table_source.reference[1:] + + def source_identity_structure(self) -> Any: + return self._table_source.source_identity_structure() + + def get_all_records( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + return self._table_source.get_all_records( + include_system_columns=include_system_columns + ) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """ + Load data from file and return a static stream. + + This is called by forward() and creates a fresh snapshot each time. + """ + return self._table_source.forward(*streams) + + def source_output_types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Return tag and packet types based on provided typespecs.""" + # TODO: add system tag + return self._table_source.source_output_types( + include_system_tags=include_system_tags + ) diff --git a/src/orcapod/core/sources/list_source.py b/src/orcapod/core/sources/list_source.py new file mode 100644 index 0000000..fdc7ffa --- /dev/null +++ b/src/orcapod/core/sources/list_source.py @@ -0,0 +1,187 @@ +from collections.abc import Callable, Collection, Iterator +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal, cast + +from deltalake import DeltaTable, write_deltalake +from pyarrow.lib import Table + +from orcapod.core.datagrams import DictTag +from orcapod.core.kernels import TrackedKernelBase +from orcapod.core.streams import ( + TableStream, + KernelStream, + StatefulStreamBase, +) +from orcapod.errors import DuplicateTagError +from orcapod.protocols import core_protocols as cp +from orcapod.types import DataValue, PythonSchema +from orcapod.utils import arrow_utils +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.system_constants import constants +from orcapod.semantic_types import infer_python_schema_from_pylist_data + +if TYPE_CHECKING: + import pandas as pd + import polars as pl + import pyarrow as pa +else: + pl = LazyModule("polars") + pd = LazyModule("pandas") + pa = LazyModule("pyarrow") + +from orcapod.core.sources.base import SourceBase + + +class ListSource(SourceBase): + """ + A stream source that sources data from a list of elements. + For each element in the list, yields a tuple containing: + - A tag generated either by the provided tag_function or defaulting to the element index + - A packet containing the element under the provided name key + Parameters + ---------- + name : str + The key name under which each list element will be stored in the packet + data : list[Any] + The list of elements to source data from + tag_function : Callable[[Any, int], Tag] | None, default=None + Optional function to generate a tag from a list element and its index. + The function receives the element and the index as arguments. + If None, uses the element index in a dict with key 'element_index' + tag_function_hash_mode : Literal["content", "signature", "name"], default="name" + How to hash the tag function for identity purposes + expected_tag_keys : Collection[str] | None, default=None + Expected tag keys for the stream + label : str | None, default=None + Optional label for the source + Examples + -------- + >>> # Simple list of file names + >>> file_list = ['/path/to/file1.txt', '/path/to/file2.txt', '/path/to/file3.txt'] + >>> source = ListSource('file_path', file_list) + >>> + >>> # Custom tag function using filename stems + >>> from pathlib import Path + >>> source = ListSource( + ... 'file_path', + ... file_list, + ... tag_function=lambda elem, idx: {'file_name': Path(elem).stem} + ... ) + >>> + >>> # List of sample IDs + >>> samples = ['sample_001', 'sample_002', 'sample_003'] + >>> source = ListSource( + ... 'sample_id', + ... samples, + ... tag_function=lambda elem, idx: {'sample': elem} + ... ) + """ + + @staticmethod + def default_tag_function(element: Any, idx: int) -> cp.Tag: + return DictTag({"element_index": idx}) + + def __init__( + self, + name: str, + data: list[Any], + tag_function: Callable[[Any, int], cp.Tag] | None = None, + label: str | None = None, + tag_function_hash_mode: Literal["content", "signature", "name"] = "name", + expected_tag_keys: Collection[str] | None = None, + **kwargs, + ) -> None: + super().__init__(label=label, **kwargs) + self.name = name + self.elements = list(data) # Create a copy to avoid external modifications + + if tag_function is None: + tag_function = self.__class__.default_tag_function + # If using default tag function and no explicit expected_tag_keys, set to default + if expected_tag_keys is None: + expected_tag_keys = ["element_index"] + + self.expected_tag_keys = expected_tag_keys + self.tag_function = tag_function + self.tag_function_hash_mode = tag_function_hash_mode + + def forward(self, *streams: SyncStream) -> SyncStream: + if len(streams) != 0: + raise ValueError( + "ListSource does not support forwarding streams. " + "It generates its own stream from the list elements." + ) + + def generator() -> Iterator[tuple[Tag, Packet]]: + for idx, element in enumerate(self.elements): + tag = self.tag_function(element, idx) + packet = {self.name: element} + yield tag, packet + + return SyncStreamFromGenerator(generator) + + def __repr__(self) -> str: + return f"ListSource({self.name}, {len(self.elements)} elements)" + + def identity_structure(self, *streams: SyncStream) -> Any: + hash_function_kwargs = {} + if self.tag_function_hash_mode == "content": + # if using content hash, exclude few + hash_function_kwargs = { + "include_name": False, + "include_module": False, + "include_declaration": False, + } + + tag_function_hash = hash_function( + self.tag_function, + function_hash_mode=self.tag_function_hash_mode, + hash_kwargs=hash_function_kwargs, + ) + + # Convert list to hashable representation + # Handle potentially unhashable elements by converting to string + try: + elements_hashable = tuple(self.elements) + except TypeError: + # If elements are not hashable, convert to string representation + elements_hashable = tuple(str(elem) for elem in self.elements) + + return ( + self.__class__.__name__, + self.name, + elements_hashable, + tag_function_hash, + ) + tuple(streams) + + def keys( + self, *streams: SyncStream, trigger_run: bool = False + ) -> tuple[Collection[str] | None, Collection[str] | None]: + """ + Returns the keys of the stream. The keys are the names of the packets + in the stream. The keys are used to identify the packets in the stream. + If expected_keys are provided, they will be used instead of the default keys. + """ + if len(streams) != 0: + raise ValueError( + "ListSource does not support forwarding streams. " + "It generates its own stream from the list elements." + ) + + if self.expected_tag_keys is not None: + return tuple(self.expected_tag_keys), (self.name,) + return super().keys(trigger_run=trigger_run) + + def claims_unique_tags( + self, *streams: "SyncStream", trigger_run: bool = True + ) -> bool | None: + if len(streams) != 0: + raise ValueError( + "ListSource does not support forwarding streams. " + "It generates its own stream from the list elements." + ) + # Claim uniqueness only if the default tag function is used + if self.tag_function == self.__class__.default_tag_function: + return True + # Otherwise, delegate to the base class + return super().claims_unique_tags(trigger_run=trigger_run) diff --git a/src/orcapod/core/sources/manual_table_source.py b/src/orcapod/core/sources/manual_table_source.py new file mode 100644 index 0000000..ba365ec --- /dev/null +++ b/src/orcapod/core/sources/manual_table_source.py @@ -0,0 +1,367 @@ +from collections.abc import Collection +from pathlib import Path +from typing import TYPE_CHECKING, Any, cast + +from deltalake import DeltaTable, write_deltalake +from deltalake.exceptions import TableNotFoundError + +from orcapod.core.sources.source_registry import SourceRegistry +from orcapod.core.streams import TableStream +from orcapod.errors import DuplicateTagError +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema, PythonSchemaLike +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pandas as pd + import polars as pl + import pyarrow as pa +else: + pl = LazyModule("polars") + pd = LazyModule("pandas") + pa = LazyModule("pyarrow") + +from orcapod.core.sources.base import SourceBase + + +class ManualDeltaTableSource(SourceBase): + """ + A source that allows manual delta updates to a table. + This is useful for testing and debugging purposes. + + Supports duplicate tag handling: + - skip_duplicates=True: Use merge operation to only insert new tag combinations + - skip_duplicates=False: Raise error if duplicate tags would be created + """ + + def __init__( + self, + table_path: str | Path, + python_schema: PythonSchemaLike | None = None, + tag_columns: Collection[str] | None = None, + source_name: str | None = None, + source_registry: SourceRegistry | None = None, + **kwargs, + ) -> None: + """ + Initialize the ManualDeltaTableSource with a label and optional data context. + """ + super().__init__(**kwargs) + + if source_name is None: + source_name = Path(table_path).name + + self._source_name = source_name + + self.table_path = Path(table_path) + self._delta_table: DeltaTable | None = None + self.load_delta_table() + + if self._delta_table is None: + if python_schema is None: + raise ValueError( + "Delta table not found and no schema provided. " + "Please provide a valid Delta table path or a schema to create a new table." + ) + if tag_columns is None: + raise ValueError( + "At least one tag column must be provided when creating a new Delta table." + ) + arrow_schema = ( + self.data_context.type_converter.python_schema_to_arrow_schema( + python_schema + ) + ) + + fields = [] + for field in arrow_schema: + if field.name in tag_columns: + field = field.with_metadata({b"tag": b"True"}) + fields.append(field) + arrow_schema = pa.schema(fields) + + else: + arrow_schema = pa.schema(self._delta_table.schema().to_arrow()) + python_schema = ( + self.data_context.type_converter.arrow_schema_to_python_schema( + arrow_schema + ) + ) + + inferred_tag_columns = [] + for field in arrow_schema: + if ( + field.metadata is not None + and field.metadata.get(b"tag", b"False").decode().lower() == "true" + ): + inferred_tag_columns.append(field.name) + tag_columns = tag_columns or inferred_tag_columns + + self.python_schema = python_schema + self.arrow_schema = arrow_schema + self.tag_columns = list(tag_columns) if tag_columns else [] + + @property + def reference(self) -> tuple[str, ...]: + return ("manual_delta", self._source_name) + + @property + def delta_table_version(self) -> int | None: + """ + Return the version of the delta table. + If the table does not exist, return None. + """ + if self._delta_table is not None: + return self._delta_table.version() + return None + + def forward(self, *streams: cp.Stream) -> cp.Stream: + """Load current delta table data as a stream.""" + if len(streams) > 0: + raise ValueError("ManualDeltaTableSource takes no input streams") + + if self._delta_table is None: + arrow_data = pa.Table.from_pylist([], schema=self.arrow_schema) + else: + arrow_data = self._delta_table.to_pyarrow_dataset( + as_large_types=True + ).to_table() + + return TableStream( + arrow_data, tag_columns=self.tag_columns, source=self, upstreams=() + ) + + def source_identity_structure(self) -> Any: + """ + Return the identity structure of the kernel. + This is a unique identifier for the kernel based on its class name and table path. + """ + return (self.__class__.__name__, str(self.table_path)) + + def source_output_types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """Return tag and packet types based on schema and tag columns.""" + # TODO: auto add system entry tag + tag_types: PythonSchema = {} + packet_types: PythonSchema = {} + for field, field_type in self.python_schema.items(): + if field in self.tag_columns: + tag_types[field] = field_type + else: + packet_types[field] = field_type + return tag_types, packet_types + + def get_all_records(self, include_system_columns: bool = False) -> pa.Table | None: + """Get all records from the delta table.""" + if self._delta_table is None: + return None + + arrow_data = self._delta_table.to_pyarrow_dataset( + as_large_types=True + ).to_table() + + if not include_system_columns: + arrow_data = arrow_data.drop( + [col for col in arrow_data.column_names if col.startswith("_")] + ) + return arrow_data + + def _normalize_data_to_table( + self, data: "dict | pa.Table | pl.DataFrame | pd.DataFrame" + ) -> pa.Table: + """Convert input data to PyArrow Table with correct schema.""" + if isinstance(data, dict): + return pa.Table.from_pylist([data], schema=self.arrow_schema) + elif isinstance(data, pa.Table): + return data + else: + # Handle polars/pandas DataFrames + if hasattr(data, "to_arrow"): # Polars DataFrame + return data.to_arrow() # type: ignore + elif hasattr(data, "to_pandas"): # Polars to pandas fallback + return pa.Table.from_pandas(data.to_pandas(), schema=self.arrow_schema) # type: ignore + else: # Assume pandas DataFrame + return pa.Table.from_pandas( + cast(pd.DataFrame, data), schema=self.arrow_schema + ) + + def _check_for_duplicates(self, new_data: pa.Table) -> None: + """ + Check if new data contains tag combinations that already exist. + Raises DuplicateTagError if duplicates found. + """ + if self._delta_table is None or not self.tag_columns: + return # No existing data or no tag columns to check + + # Get existing tag combinations + existing_data = self._delta_table.to_pyarrow_dataset( + as_large_types=True + ).to_table() + if len(existing_data) == 0: + return # No existing data + + # Extract tag combinations from existing data + existing_tags = existing_data.select(self.tag_columns) + new_tags = new_data.select(self.tag_columns) + + # Convert to sets of tuples for comparison + existing_tag_tuples = set() + for i in range(len(existing_tags)): + tag_tuple = tuple( + existing_tags.column(col)[i].as_py() for col in self.tag_columns + ) + existing_tag_tuples.add(tag_tuple) + + # Check for duplicates in new data + duplicate_tags = [] + for i in range(len(new_tags)): + tag_tuple = tuple( + new_tags.column(col)[i].as_py() for col in self.tag_columns + ) + if tag_tuple in existing_tag_tuples: + duplicate_tags.append(tag_tuple) + + if duplicate_tags: + tag_names = ", ".join(self.tag_columns) + duplicate_strs = [str(tags) for tags in duplicate_tags] + raise DuplicateTagError( + f"Duplicate tag combinations found for columns [{tag_names}]: " + f"{duplicate_strs}. Use skip_duplicates=True to merge instead." + ) + + def _merge_data(self, new_data: pa.Table) -> None: + """ + Merge new data using Delta Lake merge operation. + Only inserts rows where tag combinations don't already exist. + """ + if self._delta_table is None: + # No existing table, just write the data + write_deltalake( + self.table_path, + new_data, + mode="overwrite", + ) + else: + # Use merge operation - only insert if tag combination doesn't exist + # Build merge condition based on tag columns + # Format: "target.col1 = source.col1 AND target.col2 = source.col2" + merge_conditions = " AND ".join( + f"target.{col} = source.{col}" for col in self.tag_columns + ) + + try: + # Use Delta Lake's merge functionality + ( + self._delta_table.merge( + source=new_data, + predicate=merge_conditions, + source_alias="source", + target_alias="target", + ) + .when_not_matched_insert_all() # Insert when no match found + .execute() + ) + except Exception: + # Fallback: manual duplicate filtering if merge fails + self._manual_merge_fallback(new_data) + + def _manual_merge_fallback(self, new_data: pa.Table) -> None: + """ + Fallback merge implementation that manually filters duplicates. + """ + if self._delta_table is None or not self.tag_columns: + write_deltalake(self.table_path, new_data, mode="append") + return + + # Get existing tag combinations + existing_data = self._delta_table.to_pyarrow_dataset( + as_large_types=True + ).to_table() + existing_tags = existing_data.select(self.tag_columns) + + # Create set of existing tag tuples + existing_tag_tuples = set() + for i in range(len(existing_tags)): + tag_tuple = tuple( + existing_tags.column(col)[i].as_py() for col in self.tag_columns + ) + existing_tag_tuples.add(tag_tuple) + + # Filter new data to only include non-duplicate rows + filtered_rows = [] + new_tags = new_data.select(self.tag_columns) + + for i in range(len(new_data)): + tag_tuple = tuple( + new_tags.column(col)[i].as_py() for col in self.tag_columns + ) + if tag_tuple not in existing_tag_tuples: + # Extract this row + row_dict = {} + for col_name in new_data.column_names: + row_dict[col_name] = new_data.column(col_name)[i].as_py() + filtered_rows.append(row_dict) + + # Only append if there are new rows to add + if filtered_rows: + filtered_table = pa.Table.from_pylist( + filtered_rows, schema=self.arrow_schema + ) + write_deltalake(self.table_path, filtered_table, mode="append") + + def insert( + self, + data: "dict | pa.Table | pl.DataFrame | pd.DataFrame", + skip_duplicates: bool = False, + ) -> None: + """ + Insert data into the delta table. + + Args: + data: Data to insert (dict, PyArrow Table, Polars DataFrame, or Pandas DataFrame) + skip_duplicates: If True, use merge operation to skip duplicate tag combinations. + If False, raise error if duplicate tag combinations are found. + + Raises: + DuplicateTagError: If skip_duplicates=False and duplicate tag combinations are found. + """ + # Normalize data to PyArrow Table + new_data_table = self._normalize_data_to_table(data) + + if skip_duplicates: + # Use merge operation to only insert new tag combinations + self._merge_data(new_data_table) + else: + # Check for duplicates first, raise error if found + self._check_for_duplicates(new_data_table) + + # No duplicates found, safe to append + write_deltalake(self.table_path, new_data_table, mode="append") + + # Update our delta table reference and mark as modified + self._set_modified_time() + self._delta_table = DeltaTable(self.table_path) + + # Invalidate any cached streams + self.invalidate() + + def load_delta_table(self) -> None: + """ + Try loading the delta table from the file system. + """ + current_version = self.delta_table_version + try: + delta_table = DeltaTable(self.table_path) + except TableNotFoundError: + delta_table = None + + if delta_table is not None: + new_version = delta_table.version() + if (current_version is None) or ( + current_version is not None and new_version > current_version + ): + # Delta table has been updated + self._set_modified_time() + + self._delta_table = delta_table diff --git a/src/orcapod/core/sources/source_registry.py b/src/orcapod/core/sources/source_registry.py new file mode 100644 index 0000000..66f9bf7 --- /dev/null +++ b/src/orcapod/core/sources/source_registry.py @@ -0,0 +1,232 @@ +import logging +from collections.abc import Iterator +from orcapod.protocols.core_protocols import Source + + +logger = logging.getLogger(__name__) + + +class SourceCollisionError(Exception): + """Raised when attempting to register a source ID that already exists.""" + + pass + + +class SourceNotFoundError(Exception): + """Raised when attempting to access a source that doesn't exist.""" + + pass + + +class SourceRegistry: + """ + Registry for managing data sources. + + Provides collision detection, source lookup, and management of source lifecycles. + """ + + def __init__(self): + self._sources: dict[str, Source] = {} + + def register(self, source_id: str, source: Source) -> None: + """ + Register a source with the given ID. + + Args: + source_id: Unique identifier for the source + source: Source instance to register + + Raises: + SourceCollisionError: If source_id already exists + ValueError: If source_id or source is invalid + """ + if not source_id: + raise ValueError("Source ID cannot be empty") + + if not isinstance(source_id, str): + raise ValueError(f"Source ID must be a string, got {type(source_id)}") + + if source is None: + raise ValueError("Source cannot be None") + + if source_id in self._sources: + existing_source = self._sources[source_id] + if existing_source == source: + # Idempotent - same source already registered + logger.debug( + f"Source ID '{source_id}' already registered with the same source instance." + ) + return + raise SourceCollisionError( + f"Source ID '{source_id}' already registered with {type(existing_source).__name__}. " + f"Cannot register {type(source).__name__}. " + f"Choose a different source_id or unregister the existing source first." + ) + + self._sources[source_id] = source + logger.info(f"Registered source: '{source_id}' -> {type(source).__name__}") + + def get(self, source_id: str) -> Source: + """ + Get a source by ID. + + Args: + source_id: Source identifier + + Returns: + Source instance + + Raises: + SourceNotFoundError: If source doesn't exist + """ + if source_id not in self._sources: + available_ids = list(self._sources.keys()) + raise SourceNotFoundError( + f"Source '{source_id}' not found. Available sources: {available_ids}" + ) + + return self._sources[source_id] + + def get_optional(self, source_id: str) -> Source | None: + """ + Get a source by ID, returning None if not found. + + Args: + source_id: Source identifier + + Returns: + Source instance or None if not found + """ + return self._sources.get(source_id) + + def unregister(self, source_id: str) -> Source: + """ + Unregister a source by ID. + + Args: + source_id: Source identifier + + Returns: + The unregistered source instance + + Raises: + SourceNotFoundError: If source doesn't exist + """ + if source_id not in self._sources: + raise SourceNotFoundError(f"Source '{source_id}' not found") + + source = self._sources.pop(source_id) + logger.info(f"Unregistered source: '{source_id}'") + return source + + # TODO: consider just using __contains__ + def contains(self, source_id: str) -> bool: + """Check if a source ID is registered.""" + return source_id in self._sources + + def list_sources(self) -> list[str]: + """Get list of all registered source IDs.""" + return list(self._sources.keys()) + + # TODO: consider removing this + def list_sources_by_type(self, source_type: type) -> list[str]: + """ + Get list of source IDs filtered by source type. + + Args: + source_type: Class type to filter by + + Returns: + List of source IDs that match the type + """ + return [ + source_id + for source_id, source in self._sources.items() + if isinstance(source, source_type) + ] + + def clear(self) -> None: + """Remove all registered sources.""" + count = len(self._sources) + self._sources.clear() + logger.info(f"Cleared {count} sources from registry") + + def replace(self, source_id: str, source: Source) -> Source | None: + """ + Replace an existing source or register a new one. + + Args: + source_id: Source identifier + source: New source instance + + Returns: + Previous source if it existed, None otherwise + """ + old_source = self._sources.get(source_id) + self._sources[source_id] = source + + if old_source: + logger.info(f"Replaced source: '{source_id}' -> {type(source).__name__}") + else: + logger.info( + f"Registered new source: '{source_id}' -> {type(source).__name__}" + ) + + return old_source + + def get_source_info(self, source_id: str) -> dict: + """ + Get information about a registered source. + + Args: + source_id: Source identifier + + Returns: + Dictionary with source information + + Raises: + SourceNotFoundError: If source doesn't exist + """ + source = self.get(source_id) # This handles the not found case + + info = { + "source_id": source_id, + "type": type(source).__name__, + "reference": source.reference if hasattr(source, "reference") else None, + } + info["identity"] = source.identity_structure() + + return info + + def __len__(self) -> int: + """Return number of registered sources.""" + return len(self._sources) + + def __contains__(self, source_id: str) -> bool: + """Support 'in' operator for checking source existence.""" + return source_id in self._sources + + def __iter__(self) -> Iterator[str]: + """Iterate over source IDs.""" + return iter(self._sources) + + def items(self) -> Iterator[tuple[str, Source]]: + """Iterate over (source_id, source) pairs.""" + yield from self._sources.items() + + def __repr__(self) -> str: + return f"SourceRegistry({len(self._sources)} sources)" + + def __str__(self) -> str: + if not self._sources: + return "SourceRegistry(empty)" + + source_summary = [] + for source_id, source in self._sources.items(): + source_summary.append(f" {source_id}: {type(source).__name__}") + + return "SourceRegistry:\n" + "\n".join(source_summary) + + +# Global source registry instance +GLOBAL_SOURCE_REGISTRY = SourceRegistry() diff --git a/src/orcapod/core/streams.py b/src/orcapod/core/streams.py deleted file mode 100644 index 77cdbe3..0000000 --- a/src/orcapod/core/streams.py +++ /dev/null @@ -1,72 +0,0 @@ -from collections.abc import Callable, Collection, Iterator - -from orcapod.core.base import SyncStream -from orcapod.types import Packet, Tag - - -class SyncStreamFromLists(SyncStream): - def __init__( - self, - tags: Collection[Tag] | None = None, - packets: Collection[Packet] | None = None, - paired: Collection[tuple[Tag, Packet]] | None = None, - tag_keys: list[str] | None = None, - packet_keys: list[str] | None = None, - strict: bool = True, - **kwargs, - ) -> None: - super().__init__(**kwargs) - self.tag_keys = tag_keys - self.packet_keys = packet_keys - if tags is not None and packets is not None: - if strict and len(tags) != len(packets): - raise ValueError( - "tags and packets must have the same length if both are provided" - ) - self.paired = list(zip(tags, packets)) - elif paired is not None: - self.paired = list(paired) - else: - raise ValueError( - "Either tags and packets or paired must be provided to SyncStreamFromLists" - ) - - def keys( - self, *, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - if self.tag_keys is None or self.packet_keys is None: - return super().keys(trigger_run=trigger_run) - # If the keys are already set, return them - return self.tag_keys.copy(), self.packet_keys.copy() - - def __iter__(self) -> Iterator[tuple[Tag, Packet]]: - yield from self.paired - - -class SyncStreamFromGenerator(SyncStream): - """ - A synchronous stream that is backed by a generator function. - """ - - def __init__( - self, - generator_factory: Callable[[], Iterator[tuple[Tag, Packet]]], - tag_keys: list[str] | None = None, - packet_keys: list[str] | None = None, - **kwargs, - ) -> None: - super().__init__(**kwargs) - self.tag_keys = tag_keys - self.packet_keys = packet_keys - self.generator_factory = generator_factory - - def __iter__(self) -> Iterator[tuple[Tag, Packet]]: - yield from self.generator_factory() - - def keys( - self, *, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - if self.tag_keys is None or self.packet_keys is None: - return super().keys(trigger_run=trigger_run) - # If the keys are already set, return them - return self.tag_keys.copy(), self.packet_keys.copy() diff --git a/src/orcapod/core/streams/__init__.py b/src/orcapod/core/streams/__init__.py new file mode 100644 index 0000000..86f64bb --- /dev/null +++ b/src/orcapod/core/streams/__init__.py @@ -0,0 +1,16 @@ +from .base import StatefulStreamBase +from .kernel_stream import KernelStream +from .table_stream import TableStream +from .lazy_pod_stream import LazyPodResultStream +from .cached_pod_stream import CachedPodStream +from .wrapped_stream import WrappedStream + + +__all__ = [ + "StatefulStreamBase", + "KernelStream", + "TableStream", + "LazyPodResultStream", + "CachedPodStream", + "WrappedStream", +] diff --git a/src/orcapod/core/streams/base.py b/src/orcapod/core/streams/base.py new file mode 100644 index 0000000..a216cdc --- /dev/null +++ b/src/orcapod/core/streams/base.py @@ -0,0 +1,582 @@ +import logging +from abc import abstractmethod +from collections.abc import Collection, Iterator, Mapping +from datetime import datetime, timezone +from typing import TYPE_CHECKING, Any + +from orcapod import contexts +from orcapod.core.base import LabeledContentIdentifiableBase +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule + + +if TYPE_CHECKING: + import pyarrow as pa + import pyarrow.compute as pc + import polars as pl + import pandas as pd +else: + pa = LazyModule("pyarrow") + pc = LazyModule("pyarrow.compute") + pl = LazyModule("polars") + pd = LazyModule("pandas") + + +# TODO: consider using this instead of making copy of dicts +# from types import MappingProxyType + +logger = logging.getLogger(__name__) + + +class OperatorStreamBaseMixin: + def join(self, other_stream: cp.Stream, label: str | None = None) -> cp.Stream: + """ + Joins this stream with another stream, returning a new stream that contains + the combined data from both streams. + """ + from orcapod.core.operators import Join + + return Join()(self, other_stream, label=label) # type: ignore + + def semi_join( + self, + other_stream: cp.Stream, + label: str | None = None, + ) -> cp.Stream: + """ + Performs a semi-join with another stream, returning a new stream that contains + only the packets from this stream that have matching tags in the other stream. + """ + from orcapod.core.operators import SemiJoin + + return SemiJoin()(self, other_stream, label=label) # type: ignore + + def map_tags( + self, + name_map: Mapping[str, str], + drop_unmapped: bool = True, + label: str | None = None, + ) -> cp.Stream: + """ + Maps the tags in this stream according to the provided name_map. + If drop_unmapped is True, any tags that are not in the name_map will be dropped. + """ + from orcapod.core.operators import MapTags + + return MapTags(name_map, drop_unmapped)(self, label=label) # type: ignore + + def map_packets( + self, + name_map: Mapping[str, str], + drop_unmapped: bool = True, + label: str | None = None, + ) -> cp.Stream: + """ + Maps the packets in this stream according to the provided packet_map. + If drop_unmapped is True, any packets that are not in the packet_map will be dropped. + """ + from orcapod.core.operators import MapPackets + + return MapPackets(name_map, drop_unmapped)(self, label=label) # type: ignore + + def batch( + self: cp.Stream, + batch_size: int = 0, + drop_partial_batch: bool = False, + label: str | None = None, + ) -> cp.Stream: + """ + Batch stream into fixed-size chunks, each of size batch_size. + If drop_last is True, any remaining elements that don't fit into a full batch will be dropped. + """ + from orcapod.core.operators import Batch + + return Batch(batch_size=batch_size, drop_partial_batch=drop_partial_batch)( + self, label=label + ) # type: ignore + + def polars_filter( + self, + *predicates: Any, + constraint_map: Mapping[str, Any] | None = None, + label: str | None = None, + **constraints: Any, + ) -> cp.Stream: + from orcapod.core.operators import PolarsFilter + + total_constraints = dict(constraint_map) if constraint_map is not None else {} + + total_constraints.update(constraints) + + return PolarsFilter(predicates=predicates, constraints=total_constraints)( + self, label=label + ) + + def select_tag_columns( + self: cp.Stream, + tag_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> cp.Stream: + """ + Select the specified tag columns from the stream. A ValueError is raised + if one or more specified tag columns do not exist in the stream unless strict = False. + """ + from orcapod.core.operators import SelectTagColumns + + return SelectTagColumns(tag_columns, strict=strict)(self, label=label) + + def select_packet_columns( + self: cp.Stream, + packet_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> cp.Stream: + """ + Select the specified packet columns from the stream. A ValueError is raised + if one or more specified packet columns do not exist in the stream unless strict = False. + """ + from orcapod.core.operators import SelectPacketColumns + + return SelectPacketColumns(packet_columns, strict=strict)(self, label=label) + + def drop_tag_columns( + self: cp.Stream, + tag_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> cp.Stream: + from orcapod.core.operators import DropTagColumns + + return DropTagColumns(tag_columns, strict=strict)(self, label=label) + + def drop_packet_columns( + self: cp.Stream, + packet_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> cp.Stream: + from orcapod.core.operators import DropPacketColumns + + return DropPacketColumns(packet_columns, strict=strict)(self, label=label) + + +class StatefulStreamBase(OperatorStreamBaseMixin, LabeledContentIdentifiableBase): + """ + A stream that has a unique identity within the pipeline. + """ + + def pop(self) -> cp.Stream: + return self + + def __init__( + self, + execution_engine: cp.ExecutionEngine | None = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self._last_modified: datetime | None = None + self._set_modified_time() + # note that this is not necessary for Stream protocol, but is provided + # for convenience to resolve semantic types and other context-specific information + self._execution_engine = execution_engine + + @property + def substream_identities(self) -> tuple[str, ...]: + """ + Returns the identities of the substreams that this stream is composed of. + This is used to identify the substreams in the computational graph. + """ + return (self.content_hash().to_hex(),) + + @property + def execution_engine(self) -> cp.ExecutionEngine | None: + """ + Returns the execution engine that is used to execute this stream. + This is typically used to track the execution context of the stream. + """ + return self._execution_engine + + @execution_engine.setter + def execution_engine(self, engine: cp.ExecutionEngine | None) -> None: + """ + Sets the execution engine for the stream. + This is typically used to track the execution context of the stream. + """ + self._execution_engine = engine + + def get_substream(self, substream_id: str) -> cp.Stream: + """ + Returns the substream with the given substream_id. + This is used to retrieve a specific substream from the stream. + """ + if substream_id == self.substream_identities[0]: + return self + else: + raise ValueError(f"Substream with ID {substream_id} not found.") + + @property + @abstractmethod + def source(self) -> cp.Kernel | None: + """ + The source of the stream, which is the kernel that generated the stream. + This is typically used to track the origin of the stream in the computational graph. + """ + ... + + @property + @abstractmethod + def upstreams(self) -> tuple[cp.Stream, ...]: + """ + The upstream streams that are used to generate this stream. + This is typically used to track the origin of the stream in the computational graph. + """ + ... + + def computed_label(self) -> str | None: + if self.source is not None: + # use the invocation operation label + return self.source.label + return None + + @abstractmethod + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: ... + + def tag_keys(self, include_system_tags: bool = False) -> tuple[str, ...]: + return self.keys(include_system_tags=include_system_tags)[0] + + def packet_keys(self) -> tuple[str, ...]: + return self.keys()[1] + + @abstractmethod + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: ... + + def tag_types(self, include_system_tags: bool = False) -> PythonSchema: + return self.types(include_system_tags=include_system_tags)[0] + + def packet_types(self) -> PythonSchema: + return self.types()[1] + + @property + def last_modified(self) -> datetime | None: + """ + Returns when the stream's content was last modified. + This is used to track the time when the stream was last accessed. + Returns None if the stream has not been accessed yet. + """ + return self._last_modified + + @property + def is_current(self) -> bool: + """ + Returns whether the stream is current. + A stream is current if the content is up-to-date with respect to its source. + This can be used to determine if a stream with non-None last_modified is up-to-date. + Note that for asynchronous streams, this status is not applicable and always returns False. + """ + if self.last_modified is None: + # If there is no last_modified timestamp, we cannot determine if the stream is current + return False + + # check if the source kernel has been modified + if self.source is not None and ( + self.source.last_modified is None + or self.source.last_modified > self.last_modified + ): + return False + + # check if all upstreams are current + for upstream in self.upstreams: + if ( + not upstream.is_current + or upstream.last_modified is None + or upstream.last_modified > self.last_modified + ): + return False + return True + + def _set_modified_time( + self, timestamp: datetime | None = None, invalidate: bool = False + ) -> None: + if invalidate: + self._last_modified = None + return + + if timestamp is not None: + self._last_modified = timestamp + else: + self._last_modified = datetime.now(timezone.utc) + + def __iter__( + self, + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + return self.iter_packets() + + @abstractmethod + def iter_packets( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: ... + + @abstractmethod + def run( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> None: ... + + @abstractmethod + async def run_async( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> None: ... + + @abstractmethod + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": ... + + def as_polars_df( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pl.DataFrame": + """ + Convert the entire stream to a Polars DataFrame. + """ + return pl.DataFrame( + self.as_table( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + ) + + def as_df( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pl.DataFrame": + """ + Convert the entire stream to a Polars DataFrame. + """ + return self.as_polars_df( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + + def as_lazy_frame( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pl.LazyFrame": + """ + Convert the entire stream to a Polars LazyFrame. + """ + df = self.as_polars_df( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + return df.lazy() + + def as_pandas_df( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + index_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pd.DataFrame": + df = self.as_polars_df( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + tag_keys, _ = self.keys() + pdf = df.to_pandas() + if index_by_tags: + pdf = pdf.set_index(list(tag_keys)) + return pdf + + def flow( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> Collection[tuple[cp.Tag, cp.Packet]]: + """ + Flow everything through the stream, returning the entire collection of + (Tag, Packet) as a collection. This will tigger any upstream computation of the stream. + """ + return [e for e in self.iter_packets(execution_engine=execution_engine)] + + def _repr_html_(self) -> str: + df = self.as_polars_df() + if df is not None: + tag_map = {t: f"*{t}" for t in self.tag_keys()} + # TODO: construct repr html better + df = df.rename(tag_map) + return f"{self.__class__.__name__}[{self.label}]\n" + df._repr_html_() + return "" + + # def identity_structure(self) -> Any: + # """ + # Identity structure of a stream is deferred to the identity structure + # of the associated invocation, if present. + # A bare stream without invocation has no well-defined identity structure. + # Specialized stream subclasses should override this method to provide more meaningful identity structure + # """ + # ... + + +class StreamBase(StatefulStreamBase): + """ + A stream is a collection of tagged-packets that are generated by an operation. + The stream is iterable and can be used to access the packets in the stream. + + A stream has property `invocation` that is an instance of Invocation that generated the stream. + This may be None if the stream is not generated by a kernel (i.e. directly instantiated by a user). + """ + + def __init__( + self, + source: cp.Kernel | None = None, + upstreams: tuple[cp.Stream, ...] = (), + data_context: str | contexts.DataContext | None = None, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self._source = source + self._upstreams = upstreams + + # if data context is not provided, use that of the source kernel + if data_context is None and source is not None: + # if source is provided, use its data context + data_context = source.data_context_key + super().__init__(data_context=data_context, **kwargs) + + @property + def source(self) -> cp.Kernel | None: + """ + The source of the stream, which is the kernel that generated the stream. + This is typically used to track the origin of the stream in the computational graph. + """ + return self._source + + @property + def upstreams(self) -> tuple[cp.Stream, ...]: + """ + The upstream streams that are used to generate this stream. + This is typically used to track the origin of the stream in the computational graph. + """ + return self._upstreams + + def computed_label(self) -> str | None: + if self.source is not None: + # use the invocation operation label + return self.source.label + return None + + # @abstractmethod + # def iter_packets( + # self, + # execution_engine: dp.ExecutionEngine | None = None, + # ) -> Iterator[tuple[dp.Tag, dp.Packet]]: ... + + # @abstractmethod + # def run( + # self, + # execution_engine: dp.ExecutionEngine | None = None, + # ) -> None: ... + + # @abstractmethod + # async def run_async( + # self, + # execution_engine: dp.ExecutionEngine | None = None, + # ) -> None: ... + + # @abstractmethod + # def as_table( + # self, + # include_data_context: bool = False, + # include_source: bool = False, + # include_system_tags: bool = False, + # include_content_hash: bool | str = False, + # sort_by_tags: bool = True, + # execution_engine: dp.ExecutionEngine | None = None, + # ) -> "pa.Table": ... + + def identity_structure(self) -> Any: + """ + Identity structure of a stream is deferred to the identity structure + of the associated invocation, if present. + A bare stream without invocation has no well-defined identity structure. + Specialized stream subclasses should override this method to provide more meaningful identity structure + """ + if self.source is not None: + # if the stream is generated by an operation, use the identity structure from the invocation + return self.source.identity_structure(self.upstreams) + return super().identity_structure() + + +class ImmutableStream(StreamBase): + """ + A class of stream that is constructed from immutable/constant data and does not change over time. + Consequently, the identity of an unsourced stream should be based on the content of the stream itself. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self._data_content_identity = None + + @abstractmethod + def data_content_identity_structure(self) -> Any: + """ + Returns a hash of the content of the stream. + This is used to identify the content of the stream. + """ + ... + + def identity_structure(self) -> Any: + if self.source is not None: + # if the stream is generated by an operation, use the identity structure from the invocation + return self.source.identity_structure(self.upstreams) + # otherwise, use the content of the stream as the identity structure + if self._data_content_identity is None: + self._data_content_identity = self.data_content_identity_structure() + return self._data_content_identity diff --git a/src/orcapod/core/streams/cached_pod_stream.py b/src/orcapod/core/streams/cached_pod_stream.py new file mode 100644 index 0000000..4f85f30 --- /dev/null +++ b/src/orcapod/core/streams/cached_pod_stream.py @@ -0,0 +1,434 @@ +import logging +from collections.abc import Iterator +from pathlib import Path +from typing import TYPE_CHECKING + +from orcapod.core.system_constants import constants +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils import arrow_utils +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.streams.base import StreamBase +from orcapod.core.streams.table_stream import TableStream + + +if TYPE_CHECKING: + import pyarrow as pa + import pyarrow.compute as pc + import polars as pl + +else: + pa = LazyModule("pyarrow") + pc = LazyModule("pyarrow.compute") + pl = LazyModule("polars") + + +# TODO: consider using this instead of making copy of dicts +# from types import MappingProxyType + +logger = logging.getLogger(__name__) + + +class CachedPodStream(StreamBase): + """ + A fixed stream that lazily processes packets from a prepared input stream. + This is what Pod.process() returns - it's static/fixed but efficient. + """ + + # TODO: define interface for storage or pod storage + def __init__(self, pod: cp.CachedPod, input_stream: cp.Stream, **kwargs): + super().__init__(source=pod, upstreams=(input_stream,), **kwargs) + self.pod = pod + self.input_stream = input_stream + self._set_modified_time() # set modified time to when we obtain the iterator + # capture the immutable iterator from the input stream + + self._prepared_stream_iterator = input_stream.iter_packets() + + # Packet-level caching (from your PodStream) + self._cached_output_packets: list[tuple[cp.Tag, cp.Packet | None]] | None = None + self._cached_output_table: pa.Table | None = None + self._cached_content_hash_column: pa.Array | None = None + + async def run_async( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> None: + """ + Runs the stream, processing the input stream and preparing the output stream. + This is typically called before iterating over the packets. + """ + if self._cached_output_packets is None: + cached_results = [] + + # identify all entries in the input stream for which we still have not computed packets + target_entries = self.input_stream.as_table( + include_content_hash=constants.INPUT_PACKET_HASH, + include_source=True, + include_system_tags=True, + ) + existing_entries = self.pod.get_all_cached_outputs( + include_system_columns=True + ) + if existing_entries is None or existing_entries.num_rows == 0: + missing = target_entries.drop_columns([constants.INPUT_PACKET_HASH]) + existing = None + else: + all_results = target_entries.join( + existing_entries.append_column( + "_exists", pa.array([True] * len(existing_entries)) + ), + keys=[constants.INPUT_PACKET_HASH], + join_type="left outer", + right_suffix="_right", + ) + # grab all columns from target_entries first + missing = ( + all_results.filter(pc.is_null(pc.field("_exists"))) + .select(target_entries.column_names) + .drop_columns([constants.INPUT_PACKET_HASH]) + ) + + existing = ( + all_results.filter(pc.is_valid(pc.field("_exists"))) + .drop_columns(target_entries.column_names) + .drop_columns(["_exists"]) + ) + renamed = [ + c.removesuffix("_right") if c.endswith("_right") else c + for c in existing.column_names + ] + existing = existing.rename_columns(renamed) + + tag_keys = self.input_stream.keys()[0] + + if existing is not None and existing.num_rows > 0: + # If there are existing entries, we can cache them + existing_stream = TableStream(existing, tag_columns=tag_keys) + for tag, packet in existing_stream.iter_packets(): + cached_results.append((tag, packet)) + + pending_calls = [] + if missing is not None and missing.num_rows > 0: + for tag, packet in TableStream(missing, tag_columns=tag_keys): + # Since these packets are known to be missing, skip the cache lookup + pending = self.pod.async_call( + tag, + packet, + skip_cache_lookup=True, + execution_engine=execution_engine, + ) + pending_calls.append(pending) + import asyncio + + completed_calls = await asyncio.gather(*pending_calls) + for result in completed_calls: + cached_results.append(result) + + self._cached_output_packets = cached_results + self._set_modified_time() + + def run( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> None: + cached_results = [] + + # identify all entries in the input stream for which we still have not computed packets + target_entries = self.input_stream.as_table( + include_system_tags=True, + include_source=True, + include_content_hash=constants.INPUT_PACKET_HASH, + execution_engine=execution_engine, + ) + existing_entries = self.pod.get_all_cached_outputs(include_system_columns=True) + if existing_entries is None or existing_entries.num_rows == 0: + missing = target_entries.drop_columns([constants.INPUT_PACKET_HASH]) + existing = None + else: + # TODO: do more proper replacement operation + target_df = pl.DataFrame(target_entries) + existing_df = pl.DataFrame( + existing_entries.append_column( + "_exists", pa.array([True] * len(existing_entries)) + ) + ) + all_results_df = target_df.join( + existing_df, + on=constants.INPUT_PACKET_HASH, + how="left", + suffix="_right", + ) + all_results = all_results_df.to_arrow() + + missing = ( + all_results.filter(pc.is_null(pc.field("_exists"))) + .select(target_entries.column_names) + .drop_columns([constants.INPUT_PACKET_HASH]) + ) + + existing = all_results.filter( + pc.is_valid(pc.field("_exists")) + ).drop_columns( + [ + "_exists", + constants.INPUT_PACKET_HASH, + constants.PACKET_RECORD_ID, + *self.input_stream.keys()[1], # remove the input packet keys + ] + # TODO: look into NOT fetching back the record ID + ) + renamed = [ + c.removesuffix("_right") if c.endswith("_right") else c + for c in existing.column_names + ] + existing = existing.rename_columns(renamed) + + tag_keys = self.input_stream.keys()[0] + + if existing is not None and existing.num_rows > 0: + # If there are existing entries, we can cache them + existing_stream = TableStream(existing, tag_columns=tag_keys) + for tag, packet in existing_stream.iter_packets(): + cached_results.append((tag, packet)) + + if missing is not None and missing.num_rows > 0: + for tag, packet in TableStream(missing, tag_columns=tag_keys): + # Since these packets are known to be missing, skip the cache lookup + tag, packet = self.pod.call( + tag, + packet, + skip_cache_lookup=True, + execution_engine=execution_engine, + ) + cached_results.append((tag, packet)) + + self._cached_output_packets = cached_results + self._set_modified_time() + + def iter_packets( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + """ + Processes the input stream and prepares the output stream. + This is typically called before iterating over the packets. + """ + if self._cached_output_packets is None: + cached_results = [] + + # identify all entries in the input stream for which we still have not computed packets + target_entries = self.input_stream.as_table( + include_system_tags=True, + include_source=True, + include_content_hash=constants.INPUT_PACKET_HASH, + execution_engine=execution_engine, + ) + existing_entries = self.pod.get_all_cached_outputs( + include_system_columns=True + ) + if existing_entries is None or existing_entries.num_rows == 0: + missing = target_entries.drop_columns([constants.INPUT_PACKET_HASH]) + existing = None + else: + # missing = target_entries.join( + # existing_entries, + # keys=[constants.INPUT_PACKET_HASH], + # join_type="left anti", + # ) + # Single join that gives you both missing and existing + # More efficient - only bring the key column from existing_entries + # .select([constants.INPUT_PACKET_HASH]).append_column( + # "_exists", pa.array([True] * len(existing_entries)) + # ), + + # TODO: do more proper replacement operation + target_df = pl.DataFrame(target_entries) + existing_df = pl.DataFrame( + existing_entries.append_column( + "_exists", pa.array([True] * len(existing_entries)) + ) + ) + all_results_df = target_df.join( + existing_df, + on=constants.INPUT_PACKET_HASH, + how="left", + suffix="_right", + ) + all_results = all_results_df.to_arrow() + # all_results = target_entries.join( + # existing_entries.append_column( + # "_exists", pa.array([True] * len(existing_entries)) + # ), + # keys=[constants.INPUT_PACKET_HASH], + # join_type="left outer", + # right_suffix="_right", # rename the existing records in case of collision of output packet keys with input packet keys + # ) + # grab all columns from target_entries first + missing = ( + all_results.filter(pc.is_null(pc.field("_exists"))) + .select(target_entries.column_names) + .drop_columns([constants.INPUT_PACKET_HASH]) + ) + + existing = all_results.filter( + pc.is_valid(pc.field("_exists")) + ).drop_columns( + [ + "_exists", + constants.INPUT_PACKET_HASH, + constants.PACKET_RECORD_ID, + *self.input_stream.keys()[1], # remove the input packet keys + ] + # TODO: look into NOT fetching back the record ID + ) + renamed = [ + c.removesuffix("_right") if c.endswith("_right") else c + for c in existing.column_names + ] + existing = existing.rename_columns(renamed) + + tag_keys = self.input_stream.keys()[0] + + if existing is not None and existing.num_rows > 0: + # If there are existing entries, we can cache them + existing_stream = TableStream(existing, tag_columns=tag_keys) + for tag, packet in existing_stream.iter_packets(): + cached_results.append((tag, packet)) + yield tag, packet + + if missing is not None and missing.num_rows > 0: + for tag, packet in TableStream(missing, tag_columns=tag_keys): + # Since these packets are known to be missing, skip the cache lookup + tag, packet = self.pod.call( + tag, + packet, + skip_cache_lookup=True, + execution_engine=execution_engine, + ) + cached_results.append((tag, packet)) + if packet is not None: + yield tag, packet + + self._cached_output_packets = cached_results + self._set_modified_time() + else: + for tag, packet in self._cached_output_packets: + if packet is not None: + yield tag, packet + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """ + Returns the keys of the tag and packet columns in the stream. + This is useful for accessing the columns in the stream. + """ + + tag_keys, _ = self.input_stream.keys(include_system_tags=include_system_tags) + packet_keys = tuple(self.pod.output_packet_types().keys()) + return tag_keys, packet_keys + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_typespec, _ = self.input_stream.types( + include_system_tags=include_system_tags + ) + # TODO: check if copying can be avoided + packet_typespec = dict(self.pod.output_packet_types()) + return tag_typespec, packet_typespec + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + if self._cached_output_table is None: + all_tags = [] + all_packets = [] + tag_schema, packet_schema = None, None + for tag, packet in self.iter_packets(execution_engine=execution_engine): + if tag_schema is None: + tag_schema = tag.arrow_schema(include_system_tags=True) + if packet_schema is None: + packet_schema = packet.arrow_schema( + include_context=True, + include_source=True, + ) + all_tags.append(tag.as_dict(include_system_tags=True)) + # FIXME: using in the pinch conversion to str from path + # replace with an appropriate semantic converter-based approach! + dict_patcket = packet.as_dict(include_context=True, include_source=True) + for k, v in dict_patcket.items(): + if isinstance(v, Path): + dict_patcket[k] = str(v) + all_packets.append(dict_patcket) + + converter = self.data_context.type_converter + + struct_packets = converter.python_dicts_to_struct_dicts(all_packets) + all_tags_as_tables: pa.Table = pa.Table.from_pylist( + all_tags, schema=tag_schema + ) + all_packets_as_tables: pa.Table = pa.Table.from_pylist( + struct_packets, schema=packet_schema + ) + + self._cached_output_table = arrow_utils.hstack_tables( + all_tags_as_tables, all_packets_as_tables + ) + assert self._cached_output_table is not None, ( + "_cached_output_table should not be None here." + ) + + drop_columns = [] + if not include_source: + drop_columns.extend(f"{constants.SOURCE_PREFIX}{c}" for c in self.keys()[1]) + if not include_data_context: + drop_columns.append(constants.CONTEXT_KEY) + if not include_system_tags: + # TODO: come up with a more efficient approach + drop_columns.extend( + [ + c + for c in self._cached_output_table.column_names + if c.startswith(constants.SYSTEM_TAG_PREFIX) + ] + ) + + output_table = self._cached_output_table.drop_columns(drop_columns) + + # lazily prepare content hash column if requested + if include_content_hash: + if self._cached_content_hash_column is None: + content_hashes = [] + for tag, packet in self.iter_packets(execution_engine=execution_engine): + content_hashes.append(packet.content_hash().to_string()) + self._cached_content_hash_column = pa.array( + content_hashes, type=pa.large_string() + ) + assert self._cached_content_hash_column is not None, ( + "_cached_content_hash_column should not be None here." + ) + hash_column_name = ( + "_content_hash" + if include_content_hash is True + else include_content_hash + ) + output_table = output_table.append_column( + hash_column_name, self._cached_content_hash_column + ) + + if sort_by_tags: + try: + # TODO: consider having explicit tag/packet properties? + output_table = output_table.sort_by( + [(column, "ascending") for column in self.keys()[0]] + ) + except pa.ArrowTypeError: + pass + + return output_table diff --git a/src/orcapod/core/streams/kernel_stream.py b/src/orcapod/core/streams/kernel_stream.py new file mode 100644 index 0000000..40908db --- /dev/null +++ b/src/orcapod/core/streams/kernel_stream.py @@ -0,0 +1,189 @@ +import logging +from collections.abc import Iterator +from datetime import datetime +from typing import TYPE_CHECKING + +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.streams.base import StreamBase + + +if TYPE_CHECKING: + import pyarrow as pa + import pyarrow.compute as pc + import polars as pl + import pandas as pd + import asyncio +else: + pa = LazyModule("pyarrow") + pc = LazyModule("pyarrow.compute") + pl = LazyModule("polars") + pd = LazyModule("pandas") + asyncio = LazyModule("asyncio") + + +# TODO: consider using this instead of making copy of dicts +# from types import MappingProxyType + +logger = logging.getLogger(__name__) + + +class KernelStream(StreamBase): + """ + Recomputable stream that wraps a stream produced by a kernel to provide + an abstraction over the stream, taking the stream's source and upstreams as the basis of + recomputing the stream. + + This stream is used to represent the output of a kernel invocation. + """ + + def __init__( + self, + output_stream: cp.Stream | None = None, + source: cp.Kernel | None = None, + upstreams: tuple[ + cp.Stream, ... + ] = (), # if provided, this will override the upstreams of the output_stream + **kwargs, + ) -> None: + if (output_stream is None or output_stream.source is None) and source is None: + raise ValueError( + "Either output_stream must have a kernel assigned to it or source must be provided in order to be recomputable." + ) + if source is None: + if output_stream is None or output_stream.source is None: + raise ValueError( + "Either output_stream must have a kernel assigned to it or source must be provided in order to be recomputable." + ) + source = output_stream.source + upstreams = upstreams or output_stream.upstreams + + super().__init__(source=source, upstreams=upstreams, **kwargs) + self.kernel = source + self._cached_stream = output_stream + + def clear_cache(self) -> None: + """ + Clears the cached stream. + This is useful for re-processing the stream with the same kernel. + """ + self._cached_stream = None + self._set_modified_time(invalidate=True) + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """ + Returns the keys of the tag and packet columns in the stream. + This is useful for accessing the columns in the stream. + """ + tag_types, packet_types = self.kernel.output_types( + *self.upstreams, include_system_tags=include_system_tags + ) + return tuple(tag_types.keys()), tuple(packet_types.keys()) + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Returns the types of the tag and packet columns in the stream. + This is useful for accessing the types of the columns in the stream. + """ + return self.kernel.output_types( + *self.upstreams, include_system_tags=include_system_tags + ) + + @property + def is_current(self) -> bool: + if self._cached_stream is None or not super().is_current: + status = self.refresh() + if not status: # if it failed to update for whatever reason + return False + return True + + def refresh(self, force: bool = False) -> bool: + updated = False + if force or (self._cached_stream is not None and not super().is_current): + self.clear_cache() + + if self._cached_stream is None: + assert self.source is not None, ( + "Stream source must be set to recompute the stream." + ) + self._cached_stream = self.source.forward(*self.upstreams) + self._set_modified_time() + updated = True + + if self._cached_stream is None: + # TODO: use beter error type + raise ValueError( + "Stream could not be updated. Ensure that the source is valid and upstreams are correct." + ) + + return updated + + def invalidate(self) -> None: + """ + Invalidate the stream, marking it as needing recomputation. + This will clear the cached stream and set the last modified time to None. + """ + self.clear_cache() + self._set_modified_time(invalidate=True) + + @property + def last_modified(self) -> datetime | None: + if self._cached_stream is None: + return None + return self._cached_stream.last_modified + + def run(self, execution_engine: cp.ExecutionEngine | None = None) -> None: + self.refresh() + assert self._cached_stream is not None, ( + "Stream has not been updated or is empty." + ) + self._cached_stream.run(execution_engine=execution_engine) + + async def run_async( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> None: + self.refresh() + assert self._cached_stream is not None, ( + "Stream has not been updated or is empty." + ) + await self._cached_stream.run_async(execution_engine=execution_engine) + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + self.refresh() + assert self._cached_stream is not None, ( + "Stream has not been updated or is empty." + ) + return self._cached_stream.as_table( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + + def iter_packets( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + self.refresh() + assert self._cached_stream is not None, ( + "Stream has not been updated or is empty." + ) + return self._cached_stream.iter_packets(execution_engine=execution_engine) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(kernel={self.source}, upstreams={self.upstreams})" diff --git a/src/orcapod/core/streams/lazy_pod_stream.py b/src/orcapod/core/streams/lazy_pod_stream.py new file mode 100644 index 0000000..a80cd97 --- /dev/null +++ b/src/orcapod/core/streams/lazy_pod_stream.py @@ -0,0 +1,228 @@ +import logging +from collections.abc import Iterator +from pathlib import Path +from typing import TYPE_CHECKING + +from orcapod.core.system_constants import constants +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils import arrow_utils +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.streams.base import StreamBase + + +if TYPE_CHECKING: + import pyarrow as pa + import polars as pl + import asyncio +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + asyncio = LazyModule("asyncio") + + +# TODO: consider using this instead of making copy of dicts +# from types import MappingProxyType + +logger = logging.getLogger(__name__) + + +class LazyPodResultStream(StreamBase): + """ + A fixed stream that lazily processes packets from a prepared input stream. + This is what Pod.process() returns - it's static/fixed but efficient. + """ + + def __init__(self, pod: cp.Pod, prepared_stream: cp.Stream, **kwargs): + super().__init__(source=pod, upstreams=(prepared_stream,), **kwargs) + self.pod = pod + self.prepared_stream = prepared_stream + self._set_modified_time() # set modified time to when we obtain the iterator + # capture the immutable iterator from the prepared stream + self._prepared_stream_iterator = prepared_stream.iter_packets() + + # Packet-level caching (from your PodStream) + self._cached_output_packets: dict[int, tuple[cp.Tag, cp.Packet | None]] = {} + self._cached_output_table: pa.Table | None = None + self._cached_content_hash_column: pa.Array | None = None + + def iter_packets( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + if self._prepared_stream_iterator is not None: + for i, (tag, packet) in enumerate(self._prepared_stream_iterator): + if i in self._cached_output_packets: + # Use cached result + tag, packet = self._cached_output_packets[i] + if packet is not None: + yield tag, packet + else: + # Process packet + processed = self.pod.call( + tag, packet, execution_engine=execution_engine + ) + if processed is not None: + # Update shared cache for future iterators (optimization) + self._cached_output_packets[i] = processed + tag, packet = processed + if packet is not None: + yield tag, packet + + # Mark completion by releasing the iterator + self._prepared_stream_iterator = None + else: + # Yield from snapshot of complete cache + for i in range(len(self._cached_output_packets)): + tag, packet = self._cached_output_packets[i] + if packet is not None: + yield tag, packet + + async def run_async( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> None: + if self._prepared_stream_iterator is not None: + pending_call_lut = {} + for i, (tag, packet) in enumerate(self._prepared_stream_iterator): + if i not in self._cached_output_packets: + # Process packet + pending_call_lut[i] = self.pod.async_call( + tag, packet, execution_engine=execution_engine + ) + + indices = list(pending_call_lut.keys()) + pending_calls = [pending_call_lut[i] for i in indices] + + results = await asyncio.gather(*pending_calls) + for i, result in zip(indices, results): + self._cached_output_packets[i] = result + + # Mark completion by releasing the iterator + self._prepared_stream_iterator = None + + def run( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> None: + # Fallback to synchronous run + self.flow(execution_engine=execution_engine) + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """ + Returns the keys of the tag and packet columns in the stream. + This is useful for accessing the columns in the stream. + """ + + tag_keys, _ = self.prepared_stream.keys(include_system_tags=include_system_tags) + packet_keys = tuple(self.pod.output_packet_types().keys()) + return tag_keys, packet_keys + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_typespec, _ = self.prepared_stream.types( + include_system_tags=include_system_tags + ) + # TODO: check if copying can be avoided + packet_typespec = dict(self.pod.output_packet_types()) + return tag_typespec, packet_typespec + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + if self._cached_output_table is None: + all_tags = [] + all_packets = [] + tag_schema, packet_schema = None, None + for tag, packet in self.iter_packets(execution_engine=execution_engine): + if tag_schema is None: + tag_schema = tag.arrow_schema(include_system_tags=True) + if packet_schema is None: + packet_schema = packet.arrow_schema( + include_context=True, + include_source=True, + ) + all_tags.append(tag.as_dict(include_system_tags=True)) + # FIXME: using in the pinch conversion to str from path + # replace with an appropriate semantic converter-based approach! + dict_patcket = packet.as_dict(include_context=True, include_source=True) + for k, v in dict_patcket.items(): + if isinstance(v, Path): + dict_patcket[k] = str(v) + all_packets.append(dict_patcket) + + # TODO: re-verify the implemetation of this conversion + converter = self.data_context.type_converter + + struct_packets = converter.python_dicts_to_struct_dicts(all_packets) + all_tags_as_tables: pa.Table = pa.Table.from_pylist( + all_tags, schema=tag_schema + ) + all_packets_as_tables: pa.Table = pa.Table.from_pylist( + struct_packets, schema=packet_schema + ) + + self._cached_output_table = arrow_utils.hstack_tables( + all_tags_as_tables, all_packets_as_tables + ) + assert self._cached_output_table is not None, ( + "_cached_output_table should not be None here." + ) + + drop_columns = [] + if not include_system_tags: + # TODO: get system tags more effiicently + drop_columns.extend( + [ + c + for c in self._cached_output_table.column_names + if c.startswith(constants.SYSTEM_TAG_PREFIX) + ] + ) + if not include_source: + drop_columns.extend(f"{constants.SOURCE_PREFIX}{c}" for c in self.keys()[1]) + if not include_data_context: + drop_columns.append(constants.CONTEXT_KEY) + + output_table = self._cached_output_table.drop(drop_columns) + + # lazily prepare content hash column if requested + if include_content_hash: + if self._cached_content_hash_column is None: + content_hashes = [] + # TODO: verify that order will be preserved + for tag, packet in self.iter_packets(): + content_hashes.append(packet.content_hash().to_string()) + self._cached_content_hash_column = pa.array( + content_hashes, type=pa.large_string() + ) + assert self._cached_content_hash_column is not None, ( + "_cached_content_hash_column should not be None here." + ) + hash_column_name = ( + "_content_hash" + if include_content_hash is True + else include_content_hash + ) + output_table = output_table.append_column( + hash_column_name, self._cached_content_hash_column + ) + + if sort_by_tags: + # TODO: reimplement using polars natively + output_table = ( + pl.DataFrame(output_table) + .sort(by=self.keys()[0], descending=False) + .to_arrow() + ) + # output_table = output_table.sort_by( + # [(column, "ascending") for column in self.keys()[0]] + # ) + return output_table diff --git a/src/orcapod/core/streams/pod_node_stream.py b/src/orcapod/core/streams/pod_node_stream.py new file mode 100644 index 0000000..4ddb5c8 --- /dev/null +++ b/src/orcapod/core/streams/pod_node_stream.py @@ -0,0 +1,435 @@ +import logging +from collections.abc import Iterator +from pathlib import Path +from typing import TYPE_CHECKING + +from orcapod.core.system_constants import constants +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils import arrow_utils +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.streams.base import StreamBase +from orcapod.core.streams.table_stream import TableStream +from orcapod.protocols.pipeline_protocols import PodNode + + +if TYPE_CHECKING: + import pyarrow as pa + import pyarrow.compute as pc + import polars as pl + +else: + pa = LazyModule("pyarrow") + pc = LazyModule("pyarrow.compute") + pl = LazyModule("polars") + + +# TODO: consider using this instead of making copy of dicts +# from types import MappingProxyType + +logger = logging.getLogger(__name__) + + +class PodNodeStream(StreamBase): + """ + A fixed stream that lazily processes packets from a prepared input stream. + This is what Pod.process() returns - it's static/fixed but efficient. + """ + + # TODO: define interface for storage or pod storage + def __init__(self, pod_node: PodNode, input_stream: cp.Stream, **kwargs): + super().__init__(source=pod_node, upstreams=(input_stream,), **kwargs) + self.pod_node = pod_node + self.input_stream = input_stream + self._set_modified_time() # set modified time to when we obtain the iterator + # capture the immutable iterator from the input stream + + self._prepared_stream_iterator = input_stream.iter_packets() + + # Packet-level caching (from your PodStream) + self._cached_output_packets: list[tuple[cp.Tag, cp.Packet | None]] | None = None + self._cached_output_table: pa.Table | None = None + self._cached_content_hash_column: pa.Array | None = None + + async def run_async( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> None: + """ + Runs the stream, processing the input stream and preparing the output stream. + This is typically called before iterating over the packets. + """ + if self._cached_output_packets is None: + cached_results = [] + + # identify all entries in the input stream for which we still have not computed packets + target_entries = self.input_stream.as_table( + include_content_hash=constants.INPUT_PACKET_HASH, + include_source=True, + include_system_tags=True, + ) + existing_entries = self.pod.get_all_cached_outputs( + include_system_columns=True + ) + if existing_entries is None or existing_entries.num_rows == 0: + missing = target_entries.drop_columns([constants.INPUT_PACKET_HASH]) + existing = None + else: + all_results = target_entries.join( + existing_entries.append_column( + "_exists", pa.array([True] * len(existing_entries)) + ), + keys=[constants.INPUT_PACKET_HASH], + join_type="left outer", + right_suffix="_right", + ) + # grab all columns from target_entries first + missing = ( + all_results.filter(pc.is_null(pc.field("_exists"))) + .select(target_entries.column_names) + .drop_columns([constants.INPUT_PACKET_HASH]) + ) + + existing = ( + all_results.filter(pc.is_valid(pc.field("_exists"))) + .drop_columns(target_entries.column_names) + .drop_columns(["_exists"]) + ) + renamed = [ + c.removesuffix("_right") if c.endswith("_right") else c + for c in existing.column_names + ] + existing = existing.rename_columns(renamed) + + tag_keys = self.input_stream.keys()[0] + + if existing is not None and existing.num_rows > 0: + # If there are existing entries, we can cache them + existing_stream = TableStream(existing, tag_columns=tag_keys) + for tag, packet in existing_stream.iter_packets(): + cached_results.append((tag, packet)) + + pending_calls = [] + if missing is not None and missing.num_rows > 0: + for tag, packet in TableStream(missing, tag_columns=tag_keys): + # Since these packets are known to be missing, skip the cache lookup + pending = self.pod.async_call( + tag, + packet, + skip_cache_lookup=True, + execution_engine=execution_engine, + ) + pending_calls.append(pending) + import asyncio + + completed_calls = await asyncio.gather(*pending_calls) + for result in completed_calls: + cached_results.append(result) + + self._cached_output_packets = cached_results + self._set_modified_time() + + def run( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> None: + cached_results = [] + + # identify all entries in the input stream for which we still have not computed packets + target_entries = self.input_stream.as_table( + include_system_tags=True, + include_source=True, + include_content_hash=constants.INPUT_PACKET_HASH, + execution_engine=execution_engine, + ) + existing_entries = self.pod.get_all_cached_outputs(include_system_columns=True) + if existing_entries is None or existing_entries.num_rows == 0: + missing = target_entries.drop_columns([constants.INPUT_PACKET_HASH]) + existing = None + else: + # TODO: do more proper replacement operation + target_df = pl.DataFrame(target_entries) + existing_df = pl.DataFrame( + existing_entries.append_column( + "_exists", pa.array([True] * len(existing_entries)) + ) + ) + all_results_df = target_df.join( + existing_df, + on=constants.INPUT_PACKET_HASH, + how="left", + suffix="_right", + ) + all_results = all_results_df.to_arrow() + + missing = ( + all_results.filter(pc.is_null(pc.field("_exists"))) + .select(target_entries.column_names) + .drop_columns([constants.INPUT_PACKET_HASH]) + ) + + existing = all_results.filter( + pc.is_valid(pc.field("_exists")) + ).drop_columns( + [ + "_exists", + constants.INPUT_PACKET_HASH, + constants.PACKET_RECORD_ID, + *self.input_stream.keys()[1], # remove the input packet keys + ] + # TODO: look into NOT fetching back the record ID + ) + renamed = [ + c.removesuffix("_right") if c.endswith("_right") else c + for c in existing.column_names + ] + existing = existing.rename_columns(renamed) + + tag_keys = self.input_stream.keys()[0] + + if existing is not None and existing.num_rows > 0: + # If there are existing entries, we can cache them + existing_stream = TableStream(existing, tag_columns=tag_keys) + for tag, packet in existing_stream.iter_packets(): + cached_results.append((tag, packet)) + + if missing is not None and missing.num_rows > 0: + for tag, packet in TableStream(missing, tag_columns=tag_keys): + # Since these packets are known to be missing, skip the cache lookup + tag, packet = self.pod.call( + tag, + packet, + skip_cache_lookup=True, + execution_engine=execution_engine, + ) + cached_results.append((tag, packet)) + + self._cached_output_packets = cached_results + self._set_modified_time() + + def iter_packets( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + """ + Processes the input stream and prepares the output stream. + This is typically called before iterating over the packets. + """ + if self._cached_output_packets is None: + cached_results = [] + + # identify all entries in the input stream for which we still have not computed packets + target_entries = self.input_stream.as_table( + include_system_tags=True, + include_source=True, + include_content_hash=constants.INPUT_PACKET_HASH, + execution_engine=execution_engine, + ) + existing_entries = self.pod.get_all_cached_outputs( + include_system_columns=True + ) + if existing_entries is None or existing_entries.num_rows == 0: + missing = target_entries.drop_columns([constants.INPUT_PACKET_HASH]) + existing = None + else: + # missing = target_entries.join( + # existing_entries, + # keys=[constants.INPUT_PACKET_HASH], + # join_type="left anti", + # ) + # Single join that gives you both missing and existing + # More efficient - only bring the key column from existing_entries + # .select([constants.INPUT_PACKET_HASH]).append_column( + # "_exists", pa.array([True] * len(existing_entries)) + # ), + + # TODO: do more proper replacement operation + target_df = pl.DataFrame(target_entries) + existing_df = pl.DataFrame( + existing_entries.append_column( + "_exists", pa.array([True] * len(existing_entries)) + ) + ) + all_results_df = target_df.join( + existing_df, + on=constants.INPUT_PACKET_HASH, + how="left", + suffix="_right", + ) + all_results = all_results_df.to_arrow() + # all_results = target_entries.join( + # existing_entries.append_column( + # "_exists", pa.array([True] * len(existing_entries)) + # ), + # keys=[constants.INPUT_PACKET_HASH], + # join_type="left outer", + # right_suffix="_right", # rename the existing records in case of collision of output packet keys with input packet keys + # ) + # grab all columns from target_entries first + missing = ( + all_results.filter(pc.is_null(pc.field("_exists"))) + .select(target_entries.column_names) + .drop_columns([constants.INPUT_PACKET_HASH]) + ) + + existing = all_results.filter( + pc.is_valid(pc.field("_exists")) + ).drop_columns( + [ + "_exists", + constants.INPUT_PACKET_HASH, + constants.PACKET_RECORD_ID, + *self.input_stream.keys()[1], # remove the input packet keys + ] + # TODO: look into NOT fetching back the record ID + ) + renamed = [ + c.removesuffix("_right") if c.endswith("_right") else c + for c in existing.column_names + ] + existing = existing.rename_columns(renamed) + + tag_keys = self.input_stream.keys()[0] + + if existing is not None and existing.num_rows > 0: + # If there are existing entries, we can cache them + existing_stream = TableStream(existing, tag_columns=tag_keys) + for tag, packet in existing_stream.iter_packets(): + cached_results.append((tag, packet)) + yield tag, packet + + if missing is not None and missing.num_rows > 0: + for tag, packet in TableStream(missing, tag_columns=tag_keys): + # Since these packets are known to be missing, skip the cache lookup + tag, packet = self.pod.call( + tag, + packet, + skip_cache_lookup=True, + execution_engine=execution_engine, + ) + cached_results.append((tag, packet)) + if packet is not None: + yield tag, packet + + self._cached_output_packets = cached_results + self._set_modified_time() + else: + for tag, packet in self._cached_output_packets: + if packet is not None: + yield tag, packet + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """ + Returns the keys of the tag and packet columns in the stream. + This is useful for accessing the columns in the stream. + """ + + tag_keys, _ = self.input_stream.keys(include_system_tags=include_system_tags) + packet_keys = tuple(self.pod.output_packet_types().keys()) + return tag_keys, packet_keys + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + tag_typespec, _ = self.input_stream.types( + include_system_tags=include_system_tags + ) + # TODO: check if copying can be avoided + packet_typespec = dict(self.pod.output_packet_types()) + return tag_typespec, packet_typespec + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + if self._cached_output_table is None: + all_tags = [] + all_packets = [] + tag_schema, packet_schema = None, None + for tag, packet in self.iter_packets(execution_engine=execution_engine): + if tag_schema is None: + tag_schema = tag.arrow_schema(include_system_tags=True) + if packet_schema is None: + packet_schema = packet.arrow_schema( + include_context=True, + include_source=True, + ) + all_tags.append(tag.as_dict(include_system_tags=True)) + # FIXME: using in the pinch conversion to str from path + # replace with an appropriate semantic converter-based approach! + dict_patcket = packet.as_dict(include_context=True, include_source=True) + for k, v in dict_patcket.items(): + if isinstance(v, Path): + dict_patcket[k] = str(v) + all_packets.append(dict_patcket) + + converter = self.data_context.type_converter + + struct_packets = converter.python_dicts_to_struct_dicts(all_packets) + all_tags_as_tables: pa.Table = pa.Table.from_pylist( + all_tags, schema=tag_schema + ) + all_packets_as_tables: pa.Table = pa.Table.from_pylist( + struct_packets, schema=packet_schema + ) + + self._cached_output_table = arrow_utils.hstack_tables( + all_tags_as_tables, all_packets_as_tables + ) + assert self._cached_output_table is not None, ( + "_cached_output_table should not be None here." + ) + + drop_columns = [] + if not include_source: + drop_columns.extend(f"{constants.SOURCE_PREFIX}{c}" for c in self.keys()[1]) + if not include_data_context: + drop_columns.append(constants.CONTEXT_KEY) + if not include_system_tags: + # TODO: come up with a more efficient approach + drop_columns.extend( + [ + c + for c in self._cached_output_table.column_names + if c.startswith(constants.SYSTEM_TAG_PREFIX) + ] + ) + + output_table = self._cached_output_table.drop_columns(drop_columns) + + # lazily prepare content hash column if requested + if include_content_hash: + if self._cached_content_hash_column is None: + content_hashes = [] + for tag, packet in self.iter_packets(execution_engine=execution_engine): + content_hashes.append(packet.content_hash().to_string()) + self._cached_content_hash_column = pa.array( + content_hashes, type=pa.large_string() + ) + assert self._cached_content_hash_column is not None, ( + "_cached_content_hash_column should not be None here." + ) + hash_column_name = ( + "_content_hash" + if include_content_hash is True + else include_content_hash + ) + output_table = output_table.append_column( + hash_column_name, self._cached_content_hash_column + ) + + if sort_by_tags: + try: + # TODO: consider having explicit tag/packet properties? + output_table = output_table.sort_by( + [(column, "ascending") for column in self.keys()[0]] + ) + except pa.ArrowTypeError: + pass + + return output_table diff --git a/src/orcapod/core/streams/table_stream.py b/src/orcapod/core/streams/table_stream.py new file mode 100644 index 0000000..51cd466 --- /dev/null +++ b/src/orcapod/core/streams/table_stream.py @@ -0,0 +1,319 @@ +import logging +from collections.abc import Collection, Iterator +from itertools import repeat +from typing import TYPE_CHECKING, Any, cast + +from orcapod import contexts +from orcapod.core.datagrams import ( + ArrowPacket, + ArrowTag, + DictTag, +) +from orcapod.core.system_constants import constants +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils import arrow_utils +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.streams.base import ImmutableStream + +if TYPE_CHECKING: + import pyarrow as pa + import pyarrow.compute as pc + import polars as pl + import pandas as pd +else: + pa = LazyModule("pyarrow") + pc = LazyModule("pyarrow.compute") + pl = LazyModule("polars") + pd = LazyModule("pandas") + +logger = logging.getLogger(__name__) + + +class TableStream(ImmutableStream): + """ + An immutable stream based on a PyArrow Table. + This stream is designed to be used with data that is already in a tabular format, + such as data loaded from a file or database. The columns to be treated as tags are + specified at initialization, and the rest of the columns are treated as packets. + The stream is immutable, meaning that once it is created, it cannot be modified. + This is useful for ensuring that the data in the stream remains consistent and unchanging. + + The types of the tag and packet columns are inferred from the PyArrow Table schema. + """ + + def __init__( + self, + table: "pa.Table", + tag_columns: Collection[str] = (), + system_tag_columns: Collection[str] = (), + source_info: dict[str, str | None] | None = None, + source: cp.Kernel | None = None, + upstreams: tuple[cp.Stream, ...] = (), + **kwargs, + ) -> None: + super().__init__(source=source, upstreams=upstreams, **kwargs) + + data_table, data_context_table = arrow_utils.split_by_column_groups( + table, [constants.CONTEXT_KEY] + ) + if data_table is None: + # TODO: provide better error message + raise ValueError( + "Table must contain at least one column to be used as a stream." + ) + table = data_table + + if data_context_table is None: + data_context_table = pa.table( + { + constants.CONTEXT_KEY: pa.array( + [contexts.get_default_context_key()] * len(table), + pa.large_string(), + ) + } + ) + + prefix_info = {constants.SOURCE_PREFIX: source_info} + + # determine tag columns first and then exclude any source info + self._tag_columns = tuple(c for c in tag_columns if c in table.column_names) + self._system_tag_columns = tuple( + c for c in table.column_names if c.startswith(constants.SYSTEM_TAG_PREFIX) + ) + if len(system_tag_columns) > 0: + # rename system_tag_columns + column_name_map = { + c: f"{constants.SYSTEM_TAG_PREFIX}{c}" for c in system_tag_columns + } + table = table.rename_columns( + [column_name_map.get(c, c) for c in table.column_names] + ) + + self._system_tag_columns += tuple( + f"{constants.SYSTEM_TAG_PREFIX}{c}" for c in system_tag_columns + ) + + self._all_tag_columns = self._tag_columns + self._system_tag_columns + if delta := set(tag_columns) - set(self._tag_columns): + raise ValueError( + f"Specified tag columns {delta} are not present in the table." + ) + table, prefix_tables = arrow_utils.prepare_prefixed_columns( + table, + prefix_info, + exclude_columns=self._all_tag_columns, + ) + # now table should only contain tag columns and packet columns + self._packet_columns = tuple( + c for c in table.column_names if c not in self._all_tag_columns + ) + self._table = table + self._source_info_table = prefix_tables[constants.SOURCE_PREFIX] + self._data_context_table = data_context_table + + if len(self._packet_columns) == 0: + raise ValueError( + "No packet columns found in the table. At least one packet column is required." + ) + + tag_schema = pa.schema( + f for f in self._table.schema if f.name in self._tag_columns + ) + system_tag_schema = pa.schema( + f for f in self._table.schema if f.name in self._system_tag_columns + ) + all_tag_schema = arrow_utils.join_arrow_schemas(tag_schema, system_tag_schema) + packet_schema = pa.schema( + f for f in self._table.schema if f.name in self._packet_columns + ) + + self._tag_schema = tag_schema + self._system_tag_schema = system_tag_schema + self._all_tag_schema = all_tag_schema + self._packet_schema = packet_schema + # self._tag_converter = SemanticConverter.from_semantic_schema( + # schemas.SemanticSchema.from_arrow_schema( + # tag_schema, self._data_context.semantic_type_registry + # ) + # ) + # self._packet_converter = SemanticConverter.from_semantic_schema( + # schemas.SemanticSchema.from_arrow_schema( + # packet_schema, self._data_context.semantic_type_registry + # ) + # ) + + self._cached_elements: list[tuple[cp.Tag, ArrowPacket]] | None = None + self._set_modified_time() # set modified time to now + + def data_content_identity_structure(self) -> Any: + """ + Returns a hash of the content of the stream. + This is used to identify the content of the stream. + """ + table_hash = self.data_context.arrow_hasher.hash_table( + self.as_table( + include_data_context=True, include_source=True, include_system_tags=True + ), + ) + return ( + self.__class__.__name__, + table_hash, + self._tag_columns, + ) + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """ + Returns the keys of the tag and packet columns in the stream. + This is useful for accessing the columns in the stream. + """ + tag_columns = self._tag_columns + if include_system_tags: + tag_columns += self._system_tag_columns + return tag_columns, self._packet_columns + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Returns the types of the tag and packet columns in the stream. + This is useful for accessing the types of the columns in the stream. + """ + # TODO: consider using MappingProxyType to avoid copying the dicts + converter = self.data_context.type_converter + if include_system_tags: + tag_schema = self._all_tag_schema + else: + tag_schema = self._tag_schema + return ( + converter.arrow_schema_to_python_schema(tag_schema), + converter.arrow_schema_to_python_schema(self._packet_schema), + ) + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + """ + Returns the underlying table representation of the stream. + This is useful for converting the stream to a table format. + """ + output_table = self._table + if include_content_hash: + hash_column_name = ( + "_content_hash" + if include_content_hash is True + else include_content_hash + ) + content_hashes = [ + str(packet.content_hash()) for _, packet in self.iter_packets() + ] + output_table = output_table.append_column( + hash_column_name, pa.array(content_hashes, type=pa.large_string()) + ) + if not include_system_tags: + # Check in original implementation + output_table = output_table.drop_columns(list(self._system_tag_columns)) + table_stack = (output_table,) + if include_data_context: + table_stack += (self._data_context_table,) + if include_source: + table_stack += (self._source_info_table,) + + table = arrow_utils.hstack_tables(*table_stack) + + if sort_by_tags: + # TODO: cleanup the sorting tag selection logic + try: + target_tags = ( + self._all_tag_columns if include_system_tags else self._tag_columns + ) + return table.sort_by([(column, "ascending") for column in target_tags]) + except pa.ArrowTypeError: + # If sorting fails, fall back to unsorted table + return table + + return table + + def clear_cache(self) -> None: + """ + Resets the cached elements of the stream. + This is useful for re-iterating over the stream. + """ + self._cached_elements = None + + def iter_packets( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> Iterator[tuple[cp.Tag, ArrowPacket]]: + """ + Iterates over the packets in the stream. + Each packet is represented as a tuple of (Tag, Packet). + """ + # TODO: make it work with table batch stream + if self._cached_elements is None: + self._cached_elements = [] + tag_present = len(self._all_tag_columns) > 0 + if tag_present: + tags = self._table.select(self._all_tag_columns) + tag_batches = tags.to_batches() + else: + tag_batches = repeat(DictTag({})) + + # TODO: come back and clean up this logic + + packets = self._table.select(self._packet_columns) + for tag_batch, packet_batch in zip(tag_batches, packets.to_batches()): + for i in range(len(packet_batch)): + if tag_present: + tag = ArrowTag( + tag_batch.slice(i, 1), # type: ignore + data_context=self.data_context, + ) + + else: + tag = cast(DictTag, tag_batch) + + self._cached_elements.append( + ( + tag, + ArrowPacket( + packet_batch.slice(i, 1), + source_info=self._source_info_table.slice( + i, 1 + ).to_pylist()[0], + data_context=self.data_context, + ), + ) + ) + yield from self._cached_elements + + def run(self, execution_engine: cp.ExecutionEngine | None = None) -> None: + """ + Runs the stream, which in this case is a no-op since the stream is immutable. + This is typically used to trigger any upstream computation of the stream. + """ + # No-op for immutable streams + pass + + async def run_async( + self, execution_engine: cp.ExecutionEngine | None = None + ) -> None: + """ + Runs the stream asynchronously, which in this case is a no-op since the stream is immutable. + This is typically used to trigger any upstream computation of the stream. + """ + # No-op for immutable streams + pass + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}(table={self._table.column_names}, " + f"tag_columns={self._tag_columns})" + ) diff --git a/src/orcapod/core/streams/wrapped_stream.py b/src/orcapod/core/streams/wrapped_stream.py new file mode 100644 index 0000000..3f14203 --- /dev/null +++ b/src/orcapod/core/streams/wrapped_stream.py @@ -0,0 +1,86 @@ +import logging +from collections.abc import Iterator +from typing import TYPE_CHECKING, Any + +from orcapod.protocols import core_protocols as cp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule +from orcapod.core.streams.base import StreamBase + + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +# TODO: consider using this instead of making copy of dicts +# from types import MappingProxyType + +logger = logging.getLogger(__name__) + + +class WrappedStream(StreamBase): + def __init__( + self, + stream: cp.Stream, + source: cp.Kernel, + input_streams: tuple[cp.Stream, ...], + label: str | None = None, + **kwargs, + ) -> None: + super().__init__(source=source, upstreams=input_streams, label=label, **kwargs) + self._stream = stream + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """ + Returns the keys of the tag and packet columns in the stream. + This is useful for accessing the columns in the stream. + """ + return self._stream.keys(include_system_tags=include_system_tags) + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Returns the types of the tag and packet columns in the stream. + This is useful for accessing the types of the columns in the stream. + """ + return self._stream.types(include_system_tags=include_system_tags) + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: cp.ExecutionEngine | None = None, + ) -> "pa.Table": + """ + Returns the underlying table representation of the stream. + This is useful for converting the stream to a table format. + """ + return self._stream.as_table( + include_data_context=include_data_context, + include_source=include_source, + include_system_tags=include_system_tags, + include_content_hash=include_content_hash, + sort_by_tags=sort_by_tags, + execution_engine=execution_engine, + ) + + def iter_packets( + self, + execution_engine: cp.ExecutionEngine | None = None, + ) -> Iterator[tuple[cp.Tag, cp.Packet]]: + """ + Iterates over the packets in the stream. + Each packet is represented as a tuple of (Tag, Packet). + """ + return self._stream.iter_packets(execution_engine=execution_engine) + + def identity_structure(self) -> Any: + return self._stream.identity_structure() diff --git a/src/orcapod/core/system_constants.py b/src/orcapod/core/system_constants.py new file mode 100644 index 0000000..7dc6e49 --- /dev/null +++ b/src/orcapod/core/system_constants.py @@ -0,0 +1,74 @@ +# Constants used for source info keys +SYSTEM_COLUMN_PREFIX = "__" +DATAGRAM_PREFIX = "_" +SOURCE_INFO_PREFIX = "source_" +POD_ID_PREFIX = "pod_id_" +DATA_CONTEXT_KEY = "context_key" +INPUT_PACKET_HASH = "input_packet_hash" +PACKET_RECORD_ID = "packet_id" +SYSTEM_TAG_PREFIX = "tag" +POD_VERSION = "pod_version" +EXECUTION_ENGINE = "execution_engine" +POD_TIMESTAMP = "pod_ts" +FIELD_SEPARATOR = ":" +BLOCK_SEPARATOR = "::" + + +class SystemConstant: + def __init__(self, global_prefix: str = ""): + self._global_prefix = global_prefix + + @property + def BLOCK_SEPARATOR(self) -> str: + return BLOCK_SEPARATOR + + @property + def FIELD_SEPARATOR(self) -> str: + return FIELD_SEPARATOR + + @property + def META_PREFIX(self) -> str: + return f"{self._global_prefix}{SYSTEM_COLUMN_PREFIX}" + + @property + def DATAGRAM_PREFIX(self) -> str: + return f"{self._global_prefix}{DATAGRAM_PREFIX}" + + @property + def SOURCE_PREFIX(self) -> str: + return f"{self._global_prefix}{DATAGRAM_PREFIX}{SOURCE_INFO_PREFIX}" + + @property + def CONTEXT_KEY(self) -> str: + return f"{self._global_prefix}{DATAGRAM_PREFIX}{DATA_CONTEXT_KEY}" + + @property + def POD_ID_PREFIX(self) -> str: + return f"{self._global_prefix}{SYSTEM_COLUMN_PREFIX}{POD_ID_PREFIX}" + + @property + def INPUT_PACKET_HASH(self) -> str: + return f"{self._global_prefix}{SYSTEM_COLUMN_PREFIX}{INPUT_PACKET_HASH}" + + @property + def PACKET_RECORD_ID(self) -> str: + return f"{self._global_prefix}{SYSTEM_COLUMN_PREFIX}{PACKET_RECORD_ID}" + + @property + def SYSTEM_TAG_PREFIX(self) -> str: + return f"{self._global_prefix}{DATAGRAM_PREFIX}{SYSTEM_TAG_PREFIX}{self.BLOCK_SEPARATOR}" + + @property + def POD_VERSION(self) -> str: + return f"{self._global_prefix}{SYSTEM_COLUMN_PREFIX}{POD_VERSION}" + + @property + def EXECUTION_ENGINE(self) -> str: + return f"{self._global_prefix}{SYSTEM_COLUMN_PREFIX}{EXECUTION_ENGINE}" + + @property + def POD_TIMESTAMP(self) -> str: + return f"{self._global_prefix}{SYSTEM_COLUMN_PREFIX}{POD_TIMESTAMP}" + + +constants = SystemConstant() diff --git a/src/orcapod/core/tracker.py b/src/orcapod/core/tracker.py deleted file mode 100644 index efc2c42..0000000 --- a/src/orcapod/core/tracker.py +++ /dev/null @@ -1,78 +0,0 @@ -from orcapod.core.base import Invocation, Kernel, Tracker - - -class GraphTracker(Tracker): - """ - A tracker that records the invocations of operations and generates a graph - of the invocations and their dependencies. - """ - - # Thread-local storage to track active trackers - - def __init__(self) -> None: - super().__init__() - self.invocation_lut: dict[Kernel, list[Invocation]] = {} - - def record(self, invocation: Invocation) -> None: - invocation_list = self.invocation_lut.setdefault(invocation.kernel, []) - if invocation not in invocation_list: - invocation_list.append(invocation) - - def reset(self) -> dict[Kernel, list[Invocation]]: - """ - Reset the tracker and return the recorded invocations. - """ - recorded_invocations = self.invocation_lut - self.invocation_lut = {} - return recorded_invocations - - def generate_namemap(self) -> dict[Invocation, str]: - namemap = {} - for kernel, invocations in self.invocation_lut.items(): - # if only one entry present, use the kernel name alone - if kernel.label is not None: - node_label = kernel.label - else: - node_label = str(kernel) - if len(invocations) == 1: - namemap[invocations[0]] = node_label - continue - # if multiple entries, use the kernel name and index - for idx, invocation in enumerate(invocations): - namemap[invocation] = f"{node_label}_{idx}" - return namemap - - def generate_graph(self): - import networkx as nx - G = nx.DiGraph() - - # Add edges for each invocation - for kernel, invocations in self.invocation_lut.items(): - for invocation in invocations: - for upstream in invocation.streams: - # if upstream.invocation is not in the graph, add it - if upstream.invocation not in G: - G.add_node(upstream.invocation) - G.add_edge(upstream.invocation, invocation, stream=upstream) - - return G - - def draw_graph(self): - import networkx as nx - import matplotlib.pyplot as plt - G = self.generate_graph() - labels = self.generate_namemap() - - pos = nx.drawing.nx_agraph.graphviz_layout(G, prog="dot") - nx.draw( - G, - pos, - labels=labels, - node_size=2000, - node_color="lightblue", - with_labels=True, - font_size=10, - font_weight="bold", - arrowsize=20, - ) - plt.tight_layout() diff --git a/src/orcapod/core/trackers.py b/src/orcapod/core/trackers.py new file mode 100644 index 0000000..4ffe39a --- /dev/null +++ b/src/orcapod/core/trackers.py @@ -0,0 +1,270 @@ +from orcapod.core.base import LabeledContentIdentifiableBase +from orcapod.protocols import core_protocols as cp +from collections import defaultdict +from collections.abc import Generator +from abc import ABC, abstractmethod +from typing import Any, TYPE_CHECKING +from contextlib import contextmanager + + +if TYPE_CHECKING: + import networkx as nx + + +class BasicTrackerManager: + def __init__(self) -> None: + self._active_trackers: list[cp.Tracker] = [] + self._active = True + + def set_active(self, active: bool = True) -> None: + """ + Set the active state of the tracker manager. + This is used to enable or disable the tracker manager. + """ + self._active = active + + def register_tracker(self, tracker: cp.Tracker) -> None: + """ + Register a new tracker in the system. + This is used to add a new tracker to the list of active trackers. + """ + if tracker not in self._active_trackers: + self._active_trackers.append(tracker) + + def deregister_tracker(self, tracker: cp.Tracker) -> None: + """ + Remove a tracker from the system. + This is used to deactivate a tracker and remove it from the list of active trackers. + """ + if tracker in self._active_trackers: + self._active_trackers.remove(tracker) + + def get_active_trackers(self) -> list[cp.Tracker]: + """ + Get the list of active trackers. + This is used to retrieve the currently active trackers in the system. + """ + if not self._active: + return [] + # Filter out inactive trackers + # This is to ensure that we only return trackers that are currently active + return [t for t in self._active_trackers if t.is_active()] + + def record_kernel_invocation( + self, + kernel: cp.Kernel, + upstreams: tuple[cp.Stream, ...], + label: str | None = None, + ) -> None: + """ + Record the output stream of a kernel invocation in the tracker. + This is used to track the computational graph and the invocations of kernels. + """ + for tracker in self.get_active_trackers(): + tracker.record_kernel_invocation(kernel, upstreams, label=label) + + def record_source_invocation( + self, source: cp.Source, label: str | None = None + ) -> None: + """ + Record the output stream of a source invocation in the tracker. + This is used to track the computational graph and the invocations of sources. + """ + for tracker in self.get_active_trackers(): + tracker.record_source_invocation(source, label=label) + + def record_pod_invocation( + self, pod: cp.Pod, upstreams: tuple[cp.Stream, ...], label: str | None = None + ) -> None: + """ + Record the output stream of a pod invocation in the tracker. + This is used to track the computational graph and the invocations of pods. + """ + for tracker in self.get_active_trackers(): + tracker.record_pod_invocation(pod, upstreams, label=label) + + @contextmanager + def no_tracking(self) -> Generator[None, Any, None]: + original_state = self._active + self.set_active(False) + try: + yield + finally: + self.set_active(original_state) + + +class AutoRegisteringContextBasedTracker(ABC): + def __init__(self, tracker_manager: cp.TrackerManager | None = None) -> None: + self._tracker_manager = tracker_manager or DEFAULT_TRACKER_MANAGER + self._active = False + + def set_active(self, active: bool = True) -> None: + if active: + self._tracker_manager.register_tracker(self) + else: + self._tracker_manager.deregister_tracker(self) + self._active = active + + def is_active(self) -> bool: + return self._active + + @abstractmethod + def record_kernel_invocation( + self, + kernel: cp.Kernel, + upstreams: tuple[cp.Stream, ...], + label: str | None = None, + ) -> None: ... + + @abstractmethod + def record_source_invocation( + self, source: cp.Source, label: str | None = None + ) -> None: ... + + @abstractmethod + def record_pod_invocation( + self, pod: cp.Pod, upstreams: tuple[cp.Stream, ...], label: str | None = None + ) -> None: ... + + def __enter__(self): + self.set_active(True) + return self + + def __exit__(self, exc_type, exc_val, ext_tb): + self.set_active(False) + + +class Invocation(LabeledContentIdentifiableBase): + def __init__( + self, + kernel: cp.Kernel, + upstreams: tuple[cp.Stream, ...] = (), + label: str | None = None, + ) -> None: + """ + Represents an invocation of a kernel with its upstream streams. + This is used to track the computational graph and the invocations of kernels. + """ + super().__init__(label=label) + self.kernel = kernel + self.upstreams = upstreams + + def parents(self) -> tuple["Invocation", ...]: + parent_invoctions = [] + for stream in self.upstreams: + if stream.source is not None: + parent_invoctions.append(Invocation(stream.source, stream.upstreams)) + else: + # import JIT to avoid circular imports + from orcapod.core.sources.base import StreamSource + + source = StreamSource(stream) + parent_invoctions.append(Invocation(source)) + + return tuple(parent_invoctions) + + def computed_label(self) -> str | None: + """ + Compute a label for this invocation based on its kernel and upstreams. + If label is not explicitly set for this invocation and computed_label returns a valid value, + it will be used as label of this invocation. + """ + return self.kernel.label + + def identity_structure(self) -> Any: + """ + Return a structure that represents the identity of this invocation. + This is used to uniquely identify the invocation in the tracker. + """ + # if no upstreams, then we want to identify the source directly + if not self.upstreams: + return self.kernel.identity_structure() + return self.kernel.identity_structure(self.upstreams) + + def __repr__(self) -> str: + return f"Invocation(kernel={self.kernel}, upstreams={self.upstreams}, label={self.label})" + + +class GraphTracker(AutoRegisteringContextBasedTracker): + """ + A tracker that records the invocations of operations and generates a graph + of the invocations and their dependencies. + """ + + # Thread-local storage to track active trackers + + def __init__( + self, + tracker_manager: cp.TrackerManager | None = None, + **kwargs, + ) -> None: + super().__init__(tracker_manager=tracker_manager) + + # Dictionary to map kernels to the streams they have invoked + # This is used to track the computational graph and the invocations of kernels + self.kernel_invocations: set[Invocation] = set() + self.invocation_to_pod_lut: dict[Invocation, cp.Pod] = {} + self.invocation_to_source_lut: dict[Invocation, cp.Source] = {} + + def _record_kernel_and_get_invocation( + self, + kernel: cp.Kernel, + upstreams: tuple[cp.Stream, ...], + label: str | None = None, + ) -> Invocation: + invocation = Invocation(kernel, upstreams, label=label) + self.kernel_invocations.add(invocation) + return invocation + + def record_kernel_invocation( + self, + kernel: cp.Kernel, + upstreams: tuple[cp.Stream, ...], + label: str | None = None, + ) -> None: + """ + Record the output stream of a kernel invocation in the tracker. + This is used to track the computational graph and the invocations of kernels. + """ + self._record_kernel_and_get_invocation(kernel, upstreams, label) + + def record_source_invocation( + self, source: cp.Source, label: str | None = None + ) -> None: + """ + Record the output stream of a source invocation in the tracker. + """ + invocation = self._record_kernel_and_get_invocation(source, (), label) + self.invocation_to_source_lut[invocation] = source + + def record_pod_invocation( + self, pod: cp.Pod, upstreams: tuple[cp.Stream, ...], label: str | None = None + ) -> None: + """ + Record the output stream of a pod invocation in the tracker. + """ + invocation = self._record_kernel_and_get_invocation(pod, upstreams, label) + self.invocation_to_pod_lut[invocation] = pod + + def reset(self) -> dict[cp.Kernel, list[cp.Stream]]: + """ + Reset the tracker and return the recorded invocations. + """ + recorded_streams = self.kernel_to_invoked_stream_lut + self.kernel_to_invoked_stream_lut = defaultdict(list) + return recorded_streams + + def generate_graph(self) -> "nx.DiGraph": + import networkx as nx + + G = nx.DiGraph() + + # Add edges for each invocation + for invocation in self.kernel_invocations: + G.add_node(invocation) + for upstream_invocation in invocation.parents(): + G.add_edge(upstream_invocation, invocation) + return G + + +DEFAULT_TRACKER_MANAGER = BasicTrackerManager() diff --git a/src/orcapod/databases/__init__.py b/src/orcapod/databases/__init__.py new file mode 100644 index 0000000..f47c734 --- /dev/null +++ b/src/orcapod/databases/__init__.py @@ -0,0 +1,16 @@ +# from .legacy.types import DataStore, ArrowDataStore +# from .legacy.legacy_arrow_data_stores import MockArrowDataStore, SimpleParquetDataStore +# from .legacy.dict_data_stores import DirDataStore, NoOpDataStore +# from .legacy.safe_dir_data_store import SafeDirDataStore + +# __all__ = [ +# "DataStore", +# "ArrowDataStore", +# "DirDataStore", +# "SafeDirDataStore", +# "NoOpDataStore", +# "MockArrowDataStore", +# "SimpleParquetDataStore", +# ] + +from .delta_lake_databases import DeltaTableDatabase diff --git a/src/orcapod/databases/basic_delta_lake_arrow_database.py b/src/orcapod/databases/basic_delta_lake_arrow_database.py new file mode 100644 index 0000000..412d247 --- /dev/null +++ b/src/orcapod/databases/basic_delta_lake_arrow_database.py @@ -0,0 +1,1008 @@ +import logging +from collections import defaultdict +from collections.abc import Collection, Mapping +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal, cast + +from deltalake import DeltaTable, write_deltalake +from deltalake.exceptions import TableNotFoundError + +from orcapod.core import constants +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import polars as pl + import pyarrow as pa + import pyarrow.compute as pc +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + pc = LazyModule("pyarrow.compute") + +# Module-level logger +logger = logging.getLogger(__name__) + + +class BasicDeltaTableArrowStore: + """ + A basic Delta Table-based Arrow data store with flexible hierarchical path support. + This store does NOT implement lazy loading or streaming capabilities, therefore + being "basic" in that sense. It is designed for simple use cases where data is written + in batches and read back as complete tables. It is worth noting that the Delta table + structure created by this store IS compatible with more advanced Delta Table-based + data stores (to be implemented) that will support lazy loading and streaming. + + Uses tuple-based source paths for robust parameter handling: + - ("source_name", "source_id") -> source_name/source_id/ + - ("org", "project", "dataset") -> org/project/dataset/ + - ("year", "month", "day", "experiment") -> year/month/day/experiment/ + """ + + RECORD_ID_COLUMN = f"{constants.META_PREFIX}record_id" + + def __init__( + self, + base_path: str | Path, + duplicate_entry_behavior: str = "error", + create_base_path: bool = True, + max_hierarchy_depth: int = 10, + batch_size: int = 100, + ): + """ + Initialize the BasicDeltaTableArrowStore. + + Args: + base_path: Base directory path where Delta tables will be stored + duplicate_entry_behavior: How to handle duplicate record_ids: + - 'error': Raise ValueError when record_id already exists + - 'overwrite': Replace existing entry with new data + create_base_path: Whether to create the base path if it doesn't exist + max_hierarchy_depth: Maximum allowed depth for source paths (safety limit) + batch_size: Number of records to batch before writing to Delta table + """ + # Validate duplicate behavior + if duplicate_entry_behavior not in ["error", "overwrite"]: + raise ValueError("duplicate_entry_behavior must be 'error' or 'overwrite'") + + self.duplicate_entry_behavior = duplicate_entry_behavior + self.base_path = Path(base_path) + self.max_hierarchy_depth = max_hierarchy_depth + self.batch_size = batch_size + + if create_base_path: + self.base_path.mkdir(parents=True, exist_ok=True) + elif not self.base_path.exists(): + raise ValueError( + f"Base path {self.base_path} does not exist and create_base_path=False" + ) + + # Cache for Delta tables to avoid repeated initialization + self._delta_table_cache: dict[str, DeltaTable] = {} + + # Batch management + self._pending_batches: dict[str, dict[str, pa.Table]] = defaultdict(dict) + + logger.info( + f"Initialized DeltaTableArrowDataStore at {self.base_path} " + f"with duplicate_entry_behavior='{duplicate_entry_behavior}', " + f"batch_size={batch_size}, as" + ) + + def flush(self) -> None: + """ + Flush all pending batches immediately. + + This method is called to ensure all pending data is written to the Delta tables. + """ + try: + self.flush_all_batches() + except Exception as e: + logger.error(f"Error during flush: {e}") + + def flush_batch(self, record_path: tuple[str, ...]) -> None: + """ + Flush pending batch for a specific source path. + + Args: + record_path: Tuple of path components + """ + logger.debug("Flushing triggered!!") + source_key = self._get_source_key(record_path) + + if ( + source_key not in self._pending_batches + or not self._pending_batches[source_key] + ): + return + + # Get all pending records + pending_tables = self._pending_batches[source_key] + self._pending_batches[source_key] = {} + + try: + # Combine all tables in the batch + combined_table = pa.concat_tables(pending_tables.values()).combine_chunks() + + table_path = self._get_table_path(record_path) + table_path.mkdir(parents=True, exist_ok=True) + + # Check if table exists + delta_table = self._get_existing_delta_table(record_path) + + if delta_table is None: + # TODO: reconsider mode="overwrite" here + write_deltalake( + table_path, + combined_table, + mode="overwrite", + ) + logger.debug( + f"Created new Delta table for {source_key} with {len(combined_table)} records" + ) + else: + if self.duplicate_entry_behavior == "overwrite": + # Get entry IDs from the batch + record_ids = combined_table.column( + self.RECORD_ID_COLUMN + ).to_pylist() + unique_record_ids = cast(list[str], list(set(record_ids))) + + # Delete existing records with these IDs + if unique_record_ids: + record_ids_str = "', '".join(unique_record_ids) + delete_predicate = ( + f"{self.RECORD_ID_COLUMN} IN ('{record_ids_str}')" + ) + try: + delta_table.delete(delete_predicate) + logger.debug( + f"Deleted {len(unique_record_ids)} existing records from {source_key}" + ) + except Exception as e: + logger.debug( + f"No existing records to delete from {source_key}: {e}" + ) + + # otherwise, only insert if same record_id does not exist yet + delta_table.merge( + source=combined_table, + predicate=f"target.{self.RECORD_ID_COLUMN} = source.{self.RECORD_ID_COLUMN}", + source_alias="source", + target_alias="target", + ).when_not_matched_insert_all().execute() + + logger.debug( + f"Appended batch of {len(combined_table)} records to {source_key}" + ) + + # Update cache + self._delta_table_cache[source_key] = DeltaTable(str(table_path)) + + except Exception as e: + logger.error(f"Error flushing batch for {source_key}: {e}") + # Put the tables back in the pending queue + self._pending_batches[source_key] = pending_tables + raise + + def flush_all_batches(self) -> None: + """Flush all pending batches.""" + source_keys = list(self._pending_batches.keys()) + + # TODO: capture and re-raise exceptions at the end + for source_key in source_keys: + record_path = tuple(source_key.split("/")) + try: + self.flush_batch(record_path) + except Exception as e: + logger.error(f"Error flushing batch for {source_key}: {e}") + + def __del__(self): + """Cleanup when object is destroyed.""" + self.flush() + + def _validate_record_path(self, record_path: tuple[str, ...]) -> None: + # TODO: consider removing this as path creation can be tried directly + """ + Validate source path components. + + Args: + record_path: Tuple of path components + + Raises: + ValueError: If path is invalid + """ + if not record_path: + raise ValueError("Source path cannot be empty") + + if len(record_path) > self.max_hierarchy_depth: + raise ValueError( + f"Source path depth {len(record_path)} exceeds maximum {self.max_hierarchy_depth}" + ) + + # Validate path components + for i, component in enumerate(record_path): + if not component or not isinstance(component, str): + raise ValueError( + f"Source path component {i} is invalid: {repr(component)}" + ) + + # Check for filesystem-unsafe characters + unsafe_chars = ["/", "\\", ":", "*", "?", '"', "<", ">", "|", "\0"] + if any(char in component for char in unsafe_chars): + raise ValueError( + f"Source path {record_path} component {component} contains invalid characters: {repr(component)}" + ) + + def _get_source_key(self, record_path: tuple[str, ...]) -> str: + """Generate cache key for source storage.""" + return "/".join(record_path) + + def _get_table_path(self, record_path: tuple[str, ...]) -> Path: + """Get the filesystem path for a given source path.""" + path = self.base_path + for subpath in record_path: + path = path / subpath + return path + + def _get_existing_delta_table( + self, record_path: tuple[str, ...] + ) -> DeltaTable | None: + """ + Get or create a Delta table, handling schema initialization properly. + + Args: + record_path: Tuple of path components + + Returns: + DeltaTable instance or None if table doesn't exist + """ + source_key = self._get_source_key(record_path) + table_path = self._get_table_path(record_path) + + # Check cache first + if dt := self._delta_table_cache.get(source_key): + return dt + + try: + # Try to load existing table + delta_table = DeltaTable(str(table_path)) + self._delta_table_cache[source_key] = delta_table + logger.debug(f"Loaded existing Delta table for {source_key}") + return delta_table + except TableNotFoundError: + # Table doesn't exist + return None + except Exception as e: + logger.error(f"Error loading Delta table for {source_key}: {e}") + # Try to clear any corrupted cache and retry once + if source_key in self._delta_table_cache: + del self._delta_table_cache[source_key] + return None + + def _ensure_record_id_column( + self, arrow_data: "pa.Table", record_id: str + ) -> "pa.Table": + """Ensure the table has an record id column.""" + if self.RECORD_ID_COLUMN not in arrow_data.column_names: + # Add record_id column at the beginning + key_array = pa.array([record_id] * len(arrow_data), type=pa.large_string()) + arrow_data = arrow_data.add_column(0, self.RECORD_ID_COLUMN, key_array) + return arrow_data + + def _remove_record_id_column(self, arrow_data: "pa.Table") -> "pa.Table": + """Remove the record id column if it exists.""" + if self.RECORD_ID_COLUMN in arrow_data.column_names: + column_names = arrow_data.column_names + indices_to_keep = [ + i + for i, name in enumerate(column_names) + if name != self.RECORD_ID_COLUMN + ] + arrow_data = arrow_data.select(indices_to_keep) + return arrow_data + + def _handle_record_id_column( + self, arrow_data: "pa.Table", record_id_column: str | None = None + ) -> "pa.Table": + """ + Handle record_id column based on add_record_id_column parameter. + + Args: + arrow_data: Arrow table with record id column + record_id_column: Control entry ID column inclusion: + + """ + if not record_id_column: + # Remove the record id column + return self._remove_record_id_column(arrow_data) + + # Rename record id column + if self.RECORD_ID_COLUMN in arrow_data.column_names: + schema = arrow_data.schema + new_names = [ + record_id_column if name == self.RECORD_ID_COLUMN else name + for name in schema.names + ] + return arrow_data.rename_columns(new_names) + else: + raise ValueError( + f"Record ID column '{self.RECORD_ID_COLUMN}' not found in the table and cannot be renamed." + ) + + def _create_record_id_filter(self, record_id: str) -> list: + """ + Create a proper filter expression for Delta Lake. + + Args: + record_id: The entry ID to filter by + + Returns: + List containing the filter expression for Delta Lake + """ + return [(self.RECORD_ID_COLUMN, "=", record_id)] + + def _create_record_ids_filter(self, record_ids: list[str]) -> list: + """ + Create a proper filter expression for multiple entry IDs. + + Args: + record_ids: List of entry IDs to filter by + + Returns: + List containing the filter expression for Delta Lake + """ + return [(self.RECORD_ID_COLUMN, "in", record_ids)] + + def _read_table_with_filter( + self, + delta_table: DeltaTable, + filters: list | None = None, + ) -> "pa.Table": + """ + Read table using to_pyarrow_dataset with original schema preservation. + + Args: + delta_table: The Delta table to read from + filters: Optional filters to apply + + Returns: + Arrow table with preserved schema + """ + # Use to_pyarrow_dataset with as_large_types for Polars compatible arrow table loading + dataset = delta_table.to_pyarrow_dataset(as_large_types=True) + if filters: + # Apply filters at dataset level for better performance + import pyarrow.compute as pc + + filter_expr = None + for filt in filters: + if len(filt) == 3: + col, op, val = filt + if op == "=": + expr = pc.equal(pc.field(col), pa.scalar(val)) # type: ignore + elif op == "in": + expr = pc.is_in(pc.field(col), pa.array(val)) # type: ignore + else: + logger.warning( + f"Unsupported filter operation: {op}. Falling back to table-level filter application which may be less efficient." + ) + # Fallback to table-level filtering + return dataset.to_table()(filters=filters) + + if filter_expr is None: + filter_expr = expr + else: + filter_expr = pc.and_(filter_expr, expr) # type: ignore + + if filter_expr is not None: + return dataset.to_table(filter=filter_expr) + + return dataset.to_table() + + def add_record( + self, + record_path: tuple[str, ...], + record_id: str, + data: "pa.Table", + ignore_duplicates: bool | None = None, + overwrite_existing: bool = False, + force_flush: bool = False, + ) -> "pa.Table": + self._validate_record_path(record_path) + source_key = self._get_source_key(record_path) + + # Check for existing entry + if ignore_duplicates is None: + ignore_duplicates = self.duplicate_entry_behavior != "error" + if not ignore_duplicates: + pending_table = self._pending_batches[source_key].get(record_id, None) + if pending_table is not None: + raise ValueError( + f"Entry '{record_id}' already exists in pending batch for {source_key}. " + f"Use duplicate_entry_behavior='overwrite' to allow updates." + ) + existing_record = self.get_record_by_id(record_path, record_id, flush=False) + if existing_record is not None: + raise ValueError( + f"Entry '{record_id}' already exists in {'/'.join(record_path)}. " + f"Use duplicate_entry_behavior='overwrite' to allow updates." + ) + + # Add record_id column to the data + data_with_record_id = self._ensure_record_id_column(data, record_id) + + if force_flush: + # Write immediately + table_path = self._get_table_path(record_path) + table_path.mkdir(parents=True, exist_ok=True) + + delta_table = self._get_existing_delta_table(record_path) + + if delta_table is None: + # Create new table - save original schema first + write_deltalake(str(table_path), data_with_record_id, mode="overwrite") + logger.debug(f"Created new Delta table for {source_key}") + else: + if self.duplicate_entry_behavior == "overwrite": + try: + delta_table.delete( + f"{self.RECORD_ID_COLUMN} = '{record_id.replace(chr(39), chr(39) + chr(39))}'" + ) + logger.debug( + f"Deleted existing record {record_id} from {source_key}" + ) + except Exception as e: + logger.debug( + f"No existing record to delete for {record_id}: {e}" + ) + + write_deltalake( + table_path, + data_with_record_id, + mode="append", + schema_mode="merge", + ) + + # Update cache + self._delta_table_cache[source_key] = DeltaTable(str(table_path)) + else: + # Add to the batch for later flushing + self._pending_batches[source_key][record_id] = data_with_record_id + batch_size = len(self._pending_batches[source_key]) + + # Check if we need to flush + if batch_size >= self.batch_size: + self.flush_batch(record_path) + + logger.debug(f"Added record {record_id} to {source_key}") + return data + + def add_records( + self, + record_path: tuple[str, ...], + records: "pa.Table", + record_id_column: str | None = None, + ignore_duplicates: bool | None = None, + overwrite_existing: bool = False, + force_flush: bool = False, + ) -> list[str]: + """ + Add multiple records to the Delta table, using one column as record_id. + + Args: + record_path: Path tuple identifying the table location + records: PyArrow table containing the records to add + record_id_column: Column name to use as record_id (defaults to first column) + ignore_duplicates: Whether to ignore duplicate entries + overwrite_existing: Whether to overwrite existing records with same ID + force_flush: Whether to write immediately instead of batching + + Returns: + List of record IDs that were added + """ + self._validate_record_path(record_path) + source_key = self._get_source_key(record_path) + + # Determine record_id column + if record_id_column is None: + record_id_column = records.column_names[0] + + # Validate that the record_id column exists + if record_id_column not in records.column_names: + raise ValueError( + f"Record ID column '{record_id_column}' not found in table. " + f"Available columns: {records.column_names}" + ) + + # Rename the record_id column to the standard name + column_mapping = {record_id_column: self.RECORD_ID_COLUMN} + records_renamed = records.rename_columns( + [column_mapping.get(col, col) for col in records.column_names] + ) + + # Get unique record IDs from the data + record_ids_array = records_renamed[self.RECORD_ID_COLUMN] + unique_record_ids = pc.unique(record_ids_array).to_pylist() + + # Set default behavior for duplicates + if ignore_duplicates is None: + ignore_duplicates = self.duplicate_entry_behavior != "error" + + added_record_ids = [] + + # Check for duplicates if needed + if not ignore_duplicates: + # Check pending batches + pending_duplicates = [] + for record_id in unique_record_ids: + if record_id in self._pending_batches[source_key]: + pending_duplicates.append(record_id) + + if pending_duplicates: + raise ValueError( + f"Records {pending_duplicates} already exist in pending batch for {source_key}. " + f"Use ignore_duplicates=True or duplicate_entry_behavior='overwrite' to allow updates." + ) + + # Check existing table + existing_duplicates = [] + try: + for record_id in unique_record_ids: + existing_record = self.get_record_by_id( + record_path, str(record_id), flush=False + ) + if existing_record is not None: + existing_duplicates.append(record_id) + except Exception as e: + logger.debug(f"Error checking existing records: {e}") + + if existing_duplicates: + raise ValueError( + f"Records {existing_duplicates} already exist in {'/'.join(record_path)}. " + f"Use ignore_duplicates=True or duplicate_entry_behavior='overwrite' to allow updates." + ) + + if force_flush: + # Write immediately + table_path = self._get_table_path(record_path) + table_path.mkdir(parents=True, exist_ok=True) + + delta_table = self._get_existing_delta_table(record_path) + + if delta_table is None: + # Create new table + write_deltalake(str(table_path), records_renamed, mode="overwrite") + logger.debug(f"Created new Delta table for {source_key}") + added_record_ids = unique_record_ids + else: + # Handle existing table + if self.duplicate_entry_behavior == "overwrite" or overwrite_existing: + # Delete existing records with matching IDs + try: + # Create SQL condition for multiple record IDs + escaped_ids = [ + str(rid).replace("'", "''") for rid in unique_record_ids + ] + id_list = "', '".join(escaped_ids) + delete_condition = f"{self.RECORD_ID_COLUMN} IN ('{id_list}')" + + delta_table.delete(delete_condition) + logger.debug( + f"Deleted existing records {unique_record_ids} from {source_key}" + ) + except Exception as e: + logger.debug(f"No existing records to delete: {e}") + + # Filter out duplicates if not overwriting + if not ( + self.duplicate_entry_behavior == "overwrite" or overwrite_existing + ): + # Get existing record IDs + try: + existing_table = delta_table.to_pyarrow_table() + if len(existing_table) > 0: + existing_ids = pc.unique( + existing_table[self.RECORD_ID_COLUMN] + ) + + # Filter out records that already exist + mask = pc.invert( + pc.is_in( + records_renamed[self.RECORD_ID_COLUMN], existing_ids + ) + ) + records_renamed = pc.filter(records_renamed, mask) # type: ignore + + # Update the list of record IDs that will actually be added + if len(records_renamed) > 0: + added_record_ids = pc.unique( + records_renamed[self.RECORD_ID_COLUMN] + ).to_pylist() + else: + added_record_ids = [] + else: + added_record_ids = unique_record_ids + except Exception as e: + logger.debug(f"Error filtering duplicates: {e}") + added_record_ids = unique_record_ids + else: + added_record_ids = unique_record_ids + + # Append the (possibly filtered) records + if len(records_renamed) > 0: + write_deltalake( + table_path, + records_renamed, + mode="append", + schema_mode="merge", + ) + + # Update cache + self._delta_table_cache[source_key] = DeltaTable(str(table_path)) + + else: + # Add to batches for later flushing + # Group records by record_id for individual batch entries + for record_id in unique_record_ids: + # Filter records for this specific record_id + mask = pc.equal(records_renamed[self.RECORD_ID_COLUMN], record_id) # type: ignore + single_record = pc.filter(records_renamed, mask) # type: ignore + + # Add to pending batch (will overwrite if duplicate_entry_behavior allows) + if ( + self.duplicate_entry_behavior == "overwrite" + or overwrite_existing + or record_id not in self._pending_batches[source_key] + ): + self._pending_batches[source_key][str(record_id)] = single_record + added_record_ids.append(record_id) + elif ignore_duplicates: + logger.debug(f"Ignoring duplicate record {record_id}") + else: + # This should have been caught earlier, but just in case + logger.warning(f"Skipping duplicate record {record_id}") + + # Check if we need to flush + batch_size = len(self._pending_batches[source_key]) + if batch_size >= self.batch_size: + self.flush_batch(record_path) + + logger.debug(f"Added {len(added_record_ids)} records to {source_key}") + return [str(rid) for rid in added_record_ids] + + def get_record_by_id( + self, + record_path: tuple[str, ...], + record_id: str, + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": + """ + Get a specific record by record_id with schema preservation. + + Args: + record_path: Tuple of path components + record_id: Unique identifier for the record + + Returns: + Arrow table for the record or None if not found + """ + + if flush: + self.flush_batch(record_path) + self._validate_record_path(record_path) + + # check if record_id is found in pending batches + source_key = self._get_source_key(record_path) + if record_id in self._pending_batches[source_key]: + # Return the pending record after removing the entry id column + return self._remove_record_id_column( + self._pending_batches[source_key][record_id] + ) + + delta_table = self._get_existing_delta_table(record_path) + if delta_table is None: + return None + + try: + # Use schema-preserving read + filter_expr = self._create_record_id_filter(record_id) + result = self._read_table_with_filter(delta_table, filters=filter_expr) + + if len(result) == 0: + return None + + # Handle (remove/rename) the record id column before returning + return self._handle_record_id_column(result, record_id_column) + + except Exception as e: + logger.error( + f"Error getting record {record_id} from {'/'.join(record_path)}: {e}" + ) + raise e + + def get_all_records( + self, + record_path: tuple[str, ...], + record_id_column: str | None = None, + retrieve_pending: bool = True, + flush: bool = False, + ) -> "pa.Table | None": + """ + Retrieve all records for a given source path as a single table with schema preservation. + + Args: + record_path: Tuple of path components + record_id_column: If not None or empty, record id is returned in the result with the specified column name + + Returns: + Arrow table containing all records with original schema, or None if no records found + """ + # TODO: this currently reads everything into memory and then return. Consider implementation that performs everything lazily + + if flush: + self.flush_batch(record_path) + self._validate_record_path(record_path) + + collected_tables = [] + if retrieve_pending: + # Check if there are pending records in the batch + for record_id, arrow_table in self._pending_batches[ + self._get_source_key(record_path) + ].items(): + collected_tables.append( + self._ensure_record_id_column(arrow_table, record_id) + ) + + delta_table = self._get_existing_delta_table(record_path) + if delta_table is not None: + try: + # Use filter-based read + result = self._read_table_with_filter(delta_table) + + if len(result) != 0: + collected_tables.append(result) + + except Exception as e: + logger.error( + f"Error getting all records from {'/'.join(record_path)}: {e}" + ) + if collected_tables: + total_table = pa.concat_tables(collected_tables) + + # Handle record_id column based on parameter + return self._handle_record_id_column(total_table, record_id_column) + + return None + + def get_records_by_ids( + self, + record_path: tuple[str, ...], + record_ids: "list[str] | pl.Series | pa.Array", + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": + """ + Retrieve records by entry IDs as a single table with schema preservation. + + Args: + record_path: Tuple of path components + record_ids: Entry IDs to retrieve + add_record_id_column: Control entry ID column inclusion + preserve_input_order: If True, return results in input order with nulls for missing + + Returns: + Arrow table containing all found records with original schema, or None if no records found + """ + + if flush: + self.flush_batch(record_path) + + self._validate_record_path(record_path) + + # Convert input to list of strings for consistency + if isinstance(record_ids, list): + if not record_ids: + return None + record_ids_list = record_ids + elif isinstance(record_ids, pl.Series): + if len(record_ids) == 0: + return None + record_ids_list = record_ids.to_list() + elif isinstance(record_ids, (pa.Array, pa.ChunkedArray)): + if len(record_ids) == 0: + return None + record_ids_list = record_ids.to_pylist() + else: + raise TypeError( + f"record_ids must be list[str], pl.Series, or pa.Array, got {type(record_ids)}" + ) + + delta_table = self._get_existing_delta_table(record_path) + if delta_table is None: + return None + + try: + # Use schema-preserving read with filters + filter_expr = self._create_record_ids_filter( + cast(list[str], record_ids_list) + ) + result = self._read_table_with_filter(delta_table, filters=filter_expr) + + if len(result) == 0: + return None + + # Handle record_id column based on parameter + return self._handle_record_id_column(result, record_id_column) + + except Exception as e: + logger.error( + f"Error getting records by IDs from {'/'.join(record_path)}: {e}" + ) + return None + + def get_pending_batch_info(self) -> dict[str, int]: + """ + Get information about pending batches. + + Returns: + Dictionary mapping source keys to number of pending records + """ + return { + source_key: len(tables) + for source_key, tables in self._pending_batches.items() + if tables + } + + def list_sources(self) -> list[tuple[str, ...]]: + """ + List all available source paths. + + Returns: + List of source path tuples + """ + sources = [] + + def _scan_directory(current_path: Path, path_components: tuple[str, ...]): + """Recursively scan for Delta tables.""" + for item in current_path.iterdir(): + if not item.is_dir(): + continue + + new_path_components = path_components + (item.name,) + + # Check if this directory contains a Delta table + try: + DeltaTable(str(item)) + sources.append(new_path_components) + except TableNotFoundError: + # Not a Delta table, continue scanning subdirectories + if len(new_path_components) < self.max_hierarchy_depth: + _scan_directory(item, new_path_components) + + _scan_directory(self.base_path, ()) + return sources + + def delete_source(self, record_path: tuple[str, ...]) -> bool: + """ + Delete an entire source (all records for a source path). + + Args: + record_path: Tuple of path components + + Returns: + True if source was deleted, False if it didn't exist + """ + self._validate_record_path(record_path) + + # Flush any pending batches first + self.flush_batch(record_path) + + table_path = self._get_table_path(record_path) + source_key = self._get_source_key(record_path) + + if not table_path.exists(): + return False + + try: + # Remove from caches + if source_key in self._delta_table_cache: + del self._delta_table_cache[source_key] + + # Remove directory + import shutil + + shutil.rmtree(table_path) + + logger.info(f"Deleted source {source_key}") + return True + + except Exception as e: + logger.error(f"Error deleting source {source_key}: {e}") + return False + + def delete_record(self, record_path: tuple[str, ...], record_id: str) -> bool: + """ + Delete a specific record. + + Args: + record_path: Tuple of path components + record_id: ID of the record to delete + + Returns: + True if record was deleted, False if it didn't exist + """ + self._validate_record_path(record_path) + + # Flush any pending batches first + self.flush_batch(record_path) + + delta_table = self._get_existing_delta_table(record_path) + if delta_table is None: + return False + + try: + # Check if record exists using proper filter + filter_expr = self._create_record_id_filter(record_id) + existing = self._read_table_with_filter(delta_table, filters=filter_expr) + if len(existing) == 0: + return False + + # Delete the record using SQL-style predicate (this is correct for delete operations) + delta_table.delete( + f"{self.RECORD_ID_COLUMN} = '{record_id.replace(chr(39), chr(39) + chr(39))}'" + ) + + # Update cache + source_key = self._get_source_key(record_path) + self._delta_table_cache[source_key] = delta_table + + logger.debug(f"Deleted record {record_id} from {'/'.join(record_path)}") + return True + + except Exception as e: + logger.error( + f"Error deleting record {record_id} from {'/'.join(record_path)}: {e}" + ) + return False + + def get_table_info(self, record_path: tuple[str, ...]) -> dict[str, Any] | None: + """ + Get metadata information about a Delta table. + + Args: + record_path: Tuple of path components + + Returns: + Dictionary with table metadata, or None if table doesn't exist + """ + self._validate_record_path(record_path) + + delta_table = self._get_existing_delta_table(record_path) + if delta_table is None: + return None + + try: + # Get basic info + schema = delta_table.schema() + history = delta_table.history() + source_key = self._get_source_key(record_path) + + # Add pending batch info + pending_info = self.get_pending_batch_info() + pending_count = pending_info.get(source_key, 0) + + return { + "path": str(self._get_table_path(record_path)), + "record_path": record_path, + "schema": schema, + "version": delta_table.version(), + "num_files": len(delta_table.files()), + "history_length": len(history), + "latest_commit": history[0] if history else None, + "pending_records": pending_count, + } + + except Exception as e: + logger.error(f"Error getting table info for {'/'.join(record_path)}: {e}") + return None diff --git a/src/orcapod/databases/delta_lake_databases.py b/src/orcapod/databases/delta_lake_databases.py new file mode 100644 index 0000000..ab02802 --- /dev/null +++ b/src/orcapod/databases/delta_lake_databases.py @@ -0,0 +1,869 @@ +import logging +from collections import defaultdict +from collections.abc import Collection, Mapping +from pathlib import Path +from typing import TYPE_CHECKING, Any, Literal, cast + +from deltalake import DeltaTable, write_deltalake +from deltalake.exceptions import TableNotFoundError + +from orcapod.core import constants +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import polars as pl + import pyarrow as pa + import pyarrow.compute as pc +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + pc = LazyModule("pyarrow.compute") + +# Module-level logger +logger = logging.getLogger(__name__) + + +class DeltaTableDatabase: + """ + A Delta table database with clear insert vs update semantics. + + - insert(): Never overwrites existing records by default. Can skip duplicates if requested. + Can be batched for performance. Supports composite keys. + + - update(): Always overwrites existing records. Executes immediately. + Requires pending batches to be flushed first (or use force_flush=True). + + Supports both single column and composite (multi-column) record IDs. + """ + + # Class constants for internal column names + ROW_INDEX_COLUMN = "__row_index" + RECORD_ID_COLUMN = "__record_id" + + def __init__( + self, + base_path: str | Path, + create_base_path: bool = True, + batch_size: int = 1000, + max_hierarchy_depth: int = 10, + ): + self.base_path = Path(base_path) + self.batch_size = batch_size + self.max_hierarchy_depth = max_hierarchy_depth + + if create_base_path: + self.base_path.mkdir(parents=True, exist_ok=True) + elif not self.base_path.exists(): + raise ValueError( + f"Base path {self.base_path} does not exist and create_base_path=False" + ) + + # Cache for Delta tables to avoid repeated initialization + self._delta_table_cache: dict[str, DeltaTable] = {} + + # Batch management + self._pending_batches: dict[str, pa.Table] = {} + self._pending_record_ids: dict[str, set[str]] = defaultdict(set) + self._existing_ids_cache: dict[str, set[str]] = defaultdict(set) + # TODO: reconsider this approach as this is NOT serializable + self._cache_dirty: dict[str, bool] = defaultdict(lambda: True) + + def _clear_pending(self): + """Clear all pending state.""" + self._pending_batches = {} + self._pending_record_ids = defaultdict(set) + # Note: next_row_index continues incrementing + + def _get_record_key(self, record_path: tuple[str, ...]) -> str: + """Generate cache key for source storage.""" + return "/".join(record_path) + + def _get_table_path(self, record_path: tuple[str, ...]) -> Path: + """Get the filesystem path for a given source path.""" + path = self.base_path + for subpath in record_path: + path = path / subpath + return path + + def _validate_record_path(self, record_path: tuple[str, ...]) -> None: + # TODO: consider removing this as path creation can be tried directly + """ + Validate source path components. + + Args: + record_path: Tuple of path components + + Raises: + ValueError: If path is invalid + """ + if not record_path: + raise ValueError("Source path cannot be empty") + + if len(record_path) > self.max_hierarchy_depth: + raise ValueError( + f"Source path depth {len(record_path)} exceeds maximum {self.max_hierarchy_depth}" + ) + + # Validate path components + for i, component in enumerate(record_path): + if not component or not isinstance(component, str): + raise ValueError( + f"Source path component {i} is invalid: {repr(component)}" + ) + + # Check for filesystem-unsafe characters + unsafe_chars = ["/", "\\", ":", "*", "?", '"', "<", ">", "|", "\0"] + if any(char in component for char in unsafe_chars): + raise ValueError( + f"Source path {record_path} component {component} contains invalid characters: {repr(component)}" + ) + + def _get_delta_table(self, record_path: tuple[str, ...]) -> DeltaTable | None: + """ + Get an existing Delta table, either from cache or by loading it. + + Args: + record_path: Tuple of path components + + Returns: + DeltaTable instance or None if table doesn't exist + """ + record_key = self._get_record_key(record_path) + table_path = self._get_table_path(record_path) + + # Check cache first + if dt := self._delta_table_cache.get(record_key): + return dt + + try: + # Try to load existing table + delta_table = DeltaTable(str(table_path)) + self._delta_table_cache[record_key] = delta_table + logger.debug(f"Loaded existing Delta table for {record_key}") + return delta_table + except TableNotFoundError: + # Table doesn't exist + return None + except Exception as e: + logger.error(f"Error loading Delta table for {record_key}: {e}") + # Try to clear any corrupted cache + if record_key in self._delta_table_cache: + self._delta_table_cache.pop(record_key) + raise + + def _ensure_record_id_column( + self, arrow_data: "pa.Table", record_id: str + ) -> "pa.Table": + """Ensure the table has an record id column.""" + if self.RECORD_ID_COLUMN not in arrow_data.column_names: + # Add record_id column at the beginning + key_array = pa.array([record_id] * len(arrow_data), type=pa.large_string()) + arrow_data = arrow_data.add_column(0, self.RECORD_ID_COLUMN, key_array) + return arrow_data + + def _remove_record_id_column(self, arrow_data: "pa.Table") -> "pa.Table": + """Remove the record id column if it exists.""" + if self.RECORD_ID_COLUMN in arrow_data.column_names: + arrow_data = arrow_data.drop([self.RECORD_ID_COLUMN]) + return arrow_data + + def _handle_record_id_column( + self, arrow_data: "pa.Table", record_id_column: str | None = None + ) -> "pa.Table": + """ + Handle record_id column based on add_record_id_column parameter. + + Args: + arrow_data: Arrow table with record id column + record_id_column: Control entry ID column inclusion: + + """ + if not record_id_column: + # Remove the record id column + return self._remove_record_id_column(arrow_data) + + # Rename record id column + if self.RECORD_ID_COLUMN in arrow_data.column_names: + schema = arrow_data.schema + new_names = [ + record_id_column if name == self.RECORD_ID_COLUMN else name + for name in schema.names + ] + return arrow_data.rename_columns(new_names) + else: + raise ValueError( + f"Record ID column '{self.RECORD_ID_COLUMN}' not found in the table and cannot be renamed." + ) + + def _create_record_id_filter(self, record_id: str) -> list: + """ + Create a proper filter expression for Delta Lake. + + Args: + record_id: The entry ID to filter by + + Returns: + List containing the filter expression for Delta Lake + """ + return [(self.RECORD_ID_COLUMN, "=", record_id)] + + def _create_record_ids_filter(self, record_ids: list[str]) -> list: + """ + Create a proper filter expression for multiple entry IDs. + + Args: + record_ids: List of entry IDs to filter by + + Returns: + List containing the filter expression for Delta Lake + """ + return [(self.RECORD_ID_COLUMN, "in", record_ids)] + + def _refresh_existing_ids_cache(self, record_path: tuple[str, ...]) -> None: + """Refresh the cache of existing IDs in the Delta table.""" + record_key = self._get_record_key(record_path) + + delta_table = self._get_delta_table(record_path) + + if delta_table is None: + self._existing_ids_cache[record_key] = set() + self._cache_dirty[record_key] = False + return + + try: + # Get all existing IDs from Delta table using standard RECORD_ID_COLUMN + # TODO: replace this with more targetted loading of only the target column and in batches + arrow_table = delta_table.to_pyarrow_table() + if arrow_table.num_rows == 0: + self._existing_ids_cache[record_key] = set() + elif self.RECORD_ID_COLUMN not in arrow_table.column_names: + # TODO: replace this with proper checking of the table schema first! + logger.warning(f"Delta table missing {self.RECORD_ID_COLUMN} column") + self._existing_ids_cache[record_key] = set() + else: + existing_ids = cast( + set[str], set(arrow_table[self.RECORD_ID_COLUMN].to_pylist()) + ) + self._existing_ids_cache[record_key] = existing_ids + + self._cache_dirty[record_key] = False + logger.debug( + f"Refreshed existing IDs cache: {len(self._existing_ids_cache)} IDs" + ) + + except Exception as e: + logger.error(f"Failed to refresh existing IDs cache: {e}") + self._existing_ids_cache[record_key] = set() + self._cache_dirty[record_key] = False + raise + + def _get_existing_ids(self, record_path: tuple[str, ...]) -> set[str]: + """Get the set of existing IDs in the Delta table, using cache when possible.""" + record_key = self._get_record_key(record_path) + if ( + self._cache_dirty.get(record_key) + or record_key not in self._delta_table_cache + ): + self._refresh_existing_ids_cache(record_path) + return self._existing_ids_cache.get(record_key) or set() + + def _invalidate_cache(self, record_path: tuple[str, ...]) -> None: + """Mark the existing IDs cache as dirty.""" + self._cache_dirty[self._get_record_key(record_path)] = True + + def add_record( + self, + record_path: tuple[str, ...], + record_id: str, + record: "pa.Table", + skip_duplicates: bool = False, + flush: bool = False, + schema_handling: Literal["merge", "error", "coerce"] = "error", + ) -> None: + data_with_record_id = self._ensure_record_id_column(record, record_id) + self.add_records( + record_path=record_path, + records=data_with_record_id, + record_id_column=self.RECORD_ID_COLUMN, + schema_handling=schema_handling, + skip_duplicates=skip_duplicates, + flush=flush, + ) + + def add_records( + self, + record_path: tuple[str, ...], + records: pa.Table, + record_id_column: str | None = None, + skip_duplicates: bool = False, + flush: bool = False, + schema_handling: Literal["merge", "error", "coerce"] = "error", + ) -> None: + """ + Insert new records. By default, never overwrites existing records. + + Args: + arrow_table: Arrow table to insert + id_columns: Single column name or list of column names that form the record ID. + For composite keys, values are concatenated with '|' separator. + schema_handling: How to handle schema differences + skip_duplicates: If True, skip records with IDs that already exist. + If False, raise error on duplicates. + flush: Whether to flush immediately after the insert + + Raises: + ValueError: If any record IDs already exist and skip_duplicates=False + """ + if records.num_rows == 0: + return + + if record_id_column is None: + record_id_column = records.column_names[0] + + # Step 1: Validate that record ID column exist + if record_id_column not in records.column_names: + raise ValueError( + f"Specified record ID column {record_id_column} not found in input table {records.column_names}" + ) + + # rename record ID column to a standard name + if record_id_column != self.RECORD_ID_COLUMN: + rename_map = {record_id_column: self.RECORD_ID_COLUMN} + total_name_map = {k: rename_map.get(k, k) for k in records.column_names} + records = records.rename_columns(total_name_map) + + # Step 2: Deduplicate within input table (keep last occurrence) + deduplicated_table = self._deduplicate_within_table(records) + + # Step 3: Handle conflicts based on skip_duplicates setting + if skip_duplicates: + filtered_table = self._filter_existing_records( + record_path, deduplicated_table + ) + if filtered_table.num_rows == 0: + logger.debug("All records were duplicates, nothing to insert") + return None + else: + # Check for conflicts - insert never allows duplicates when skip_duplicates=False + # self._check_all_conflicts(record_path, deduplicated_table) + filtered_table = deduplicated_table + + # Step 4: Handle schema compatibility + schema_compatible_table = self._handle_schema_compatibility( + record_path, filtered_table, schema_handling + ) + + # Step 5: Add to pending batch (no overwrite logic needed) + self._add_to_pending_batch(record_path, schema_compatible_table) + + # Step 6: Auto-flush if needed + if flush or self._should_auto_flush(record_path): + self.flush() + + def _deduplicate_within_table(self, table: pa.Table) -> pa.Table: + # TODO: consider erroring out if duplicates are found + """Remove duplicates within the input table, keeping the last occurrence.""" + if table.num_rows <= 1: + return table + + # Create row indices + indices = pa.array(range(table.num_rows)) + + # Add row index column temporarily + table_with_indices = table.add_column(0, self.ROW_INDEX_COLUMN, indices) + + # Group by RECORD_ID_COLUMN and get the maximum row index for each group + # This gives us the last occurrence of each ID + grouped = table_with_indices.group_by([self.RECORD_ID_COLUMN]).aggregate( + [(self.ROW_INDEX_COLUMN, "max")] + ) + + # Get the row indices to keep - the aggregated column name has "_max" suffix + max_indices_column = f"{self.ROW_INDEX_COLUMN}_max" + indices_to_keep = grouped[max_indices_column].to_pylist() + + # Filter original table to keep only these rows + mask = pc.is_in(indices, pa.array(indices_to_keep)) + return table.filter(mask) + + def _filter_existing_records( + self, record_path: tuple[str, ...], table: pa.Table + ) -> pa.Table: + """Filter out records that already exist (for skip_duplicates=True).""" + input_ids = set(table[self.RECORD_ID_COLUMN].to_pylist()) + record_key = self._get_record_key(record_path) + + # Get IDs that already exist in pending batch or Delta table + existing_in_pending = input_ids.intersection( + self._pending_record_ids[record_key] + ) + existing_in_delta = input_ids.intersection(self._get_existing_ids(record_path)) + all_existing = existing_in_pending.union(existing_in_delta) + + if not all_existing: + return table # No duplicates found + + # Filter out existing records + mask = pc.invert( + pc.is_in(table[self.RECORD_ID_COLUMN], pa.array(list(all_existing))) + ) + filtered = table.filter(mask) + + logger.debug(f"Skipped {len(all_existing)} duplicate records") + return filtered + + def _check_all_conflicts( + self, record_path: tuple[str, ...], table: pa.Table + ) -> None: + """Check for conflicts with both pending batch and Delta table.""" + input_ids = set(table[self.RECORD_ID_COLUMN].to_pylist()) + record_key = self._get_record_key(record_path) + # Check conflicts with pending batch + pending_conflicts = input_ids.intersection(self._pending_record_ids[record_key]) + if pending_conflicts: + raise ValueError( + f"Cannot insert records with IDs that already exist in pending batch: {pending_conflicts}. " + f"Use skip_duplicates=True to skip existing records or update() method to overwrite." + ) + + # Check conflicts with Delta table + existing_ids = self._get_existing_ids(record_path) + delta_conflicts = input_ids.intersection(existing_ids) + if delta_conflicts: + raise ValueError( + f"Cannot insert records with IDs that already exist in Delta table: {delta_conflicts}. " + f"Use skip_duplicates=True to skip existing records or update() method to overwrite." + ) + + def _handle_schema_compatibility( + self, record_path: tuple[str, ...], table: pa.Table, schema_handling: str + ) -> pa.Table: + """Handle schema differences between input and pending batch.""" + record_key = self._get_record_key(record_path) + pending_batch = self._pending_batches.get(record_key) + if pending_batch is None: + return table + + if pending_batch.schema.equals(table.schema): + # TODO: perform more careful check + return table + + if schema_handling == "error": + raise ValueError( + f"Schema mismatch between input {table.schema} and pending batch {pending_batch.schema}" + ) + elif schema_handling == "merge": + try: + # Unify schemas and cast both input table and pending batch + unified_schema = pa.unify_schemas([pending_batch.schema, table.schema]) + + # Cast the pending batch to unified schema (excluding tracking columns) + self._pending_batches[record_key] = pending_batch.cast(unified_schema) + + # Cast and return the input table + return table.cast(unified_schema) + except Exception as e: + # TODO: perform more careful error check + raise ValueError(f"Cannot merge schemas: {e}") + elif schema_handling == "coerce": + try: + # Coerce input table to match existing pending batch schema + return table.cast(pending_batch.schema) + except Exception as e: + raise ValueError(f"Cannot coerce schema: {e}") + else: + raise ValueError(f"Unknown schema handling: {schema_handling}") + + def _handle_delta_schema_compatibility( + self, record_path: tuple[str, ...], table: pa.Table, schema_handling: str + ) -> pa.Table: + """Handle schema differences between input and Delta table for updates.""" + record_key = self._get_record_key(record_path) + if self._delta_table_cache.get(record_key) is None: + return table + + delta_table = self._delta_table_cache[record_key] + + try: + # Get Delta table schema and convert from arro3 to pyarrow + arro3_schema = delta_table.schema().to_arrow() + delta_schema = pa.schema(arro3_schema) # type: ignore + except Exception as e: + logger.warning(f"Could not get Delta table schema: {e}") + return table + + if delta_schema.equals(table.schema): + return table + + if schema_handling == "error": + raise ValueError("Schema mismatch between input and Delta table") + elif schema_handling == "merge": + try: + # Unify schemas - this might require adding null columns + unified_schema = pa.unify_schemas([delta_schema, table.schema]) + return table.cast(unified_schema) + except Exception as e: + raise ValueError(f"Cannot merge schemas: {e}") + elif schema_handling == "coerce": + try: + # Coerce input table to match Delta table schema + return table.cast(delta_schema) + except Exception as e: + raise ValueError(f"Cannot coerce schema: {e}") + else: + raise ValueError(f"Unknown schema handling: {schema_handling}") + + def _add_to_pending_batch(self, record_path: tuple[str, ...], table: pa.Table): + """Add table to pending batch.""" + # Add row index column for internal tracking + record_key = self._get_record_key(record_path) + pending_batch = self._pending_batches.get(record_key) + if pending_batch is None: + self._pending_batches[record_key] = table + else: + self._pending_batches[record_key] = pa.concat_tables([pending_batch, table]) + + pending_ids = cast(list[str], table[self.RECORD_ID_COLUMN].to_pylist()) + self._pending_record_ids[record_key].update(pending_ids) + + def _should_auto_flush(self, record_path: tuple[str, ...]) -> bool: + """Check if auto-flush should be triggered.""" + record_key = self._get_record_key(record_path) + return ( + self._pending_batches.get(record_key) is not None + and self._pending_batches[record_key].num_rows >= self.batch_size + ) + + def get_all_records( + self, + record_path: tuple[str, ...], + record_id_column: str | None = None, + retrieve_pending: bool = True, + ) -> pa.Table | None: + """ + Get all records from both pending batch and Delta table. + + Returns: + Combined Arrow table with all records, or None if no records exist + """ + record_key = self._get_record_key(record_path) + + tables_to_combine = [] + + # Add Delta table data + if (delta_table := self._get_delta_table(record_path)) is not None: + try: + delta_table_data = delta_table.to_pyarrow_dataset( + as_large_types=True + ).to_table() + if delta_table_data.num_rows > 0: + tables_to_combine.append(delta_table_data) + except Exception as e: + logger.warning(f"Error reading Delta table: {e}") + + # Add pending batch data + if ( + retrieve_pending + and (pending_batch := self._pending_batches.get(record_key)) is not None + ): + if pending_batch.num_rows > 0: + tables_to_combine.append(pending_batch) + + if not tables_to_combine: + return None + + if len(tables_to_combine) == 1: + table_to_return = tables_to_combine[0] + else: + table_to_return = pa.concat_tables(tables_to_combine) + + # Handle record_id_column if specified + return self._handle_record_id_column(table_to_return, record_id_column) + + def get_records_with_column_value( + self, + record_path: tuple[str, ...], + column_values: Collection[tuple[str, Any]] | Mapping[str, Any], + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": + if flush: + self.flush_batch(record_path) + # check if record_id is found in pending batches + record_key = self._get_record_key(record_path) + pending_batch = self._pending_batches.get(record_key) + + if isinstance(column_values, Mapping): + # Convert Mapping to list of tuples + pair_list = list(column_values.items()) + elif isinstance(column_values, Collection): + # Ensure it's a list of tuples + pair_list = cast(list[tuple[str, Any]], list(column_values)) + + expressions = [pc.field(c) == v for c, v in pair_list] + combined_expression = expressions[0] + for next_expression in expressions[1:]: + combined_expression = combined_expression & next_expression + + if pending_batch is not None: + filtered_table = pending_batch.filter(combined_expression) + return self._handle_record_id_column(filtered_table, record_id_column) + + # Now check the Delta table + delta_table = self._get_delta_table(record_path) + if delta_table is None: + return None + + try: + # Use schema-preserving read + result = self._read_delta_table(delta_table, expression=combined_expression) + + if len(result) == 0: + return None + + # Handle (remove/rename) the record id column before returning + return self._handle_record_id_column(result, record_id_column) + + except Exception as e: + logger.error( + f"Error getting record with {column_values} from {'/'.join(record_path)}: {e}" + ) + raise e + + def get_record_by_id( + self, + record_path: tuple[str, ...], + record_id: str, + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": + """ + Get a specific record by record_id with schema preservation. + + Args: + record_path: Tuple of path components + record_id: Unique identifier for the record + + Returns: + Arrow table for the record or None if not found + """ + + if flush: + self.flush_batch(record_path) + + # check if record_id is found in pending batches + record_key = self._get_record_key(record_path) + if record_id in self._pending_record_ids[record_key]: + # Return the pending record after removing the entry id column + pending_batch = self._pending_batches[record_key] + assert pending_batch is not None, "Pending batch should not be None" + filtered_table = pending_batch.filter( + pc.field(self.RECORD_ID_COLUMN) == record_id + ) + if filtered_table.num_rows != 1: + raise ValueError( + f"Expected exactly one record in pending batch with record ID {record_id}, but found {filtered_table.num_rows}" + ) + return self._handle_record_id_column(filtered_table, record_id_column) + + # Now check the Delta table + delta_table = self._get_delta_table(record_path) + if delta_table is None: + return None + + try: + # Use schema-preserving read + filter_expr = self._create_record_id_filter(record_id) + result = self._read_delta_table(delta_table, filters=filter_expr) + + if len(result) == 0: + return None + + # Handle (remove/rename) the record id column before returning + return self._handle_record_id_column(result, record_id_column) + + except Exception as e: + logger.error( + f"Error getting record {record_id} from {'/'.join(record_path)}: {e}" + ) + raise e + + def get_records_by_ids( + self, + record_path: tuple[str, ...], + record_ids: "Collection[str] | pl.Series | pa.Array", + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": + """ + Retrieve records by entry IDs as a single table with schema preservation. + + Args: + record_path: Tuple of path components + record_ids: Entry IDs to retrieve + add_record_id_column: Control entry ID column inclusion + preserve_input_order: If True, return results in input order with nulls for missing + + Returns: + Arrow table containing all found records with original schema, or None if no records found + """ + _ = self._get_record_key(record_path) + if flush: + self.flush_batch(record_path) + + # Convert input to list of strings for consistency + + if isinstance(record_ids, pl.Series): + record_ids_list = cast(list[str], record_ids.to_list()) + elif isinstance(record_ids, (pa.Array, pa.ChunkedArray)): + record_ids_list = cast(list[str], record_ids.to_pylist()) + elif isinstance(record_ids, Collection): + record_ids_list = list(record_ids) + else: + raise TypeError( + f"record_ids must be list[str], pl.Series, or pa.Array, got {type(record_ids)}" + ) + if len(record_ids) == 0: + return None + + # check inside the batch + delta_table = self._get_delta_table(record_path) + if delta_table is None: + return None + try: + # Use schema-preserving read with filters + filter_expr = self._create_record_ids_filter(record_ids_list) + result = self._read_delta_table(delta_table, filters=filter_expr) + + if len(result) == 0: + return None + + # Handle record_id column based on parameter + return self._handle_record_id_column(result, record_id_column) + + except Exception as e: + logger.error( + f"Error getting records by IDs from {'/'.join(record_path)}: {e}" + ) + return None + + def _read_delta_table( + self, + delta_table: DeltaTable, + filters: list | None = None, + expression: "pc.Expression | None" = None, + ) -> "pa.Table": + """ + Read table using to_pyarrow_dataset with original schema preservation. + + Args: + delta_table: The Delta table to read from + filters: Optional filters to apply + + Returns: + Arrow table with preserved schema + """ + filter_expr = None + # Use to_pyarrow_dataset with as_large_types for Polars compatible arrow table loading + dataset = delta_table.to_pyarrow_dataset(as_large_types=True) + if filters and expression is None: + for filt in filters: + if len(filt) == 3: + col, op, val = filt + if op == "=": + expr = pc.equal(pc.field(col), pa.scalar(val)) # type: ignore + elif op == "in": + expr = pc.is_in(pc.field(col), pa.array(val)) # type: ignore + else: + logger.warning( + f"Unsupported filter operation: {op}. Falling back to table-level filter application which may be less efficient." + ) + # Fallback to table-level filtering + return dataset.to_table()(filters=filters) + + if filter_expr is None: + filter_expr = expr + else: + filter_expr = pc.and_(filter_expr, expr) # type: ignore + elif expression is not None: + filter_expr = expression + + if filter_expr is not None: + return dataset.to_table(filter=filter_expr) + + return dataset.to_table() + + def flush(self) -> None: + """Flush all pending batches.""" + # TODO: capture and re-raise exceptions at the end + for record_key in list(self._pending_batches.keys()): + record_path = tuple(record_key.split("/")) + try: + self.flush_batch(record_path) + except Exception as e: + logger.error(f"Error flushing batch for {record_key}: {e}") + + def flush_batch(self, record_path: tuple[str, ...]) -> None: + """ + Flush pending batch for a specific source path. + + Args: + record_path: Tuple of path components + """ + logger.debug("Flushing triggered!!") + record_key = self._get_record_key(record_path) + + if ( + record_key not in self._pending_batches + or not self._pending_batches[record_key] + ): + return + + # Get all pending records + pending_batch = self._pending_batches.pop(record_key) + pending_ids = self._pending_record_ids.pop(record_key) + + try: + # Combine all tables in the batch + combined_table = pending_batch.combine_chunks() + + table_path = self._get_table_path(record_path) + table_path.mkdir(parents=True, exist_ok=True) + + # Check if table exists + delta_table = self._get_delta_table(record_path) + + if delta_table is None: + # TODO: reconsider mode="overwrite" here + write_deltalake( + table_path, + combined_table, + mode="overwrite", + ) + logger.debug( + f"Created new Delta table for {record_key} with {len(combined_table)} records" + ) + else: + delta_table.merge( + source=combined_table, + predicate=f"target.{self.RECORD_ID_COLUMN} = source.{self.RECORD_ID_COLUMN}", + source_alias="source", + target_alias="target", + ).when_not_matched_insert_all().execute() + + logger.debug( + f"Appended batch of {len(combined_table)} records to {record_key}" + ) + + # Update cache + self._delta_table_cache[record_key] = DeltaTable(str(table_path)) + + # invalide record id cache + self._invalidate_cache(record_path) + + except Exception as e: + logger.error(f"Error flushing batch for {record_key}: {e}") + # Put the tables back in the pending queue + self._pending_batches[record_key] = pending_batch + self._pending_record_ids[record_key] = pending_ids + raise diff --git a/src/orcapod/databases/file_utils.py b/src/orcapod/databases/file_utils.py new file mode 100644 index 0000000..8fb6191 --- /dev/null +++ b/src/orcapod/databases/file_utils.py @@ -0,0 +1,434 @@ +# # file_ops.py - Atomic file operations module + +# import builtins +# import contextlib +# import inspect +# import logging +# import os +# from pathlib import Path + +# from orcapod.types import PathLike, PathSet, PacketLike +# from collections.abc import Collection, Callable + + +# logger = logging.getLogger(__name__) + + +# def atomic_write(file_path: PathLike, content: str) -> Path: +# """ +# Atomically write content to a file. + +# This function writes content to a temporary file and then atomically +# renames it to the target file path, ensuring that other processes never +# see a partially-written file. + +# Args: +# file_path: Target file path +# content: Content to write + +# Returns: +# Path object to the written file + +# Raises: +# OSError: If the file cannot be written +# """ +# file_path = Path(file_path) +# temp_path = file_path.with_name(f"{file_path.name}.tmp{os.getpid()}") + +# # Ensure parent directory exists +# file_path.parent.mkdir(parents=True, exist_ok=True) + +# try: +# # Write content to a temporary file +# with open(temp_path, "w") as f: +# f.write(content) +# f.flush() +# os.fsync(f.fileno()) # Force flush to disk + +# # Atomic rename +# os.rename(temp_path, file_path) +# return file_path +# except Exception as e: +# logger.error(f"Error writing file {file_path}: {str(e)}") +# raise +# finally: +# # Clean up the temporary file if it exists +# if temp_path.exists(): +# temp_path.unlink(missing_ok=True) + + +# def atomic_write_bytes(file_path: PathLike, content: bytes) -> Path: +# """ +# Atomically write binary content to a file. + +# This function writes binary content to a temporary file and then atomically +# renames it to the target file path. + +# Args: +# file_path: Target file path +# content: Binary content to write + +# Returns: +# Path object to the written file + +# Raises: +# OSError: If the file cannot be written +# """ +# file_path = Path(file_path) +# temp_path = file_path.with_name(f"{file_path.name}.tmp{os.getpid()}") + +# # Ensure parent directory exists +# file_path.parent.mkdir(parents=True, exist_ok=True) + +# try: +# # Write content to a temporary file +# with open(temp_path, "wb") as f: +# f.write(content) +# f.flush() +# os.fsync(f.fileno()) # Force flush to disk + +# # Atomic rename +# os.rename(temp_path, file_path) +# return file_path +# except Exception as e: +# logger.error(f"Error writing file {file_path}: {str(e)}") +# raise +# finally: +# # Clean up the temporary file if it exists +# if temp_path.exists(): +# temp_path.unlink(missing_ok=True) + + +# def atomic_copy(source_path: PathLike, dest_path: PathLike) -> Path: +# """ +# Atomically copy a file. + +# This function copies a file to a temporary location and then atomically +# renames it to the target path, ensuring that other processes never +# see a partially-copied file. + +# Args: +# source_path: Source file path +# dest_path: Destination file path + +# Returns: +# Path object to the copied file + +# Raises: +# OSError: If the file cannot be copied +# FileNotFoundError: If the source file does not exist +# """ +# import shutil + +# source_path = Path(source_path) +# dest_path = Path(dest_path) +# temp_path = dest_path.with_name(f"{dest_path.name}.tmp{os.getpid()}") + +# # Check if source exists +# if not source_path.exists(): +# raise FileNotFoundError(f"Source file does not exist: {source_path}") + +# # Ensure parent directory exists +# dest_path.parent.mkdir(parents=True, exist_ok=True) + +# try: +# # Copy to temporary file +# shutil.copy2(source_path, temp_path) + +# # Ensure the data is written to disk +# with open(temp_path, "a") as f: +# os.fsync(f.fileno()) + +# # Atomic rename +# os.rename(temp_path, dest_path) +# return dest_path +# except Exception as e: +# logger.error(f"Error copying file from {source_path} to {dest_path}: {str(e)}") +# raise +# finally: +# # Clean up the temporary file if it exists +# if temp_path.exists(): +# temp_path.unlink(missing_ok=True) + + +# def atomic_append(file_path: PathLike, content: str) -> Path: +# """ +# Atomically append content to a file. + +# This function reads the existing content, appends the new content, +# and then atomically writes the result back to the file. + +# Args: +# file_path: Target file path +# content: Content to append + +# Returns: +# Path object to the appended file + +# Raises: +# OSError: If the file cannot be written +# """ +# file_path = Path(file_path) + +# # Read existing content if file exists +# existing_content = "" +# if file_path.exists(): +# try: +# with open(file_path, "r") as f: +# existing_content = f.read() +# except Exception as e: +# logger.error(f"Error reading file {file_path} for append: {str(e)}") +# raise + +# # Write the combined content atomically +# return atomic_write(file_path, existing_content + content) + + +# def atomic_replace( +# file_path: PathLike, pattern: str, replacement: str, count: int = -1 +# ) -> tuple[Path, int]: +# """ +# Atomically replace text in a file. + +# This function reads the existing content, performs the replacement, +# and then atomically writes the result back to the file. + +# Args: +# file_path: Target file path +# pattern: Pattern to replace +# replacement: Replacement text +# count: Maximum number of replacements (default: unlimited) + +# Returns: +# Tuple of (Path object to the file, number of replacements made) + +# Raises: +# OSError: If the file cannot be read or written +# FileNotFoundError: If the file does not exist +# """ +# file_path = Path(file_path) + +# # Check if file exists +# if not file_path.exists(): +# raise FileNotFoundError(f"File does not exist: {file_path}") + +# # Read existing content +# try: +# with open(file_path, "r") as f: +# existing_content = f.read() +# except Exception as e: +# logger.error(f"Error reading file {file_path} for replacement: {str(e)}") +# raise + +# # Perform replacement +# new_content, num_replacements = existing_content, 0 +# if count == -1: +# # Replace all occurrences +# new_content = existing_content.replace(pattern, replacement) +# num_replacements = existing_content.count(pattern) +# else: +# # Replace only up to count occurrences +# new_content = "" +# remaining = existing_content +# for _ in range(count): +# if pattern not in remaining: +# break +# pos = remaining.find(pattern) +# new_content += remaining[:pos] + replacement +# remaining = remaining[pos + len(pattern) :] +# num_replacements += 1 +# new_content += remaining + +# # Write the new content atomically +# return atomic_write(file_path, new_content), num_replacements + + +# def is_file_locked(file_path: PathLike) -> bool: +# """ +# Check if a file is locked. + +# This function attempts to open the file for writing in non-blocking mode +# and checks if it fails with a "resource temporarily unavailable" error. + +# Args: +# file_path: File path to check + +# Returns: +# True if the file is locked, False otherwise +# """ +# import errno +# import fcntl + +# file_path = Path(file_path) + +# # If file doesn't exist, it's not locked +# if not file_path.exists(): +# return False + +# try: +# # Try to open the file and get an exclusive lock in non-blocking mode +# with open(file_path, "r+") as f: +# fcntl.flock(f, fcntl.LOCK_EX | fcntl.LOCK_NB) +# # If we get here, the file is not locked +# fcntl.flock(f, fcntl.LOCK_UN) +# return False +# except IOError as e: +# if e.errno == errno.EAGAIN: +# # Resource temporarily unavailable = the file is locked +# return True +# # Some other error - assume not locked +# return False +# except Exception: +# # Any other exception - assume not locked +# return False + + +# @contextlib.contextmanager +# def redirect_open( +# mapping: dict[str, str] | Callable[[str], str | None], +# ): +# """ +# Context manager to intercept file opening operations. + +# Args: +# mapping: Either a dictionary mapping original paths to their replacements, +# or a function that takes a path string and returns a replacement path +# (or None to indicate the file should not be opened). + +# Raises: +# FileNotFoundError: If using a dictionary and the path is not found in it. +# """ +# # Track all places that might store an open() function +# places_to_patch = [] + +# # 1. Standard builtins.open +# original_builtin_open = builtins.open +# places_to_patch.append((builtins, "open", original_builtin_open)) + +# # 2. __builtins__ (could be different in some contexts, especially IPython) +# if isinstance(__builtins__, dict) and "open" in __builtins__: +# places_to_patch.append((__builtins__, "open", __builtins__["open"])) + +# # 3. Current module's globals (for the calling namespace) +# current_frame = inspect.currentframe() +# if current_frame is not None: +# caller_globals = current_frame.f_back.f_globals if current_frame.f_back else {} +# if "open" in caller_globals: +# places_to_patch.append((caller_globals, "open", caller_globals["open"])) + +# # 4. Check for IPython user namespace +# try: +# import IPython + +# ip = IPython.get_ipython() # type: ignore +# if ip and "open" in ip.user_ns: +# places_to_patch.append((ip.user_ns, "open", ip.user_ns["open"])) +# except (ImportError, AttributeError): +# pass + +# def patched_open(file, *args, **kwargs): +# # Convert PathLike objects to string if needed +# if hasattr(file, "__fspath__"): +# file_path = os.fspath(file) +# else: +# file_path = str(file) + +# if isinstance(mapping, dict): +# if file_path in mapping: +# redirected_path = mapping[file_path] +# print(f"Redirecting '{file_path}' to '{redirected_path}'") +# return original_builtin_open(redirected_path, *args, **kwargs) +# else: +# raise FileNotFoundError( +# f"Path '{file_path}' not found in redirection mapping" +# ) +# else: # mapping is a function +# redirected_path = mapping(file_path) +# if redirected_path is not None: +# print(f"Redirecting '{file_path}' to '{redirected_path}'") +# return original_builtin_open(redirected_path, *args, **kwargs) +# else: +# raise FileNotFoundError(f"Path '{file_path}' could not be redirected") + +# # Apply the patch to all places +# for obj, attr, _ in places_to_patch: +# if isinstance(obj, dict): +# obj[attr] = patched_open +# else: +# setattr(obj, attr, patched_open) + +# try: +# yield +# finally: +# # Restore all original functions +# for obj, attr, original in places_to_patch: +# if isinstance(obj, dict): +# obj[attr] = original +# else: +# setattr(obj, attr, original) + + +# def virtual_mount( +# packet: PacketLike, +# ) -> tuple[PacketLike, dict[str, str], dict[str, str]]: +# """ +# Visit all pathset within the packet, and convert them to alternative path +# representation. By default, full path is mapped to the file name. If two or +# more paths have the same file name, the second one is suffixed with "_1", the +# third one with "_2", etc. This is useful for creating a virtual mount point +# for a set of files, where the original paths are not important, but the file +# names can be used to identify the files. +# """ +# forward_lut = {} # mapping from original path to new path +# reverse_lut = {} # mapping from new path to original path +# new_packet = {} + +# for key, value in packet.items(): +# new_packet[key] = convert_pathset(value, forward_lut, reverse_lut) # type: ignore + +# return new_packet, forward_lut, reverse_lut + + +# # TODO: re-assess the structure of PathSet and consider making it recursive +# def convert_pathset(pathset: PathSet, forward_lut, reverse_lut) -> PathSet: +# """ +# Convert a pathset to a new pathset. forward_lut and reverse_lut are updated +# with the new paths. The new paths are created by replacing the original paths +# with the new paths in the forward_lut. The reverse_lut is updated with the +# original paths. If name already exists, a suffix is added to the new name to avoid +# collisions. +# """ +# if isinstance(pathset, (str, bytes)): +# new_name = Path(pathset).name +# if new_name in reverse_lut: +# # if the name already exists, add a suffix +# i = 1 +# while f"{new_name}_{i}" in reverse_lut: +# i += 1 +# new_name = f"{new_name}_{i}" +# forward_lut[pathset] = new_name +# reverse_lut[new_name] = pathset +# return new_name +# elif isinstance(pathset, Collection): +# return [convert_pathset(p, forward_lut, reverse_lut) for p in pathset] # type: ignore +# else: +# raise ValueError( +# f"Unsupported pathset type: {type(pathset)}. Expected str, bytes, or Collection." +# ) + + +# class WrappedPath: +# def __init__(self, path, name=None): +# self.path = Path(path) +# if name is None: +# name = self.path.name +# self.name = name + +# def __fspath__(self) -> str | bytes: +# return self.path.__fspath__() + +# def __str__(self) -> str: +# return self.name + +# def __repr__(self) -> str: +# return f"WrappedPath({self.path}): {self.name}" diff --git a/src/orcapod/databases/legacy/delta_table_arrow_data_store.py b/src/orcapod/databases/legacy/delta_table_arrow_data_store.py new file mode 100644 index 0000000..56bbbfa --- /dev/null +++ b/src/orcapod/databases/legacy/delta_table_arrow_data_store.py @@ -0,0 +1,864 @@ +import pyarrow as pa +import pyarrow.compute as pc +import pyarrow.dataset as ds +import polars as pl +from pathlib import Path +from typing import Any +import logging +from deltalake import DeltaTable, write_deltalake +from deltalake.exceptions import TableNotFoundError +from collections import defaultdict + + +# Module-level logger +logger = logging.getLogger(__name__) + + +class DeltaTableArrowDataStore: + """ + Delta Table-based Arrow data store with flexible hierarchical path support and schema preservation. + + Uses tuple-based source paths for robust parameter handling: + - ("source_name", "source_id") -> source_name/source_id/ + - ("org", "project", "dataset") -> org/project/dataset/ + - ("year", "month", "day", "experiment") -> year/month/day/experiment/ + """ + + def __init__( + self, + base_path: str | Path, + duplicate_entry_behavior: str = "error", + create_base_path: bool = True, + max_hierarchy_depth: int = 10, + batch_size: int = 100, + ): + """ + Initialize the DeltaTableArrowDataStore. + + Args: + base_path: Base directory path where Delta tables will be stored + duplicate_entry_behavior: How to handle duplicate entry_ids: + - 'error': Raise ValueError when entry_id already exists + - 'overwrite': Replace existing entry with new data + create_base_path: Whether to create the base path if it doesn't exist + max_hierarchy_depth: Maximum allowed depth for source paths (safety limit) + batch_size: Number of records to batch before writing to Delta table + auto_flush_interval: Time in seconds to auto-flush pending batches (0 to disable) + """ + # Validate duplicate behavior + if duplicate_entry_behavior not in ["error", "overwrite"]: + raise ValueError("duplicate_entry_behavior must be 'error' or 'overwrite'") + + self.duplicate_entry_behavior = duplicate_entry_behavior + self.base_path = Path(base_path) + self.max_hierarchy_depth = max_hierarchy_depth + self.batch_size = batch_size + + if create_base_path: + self.base_path.mkdir(parents=True, exist_ok=True) + elif not self.base_path.exists(): + raise ValueError( + f"Base path {self.base_path} does not exist and create_base_path=False" + ) + + # Cache for Delta tables to avoid repeated initialization + self._delta_table_cache: dict[str, DeltaTable] = {} + + # Batch management + self._pending_batches: dict[str, dict[str, pa.Table]] = defaultdict(dict) + + logger.info( + f"Initialized DeltaTableArrowDataStore at {self.base_path} " + f"with duplicate_entry_behavior='{duplicate_entry_behavior}', " + f"batch_size={batch_size}, as" + ) + + def flush(self) -> None: + """ + Flush all pending batches immediately. + + This method is called to ensure all pending data is written to the Delta tables. + """ + try: + self.flush_all_batches() + except Exception as e: + logger.error(f"Error during flush: {e}") + + def flush_batch(self, source_path: tuple[str, ...]) -> None: + """ + Flush pending batch for a specific source path. + + Args: + source_path: Tuple of path components + """ + logger.debug("Flushing triggered!!") + source_key = self._get_source_key(source_path) + + if ( + source_key not in self._pending_batches + or not self._pending_batches[source_key] + ): + return + + # Get all pending records + pending_tables = self._pending_batches[source_key] + self._pending_batches[source_key] = {} + + try: + # Combine all tables in the batch + combined_table = pa.concat_tables(pending_tables.values()).combine_chunks() + + table_path = self._get_table_path(source_path) + table_path.mkdir(parents=True, exist_ok=True) + + # Check if table exists + delta_table = self._get_existing_delta_table(source_path) + + if delta_table is None: + # TODO: reconsider mode="overwrite" here + write_deltalake( + table_path, + combined_table, + mode="overwrite", + ) + logger.debug( + f"Created new Delta table for {source_key} with {len(combined_table)} records" + ) + else: + if self.duplicate_entry_behavior == "overwrite": + # Get entry IDs from the batch + entry_ids = combined_table.column("__entry_id").to_pylist() + unique_entry_ids = list(set(entry_ids)) + + # Delete existing records with these IDs + if unique_entry_ids: + entry_ids_str = "', '".join(unique_entry_ids) + delete_predicate = f"__entry_id IN ('{entry_ids_str}')" + try: + delta_table.delete(delete_predicate) + logger.debug( + f"Deleted {len(unique_entry_ids)} existing records from {source_key}" + ) + except Exception as e: + logger.debug( + f"No existing records to delete from {source_key}: {e}" + ) + + # otherwise, only insert if same entry_id does not exist yet + delta_table.merge( + source=combined_table, + predicate="target.__entry_id = source.__entry_id", + source_alias="source", + target_alias="target", + ).when_not_matched_insert_all().execute() + + logger.debug( + f"Appended batch of {len(combined_table)} records to {source_key}" + ) + + # Update cache + self._delta_table_cache[source_key] = DeltaTable(str(table_path)) + + except Exception as e: + logger.error(f"Error flushing batch for {source_key}: {e}") + # Put the tables back in the pending queue + self._pending_batches[source_key] = pending_tables + raise + + def flush_all_batches(self) -> None: + """Flush all pending batches.""" + source_keys = list(self._pending_batches.keys()) + + # TODO: capture and re-raise exceptions at the end + for source_key in source_keys: + source_path = tuple(source_key.split("/")) + try: + self.flush_batch(source_path) + except Exception as e: + logger.error(f"Error flushing batch for {source_key}: {e}") + + def __del__(self): + """Cleanup when object is destroyed.""" + self.flush() + + def _validate_source_path(self, source_path: tuple[str, ...]) -> None: + # TODO: consider removing this as path creation can be tried directly + """ + Validate source path components. + + Args: + source_path: Tuple of path components + + Raises: + ValueError: If path is invalid + """ + if not source_path: + raise ValueError("Source path cannot be empty") + + if len(source_path) > self.max_hierarchy_depth: + raise ValueError( + f"Source path depth {len(source_path)} exceeds maximum {self.max_hierarchy_depth}" + ) + + # Validate path components + for i, component in enumerate(source_path): + if not component or not isinstance(component, str): + raise ValueError( + f"Source path component {i} is invalid: {repr(component)}" + ) + + # Check for filesystem-unsafe characters + unsafe_chars = ["/", "\\", ":", "*", "?", '"', "<", ">", "|", "\0"] + if any(char in component for char in unsafe_chars): + raise ValueError( + f"Source path component contains invalid characters: {repr(component)}" + ) + + def _get_source_key(self, source_path: tuple[str, ...]) -> str: + """Generate cache key for source storage.""" + return "/".join(source_path) + + def _get_table_path(self, source_path: tuple[str, ...]) -> Path: + """Get the filesystem path for a given source path.""" + path = self.base_path + for subpath in source_path: + path = path / subpath + return path + + def _get_existing_delta_table( + self, source_path: tuple[str, ...] + ) -> DeltaTable | None: + """ + Get or create a Delta table, handling schema initialization properly. + + Args: + source_path: Tuple of path components + + Returns: + DeltaTable instance or None if table doesn't exist + """ + source_key = self._get_source_key(source_path) + table_path = self._get_table_path(source_path) + + # Check cache first + if dt := self._delta_table_cache.get(source_key): + return dt + + try: + # Try to load existing table + delta_table = DeltaTable(str(table_path)) + self._delta_table_cache[source_key] = delta_table + logger.debug(f"Loaded existing Delta table for {source_key}") + return delta_table + except TableNotFoundError: + # Table doesn't exist + return None + except Exception as e: + logger.error(f"Error loading Delta table for {source_key}: {e}") + # Try to clear any corrupted cache and retry once + if source_key in self._delta_table_cache: + del self._delta_table_cache[source_key] + return None + + def _ensure_entry_id_column(self, arrow_data: pa.Table, entry_id: str) -> pa.Table: + """Ensure the table has an __entry_id column.""" + if "__entry_id" not in arrow_data.column_names: + # Add entry_id column at the beginning + key_array = pa.array([entry_id] * len(arrow_data), type=pa.large_string()) + arrow_data = arrow_data.add_column(0, "__entry_id", key_array) + return arrow_data + + def _remove_entry_id_column(self, arrow_data: pa.Table) -> pa.Table: + """Remove the __entry_id column if it exists.""" + if "__entry_id" in arrow_data.column_names: + column_names = arrow_data.column_names + indices_to_keep = [ + i for i, name in enumerate(column_names) if name != "__entry_id" + ] + arrow_data = arrow_data.select(indices_to_keep) + return arrow_data + + def _handle_entry_id_column( + self, arrow_data: pa.Table, add_entry_id_column: bool | str = False + ) -> pa.Table: + """ + Handle entry_id column based on add_entry_id_column parameter. + + Args: + arrow_data: Arrow table with __entry_id column + add_entry_id_column: Control entry ID column inclusion: + - False: Remove __entry_id column + - True: Keep __entry_id column as is + - str: Rename __entry_id column to custom name + """ + if add_entry_id_column is False: + # Remove the __entry_id column + return self._remove_entry_id_column(arrow_data) + elif isinstance(add_entry_id_column, str): + # Rename __entry_id to custom name + if "__entry_id" in arrow_data.column_names: + schema = arrow_data.schema + new_names = [ + add_entry_id_column if name == "__entry_id" else name + for name in schema.names + ] + return arrow_data.rename_columns(new_names) + # If add_entry_id_column is True, keep __entry_id as is + return arrow_data + + def _create_entry_id_filter(self, entry_id: str) -> list: + """ + Create a proper filter expression for Delta Lake. + + Args: + entry_id: The entry ID to filter by + + Returns: + List containing the filter expression for Delta Lake + """ + return [("__entry_id", "=", entry_id)] + + def _create_entry_ids_filter(self, entry_ids: list[str]) -> list: + """ + Create a proper filter expression for multiple entry IDs. + + Args: + entry_ids: List of entry IDs to filter by + + Returns: + List containing the filter expression for Delta Lake + """ + return [("__entry_id", "in", entry_ids)] + + def _read_table_with_filter( + self, + delta_table: DeltaTable, + filters: list | None = None, + ) -> pa.Table: + """ + Read table using to_pyarrow_dataset with original schema preservation. + + Args: + delta_table: The Delta table to read from + filters: Optional filters to apply + + Returns: + Arrow table with preserved schema + """ + # Use to_pyarrow_dataset with as_large_types for Polars compatible arrow table loading + dataset: ds.Dataset = delta_table.to_pyarrow_dataset(as_large_types=True) + if filters: + # Apply filters at dataset level for better performance + import pyarrow.compute as pc + + filter_expr = None + for filt in filters: + if len(filt) == 3: + col, op, val = filt + if op == "=": + expr = pc.equal(pc.field(col), pa.scalar(val)) # type: ignore + elif op == "in": + expr = pc.is_in(pc.field(col), pa.array(val)) # type: ignore + else: + logger.warning( + f"Unsupported filter operation: {op}. Falling back to table-level filter application which may be less efficient." + ) + # Fallback to table-level filtering + return dataset.to_table()(filters=filters) + + if filter_expr is None: + filter_expr = expr + else: + filter_expr = pc.and_(filter_expr, expr) # type: ignore + + if filter_expr is not None: + return dataset.to_table(filter=filter_expr) + + return dataset.to_table() + + def add_record( + self, + source_path: tuple[str, ...], + entry_id: str, + arrow_data: pa.Table, + force_flush: bool = False, + ) -> pa.Table: + """ + Add a record to the Delta table (batched). + + Args: + source_path: Tuple of path components (e.g., ("org", "project", "dataset")) + entry_id: Unique identifier for this record + arrow_data: The Arrow table data to store + ignore_duplicate: If True, ignore duplicate entry error + force_flush: If True, immediately flush this record to disk + + Returns: + The Arrow table data that was stored + + Raises: + ValueError: If entry_id already exists and duplicate_entry_behavior is 'error' + """ + self._validate_source_path(source_path) + source_key = self._get_source_key(source_path) + + # Check for existing entry + if self.duplicate_entry_behavior == "error": + # Only check existing table, not pending batch for performance + pending_table = self._pending_batches[source_key].get(entry_id, None) + if pending_table is not None: + raise ValueError( + f"Entry '{entry_id}' already exists in pending batch for {source_key}. " + f"Use duplicate_entry_behavior='overwrite' to allow updates." + ) + existing_record = self.get_record(source_path, entry_id, flush=False) + if existing_record is not None: + raise ValueError( + f"Entry '{entry_id}' already exists in {'/'.join(source_path)}. " + f"Use duplicate_entry_behavior='overwrite' to allow updates." + ) + + # Add entry_id column to the data + data_with_entry_id = self._ensure_entry_id_column(arrow_data, entry_id) + + if force_flush: + # Write immediately + table_path = self._get_table_path(source_path) + table_path.mkdir(parents=True, exist_ok=True) + + delta_table = self._get_existing_delta_table(source_path) + + if delta_table is None: + # Create new table - save original schema first + write_deltalake(str(table_path), data_with_entry_id, mode="overwrite") + logger.debug(f"Created new Delta table for {source_key}") + else: + if self.duplicate_entry_behavior == "overwrite": + try: + delta_table.delete( + f"__entry_id = '{entry_id.replace(chr(39), chr(39) + chr(39))}'" + ) + logger.debug( + f"Deleted existing record {entry_id} from {source_key}" + ) + except Exception as e: + logger.debug( + f"No existing record to delete for {entry_id}: {e}" + ) + + write_deltalake( + table_path, + data_with_entry_id, + mode="append", + schema_mode="merge", + ) + + # Update cache + self._delta_table_cache[source_key] = DeltaTable(str(table_path)) + else: + # Add to the batch for later flushing + self._pending_batches[source_key][entry_id] = data_with_entry_id + batch_size = len(self._pending_batches[source_key]) + + # Check if we need to flush + if batch_size >= self.batch_size: + self.flush_batch(source_path) + + logger.debug(f"Added record {entry_id} to {source_key}") + return arrow_data + + def get_pending_batch_info(self) -> dict[str, int]: + """ + Get information about pending batches. + + Returns: + Dictionary mapping source keys to number of pending records + """ + return { + source_key: len(tables) + for source_key, tables in self._pending_batches.items() + if tables + } + + def get_record( + self, source_path: tuple[str, ...], entry_id: str, flush: bool = False + ) -> pa.Table | None: + """ + Get a specific record by entry_id with schema preservation. + + Args: + source_path: Tuple of path components + entry_id: Unique identifier for the record + + Returns: + Arrow table for the record or None if not found + """ + if flush: + self.flush_batch(source_path) + self._validate_source_path(source_path) + + # check if entry_id is found in pending batches + source_key = self._get_source_key(source_path) + if entry_id in self._pending_batches[source_key]: + # Return the pending record directly + return self._pending_batches[source_key][entry_id] + + delta_table = self._get_existing_delta_table(source_path) + if delta_table is None: + return None + + try: + # Use schema-preserving read + filter_expr = self._create_entry_id_filter(entry_id) + result = self._read_table_with_filter(delta_table, filters=filter_expr) + + if len(result) == 0: + return None + + # Remove the __entry_id column before returning + return self._remove_entry_id_column(result) + + except Exception as e: + logger.error( + f"Error getting record {entry_id} from {'/'.join(source_path)}: {e}" + ) + raise e + + def get_all_records( + self, + source_path: tuple[str, ...], + add_entry_id_column: bool | str = False, + retrieve_pending: bool = True, + flush: bool = False, + ) -> pa.Table | None: + """ + Retrieve all records for a given source path as a single table with schema preservation. + + Args: + source_path: Tuple of path components + add_entry_id_column: Control entry ID column inclusion: + - False: Don't include entry ID column (default) + - True: Include entry ID column as "__entry_id" + - str: Include entry ID column with custom name + + Returns: + Arrow table containing all records with original schema, or None if no records found + """ + if flush: + self.flush_batch(source_path) + self._validate_source_path(source_path) + + collected_arrays = [] + if retrieve_pending: + # Check if there are pending records in the batch + for entry_id, arrow_table in self._pending_batches[ + self._get_source_key(source_path) + ].items(): + collected_arrays.append( + self._ensure_entry_id_column(arrow_table, entry_id) + ) + + delta_table = self._get_existing_delta_table(source_path) + if delta_table is not None: + try: + # Use filter-based read + result = self._read_table_with_filter(delta_table) + + if len(result) != 0: + collected_arrays.append(result) + + except Exception as e: + logger.error( + f"Error getting all records from {'/'.join(source_path)}: {e}" + ) + if collected_arrays: + total_table = pa.Table.concatenate(collected_arrays) + + # Handle entry_id column based on parameter + return self._handle_entry_id_column(total_table, add_entry_id_column) + + return None + + def get_all_records_as_polars( + self, source_path: tuple[str, ...], flush: bool = True + ) -> pl.LazyFrame | None: + """ + Retrieve all records for a given source path as a single Polars LazyFrame. + + Args: + source_path: Tuple of path components + + Returns: + Polars LazyFrame containing all records, or None if no records found + """ + all_records = self.get_all_records(source_path, flush=flush) + if all_records is None: + return None + return pl.LazyFrame(all_records) + + def get_records_by_ids( + self, + source_path: tuple[str, ...], + entry_ids: list[str] | pl.Series | pa.Array, + add_entry_id_column: bool | str = False, + preserve_input_order: bool = False, + flush: bool = False, + ) -> pa.Table | None: + """ + Retrieve records by entry IDs as a single table with schema preservation. + + Args: + source_path: Tuple of path components + entry_ids: Entry IDs to retrieve + add_entry_id_column: Control entry ID column inclusion + preserve_input_order: If True, return results in input order with nulls for missing + + Returns: + Arrow table containing all found records with original schema, or None if no records found + """ + if flush: + self.flush_batch(source_path) + + self._validate_source_path(source_path) + + # Convert input to list of strings for consistency + if isinstance(entry_ids, list): + if not entry_ids: + return None + entry_ids_list = entry_ids + elif isinstance(entry_ids, pl.Series): + if len(entry_ids) == 0: + return None + entry_ids_list = entry_ids.to_list() + elif isinstance(entry_ids, pa.Array): + if len(entry_ids) == 0: + return None + entry_ids_list = entry_ids.to_pylist() + else: + raise TypeError( + f"entry_ids must be list[str], pl.Series, or pa.Array, got {type(entry_ids)}" + ) + + delta_table = self._get_existing_delta_table(source_path) + if delta_table is None: + return None + + try: + # Use schema-preserving read with filters + filter_expr = self._create_entry_ids_filter(entry_ids_list) + result = self._read_table_with_filter(delta_table, filters=filter_expr) + + if len(result) == 0: + return None + + if preserve_input_order: + # Need to reorder results and add nulls for missing entries + import pandas as pd + + df = result.to_pandas() + df = df.set_index("__entry_id") + + # Create a DataFrame with the desired order, filling missing with NaN + ordered_df = df.reindex(entry_ids_list) + + # Convert back to Arrow + result = pa.Table.from_pandas(ordered_df.reset_index()) + + # Handle entry_id column based on parameter + return self._handle_entry_id_column(result, add_entry_id_column) + + except Exception as e: + logger.error( + f"Error getting records by IDs from {'/'.join(source_path)}: {e}" + ) + return None + + def get_records_by_ids_as_polars( + self, + source_path: tuple[str, ...], + entry_ids: list[str] | pl.Series | pa.Array, + add_entry_id_column: bool | str = False, + preserve_input_order: bool = False, + flush: bool = False, + ) -> pl.LazyFrame | None: + """ + Retrieve records by entry IDs as a single Polars LazyFrame. + + Args: + source_path: Tuple of path components + entry_ids: Entry IDs to retrieve + add_entry_id_column: Control entry ID column inclusion + preserve_input_order: If True, return results in input order with nulls for missing + + Returns: + Polars LazyFrame containing all found records, or None if no records found + """ + arrow_result = self.get_records_by_ids( + source_path, + entry_ids, + add_entry_id_column, + preserve_input_order, + flush=flush, + ) + + if arrow_result is None: + return None + + # Convert to Polars LazyFrame + return pl.LazyFrame(arrow_result) + + # Additional utility methods + def list_sources(self) -> list[tuple[str, ...]]: + """ + List all available source paths. + + Returns: + List of source path tuples + """ + sources = [] + + def _scan_directory(current_path: Path, path_components: tuple[str, ...]): + """Recursively scan for Delta tables.""" + for item in current_path.iterdir(): + if not item.is_dir(): + continue + + new_path_components = path_components + (item.name,) + + # Check if this directory contains a Delta table + try: + DeltaTable(str(item)) + sources.append(new_path_components) + except TableNotFoundError: + # Not a Delta table, continue scanning subdirectories + if len(new_path_components) < self.max_hierarchy_depth: + _scan_directory(item, new_path_components) + + _scan_directory(self.base_path, ()) + return sources + + def delete_source(self, source_path: tuple[str, ...]) -> bool: + """ + Delete an entire source (all records for a source path). + + Args: + source_path: Tuple of path components + + Returns: + True if source was deleted, False if it didn't exist + """ + self._validate_source_path(source_path) + + # Flush any pending batches first + self.flush_batch(source_path) + + table_path = self._get_table_path(source_path) + source_key = self._get_source_key(source_path) + + if not table_path.exists(): + return False + + try: + # Remove from caches + if source_key in self._delta_table_cache: + del self._delta_table_cache[source_key] + if source_key in self._schema_cache: + del self._schema_cache[source_key] + + # Remove directory + import shutil + + shutil.rmtree(table_path) + + logger.info(f"Deleted source {source_key}") + return True + + except Exception as e: + logger.error(f"Error deleting source {source_key}: {e}") + return False + + def delete_record(self, source_path: tuple[str, ...], entry_id: str) -> bool: + """ + Delete a specific record. + + Args: + source_path: Tuple of path components + entry_id: ID of the record to delete + + Returns: + True if record was deleted, False if it didn't exist + """ + self._validate_source_path(source_path) + + # Flush any pending batches first + self._flush_batch(source_path) + + delta_table = self._get_existing_delta_table(source_path) + if delta_table is None: + return False + + try: + # Check if record exists using proper filter + filter_expr = self._create_entry_id_filter(entry_id) + existing = self._read_table_with_filter(delta_table, filters=filter_expr) + if len(existing) == 0: + return False + + # Delete the record using SQL-style predicate (this is correct for delete operations) + delta_table.delete( + f"__entry_id = '{entry_id.replace(chr(39), chr(39) + chr(39))}'" + ) + + # Update cache + source_key = self._get_source_key(source_path) + self._delta_table_cache[source_key] = delta_table + + logger.debug(f"Deleted record {entry_id} from {'/'.join(source_path)}") + return True + + except Exception as e: + logger.error( + f"Error deleting record {entry_id} from {'/'.join(source_path)}: {e}" + ) + return False + + def get_table_info(self, source_path: tuple[str, ...]) -> dict[str, Any] | None: + """ + Get metadata information about a Delta table. + + Args: + source_path: Tuple of path components + + Returns: + Dictionary with table metadata, or None if table doesn't exist + """ + self._validate_source_path(source_path) + + delta_table = self._get_existing_delta_table(source_path) + if delta_table is None: + return None + + try: + # Get basic info + schema = delta_table.schema() + history = delta_table.history() + source_key = self._get_source_key(source_path) + + # Add pending batch info + pending_info = self.get_pending_batch_info() + pending_count = pending_info.get(source_key, 0) + + return { + "path": str(self._get_table_path(source_path)), + "source_path": source_path, + "schema": schema, + "version": delta_table.version(), + "num_files": len(delta_table.files()), + "history_length": len(history), + "latest_commit": history[0] if history else None, + "pending_records": pending_count, + } + + except Exception as e: + logger.error(f"Error getting table info for {'/'.join(source_path)}: {e}") + return None diff --git a/src/orcapod/store/core.py b/src/orcapod/databases/legacy/dict_data_stores.py similarity index 95% rename from src/orcapod/store/core.py rename to src/orcapod/databases/legacy/dict_data_stores.py index c41dd55..63d7974 100644 --- a/src/orcapod/store/core.py +++ b/src/orcapod/databases/legacy/dict_data_stores.py @@ -4,11 +4,11 @@ from os import PathLike from pathlib import Path -from orcapod.hashing import hash_packet +from orcapod.hashing.legacy_core import hash_packet +from orcapod.hashing.types import LegacyPacketHasher from orcapod.hashing.defaults import get_default_composite_file_hasher -from orcapod.hashing.types import PacketHasher -from orcapod.store.types import DataStore -from orcapod.types import Packet +from orcapod.databases.legacy.types import DataStore +from orcapod.types import Packet, PacketLike logger = logging.getLogger(__name__) @@ -30,15 +30,15 @@ def memoize( self, function_name: str, function_hash: str, - packet: Packet, - output_packet: Packet, + packet: PacketLike, + output_packet: PacketLike, overwrite: bool = False, - ) -> Packet: + ) -> PacketLike: return output_packet def retrieve_memoized( - self, function_name: str, function_hash: str, packet: Packet - ) -> Packet | None: + self, function_name: str, function_hash: str, packet: PacketLike + ) -> PacketLike | None: return None @@ -46,7 +46,7 @@ class DirDataStore(DataStore): def __init__( self, store_dir: str | PathLike = "./pod_data", - packet_hasher: PacketHasher | None = None, + packet_hasher: LegacyPacketHasher | None = None, copy_files=True, preserve_filename=True, overwrite=False, @@ -71,9 +71,9 @@ def memoize( self, function_name: str, function_hash: str, - packet: Packet, - output_packet: Packet, - ) -> Packet: + packet: PacketLike, + output_packet: PacketLike, + ) -> PacketLike: if self.legacy_mode: packet_hash = hash_packet(packet, algorithm=self.legacy_algorithm) else: @@ -139,7 +139,7 @@ def memoize( return retrieved_output_packet def retrieve_memoized( - self, function_name: str, function_hash: str, packet: Packet + self, function_name: str, function_hash: str, packet: PacketLike ) -> Packet | None: if self.legacy_mode: packet_hash = hash_packet(packet, algorithm=self.legacy_algorithm) diff --git a/src/orcapod/store/transfer.py b/src/orcapod/databases/legacy/dict_transfer_data_store.py similarity index 82% rename from src/orcapod/store/transfer.py rename to src/orcapod/databases/legacy/dict_transfer_data_store.py index c9a4e5d..99709e8 100644 --- a/src/orcapod/store/transfer.py +++ b/src/orcapod/databases/legacy/dict_transfer_data_store.py @@ -1,12 +1,12 @@ # Implements transfer data store that lets you transfer memoized packets between data stores. -from orcapod.store.types import DataStore -from orcapod.types import Packet +from orcapod.databases.legacy.types import DataStore +from orcapod.types import PacketLike class TransferDataStore(DataStore): """ - A data store that allows transferring memoized packets between different data stores. + A data store that allows transferring recorded data between different data stores. This is useful for moving data between different storage backends. """ @@ -14,7 +14,9 @@ def __init__(self, source_store: DataStore, target_store: DataStore) -> None: self.source_store = source_store self.target_store = target_store - def transfer(self, function_name: str, content_hash: str, packet: Packet) -> Packet: + def transfer( + self, function_name: str, content_hash: str, packet: PacketLike + ) -> PacketLike: """ Transfer a memoized packet from the source store to the target store. """ @@ -29,8 +31,8 @@ def transfer(self, function_name: str, content_hash: str, packet: Packet) -> Pac ) def retrieve_memoized( - self, function_name: str, function_hash: str, packet: Packet - ) -> Packet | None: + self, function_name: str, function_hash: str, packet: PacketLike + ) -> PacketLike | None: """ Retrieve a memoized packet from the target store. """ @@ -57,9 +59,9 @@ def memoize( self, function_name: str, function_hash: str, - packet: Packet, - output_packet: Packet, - ) -> Packet: + packet: PacketLike, + output_packet: PacketLike, + ) -> PacketLike: """ Memoize a packet in the target store. """ diff --git a/src/orcapod/store/arrow_data_stores.py b/src/orcapod/databases/legacy/legacy_arrow_data_stores.py similarity index 72% rename from src/orcapod/store/arrow_data_stores.py rename to src/orcapod/databases/legacy/legacy_arrow_data_stores.py index 4be9698..acac198 100644 --- a/src/orcapod/store/arrow_data_stores.py +++ b/src/orcapod/databases/legacy/legacy_arrow_data_stores.py @@ -7,7 +7,8 @@ from dataclasses import dataclass from datetime import datetime, timedelta import logging -from orcapod.store.types import DuplicateError +from orcapod.databases.types import DuplicateError +from pathlib import Path # Module-level logger logger = logging.getLogger(__name__) @@ -16,40 +17,47 @@ class MockArrowDataStore: """ Mock Arrow data store for testing purposes. - This class simulates the behavior of ParquetArrowDataStore without actually saving anything. - It is useful for unit tests where you want to avoid filesystem dependencies. + This class simulates the behavior of ArrowDataStore without actually saving anything. + It is useful for unit tests where you want to avoid any I/O operations or when you need + to test the behavior of your code without relying on external systems. If you need some + persistence of saved data, consider using SimpleParquetDataStore without providing a + file path instead. """ def __init__(self): logger.info("Initialized MockArrowDataStore") - def add_record(self, - source_name: str, + def add_record( + self, + source_pathh: tuple[str, ...], source_id: str, entry_id: str, - arrow_data: pa.Table) -> pa.Table: + arrow_data: pa.Table, + ) -> pa.Table: """Add a record to the mock store.""" return arrow_data - def get_record(self, source_name: str, - source_id: str, - entry_id: str) -> pa.Table | None: + def get_record( + self, source_path: tuple[str, ...], source_id: str, entry_id: str + ) -> pa.Table | None: """Get a specific record.""" return None - def get_all_records(self, source_name: str, source_id: str) -> pa.Table | None: + def get_all_records( + self, source_path: tuple[str, ...], source_id: str + ) -> pa.Table | None: """Retrieve all records for a given source as a single table.""" return None def get_all_records_as_polars( - self, source_name: str, source_id: str + self, source_path: tuple[str, ...], source_id: str ) -> pl.LazyFrame | None: """Retrieve all records for a given source as a single Polars LazyFrame.""" return None def get_records_by_ids( self, - source_name: str, + source_path: tuple[str, ...], source_id: str, entry_ids: list[str] | pl.Series | pa.Array, add_entry_id_column: bool | str = False, @@ -76,10 +84,10 @@ def get_records_by_ids( Arrow table containing all found records, or None if no records found """ return None - + def get_records_by_ids_as_polars( self, - source_name: str, + source_path: tuple[str, ...], source_id: str, entry_ids: list[str] | pl.Series | pa.Array, add_entry_id_column: bool | str = False, @@ -88,21 +96,28 @@ def get_records_by_ids_as_polars( return None - - -class InMemoryArrowDataStore: +class SimpleParquetDataStore: """ - In-memory Arrow data store for testing purposes. - This class simulates the behavior of ParquetArrowDataStore without actual file I/O. - It is useful for unit tests where you want to avoid filesystem dependencies. - - Uses dict of dict of Arrow tables for efficient storage and retrieval. + Simple Parquet-based Arrow data store, primarily to be used for development purposes. + If no file path is provided, it will not save anything to disk. Instead, all data will be stored in memory. + If a file path is provided, it will save data to a single Parquet files in a directory structure reflecting + the provided source_path. To speed up the process, data will be stored in memory and only saved to disk + when the `flush` method is called. If used as part of pipeline, flush is automatically called + at the end of pipeline execution. + Note that this store provides only very basic functionality and is not suitable for production use. + For each distinct source_path, only a single parquet file is created to store all data entries. + Appending is not efficient as it requires reading the entire file into the memory, appending new data, + and then writing the entire file back to disk. This is not suitable for large datasets or frequent updates. + However, for development/testing purposes, this data store provides a simple way to store and retrieve + data without the overhead of a full database or file system and provides very high performance. """ - def __init__(self, duplicate_entry_behavior: str = "error"): + def __init__( + self, path: str | Path | None = None, duplicate_entry_behavior: str = "error" + ): """ Initialize the InMemoryArrowDataStore. - + Args: duplicate_entry_behavior: How to handle duplicate entry_ids: - 'error': Raise ValueError when entry_id already exists @@ -112,107 +127,127 @@ def __init__(self, duplicate_entry_behavior: str = "error"): if duplicate_entry_behavior not in ["error", "overwrite"]: raise ValueError("duplicate_entry_behavior must be 'error' or 'overwrite'") self.duplicate_entry_behavior = duplicate_entry_behavior - + # Store Arrow tables: {source_key: {entry_id: arrow_table}} self._in_memory_store: dict[str, dict[str, pa.Table]] = {} - logger.info(f"Initialized InMemoryArrowDataStore with duplicate_entry_behavior='{duplicate_entry_behavior}'") + logger.info( + f"Initialized InMemoryArrowDataStore with duplicate_entry_behavior='{duplicate_entry_behavior}'" + ) + self.base_path = Path(path) if path else None + if self.base_path: + try: + self.base_path.mkdir(parents=True, exist_ok=True) + except Exception as e: + logger.error(f"Error creating base path {self.base_path}: {e}") - def _get_source_key(self, source_name: str, source_id: str) -> str: + def _get_source_key(self, source_path: tuple[str, ...]) -> str: """Generate key for source storage.""" - return f"{source_name}:{source_id}" + return "/".join(source_path) def add_record( self, - source_name: str, - source_id: str, + source_path: tuple[str, ...], entry_id: str, arrow_data: pa.Table, + ignore_duplicate: bool = False, ) -> pa.Table: """ Add a record to the in-memory store. - + Args: source_name: Name of the data source source_id: ID of the specific dataset within the source entry_id: Unique identifier for this record arrow_data: The Arrow table data to store - + Returns: - The original arrow_data table - + arrow_data equivalent to having loaded the corresponding entry that was just saved + Raises: ValueError: If entry_id already exists and duplicate_entry_behavior is 'error' """ - source_key = self._get_source_key(source_name, source_id) - + source_key = self._get_source_key(source_path) + # Initialize source if it doesn't exist if source_key not in self._in_memory_store: self._in_memory_store[source_key] = {} - + local_data = self._in_memory_store[source_key] - + # Check for duplicate entry - if entry_id in local_data and self.duplicate_entry_behavior == "error": - raise ValueError( - f"Entry '{entry_id}' already exists in {source_name}/{source_id}. " - f"Use duplicate_entry_behavior='overwrite' to allow updates." - ) - + if entry_id in local_data: + if not ignore_duplicate and self.duplicate_entry_behavior == "error": + raise ValueError( + f"Entry '{entry_id}' already exists in {source_key}. " + f"Use duplicate_entry_behavior='overwrite' to allow updates." + ) + # Store the record local_data[entry_id] = arrow_data - + action = "Updated" if entry_id in local_data else "Added" logger.debug(f"{action} record {entry_id} in {source_key}") return arrow_data + def load_existing_record(self, source_path: tuple[str, ...]): + source_key = self._get_source_key(source_path) + if self.base_path is not None and source_key not in self._in_memory_store: + self.load_from_parquet(self.base_path, source_path) + def get_record( - self, source_name: str, source_id: str, entry_id: str + self, source_path: tuple[str, ...], entry_id: str ) -> pa.Table | None: """Get a specific record.""" - source_key = self._get_source_key(source_name, source_id) + self.load_existing_record(source_path) + source_key = self._get_source_key(source_path) local_data = self._in_memory_store.get(source_key, {}) return local_data.get(entry_id) - def get_all_records(self, source_name: str, source_id: str) -> pa.Table | None: + def get_all_records( + self, source_path: tuple[str, ...], add_entry_id_column: bool | str = False + ) -> pa.Table | None: """Retrieve all records for a given source as a single table.""" - source_key = self._get_source_key(source_name, source_id) + self.load_existing_record(source_path) + source_key = self._get_source_key(source_path) local_data = self._in_memory_store.get(source_key, {}) - + if not local_data: return None tables_with_keys = [] for key, table in local_data.items(): # Add entry_id column to each table - key_array = pa.array([key] * len(table), type=pa.string()) + key_array = pa.array([key] * len(table), type=pa.large_string()) table_with_key = table.add_column(0, "__entry_id", key_array) tables_with_keys.append(table_with_key) # Concatenate all tables if tables_with_keys: - return pa.concat_tables(tables_with_keys) + combined_table = pa.concat_tables(tables_with_keys) + if not add_entry_id_column: + combined_table = combined_table.drop(columns=["__entry_id"]) + return combined_table return None def get_all_records_as_polars( - self, source_name: str, source_id: str + self, source_path: tuple[str, ...] ) -> pl.LazyFrame | None: """Retrieve all records for a given source as a single Polars LazyFrame.""" - all_records = self.get_all_records(source_name, source_id) + all_records = self.get_all_records(source_path) if all_records is None: return None return pl.LazyFrame(all_records) def get_records_by_ids( self, - source_name: str, - source_id: str, + source_path: tuple[str, ...], entry_ids: list[str] | pl.Series | pa.Array, add_entry_id_column: bool | str = False, preserve_input_order: bool = False, ) -> pa.Table | None: """ Retrieve records by entry IDs as a single table. - + Args: source_name: Name of the data source source_id: ID of the specific dataset within the source @@ -226,7 +261,7 @@ def get_records_by_ids( - str: Include entry ID column with custom name preserve_input_order: If True, return results in the same order as input entry_ids, with null rows for missing entries. If False, return in storage order. - + Returns: Arrow table containing all found records, or None if no records found """ @@ -248,20 +283,22 @@ def get_records_by_ids( f"entry_ids must be list[str], pl.Series, or pa.Array, got {type(entry_ids)}" ) - source_key = self._get_source_key(source_name, source_id) + self.load_existing_record(source_path) + + source_key = self._get_source_key(source_path) local_data = self._in_memory_store.get(source_key, {}) - + if not local_data: return None # Collect matching tables found_tables = [] found_entry_ids = [] - + if preserve_input_order: # Preserve input order, include nulls for missing entries first_table_schema = None - + for entry_id in entry_ids_list: if entry_id in local_data: table = local_data[entry_id] @@ -270,7 +307,7 @@ def get_records_by_ids( table_with_key = table.add_column(0, "__entry_id", key_array) found_tables.append(table_with_key) found_entry_ids.append(entry_id) - + # Store schema for creating null rows if first_table_schema is None: first_table_schema = table_with_key.schema @@ -281,12 +318,14 @@ def get_records_by_ids( null_data = {} for field in first_table_schema: if field.name == "__entry_id": - null_data[field.name] = pa.array([entry_id], type=field.type) + null_data[field.name] = pa.array( + [entry_id], type=field.type + ) else: # Create null array with proper type null_array = pa.array([None], type=field.type) null_data[field.name] = null_array - + null_table = pa.table(null_data, schema=first_table_schema) found_tables.append(null_table) found_entry_ids.append(entry_id) @@ -315,12 +354,17 @@ def get_records_by_ids( # Remove the __entry_id column column_names = combined_table.column_names if "__entry_id" in column_names: - indices_to_keep = [i for i, name in enumerate(column_names) if name != "__entry_id"] + indices_to_keep = [ + i for i, name in enumerate(column_names) if name != "__entry_id" + ] combined_table = combined_table.select(indices_to_keep) elif isinstance(add_entry_id_column, str): # Rename __entry_id to custom name schema = combined_table.schema - new_names = [add_entry_id_column if name == "__entry_id" else name for name in schema.names] + new_names = [ + add_entry_id_column if name == "__entry_id" else name + for name in schema.names + ] combined_table = combined_table.rename_columns(new_names) # If add_entry_id_column is True, keep __entry_id as is @@ -328,15 +372,14 @@ def get_records_by_ids( def get_records_by_ids_as_polars( self, - source_name: str, - source_id: str, + source_path: tuple[str, ...], entry_ids: list[str] | pl.Series | pa.Array, add_entry_id_column: bool | str = False, preserve_input_order: bool = False, ) -> pl.LazyFrame | None: """ Retrieve records by entry IDs as a single Polars LazyFrame. - + Args: source_name: Name of the data source source_id: ID of the specific dataset within the source @@ -350,50 +393,43 @@ def get_records_by_ids_as_polars( - str: Include entry ID column with custom name preserve_input_order: If True, return results in the same order as input entry_ids, with null rows for missing entries. If False, return in storage order. - + Returns: Polars LazyFrame containing all found records, or None if no records found """ # Get Arrow result and convert to Polars arrow_result = self.get_records_by_ids( - source_name, source_id, entry_ids, add_entry_id_column, preserve_input_order + source_path, entry_ids, add_entry_id_column, preserve_input_order ) - + if arrow_result is None: return None - + # Convert to Polars LazyFrame return pl.LazyFrame(arrow_result) def save_to_parquet(self, base_path: str | Path) -> None: """ Save all data to Parquet files in a directory structure. - + Directory structure: base_path/source_name/source_id/data.parquet - + Args: base_path: Base directory path where to save the Parquet files """ base_path = Path(base_path) base_path.mkdir(parents=True, exist_ok=True) - + saved_count = 0 - - for source_key, local_data in self._in_memory_store.items(): + + for source_id, local_data in self._in_memory_store.items(): if not local_data: continue - - # Parse source_name and source_id from the key - if ":" not in source_key: - logger.warning(f"Invalid source key format: {source_key}, skipping") - continue - - source_name, source_id = source_key.split(":", 1) - + # Create directory structure - source_dir = base_path / source_name / source_id + source_dir = base_path / source_id source_dir.mkdir(parents=True, exist_ok=True) - + # Combine all tables for this source with entry_id column tables_with_keys = [] for entry_id, table in local_data.items(): @@ -401,126 +437,130 @@ def save_to_parquet(self, base_path: str | Path) -> None: key_array = pa.array([entry_id] * len(table), type=pa.string()) table_with_key = table.add_column(0, "__entry_id", key_array) tables_with_keys.append(table_with_key) - + # Concatenate all tables if tables_with_keys: combined_table = pa.concat_tables(tables_with_keys) - + # Save as Parquet file + # TODO: perform safe "atomic" write parquet_path = source_dir / "data.parquet" import pyarrow.parquet as pq + pq.write_table(combined_table, parquet_path) - + saved_count += 1 - logger.debug(f"Saved {len(combined_table)} records for {source_key} to {parquet_path}") - + logger.debug( + f"Saved {len(combined_table)} records for {source_id} to {parquet_path}" + ) + logger.info(f"Saved {saved_count} sources to Parquet files in {base_path}") - def load_from_parquet(self, base_path: str | Path) -> None: + def load_from_parquet( + self, base_path: str | Path, source_path: tuple[str, ...] + ) -> None: """ Load data from Parquet files with the expected directory structure. - + Expected structure: base_path/source_name/source_id/data.parquet - + Args: base_path: Base directory path containing the Parquet files """ - base_path = Path(base_path) - - if not base_path.exists(): - logger.warning(f"Base path {base_path} does not exist") + + source_key = self._get_source_key(source_path) + target_path = Path(base_path) / source_key + + if not target_path.exists(): + logger.info(f"Base path {base_path} does not exist") return - - # Clear existing data - self._in_memory_store.clear() - + loaded_count = 0 - - # Traverse directory structure: source_name/source_id/ - for source_name_dir in base_path.iterdir(): - if not source_name_dir.is_dir(): - continue - - source_name = source_name_dir.name - - for source_id_dir in source_name_dir.iterdir(): - if not source_id_dir.is_dir(): - continue - - source_id = source_id_dir.name - source_key = self._get_source_key(source_name, source_id) - - # Look for Parquet files in this directory - parquet_files = list(source_id_dir.glob("*.parquet")) - - if not parquet_files: - logger.debug(f"No Parquet files found in {source_id_dir}") + + # Look for Parquet files in this directory + parquet_files = list(target_path.glob("*.parquet")) + if not parquet_files: + logger.debug(f"No Parquet files found in {target_path}") + return + + # Load all Parquet files and combine them + all_records = [] + + for parquet_file in parquet_files: + try: + import pyarrow.parquet as pq + + table = pq.read_table(parquet_file) + + # Validate that __entry_id column exists + if "__entry_id" not in table.column_names: + logger.warning( + f"Parquet file {parquet_file} missing __entry_id column, skipping" + ) continue - - # Load all Parquet files and combine them - all_records = [] - - for parquet_file in parquet_files: - try: - import pyarrow.parquet as pq - table = pq.read_table(parquet_file) - - # Validate that __entry_id column exists - if "__entry_id" not in table.column_names: - logger.warning(f"Parquet file {parquet_file} missing __entry_id column, skipping") - continue - - all_records.append(table) - logger.debug(f"Loaded {len(table)} records from {parquet_file}") - - except Exception as e: - logger.error(f"Failed to load Parquet file {parquet_file}: {e}") - continue - - # Process all records for this source - if all_records: - # Combine all tables - if len(all_records) == 1: - combined_table = all_records[0] - else: - combined_table = pa.concat_tables(all_records) - - # Split back into individual records by entry_id - local_data = {} - entry_ids = combined_table.column("__entry_id").to_pylist() - - # Group records by entry_id - entry_id_groups = {} - for i, entry_id in enumerate(entry_ids): - if entry_id not in entry_id_groups: - entry_id_groups[entry_id] = [] - entry_id_groups[entry_id].append(i) - - # Extract each entry_id's records - for entry_id, indices in entry_id_groups.items(): - # Take rows for this entry_id and remove __entry_id column - entry_table = combined_table.take(indices) - - # Remove __entry_id column - column_names = entry_table.column_names - if "__entry_id" in column_names: - indices_to_keep = [i for i, name in enumerate(column_names) if name != "__entry_id"] - entry_table = entry_table.select(indices_to_keep) - - local_data[entry_id] = entry_table - - self._in_memory_store[source_key] = local_data - loaded_count += 1 - - record_count = len(combined_table) - unique_entries = len(entry_id_groups) - logger.debug(f"Loaded {record_count} records ({unique_entries} unique entries) for {source_key}") - - logger.info(f"Loaded {loaded_count} sources from Parquet files in {base_path}") - - # Log summary of loaded data - total_records = sum(len(local_data) for local_data in self._in_memory_store.values()) - logger.info(f"Total records loaded: {total_records}") + + all_records.append(table) + logger.debug(f"Loaded {len(table)} records from {parquet_file}") + + except Exception as e: + logger.error(f"Failed to load Parquet file {parquet_file}: {e}") + continue + + # Process all records for this source + if all_records: + # Combine all tables + if len(all_records) == 1: + combined_table = all_records[0] + else: + combined_table = pa.concat_tables(all_records) + + # Split back into individual records by entry_id + local_data = {} + entry_ids = combined_table.column("__entry_id").to_pylist() + + # Group records by entry_id + entry_id_groups = {} + for i, entry_id in enumerate(entry_ids): + if entry_id not in entry_id_groups: + entry_id_groups[entry_id] = [] + entry_id_groups[entry_id].append(i) + + # Extract each entry_id's records + for entry_id, indices in entry_id_groups.items(): + # Take rows for this entry_id and remove __entry_id column + entry_table = combined_table.take(indices) + + # Remove __entry_id column + column_names = entry_table.column_names + if "__entry_id" in column_names: + indices_to_keep = [ + i for i, name in enumerate(column_names) if name != "__entry_id" + ] + entry_table = entry_table.select(indices_to_keep) + + local_data[entry_id] = entry_table + + self._in_memory_store[source_key] = local_data + loaded_count += 1 + + record_count = len(combined_table) + unique_entries = len(entry_id_groups) + logger.info( + f"Loaded {record_count} records ({unique_entries} unique entries) for {source_key}" + ) + + def flush(self): + """ + Flush all in-memory data to Parquet files in the base path. + This will overwrite existing files. + """ + if self.base_path is None: + logger.warning("Base path is not set, cannot flush data") + return + + logger.info(f"Flushing data to Parquet files in {self.base_path}") + self.save_to_parquet(self.base_path) + @dataclass class RecordMetadata: @@ -1634,7 +1674,7 @@ def create_multi_row_record(entry_id: str, num_rows: int = 3) -> pa.Table: store.add_record( "experiments", "dataset_A", valid_entries[0], overwrite_data ) - print(f"✓ Overwrote existing record") + print("✓ Overwrote existing record") # Verify overwrite updated_record = store.get_record( @@ -1648,7 +1688,7 @@ def create_multi_row_record(entry_id: str, num_rows: int = 3) -> pa.Table: # Sync and show final stats store.force_sync() stats = store.get_stats() - print(f"\n=== Final Statistics ===") + print("\n=== Final Statistics ===") print(f"Total records: {stats['total_records']}") print(f"Loaded caches: {stats['loaded_source_caches']}") print(f"Dirty caches: {stats['dirty_caches']}") @@ -1659,6 +1699,380 @@ def create_multi_row_record(entry_id: str, num_rows: int = 3) -> pa.Table: print("\n✓ Single-row constraint testing completed successfully!") +class InMemoryPolarsDataStore: + """ + In-memory Arrow data store using Polars DataFrames for efficient storage and retrieval. + This class provides the same interface as InMemoryArrowDataStore but uses Polars internally + for better performance with large datasets and complex queries. + + Uses dict of Polars DataFrames for efficient storage and retrieval. + Each DataFrame contains all records for a source with an __entry_id column. + """ + + def __init__(self, duplicate_entry_behavior: str = "error"): + """ + Initialize the InMemoryPolarsDataStore. + + Args: + duplicate_entry_behavior: How to handle duplicate entry_ids: + - 'error': Raise ValueError when entry_id already exists + - 'overwrite': Replace existing entry with new data + """ + # Validate duplicate behavior + if duplicate_entry_behavior not in ["error", "overwrite"]: + raise ValueError("duplicate_entry_behavior must be 'error' or 'overwrite'") + self.duplicate_entry_behavior = duplicate_entry_behavior + + # Store Polars DataFrames: {source_key: polars_dataframe} + # Each DataFrame has an __entry_id column plus user data columns + self._in_memory_store: dict[str, pl.DataFrame] = {} + logger.info( + f"Initialized InMemoryPolarsDataStore with duplicate_entry_behavior='{duplicate_entry_behavior}'" + ) + + def _get_source_key(self, source_name: str, source_id: str) -> str: + """Generate key for source storage.""" + return f"{source_name}:{source_id}" + + def add_record( + self, + source_name: str, + source_id: str, + entry_id: str, + arrow_data: pa.Table, + ) -> pa.Table: + """ + Add a record to the in-memory store. + + Args: + source_name: Name of the data source + source_id: ID of the specific dataset within the source + entry_id: Unique identifier for this record + arrow_data: The Arrow table data to store + + Returns: + arrow_data equivalent to having loaded the corresponding entry that was just saved + + Raises: + ValueError: If entry_id already exists and duplicate_entry_behavior is 'error' + """ + source_key = self._get_source_key(source_name, source_id) + + # Convert Arrow table to Polars DataFrame and add entry_id column + polars_data = cast(pl.DataFrame, pl.from_arrow(arrow_data)) + + # Add __entry_id column + polars_data = polars_data.with_columns(pl.lit(entry_id).alias("__entry_id")) + + # Check if source exists + if source_key not in self._in_memory_store: + # First record for this source + self._in_memory_store[source_key] = polars_data + logger.debug(f"Created new source {source_key} with entry {entry_id}") + else: + existing_df = self._in_memory_store[source_key] + + # Check for duplicate entry + entry_exists = ( + existing_df.filter(pl.col("__entry_id") == entry_id).shape[0] > 0 + ) + + if entry_exists: + if self.duplicate_entry_behavior == "error": + raise ValueError( + f"Entry '{entry_id}' already exists in {source_name}/{source_id}. " + f"Use duplicate_entry_behavior='overwrite' to allow updates." + ) + else: # validity of value is checked in constructor so it must be "ovewrite" + # Remove existing entry and add new one + existing_df = existing_df.filter(pl.col("__entry_id") != entry_id) + self._in_memory_store[source_key] = pl.concat( + [existing_df, polars_data] + ) + logger.debug(f"Overwrote entry {entry_id} in {source_key}") + else: + # Append new entry + try: + self._in_memory_store[source_key] = pl.concat( + [existing_df, polars_data] + ) + logger.debug(f"Added entry {entry_id} to {source_key}") + except Exception as e: + # Handle schema mismatch + existing_cols = set(existing_df.columns) - {"__entry_id"} + new_cols = set(polars_data.columns) - {"__entry_id"} + + if existing_cols != new_cols: + raise ValueError( + f"Schema mismatch for {source_key}. " + f"Existing columns: {sorted(existing_cols)}, " + f"New columns: {sorted(new_cols)}" + ) from e + else: + raise e + + return arrow_data + + def get_record( + self, source_name: str, source_id: str, entry_id: str + ) -> pa.Table | None: + """Get a specific record.""" + source_key = self._get_source_key(source_name, source_id) + + if source_key not in self._in_memory_store: + return None + + df = self._in_memory_store[source_key] + + # Filter for the specific entry_id + filtered_df = df.filter(pl.col("__entry_id") == entry_id) + + if filtered_df.shape[0] == 0: + return None + + # Remove __entry_id column and convert to Arrow + result_df = filtered_df.drop("__entry_id") + return result_df.to_arrow() + + def get_all_records( + self, source_name: str, source_id: str, add_entry_id_column: bool | str = False + ) -> pa.Table | None: + """Retrieve all records for a given source as a single table.""" + df = self.get_all_records_as_polars( + source_name, source_id, add_entry_id_column=add_entry_id_column + ) + if df is None: + return None + return df.collect().to_arrow() + + def get_all_records_as_polars( + self, source_name: str, source_id: str, add_entry_id_column: bool | str = False + ) -> pl.LazyFrame | None: + """Retrieve all records for a given source as a single Polars LazyFrame.""" + source_key = self._get_source_key(source_name, source_id) + + if source_key not in self._in_memory_store: + return None + + df = self._in_memory_store[source_key] + + if df.shape[0] == 0: + return None + + # perform column selection lazily + df = df.lazy() + + # Handle entry_id column based on parameter + if add_entry_id_column is False: + # Remove __entry_id column + result_df = df.drop("__entry_id") + elif add_entry_id_column is True: + # Keep __entry_id column as is + result_df = df + elif isinstance(add_entry_id_column, str): + # Rename __entry_id to custom name + result_df = df.rename({"__entry_id": add_entry_id_column}) + else: + raise ValueError( + f"add_entry_id_column must be a bool or str but {add_entry_id_column} was given" + ) + + return result_df + + def get_records_by_ids( + self, + source_name: str, + source_id: str, + entry_ids: list[str] | pl.Series | pa.Array, + add_entry_id_column: bool | str = False, + preserve_input_order: bool = False, + ) -> pa.Table | None: + """ + Retrieve records by entry IDs as a single table. + + Args: + source_name: Name of the data source + source_id: ID of the specific dataset within the source + entry_ids: Entry IDs to retrieve. Can be: + - list[str]: List of entry ID strings + - pl.Series: Polars Series containing entry IDs + - pa.Array: PyArrow Array containing entry IDs + add_entry_id_column: Control entry ID column inclusion: + - False: Don't include entry ID column (default) + - True: Include entry ID column as "__entry_id" + - str: Include entry ID column with custom name + preserve_input_order: If True, return results in the same order as input entry_ids, + with null rows for missing entries. If False, return in storage order. + + Returns: + Arrow table containing all found records, or None if no records found + """ + # Convert input to Polars Series + if isinstance(entry_ids, list): + if not entry_ids: + return None + entry_ids_series = pl.Series("entry_id", entry_ids) + elif isinstance(entry_ids, pl.Series): + if len(entry_ids) == 0: + return None + entry_ids_series = entry_ids + elif isinstance(entry_ids, pa.Array): + if len(entry_ids) == 0: + return None + entry_ids_series: pl.Series = pl.from_arrow( + pa.table({"entry_id": entry_ids}) + )["entry_id"] # type: ignore + else: + raise TypeError( + f"entry_ids must be list[str], pl.Series, or pa.Array, got {type(entry_ids)}" + ) + + source_key = self._get_source_key(source_name, source_id) + + if source_key not in self._in_memory_store: + return None + + df = self._in_memory_store[source_key] + + if preserve_input_order: + # Create DataFrame with input order and join to preserve order with nulls + ordered_df = pl.DataFrame({"__entry_id": entry_ids_series}) + result_df = ordered_df.join(df, on="__entry_id", how="left") + else: + # Filter for matching entry_ids (storage order) + result_df = df.filter(pl.col("__entry_id").is_in(entry_ids_series)) + + if result_df.shape[0] == 0: + return None + + # Handle entry_id column based on parameter + if add_entry_id_column is False: + # Remove __entry_id column + result_df = result_df.drop("__entry_id") + elif add_entry_id_column is True: + # Keep __entry_id column as is + pass + elif isinstance(add_entry_id_column, str): + # Rename __entry_id to custom name + result_df = result_df.rename({"__entry_id": add_entry_id_column}) + + return result_df.to_arrow() + + def get_records_by_ids_as_polars( + self, + source_name: str, + source_id: str, + entry_ids: list[str] | pl.Series | pa.Array, + add_entry_id_column: bool | str = False, + preserve_input_order: bool = False, + ) -> pl.LazyFrame | None: + """ + Retrieve records by entry IDs as a single Polars LazyFrame. + + Args: + source_name: Name of the data source + source_id: ID of the specific dataset within the source + entry_ids: Entry IDs to retrieve. Can be: + - list[str]: List of entry ID strings + - pl.Series: Polars Series containing entry IDs + - pa.Array: PyArrow Array containing entry IDs + add_entry_id_column: Control entry ID column inclusion: + - False: Don't include entry ID column (default) + - True: Include entry ID column as "__entry_id" + - str: Include entry ID column with custom name + preserve_input_order: If True, return results in the same order as input entry_ids, + with null rows for missing entries. If False, return in storage order. + + Returns: + Polars LazyFrame containing all found records, or None if no records found + """ + # Get Arrow result and convert to Polars LazyFrame + arrow_result = self.get_records_by_ids( + source_name, source_id, entry_ids, add_entry_id_column, preserve_input_order + ) + + if arrow_result is None: + return None + + # Convert to Polars LazyFrame + df = cast(pl.DataFrame, pl.from_arrow(arrow_result)) + return df.lazy() + + def entry_exists(self, source_name: str, source_id: str, entry_id: str) -> bool: + """Check if a specific entry exists.""" + source_key = self._get_source_key(source_name, source_id) + + if source_key not in self._in_memory_store: + return False + + df = self._in_memory_store[source_key] + return df.filter(pl.col("__entry_id") == entry_id).shape[0] > 0 + + def list_entries(self, source_name: str, source_id: str) -> set[str]: + """List all entry IDs for a specific source.""" + source_key = self._get_source_key(source_name, source_id) + + if source_key not in self._in_memory_store: + return set() + + df = self._in_memory_store[source_key] + return set(df["__entry_id"].to_list()) + + def list_sources(self) -> set[tuple[str, str]]: + """List all (source_name, source_id) combinations.""" + sources = set() + for source_key in self._in_memory_store.keys(): + if ":" in source_key: + source_name, source_id = source_key.split(":", 1) + sources.add((source_name, source_id)) + return sources + + def clear_source(self, source_name: str, source_id: str) -> None: + """Clear all records for a specific source.""" + source_key = self._get_source_key(source_name, source_id) + if source_key in self._in_memory_store: + del self._in_memory_store[source_key] + logger.debug(f"Cleared source {source_key}") + + def clear_all(self) -> None: + """Clear all records from the store.""" + self._in_memory_store.clear() + logger.info("Cleared all records from store") + + def get_stats(self) -> dict[str, Any]: + """Get comprehensive statistics about the data store.""" + total_records = 0 + total_memory_mb = 0 + source_stats = [] + + for source_key, df in self._in_memory_store.items(): + record_count = df.shape[0] + total_records += record_count + + # Estimate memory usage (rough approximation) + memory_bytes = df.estimated_size() + memory_mb = memory_bytes / (1024 * 1024) + total_memory_mb += memory_mb + + source_stats.append( + { + "source_key": source_key, + "record_count": record_count, + "column_count": df.shape[1] - 1, # Exclude __entry_id + "memory_mb": round(memory_mb, 2), + "columns": [col for col in df.columns if col != "__entry_id"], + } + ) + + return { + "total_records": total_records, + "total_sources": len(self._in_memory_store), + "total_memory_mb": round(total_memory_mb, 2), + "duplicate_entry_behavior": self.duplicate_entry_behavior, + "source_details": source_stats, + } + + if __name__ == "__main__": logging.basicConfig(level=logging.INFO) demo_single_row_constraint() diff --git a/src/orcapod/store/safe_dir_data_store.py b/src/orcapod/databases/legacy/safe_dir_data_store.py similarity index 99% rename from src/orcapod/store/safe_dir_data_store.py rename to src/orcapod/databases/legacy/safe_dir_data_store.py index 0f0ce6a..72f8ef0 100644 --- a/src/orcapod/store/safe_dir_data_store.py +++ b/src/orcapod/databases/legacy/safe_dir_data_store.py @@ -10,7 +10,7 @@ from pathlib import Path from typing import Optional, Union -from .file_ops import atomic_copy, atomic_write +from ..file_utils import atomic_copy, atomic_write logger = logging.getLogger(__name__) @@ -23,7 +23,7 @@ class FileLockError(Exception): @contextmanager def file_lock( - lock_path: Union[str, Path], + lock_path: str | Path, shared: bool = False, timeout: float = 30.0, delay: float = 0.1, @@ -205,7 +205,7 @@ def __init__( def _get_output_dir(self, function_name, content_hash, packet): """Get the output directory for a specific packet""" - from orcapod.hashing.core import hash_dict + from orcapod.hashing.legacy_core import hash_dict packet_hash = hash_dict(packet) return self.store_dir / function_name / content_hash / str(packet_hash) diff --git a/src/orcapod/store/types.py b/src/orcapod/databases/legacy/types.py similarity index 75% rename from src/orcapod/store/types.py rename to src/orcapod/databases/legacy/types.py index 6c1b5af..42b0ed5 100644 --- a/src/orcapod/store/types.py +++ b/src/orcapod/databases/legacy/types.py @@ -1,6 +1,6 @@ from typing import Protocol, runtime_checkable -from orcapod.types import Tag, Packet +from orcapod.types import Tag, PacketLike import pyarrow as pa import polars as pl @@ -21,13 +21,13 @@ def memoize( self, function_name: str, function_hash: str, - packet: Packet, - output_packet: Packet, - ) -> Packet: ... + packet: PacketLike, + output_packet: PacketLike, + ) -> PacketLike: ... def retrieve_memoized( - self, function_name: str, function_hash: str, packet: Packet - ) -> Packet | None: ... + self, function_name: str, function_hash: str, packet: PacketLike + ) -> PacketLike | None: ... @runtime_checkable @@ -41,30 +41,29 @@ def __init__(self, *args, **kwargs) -> None: ... def add_record( self, - source_name: str, - source_id: str, + source_path: tuple[str, ...], entry_id: str, arrow_data: pa.Table, + ignore_duplicate: bool = False, ) -> pa.Table: ... def get_record( - self, source_name: str, source_id: str, entry_id: str + self, source_path: tuple[str, ...], entry_id: str ) -> pa.Table | None: ... - def get_all_records(self, source_name: str, source_id: str) -> pa.Table | None: + def get_all_records(self, source_path: tuple[str, ...]) -> pa.Table | None: """Retrieve all records for a given source as a single table.""" ... def get_all_records_as_polars( - self, source_name: str, source_id: str + self, source_path: tuple[str, ...] ) -> pl.LazyFrame | None: """Retrieve all records for a given source as a single Polars DataFrame.""" ... def get_records_by_ids( self, - source_name: str, - source_id: str, + source_path: tuple[str, ...], entry_ids: list[str] | pl.Series | pa.Array, add_entry_id_column: bool | str = False, preserve_input_order: bool = False, @@ -74,11 +73,14 @@ def get_records_by_ids( def get_records_by_ids_as_polars( self, - source_name: str, - source_id: str, + source_path: tuple[str, ...], entry_ids: list[str] | pl.Series | pa.Array, add_entry_id_column: bool | str = False, preserve_input_order: bool = False, ) -> pl.LazyFrame | None: """Retrieve records by entry IDs as a single Polars DataFrame.""" ... + + def flush(self) -> None: + """Flush all pending writes/saves to the data store.""" + ... diff --git a/src/orcapod/dj/mapper.py b/src/orcapod/dj/mapper.py deleted file mode 100644 index a38fdaf..0000000 --- a/src/orcapod/dj/mapper.py +++ /dev/null @@ -1,127 +0,0 @@ -import warnings -from typing import Optional - -from orcapod.mappers import Join, MapPackets, Mapper, MapTags -from .operation import QueryOperation -from .stream import QueryStream - - -class QueryMapper(QueryOperation, Mapper): - """ - A special type of mapper that returns and works with QueryStreams - """ - - -def convert_to_query_mapper(operation: Mapper) -> QueryMapper: - """ - Convert a generic mapper to an equivalent, Query mapper - """ - - if isinstance(operation, Join): - return JoinQuery() - elif isinstance(operation, MapPackets): - proj_map = {v: k for k, v in operation.key_map.items()} - # if drop_unmapped is True, we need to project the keys - args = [] if operation.drop_unmapped else [...] - return ProjectQuery(*args, **proj_map) - elif isinstance(operation, MapTags): - proj_map = {v: k for k, v in operation.key_map.items()} - if operation.drop_unmapped: - warnings.warn("Dropping unmapped tags is not supported in DataJoint") - return ProjectQuery(..., **proj_map) - elif isinstance(operation, QueryOperation): - # if the operation is already a QueryOperation, just return it - return operation - else: - raise ValueError(f"Unknown operation: {operation}") - - -class JoinQuery(QueryMapper): - """ - DataJoint specific Join operation that only works on QueryStream - """ - - def forward(self, *streams: QueryStream, project=False) -> QueryStream: - if len(streams) < 2: - raise ValueError("Join operation requires at least two streams") - - if not all(isinstance(s, QueryStream) for s in streams): - raise ValueError("All streams must be QueryStreams") - - # join the tables - joined_query = None - upstream_tables = set() - for stream in streams: - next_query = stream.query.proj() if project else stream.query - if joined_query is None: - joined_query = next_query - else: - joined_query = joined_query * stream.query - upstream_tables.update(stream.upstream_tables) - - return QueryStream(joined_query, upstream_tables) - - -class ProjectQuery(QueryMapper): - """ - Project (rename/remove) tag and packet keys - """ - - def __init__(self, *args, _label: Optional[str] = None, **projection_kwargs): - super().__init__(label=_label) - self.projection_args = args - self.projection_kwargs = projection_kwargs - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - str(self.projection_args), - str(self.projection_kwargs), - ) + tuple(streams) - - def forward(self, *streams: QueryStream) -> QueryStream: - if len(streams) != 1: - raise ValueError("Project operation requires exactly one stream") - - stream = streams[0] - - # project the query - projected_query = stream.query.proj( - *self.projection_args, **self.projection_kwargs - ) - - projected_upstreams = [ - table.proj(*self.projection_args, **self.projection_kwargs) - for table in stream.upstream_tables - ] - - return QueryStream(projected_query, projected_upstreams) - - -class RestrictQuery(QueryMapper): - """ - Restrict (filter) tag and packet keys - """ - - def __init__(self, *restrictions, label: Optional[str] = None): - super().__init__(label=label) - self.restrictions = restrictions - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - str(self.restrictions), - ) + tuple(streams) - - def forward(self, *streams: QueryStream) -> QueryStream: - if len(streams) != 1: - raise ValueError("Restrict operation requires exactly one stream") - - stream = streams[0] - - # restrict the query - restricted_query = stream.query - for restriction in self.restrictions: - restricted_query = restricted_query & restriction - - return QueryStream(restricted_query, stream.upstream_tables) diff --git a/src/orcapod/dj/operation.py b/src/orcapod/dj/operation.py deleted file mode 100644 index d4d5a81..0000000 --- a/src/orcapod/dj/operation.py +++ /dev/null @@ -1,12 +0,0 @@ -from ..base import Operation -from .stream import QueryStream - - -class QueryOperation(Operation): - """ - A special type of operation that returns and works with - QueryStreams - """ - - def __call__(self, *streams: QueryStream, **kwargs) -> QueryStream: - return super().__call__(*streams, **kwargs) diff --git a/src/orcapod/dj/pod.py b/src/orcapod/dj/pod.py deleted file mode 100644 index 815b2dc..0000000 --- a/src/orcapod/dj/pod.py +++ /dev/null @@ -1,149 +0,0 @@ -import logging -from typing import Collection, Optional, Tuple - -import datajoint as dj -from datajoint import Schema -from datajoint.table import Table - -from ..pod import FunctionPod, Pod -from ..utils.name import pascal_to_snake, snake_to_pascal -from .mapper import JoinQuery -from .operation import QueryOperation -from .source import QuerySource -from .stream import QueryStream, TableCachedStream, TableStream - -logger = logging.getLogger(__name__) - - -class QueryPod(Pod, QueryOperation): - """ - A special type of operation that returns and works with - QueryStreams - """ - - -class TableCachedPod(QueryPod, QuerySource): - def __init__( - self, - fp: FunctionPod, - schema: Schema, - table_name: str = None, - table_postfix: str = "", - streams: Collection[QueryStream] = None, - create_table: bool = True, - label: Optional[str] = None, - **kwargs, - ) -> None: - super().__init__(label=label, **kwargs) - self.fp = fp - self.schema = schema - self.table_name = ( - table_name if table_name is not None else pascal_to_snake(fp.function_name) - ) + (f"_{table_postfix}" if table_postfix else "") - self.streams = streams if streams is not None else [] - self.table = None - if create_table: - self.compile() - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - str(self.schema), - self.table_name, - self.fp, - ) + tuple(self.streams) - - @property - def label(self) -> str: - if self._label is None: - return snake_to_pascal(self.fp.function_name) - return self._label - - def prepare_source_query(self) -> Tuple[QueryStream, Collection[Table]]: - if len(self.streams) > 1: - query_stream = JoinQuery()(*self.streams) - else: - query_stream = self.streams[0] - - source_query = query_stream.query - - upstream_tables = query_stream.upstream_tables - - return source_query, upstream_tables - - def compile(self) -> None: - if not all(isinstance(s, QueryStream) for s in self.streams): - raise ValueError("All streams must be QueryStreams") - - source_query, upstream_tables = self.prepare_source_query() - - # upstreams = '\n'.join([f"-> self.streams[{i}].upstream_tables[{j}]" for i, stream in enumerate(self.streams) for j in range(len(stream.upstream_tables))]) - upstreams = "\n".join( - f"-> upstream_tables[{i}]" for i in range(len(upstream_tables)) - ) - outputs = "\n".join([f"{k}: varchar(255)" for k in self.fp.output_keys]) - - class PodTable(dj.Computed): - definition = f""" - # {self.table_name} outputs - {upstreams} - --- - {outputs} - """ - - source = self - - @property - def key_source(self): - return source_query - - def make(self, key): - # form QueryStream using key - query_stream = QueryStream(source_query & key, upstream_tables) - - for key, packet in self.source.fp(query_stream): - key.update(packet) - self.insert1(key) - logger.info(f"Inserted key: {key}") - - PodTable.__name__ = snake_to_pascal(self.table_name) - PodTable = self.schema(PodTable) - self.table = PodTable - - def forward(self, *streams: QueryStream) -> QueryStream: - """ - This method has two modes of operations: as a source and as a pod. - When used as a source, it will return a stream of data from what's already - in the table. When used as a pod, it will execute the function as necessary - and insert the results into the table. - """ - - # if no stream are provided, act as a source and return the table stream - if len(streams) < 1: - return TableStream(self.table) - - # verify that all stream are QueryStreams - if not all(isinstance(s, QueryStream) for s in streams): - raise ValueError("All streams must be QueryStreams") - - # be sure to project out non primary keys - if len(streams) > 1: - joined_streams = JoinQuery()(*streams, project=True) - else: - # TODO: add proj method onto QueryStream - joined_streams = QueryStream( - streams[0].query.proj(), streams[0].upstream_tables - ) - - source_query, upstream_tables = self.prepare_source_query() - - # restrict the source using the passed in query - source_query = source_query & joined_streams.query - - # form QueryStream using key - query_stream = QueryStream(source_query, upstream_tables) - - # set table to allow direct insert - self.table._allow_insert = True - - return TableCachedStream(self.table, self.fp(query_stream)) diff --git a/src/orcapod/dj/source.py b/src/orcapod/dj/source.py deleted file mode 100644 index 8af3f23..0000000 --- a/src/orcapod/dj/source.py +++ /dev/null @@ -1,425 +0,0 @@ -import logging -from typing import Any, Collection, Optional, Union - -import datajoint as dj -from datajoint import Schema, Table - -from orcapod.hashing import hash_to_uuid - -from orcapod.sources import Source -from orcapod.streams import SyncStream -from ..utils.name import pascal_to_snake, snake_to_pascal -from ..utils.stream_utils import common_elements -from .operation import QueryOperation -from .stream import QueryStream, TableCachedStream, TableStream - -logger = logging.getLogger(__name__) - - -class QuerySource(Source, QueryOperation): - """ - A speical type of source that returns and works with QueryStreams - """ - - -class TableSource(QuerySource): - """ - A source that reads from a table. - """ - - def __init__( - self, table: Union[Table, type[Table]], label: Optional[str] = None - ) -> None: - super().__init__(label=label) - # if table is an instance, grab the class for consistency - if not isinstance(table, type): - table = table.__class__ - self.table = table - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - str(self.table.full_table_name), - ) + tuple(streams) - - @property - def label(self) -> str: - if self._label is None: - return self.table.__name__ - return self._label - - def forward(self, *streams: SyncStream) -> QueryStream: - """ - Read from the table and return a stream of packets. - """ - if len(streams) > 0: - raise ValueError("No streams should be passed to TableSource") - # make sure to pass in an instance of the table for the query - return QueryStream(self.table(), [self.table()]) - - def proj(self, *args, **kwargs) -> "TableSource": - """ - Project the table and return a new source. - """ - return TableSource(self.table.proj(*args, **kwargs)) - - def __and__(self, other: Any) -> "TableSource": - """ - Join the table with another table and return a new source. - """ - if isinstance(other, TableSource): - other = other.table - elif isinstance(other, QueryStream): - other = other.query - else: - raise ValueError(f"Object of type {type(other)} is not supported.") - return TableSource(self.table & other) - - def __repr__(self): - return self.table().__repr__() - - def preview(self, limit=None, width=None): - return self.table().preview(limit=limit, width=width) - - def _repr_html_(self): - """:return: HTML to display table in Jupyter notebook.""" - return self.table()._repr_html_() - - -class TableCachedStreamSource(QuerySource): - """ - This class wraps any `Stream` and caches the output into a DataJoint table. - The class instance acts as a source and returns a `QueryStream` when invoked. - """ - - def __init__( - self, - stream: SyncStream, - schema: Schema, - table_name: str | None = None, - label: str | None = None, - ): - super().__init__(label=label) - self.stream = stream - self.schema = schema - # if table name is not provided, use the name of the stream source - if table_name is None: - if stream.invocation is not None: - table_name = stream.invocation.operation.__class__.__name__ - else: - table_name = stream.__class__.__name__ - # make sure the table name is in snake case - self.table_name = pascal_to_snake(table_name) - - self.table = None - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - self.stream, - str(self.schema), - self.table_name, - ) + tuple(streams) - - @property - def label(self) -> str: - if self._label is None: - if ( - hasattr(self.stream.invocation, "label") - and self.stream.invocation.label is not None - ): - return self.stream.invocation.label - else: - return snake_to_pascal(self.table_name) - return self._label - - def compile(self, tag_keys: Collection[str], packet_keys: Collection[str]) -> None: - # create a table to store the cached packets - - key_fields = "\n".join([f"{k}: varchar(255)" for k in tag_keys]) - output_fields = "\n".join([f"{k}: varchar(255)" for k in packet_keys]) - - class CachedTable(dj.Manual): - source = self # this refers to the outer class instance - definition = f""" - # {self.table_name} outputs - {key_fields} - --- - {output_fields} - """ - - CachedTable.__name__ = snake_to_pascal(self.table_name) - CachedTable = self.schema(CachedTable) - self.table = CachedTable - - def forward(self, *streams: QueryStream) -> QueryStream: - if len(streams) > 0: - raise ValueError("No streams should be passed to TableCachedStreamSource") - - if self.table is None: - # TODO: consider handling this lazily - self.compile(*self.stream.keys()) - - return TableCachedStream(self.table, self.stream) - - -class TableCachedSource(QuerySource): - """ - This class wraps any `Source` and caches the output into a DataJoint table. - Consequently, the table returns a `QueryStream` that can be used by any downstraem - processes that relies on DJ-based streams (e.g. `TableCachedPod`). - """ - - def __init__( - self, - source: Source, - schema: Schema, - table_name: str = None, - table_postfix: str = "", - label: Optional[str] = None, - ): - super().__init__(label=label) - self.source = source - self.schema = schema - # if table name is not provided, use the name of the source - self.table_name = ( - table_name - if table_name is not None - else pascal_to_snake(source.__class__.__name__) - ) + (f"_{table_postfix}" if table_postfix else "") - self.table = None - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - self.source, - str(self.schema), - self.table_name, - ) + tuple(streams) - - @property - def label(self) -> str: - if self._label is None: - return self.source.label - return self._label - - def compile(self) -> None: - # create a table to store the cached packets - tag_keys, packet_keys = self.source().keys() - key_fields = "\n".join([f"{k}: varchar(255)" for k in tag_keys]) - output_fields = "\n".join([f"{k}: varchar(255)" for k in packet_keys]) - - class CachedTable(dj.Manual): - source = self # this refers to the outer class instance - definition = f""" - # {self.table_name} outputs - {key_fields} - --- - {output_fields} - """ - - def populate( - self, batch_size: int = 10, use_skip_duplicates: bool = False - ) -> int: - return sum( - 1 - for _ in self.source( - batch_size=batch_size, - use_skip_duplicates=use_skip_duplicates, - ) - ) - - CachedTable.__name__ = snake_to_pascal(self.table_name) - CachedTable = self.schema(CachedTable) - self.table = CachedTable - - def forward( - self, - *streams: QueryStream, - batch_size: int = 10, - use_skip_duplicates: bool = False, - ) -> QueryStream: - if len(streams) > 0: - raise ValueError("No streams should be passed to TableCachedSource") - - if self.table is None: - self.compile() - return TableCachedStream( - self.table, - self.source(), - batch_size=batch_size, - use_skip_duplicates=use_skip_duplicates, - ) - - -class MergedQuerySource(QuerySource): - """ - A source that represents multiple merged query. - """ - - def __init__( - self, - *streams: QueryStream, - schema: Schema, - table_name: str = None, - table_postfix: str = "", - label: Optional[str] = None, - lazy_build: bool = True, - ) -> None: - super().__init__(label=label) - self.streams = streams - self.schema = schema - self.table = None - if table_name is None: - table_name = self.label if self.label is not None else "MergedData" - - self.table_name = pascal_to_snake(table_name) + ( - f"_{table_postfix}" if table_postfix else "" - ) - if not lazy_build: - self.compile() - - @property - def label(self) -> str: - if self._label is None: - return "_".join([stream.label for stream in self.streams]) - return self._label - - def identity_structure(self, *streams): - return ( - self.__class__.__name__, - self.streams, - ) + tuple(streams) - - def forward(self, *streams: SyncStream) -> QueryStream: - if len(streams) > 0: - logger.warning( - "Handling multiple streams in forward is not implemented yet in " - "MergedQuerySource and this will be silently ignored" - ) - if self.table is None: - self.compile() - - return TableStream(self.table) - - def compile(self) -> None: - part_tag_keys = [] - part_packet_keys = [] - for stream in self.streams: - tag_key, packet_key = stream.keys() - part_tag_keys.append(tag_key) - part_packet_keys.append(packet_key) - - # find common keys among all tags and use that as primary key - common_tag_keys = common_elements(*part_tag_keys) - common_packet_keys = common_elements(*part_packet_keys) - - use_uuid = True - if all([len(k) == len(common_tag_keys) for k in part_tag_keys]): - # if all tags have the same number of keys, it is not necessary - # to include an additional UUID - use_uuid = False - - # create a table to store the cached packets - key_fields = "\n".join([f"{k}: varchar(255)" for k in common_tag_keys]) - output_fields = "\n".join([f"{k}: varchar(255)" for k in common_packet_keys]) - table_field = f"{self.table_name}_part" - uuid_field = f"{self.table_name}_uuid" if use_uuid else "" - table_entry = f"{table_field}: varchar(255)" - uuid_entry = f"{uuid_field}: uuid" if use_uuid else "" - - class MergedTable(dj.Manual): - source = self - definition = f""" - # {self.table_name} inputs - {key_fields} - {table_entry} - {uuid_entry} - --- - {output_fields} - """ - - for stream in self.streams: - if not isinstance(stream, QueryStream): - raise ValueError( - f"Stream {stream} is not a QueryStream. " - "Please use a QueryStream as input." - ) - part_table = make_part_table( - stream, - common_tag_keys, - common_packet_keys, - table_field, - uuid_field, - ) - setattr(MergedTable, snake_to_pascal(stream.label), part_table) - - MergedTable.__name__ = snake_to_pascal(self.table_name) - MergedTable = self.schema(MergedTable) - self.table = MergedTable - - # class CachedTable(dj.Manual): - # source = self # this refers to the outer class instance - # definition = f""" - # # {self.table_name} outputs - # {key_fields} - # --- - # {output_fields} - # """ - - # def populate( - # self, batch_size: int = 10, use_skip_duplicates: bool = False - # ) -> int: - # return sum( - # 1 - # for _ in self.operation( - # batch_size=batch_size, - # use_skip_duplicates=use_skip_duplicates, - # ) - # ) - - # CachedTable.__name__ = snake_to_pascal(self.table_name) - # CachedTable = self.schema(CachedTable) - # self.table = CachedTable - - -def make_part_table( - stream: QueryStream, - common_tag_keys, - common_packet_keys, - table_field, - uuid_field, -) -> type[dj.Part]: - upstreams = "\n".join( - f"-> self.upstream_tables[{i}]" for i in range(len(stream.upstream_tables)) - ) - - tag_keys, packet_keys = stream.keys() - - extra_packet_keys = [k for k in packet_keys if k not in common_packet_keys] - - extra_output_fields = "\n".join([f"{k}: varchar(255)" for k in extra_packet_keys]) - - class PartTable(dj.Part, dj.Computed): - upstream_tables = stream.upstream_tables - definition = f""" - -> master - --- - {upstreams} - {extra_output_fields} - """ - - @property - def key_source(self): - return stream.query - - def make(self, key): - content = (stream.query & key).fetch1() - content[table_field] = self.__class__.__name__ - if uuid_field: - content[uuid_field] = hash_to_uuid(key) - self.master.insert1(content, ignore_extra_fields=True) - self.insert1(content, ignore_extra_fields=True) - - PartTable.__name__ = snake_to_pascal(stream.label) - return PartTable diff --git a/src/orcapod/dj/stream.py b/src/orcapod/dj/stream.py deleted file mode 100644 index 3e4eb08..0000000 --- a/src/orcapod/dj/stream.py +++ /dev/null @@ -1,155 +0,0 @@ -import copy -import logging -from typing import Any, Collection, Union - -from datajoint.expression import QueryExpression -from datajoint.table import Table - -from orcapod.streams import SyncStream - -logger = logging.getLogger(__name__) - - -def query_without_restriction(query: QueryExpression) -> QueryExpression: - """ - Make a new QueryExpression, copying all attributes except for the restriction - """ - new_query = copy.copy(query) - new_query._restriction = None - new_query._restriction_attributes = None - - return new_query - - -class QueryStream(SyncStream): - """ - DataJoint query-based data stream. Critically, `QueryStream` is backed - by a DataJoint query, and iterating over it will yield packets equivalent to - iterating over the query results. - """ - - def __init__( - self, query: QueryExpression, upstream_tables: Collection[Table] - ) -> None: - super().__init__() - self.query = query - # remove the restriction from the query - self.upstream_tables = [ - query_without_restriction(table) for table in upstream_tables - ] - - def __iter__(self): - for row in self.query.fetch(as_dict=True): - tag = {k: row[k] for k in self.query.primary_key} - packet = {k: row[k] for k in row if k not in self.query.primary_key} - yield tag, packet - - def proj(self, *args, **kwargs) -> "QueryStream": - """ - Project the query stream and return a new query stream - """ - from .mapper import ProjectQuery - - return ProjectQuery(*args, **kwargs)(self) - - def __and__(self, other: Any) -> "QueryStream": - """ - Restrict the query stream by `other` and return a new query stream - """ - # lazy load to avoid circular import - from .source import TableSource - from .mapper import RestrictQuery - - if isinstance(other, TableSource): - other = other.table - elif isinstance(other, QueryStream): - other = other.query - else: - raise ValueError(f"Object of type {type(other)} is not supported.") - return RestrictQuery(other)(self) - - def __mul__(self, other: Any) -> Union["QueryStream", SyncStream]: - from .mapper import JoinQuery - - if isinstance(other, QueryStream): - return JoinQuery()(self, other) - else: - # fallback to default handling that returns a SyncStream - return super().__mul__(other) - - def __repr__(self): - return self.query.__repr__() - - def preview(self, limit=None, width=None): - return self.query.preview(limit=limit, width=width) - - def _repr_html_(self): - """:return: HTML to display table in Jupyter notebook.""" - return self.query._repr_html_() - - def identity_structure(self, *streams) -> Any: - return (self.__class__.__name__, self.query.make_sql()) - - -class TableStream(QueryStream): - """ - DataJoint table-based data stream - """ - - def __init__(self, table: Table) -> None: - if isinstance(table, type): - table = table() - super().__init__(table, [table]) - self.table = table - - -class TableCachedStream(TableStream): - """ - A stream that caches the output from another stream into a DJ table - and then returns the content. Effectively act as a TableStream as - all returned packets can be found in the table. - """ - - def __init__( - self, - table: Table, - stream: SyncStream, - batch_size: int = 1, - use_skip_duplicates=False, - ) -> None: - if isinstance(table, type): - table = table() - super().__init__(table) - self.stream = stream - self.batch_size = batch_size - self.use_skip_duplicates = use_skip_duplicates - - def __iter__(self): - logger.info( - f"Caching stream into table {self.table.table_name} with batch size {self.batch_size}, use_skip_duplicates={self.use_skip_duplicates}" - ) - batch = [] - batch_count = 0 - for tag, packet in self.stream: - # cache the packet into the table - if self.use_skip_duplicates or not self.table & tag: - # if use_skip_duplicates is True, skip duplicates - # if not, insert the packet into the table - batch.append(tag | packet) - batch_count += 1 - - # if batch_size is <= 0, will accumulate all packets into the batch - # and insert at the very end - if self.batch_size > 0 and batch_count >= self.batch_size: - self.table.insert(batch, skip_duplicates=self.use_skip_duplicates) - logger.debug( - f"Inserted batch of size {len(batch)} into table {self.table.table_name}" - ) - batch = [] - batch_count = 0 - yield tag, packet - if batch: - logger.debug( - f"Inserted the final batch of size {len(batch)} into table {self.table.table_name}" - ) - self.table.insert(batch, skip_duplicates=self.use_skip_duplicates) diff --git a/src/orcapod/dj/tracker.py b/src/orcapod/dj/tracker.py deleted file mode 100644 index b137e54..0000000 --- a/src/orcapod/dj/tracker.py +++ /dev/null @@ -1,155 +0,0 @@ -import sys -from collections import defaultdict -from types import ModuleType -from typing import Any, Collection, Optional, Tuple - -import networkx as nx -from datajoint import Schema - -from orcapod.base import Operation, Source -from orcapod.mappers import Mapper, Merge -from orcapod.pod import FunctionPod -from orcapod.pipeline import GraphTracker - -from .mapper import convert_to_query_mapper -from .operation import QueryOperation -from .pod import TableCachedPod -from .source import MergedQuerySource, TableCachedSource -from .stream import QueryStream - - -def convert_to_query_operation( - operation: Operation, - schema: Schema, - table_name: str = None, - table_postfix: str = "", - upstreams: Optional[Collection[QueryStream]] = None, -) -> Tuple[QueryOperation, bool]: - """ - Convert a generic operation to an equivalent, DataJoint specific operation - """ - if upstreams is None: - upstreams = [] - - if isinstance(operation, QueryOperation): - return operation, False - - if isinstance(operation, Source) and len(upstreams) == 0: - return ( - TableCachedSource( - operation, - schema=schema, - table_name=table_name, - table_postfix=table_postfix, - ), - True, - ) - - if isinstance(operation, FunctionPod): - return ( - TableCachedPod( - operation, - schema=schema, - table_name=table_name, - table_postfix=table_postfix, - streams=upstreams, - ), - True, - ) - - if isinstance(operation, Merge): - return ( - MergedQuerySource( - *upstreams, - schema=schema, - table_name=table_name, - table_postfix=table_postfix, - ), - True, - ) - - if isinstance(operation, Mapper): - return convert_to_query_mapper(operation), True - - # operation conversion is not supported, raise an error - raise ValueError(f"Unsupported operation for DJ conversion: {operation}") - - -class QueryTracker(GraphTracker): - """ - Query-specific tracker that tracks the invocations of operations - and their associated streams. - """ - - def __init__(self) -> None: - super().__init__() - self._converted_graph = None - - def generate_tables( - self, schema: Schema, module_name="pipeline" - ) -> Tuple[Any, ModuleType, ModuleType]: - G = self.generate_graph() - - # create a new module and add the tables to it - table_module = ModuleType(module_name) - table_module.__name__ = module_name + "_tables" - op_module = ModuleType(module_name) - op_module.__name__ = module_name + "_op" - - desired_labels_lut = defaultdict(list) - node_lut = {} - edge_lut = {} - for invocation in nx.topological_sort(G): - streams = [edge_lut.get(stream, stream) for stream in invocation.streams] - new_node, converted = convert_to_query_operation( - invocation.operation, - schema, - table_name=None, - table_postfix=invocation.content_hash_int(), - upstreams=streams, - ) - - node_lut[invocation] = new_node - desired_labels_lut[new_node.label].append(new_node) - - if converted: - output_stream = new_node(*streams) - for edge in G.out_edges(invocation): - edge_lut[G.edges[edge]["stream"]] = output_stream - - # construct labels for the oprations - node_label_lut = {} - for label, nodes in desired_labels_lut.items(): - if len(nodes) > 1: - for idx, node in enumerate(nodes): - node_label_lut[node] = f"{label}Id{idx}" - else: - node_label_lut[nodes[0]] = label - # generate the new converted computation graph - G_dj = nx.DiGraph() - for invocation in G: - G_dj.add_node(node_lut[invocation]) - - for edge in G.edges: - stream = G.edges[edge]["stream"] - G_dj.add_edge( - node_lut[edge[0]], - node_lut[edge[1]], - stream=edge_lut.get(stream, stream), - ) - - for op in G_dj: - if hasattr(op, "table"): - op.__module__ = str(op_module) - op.__name__ = node_label_lut[op] - setattr(op_module, node_label_lut[op], op) - - table = op.table - table.__module__ = str(table_module) - table_name = node_label_lut[op] - setattr(table_module, table_name, table) - - setattr(table_module, "schema", schema) - sys.modules[module_name] = table_module - - return G_dj, op_module, table_module diff --git a/src/orcapod/errors.py b/src/orcapod/errors.py new file mode 100644 index 0000000..3775ee9 --- /dev/null +++ b/src/orcapod/errors.py @@ -0,0 +1,11 @@ +class InputValidationError(Exception): + """ + Exception raised when the inputs are not valid. + This is used to indicate that the inputs do not meet the requirements of the operator. + """ + + +class DuplicateTagError(ValueError): + """Raised when duplicate tag values are found and skip_duplicates=False""" + + pass diff --git a/src/orcapod/execution_engines/__init__.py b/src/orcapod/execution_engines/__init__.py new file mode 100644 index 0000000..24050d5 --- /dev/null +++ b/src/orcapod/execution_engines/__init__.py @@ -0,0 +1 @@ +from .ray_execution_engine import RayEngine diff --git a/src/orcapod/execution_engines/ray_execution_engine.py b/src/orcapod/execution_engines/ray_execution_engine.py new file mode 100644 index 0000000..d3443df --- /dev/null +++ b/src/orcapod/execution_engines/ray_execution_engine.py @@ -0,0 +1,123 @@ +from orcapod.utils.lazy_module import LazyModule +from typing import TYPE_CHECKING, Any, TypeVar +import logging +from collections.abc import Callable + +if TYPE_CHECKING: + import asyncio + import ray +else: + asyncio = LazyModule("asyncio") + ray = LazyModule("ray") + +logger = logging.getLogger(__name__) + + +T = TypeVar("T") + + +class RayEngine: + """ + Ray execution engine using native asyncio support. + + This approach uses Ray's built-in async capabilities: + 1. For tasks: Uses ObjectRef.future() + asyncio.wrap_future() + 2. For batch operations: Uses ray's native async support + 3. No polling needed - Ray handles async integration + """ + + def __init__(self, ray_address: str | None = None, **ray_init_kwargs): + """Initialize Ray with native async support.""" + + if not ray.is_initialized(): + ray.init(address=ray_address, **ray_init_kwargs) + self._ray_initialized_here = True + else: + self._ray_initialized_here = False + + logger.info("Native Ray async engine initialized") + logger.info(f"Cluster resources: {ray.cluster_resources()}") + + @property + def name(self) -> str: + return "ray" + + def submit_sync(self, func: Callable[..., T], *args, **kwargs) -> T: + """ + Submit a function synchronously using Ray. + + This is a blocking call that waits for the result. + """ + # Create remote function and submit + remote_func = ray.remote(func) + object_ref = remote_func.remote(*args, **kwargs) + + # Wait for the result - this is blocking + result = ray.get(object_ref) + return result + + async def submit_async(self, func: Callable[..., T], *args, **kwargs) -> T: + """ + Submit a function using Ray's native async support. + + Uses ObjectRef.future() which Ray converts to asyncio.Future natively. + """ + # Create remote function and submit + remote_func = ray.remote(func) + object_ref = remote_func.remote(*args, **kwargs) + + # Use Ray's native async support - this is the key insight! + # ObjectRef.future() returns a concurrent.futures.Future that works with asyncio + future = object_ref.future() # type: ignore + asyncio_future = asyncio.wrap_future(future) + + # This is truly non-blocking and integrates with asyncio event loop + result = await asyncio_future + return result + + async def submit_batch_async( + self, + func: Callable[..., T], + args_list: list[tuple], + kwargs_list: list[dict] | None = None, + ) -> list[T]: + """ + Submit batch using Ray's native async support. + + This is much more efficient than individual submissions. + """ + if kwargs_list is None: + kwargs_list = [{}] * len(args_list) + + # Create remote function once + remote_func = ray.remote(func) + + # Submit all tasks and get ObjectRefs + object_refs = [ + remote_func.remote(*args, **kwargs) + for args, kwargs in zip(args_list, kwargs_list) + ] + + # Convert all ObjectRefs to asyncio futures + asyncio_futures = [ + asyncio.wrap_future(obj_ref.future()) # type: ignore + for obj_ref in object_refs + ] + + # Use asyncio.gather for efficient concurrent execution + results = await asyncio.gather(*asyncio_futures) + return results + + def get_cluster_info(self) -> dict[str, Any]: + """Get Ray cluster information.""" + return { + "cluster_resources": ray.cluster_resources(), + "available_resources": ray.available_resources(), + "nodes": ray.nodes(), + } + + def shutdown(self) -> None: + """Shutdown Ray if we initialized it.""" + if self._ray_initialized_here and ray.is_initialized(): + ray.shutdown() + logger.info("Native Ray async engine shut down") diff --git a/src/orcapod/hashing/__init__.py b/src/orcapod/hashing/__init__.py index 98a15da..b90f228 100644 --- a/src/orcapod/hashing/__init__.py +++ b/src/orcapod/hashing/__init__.py @@ -1,37 +1,15 @@ -from .core import ( - HashableMixin, - function_content_hash, - get_function_signature, - hash_file, - hash_function, - hash_packet, - hash_pathset, - hash_to_hex, - hash_to_int, - hash_to_uuid, -) -from .defaults import ( - get_default_composite_file_hasher, - get_default_object_hasher, - get_default_arrow_hasher, -) -from .types import ( - FileHasher, - PacketHasher, - ArrowHasher, - ObjectHasher, - StringCacher, - FunctionInfoExtractor, - CompositeFileHasher, -) +# from .defaults import ( +# get_default_object_hasher, +# get_default_arrow_hasher, +# ) + __all__ = [ - "FileHasher", - "PacketHasher", - "ArrowHasher", + "FileContentHasher", + "LegacyPacketHasher", "StringCacher", "ObjectHasher", - "CompositeFileHasher", + "LegacyCompositeFileHasher", "FunctionInfoExtractor", "hash_file", "hash_pathset", @@ -46,4 +24,5 @@ "get_default_composite_file_hasher", "get_default_object_hasher", "get_default_arrow_hasher", + "ContentIdentifiableBase", ] diff --git a/src/orcapod/hashing/arrow_hashers.py b/src/orcapod/hashing/arrow_hashers.py new file mode 100644 index 0000000..ebe797e --- /dev/null +++ b/src/orcapod/hashing/arrow_hashers.py @@ -0,0 +1,234 @@ +import hashlib +from typing import Any +import pyarrow as pa +import json +from orcapod.semantic_types import SemanticTypeRegistry +from orcapod.hashing import arrow_serialization +from collections.abc import Callable +from orcapod.hashing.visitors import SemanticHashingVisitor +from orcapod.utils import arrow_utils +from orcapod.protocols.hashing_protocols import ContentHash + + +SERIALIZATION_METHOD_LUT: dict[str, Callable[[pa.Table], bytes]] = { + "logical": arrow_serialization.serialize_table_logical, +} + + +def json_pyarrow_table_serialization(table: pa.Table) -> str: + """ + Serialize a PyArrow table to a stable JSON string by converting to dictionary of lists. + + Args: + table: PyArrow table to serialize + + Returns: + JSON string representation with sorted keys and no whitespace + """ + # Convert table to dictionary of lists using to_pylist() + data_dict = {} + + for column_name in table.column_names: + # Convert Arrow column to Python list, which visits all elements + data_dict[column_name] = table.column(column_name).to_pylist() + + # Serialize to JSON with sorted keys and no whitespace + return json.dumps( + data_dict, + separators=(",", ":"), + sort_keys=True, + ) + + +class SemanticArrowHasher: + """ + Stable hasher for Arrow tables with semantic type support. + + This hasher: + 1. Uses visitor pattern to recursively process nested data structures + 2. Replaces semantic types with their hash strings using registered converters + 3. Sorts columns by name for deterministic ordering + 4. Uses Arrow serialization for stable binary representation + 5. Computes final hash of the processed table + """ + + def __init__( + self, + semantic_registry: SemanticTypeRegistry, + hasher_id: str | None = None, + hash_algorithm: str = "sha256", + chunk_size: int = 8192, + handle_missing: str = "error", + serialization_method: str = "logical", + # TODO: consider passing options for serialization method + ): + """ + Initialize SemanticArrowHasher. + + Args: + semantic_registry: Registry containing semantic type converters with hashing + hash_algorithm: Hash algorithm to use for final table hash + chunk_size: Size of chunks to read files in bytes (legacy, may be removed) + hasher_id: Unique identifier for this hasher instance + handle_missing: How to handle missing files ('error', 'skip', 'null_hash') + serialization_method: Method for serializing Arrow table + """ + if hasher_id is None: + hasher_id = f"semantic_arrow_hasher:{hash_algorithm}:{serialization_method}" + + self._hasher_id = hasher_id + self.semantic_registry = semantic_registry + self.chunk_size = chunk_size + self.handle_missing = handle_missing + self.hash_algorithm = hash_algorithm + + if serialization_method not in SERIALIZATION_METHOD_LUT: + raise ValueError( + f"Invalid serialization method '{serialization_method}'. " + f"Supported methods: {list(SERIALIZATION_METHOD_LUT.keys())}" + ) + self.serialization_method = serialization_method + + @property + def hasher_id(self) -> str: + return self._hasher_id + + def _process_table_columns(self, table: pa.Table) -> pa.Table: + """ + Process table columns using visitor pattern to handle nested semantic types. + + This replaces the old column-by-column processing with a visitor-based approach + that can handle semantic types nested inside complex data structures. + """ + # TODO: Process in batchwise/chunk-wise fashion for memory efficiency + # Currently using to_pylist() for simplicity but this loads entire table into memory + + new_columns = [] + new_fields = [] + + # Import here to avoid circular dependencies + for i, field in enumerate(table.schema): + # Convert column to struct dicts for processing + column_data = table.column(i).to_pylist() + + # TODO: verify the functioning of the visitor pattern + # Create fresh visitor for each column (stateless approach) + visitor = SemanticHashingVisitor(self.semantic_registry) + + try: + # Use visitor to transform both type and data + new_type = None + processed_data = [] + for c in column_data: + processed_type, processed_value = visitor.visit(field.type, c) + if new_type is None: + new_type = processed_type + processed_data.append(processed_value) + + # Create new Arrow column from processed data + assert new_type is not None, "Failed to infer new column type" + # TODO: revisit this logic + new_column = pa.array(processed_data, type=new_type) + new_field = pa.field(field.name, new_type) + + new_columns.append(new_column) + new_fields.append(new_field) + + except Exception as e: + # Add context about which column failed + raise RuntimeError( + f"Failed to process column '{field.name}': {str(e)}" + ) from e + + # Return new table with processed columns + return pa.table(new_columns, schema=pa.schema(new_fields)) + + def _sort_table_columns(self, table: pa.Table) -> pa.Table: + """Sort table columns by field name for deterministic ordering.""" + # Get sorted column names + sorted_column_names = sorted(table.column_names) + + # Use select to reorder columns - much cleaner! + return table.select(sorted_column_names) + + def serialize_arrow_table(self, table: pa.Table) -> bytes: + """ + Serialize Arrow table using the configured serialization method. + + Args: + table: Arrow table to serialize + + Returns: + Serialized bytes of the table + """ + serialization_method_function = SERIALIZATION_METHOD_LUT[ + self.serialization_method + ] + return serialization_method_function(table) + + def hash_table(self, table: pa.Table) -> ContentHash: + """ + Compute stable hash of Arrow table with semantic type processing. + + Args: + table: Arrow table to hash + prefix_hasher_id: Whether to prefix hash with hasher ID + + Returns: + Hex string of the computed hash + """ + + # Step 1: Process columns with semantic types using visitor pattern + processed_table = self._process_table_columns(table) + + # Step 2: Sort columns by name for deterministic ordering + sorted_table = self._sort_table_columns(processed_table) + + # normalize all string to large strings (for compatibility with Polars) + normalized_table = arrow_utils.normalize_table_to_large_types(sorted_table) + + # Step 3: Serialize using configured serialization method + serialized_bytes = self.serialize_arrow_table(normalized_table) + + # Step 4: Compute final hash + hasher = hashlib.new(self.hash_algorithm) + hasher.update(serialized_bytes) + + return ContentHash(method=self.hasher_id, digest=hasher.digest()) + + def hash_table_with_metadata(self, table: pa.Table) -> dict[str, Any]: + """ + Compute hash with additional metadata about the process. + + Returns: + Dictionary containing hash, metadata, and processing info + """ + # Process table to see what transformations were made + processed_table = self._process_table_columns(table) + + # Track processing steps + processed_columns = [] + for i, (original_field, processed_field) in enumerate( + zip(table.schema, processed_table.schema) + ): + column_info = { + "name": original_field.name, + "original_type": str(original_field.type), + "processed_type": str(processed_field.type), + "was_processed": str(original_field.type) != str(processed_field.type), + } + processed_columns.append(column_info) + + # Compute hash + table_hash = self.hash_table(table) + + return { + "hash": table_hash, + "hasher_id": self.hasher_id, + "serialization_method": self.serialization_method, + "hash_algorithm": self.hash_algorithm, + "num_rows": len(table), + "num_columns": len(table.schema), + "processed_columns": processed_columns, + "column_order": [field.name for field in table.schema], + } diff --git a/src/orcapod/hashing/arrow_serialization.py b/src/orcapod/hashing/arrow_serialization.py new file mode 100644 index 0000000..fa0500f --- /dev/null +++ b/src/orcapod/hashing/arrow_serialization.py @@ -0,0 +1,1246 @@ +import pyarrow as pa +import pyarrow.compute as pc +from io import BytesIO +import struct +from typing import Any +import hashlib + + +def bool_sequence_to_byte(sequence: list[bool]) -> bytes: + """Convert a sequence of booleans to a byte array.""" + if len(sequence) > 8: + raise ValueError("Sequence length exceeds 8 bits, cannot fit in a byte.") + mask = 1 + flags = 0 + for value in sequence: + if value: + flags |= mask + mask <<= 1 + return struct.pack(" bytes: + """Serialize order options to bytes for inclusion in format.""" + flags = 0 + if self.ignore_column_order: + flags |= 1 + if self.ignore_row_order: + flags |= 2 + return struct.pack(" "OrderOptions": + """Deserialize order options from bytes.""" + flags = struct.unpack(" pa.Array: + """ + Convert any Arrow array to string representation for sorting purposes. + Handles all data types including complex ones. + """ + if pa.types.is_string(array.type) or pa.types.is_large_string(array.type): + # Already string + return array + + elif pa.types.is_binary(array.type) or pa.types.is_large_binary(array.type): + # Convert binary to base64 string representation for deterministic sorting + try: + # Use Arrow's base64 encoding if available + import base64 + + str_values = [] + # Get null mask + null_mask = pc.is_null(array) # type: ignore + for i in range(len(array)): + if null_mask[i].as_py(): + str_values.append(None) # Will be handled by fill_null later + else: + binary_val = array[i].as_py() + if binary_val is not None: + str_values.append(base64.b64encode(binary_val).decode("ascii")) + else: + str_values.append(None) + return pa.array(str_values, type=pa.string()) + except Exception: + # Fallback: convert to hex string + str_values = [] + try: + null_mask = pc.is_null(array) # type: ignore + for i in range(len(array)): + if null_mask[i].as_py(): + str_values.append(None) + else: + try: + binary_val = array[i].as_py() + if binary_val is not None: + str_values.append(binary_val.hex()) + else: + str_values.append(None) + except Exception: + str_values.append(f"BINARY_{i}") + except Exception: + # If null checking fails, just convert all values + for i in range(len(array)): + try: + binary_val = array[i].as_py() + if binary_val is not None: + str_values.append(binary_val.hex()) + else: + str_values.append(None) + except Exception: + str_values.append(f"BINARY_{i}") + return pa.array(str_values, type=pa.string()) + + elif _is_primitive_type(array.type): + # Convert primitive types to string + try: + return pc.cast(array, pa.string()) + except Exception: + # Manual conversion for types that don't cast well + str_values = [] + try: + null_mask = pc.is_null(array) # type: ignore + for i in range(len(array)): + if null_mask[i].as_py(): + str_values.append(None) + else: + try: + value = array[i].as_py() + str_values.append(str(value)) + except Exception: + str_values.append(f"PRIMITIVE_{i}") + except Exception: + # If null checking fails, just convert all values + for i in range(len(array)): + try: + value = array[i].as_py() + if value is not None: + str_values.append(str(value)) + else: + str_values.append(None) + except Exception: + str_values.append(f"PRIMITIVE_{i}") + return pa.array(str_values, type=pa.string()) + + elif pa.types.is_list(array.type) or pa.types.is_large_list(array.type): + # Convert list to string representation + str_values = [] + try: + null_mask = pc.is_null(array) # type: ignore + for i in range(len(array)): + if null_mask[i].as_py(): + str_values.append(None) + else: + try: + value = array[i].as_py() + # Sort list elements for consistent representation + if value is not None: + sorted_value = sorted( + value, key=lambda x: (x is None, str(x)) + ) + str_values.append(str(sorted_value)) + else: + str_values.append(None) + except Exception: + str_values.append(f"LIST_{i}") + except Exception: + # If null checking fails, just convert all values + for i in range(len(array)): + try: + value = array[i].as_py() + if value is not None: + sorted_value = sorted(value, key=lambda x: (x is None, str(x))) + str_values.append(str(sorted_value)) + else: + str_values.append(None) + except Exception: + str_values.append(f"LIST_{i}") + return pa.array(str_values, type=pa.string()) + + elif pa.types.is_struct(array.type): + # Convert struct to string representation + str_values = [] + try: + null_mask = pc.is_null(array) # type: ignore + for i in range(len(array)): + if null_mask[i].as_py(): + str_values.append(None) + else: + try: + value = array[i].as_py() + if value is not None: + # Sort dict keys for consistent representation + if isinstance(value, dict): + sorted_items = sorted( + value.items(), key=lambda x: str(x[0]) + ) + str_values.append(str(dict(sorted_items))) + else: + str_values.append(str(value)) + else: + str_values.append(None) + except Exception: + str_values.append(f"STRUCT_{i}") + except Exception: + # If null checking fails, just convert all values + for i in range(len(array)): + try: + value = array[i].as_py() + if value is not None: + if isinstance(value, dict): + sorted_items = sorted( + value.items(), key=lambda x: str(x[0]) + ) + str_values.append(str(dict(sorted_items))) + else: + str_values.append(str(value)) + else: + str_values.append(None) + except Exception: + str_values.append(f"STRUCT_{i}") + return pa.array(str_values, type=pa.string()) + + elif pa.types.is_dictionary(array.type): + # Convert dictionary to string representation using the decoded values + str_values = [] + try: + null_mask = pc.is_null(array) # type: ignore + for i in range(len(array)): + if null_mask[i].as_py(): + str_values.append(None) + else: + try: + value = array[i].as_py() + str_values.append(str(value)) + except Exception: + str_values.append(f"DICT_{i}") + except Exception: + # If null checking fails, just convert all values + for i in range(len(array)): + try: + value = array[i].as_py() + if value is not None: + str_values.append(str(value)) + else: + str_values.append(None) + except Exception: + str_values.append(f"DICT_{i}") + return pa.array(str_values, type=pa.string()) + + else: + # Generic fallback for any other types + try: + return pc.cast(array, pa.string()) + except Exception: + # Manual conversion as last resort + str_values = [] + try: + null_mask = pc.is_null(array) # type: ignore + for i in range(len(array)): + if null_mask[i].as_py(): + str_values.append(None) + else: + try: + value = array[i].as_py() + str_values.append(str(value)) + except Exception: + str_values.append(f"UNKNOWN_{array.type}_{i}") + except Exception: + # If null checking fails, just convert all values + for i in range(len(array)): + try: + value = array[i].as_py() + if value is not None: + str_values.append(str(value)) + else: + str_values.append(None) + except Exception: + str_values.append(f"UNKNOWN_{array.type}_{i}") + return pa.array(str_values, type=pa.string()) + + +def _create_row_sort_key(table: pa.Table) -> pa.Array: + """ + Create a deterministic sort key for rows by combining all column values. + This ensures consistent row ordering regardless of input order. + """ + if table.num_rows == 0: + return pa.array([], type=pa.string()) + + # Convert each column to string representation for sorting + sort_components = [] + + for i in range(table.num_columns): + column = table.column(i) + field = table.schema.field(i) + + # Combine all chunks into a single array + if column.num_chunks > 1: + combined_array = pa.concat_arrays(column.chunks) + elif column.num_chunks == 1: + combined_array = column.chunk(0) + else: + combined_array = pa.array([], type=field.type) + + # Convert to string representation for sorting + str_array = _convert_array_to_string_for_sorting(combined_array) + + # Handle nulls by replacing with a consistent null representation + str_array = pc.fill_null(str_array, "NULL") + sort_components.append(str_array) + + # Combine all columns into a single sort key + if len(sort_components) == 1: + return sort_components[0] + else: + # Concatenate all string representations with separators + separator = pa.scalar("||") + combined = sort_components[0] + for component in sort_components[1:]: + combined = pc.binary_join_element_wise(combined, separator, component) # type: ignore + return combined + + +def _sort_table_by_content(table: pa.Table) -> pa.Table: + """Sort table rows based on content for deterministic ordering.""" + if table.num_rows <= 1: + return table + + # Create sort key + sort_key = _create_row_sort_key(table) + + # Get sort indices + sort_indices = pc.sort_indices(sort_key) # type: ignore + + # Apply sort to table + return pc.take(table, sort_indices) + + +def _sort_table_columns_by_name(table: pa.Table) -> pa.Table: + """Sort table columns alphabetically by name for deterministic ordering.""" + if table.num_columns <= 1: + return table + + # Get column names and sort them + column_names = [field.name for field in table.schema] + sorted_names = sorted(column_names) + + # If already sorted, return as-is + if column_names == sorted_names: + return table + + # Reorder columns + return table.select(sorted_names) + + +def serialize_table_logical( + table: pa.Table, order_options: OrderOptions | None = None +) -> bytes: + """ + Serialize table using column-wise processing with direct binary data access. + + This implementation works directly with Arrow's underlying binary buffers + without converting to Python objects, making it much faster and more + memory efficient while maintaining high repeatability. + + Args: + table: PyArrow table to serialize + order_options: Options for handling column and row order independence + """ + if order_options is None: + order_options = OrderOptions() + + buffer = BytesIO() + + # Write format version + buffer.write(b"ARROW_BINARY_V1") # Updated version to include order options + + # Write order options + buffer.write(order_options.to_bytes()) + + # Apply ordering transformations if requested + processed_table = table + + if order_options.ignore_column_order: + processed_table = _sort_table_columns_by_name(processed_table) + + if order_options.ignore_row_order: + processed_table = _sort_table_by_content(processed_table) + + # Serialize schema deterministically + _serialize_schema_deterministic(buffer, processed_table.schema) + + # Process each column using direct binary access + column_digests = [] + for i in range(processed_table.num_columns): + column = processed_table.column(i) + field = processed_table.schema.field(i) + column_digest = _serialize_column_binary(column, field) + column_digests.append(column_digest) + + # Combine column digests + for digest in column_digests: + buffer.write(digest) + + return buffer.getvalue() + + +def _serialize_schema_deterministic(buffer: BytesIO, schema: pa.Schema) -> None: + """Serialize schema information deterministically.""" + buffer.write(struct.pack(" None: + """Serialize Arrow data type deterministically.""" + type_id = data_type.id + buffer.write(struct.pack(" bytes: + """ + Serialize column using direct binary buffer access. + + To ensure chunking independence, we combine chunks into a single array + before processing. This ensures identical output regardless of chunk boundaries. + """ + buffer = BytesIO() + + # Combine all chunks into a single array for consistent processing + if column.num_chunks > 1: + # Multiple chunks - combine them using pa.concat_arrays + combined_array = pa.concat_arrays(column.chunks) + elif column.num_chunks == 1: + # Single chunk - use directly + combined_array = column.chunk(0) + else: + # No chunks - create empty array + combined_array = pa.array([], type=field.type) + + # Process the combined array + chunk_result = _serialize_array_binary(combined_array, field.type) + buffer.write(chunk_result) + + return buffer.getvalue() + + +def _serialize_array_binary(array: pa.Array, data_type: pa.DataType) -> bytes: + """Serialize array using direct access to Arrow's binary buffers.""" + buffer = BytesIO() + + # Get validity buffer (null bitmap) if it exists + validity_buffer = None + if array.buffers()[0] is not None: + validity_buffer = array.buffers()[0] + + # Process based on Arrow type, accessing buffers directly + try: + if _is_primitive_type(data_type): + _serialize_primitive_array_binary(buffer, array, data_type, validity_buffer) + + elif pa.types.is_string(data_type) or pa.types.is_large_string(data_type): + _serialize_string_array_binary(buffer, array, data_type, validity_buffer) + + elif pa.types.is_binary(data_type) or pa.types.is_large_binary(data_type): + _serialize_binary_array_binary(buffer, array, data_type, validity_buffer) + + elif pa.types.is_list(data_type) or pa.types.is_large_list(data_type): + _serialize_list_array_binary(buffer, array, data_type, validity_buffer) + + elif pa.types.is_struct(data_type): + _serialize_struct_array_binary(buffer, array, data_type, validity_buffer) + + elif pa.types.is_dictionary(data_type): + _serialize_dictionary_array_binary( + buffer, array, data_type, validity_buffer + ) + + else: + # Fallback to element-wise processing for complex types + _serialize_array_fallback(buffer, array, data_type, validity_buffer) + + except Exception as e: + # If binary serialization fails, fall back to element-wise processing + print( + f"Warning: Binary serialization failed for {data_type}, falling back to element-wise: {e}" + ) + buffer = BytesIO() # Reset buffer + _serialize_array_fallback(buffer, array, data_type, validity_buffer) + + return buffer.getvalue() + + +def _is_primitive_type(data_type: pa.DataType) -> bool: + """Check if type can be processed as primitive (fixed-size) data.""" + return ( + pa.types.is_integer(data_type) + or pa.types.is_floating(data_type) + or pa.types.is_boolean(data_type) + or pa.types.is_date(data_type) + or pa.types.is_time(data_type) + or pa.types.is_timestamp(data_type) + ) + + +def _serialize_primitive_array_binary( + buffer: BytesIO, array: pa.Array, data_type: pa.DataType, validity_buffer +): + """Serialize primitive arrays by directly copying binary data.""" + # Write validity bitmap + _serialize_validity_buffer(buffer, validity_buffer) + + # Get data buffer (buffer[1] for primitive types) + data_buffer = array.buffers()[1] + if data_buffer is not None: + # For primitive types, copy the buffer directly + if pa.types.is_boolean(data_type): + # Boolean needs the length for bit interpretation + buffer.write(struct.pack(" 0: + child_array = array.children[0] + + # Method 3: Try accessing via flatten() for some list types + elif hasattr(array, "flatten"): + try: + child_array = array.flatten() + except Exception: + pass + + # Recursively serialize child array + if child_array is not None: + child_data = _serialize_array_binary(child_array, data_type.value_type) + buffer.write(child_data) + else: + # If we can't access child arrays directly, fall back to element-wise processing + _serialize_array_fallback(buffer, array, data_type, validity_buffer) + + +def _serialize_struct_array_binary( + buffer: BytesIO, array: pa.Array, data_type: pa.DataType, validity_buffer +): + """Serialize struct arrays by processing child arrays.""" + # Write validity bitmap + _serialize_validity_buffer(buffer, validity_buffer) + + # Get child arrays - handle different access patterns for StructArray + child_arrays = [] + if hasattr(array, "field"): + # StructArray uses .field(i) to access child arrays + for i in range(len(data_type)): + child_arrays.append(array.field(i)) + elif hasattr(array, "children") and array.children: + # Some array types use .children + child_arrays = array.children + else: + # Fallback: try to access fields by iterating + try: + for i in range(len(data_type)): + child_arrays.append(array.field(i)) + except (AttributeError, IndexError): + # If all else fails, use element-wise processing + _serialize_array_fallback(buffer, array, data_type, validity_buffer) + return + + # Serialize each child field + for i, child_array in enumerate(child_arrays): + field_type = data_type[i].type + child_data = _serialize_array_binary(child_array, field_type) + buffer.write(child_data) + + +def _serialize_dictionary_array_binary( + buffer: BytesIO, array: pa.Array, data_type: pa.DataType, validity_buffer +): + """Serialize dictionary arrays using indices + dictionary.""" + # Write validity bitmap + _serialize_validity_buffer(buffer, validity_buffer) + + # Serialize indices array + indices_data = _serialize_array_binary(array.indices, data_type.index_type) + buffer.write(indices_data) + + # Serialize dictionary array + dict_data = _serialize_array_binary(array.dictionary, data_type.value_type) + buffer.write(dict_data) + + +def _serialize_validity_buffer(buffer: BytesIO, validity_buffer): + """Serialize validity (null) bitmap.""" + if validity_buffer is not None: + # Copy validity bitmap directly + buffer.write(validity_buffer.to_pybytes()) + # If no validity buffer, there are no nulls (implicit) + + +def _serialize_array_fallback( + buffer: BytesIO, array: pa.Array, data_type: pa.DataType, validity_buffer +): + """Fallback to element-wise processing for complex types.""" + # Write validity bitmap + _serialize_validity_buffer(buffer, validity_buffer) + + # Process element by element + for i in range(len(array)): + try: + null_mask = pc.is_null(array) # type: ignore + is_null = null_mask[i].as_py() + except: + # Fallback null check + try: + value = array[i].as_py() + is_null = value is None + except: + is_null = False + + if is_null: + buffer.write(b"\x00") + else: + buffer.write(b"\x01") + + # For complex nested types, convert to Python and serialize + try: + value = array[i].as_py() + _serialize_complex_value(buffer, value, data_type) + except Exception as e: + # If .as_py() fails, try alternative approaches + try: + # For some array types, we can access scalar values differently + scalar = array[i] + if hasattr(scalar, "value"): + value = scalar.value + else: + value = str(scalar) # Convert to string as last resort + _serialize_complex_value(buffer, value, data_type) + except Exception: + # Absolute fallback - serialize type name and index + fallback_str = f"{data_type}[{i}]" + fallback_bytes = fallback_str.encode("utf-8") + buffer.write(struct.pack(" str: + """Create deterministic hash using binary serialization.""" + serialized = serialize_table_logical(table, order_options) + + if algorithm == "sha256": + hasher = hashlib.sha256() + elif algorithm == "sha3_256": + hasher = hashlib.sha3_256() + elif algorithm == "blake2b": + hasher = hashlib.blake2b() + else: + raise ValueError(f"Unsupported hash algorithm: {algorithm}") + + hasher.update(serialized) + return hasher.hexdigest() + + +def serialize_table_logical_streaming( + table: pa.Table, order_options: OrderOptions | None = None +) -> str: + """ + Memory-efficient streaming version that produces the same hash as serialize_table_logical_hash. + + This version processes data in streaming fashion but maintains the same logical structure + as the non-streaming version to ensure identical hashes and chunking independence. + """ + if order_options is None: + order_options = OrderOptions() + + hasher = hashlib.sha256() + + # Hash format version (same as non-streaming) + hasher.update(b"ARROW_BINARY_V1") + + # Hash order options + hasher.update(order_options.to_bytes()) + + # Apply ordering transformations if requested + processed_table = table + + if order_options.ignore_column_order: + processed_table = _sort_table_columns_by_name(processed_table) + + if order_options.ignore_row_order: + processed_table = _sort_table_by_content(processed_table) + + # Hash schema (same as non-streaming) + schema_buffer = BytesIO() + _serialize_schema_deterministic(schema_buffer, processed_table.schema) + hasher.update(schema_buffer.getvalue()) + + # Process each column using the same logic as non-streaming + for i in range(processed_table.num_columns): + column = processed_table.column(i) + field = processed_table.schema.field(i) + + # Use the same column serialization logic for chunking independence + column_data = _serialize_column_binary(column, field) + + # Hash the column data + hasher.update(column_data) + + return hasher.hexdigest() + + +# IPC serialization for comparison (updated to include order options for fair comparison) +def serialize_table_ipc( + table: pa.Table, order_options: OrderOptions | None = None +) -> bytes: + """Serialize table using Arrow IPC format for comparison.""" + from io import BytesIO + import pyarrow.ipc as ipc + + if order_options is None: + order_options = OrderOptions() + + buffer = BytesIO() + + # Add format version for consistency with logical serialization + buffer.write(b"ARROW_IPC_V2") + + # Add order options + buffer.write(order_options.to_bytes()) + + # Apply ordering transformations if requested + processed_table = table + + if order_options.ignore_column_order: + processed_table = _sort_table_columns_by_name(processed_table) + + if order_options.ignore_row_order: + processed_table = _sort_table_by_content(processed_table) + + # Standard IPC serialization + ipc_buffer = BytesIO() + with ipc.new_stream(ipc_buffer, processed_table.schema) as writer: + writer.write_table(processed_table) + + # Append IPC data + buffer.write(ipc_buffer.getvalue()) + + return buffer.getvalue() + + +# Test utilities (updated to test order independence) +def create_test_table_1(): + """Create a basic test table with various data types.""" + return pa.table( + { + "int32_col": pa.array([1, 2, None, 4, 5], type=pa.int32()), + "float64_col": pa.array([1.1, 2.2, 3.3, None, 5.5], type=pa.float64()), + "string_col": pa.array(["hello", "world", None, "arrow", "fast"]), + "bool_col": pa.array([True, False, None, True, False]), + "binary_col": pa.array([b"data1", b"data2", None, b"data4", b"data5"]), + } + ) + + +def create_test_table_reordered_columns(): + """Same data as test_table_1 but with different column order.""" + return pa.table( + { + "string_col": pa.array(["hello", "world", None, "arrow", "fast"]), + "bool_col": pa.array([True, False, None, True, False]), + "int32_col": pa.array([1, 2, None, 4, 5], type=pa.int32()), + "binary_col": pa.array([b"data1", b"data2", None, b"data4", b"data5"]), + "float64_col": pa.array([1.1, 2.2, 3.3, None, 5.5], type=pa.float64()), + } + ) + + +def create_test_table_reordered_rows(): + """Same data as test_table_1 but with different row order.""" + return pa.table( + { + "int32_col": pa.array([5, 4, None, 2, 1], type=pa.int32()), + "float64_col": pa.array([5.5, None, 3.3, 2.2, 1.1], type=pa.float64()), + "string_col": pa.array(["fast", "arrow", None, "world", "hello"]), + "bool_col": pa.array([False, True, None, False, True]), + "binary_col": pa.array([b"data5", b"data4", None, b"data2", b"data1"]), + } + ) + + +def create_test_table_different_types(): + """Same logical data but with different Arrow types where possible.""" + return pa.table( + { + "int32_col": pa.array( + [1, 2, None, 4, 5], type=pa.int64() + ), # int64 instead of int32 + "float64_col": pa.array( + [1.1, 2.2, 3.3, None, 5.5], type=pa.float32() + ), # float32 instead of float64 + "string_col": pa.array(["hello", "world", None, "arrow", "fast"]), + "bool_col": pa.array([True, False, None, True, False]), + "binary_col": pa.array([b"data1", b"data2", None, b"data4", b"data5"]), + } + ) + + +def create_test_table_different_chunking(): + """Same data as test_table_1 but with different chunking.""" + # Create arrays with explicit chunking + int_chunks = [ + pa.array([1, 2], type=pa.int32()), + pa.array([None, 4, 5], type=pa.int32()), + ] + float_chunks = [ + pa.array([1.1], type=pa.float64()), + pa.array([2.2, 3.3, None, 5.5], type=pa.float64()), + ] + string_chunks = [pa.array(["hello", "world"]), pa.array([None, "arrow", "fast"])] + bool_chunks = [pa.array([True, False, None]), pa.array([True, False])] + binary_chunks = [ + pa.array([b"data1"]), + pa.array([b"data2", None, b"data4", b"data5"]), + ] + + return pa.table( + { + "int32_col": pa.chunked_array(int_chunks), + "float64_col": pa.chunked_array(float_chunks), + "string_col": pa.chunked_array(string_chunks), + "bool_col": pa.chunked_array(bool_chunks), + "binary_col": pa.chunked_array(binary_chunks), + } + ) + + +def create_test_table_complex_types(): + """Create a table with complex nested types.""" + return pa.table( + { + "list_col": pa.array( + [[1, 2], [3, 4, 5], None, [], [6]], type=pa.list_(pa.int32()) + ), + "struct_col": pa.array( + [ + {"a": 1, "b": "x"}, + {"a": 2, "b": "y"}, + None, + {"a": 3, "b": "z"}, + {"a": 4, "b": "w"}, + ], + type=pa.struct([("a", pa.int32()), ("b", pa.string())]), + ), + "dict_col": pa.array( + ["apple", "banana", "apple", None, "cherry"] + ).dictionary_encode(), + } + ) + + +def run_comprehensive_tests(): + """Run comprehensive test suite for serialization with order independence.""" + import time + + print("=" * 70) + print("COMPREHENSIVE ARROW SERIALIZATION TEST SUITE (WITH ORDER OPTIONS)") + print("=" * 70) + + # Test cases + test_cases = [ + ("Basic table", create_test_table_1), + ("Reordered columns", create_test_table_reordered_columns), + ("Reordered rows", create_test_table_reordered_rows), + ("Different types", create_test_table_different_types), + ("Different chunking", create_test_table_different_chunking), + ("Complex types", create_test_table_complex_types), + ] + + # Order option combinations to test + order_configs = [ + ("Default (order-sensitive)", OrderOptions(False, False)), + ("Column-order independent", OrderOptions(True, False)), + ("Row-order independent", OrderOptions(False, True)), + ("Fully order-independent", OrderOptions(True, True)), + ] + + print("\n1. ORDER INDEPENDENCE TESTS") + print("-" * 50) + + base_table = create_test_table_1() + reordered_cols = create_test_table_reordered_columns() + reordered_rows = create_test_table_reordered_rows() + + for config_name, order_opts in order_configs: + print(f"\n{config_name}:") + print(f" Config: {order_opts}") + + # Test with base table + base_hash = serialize_table_logical_hash(base_table, order_options=order_opts) + cols_hash = serialize_table_logical_hash( + reordered_cols, order_options=order_opts + ) + rows_hash = serialize_table_logical_hash( + reordered_rows, order_options=order_opts + ) + + # Test streaming consistency + base_stream = serialize_table_logical_streaming( + base_table, order_options=order_opts + ) + + print(f" Base table: {base_hash[:12]}...") + print(f" Reordered columns: {cols_hash[:12]}...") + print(f" Reordered rows: {rows_hash[:12]}...") + print(f" Streaming matches: {base_hash == base_stream}") + + # Check expected behavior + cols_should_match = order_opts.ignore_column_order + rows_should_match = order_opts.ignore_row_order + + cols_match = base_hash == cols_hash + rows_match = base_hash == rows_hash + + cols_status = "✓" if cols_match == cols_should_match else "✗" + rows_status = "✓" if rows_match == rows_should_match else "✗" + + print( + f" {cols_status} Column order independence: {cols_match} (expected: {cols_should_match})" + ) + print( + f" {rows_status} Row order independence: {rows_match} (expected: {rows_should_match})" + ) + + print("\n2. CHUNKING INDEPENDENCE WITH ORDER OPTIONS") + print("-" * 50) + + original = create_test_table_1() + combined = original.combine_chunks() + different_chunking = create_test_table_different_chunking() + + for config_name, order_opts in order_configs: + orig_hash = serialize_table_logical_hash(original, order_options=order_opts) + comb_hash = serialize_table_logical_hash(combined, order_options=order_opts) + diff_hash = serialize_table_logical_hash( + different_chunking, order_options=order_opts + ) + + chunking_independent = orig_hash == comb_hash == diff_hash + status = "✓" if chunking_independent else "✗" + + print( + f"{status} {config_name:25} | Chunking independent: {chunking_independent}" + ) + + print("\n3. FORMAT VERSION COMPATIBILITY") + print("-" * 50) + + # Test that different order options produce different hashes when they should + test_table = create_test_table_1() + + hashes = {} + for config_name, order_opts in order_configs: + hash_value = serialize_table_logical_hash(test_table, order_options=order_opts) + hashes[config_name] = hash_value + print(f"{config_name:25} | {hash_value[:16]}...") + + # Verify that order-sensitive vs order-independent produce different hashes + default_hash = hashes["Default (order-sensitive)"] + col_indep_hash = hashes["Column-order independent"] + row_indep_hash = hashes["Row-order independent"] + full_indep_hash = hashes["Fully order-independent"] + + print(f"\nHash uniqueness:") + print(f" Default != Col-independent: {default_hash != col_indep_hash}") + print(f" Default != Row-independent: {default_hash != row_indep_hash}") + print(f" Default != Fully independent: {default_hash != full_indep_hash}") + + print("\n4. CONTENT EQUIVALENCE TEST") + print("-" * 50) + + # Create tables with same content but different presentation + table_a = pa.table({"col1": pa.array([1, 2, 3]), "col2": pa.array(["a", "b", "c"])}) + + table_b = pa.table( + { + "col2": pa.array(["a", "b", "c"]), # Different column order + "col1": pa.array([1, 2, 3]), + } + ) + + table_c = pa.table( + { + "col1": pa.array([3, 1, 2]), # Different row order + "col2": pa.array(["c", "a", "b"]), + } + ) + + table_d = pa.table( + { + "col2": pa.array(["c", "a", "b"]), # Both different + "col1": pa.array([3, 1, 2]), + } + ) + + full_indep_opts = OrderOptions(True, True) + + hash_a = serialize_table_logical_hash(table_a, order_options=full_indep_opts) + hash_b = serialize_table_logical_hash(table_b, order_options=full_indep_opts) + hash_c = serialize_table_logical_hash(table_c, order_options=full_indep_opts) + hash_d = serialize_table_logical_hash(table_d, order_options=full_indep_opts) + + all_match = hash_a == hash_b == hash_c == hash_d + status = "✓" if all_match else "✗" + + print(f"{status} Content equivalence test:") + print(f" Table A (original): {hash_a[:12]}...") + print(f" Table B (reord cols): {hash_b[:12]}...") + print(f" Table C (reord rows): {hash_c[:12]}...") + print(f" Table D (both reord): {hash_d[:12]}...") + print(f" All hashes match: {all_match}") + + print("\n5. PERFORMANCE COMPARISON") + print("-" * 50) + + # Create larger table for performance testing + large_size = 10000 + large_table = pa.table( + { + "int_col": pa.array(list(range(large_size)), type=pa.int32()), + "float_col": pa.array( + [i * 1.5 for i in range(large_size)], type=pa.float64() + ), + "string_col": pa.array([f"item_{i}" for i in range(large_size)]), + "bool_col": pa.array([i % 2 == 0 for i in range(large_size)]), + } + ) + + # Time each method with different order options + for config_name, order_opts in order_configs: + times = [] + for _ in range(3): # Run 3 times for average + start = time.time() + hash_result = serialize_table_logical_hash( + large_table, order_options=order_opts + ) + end = time.time() + times.append(end - start) + + avg_time = sum(times) / len(times) + throughput = (large_size * 4) / avg_time # 4 columns + + print( + f"{config_name:25} | {avg_time * 1000:6.1f}ms | {throughput:8.0f} values/sec" + ) + + print(f"\n{'=' * 70}") + print("ORDER-INDEPENDENT SERIALIZATION TEST SUITE COMPLETE") + print(f"{'=' * 70}") + + +# Main execution +if __name__ == "__main__": + # Run the comprehensive test suite + test_results = run_comprehensive_tests() diff --git a/src/orcapod/hashing/arrow_utils.py b/src/orcapod/hashing/arrow_utils.py new file mode 100644 index 0000000..b9f35d6 --- /dev/null +++ b/src/orcapod/hashing/arrow_utils.py @@ -0,0 +1,409 @@ +import base64 +import hashlib +import json +from decimal import Decimal +from typing import TYPE_CHECKING, Any + +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +def serialize_pyarrow_table_schema(table: "pa.Table") -> str: + """ + Serialize PyArrow table schema to JSON with Python type names and filtered metadata. + + Args: + table: PyArrow table + + Returns: + JSON string representation of schema + """ + schema_info = [] + + for field in table.schema: + field_info = { + "name": field.name, + "type": _arrow_type_to_python_type(field.type), + "metadata": _extract_semantic_metadata(field.metadata), + } + schema_info.append(field_info) + + return json.dumps(schema_info, separators=(",", ":"), sort_keys=True) + + +def serialize_pyarrow_table(table: "pa.Table") -> str: + """ + Serialize a PyArrow table to a stable JSON string with both schema and data. + + Args: + table: PyArrow table to serialize + + Returns: + JSON string representation with schema and data sections + """ + # Convert table to dictionary of lists using to_pylist() + data_dict = {} + + for column_name in table.column_names: + column = table.column(column_name) + # Convert Arrow column to Python list, which visits all elements + column_values = column.to_pylist() + + # Handle special types that need encoding for JSON + data_dict[column_name] = [ + _serialize_value_for_json(val) for val in column_values + ] + + # Serialize schema + schema_info = [] + for field in table.schema: + field_info = { + "name": field.name, + "type": _arrow_type_to_python_type(field.type), + "metadata": _extract_semantic_metadata(field.metadata), + } + schema_info.append(field_info) + + # Combine schema and data + serialized_table = {"schema": schema_info, "data": data_dict} + + # Serialize to JSON with sorted keys and no whitespace + return json.dumps( + serialized_table, + separators=(",", ":"), + sort_keys=True, + default=_json_serializer, + ) + + +def get_pyarrow_table_hash(table: "pa.Table") -> str: + """ + Get a stable SHA-256 hash of the table content. + + Args: + table: PyArrow table + + Returns: + SHA-256 hash of the serialized table + """ + serialized = serialize_pyarrow_table(table) + return hashlib.sha256(serialized.encode("utf-8")).hexdigest() + + +def deserialize_to_pyarrow_table(serialized_str: str) -> "pa.Table": + """ + Deserialize JSON string back to a PyArrow table. + + Args: + serialized_str: JSON string from serialize_pyarrow_table + + Returns: + Reconstructed PyArrow table + """ + parsed_data = json.loads(serialized_str) + + # Handle both old format (dict of lists) and new format (schema + data) + if "data" in parsed_data and "schema" in parsed_data: + # New format with schema and data + data_dict = parsed_data["data"] + schema_info = parsed_data["schema"] + else: + # Old format - just data dict + data_dict = parsed_data + schema_info = None + + if not data_dict: + return pa.table([]) + + # Deserialize each column + arrays = [] + names = [] + + for column_name in sorted(data_dict.keys()): # Sort for consistency + column_values = [_deserialize_value(val) for val in data_dict[column_name]] + arrays.append(pa.array(column_values)) + names.append(column_name) + + return pa.table(arrays, names=names) + + +def _arrow_type_to_python_type(arrow_type: pa.DataType) -> str: + """ + Convert PyArrow data type to standard Python type name. + + Args: + arrow_type: PyArrow data type + + Returns: + Python type name as string + """ + if pa.types.is_boolean(arrow_type): + return "bool" + elif pa.types.is_integer(arrow_type): + return "int" + elif pa.types.is_floating(arrow_type): + return "float" + elif pa.types.is_string(arrow_type) or pa.types.is_large_string(arrow_type): + return "str" + elif pa.types.is_binary(arrow_type) or pa.types.is_large_binary(arrow_type): + return "bytes" + elif pa.types.is_date(arrow_type): + return "date" + elif pa.types.is_timestamp(arrow_type): + return "datetime" + elif pa.types.is_time(arrow_type): + return "time" + elif pa.types.is_decimal(arrow_type): + return "decimal" + elif pa.types.is_list(arrow_type) or pa.types.is_large_list(arrow_type): + element_type = _arrow_type_to_python_type(arrow_type.value_type) + return f"list[{element_type}]" + elif pa.types.is_struct(arrow_type): + return "dict" + elif pa.types.is_dictionary(arrow_type): + value_type = _arrow_type_to_python_type(arrow_type.value_type) + return value_type # Dictionary encoding is transparent + elif pa.types.is_null(arrow_type): + return "NoneType" + else: + # Fallback for other types + return str(arrow_type).lower() + + +def _extract_semantic_metadata(field_metadata) -> dict[str, str]: + """ + Extract only 'semantic_type' metadata from field metadata. + + Args: + field_metadata: PyArrow field metadata (can be None) + + Returns: + Dictionary containing only semantic_type if present, empty dict otherwise + """ + if field_metadata is None: + return {} + + metadata_dict = dict(field_metadata) + + # Only keep semantic_type if it exists + if "semantic_type" in metadata_dict: + return { + "semantic_type": metadata_dict["semantic_type"].decode("utf-8") + if isinstance(metadata_dict["semantic_type"], bytes) + else metadata_dict["semantic_type"] + } + else: + return {} + + +def _serialize_value_for_json(value: Any) -> Any: + """ + Prepare a Python value for JSON serialization. + + Args: + value: Python value from to_pylist() + + Returns: + JSON-serializable value + """ + if value is None: + return None + elif isinstance(value, bytes): + return { + "__type__": "bytes", + "__value__": base64.b64encode(value).decode("ascii"), + } + elif isinstance(value, Decimal): + return {"__type__": "decimal", "__value__": str(value)} + elif hasattr(value, "date") and hasattr(value, "time"): # datetime objects + return {"__type__": "datetime", "__value__": value.isoformat()} + elif hasattr(value, "isoformat") and not hasattr( + value, "time" + ): # date objects (no time component) + return {"__type__": "date", "__value__": value.isoformat()} + elif isinstance(value, (list, tuple)): + return [_serialize_value_for_json(item) for item in value] + elif isinstance(value, dict): + return {k: _serialize_value_for_json(v) for k, v in sorted(value.items())} + else: + return value + + +def _deserialize_value(value: Any) -> Any: + """ + Deserialize a value from the JSON representation. + + Args: + value: Value from JSON + + Returns: + Python value suitable for PyArrow + """ + if value is None: + return None + elif isinstance(value, dict) and "__type__" in value: + type_name = value["__type__"] + val = value["__value__"] + + if type_name == "bytes": + return base64.b64decode(val.encode("ascii")) + elif type_name == "decimal": + return Decimal(val) + elif type_name == "datetime": + from datetime import datetime + + return datetime.fromisoformat(val) + elif type_name == "date": + from datetime import date + + return date.fromisoformat(val) + else: + return val + elif isinstance(value, list): + return [_deserialize_value(item) for item in value] + elif isinstance(value, dict): + return {k: _deserialize_value(v) for k, v in value.items()} + else: + return value + + +def _json_serializer(obj): + """Custom JSON serializer for edge cases.""" + if hasattr(obj, "date") and hasattr(obj, "time"): # datetime objects + return {"__type__": "datetime", "__value__": obj.isoformat()} + elif hasattr(obj, "isoformat") and not hasattr(obj, "time"): # date objects + return {"__type__": "date", "__value__": obj.isoformat()} + elif isinstance(obj, bytes): + return {"__type__": "bytes", "__value__": base64.b64encode(obj).decode("ascii")} + elif isinstance(obj, Decimal): + return {"__type__": "decimal", "__value__": str(obj)} + else: + return str(obj) # Fallback to string representation + + +# Example usage and testing +if __name__ == "__main__": + import datetime + + # Create a sample PyArrow table with various types + data = { + "integers": [1, 2, 3, 4, 5], + "floats": [1.1, 2.2, 3.3, 4.4, 5.5], + "strings": ["a", "b", "c", "d", "e"], + "booleans": [True, False, True, False, True], + "nulls": [1, None, 3, None, 5], + "dates": [ + datetime.date(2023, 1, 1), + datetime.date(2023, 1, 2), + None, + datetime.date(2023, 1, 4), + datetime.date(2023, 1, 5), + ], + } + + table = pa.table(data) + print("Original table:") + print(table) + print() + + # Serialize the table + serialized = serialize_pyarrow_table(table) + print("Serialized JSON (first 200 chars):") + print(serialized[:200] + "..." if len(serialized) > 200 else serialized) + print() + + # Get hash + table_hash = get_pyarrow_table_hash(table) + print(f"Table hash: {table_hash}") + print() + + # Test stability + serialized2 = serialize_pyarrow_table(table) + hash2 = get_pyarrow_table_hash(table) + + print(f"Serialization is stable: {serialized == serialized2}") + print(f"Hash is stable: {table_hash == hash2}") + print() + + # Test with different column order + print("--- Testing column order stability ---") + data_reordered = { + "strings": ["a", "b", "c", "d", "e"], + "integers": [1, 2, 3, 4, 5], + "nulls": [1, None, 3, None, 5], + "floats": [1.1, 2.2, 3.3, 4.4, 5.5], + "booleans": [True, False, True, False, True], + "dates": [ + datetime.date(2023, 1, 1), + datetime.date(2023, 1, 2), + None, + datetime.date(2023, 1, 4), + datetime.date(2023, 1, 5), + ], + } + + table_reordered = pa.table(data_reordered) + serialized_reordered = serialize_pyarrow_table(table_reordered) + hash_reordered = get_pyarrow_table_hash(table_reordered) + + print( + f"Same content, different column order produces same serialization: {serialized == serialized_reordered}" + ) + print( + f"Same content, different column order produces same hash: {table_hash == hash_reordered}" + ) + print() + + # Test schema serialization + print("\n--- Testing schema serialization ---") + + # Create table with metadata + schema = pa.schema( + [ + pa.field( + "integers", + pa.int64(), + metadata={"semantic_type": "id", "other_meta": "ignored"}, + ), + pa.field("floats", pa.float64(), metadata={"semantic_type": "measurement"}), + pa.field("strings", pa.string()), # No metadata + pa.field( + "booleans", pa.bool_(), metadata={"other_meta": "ignored"} + ), # No semantic_type + pa.field("dates", pa.date32(), metadata={"semantic_type": "event_date"}), + ] + ) + + table_with_schema = pa.table(data, schema=schema) + schema_json = serialize_pyarrow_table_schema(table_with_schema) + print(f"Schema JSON: {schema_json}") + + # Parse and display nicely + import json as json_module + + schema_parsed = json_module.loads(schema_json) + print("\nParsed schema:") + for field in schema_parsed: + print(f" {field['name']}: {field['type']} (metadata: {field['metadata']})") + + # Test deserialization + reconstructed = deserialize_to_pyarrow_table(serialized) + print("Reconstructed table:") + print(reconstructed) + print() + + # Verify round-trip + reconstructed_hash = get_pyarrow_table_hash(reconstructed) + print(f"Round-trip hash matches: {table_hash == reconstructed_hash}") + + # Show actual JSON structure for small example + print("\n--- Small example JSON structure ---") + small_table = pa.table( + {"numbers": [1, 2, None], "text": ["hello", "world", "test"]} + ) + small_json = serialize_pyarrow_table(small_table) + print(f"Small table JSON: {small_json}") diff --git a/src/orcapod/hashing/defaults.py b/src/orcapod/hashing/defaults.py index 85e1405..2006761 100644 --- a/src/orcapod/hashing/defaults.py +++ b/src/orcapod/hashing/defaults.py @@ -1,43 +1,62 @@ # A collection of utility function that provides a "default" implementation of hashers. # This is often used as the fallback hasher in the library code. -from orcapod.hashing.types import CompositeFileHasher, ArrowHasher -from orcapod.hashing.file_hashers import PathLikeHasherFactory +from orcapod.protocols import hashing_protocols as hp + from orcapod.hashing.string_cachers import InMemoryCacher -from orcapod.hashing.object_hashers import ObjectHasher -from orcapod.hashing.object_hashers import LegacyObjectHasher + +# from orcapod.hashing.object_hashers import LegacyObjectHasher from orcapod.hashing.function_info_extractors import FunctionInfoExtractorFactory -from orcapod.hashing.semantic_arrow_hasher import SemanticArrowHasher, PathHasher +from orcapod.hashing.versioned_hashers import ( + get_versioned_semantic_arrow_hasher, + get_versioned_object_hasher, +) -def get_default_composite_file_hasher(with_cache=True) -> CompositeFileHasher: - if with_cache: +def get_default_arrow_hasher( + cache_file_hash: bool | hp.StringCacher = True, +) -> hp.ArrowHasher: + """ + Get the default Arrow hasher with semantic type support. + If `cache_file_hash` is True, it uses an in-memory cacher for caching hash values. If a `StringCacher` is provided, it uses that for caching file hashes. + """ + arrow_hasher = get_versioned_semantic_arrow_hasher() + if cache_file_hash: # use unlimited caching - string_cacher = InMemoryCacher(max_size=None) - return PathLikeHasherFactory.create_cached_composite(string_cacher) - return PathLikeHasherFactory.create_basic_composite() + if cache_file_hash is True: + string_cacher = InMemoryCacher(max_size=None) + else: + string_cacher = cache_file_hash + arrow_hasher.set_cacher("path", string_cacher) -def get_default_composite_file_hasher_with_cacher(cacher=None) -> CompositeFileHasher: - if cacher is None: - cacher = InMemoryCacher(max_size=None) - return PathLikeHasherFactory.create_cached_composite(cacher) + return arrow_hasher -def get_default_object_hasher() -> ObjectHasher: - function_info_extractor = ( - FunctionInfoExtractorFactory.create_function_info_extractor( - strategy="signature" - ) - ) - return LegacyObjectHasher( - char_count=32, function_info_extractor=function_info_extractor - ) +def get_default_object_hasher() -> hp.ObjectHasher: + object_hasher = get_versioned_object_hasher() + return object_hasher -def get_default_arrow_hasher( - chunk_size: int = 8192, handle_missing: str = "error" -) -> ArrowHasher: - hasher = SemanticArrowHasher(chunk_size=chunk_size, handle_missing=handle_missing) - # register semantic hasher for Path - hasher.register_semantic_hasher("Path", PathHasher()) - return hasher +# def get_legacy_object_hasher() -> hp.ObjectHasher: +# function_info_extractor = ( +# FunctionInfoExtractorFactory.create_function_info_extractor( +# strategy="signature" +# ) +# ) +# return LegacyObjectHasher(function_info_extractor=function_info_extractor) + + +# def get_default_composite_file_hasher(with_cache=True) -> LegacyCompositeFileHasher: +# if with_cache: +# # use unlimited caching +# string_cacher = InMemoryCacher(max_size=None) +# return LegacyPathLikeHasherFactory.create_cached_legacy_composite(string_cacher) +# return LegacyPathLikeHasherFactory.create_basic_legacy_composite() + + +# def get_default_composite_file_hasher_with_cacher( +# cacher=None, +# ) -> LegacyCompositeFileHasher: +# if cacher is None: +# cacher = InMemoryCacher(max_size=None) +# return LegacyPathLikeHasherFactory.create_cached_legacy_composite(cacher) diff --git a/src/orcapod/hashing/file_hashers.py b/src/orcapod/hashing/file_hashers.py index 77833ee..fd3cd81 100644 --- a/src/orcapod/hashing/file_hashers.py +++ b/src/orcapod/hashing/file_hashers.py @@ -1,15 +1,11 @@ -from orcapod.hashing.core import hash_file, hash_pathset, hash_packet -from orcapod.hashing.types import ( - FileHasher, - PathSetHasher, +from orcapod.hashing.hash_utils import hash_file +from orcapod.protocols.hashing_protocols import ( + FileContentHasher, StringCacher, - CompositeFileHasher, ) -from orcapod.types import Packet, PathLike, PathSet +from orcapod.types import PathLike -# Completely unnecessary to inherit from FileHasher, but this -# allows for type checking based on isinstance class BasicFileHasher: """Basic implementation for file hashing.""" @@ -21,7 +17,7 @@ def __init__( self.algorithm = algorithm self.buffer_size = buffer_size - def hash_file(self, file_path: PathLike) -> str: + def hash_file(self, file_path: PathLike) -> bytes: return hash_file( file_path, algorithm=self.algorithm, buffer_size=self.buffer_size ) @@ -32,133 +28,192 @@ class CachedFileHasher: def __init__( self, - file_hasher: FileHasher, + file_hasher: FileContentHasher, string_cacher: StringCacher, ): self.file_hasher = file_hasher self.string_cacher = string_cacher - def hash_file(self, file_path: PathLike) -> str: + def hash_file(self, file_path: PathLike) -> bytes: cache_key = f"file:{file_path}" cached_value = self.string_cacher.get_cached(cache_key) if cached_value is not None: - return cached_value + return bytes.fromhex(cached_value) value = self.file_hasher.hash_file(file_path) - self.string_cacher.set_cached(cache_key, value) + self.string_cacher.set_cached(cache_key, value.hex()) return value -class DefaultPathsetHasher: - """Default pathset hasher that composes file hashing.""" - - def __init__( - self, - file_hasher: FileHasher, - char_count: int | None = 32, - ): - self.file_hasher = file_hasher - self.char_count = char_count - - def hash_pathset(self, pathset: PathSet) -> str: - """Hash a pathset using the injected file hasher.""" - return hash_pathset( - pathset, - char_count=self.char_count, - file_hasher=self.file_hasher.hash_file, # Inject the method - ) - - -class DefaultPacketHasher: - """Default packet hasher that composes pathset hashing.""" - - def __init__( - self, - pathset_hasher: PathSetHasher, - char_count: int | None = 32, - prefix: str = "", - ): - self.pathset_hasher = pathset_hasher - self.char_count = char_count - self.prefix = prefix - - def hash_packet(self, packet: Packet) -> str: - """Hash a packet using the injected pathset hasher.""" - hash_str = hash_packet( - packet, - char_count=self.char_count, - prefix_algorithm=False, # Will apply prefix on our own - pathset_hasher=self.pathset_hasher.hash_pathset, # Inject the method - ) - return f"{self.prefix}-{hash_str}" if self.prefix else hash_str - - -# Convenience composite implementation -class DefaultCompositeFileHasher: - """Composite hasher that implements all interfaces.""" - - def __init__( - self, - file_hasher: FileHasher, - char_count: int | None = 32, - packet_prefix: str = "", - ): - self.file_hasher = file_hasher - self.pathset_hasher = DefaultPathsetHasher(self.file_hasher, char_count) - self.packet_hasher = DefaultPacketHasher( - self.pathset_hasher, char_count, packet_prefix - ) - - def hash_file(self, file_path: PathLike) -> str: - return self.file_hasher.hash_file(file_path) - - def hash_pathset(self, pathset: PathSet) -> str: - return self.pathset_hasher.hash_pathset(pathset) - - def hash_packet(self, packet: Packet) -> str: - return self.packet_hasher.hash_packet(packet) - - -# Factory for easy construction -class PathLikeHasherFactory: - """Factory for creating various hasher combinations.""" - - @staticmethod - def create_basic_composite( - algorithm: str = "sha256", - buffer_size: int = 65536, - char_count: int | None = 32, - ) -> CompositeFileHasher: - """Create a basic composite hasher.""" - file_hasher = BasicFileHasher(algorithm, buffer_size) - # use algorithm as the prefix for the packet hasher - return DefaultCompositeFileHasher( - file_hasher, char_count, packet_prefix=algorithm - ) - - @staticmethod - def create_cached_composite( - string_cacher: StringCacher, - algorithm: str = "sha256", - buffer_size: int = 65536, - char_count: int | None = 32, - ) -> CompositeFileHasher: - """Create a composite hasher with file caching.""" - basic_file_hasher = BasicFileHasher(algorithm, buffer_size) - cached_file_hasher = CachedFileHasher(basic_file_hasher, string_cacher) - return DefaultCompositeFileHasher( - cached_file_hasher, char_count, packet_prefix=algorithm - ) - - @staticmethod - def create_file_hasher( - string_cacher: StringCacher | None = None, - algorithm: str = "sha256", - buffer_size: int = 65536, - ) -> FileHasher: - """Create just a file hasher, optionally with caching.""" - basic_hasher = BasicFileHasher(algorithm, buffer_size) - if string_cacher is None: - return basic_hasher - else: - return CachedFileHasher(basic_hasher, string_cacher) +# ----------------Legacy implementations for backward compatibility----------------- + + +# class LegacyDefaultFileHasher: +# def __init__( +# self, +# algorithm: str = "sha256", +# buffer_size: int = 65536, +# ): +# self.algorithm = algorithm +# self.buffer_size = buffer_size + +# def hash_file(self, file_path: PathLike) -> str: +# return legacy_core.hash_file( +# file_path, algorithm=self.algorithm, buffer_size=self.buffer_size +# ) + + +# class LegacyCachedFileHasher: +# """File hasher with caching.""" + +# def __init__( +# self, +# file_hasher: LegacyFileHasher, +# string_cacher: StringCacher, +# ): +# self.file_hasher = file_hasher +# self.string_cacher = string_cacher + +# def hash_file(self, file_path: PathLike) -> str: +# cache_key = f"file:{file_path}" +# cached_value = self.string_cacher.get_cached(cache_key) +# if cached_value is not None: +# return cached_value + +# value = self.file_hasher.hash_file(file_path) +# self.string_cacher.set_cached(cache_key, value) +# return value + + +# class LegacyDefaultPathsetHasher: +# """Default pathset hasher that composes file hashing.""" + +# def __init__( +# self, +# file_hasher: LegacyFileHasher, +# char_count: int | None = 32, +# ): +# self.file_hasher = file_hasher +# self.char_count = char_count + +# def _hash_file_to_hex(self, file_path: PathLike) -> str: +# return self.file_hasher.hash_file(file_path) + +# def hash_pathset(self, pathset: PathSet) -> str: +# """Hash a pathset using the injected file hasher.""" +# return legacy_core.hash_pathset( +# pathset, +# char_count=self.char_count, +# file_hasher=self.file_hasher.hash_file, # Inject the method +# ) + + +# class LegacyDefaultPacketHasher: +# """Default packet hasher that composes pathset hashing.""" + +# def __init__( +# self, +# pathset_hasher: LegacyPathSetHasher, +# char_count: int | None = 32, +# prefix: str = "", +# ): +# self.pathset_hasher = pathset_hasher +# self.char_count = char_count +# self.prefix = prefix + +# def _hash_pathset_to_hex(self, pathset: PathSet): +# return self.pathset_hasher.hash_pathset(pathset) + +# def hash_packet(self, packet: PacketLike) -> str: +# """Hash a packet using the injected pathset hasher.""" +# hash_str = legacy_core.hash_packet( +# packet, +# char_count=self.char_count, +# prefix_algorithm=False, # Will apply prefix on our own +# pathset_hasher=self._hash_pathset_to_hex, # Inject the method +# ) +# return f"{self.prefix}-{hash_str}" if self.prefix else hash_str + + +# # Convenience composite implementation +# class LegacyDefaultCompositeFileHasher: +# """Composite hasher that implements all interfaces.""" + +# def __init__( +# self, +# file_hasher: LegacyFileHasher, +# char_count: int | None = 32, +# packet_prefix: str = "", +# ): +# self.file_hasher = file_hasher +# self.pathset_hasher = LegacyDefaultPathsetHasher(self.file_hasher, char_count) +# self.packet_hasher = LegacyDefaultPacketHasher( +# self.pathset_hasher, char_count, packet_prefix +# ) + +# def hash_file(self, file_path: PathLike) -> str: +# return self.file_hasher.hash_file(file_path) + +# def hash_pathset(self, pathset: PathSet) -> str: +# return self.pathset_hasher.hash_pathset(pathset) + +# def hash_packet(self, packet: PacketLike) -> str: +# return self.packet_hasher.hash_packet(packet) + + +# # Factory for easy construction +# class LegacyPathLikeHasherFactory: +# """Factory for creating various hasher combinations.""" + +# @staticmethod +# def create_basic_legacy_composite( +# algorithm: str = "sha256", +# buffer_size: int = 65536, +# char_count: int | None = 32, +# ) -> LegacyCompositeFileHasher: +# """Create a basic composite hasher.""" +# file_hasher = LegacyDefaultFileHasher(algorithm, buffer_size) +# # use algorithm as the prefix for the packet hasher +# return LegacyDefaultCompositeFileHasher( +# file_hasher, char_count, packet_prefix=algorithm +# ) + +# @staticmethod +# def create_cached_legacy_composite( +# string_cacher: StringCacher, +# algorithm: str = "sha256", +# buffer_size: int = 65536, +# char_count: int | None = 32, +# ) -> LegacyCompositeFileHasher: +# """Create a composite hasher with file caching.""" +# basic_file_hasher = LegacyDefaultFileHasher(algorithm, buffer_size) +# cached_file_hasher = LegacyCachedFileHasher(basic_file_hasher, string_cacher) +# return LegacyDefaultCompositeFileHasher( +# cached_file_hasher, char_count, packet_prefix=algorithm +# ) + +# @staticmethod +# def create_legacy_file_hasher( +# string_cacher: StringCacher | None = None, +# algorithm: str = "sha256", +# buffer_size: int = 65536, +# ) -> LegacyFileHasher: +# """Create just a file hasher, optionally with caching.""" +# default_hasher = LegacyDefaultFileHasher(algorithm, buffer_size) +# if string_cacher is None: +# return default_hasher +# else: +# return LegacyCachedFileHasher(default_hasher, string_cacher) + +# @staticmethod +# def create_file_hasher( +# string_cacher: StringCacher | None = None, +# algorithm: str = "sha256", +# buffer_size: int = 65536, +# ) -> FileContentHasher: +# """Create just a file hasher, optionally with caching.""" +# basic_hasher = BasicFileHasher(algorithm, buffer_size) +# if string_cacher is None: +# return basic_hasher +# else: +# return CachedFileHasher(basic_hasher, string_cacher) diff --git a/src/orcapod/hashing/function_info_extractors.py b/src/orcapod/hashing/function_info_extractors.py index 2c32f05..0b5d448 100644 --- a/src/orcapod/hashing/function_info_extractors.py +++ b/src/orcapod/hashing/function_info_extractors.py @@ -1,7 +1,7 @@ -from .types import FunctionInfoExtractor +from orcapod.protocols.hashing_protocols import FunctionInfoExtractor from collections.abc import Callable from typing import Any, Literal -from orcapod.types import TypeSpec +from orcapod.types import PythonSchema import inspect @@ -14,8 +14,8 @@ def extract_function_info( self, func: Callable[..., Any], function_name: str | None = None, - input_types: TypeSpec | None = None, - output_types: TypeSpec | None = None, + input_typespec: PythonSchema | None = None, + output_typespec: PythonSchema | None = None, ) -> dict[str, Any]: if not callable(func): raise TypeError("Provided object is not callable") @@ -38,8 +38,8 @@ def extract_function_info( self, func: Callable[..., Any], function_name: str | None = None, - input_types: TypeSpec | None = None, - output_types: TypeSpec | None = None, + input_typespec: PythonSchema | None = None, + output_typespec: PythonSchema | None = None, ) -> dict[str, Any]: if not callable(func): raise TypeError("Provided object is not callable") diff --git a/src/orcapod/hashing/hash_utils.py b/src/orcapod/hashing/hash_utils.py new file mode 100644 index 0000000..292aa30 --- /dev/null +++ b/src/orcapod/hashing/hash_utils.py @@ -0,0 +1,562 @@ +import logging +import json +from pathlib import Path +from collections.abc import Collection, Callable +import hashlib +import xxhash +import zlib +import inspect + +logger = logging.getLogger(__name__) + + +# TODO: extract default char count as config +def combine_hashes( + *hashes: str, + order: bool = False, + prefix_hasher_id: bool = False, + hex_char_count: int | None = 20, +) -> str: + """Combine hashes into a single hash string.""" + + # Sort for deterministic order regardless of input order + if order: + prepared_hashes = sorted(hashes) + else: + prepared_hashes = list(hashes) + combined = "".join(prepared_hashes) + combined_hash = hashlib.sha256(combined.encode()).hexdigest() + if hex_char_count is not None: + combined_hash = combined_hash[:hex_char_count] + if prefix_hasher_id: + return "sha256@" + combined_hash + return combined_hash + + +def serialize_through_json(processed_obj) -> bytes: + """ + Create a deterministic string representation of a processed object structure. + + Args: + processed_obj: The processed object to serialize + + Returns: + A bytes object ready for hashing + """ + # TODO: add type check of processed obj + return json.dumps(processed_obj, sort_keys=True, separators=(",", ":")).encode( + "utf-8" + ) + + +# def process_structure( +# obj: Any, +# visited: set[int] | None = None, +# object_hasher: ObjectHasher | None = None, +# function_info_extractor: FunctionInfoExtractor | None = None, +# compressed: bool = False, +# force_hash: bool = True, +# ) -> Any: +# """ +# Recursively process a structure to prepare it for hashing. + +# Args: +# obj: The object or structure to process +# visited: Set of object ids already visited (to handle circular references) +# function_info_extractor: FunctionInfoExtractor to be used for extracting necessary function representation + +# Returns: +# A processed version of the structure suitable for stable hashing +# """ +# # Initialize the visited set if this is the top-level call +# if visited is None: +# visited = set() +# else: +# visited = visited.copy() # Copy to avoid modifying the original set + +# # Check for circular references - use object's memory address +# # NOTE: While id() is not stable across sessions, we only use it within a session +# # to detect circular references, not as part of the final hash +# obj_id = id(obj) +# if obj_id in visited: +# logger.debug( +# f"Detected circular reference for object of type {type(obj).__name__}" +# ) +# return "CircularRef" # Don't include the actual id in hash output + +# # For objects that could contain circular references, add to visited +# if isinstance(obj, (dict, list, tuple, set)) or not isinstance( +# obj, (str, int, float, bool, type(None)) +# ): +# visited.add(obj_id) + +# # Handle None +# if obj is None: +# return None + +# # TODO: currently using runtime_checkable on ContentIdentifiable protocol +# # Re-evaluate this strategy to see if a faster / more robust check could be used +# if isinstance(obj, ContentIdentifiable): +# logger.debug( +# f"Processing ContentHashableBase instance of type {type(obj).__name__}" +# ) +# if compressed: +# # if compressed, the content identifiable object is immediately replaced with +# # its hashed string identity +# if object_hasher is None: +# raise ValueError( +# "ObjectHasher must be provided to hash ContentIdentifiable objects with compressed=True" +# ) +# return object_hasher.hash_object(obj.identity_structure(), compressed=True) +# else: +# # if not compressed, replace the object with expanded identity structure and re-process +# return process_structure( +# obj.identity_structure(), +# visited, +# object_hasher=object_hasher, +# function_info_extractor=function_info_extractor, +# ) + +# # Handle basic types +# if isinstance(obj, (str, int, float, bool)): +# return obj + +# # Handle bytes and bytearray +# if isinstance(obj, (bytes, bytearray)): +# logger.debug( +# f"Converting bytes/bytearray of length {len(obj)} to hex representation" +# ) +# return obj.hex() + +# # Handle Path objects +# if isinstance(obj, Path): +# logger.debug(f"Converting Path object to string: {obj}") +# return str(obj) + +# # Handle UUID objects +# if isinstance(obj, UUID): +# logger.debug(f"Converting UUID to string: {obj}") +# return str(obj) + +# # Handle named tuples (which are subclasses of tuple) +# if hasattr(obj, "_fields") and isinstance(obj, tuple): +# logger.debug(f"Processing named tuple of type {type(obj).__name__}") +# # For namedtuples, convert to dict and then process +# d = {field: getattr(obj, field) for field in obj._fields} # type: ignore +# return process_structure( +# d, +# visited, +# object_hasher=object_hasher, +# function_info_extractor=function_info_extractor, +# compressed=compressed, +# ) + +# # Handle mappings (dict-like objects) +# if isinstance(obj, Mapping): +# # Process both keys and values +# processed_items = [ +# ( +# process_structure( +# k, +# visited, +# object_hasher=object_hasher, +# function_info_extractor=function_info_extractor, +# compressed=compressed, +# ), +# process_structure( +# v, +# visited, +# object_hasher=object_hasher, +# function_info_extractor=function_info_extractor, +# compressed=compressed, +# ), +# ) +# for k, v in obj.items() +# ] + +# # Sort by the processed keys for deterministic order +# processed_items.sort(key=lambda x: str(x[0])) + +# # Create a new dictionary with string keys based on processed keys +# # TODO: consider checking for possibly problematic values in processed_k +# # and issue a warning +# return { +# str(processed_k): processed_v +# for processed_k, processed_v in processed_items +# } + +# # Handle sets and frozensets +# if isinstance(obj, (set, frozenset)): +# logger.debug( +# f"Processing set/frozenset of type {type(obj).__name__} with {len(obj)} items" +# ) +# # Process each item first, then sort the processed results +# processed_items = [ +# process_structure( +# item, +# visited, +# object_hasher=object_hasher, +# function_info_extractor=function_info_extractor, +# compressed=compressed, +# ) +# for item in obj +# ] +# return sorted(processed_items, key=str) + +# # Handle collections (list-like objects) +# if isinstance(obj, Collection): +# logger.debug( +# f"Processing collection of type {type(obj).__name__} with {len(obj)} items" +# ) +# return [ +# process_structure( +# item, +# visited, +# object_hasher=object_hasher, +# function_info_extractor=function_info_extractor, +# compressed=compressed, +# ) +# for item in obj +# ] + +# # For functions, use the function_content_hash +# if callable(obj) and hasattr(obj, "__code__"): +# logger.debug(f"Processing function: {getattr(obj, '__name__')}") +# if function_info_extractor is not None: +# # Use the extractor to get a stable representation +# function_info = function_info_extractor.extract_function_info(obj) +# logger.debug(f"Extracted function info: {function_info} for {obj.__name__}") + +# # simply return the function info as a stable representation +# return function_info +# else: +# raise ValueError( +# f"Function {obj} encountered during processing but FunctionInfoExtractor is missing" +# ) + +# # handle data types +# if isinstance(obj, type): +# logger.debug(f"Processing class/type: {obj.__name__}") +# return f"type:{obj.__name__}" + +# # For other objects, attempt to create deterministic representation only if force_hash=True +# class_name = obj.__class__.__name__ +# module_name = obj.__class__.__module__ +# if force_hash: +# try: +# import re + +# logger.debug( +# f"Processing generic object of type {module_name}.{class_name}" +# ) + +# # Try to get a stable dict representation if possible +# if hasattr(obj, "__dict__"): +# # Sort attributes to ensure stable order +# attrs = sorted( +# (k, v) for k, v in obj.__dict__.items() if not k.startswith("_") +# ) +# # Limit to first 10 attributes to avoid extremely long representations +# if len(attrs) > 10: +# logger.debug( +# f"Object has {len(attrs)} attributes, limiting to first 10" +# ) +# attrs = attrs[:10] +# attr_strs = [f"{k}={type(v).__name__}" for k, v in attrs] +# obj_repr = f"{{{', '.join(attr_strs)}}}" +# else: +# # Get basic repr but remove memory addresses +# logger.debug( +# "Object has no __dict__, using repr() with memory address removal" +# ) +# obj_repr = repr(obj) +# if len(obj_repr) > 1000: +# logger.debug( +# f"Object repr is {len(obj_repr)} chars, truncating to 1000" +# ) +# obj_repr = obj_repr[:1000] + "..." +# # Remove memory addresses which look like '0x7f9a1c2b3d4e' +# obj_repr = re.sub(r" at 0x[0-9a-f]+", " at 0xMEMADDR", obj_repr) + +# return f"{module_name}.{class_name}:{obj_repr}" +# except Exception as e: +# # Last resort - use class name only +# logger.warning(f"Failed to process object representation: {e}") +# try: +# return f"object:{obj.__class__.__module__}.{obj.__class__.__name__}" +# except AttributeError: +# logger.error("Could not determine object class, using UnknownObject") +# return "UnknownObject" +# else: +# raise ValueError( +# f"Processing of {obj} of type {module_name}.{class_name} is not supported" +# ) + + +# def hash_object( +# obj: Any, +# function_info_extractor: FunctionInfoExtractor | None = None, +# compressed: bool = False, +# ) -> bytes: +# # Process the object to handle nested structures and HashableMixin instances +# processed = process_structure( +# obj, function_info_extractor=function_info_extractor, compressed=compressed +# ) + +# # Serialize the processed structure +# json_str = json.dumps(processed, sort_keys=True, separators=(",", ":")).encode( +# "utf-8" +# ) +# logger.debug( +# f"Successfully serialized {type(obj).__name__} using custom serializer" +# ) + +# # Create the hash +# return hashlib.sha256(json_str).digest() + + +def hash_file(file_path, algorithm="sha256", buffer_size=65536) -> bytes: + """ + Calculate the hash of a file using the specified algorithm. + + Parameters: + file_path (str): Path to the file to hash + algorithm (str): Hash algorithm to use - options include: + 'md5', 'sha1', 'sha256', 'sha512', 'xxh64', 'crc32', 'hash_path' + buffer_size (int): Size of chunks to read from the file at a time + + Returns: + str: Hexadecimal digest of the hash + """ + # Verify the file exists + if not Path(file_path).is_file(): + raise FileNotFoundError(f"The file {file_path} does not exist") + + # Handle special case for 'hash_path' algorithm + if algorithm == "hash_path": + # Hash the name of the file instead of its content + # This is useful for cases where the file content is well known or + # not relevant + hasher = hashlib.sha256() + hasher.update(file_path.encode("utf-8")) + return hasher.digest() + + # Handle non-cryptographic hash functions + if algorithm == "xxh64": + hasher = xxhash.xxh64() + with open(file_path, "rb") as file: + while True: + data = file.read(buffer_size) + if not data: + break + hasher.update(data) + return hasher.digest() + + if algorithm == "crc32": + crc = 0 + with open(file_path, "rb") as file: + while True: + data = file.read(buffer_size) + if not data: + break + crc = zlib.crc32(data, crc) + return (crc & 0xFFFFFFFF).to_bytes(4, byteorder="big") + + # Handle cryptographic hash functions from hashlib + try: + hasher = hashlib.new(algorithm) + except ValueError: + valid_algorithms = ", ".join(sorted(hashlib.algorithms_available)) + raise ValueError( + f"Invalid algorithm: {algorithm}. Available algorithms: {valid_algorithms}, xxh64, crc32" + ) + + with open(file_path, "rb") as file: + while True: + data = file.read(buffer_size) + if not data: + break + hasher.update(data) + + return hasher.digest() + + +def get_function_signature( + func: Callable, + name_override: str | None = None, + include_defaults: bool = True, + include_module: bool = True, + output_names: Collection[str] | None = None, +) -> str: + """ + Get a stable string representation of a function's signature. + + Args: + func: The function to process + include_defaults: Whether to include default values + include_module: Whether to include the module name + + Returns: + A string representation of the function signature + """ + sig = inspect.signature(func) + + # Build the signature string + parts = {} + + # Add module if requested + if include_module and hasattr(func, "__module__"): + parts["module"] = func.__module__ + + # Add function name + parts["name"] = name_override or func.__name__ + + # Add parameters + param_strs = [] + for name, param in sig.parameters.items(): + param_str = str(param) + if not include_defaults and "=" in param_str: + param_str = param_str.split("=")[0].strip() + param_strs.append(param_str) + + parts["params"] = f"({', '.join(param_strs)})" + + # Add return annotation if present + if sig.return_annotation is not inspect.Signature.empty: + parts["returns"] = sig.return_annotation + + # TODO: fix return handling + fn_string = f"{parts['module'] + '.' if 'module' in parts else ''}{parts['name']}{parts['params']}" + if "returns" in parts: + fn_string = fn_string + f"-> {str(parts['returns'])}" + return fn_string + + +def _is_in_string(line, pos): + """Helper to check if a position in a line is inside a string literal.""" + # This is a simplified check - would need proper parsing for robust handling + in_single = False + in_double = False + for i in range(pos): + if line[i] == "'" and not in_double and (i == 0 or line[i - 1] != "\\"): + in_single = not in_single + elif line[i] == '"' and not in_single and (i == 0 or line[i - 1] != "\\"): + in_double = not in_double + return in_single or in_double + + +def get_function_components( + func: Callable, + name_override: str | None = None, + include_name: bool = True, + include_module: bool = True, + include_declaration: bool = True, + include_docstring: bool = True, + include_comments: bool = True, + preserve_whitespace: bool = True, + include_annotations: bool = True, + include_code_properties: bool = True, +) -> list: + """ + Extract the components of a function that determine its identity for hashing. + + Args: + func: The function to process + include_name: Whether to include the function name + include_module: Whether to include the module name + include_declaration: Whether to include the function declaration line + include_docstring: Whether to include the function's docstring + include_comments: Whether to include comments in the function body + preserve_whitespace: Whether to preserve original whitespace/indentation + include_annotations: Whether to include function type annotations + include_code_properties: Whether to include code object properties + + Returns: + A list of string components + """ + components = [] + + # Add function name + if include_name: + components.append(f"name:{name_override or func.__name__}") + + # Add module + if include_module and hasattr(func, "__module__"): + components.append(f"module:{func.__module__}") + + # Get the function's source code + try: + source = inspect.getsource(func) + + # Handle whitespace preservation + if not preserve_whitespace: + source = inspect.cleandoc(source) + + # Process source code components + if not include_declaration: + # Remove function declaration line + lines = source.split("\n") + for i, line in enumerate(lines): + if line.strip().startswith("def "): + lines.pop(i) + break + source = "\n".join(lines) + + # Extract and handle docstring separately if needed + if not include_docstring and func.__doc__: + # This approach assumes the docstring is properly indented + # For multi-line docstrings, we need more sophisticated parsing + doc_str = inspect.getdoc(func) + if doc_str: + doc_lines = doc_str.split("\n") + else: + doc_lines = [] + doc_pattern = '"""' + "\\n".join(doc_lines) + '"""' + # Try different quote styles + if doc_pattern not in source: + doc_pattern = "'''" + "\\n".join(doc_lines) + "'''" + source = source.replace(doc_pattern, "") + + # Handle comments (this is more complex and may need a proper parser) + if not include_comments: + # This is a simplified approach - would need a proper parser for robust handling + lines = source.split("\n") + for i, line in enumerate(lines): + comment_pos = line.find("#") + if comment_pos >= 0 and not _is_in_string(line, comment_pos): + lines[i] = line[:comment_pos].rstrip() + source = "\n".join(lines) + + components.append(f"source:{source}") + + except (IOError, TypeError): + # If source can't be retrieved, fall back to signature + components.append(f"name:{name_override or func.__name__}") + try: + sig = inspect.signature(func) + components.append(f"signature:{str(sig)}") + except ValueError: + components.append("builtin:True") + + # Add function annotations if requested + if ( + include_annotations + and hasattr(func, "__annotations__") + and func.__annotations__ + ): + sorted_annotations = sorted(func.__annotations__.items()) + annotations_str = ";".join(f"{k}:{v}" for k, v in sorted_annotations) + components.append(f"annotations:{annotations_str}") + + # Add code object properties if requested + if include_code_properties: + code = func.__code__ + stable_code_props = { + "co_argcount": code.co_argcount, + "co_kwonlyargcount": getattr(code, "co_kwonlyargcount", 0), + "co_nlocals": code.co_nlocals, + "co_varnames": code.co_varnames[: code.co_argcount], + } + components.append(f"code_properties:{stable_code_props}") + + return components diff --git a/src/orcapod/hashing/hashing_legacy.py b/src/orcapod/hashing/hashing_legacy.py deleted file mode 100644 index 353a4f9..0000000 --- a/src/orcapod/hashing/hashing_legacy.py +++ /dev/null @@ -1,269 +0,0 @@ -# # a function to hash a dictionary of key value pairs into uuid -# from collections.abc import Collection, Mapping -# import hashlib -# import uuid -# from uuid import UUID -# from typing import Any, Dict, Optional, Union -# import inspect -# import json - -# import hashlib - -# # arbitrary depth of nested dictionaries -# T = Dict[str, Union[str, "T"]] - - -# # TODO: implement proper recursive hashing - - -# def hash_dict(d: T) -> UUID: -# # Convert the dictionary to a string representation -# dict_str = str(sorted(d.items())) - -# # Create a hash of the string representation -# hash_object = hashlib.sha256(dict_str.encode("utf-8")) - -# # Convert the hash to a UUID -# hash_uuid = uuid.UUID(hash_object.hexdigest()) - -# return hash_uuid - - -# def stable_hash(s): -# """Create a stable hash that returns the same integer value across sessions.""" -# # Convert input to bytes if it's not already -# if not isinstance(s, bytes): -# s = str(s).encode("utf-8") - -# hash_hex = hashlib.sha256(s).hexdigest() -# return int(hash_hex[:16], 16) - - -# def hash_function(function, function_hash_mode: str = "content", hasher_kwargs=None) -> str: -# """ -# Hash a function based on its content, signature, or name. -# -# Args: -# function: The function to hash -# function_hash_mode: The mode of hashing ('content', 'signature', 'name') -# function_name: Optional name for the function (if not provided, uses function's __name__) - -# Returns: -# A string representing the hash of the function -# """ -# if hasher_kwargs is None: -# hasher_kwargs = {} - -# if function_hash_mode == "content": -# function_hash = function_content_hash(function, **hasher_kwargs) -# elif function_hash_mode == "signature": -# function_hash = stable_hash(get_function_signature(function, **hasher_kwargs)) -# elif function_hash_mode == "name": -# function_hash = stable_hash(function.__name__) - -# return function_hash - - -# def function_content_hash( -# func, exclude_name=False, exclude_module=False, exclude_declaration=False, return_components=False -# ): -# """ -# Compute a hash based on the function's source code, name, module, and closure variables. -# """ -# components = [] - -# # Add function name -# if not exclude_name: -# components.append(f"name:{func.__name__}") - -# # Add module -# if not exclude_module: -# components.append(f"module:{func.__module__}") - -# # Get the function's source code -# try: -# source = inspect.getsource(func) -# # Clean up the source code -# source = source.strip() -# # Remove the function definition line -# if exclude_declaration: -# # find the line that starts with def and remove it -# # TODO: consider dealing with more sophisticated cases like decorators -# source = "\n".join(line for line in source.split("\n") if not line.startswith("def ")) -# components.append(f"source:{source}") -# except (IOError, TypeError): -# # If we can't get the source (e.g., built-in function), use the function's string representation -# components.append(f"repr:{repr(func)}") - -# # Add closure variables if any -# if func.__closure__: -# closure_values = [] -# for cell in func.__closure__: -# # Try to get a stable representation of the cell content -# try: -# # For simple immutable objects -# if isinstance(cell.cell_contents, (int, float, str, bool, type(None))): -# closure_values.append(repr(cell.cell_contents)) -# # For other objects, we'll use their string representation -# else: -# closure_values.append(str(cell.cell_contents)) -# except: -# # If we can't get a stable representation, use the cell's id -# closure_values.append(f"cell_id:{id(cell)}") - -# components.append(f"closure:{','.join(closure_values)}") - -# # Add function attributes that affect behavior -# if hasattr(func, "__defaults__") and func.__defaults__: -# defaults_str = ",".join(repr(d) for d in func.__defaults__) -# components.append(f"defaults:{defaults_str}") - -# if hasattr(func, "__kwdefaults__") and func.__kwdefaults__: -# kwdefaults_str = ",".join(f"{k}={repr(v)}" for k, v in func.__kwdefaults__.items()) -# components.append(f"kwdefaults:{kwdefaults_str}") - -# # Function's code object properties (excluding filename and line numbers) -# code = func.__code__ -# code_props = { -# "co_argcount": code.co_argcount, -# "co_posonlyargcount": getattr(code, "co_posonlyargcount", 0), # Python 3.8+ -# "co_kwonlyargcount": code.co_kwonlyargcount, -# "co_nlocals": code.co_nlocals, -# "co_stacksize": code.co_stacksize, -# "co_flags": code.co_flags, -# "co_code": code.co_code, -# "co_names": code.co_names, -# "co_varnames": code.co_varnames, -# } -# components.append(f"code_properties:{repr(code_props)}") -# if return_components: -# return components - -# # Join all components and compute hash -# combined = "\n".join(components) -# return hashlib.sha256(combined.encode("utf-8")).hexdigest() - - -# class HashableMixin: -# """A mixin that provides content-based hashing functionality.""" - -# def identity_structure(self) -> Any: -# """ -# Return a structure that represents the identity of this object. -# By default, returns None to indicate that no custom structure is provided. -# Subclasses should override this method to provide meaningful representations. - -# Returns: -# None to indicate no custom structure (use default hash) -# """ -# return None - -# def content_hash(self, char_count: Optional[int] = 16) -> str: -# """ -# Generate a stable string hash based on the object's content. - -# Returns: -# str: A hexadecimal digest representing the object's content -# """ -# # Get the identity structure -# structure = self.identity_structure() - -# # TODO: consider returning __hash__ based value if structure is None - -# # Generate a hash from the identity structure -# return self._hash_structure(structure, char_count=char_count) - -# def content_hash_int(self, hexdigits=16) -> int: -# """ -# Generate a stable integer hash based on the object's content. - -# Returns: -# int: An integer representing the object's content -# """ -# return int(self.content_hash(char_count=None)[:hexdigits], 16) - -# def __hash__(self) -> int: -# """ -# Hash implementation that uses the identity structure if provided, -# otherwise falls back to the superclass's hash method. - -# Returns: -# int: A hash value based on either content or identity -# """ -# # Get the identity structure -# structure = self.identity_structure() - -# # If no custom structure is provided, use the superclass's hash -# if structure is None: -# return super().__hash__() - -# # Generate a hash and convert to integer -# hash_hex = self._hash_structure(structure, char_count=None) -# return int(hash_hex[:16], 16) - -# def _hash_structure(self, structure: Any, char_count: Optional[int] = 16) -> str: -# """ -# Helper method to compute a hash string from a structure. - -# Args: -# structure: The structure to hash - -# Returns: -# str: A hexadecimal hash digest of the structure -# """ -# processed = self._process_structure(structure) -# json_str = json.dumps(processed, sort_keys=True).encode() -# return hashlib.sha256(json_str).hexdigest()[:char_count] - -# def _process_structure(self, obj: Any) -> Any: -# """ -# Recursively process a structure to prepare it for hashing. - -# Args: -# obj: The object or structure to process - -# Returns: -# A processed version of the structure with HashableMixin objects replaced by their hashes -# """ -# # Handle None -# if obj is None: -# return "None" - -# # If the object is a HashableMixin, use its content_hash -# if isinstance(obj, HashableMixin): -# # Don't call content_hash on self to avoid cycles -# if obj is self: -# # TODO: carefully consider this case -# # Use the superclass's hash for self -# return str(super(HashableMixin, self).__hash__()) -# return obj.content_hash() - -# # Handle basic types -# if isinstance(obj, (str, int, float, bool)): -# return str(obj) - -# # Handle named tuples (which are subclasses of tuple) -# if hasattr(obj, "_fields") and isinstance(obj, tuple): -# # For namedtuples, convert to dict and then process -# return self._process_structure({field: value for field, value in zip(obj._fields, obj)}) - -# # Handle mappings (dict-like objects) -# if isinstance(obj, Mapping): -# return {str(k): self._process_structure(v) for k, v in sorted(obj.items(), key=lambda x: str(x[0]))} - -# # Handle sets and frozensets specifically -# if isinstance(obj, (set, frozenset)): -# # Process each item first, then sort the processed results -# processed_items = [self._process_structure(item) for item in obj] -# return sorted(processed_items, key=str) - -# # Handle collections (list-like objects) -# if isinstance(obj, Collection): -# return [self._process_structure(item) for item in obj] - -# # For bytes and bytearray, convert to hex representation -# if isinstance(obj, (bytes, bytearray)): -# return obj.hex() - -# # For other objects, just use their string representation -# return str(obj) diff --git a/src/orcapod/hashing/core.py b/src/orcapod/hashing/legacy_core.py similarity index 94% rename from src/orcapod/hashing/core.py rename to src/orcapod/hashing/legacy_core.py index c711f63..83d172b 100644 --- a/src/orcapod/hashing/core.py +++ b/src/orcapod/hashing/legacy_core.py @@ -1,17 +1,9 @@ -""" -Stable Hashing Library -====================== - -A library for creating stable, content-based hashes that remain consistent across Python sessions, -suitable for arbitrarily nested data structures and custom objects via HashableMixin. -""" - import hashlib import inspect import json import logging import zlib -from .types import FunctionInfoExtractor +from orcapod.protocols.hashing_protocols import FunctionInfoExtractor from functools import partial from os import PathLike from pathlib import Path @@ -29,11 +21,22 @@ ) from uuid import UUID + import xxhash -from orcapod.types import Packet, PathSet +from orcapod.types import PathSet, Packet, PacketLike from orcapod.utils.name import find_noncolliding_name +WARN_NONE_IDENTITY = False +""" +Stable Hashing Library +====================== + +A library for creating stable, content-based hashes that remain consistent across Python sessions, +suitable for arbitrarily nested data structures and custom objects via HashableMixin. +""" + + # Configure logging with __name__ for proper hierarchy logger = logging.getLogger(__name__) @@ -175,11 +178,12 @@ def content_hash(self, char_count: Optional[int] = 16) -> str: # If no custom structure is provided, use the class name # We avoid using id() since it's not stable across sessions if structure is None: - logger.warning( - f"HashableMixin.content_hash called on {self.__class__.__name__} " - "instance that returned identity_structure() of None. " - "Using class name as default identity, which may not correctly reflect object uniqueness." - ) + if WARN_NONE_IDENTITY: + logger.warning( + f"HashableMixin.content_hash called on {self.__class__.__name__} " + "instance that returned identity_structure() of None. " + "Using class name as default identity, which may not correctly reflect object uniqueness." + ) # Fall back to class name for consistent behavior return f"HashableMixin-DefaultIdentity-{self.__class__.__name__}" @@ -205,11 +209,12 @@ def content_hash_int(self, hexdigits: int = 16) -> int: # If no custom structure is provided, use the class name # We avoid using id() since it's not stable across sessions if structure is None: - logger.warning( - f"HashableMixin.content_hash_int called on {self.__class__.__name__} " - "instance without identity_structure() implementation. " - "Using class name as default identity, which may not correctly reflect object uniqueness." - ) + if WARN_NONE_IDENTITY: + logger.warning( + f"HashableMixin.content_hash_int called on {self.__class__.__name__} " + "instance that returned identity_structure() of None. " + "Using class name as default identity, which may not correctly reflect object uniqueness." + ) # Use the same default identity as content_hash for consistency default_identity = ( f"HashableMixin-DefaultIdentity-{self.__class__.__name__}" @@ -235,11 +240,12 @@ def content_hash_uuid(self) -> UUID: # If no custom structure is provided, use the class name # We avoid using id() since it's not stable across sessions if structure is None: - logger.warning( - f"HashableMixin.content_hash_uuid called on {self.__class__.__name__} " - "instance without identity_structure() implementation. " - "Using class name as default identity, which may not correctly reflect object uniqueness." - ) + if WARN_NONE_IDENTITY: + logger.warning( + f"HashableMixin.content_hash_uuid called on {self.__class__.__name__} " + "instance without identity_structure() implementation. " + "Using class name as default identity, which may not correctly reflect object uniqueness." + ) # Use the same default identity as content_hash for consistency default_identity = ( f"HashableMixin-DefaultIdentity-{self.__class__.__name__}" @@ -433,6 +439,16 @@ def process_structure( logger.debug(f"Processing HashableMixin instance of type {type(obj).__name__}") return obj.content_hash() + from .content_identifiable import ContentIdentifiableBase + + if isinstance(obj, ContentIdentifiableBase): + logger.debug( + f"Processing ContentHashableBase instance of type {type(obj).__name__}" + ) + return process_structure( + obj.identity_structure(), visited, function_info_extractor + ) + # Handle basic types if isinstance(obj, (str, int, float, bool)): return obj @@ -666,7 +682,7 @@ def hash_packet_with_psh( def hash_packet( - packet: Packet, + packet: PacketLike, algorithm: str = "sha256", buffer_size: int = 65536, char_count: Optional[int] = 32, @@ -829,6 +845,7 @@ def get_function_signature( name_override: str | None = None, include_defaults: bool = True, include_module: bool = True, + output_names: Collection[str] | None = None, ) -> str: """ Get a stable string representation of a function's signature. @@ -844,14 +861,14 @@ def get_function_signature( sig = inspect.signature(func) # Build the signature string - parts = [] + parts = {} # Add module if requested if include_module and hasattr(func, "__module__"): - parts.append(f"module:{func.__module__}") + parts["module"] = func.__module__ # Add function name - parts.append(f"name:{name_override or func.__name__}") + parts["name"] = name_override or func.__name__ # Add parameters param_strs = [] @@ -861,13 +878,17 @@ def get_function_signature( param_str = param_str.split("=")[0].strip() param_strs.append(param_str) - parts.append(f"params:({', '.join(param_strs)})") + parts["params"] = f"({', '.join(param_strs)})" # Add return annotation if present if sig.return_annotation is not inspect.Signature.empty: - parts.append(f"returns:{sig.return_annotation}") + parts["returns"] = sig.return_annotation - return " ".join(parts) + # TODO: fix return handling + fn_string = f"{parts['module'] + '.' if 'module' in parts else ''}{parts['name']}{parts['params']}" + if "returns" in parts: + fn_string = fn_string + f"-> {str(parts['returns'])}" + return fn_string def _is_in_string(line, pos): diff --git a/src/orcapod/hashing/object_hashers.py b/src/orcapod/hashing/object_hashers.py index a3f4b39..09b01dd 100644 --- a/src/orcapod/hashing/object_hashers.py +++ b/src/orcapod/hashing/object_hashers.py @@ -1,38 +1,306 @@ -from .types import FunctionInfoExtractor, ObjectHasher -from .core import legacy_hash +import hashlib +import json +import logging +import uuid +from abc import ABC, abstractmethod +from collections.abc import Collection, Mapping +from pathlib import Path +from typing import Any +from uuid import UUID +from orcapod.protocols import hashing_protocols as hp -class LegacyObjectHasher(ObjectHasher): - """ - Legacy object hasher that returns the string representation of the object. +logger = logging.getLogger(__name__) - Note that this is "legacy" in the sense that it is not recommended for use in new code. - It is provided for compatibility with existing code that relies on this behavior. - Namely, this algorithm makes use of the - """ - def __init__( - self, - char_count: int | None = 32, - function_info_extractor: FunctionInfoExtractor | None = None, - ): +class ObjectHasherBase(ABC): + @abstractmethod + def hash_object(self, obj: object) -> hp.ContentHash: ... + + @property + @abstractmethod + def hasher_id(self) -> str: ... + + def hash_to_hex( + self, obj: Any, char_count: int | None = None, prefix_hasher_id: bool = False + ) -> str: + content_hash = self.hash_object(obj) + hex_str = content_hash.to_hex() + + # TODO: clean up this logic, as char_count handling is messy + if char_count is not None: + if char_count > len(hex_str): + raise ValueError( + f"Cannot truncate to {char_count} chars, hash only has {len(hex_str)}" + ) + hex_str = hex_str[:char_count] + if prefix_hasher_id: + hex_str = self.hasher_id + "@" + hex_str + return hex_str + + def hash_to_int(self, obj: Any, hexdigits: int = 16) -> int: """ - Initializes the hasher with an optional function info extractor. + Hash an object to an integer. Args: - function_info_extractor (FunctionInfoExtractor | None): Optional extractor for function information. This must be provided if an object containing function information is to be hashed. + obj (Any): The object to hash. + hexdigits (int): Number of hexadecimal digits to use for the hash. + + Returns: + int: The integer representation of the hash. """ - self.char_count = char_count + hex_hash = self.hash_to_hex(obj, char_count=hexdigits) + return int(hex_hash, 16) + + def hash_to_uuid( + self, + obj: Any, + namespace: uuid.UUID = uuid.NAMESPACE_OID, + ) -> uuid.UUID: + """Convert hash to proper UUID5.""" + # TODO: decide whether to use to_hex or digest here + return uuid.uuid5(namespace, self.hash_object(obj).to_hex()) + + +class BasicObjectHasher(ObjectHasherBase): + """ + Default object hasher used throughout the codebase. + """ + + def __init__( + self, + hasher_id: str, + function_info_extractor: hp.FunctionInfoExtractor | None = None, + ): + self._hasher_id = hasher_id self.function_info_extractor = function_info_extractor - def hash(self, obj: object) -> bytes: + @property + def hasher_id(self) -> str: + return self._hasher_id + + def process_structure( + self, + obj: Any, + visited: set[int] | None = None, + force_hash: bool = True, + ) -> Any: """ - Hash an object to a byte representation. + Recursively process a structure to prepare it for hashing. Args: - obj (object): The object to hash. + obj: The object or structure to process + visited: Set of object ids already visited (to handle circular references) + function_info_extractor: FunctionInfoExtractor to be used for extracting necessary function representation Returns: - bytes: The byte representation of the hash. + A processed version of the structure suitable for stable hashing """ - return legacy_hash(obj, function_info_extractor=self.function_info_extractor) + # Initialize the visited set if this is the top-level call + if visited is None: + visited = set() + else: + visited = visited.copy() # Copy to avoid modifying the original set + + # Check for circular references - use object's memory address + # NOTE: While id() is not stable across sessions, we only use it within a session + # to detect circular references, not as part of the final hash + obj_id = id(obj) + if obj_id in visited: + logger.debug( + f"Detected circular reference for object of type {type(obj).__name__}" + ) + return "CircularRef" # Don't include the actual id in hash output + + # TODO: revisit the hashing of the ContentHash + if isinstance(obj, hp.ContentHash): + return (obj.method, obj.digest.hex()) + + # For objects that could contain circular references, add to visited + if isinstance(obj, (dict, list, tuple, set)) or not isinstance( + obj, (str, int, float, bool, type(None)) + ): + visited.add(obj_id) + + # Handle None + if obj is None: + return None + + # TODO: currently using runtime_checkable on ContentIdentifiable protocol + # Re-evaluate this strategy to see if a faster / more robust check could be used + if isinstance(obj, hp.ContentIdentifiable): + logger.debug( + f"Processing ContentHashableBase instance of type {type(obj).__name__}" + ) + return self._hash_object(obj.identity_structure(), visited=visited).to_hex() + + # Handle basic types + if isinstance(obj, (str, int, float, bool)): + return obj + + # Handle bytes and bytearray + if isinstance(obj, (bytes, bytearray)): + logger.debug( + f"Converting bytes/bytearray of length {len(obj)} to hex representation" + ) + return obj.hex() + + # Handle Path objects + if isinstance(obj, Path): + logger.debug(f"Converting Path object to string: {obj}") + raise NotImplementedError( + "Path objects are not supported in this hasher. Please convert to string." + ) + return str(obj) + + # Handle UUID objects + if isinstance(obj, UUID): + logger.debug(f"Converting UUID to string: {obj}") + raise NotImplementedError( + "UUID objects are not supported in this hasher. Please convert to string." + ) + return str(obj) + + # Handle named tuples (which are subclasses of tuple) + if hasattr(obj, "_fields") and isinstance(obj, tuple): + logger.debug(f"Processing named tuple of type {type(obj).__name__}") + # For namedtuples, convert to dict and then process + d = {field: getattr(obj, field) for field in obj._fields} # type: ignore + return self.process_structure(d, visited) + + # Handle mappings (dict-like objects) + if isinstance(obj, Mapping): + # Process both keys and values + processed_items = [ + ( + self.process_structure(k, visited), + self.process_structure(v, visited), + ) + for k, v in obj.items() + ] + + # Sort by the processed keys for deterministic order + processed_items.sort(key=lambda x: str(x[0])) + + # Create a new dictionary with string keys based on processed keys + # TODO: consider checking for possibly problematic values in processed_k + # and issue a warning + return { + str(processed_k): processed_v + for processed_k, processed_v in processed_items + } + + # Handle sets and frozensets + if isinstance(obj, (set, frozenset)): + logger.debug( + f"Processing set/frozenset of type {type(obj).__name__} with {len(obj)} items" + ) + # Process each item first, then sort the processed results + processed_items = [self.process_structure(item, visited) for item in obj] + return sorted(processed_items, key=str) + + # Handle collections (list-like objects) + if isinstance(obj, Collection): + logger.debug( + f"Processing collection of type {type(obj).__name__} with {len(obj)} items" + ) + return [self.process_structure(item, visited) for item in obj] + + # For functions, use the function_content_hash + if callable(obj) and hasattr(obj, "__code__"): + logger.debug(f"Processing function: {getattr(obj, '__name__')}") + if self.function_info_extractor is not None: + # Use the extractor to get a stable representation + function_info = self.function_info_extractor.extract_function_info(obj) + logger.debug( + f"Extracted function info: {function_info} for {obj.__name__}" + ) + + # simply return the function info as a stable representation + return function_info + else: + raise ValueError( + f"Function {obj} encountered during processing but FunctionInfoExtractor is missing" + ) + + # handle data types + if isinstance(obj, type): + logger.debug(f"Processing class/type: {obj.__name__}") + return f"type:{obj.__name__}" + + # For other objects, attempt to create deterministic representation only if force_hash=True + class_name = obj.__class__.__name__ + module_name = obj.__class__.__module__ + if force_hash: + try: + import re + + logger.debug( + f"Processing generic object of type {module_name}.{class_name}" + ) + + # Try to get a stable dict representation if possible + if hasattr(obj, "__dict__"): + # Sort attributes to ensure stable order + attrs = sorted( + (k, v) for k, v in obj.__dict__.items() if not k.startswith("_") + ) + # Limit to first 10 attributes to avoid extremely long representations + if len(attrs) > 10: + logger.debug( + f"Object has {len(attrs)} attributes, limiting to first 10" + ) + attrs = attrs[:10] + attr_strs = [f"{k}={type(v).__name__}" for k, v in attrs] + obj_repr = f"{{{', '.join(attr_strs)}}}" + else: + # Get basic repr but remove memory addresses + logger.debug( + "Object has no __dict__, using repr() with memory address removal" + ) + obj_repr = repr(obj) + if len(obj_repr) > 1000: + logger.debug( + f"Object repr is {len(obj_repr)} chars, truncating to 1000" + ) + obj_repr = obj_repr[:1000] + "..." + # Remove memory addresses which look like '0x7f9a1c2b3d4e' + obj_repr = re.sub(r" at 0x[0-9a-f]+", " at 0xMEMADDR", obj_repr) + + return f"{module_name}.{class_name}:{obj_repr}" + except Exception as e: + # Last resort - use class name only + logger.warning(f"Failed to process object representation: {e}") + try: + return f"object:{obj.__class__.__module__}.{obj.__class__.__name__}" + except AttributeError: + logger.error( + "Could not determine object class, using UnknownObject" + ) + return "UnknownObject" + else: + raise ValueError( + f"Processing of {obj} of type {module_name}.{class_name} is not supported" + ) + + def _hash_object( + self, + obj: Any, + visited: set[int] | None = None, + ) -> hp.ContentHash: + # Process the object to handle nested structures and HashableMixin instances + processed = self.process_structure(obj, visited=visited) + + # Serialize the processed structure + json_str = json.dumps(processed, sort_keys=True, separators=(",", ":")).encode( + "utf-8" + ) + logger.debug( + f"Successfully serialized {type(obj).__name__} using custom serializer" + ) + + # Create the hash + return hp.ContentHash(self.hasher_id, hashlib.sha256(json_str).digest()) + + def hash_object(self, obj: object) -> hp.ContentHash: + return self._hash_object(obj) diff --git a/src/orcapod/hashing/semantic_arrow_hasher.py b/src/orcapod/hashing/semantic_arrow_hasher.py deleted file mode 100644 index f3682ed..0000000 --- a/src/orcapod/hashing/semantic_arrow_hasher.py +++ /dev/null @@ -1,269 +0,0 @@ -import hashlib -import os -from typing import Any, Protocol -from abc import ABC, abstractmethod -import pyarrow as pa -import pyarrow.ipc as ipc -from io import BytesIO -import polars as pl - - -class SemanticTypeHasher(Protocol): - """Abstract base class for semantic type-specific hashers.""" - - @abstractmethod - def hash_column(self, column: pa.Array) -> bytes: - """Hash a column with this semantic type and return the hash bytes.""" - pass - - -class PathHasher(SemanticTypeHasher): - """Hasher for Path semantic type columns - hashes file contents.""" - - def __init__(self, chunk_size: int = 8192, handle_missing: str = "error"): - """ - Initialize PathHasher. - - Args: - chunk_size: Size of chunks to read files in bytes - handle_missing: How to handle missing files ('error', 'skip', 'null_hash') - """ - self.chunk_size = chunk_size - self.handle_missing = handle_missing - - def _hash_file_content(self, file_path: str) -> str: - """Hash the content of a single file and return hex string.""" - import os - - try: - if not os.path.exists(file_path): - if self.handle_missing == "error": - raise FileNotFoundError(f"File not found: {file_path}") - elif self.handle_missing == "skip": - return hashlib.sha256(b"").hexdigest() - elif self.handle_missing == "null_hash": - return hashlib.sha256(b"").hexdigest() - - hasher = hashlib.sha256() - - # Read file in chunks to handle large files efficiently - with open(file_path, "rb") as f: - while chunk := f.read(self.chunk_size): - hasher.update(chunk) - - return hasher.hexdigest() - - except (IOError, OSError, PermissionError) as e: - if self.handle_missing == "error": - raise IOError(f"Cannot read file {file_path}: {e}") - else: # skip or null_hash - error_msg = f"" - return hashlib.sha256(error_msg.encode("utf-8")).hexdigest() - - def hash_column(self, column: pa.Array) -> pa.Array: - """ - Replace path column with file content hashes. - Returns a new array where each path is replaced with its file content hash. - """ - - # Convert to python list for processing - paths = column.to_pylist() - - # Hash each file's content individually - content_hashes = [] - for path in paths: - if path is not None: - # Normalize path for consistency - normalized_path = os.path.normpath(str(path)) - file_content_hash = self._hash_file_content(normalized_path) - content_hashes.append(file_content_hash) - else: - content_hashes.append(None) # Preserve nulls - - # Return new array with content hashes instead of paths - return pa.array(content_hashes) - - -class SemanticArrowHasher: - """ - Stable hasher for Arrow tables with semantic type support. - - This hasher: - 1. Processes columns with special semantic types using dedicated hashers - 2. Sorts columns by name for deterministic ordering - 3. Uses Arrow IPC format for stable serialization - 4. Computes final hash of the processed packet - """ - - def __init__(self, chunk_size: int = 8192, handle_missing: str = "error"): - """ - Initialize SemanticArrowHasher. - - Args: - chunk_size: Size of chunks to read files in bytes - handle_missing: How to handle missing files ('error', 'skip', 'null_hash') - """ - self.chunk_size = chunk_size - self.handle_missing = handle_missing - self.semantic_type_hashers: dict[str, SemanticTypeHasher] = {} - - def register_semantic_hasher(self, semantic_type: str, hasher: SemanticTypeHasher): - """Register a custom hasher for a semantic type.""" - self.semantic_type_hashers[semantic_type] = hasher - - def _get_semantic_type(self, field: pa.Field) -> str | None: - """Extract semantic_type from field metadata.""" - if field.metadata is None: - return None - - metadata = field.metadata - if b"semantic_type" in metadata: - return metadata[b"semantic_type"].decode("utf-8") - elif "semantic_type" in metadata: - return metadata["semantic_type"] - - return None - - def _create_hash_column( - self, original_column: pa.Array, hash_bytes: bytes, original_field: pa.Field - ) -> tuple[pa.Array, pa.Field]: - """Create a new column containing the hash bytes.""" - # Create array of hash bytes (one hash value repeated for each row) - hash_value = hash_bytes.hex() # Convert to hex string for readability - hash_array = pa.array([hash_value] * len(original_column)) - - # Create new field with modified metadata - new_metadata = dict(original_field.metadata) if original_field.metadata else {} - new_metadata["original_semantic_type"] = new_metadata.get( - "semantic_type", "unknown" - ) - new_metadata["semantic_type"] = "hash" - new_metadata["hash_algorithm"] = "sha256" - - new_field = pa.field( - original_field.name, - pa.string(), # Hash stored as string - nullable=original_field.nullable, - metadata=new_metadata, - ) - - return hash_array, new_field - - def _process_table_columns(self, table: pa.Table) -> pa.Table: - # TODO: add copy of table-level metadata to the new table - """Process table columns, replacing semantic type columns with their hashes.""" - new_columns = [] - new_fields = [] - - for i, field in enumerate(table.schema): - column = table.column(i) - semantic_type = self._get_semantic_type(field) - - if semantic_type in self.semantic_type_hashers: - # Hash the column using the appropriate semantic hasher - hasher = self.semantic_type_hashers[semantic_type] - hash_bytes = hasher.hash_column(column) - - # Replace column with hash - hash_column, hash_field = self._create_hash_column( - column, hash_bytes, field - ) - new_columns.append(hash_column) - new_fields.append(hash_field) - else: - # Keep original column - new_columns.append(column) - new_fields.append(field) - - # Create new table with processed columns - new_schema = pa.schema(new_fields) - return pa.table(new_columns, schema=new_schema) - - def _sort_table_columns(self, table: pa.Table) -> pa.Table: - """Sort table columns by field name for deterministic ordering.""" - # Get column indices sorted by field name - sorted_indices = sorted( - range(len(table.schema)), key=lambda i: table.schema.field(i).name - ) - - # Reorder columns - sorted_columns = [table.column(i) for i in sorted_indices] - sorted_fields = [table.schema.field(i) for i in sorted_indices] - - sorted_schema = pa.schema(sorted_fields) - return pa.table(sorted_columns, schema=sorted_schema) - - def _serialize_table_ipc(self, table: pa.Table) -> bytes: - """Serialize table using Arrow IPC format for stable binary representation.""" - buffer = BytesIO() - - # Use IPC stream format for deterministic serialization - with ipc.new_stream(buffer, table.schema) as writer: - writer.write_table(table) - - return buffer.getvalue() - - def hash_table(self, table: pa.Table, algorithm: str = "sha256") -> str: - """ - Compute stable hash of Arrow table. - - Args: - table: Arrow table to hash - algorithm: Hash algorithm to use ('sha256', 'md5', etc.) - - Returns: - Hex string of the computed hash - """ - - # Step 1: Process columns with semantic types - processed_table = self._process_table_columns(table) - - # Step 2: Sort columns by name for deterministic ordering - sorted_table = self._sort_table_columns(processed_table) - - # normalize all string to large strings by passing through polars - # TODO: consider cleaner approach in the future - sorted_table = pl.DataFrame(sorted_table).to_arrow() - - # Step 3: Serialize using Arrow IPC format - serialized_bytes = self._serialize_table_ipc(sorted_table) - - # Step 4: Compute final hash - hasher = hashlib.new(algorithm) - hasher.update(serialized_bytes) - - return hasher.hexdigest() - - def hash_table_with_metadata( - self, table: pa.Table, algorithm: str = "sha256" - ) -> dict[str, Any]: - """ - Compute hash with additional metadata about the process. - - Returns: - Dictionary containing hash, metadata, and processing info - """ - processed_columns = [] - - # Track processing steps - for i, field in enumerate(table.schema): - semantic_type = self._get_semantic_type(field) - column_info = { - "name": field.name, - "original_type": str(field.type), - "semantic_type": semantic_type, - "processed": semantic_type in self.semantic_type_hashers, - } - processed_columns.append(column_info) - - # Compute hash - table_hash = self.hash_table(table, algorithm) - - return { - "hash": table_hash, - "algorithm": algorithm, - "num_rows": len(table), - "num_columns": len(table.schema), - "processed_columns": processed_columns, - "column_order": [field.name for field in table.schema], - } diff --git a/src/orcapod/hashing/semantic_type_hashers.py b/src/orcapod/hashing/semantic_type_hashers.py new file mode 100644 index 0000000..712d019 --- /dev/null +++ b/src/orcapod/hashing/semantic_type_hashers.py @@ -0,0 +1,100 @@ +import hashlib +import os + +import pyarrow as pa + +from orcapod.protocols.hashing_protocols import ( + FileContentHasher, + SemanticTypeHasher, + StringCacher, +) + + +class PathHasher(SemanticTypeHasher): + """Hasher for Path semantic type columns - hashes file contents.""" + + def __init__( + self, + file_hasher: FileContentHasher, + handle_missing: str = "error", + string_cacher: StringCacher | None = None, + cache_key_prefix: str = "path_hasher", + ): + """ + Initialize PathHasher. + + Args: + chunk_size: Size of chunks to read files in bytes + handle_missing: How to handle missing files ('error', 'skip', 'null_hash') + """ + self.file_hasher = file_hasher + self.handle_missing = handle_missing + self.cacher = string_cacher + self.cache_key_prefix = cache_key_prefix + + def _hash_file_content(self, file_path: str) -> bytes: + """Hash the content of a single file""" + import os + + # if cacher exists, check if the hash is cached + if self.cacher: + cache_key = f"{self.cache_key_prefix}:{file_path}" + cached_hash_hex = self.cacher.get_cached(cache_key) + if cached_hash_hex is not None: + return bytes.fromhex(cached_hash_hex) + + try: + if not os.path.exists(file_path): + if self.handle_missing == "error": + raise FileNotFoundError(f"File not found: {file_path}") + elif self.handle_missing == "skip": + return hashlib.sha256(b"").digest() + elif self.handle_missing == "null_hash": + return hashlib.sha256(b"").digest() + + hashed_value = self.file_hasher.hash_file(file_path) + if self.cacher: + # Cache the computed hash hex + self.cacher.set_cached( + f"{self.cache_key_prefix}:{file_path}", hashed_value.to_hex() + ) + # TODO: make consistent use of bytes/string for hash + return hashed_value.digest + + except (IOError, OSError, PermissionError) as e: + if self.handle_missing == "error": + raise IOError(f"Cannot read file {file_path}: {e}") + else: # skip or null_hash + error_msg = f"" + return hashlib.sha256(error_msg.encode("utf-8")).digest() + + def hash_column(self, column: pa.Array) -> pa.Array: + """ + Replace path column with file content hashes. + Returns a new array where each path is replaced with its file content hash. + """ + + # Convert to python list for processing + paths = column.to_pylist() + + # Hash each file's content individually + content_hashes = [] + for path in paths: + if path is not None: + # Normalize path for consistency + normalized_path = os.path.normpath(str(path)) + file_content_hash = self._hash_file_content(normalized_path) + content_hashes.append(file_content_hash) + else: + content_hashes.append(None) # Preserve nulls + + # Return new array with content hashes instead of paths + return pa.array(content_hashes) + + def set_cacher(self, cacher: StringCacher) -> None: + """ + Add a string cacher for caching hash values. + This is a no-op for PathHasher since it hashes file contents directly. + """ + # PathHasher does not use string caching, so this is a no-op + self.cacher = cacher diff --git a/src/orcapod/hashing/string_cachers.py b/src/orcapod/hashing/string_cachers.py index 9b2244a..caa6c93 100644 --- a/src/orcapod/hashing/string_cachers.py +++ b/src/orcapod/hashing/string_cachers.py @@ -4,23 +4,15 @@ import sqlite3 import threading from pathlib import Path -from typing import TYPE_CHECKING, Any, TYPE_CHECKING +from typing import TYPE_CHECKING, Any -from orcapod.hashing.types import StringCacher +from orcapod.protocols.hashing_protocols import StringCacher logger = logging.getLogger(__name__) if TYPE_CHECKING: import redis -def _get_redis(): - """Lazy import for Redis to avoid circular dependencies.""" - try: - import redis - return redis - except ImportError as e: - return None - class TransferCacher(StringCacher): """ @@ -613,9 +605,14 @@ def __init__( socket_timeout: Socket timeout in seconds """ # TODO: cleanup the redis use pattern - self._redis_module = _get_redis() - if self._redis_module is None: - raise ImportError("Could not import Redis module. redis package is required for RedisCacher") + try: + import redis + except ImportError as e: + raise ImportError( + "Could not import Redis module. redis package is required for RedisCacher" + ) from e + + self._redis_module = redis self.key_prefix = key_prefix self._connection_failed = False self._lock = threading.RLock() @@ -658,7 +655,10 @@ def _test_connection(self) -> None: f"Redis connection established successfully with prefix '{self.key_prefix}'" ) - except (self._redis_module.RedisError, self._redis_module.ConnectionError) as e: + except ( + self._redis_module.RedisError, + self._redis_module.ConnectionError, + ) as e: logging.error(f"Failed to establish Redis connection: {e}") raise RuntimeError(f"Redis connection test failed: {e}") @@ -690,7 +690,10 @@ def get_cached(self, cache_key: str) -> str | None: return str(result) - except (self._redis_module.RedisError, self._redis_module.ConnectionError) as e: + except ( + self._redis_module.RedisError, + self._redis_module.ConnectionError, + ) as e: self._handle_redis_error("get", e) return None @@ -708,7 +711,10 @@ def set_cached(self, cache_key: str, value: str) -> None: self.redis.set(self._get_prefixed_key(cache_key), value) - except (self._redis_module.RedisError, self._redis_module.ConnectionError) as e: + except ( + self._redis_module.RedisError, + self._redis_module.ConnectionError, + ) as e: self._handle_redis_error("set", e) def clear_cache(self) -> None: @@ -722,7 +728,10 @@ def clear_cache(self) -> None: if keys: self.redis.delete(*list(keys)) # type: ignore[arg-type] - except (self._redis_module.RedisError, self._redis_module.ConnectionError) as e: + except ( + self._redis_module.RedisError, + self._redis_module.ConnectionError, + ) as e: self._handle_redis_error("clear", e) def is_connected(self) -> bool: diff --git a/src/orcapod/hashing/types.py b/src/orcapod/hashing/types.py index 5e8b07c..027b9e2 100644 --- a/src/orcapod/hashing/types.py +++ b/src/orcapod/hashing/types.py @@ -1,142 +1,178 @@ -"""Hash strategy protocols for dependency injection.""" +# """Hash strategy protocols for dependency injection.""" -from abc import ABC, abstractmethod -from collections.abc import Callable -from typing import Any, Protocol, runtime_checkable -import uuid +# from abc import ABC, abstractmethod +# from collections.abc import Callable +# from typing import Any, Protocol, runtime_checkable +# import uuid -from orcapod.types import Packet, PathLike, PathSet, TypeSpec +# from orcapod.types import PacketLike, PathLike, PathSet, TypeSpec -import pyarrow as pa +# import pyarrow as pa -@runtime_checkable -class Identifiable(Protocol): - """Protocol for objects that can provide an identity structure.""" +# @runtime_checkable +# class Identifiable(Protocol): +# """Protocol for objects that can provide an identity structure.""" - def identity_structure(self) -> Any: - """ - Return a structure that represents the identity of this object. +# def identity_structure(self) -> Any: +# """ +# Return a structure that represents the identity of this object. - Returns: - Any: A structure representing this object's content. - Should be deterministic and include all identity-relevant data. - Return None to indicate no custom identity is available. - """ - pass # pragma: no cover +# Returns: +# Any: A structure representing this object's content. +# Should be deterministic and include all identity-relevant data. +# Return None to indicate no custom identity is available. +# """ +# pass # pragma: no cover -class ObjectHasher(ABC): - """Abstract class for general object hashing.""" +# class ObjectHasher(ABC): +# """Abstract class for general object hashing.""" - @abstractmethod - def hash(self, obj: Any) -> bytes: - """ - Hash an object to a byte representation. +# # TODO: consider more explicitly stating types of objects accepted +# @abstractmethod +# def hash(self, obj: Any) -> bytes: +# """ +# Hash an object to a byte representation. - Args: - obj (Any): The object to hash. +# Args: +# obj (Any): The object to hash. - Returns: - bytes: The byte representation of the hash. - """ - ... +# Returns: +# bytes: The byte representation of the hash. +# """ +# ... - def hash_to_hex(self, obj: Any, char_count: int | None = None) -> str: - hash_bytes = self.hash(obj) - hex_str = hash_bytes.hex() +# @abstractmethod +# def get_hasher_id(self) -> str: +# """ +# Returns a unique identifier/name assigned to the hasher +# """ - # TODO: clean up this logic, as char_count handling is messy - if char_count is not None: - if char_count > len(hex_str): - raise ValueError( - f"Cannot truncate to {char_count} chars, hash only has {len(hex_str)}" - ) - return hex_str[:char_count] - return hex_str +# def hash_to_hex( +# self, obj: Any, char_count: int | None = None, prefix_hasher_id: bool = False +# ) -> str: +# hash_bytes = self.hash(obj) +# hex_str = hash_bytes.hex() - def hash_to_int(self, obj: Any, hexdigits: int = 16) -> int: - """ - Hash an object to an integer. +# # TODO: clean up this logic, as char_count handling is messy +# if char_count is not None: +# if char_count > len(hex_str): +# raise ValueError( +# f"Cannot truncate to {char_count} chars, hash only has {len(hex_str)}" +# ) +# hex_str = hex_str[:char_count] +# if prefix_hasher_id: +# hex_str = self.get_hasher_id() + "@" + hex_str +# return hex_str - Args: - obj (Any): The object to hash. - hexdigits (int): Number of hexadecimal digits to use for the hash. +# def hash_to_int(self, obj: Any, hexdigits: int = 16) -> int: +# """ +# Hash an object to an integer. - Returns: - int: The integer representation of the hash. - """ - hex_hash = self.hash_to_hex(obj, char_count=hexdigits) - return int(hex_hash, 16) +# Args: +# obj (Any): The object to hash. +# hexdigits (int): Number of hexadecimal digits to use for the hash. - def hash_to_uuid( - self, obj: Any, namespace: uuid.UUID = uuid.NAMESPACE_OID - ) -> uuid.UUID: - """Convert hash to proper UUID5.""" - # Use the hex representation as input to UUID5 - return uuid.uuid5(namespace, self.hash(obj)) +# Returns: +# int: The integer representation of the hash. +# """ +# hex_hash = self.hash_to_hex(obj, char_count=hexdigits) +# return int(hex_hash, 16) +# def hash_to_uuid( +# self, obj: Any, namespace: uuid.UUID = uuid.NAMESPACE_OID +# ) -> uuid.UUID: +# """Convert hash to proper UUID5.""" +# return uuid.uuid5(namespace, self.hash(obj)) -@runtime_checkable -class FileHasher(Protocol): - """Protocol for file-related hashing.""" - def hash_file(self, file_path: PathLike) -> str: ... +# @runtime_checkable +# class FileContentHasher(Protocol): +# """Protocol for file-related hashing.""" +# def hash_file(self, file_path: PathLike) -> bytes: ... -# Higher-level operations that compose file hashing -@runtime_checkable -class PathSetHasher(Protocol): - """Protocol for hashing pathsets (files, directories, collections).""" - def hash_pathset(self, pathset: PathSet) -> str: ... +# @runtime_checkable +# class ArrowHasher(Protocol): +# """Protocol for hashing arrow packets.""" +# def get_hasher_id(self) -> str: ... -@runtime_checkable -class SemanticHasher(Protocol): - pass +# def hash_table(self, table: pa.Table, prefix_hasher_id: bool = True) -> str: ... -@runtime_checkable -class PacketHasher(Protocol): - """Protocol for hashing packets.""" +# @runtime_checkable +# class StringCacher(Protocol): +# """Protocol for caching string key value pairs.""" - def hash_packet(self, packet: Packet) -> str: ... +# def get_cached(self, cache_key: str) -> str | None: ... +# def set_cached(self, cache_key: str, value: str) -> None: ... +# def clear_cache(self) -> None: ... -@runtime_checkable -class ArrowHasher(Protocol): - """Protocol for hashing arrow packets.""" +# # Function hasher protocol +# @runtime_checkable +# class FunctionInfoExtractor(Protocol): +# """Protocol for extracting function information.""" - def hash_table(self, table: pa.Table) -> str: ... +# def extract_function_info( +# self, +# func: Callable[..., Any], +# function_name: str | None = None, +# input_typespec: TypeSpec | None = None, +# output_typespec: TypeSpec | None = None, +# ) -> dict[str, Any]: ... -@runtime_checkable -class StringCacher(Protocol): - """Protocol for caching string key value pairs.""" +# class SemanticTypeHasher(Protocol): +# """Abstract base class for semantic type-specific hashers.""" - def get_cached(self, cache_key: str) -> str | None: ... - def set_cached(self, cache_key: str, value: str) -> None: ... - def clear_cache(self) -> None: ... +# @abstractmethod +# def hash_column( +# self, +# column: pa.Array, +# ) -> pa.Array: +# """Hash a column with this semantic type and return the hash bytes.""" +# pass +# @abstractmethod +# def set_cacher(self, cacher: StringCacher) -> None: +# """Add a string cacher for caching hash values.""" +# pass -# Combined interface for convenience (optional) -@runtime_checkable -class CompositeFileHasher(FileHasher, PathSetHasher, PacketHasher, Protocol): - """Combined interface for all file-related hashing operations.""" - pass +# # ---------------Legacy implementations and protocols to be deprecated--------------------- -# Function hasher protocol -@runtime_checkable -class FunctionInfoExtractor(Protocol): - """Protocol for extracting function information.""" +# @runtime_checkable +# class LegacyFileHasher(Protocol): +# """Protocol for file-related hashing.""" - def extract_function_info( - self, - func: Callable[..., Any], - function_name: str | None = None, - input_types: TypeSpec | None = None, - output_types: TypeSpec | None = None, - ) -> dict[str, Any]: ... +# def hash_file(self, file_path: PathLike) -> str: ... + + +# # Higher-level operations that compose file hashing +# @runtime_checkable +# class LegacyPathSetHasher(Protocol): +# """Protocol for hashing pathsets (files, directories, collections).""" + +# def hash_pathset(self, pathset: PathSet) -> str: ... + + +# @runtime_checkable +# class LegacyPacketHasher(Protocol): +# """Protocol for hashing packets.""" + +# def hash_packet(self, packet: PacketLike) -> str: ... + + +# # Combined interface for convenience (optional) +# @runtime_checkable +# class LegacyCompositeFileHasher( +# LegacyFileHasher, LegacyPathSetHasher, LegacyPacketHasher, Protocol +# ): +# """Combined interface for all file-related hashing operations.""" + +# pass diff --git a/src/orcapod/hashing/visitors.py b/src/orcapod/hashing/visitors.py new file mode 100644 index 0000000..e205a12 --- /dev/null +++ b/src/orcapod/hashing/visitors.py @@ -0,0 +1,377 @@ +""" +SUGGESTED FILE: src/orcapod/hashing/visitors.py + +Generic visitor pattern for traversing Arrow types and data simultaneously. + +This provides a base visitor class that can be extended for various processing needs +like semantic hashing, validation, data cleaning, etc. +""" + +from abc import ABC, abstractmethod +from typing import Any, TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule +from orcapod.semantic_types.semantic_registry import SemanticTypeRegistry + + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class ArrowTypeDataVisitor(ABC): + """ + Base visitor for traversing Arrow types and data simultaneously. + + This enables processing that needs to transform both the Arrow schema + and the corresponding data in a single pass. + """ + + @abstractmethod + def visit_struct( + self, struct_type: "pa.StructType", data: dict | None + ) -> tuple["pa.DataType", Any]: + """Visit a struct type with its data""" + pass + + @abstractmethod + def visit_list( + self, list_type: "pa.ListType", data: list | None + ) -> tuple["pa.DataType", Any]: + """Visit a list type with its data""" + pass + + @abstractmethod + def visit_map( + self, map_type: "pa.MapType", data: dict | None + ) -> tuple["pa.DataType", Any]: + """Visit a map type with its data""" + pass + + @abstractmethod + def visit_primitive( + self, primitive_type: "pa.DataType", data: Any + ) -> tuple["pa.DataType", Any]: + """Visit a primitive type with its data""" + pass + + def visit(self, arrow_type: "pa.DataType", data: Any) -> tuple["pa.DataType", Any]: + """ + Main dispatch method that routes to appropriate visit method. + + Args: + arrow_type: Arrow data type to process + data: Corresponding data value + + Returns: + Tuple of (new_arrow_type, new_data) + """ + if pa.types.is_struct(arrow_type): + return self.visit_struct(arrow_type, data) + elif pa.types.is_list(arrow_type) or pa.types.is_large_list(arrow_type): + return self.visit_list(arrow_type, data) + elif pa.types.is_fixed_size_list(arrow_type): + # Treat fixed-size lists like regular lists for processing + return self.visit_list(arrow_type, data) + elif pa.types.is_map(arrow_type): + return self.visit_map(arrow_type, data) + else: + return self.visit_primitive(arrow_type, data) + + def _visit_struct_fields( + self, struct_type: "pa.StructType", data: dict | None + ) -> tuple["pa.StructType", dict]: + """ + Helper method to recursively process struct fields. + + This is the default behavior for regular (non-semantic) structs. + """ + if data is None: + return struct_type, None + + new_fields = [] + new_data = {} + + for field in struct_type: + field_data = data.get(field.name) + new_field_type, new_field_data = self.visit(field.type, field_data) + + new_fields.append(pa.field(field.name, new_field_type)) + new_data[field.name] = new_field_data + + return pa.struct(new_fields), new_data + + def _visit_list_elements( + self, list_type: "pa.ListType", data: list | None + ) -> tuple["pa.DataType", list]: + """ + Helper method to recursively process list elements. + + This is the default behavior for lists. + """ + if data is None: + return list_type, None + + element_type = list_type.value_type + processed_elements = [] + new_element_type = None + + for item in data: + current_element_type, processed_item = self.visit(element_type, item) + processed_elements.append(processed_item) + + # Use the first non-None element to determine new element type + if new_element_type is None: + new_element_type = current_element_type + + # If list was empty or all None, keep original element type + if new_element_type is None: + new_element_type = element_type + + # Create appropriate list type based on original type + if pa.types.is_large_list(list_type): + return pa.large_list(new_element_type), processed_elements + elif pa.types.is_fixed_size_list(list_type): + return pa.list_(new_element_type, list_type.list_size), processed_elements + else: + return pa.list_(new_element_type), processed_elements + + +class PassThroughVisitor(ArrowTypeDataVisitor): + """ + A visitor that passes through data unchanged. + + Useful as a base class or for testing the visitor pattern. + """ + + def visit_struct( + self, struct_type: "pa.StructType", data: dict | None + ) -> tuple["pa.DataType", Any]: + return self._visit_struct_fields(struct_type, data) + + def visit_list( + self, list_type: "pa.ListType", data: list | None + ) -> tuple["pa.DataType", Any]: + return self._visit_list_elements(list_type, data) + + def visit_map( + self, map_type: "pa.MapType", data: dict | None + ) -> tuple["pa.DataType", Any]: + # For simplicity, treat maps like structs for now + # TODO: Implement proper map handling if needed + return map_type, data + + def visit_primitive( + self, primitive_type: "pa.DataType", data: Any + ) -> tuple["pa.DataType", Any]: + return primitive_type, data + + +class SemanticHashingError(Exception): + """Exception raised when semantic hashing fails""" + + pass + + +class SemanticHashingVisitor(ArrowTypeDataVisitor): + """ + Visitor that replaces semantic types with their hash strings. + + This visitor traverses Arrow type structures and data simultaneously, + identifying semantic types by their struct signatures and replacing + them with hash strings computed by their respective converters. + """ + + def __init__(self, semantic_registry: SemanticTypeRegistry): + """ + Initialize the semantic hashing visitor. + + Args: + semantic_registry: Registry containing semantic type converters + """ + self.registry = semantic_registry + self._current_field_path: list[str] = [] + + def visit_struct( + self, struct_type: "pa.StructType", data: dict | None + ) -> tuple["pa.DataType", Any]: + """ + Visit a struct type, checking if it's a semantic type. + + If the struct is a semantic type (recognized by signature), replace it + with a hash string. Otherwise, recursively process its fields. + """ + if data is None: + return struct_type, None + + # Check if this struct IS a semantic type by signature recognition + converter = self.registry.get_converter_for_struct_signature(struct_type) + if converter: + # This is a semantic type - hash it + try: + hash_string = converter.hash_struct_dict(data, add_prefix=True) + return pa.large_string(), hash_string + except Exception as e: + field_path = ( + ".".join(self._current_field_path) + if self._current_field_path + else "" + ) + converter_name = getattr( + converter, "semantic_type_name", str(type(converter).__name__) + ) + raise SemanticHashingError( + f"Failed to hash semantic type '{converter_name}' at field path '{field_path}': {str(e)}" + ) from e + else: + # Regular struct - recursively process fields + return self._visit_struct_fields(struct_type, data) + + def visit_list( + self, list_type: "pa.ListType", data: list | None + ) -> tuple["pa.DataType", Any]: + """ + Visit a list type, recursively processing elements. + + Elements that are semantic types will be replaced with hash strings. + """ + if data is None: + return list_type, None + + # Add list indicator to field path for error context + self._current_field_path.append("[*]") + try: + return self._visit_list_elements(list_type, data) + finally: + self._current_field_path.pop() + + def visit_map( + self, map_type: "pa.MapType", data: dict | None + ) -> tuple["pa.DataType", Any]: + """ + Visit a map type. + + For now, we treat maps as pass-through since they're less common. + TODO: Implement proper map traversal if needed for semantic types in keys/values. + """ + return map_type, data + + def visit_primitive( + self, primitive_type: "pa.DataType", data: Any + ) -> tuple["pa.DataType", Any]: + """ + Visit a primitive type - pass through unchanged. + + Primitive types cannot be semantic types (which are always structs). + """ + return primitive_type, data + + def _visit_struct_fields( + self, struct_type: "pa.StructType", data: dict | None + ) -> tuple["pa.StructType", dict]: + """Override to add field path tracking for better error messages""" + if data is None: + return struct_type, None + + new_fields = [] + new_data = {} + + for field in struct_type: + # Add field name to path for error context + self._current_field_path.append(field.name) + try: + field_data = data.get(field.name) + new_field_type, new_field_data = self.visit(field.type, field_data) + + new_fields.append(pa.field(field.name, new_field_type)) + new_data[field.name] = new_field_data + finally: + self._current_field_path.pop() + + return pa.struct(new_fields), new_data + + +class ValidationVisitor(ArrowTypeDataVisitor): + """ + Example visitor for data validation. + + This demonstrates how the visitor pattern can be extended for other use cases. + """ + + def __init__(self): + self.errors: list[str] = [] + self._current_field_path: list[str] = [] + + def visit_struct( + self, struct_type: "pa.StructType", data: dict | None + ) -> tuple["pa.DataType", Any]: + if data is None: + return struct_type, None + + # Check for missing required fields + field_names = {field.name for field in struct_type} + data_keys = set(data.keys()) + missing_fields = field_names - data_keys + + if missing_fields: + field_path = ( + ".".join(self._current_field_path) + if self._current_field_path + else "" + ) + self.errors.append( + f"Missing required fields {missing_fields} at '{field_path}'" + ) + + return self._visit_struct_fields(struct_type, data) + + def visit_list( + self, list_type: "pa.ListType", data: list | None + ) -> tuple["pa.DataType", Any]: + if data is None: + return list_type, None + + self._current_field_path.append("[*]") + try: + return self._visit_list_elements(list_type, data) + finally: + self._current_field_path.pop() + + def visit_map( + self, map_type: "pa.MapType", data: dict | None + ) -> tuple["pa.DataType", Any]: + return map_type, data + + def visit_primitive( + self, primitive_type: "pa.DataType", data: Any + ) -> tuple["pa.DataType", Any]: + return primitive_type, data + + def _visit_struct_fields( + self, struct_type: "pa.StructType", data: dict | None + ) -> tuple["pa.StructType", dict]: + """Override to add field path tracking""" + if data is None: + return struct_type, None + + new_fields = [] + new_data = {} + + for field in struct_type: + self._current_field_path.append(field.name) + try: + field_data = data.get(field.name) + new_field_type, new_field_data = self.visit(field.type, field_data) + + new_fields.append(pa.field(field.name, new_field_type)) + new_data[field.name] = new_field_data + finally: + self._current_field_path.pop() + + return pa.struct(new_fields), new_data diff --git a/src/orcapod/pipeline/__init__.py b/src/orcapod/pipeline/__init__.py new file mode 100644 index 0000000..616846a --- /dev/null +++ b/src/orcapod/pipeline/__init__.py @@ -0,0 +1,11 @@ +# from .legacy_pipeline import Pipeline + +# __all__ = [ +# "Pipeline", +# ] + +from .graph import Pipeline + +__all__ = [ + "Pipeline", +] diff --git a/src/orcapod/pipeline/graph.py b/src/orcapod/pipeline/graph.py new file mode 100644 index 0000000..84b9eab --- /dev/null +++ b/src/orcapod/pipeline/graph.py @@ -0,0 +1,839 @@ +from orcapod.core.trackers import GraphTracker, Invocation +from orcapod.pipeline.nodes import KernelNode, PodNode +from orcapod.protocols.pipeline_protocols import Node +from orcapod import contexts +from orcapod.protocols import core_protocols as cp +from orcapod.protocols import database_protocols as dbp +from typing import Any +from collections.abc import Collection +import os +import tempfile +import logging +import asyncio +from typing import TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import networkx as nx +else: + nx = LazyModule("networkx") + + +def synchronous_run(async_func, *args, **kwargs): + """ + Use existing event loop if available. + + Pros: Reuses existing loop, more efficient + Cons: More complex, need to handle loop detection + """ + try: + # Check if we're already in an event loop + _ = asyncio.get_running_loop() + + def run_in_thread(): + return asyncio.run(async_func(*args, **kwargs)) + + import concurrent.futures + + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit(run_in_thread) + return future.result() + except RuntimeError: + # No event loop running, safe to use asyncio.run() + return asyncio.run(async_func(*args, **kwargs)) + + +logger = logging.getLogger(__name__) + + +class GraphNode: + def __init__(self, label: str, id: int, kernel_type: str): + self.label = label + self.id = id + self.kernel_type = kernel_type + + def __hash__(self): + return hash((self.id, self.kernel_type)) + + def __eq__(self, other): + if not isinstance(other, GraphNode): + return NotImplemented + return (self.id, self.kernel_type) == ( + other.id, + other.kernel_type, + ) + + +class Pipeline(GraphTracker): + """ + Represents a pipeline in the system. + This class extends GraphTracker to manage the execution of kernels and pods in a pipeline. + """ + + def __init__( + self, + name: str | tuple[str, ...], + pipeline_database: dbp.ArrowDatabase, + results_database: dbp.ArrowDatabase | None = None, + tracker_manager: cp.TrackerManager | None = None, + data_context: str | contexts.DataContext | None = None, + auto_compile: bool = True, + ): + super().__init__(tracker_manager=tracker_manager, data_context=data_context) + if not isinstance(name, tuple): + name = (name,) + self.name = name + self.pipeline_store_path_prefix = self.name + self.results_store_path_prefix = () + if results_database is None: + if pipeline_database is None: + raise ValueError( + "Either pipeline_database or results_database must be provided" + ) + results_database = pipeline_database + self.results_store_path_prefix = self.name + ("_results",) + self.pipeline_database = pipeline_database + self.results_database = results_database + self.nodes: dict[str, Node] = {} + self.auto_compile = auto_compile + self._dirty = False + self._ordered_nodes = [] # Track order of invocations + + def __exit__(self, exc_type=None, exc_value=None, traceback=None): + """ + Exit the pipeline context, ensuring all nodes are properly closed. + """ + super().__exit__(exc_type, exc_value, traceback) + if self.auto_compile: + self.compile() + + def flush(self) -> None: + self.pipeline_database.flush() + self.results_database.flush() + + def record_kernel_invocation( + self, + kernel: cp.Kernel, + upstreams: tuple[cp.Stream, ...], + label: str | None = None, + ) -> None: + super().record_kernel_invocation(kernel, upstreams, label) + self._dirty = True + + def record_pod_invocation( + self, + pod: cp.Pod, + upstreams: tuple[cp.Stream, ...], + label: str | None = None, + ) -> None: + super().record_pod_invocation(pod, upstreams, label) + self._dirty = True + + def compile(self) -> None: + import networkx as nx + + name_candidates = {} + + invocation_to_stream_lut = {} + G = self.generate_graph() + node_graph = nx.DiGraph() + for invocation in nx.topological_sort(G): + input_streams = [ + invocation_to_stream_lut[parent] for parent in invocation.parents() + ] + + node = self.wrap_invocation(invocation, new_input_streams=input_streams) + + for parent in node.upstreams: + node_graph.add_edge(parent.source, node) + + invocation_to_stream_lut[invocation] = node() + name_candidates.setdefault(node.label, []).append(node) + + # visit through the name candidates and resolve any collisions + for label, nodes in name_candidates.items(): + if len(nodes) > 1: + # If there are multiple nodes with the same label, we need to resolve the collision + logger.info(f"Collision detected for label '{label}': {nodes}") + for i, node in enumerate(nodes, start=1): + self.nodes[f"{label}_{i}"] = node + node.label = f"{label}_{i}" + else: + self.nodes[label] = nodes[0] + nodes[0].label = label + + self.label_lut = {v: k for k, v in self.nodes.items()} + + self.graph = node_graph + + def show_graph(self, **kwargs) -> None: + render_graph(self.graph, **kwargs) + + def run( + self, + execution_engine: cp.ExecutionEngine | None = None, + run_async: bool | None = None, + ) -> None: + """Execute the pipeline by running all nodes in the graph. + + This method traverses through all nodes in the graph and executes them sequentially + using the specified execution engine. After execution, flushes the pipeline. + + Args: + execution_engine (dp.ExecutionEngine | None): The execution engine to use for running + the nodes. If None, creates a new default ExecutionEngine instance. + run_async (bool | None): Whether to run nodes asynchronously. If None, defaults to + the preferred mode based on the execution engine. + + Returns: + None + + Note: + Current implementation uses a simple traversal through all nodes. Future versions + may implement more efficient graph traversal algorithms. + """ + for node in self.nodes.values(): + if run_async: + synchronous_run(node.run_async, execution_engine=execution_engine) + else: + node.run(execution_engine=execution_engine) + + self.flush() + + def wrap_invocation( + self, + invocation: Invocation, + new_input_streams: Collection[cp.Stream], + ) -> Node: + if invocation in self.invocation_to_pod_lut: + pod = self.invocation_to_pod_lut[invocation] + node = PodNode( + pod=pod, + input_streams=new_input_streams, + result_database=self.results_database, + record_path_prefix=self.results_store_path_prefix, + pipeline_database=self.pipeline_database, + pipeline_path_prefix=self.pipeline_store_path_prefix, + label=invocation.label, + kernel_type="pod", + ) + elif invocation in self.invocation_to_source_lut: + source = self.invocation_to_source_lut[invocation] + node = KernelNode( + kernel=source, + input_streams=new_input_streams, + pipeline_database=self.pipeline_database, + pipeline_path_prefix=self.pipeline_store_path_prefix, + label=invocation.label, + kernel_type="source", + ) + else: + node = KernelNode( + kernel=invocation.kernel, + input_streams=new_input_streams, + pipeline_database=self.pipeline_database, + pipeline_path_prefix=self.pipeline_store_path_prefix, + label=invocation.label, + kernel_type="operator", + ) + return node + + def __getattr__(self, item: str) -> Any: + """Allow direct access to pipeline attributes.""" + if item in self.nodes: + return self.nodes[item] + raise AttributeError(f"Pipeline has no attribute '{item}'") + + def __dir__(self) -> list[str]: + """Return a list of attributes and methods of the pipeline.""" + return list(super().__dir__()) + list(self.nodes.keys()) + + def rename(self, old_name: str, new_name: str) -> None: + """ + Rename a node in the pipeline. + This will update the label and the internal mapping. + """ + if old_name not in self.nodes: + raise KeyError(f"Node '{old_name}' does not exist in the pipeline.") + if new_name in self.nodes: + raise KeyError(f"Node '{new_name}' already exists in the pipeline.") + node = self.nodes[old_name] + del self.nodes[old_name] + node.label = new_name + self.nodes[new_name] = node + logger.info(f"Node '{old_name}' renamed to '{new_name}'") + + +from typing import Optional, Dict, Any + + +class GraphRenderer: + """Improved GraphRenderer with centralized default styling""" + + # ==================== + # CENTRALIZED DEFAULTS + # ==================== + DEFAULT_STYLES = { + "rankdir": "TB", + "node_shape": "box", + "node_style": "filled", + "node_color": "navy", + "font_color": "white", + "type_font_color": "#54508C", # muted navy blue + "font_name": "sans-serif", + "font_path": None, # Set to None by default + # 'font_path': './assets/fonts/LexendDeca-Medium.ttf', + "edge_color": "black", + "dpi": 150, + # HTML Label defaults + "main_font_size": 14, # Main label font size + "type_font_size": 11, # Pod type font size (small) + "type_style": "normal", # Pod type text style + } + + DEFAULT_STYLE_RULES = { + "source": { + "fillcolor": "white", + "shape": "rect", + "fontcolor": "black", + "style": "filled", + "typefontcolor": "#3A3737", # dark gray + }, + "operator": { + "fillcolor": "#DFD6CF", # pale beige + "shape": "diamond", + "fontcolor": "black", + "style": "filled", + "typefontcolor": "#3A3737", # dark gray + }, + "pod": { + "fillcolor": "#f5f5f5", # off white + "shape": "cylinder", + "fontcolor": "#090271", # darker navy blue + "style": "filled", + "typefontcolor": "#3A3737", # dark gray + }, + } + + DARK_THEME_RULES = { + "source": { + "fillcolor": "black", + "shape": "rect", + "fontcolor": "white", + "style": "filled", + "typefontcolor": "lightgray", # Light text for dark background + }, + "operator": { + "fillcolor": "#026e8e", # ocean blue + "shape": "diamond", + "fontcolor": "white", + "style": "filled", + "typefontcolor": "lightgray", # Light text for dark background + }, + "pod": { + "fillcolor": "#090271", # darker navy blue + "shape": "cylinder", + "fontcolor": "white", + "style": "filled", + "typefontcolor": "lightgray", # Light text for dark background + }, + } + + def __init__(self): + pass + + def _sanitize_node_id(self, node_id: Any) -> str: + return f"node_{hash(node_id)}" + + def _create_default_html_label(self, node, node_attrs) -> str: + """ + Create HTML for the label (text) section of the node + + Format: + kernel_type (11pt, small text) + main_label (14pt, normal text) + """ + + main_label = str(node.label) if hasattr(node, "label") else str(node) + kernel_type = str(node.kernel_type) if hasattr(node, "kernel_type") else "" + + if not kernel_type: + # No kernel_type, just return main label + return f'{main_label}' + + # Create HTML label: small kernel_type above, main label below + main_size = self.DEFAULT_STYLES["main_font_size"] + type_size = self.DEFAULT_STYLES["type_font_size"] + font_name = self.DEFAULT_STYLES["font_name"] + type_font_color = node_attrs.get( + "typefontcolor", self.DEFAULT_STYLES["type_font_color"] + ) + + html_label = f'''< + + + +
{kernel_type}
{main_label}
+ >''' + + return html_label + + def _get_node_label( + self, node_id: Any, label_lut: Optional[Dict[Any, str]] = None + ) -> str: + if label_lut and node_id in label_lut: + return label_lut[node_id] + return str(node_id) + + def _get_node_attributes( + self, node_id: Any, style_rules: Dict | None = None + ) -> Dict[str, str]: + """ + Get styling attributes for a specific node based on its properties + """ + # Use provided rules or defaults + rules = style_rules or self.DEFAULT_STYLE_RULES + + # Default attributes + default_attrs = { + "fillcolor": self.DEFAULT_STYLES["node_color"], + "shape": self.DEFAULT_STYLES["node_shape"], + "fontcolor": self.DEFAULT_STYLES["font_color"], + "fontname": self.DEFAULT_STYLES["font_name"], + "fontsize": self.DEFAULT_STYLES.get("fontsize", "14"), + "style": self.DEFAULT_STYLES["node_style"], + "typefontcolor": self.DEFAULT_STYLES["type_font_color"], + } + + # Check if node has kernel_type attribute + if hasattr(node_id, "kernel_type"): + kernel_type = node_id.kernel_type + if kernel_type in rules: + # Override defaults with rule-specific attributes + rule_attrs = rules[kernel_type].copy() + default_attrs.update(rule_attrs) + + return default_attrs + + def _merge_styles(self, **override_styles) -> dict: + """ + CENTRAL STYLE MERGING + Takes the default styles and overrides them with any user-provided styles. + """ + merged = self.DEFAULT_STYLES.copy() + merged.update(override_styles) # Override defaults with user choices + return merged + + def generate_dot( + self, + graph: "nx.DiGraph", + label_lut: Optional[Dict[Any, str]] = None, + style_rules: Optional[Dict] = None, + **style_overrides, + ) -> str: + # Get final styles (defaults + overrides) + styles = self._merge_styles(**style_overrides) + + import graphviz + + dot = graphviz.Digraph(comment="NetworkX Graph") + + # Apply global styles + dot.attr(rankdir=styles["rankdir"], dpi=str(styles["dpi"])) + dot.attr(fontname=styles["font_name"]) + if styles.get("font_size"): + dot.attr(fontsize=styles["fontsize"]) + if styles["font_path"]: + dot.attr(fontpath=styles["font_path"]) + + # Set default edge attributes + dot.attr("edge", color=styles["edge_color"]) + + # Add nodes with default attribute specific styling + for node_id in graph.nodes(): + sanitized_id = self._sanitize_node_id(node_id) + + node_attrs = self._get_node_attributes(node_id, style_rules) + + if label_lut and node_id in label_lut: + # Use custom label if provided + label = label_lut[node_id] + else: + # Use default HTML label with kernel_type above main label + label = self._create_default_html_label(node_id, node_attrs) + + # Add nodes with its specific attributes + dot.node(sanitized_id, label=label, **node_attrs) + + # Add edges + for source, target in graph.edges(): + source_id = self._sanitize_node_id(source) + target_id = self._sanitize_node_id(target) + dot.edge(source_id, target_id) + + return dot.source + + def render_graph( + self, + graph: "nx.DiGraph", + label_lut: Optional[Dict[Any, str]] = None, + show: bool = True, + output_path: Optional[str] = None, + raw_output: bool = False, + figsize: tuple = (12, 8), + dpi: int = 150, + style_rules: Optional[Dict] = None, + **style_overrides, + ) -> Optional[str]: + # Always generate DOT first + dot_text = self.generate_dot(graph, label_lut, style_rules, **style_overrides) + + if raw_output: + return dot_text + + # For rendering, continue with the existing logic but return DOT text + styles = self._merge_styles(**style_overrides) + + import graphviz + + dot = graphviz.Digraph(comment="NetworkX Graph") + + # Apply styles directly + dot.attr(rankdir=styles["rankdir"], dpi=str(dpi)) + dot.attr(fontname=styles["font_name"]) + if styles.get("fontsize"): + dot.attr(fontsize=styles["fontsize"]) + if styles["font_path"]: + dot.attr(fontpath=styles["font_path"]) + + # Set default edge attributes + dot.attr("edge", color=styles["edge_color"]) + + # Add nodes with specific styling + for node_id in graph.nodes(): + sanitized_id = self._sanitize_node_id(node_id) + node_attrs = self._get_node_attributes(node_id, style_rules) + + if label_lut and node_id in label_lut: + label = label_lut[node_id] + else: + label = self._create_default_html_label(node_id, node_attrs) + + dot.node(sanitized_id, label=label, **node_attrs) + + # Add edges + for source, target in graph.edges(): + source_id = self._sanitize_node_id(source) + target_id = self._sanitize_node_id(target) + dot.edge(source_id, target_id) + + if output_path: + name, ext = os.path.splitext(output_path) + format_type = ext[1:] if ext else "png" + dot.render(name, format=format_type, cleanup=True) + print(f"Graph saved to {output_path}") + + import matplotlib.pyplot as plt + import matplotlib.image as mpimg + + if show: + with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp: + dot.render(tmp.name[:-4], format="png", cleanup=True) + img = mpimg.imread(tmp.name) + plt.figure(figsize=figsize, dpi=dpi) + plt.imshow(img) + plt.axis("off") + # plt.title("Example Graph") + plt.tight_layout() + plt.show() + os.unlink(tmp.name) + + # Always return DOT text (like the spec) + return dot_text + + +# ===================== +# CONVENIENCE FUNCTION +# ===================== +def render_graph( + graph: "nx.DiGraph", + label_lut: Optional[Dict[Any, str]] = None, + style_rules: Optional[Dict] = None, + **kwargs, +) -> Optional[str]: + """ + Convenience function with conditional node styling + + Args: + graph: NetworkX DiGraph + label_lut: Optional node labels + style_rules: Dict mapping node attributes to styling rules + **kwargs: Other styling arguments + """ + renderer = GraphRenderer() + return renderer.render_graph(graph, label_lut, style_rules=style_rules, **kwargs) + + +def render_graph_dark_theme( + graph: "nx.DiGraph", label_lut: Optional[Dict[Any, str]] = None, **kwargs +) -> Optional[str]: + """ + Render with dark theme - all backgrounds dark, all pod type fonts light + Perfect for dark themed presentations or displays + """ + renderer = GraphRenderer() + return renderer.render_graph( + graph, label_lut, style_rules=renderer.DARK_THEME_RULES, **kwargs + ) + + +# ============================================= +# STYLE RULE SETS +# ============================================= + + +class StyleRuleSets: + """Access to different theme style rules""" + + @staticmethod + def get_default_rules(): + """Mixed theme - light node fill colors with dark colored fonts""" + return GraphRenderer.DEFAULT_STYLE_RULES + + @staticmethod + def get_dark_rules(): + """Dark theme - dark node fill colors with light colored fonts""" + return GraphRenderer.DARK_THEME_RULES + + @staticmethod + def create_custom_rules( + source_bg="lightgreen", + operator_bg="orange", + pod_bg="darkslateblue", + source_main_fcolor="black", + operator_main_fcolor="black", + pod_main_fcolor="white", + source_type_fcolor="darkgray", + operator_type_fcolor="darkgray", + kernel_type_fcolor="lightgray", + ): + """Create custom theme rules""" + return { + "source": { + "fillcolor": source_bg, + "shape": "ellipse", + "fontcolor": source_main_fcolor, + "style": "filled", + "type_font_color": source_type_fcolor, + }, + "operator": { + "fillcolor": operator_bg, + "shape": "diamond", + "fontcolor": operator_main_fcolor, + "style": "filled", + "type_font_color": operator_type_fcolor, + }, + "pod": { + "fillcolor": pod_bg, + "shape": "box", + "fontcolor": pod_main_fcolor, + "style": "filled,rounded", + "type_font_color": kernel_type_fcolor, + }, + } + + +# import networkx as nx +# # import graphviz +# import matplotlib.pyplot as plt +# import matplotlib.image as mpimg +# import tempfile +# import os + + +# class GraphRenderer: +# """Simple renderer for NetworkX graphs using Graphviz DOT format""" + +# def __init__(self): +# """Initialize the renderer""" +# pass + +# def _sanitize_node_id(self, node_id: Any) -> str: +# """Convert node_id to a valid DOT identifier using hash""" +# return f"node_{hash(node_id)}" + +# def _get_node_label( +# self, node_id: Any, label_lut: dict[Any, str] | None = None +# ) -> str: +# """Get label for a node""" +# if label_lut and node_id in label_lut: +# return label_lut[node_id] +# return str(node_id) + +# def generate_dot( +# self, +# graph: "nx.DiGraph", +# label_lut: dict[Any, str] | None = None, +# rankdir: str = "TB", +# node_shape: str = "box", +# node_style: str = "filled", +# node_color: str = "lightblue", +# edge_color: str = "black", +# dpi: int = 150, +# ) -> str: +# """ +# Generate DOT syntax from NetworkX graph + +# Args: +# graph: NetworkX DiGraph to render +# label_lut: Optional dictionary mapping node_id -> display_label +# rankdir: Graph direction ('TB', 'BT', 'LR', 'RL') +# node_shape: Shape for all nodes +# node_style: Style for all nodes +# node_color: Fill color for all nodes +# edge_color: Color for all edges +# dpi: Resolution for rendered image (default 150) + +# Returns: +# DOT format string +# """ +# try: +# import graphviz +# except ImportError as e: +# raise ImportError( +# "Graphviz is not installed. Please install graphviz to render graph of the pipeline." +# ) from e + +# dot = graphviz.Digraph(comment="NetworkX Graph") + +# # Set graph attributes +# dot.attr(rankdir=rankdir, dpi=str(dpi)) +# dot.attr("node", shape=node_shape, style=node_style, fillcolor=node_color) +# dot.attr("edge", color=edge_color) + +# # Add nodes +# for node_id in graph.nodes(): +# sanitized_id = self._sanitize_node_id(node_id) +# label = self._get_node_label(node_id, label_lut) +# dot.node(sanitized_id, label=label) + +# # Add edges +# for source, target in graph.edges(): +# source_id = self._sanitize_node_id(source) +# target_id = self._sanitize_node_id(target) +# dot.edge(source_id, target_id) + +# return dot.source + +# def render_graph( +# self, +# graph: nx.DiGraph, +# label_lut: dict[Any, str] | None = None, +# show: bool = True, +# output_path: str | None = None, +# raw_output: bool = False, +# rankdir: str = "TB", +# figsize: tuple = (6, 4), +# dpi: int = 150, +# **style_kwargs, +# ) -> str | None: +# """ +# Render NetworkX graph using Graphviz + +# Args: +# graph: NetworkX DiGraph to render +# label_lut: Optional dictionary mapping node_id -> display_label +# show: Display the graph using matplotlib +# output_path: Save graph to file (e.g., 'graph.png', 'graph.pdf') +# raw_output: Return DOT syntax instead of rendering +# rankdir: Graph direction ('TB', 'BT', 'LR', 'RL') +# figsize: Figure size for matplotlib display +# dpi: Resolution for rendered image (default 150) +# **style_kwargs: Additional styling (node_color, edge_color, node_shape, etc.) + +# Returns: +# DOT syntax if raw_output=True, None otherwise +# """ +# try: +# import graphviz +# except ImportError as e: +# raise ImportError( +# "Graphviz is not installed. Please install graphviz to render graph of the pipeline." +# ) from e + +# if raw_output: +# return self.generate_dot(graph, label_lut, rankdir, dpi=dpi, **style_kwargs) + +# # Create Graphviz object +# dot = graphviz.Digraph(comment="NetworkX Graph") +# dot.attr(rankdir=rankdir, dpi=str(dpi)) + +# # Apply styling +# node_shape = style_kwargs.get("node_shape", "box") +# node_style = style_kwargs.get("node_style", "filled") +# node_color = style_kwargs.get("node_color", "lightblue") +# edge_color = style_kwargs.get("edge_color", "black") + +# dot.attr("node", shape=node_shape, style=node_style, fillcolor=node_color) +# dot.attr("edge", color=edge_color) + +# # Add nodes with labels +# for node_id in graph.nodes(): +# sanitized_id = self._sanitize_node_id(node_id) +# label = self._get_node_label(node_id, label_lut) +# dot.node(sanitized_id, label=label) + +# # Add edges +# for source, target in graph.edges(): +# source_id = self._sanitize_node_id(source) +# target_id = self._sanitize_node_id(target) +# dot.edge(source_id, target_id) + +# # Handle output +# if output_path: +# # Save to file +# name, ext = os.path.splitext(output_path) +# format_type = ext[1:] if ext else "png" +# dot.render(name, format=format_type, cleanup=True) +# print(f"Graph saved to {output_path}") + +# if show: +# # Display with matplotlib +# with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp: +# dot.render(tmp.name[:-4], format="png", cleanup=True) + +# import matplotlib.pyplot as plt +# import matplotlib.image as mpimg + +# # Display with matplotlib +# img = mpimg.imread(tmp.name) +# plt.figure(figsize=figsize) +# plt.imshow(img) +# plt.axis("off") +# plt.title("Graph Visualization") +# plt.tight_layout() +# plt.show() + +# # Clean up +# os.unlink(tmp.name) + +# return None + + +# # Convenience function for quick rendering +# def render_graph( +# graph: nx.DiGraph, label_lut: dict[Any, str] | None = None, **kwargs +# ) -> str | None: +# """ +# Convenience function to quickly render a NetworkX graph + +# Args: +# graph: NetworkX DiGraph to render +# label_lut: Optional dictionary mapping node_id -> display_label +# **kwargs: All other arguments passed to GraphRenderer.render_graph() + +# Returns: +# DOT syntax if raw_output=True, None otherwise +# """ +# renderer = GraphRenderer() +# return renderer.render_graph(graph, label_lut, **kwargs) diff --git a/src/orcapod/pipeline/nodes.py b/src/orcapod/pipeline/nodes.py new file mode 100644 index 0000000..27f2abb --- /dev/null +++ b/src/orcapod/pipeline/nodes.py @@ -0,0 +1,445 @@ +from abc import abstractmethod +from orcapod.core.kernels import KernelStream, WrappedKernel +from orcapod.core.sources.base import SourceBase, InvocationBase +from orcapod.core.pods import CachedPod +from orcapod.protocols import core_protocols as cp, database_protocols as dbp +from orcapod.types import PythonSchema +from orcapod.utils.lazy_module import LazyModule +from typing import TYPE_CHECKING, Any +from orcapod.core.system_constants import constants +from orcapod.utils import arrow_utils +from collections.abc import Collection + +if TYPE_CHECKING: + import pyarrow as pa + import polars as pl + import pandas as pd +else: + pa = LazyModule("pyarrow") + pl = LazyModule("polars") + pd = LazyModule("pandas") + + +class NodeBase( + InvocationBase, +): + """ + Mixin class for pipeline nodes + """ + + def __init__( + self, + input_streams: Collection[cp.Stream], + pipeline_database: dbp.ArrowDatabase, + pipeline_path_prefix: tuple[str, ...] = (), + kernel_type: str = "operator", + **kwargs, + ): + super().__init__(**kwargs) + self.kernel_type = kernel_type + self._cached_stream: KernelStream | None = None + self._input_streams = tuple(input_streams) + self._pipeline_path_prefix = pipeline_path_prefix + # compute invocation hash - note that empty () is passed into identity_structure to signify + # identity structure of invocation with no input streams + self.pipeline_node_hash = self.data_context.object_hasher.hash_object( + self.identity_structure(()) + ).to_string() + tag_types, packet_types = self.types(include_system_tags=True) + + self.tag_schema_hash = self.data_context.object_hasher.hash_object( + tag_types + ).to_string() + + self.packet_schema_hash = self.data_context.object_hasher.hash_object( + packet_types + ).to_string() + + self.pipeline_database = pipeline_database + + @property + def id(self) -> str: + return self.content_hash().to_string() + + @property + def upstreams(self) -> tuple[cp.Stream, ...]: + return self._input_streams + + def track_invocation(self, *streams: cp.Stream, label: str | None = None) -> None: + # Node invocation should not be tracked + return None + + @property + def contained_kernel(self) -> cp.Kernel: + raise NotImplementedError( + "This property should be implemented by subclasses to return the contained kernel." + ) + + @property + def reference(self) -> tuple[str, ...]: + return self.contained_kernel.reference + + @property + @abstractmethod + def pipeline_path(self) -> tuple[str, ...]: + """ + Return the path to the pipeline run records. + This is used to store the run-associated tag info. + """ + ... + + def validate_inputs(self, *streams: cp.Stream) -> None: + """Sources take no input streams.""" + if len(streams) > 0: + raise NotImplementedError( + "At this moment, Node does not yet support handling additional input streams." + ) + + def forward(self, *streams: cp.Stream) -> cp.Stream: + # TODO: re-evaluate the use here -- consider semi joining with input streams + # super().validate_inputs(*self.input_streams) + return super().forward(*self.upstreams) # type: ignore[return-value] + + def kernel_output_types( + self, *streams: cp.Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Return the output types of the node. + This is used to determine the types of the output streams. + """ + return self.contained_kernel.output_types( + *self.upstreams, include_system_tags=include_system_tags + ) + + def kernel_identity_structure( + self, streams: Collection[cp.Stream] | None = None + ) -> Any: + # construct identity structure from the node's information and the + return self.contained_kernel.identity_structure(self.upstreams) + + def get_all_records( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + """ + Retrieve all records associated with the node. + If include_system_columns is True, system columns will be included in the result. + """ + raise NotImplementedError("This method should be implemented by subclasses.") + + +class KernelNode(NodeBase, WrappedKernel): + """ + A node in the pipeline that represents a kernel. + This node can be used to execute the kernel and process data streams. + """ + + HASH_COLUMN_NAME = "_record_hash" + + def __init__( + self, + kernel: cp.Kernel, + input_streams: Collection[cp.Stream], + pipeline_database: dbp.ArrowDatabase, + pipeline_path_prefix: tuple[str, ...] = (), + **kwargs, + ) -> None: + super().__init__( + kernel=kernel, + input_streams=input_streams, + pipeline_database=pipeline_database, + pipeline_path_prefix=pipeline_path_prefix, + **kwargs, + ) + + @property + def contained_kernel(self) -> cp.Kernel: + return self.kernel + + def __repr__(self): + return f"KernelNode(kernel={self.kernel!r})" + + def __str__(self): + return f"KernelNode:{self.kernel!s}" + + def forward(self, *streams: cp.Stream) -> cp.Stream: + output_stream = super().forward(*streams) + + self.record_pipeline_output(output_stream) + return output_stream + + def record_pipeline_output(self, output_stream: cp.Stream) -> None: + key_column_name = self.HASH_COLUMN_NAME + output_table = output_stream.as_table( + include_data_context=True, + include_system_tags=True, + include_source=True, + include_content_hash=key_column_name, + ) + self.pipeline_database.add_records( + self.pipeline_path, + output_table, + record_id_column=key_column_name, + skip_duplicates=True, + ) + + @property + def pipeline_path(self) -> tuple[str, ...]: + """ + Return the path to the pipeline run records. + This is used to store the run-associated tag info. + """ + return ( + self._pipeline_path_prefix # pipeline ID + + self.reference # node ID + + ( + f"node:{self.pipeline_node_hash}", # pipeline node ID + f"packet:{self.packet_schema_hash}", # packet schema ID + f"tag:{self.tag_schema_hash}", # tag schema ID + ) + ) + + def get_all_records( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + results = self.pipeline_database.get_all_records(self.pipeline_path) + + if results is None: + return None + + if not include_system_columns: + system_columns = [ + c + for c in results.column_names + if c.startswith(constants.META_PREFIX) + or c.startswith(constants.DATAGRAM_PREFIX) + ] + results = results.drop(system_columns) + + return results + + +class PodNode(NodeBase, CachedPod): + def __init__( + self, + pod: cp.Pod, + input_streams: Collection[cp.Stream], + pipeline_database: dbp.ArrowDatabase, + result_database: dbp.ArrowDatabase | None = None, + record_path_prefix: tuple[str, ...] = (), + pipeline_path_prefix: tuple[str, ...] = (), + **kwargs, + ) -> None: + super().__init__( + pod=pod, + result_database=result_database, + record_path_prefix=record_path_prefix, + input_streams=input_streams, + pipeline_database=pipeline_database, + pipeline_path_prefix=pipeline_path_prefix, + **kwargs, + ) + + @property + def contained_kernel(self) -> cp.Kernel: + return self.pod + + @property + def pipeline_path(self) -> tuple[str, ...]: + """ + Return the path to the pipeline run records. + This is used to store the run-associated tag info. + """ + return ( + self._pipeline_path_prefix # pipeline ID + + self.reference # node ID + + ( + f"node:{self.pipeline_node_hash}", # pipeline node ID + f"tag:{self.tag_schema_hash}", # tag schema ID + ) + ) + + def __repr__(self): + return f"PodNode(pod={self.pod!r})" + + def __str__(self): + return f"PodNode:{self.pod!s}" + + def call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + skip_cache_lookup: bool = False, + skip_cache_insert: bool = False, + ) -> tuple[cp.Tag, cp.Packet | None]: + execution_engine_hash = execution_engine.name if execution_engine else "default" + if record_id is None: + record_id = self.get_record_id(packet, execution_engine_hash) + + tag, output_packet = super().call( + tag, + packet, + record_id=record_id, + skip_cache_lookup=skip_cache_lookup, + skip_cache_insert=skip_cache_insert, + execution_engine=execution_engine, + ) + + if output_packet is not None: + retrieved = ( + output_packet.get_meta_value(self.DATA_RETRIEVED_FLAG) is not None + ) + # add pipeline record if the output packet is not None + # TODO: verify cache lookup logic + self.add_pipeline_record( + tag, + packet, + record_id, + retrieved=retrieved, + skip_cache_lookup=skip_cache_lookup, + ) + return tag, output_packet + + async def async_call( + self, + tag: cp.Tag, + packet: cp.Packet, + record_id: str | None = None, + execution_engine: cp.ExecutionEngine | None = None, + skip_cache_lookup: bool = False, + skip_cache_insert: bool = False, + ) -> tuple[cp.Tag, cp.Packet | None]: + execution_engine_hash = execution_engine.name if execution_engine else "default" + if record_id is None: + record_id = self.get_record_id(packet, execution_engine_hash) + + tag, output_packet = await super().async_call( + tag, + packet, + record_id=record_id, + skip_cache_lookup=skip_cache_lookup, + skip_cache_insert=skip_cache_insert, + execution_engine=execution_engine, + ) + + if output_packet is not None: + retrieved = ( + output_packet.get_meta_value(self.DATA_RETRIEVED_FLAG) is not None + ) + # add pipeline record if the output packet is not None + # TODO: verify cache lookup logic + self.add_pipeline_record( + tag, + packet, + record_id, + retrieved=retrieved, + skip_cache_lookup=skip_cache_lookup, + ) + return tag, output_packet + + def add_pipeline_record( + self, + tag: cp.Tag, + input_packet: cp.Packet, + packet_record_id: str, + retrieved: bool | None = None, + skip_cache_lookup: bool = False, + ) -> None: + # combine dp.Tag with packet content hash to compute entry hash + # TODO: add system tag columns + # TODO: consider using bytes instead of string representation + tag_with_hash = tag.as_table(include_system_tags=True).append_column( + constants.INPUT_PACKET_HASH, + pa.array([input_packet.content_hash().to_string()], type=pa.large_string()), + ) + + # unique entry ID is determined by the combination of tags, system_tags, and input_packet hash + entry_id = self.data_context.arrow_hasher.hash_table(tag_with_hash).to_string() + + # check presence of an existing entry with the same entry_id + existing_record = None + if not skip_cache_lookup: + existing_record = self.pipeline_database.get_record_by_id( + self.pipeline_path, + entry_id, + ) + + if existing_record is not None: + # if the record already exists, then skip + return + + # rename all keys to avoid potential collision with result columns + renamed_input_packet = input_packet.rename( + {k: f"_input_{k}" for k in input_packet.keys()} + ) + input_packet_info = ( + renamed_input_packet.as_table(include_source=True) + .append_column( + constants.PACKET_RECORD_ID, + pa.array([packet_record_id], type=pa.large_string()), + ) + .append_column( + f"{constants.META_PREFIX}input_packet{constants.CONTEXT_KEY}", + pa.array([input_packet.data_context_key], type=pa.large_string()), + ) + .append_column( + self.DATA_RETRIEVED_FLAG, + pa.array([retrieved], type=pa.bool_()), + ) + .drop_columns(list(renamed_input_packet.keys())) + ) + + combined_record = arrow_utils.hstack_tables( + tag.as_table(include_system_tags=True), input_packet_info + ) + + self.pipeline_database.add_record( + self.pipeline_path, + entry_id, + combined_record, + skip_duplicates=False, + ) + + def get_all_records( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + results = self.result_database.get_all_records( + self.record_path, record_id_column=constants.PACKET_RECORD_ID + ) + + if self.pipeline_database is None: + raise ValueError( + "Pipeline database is not configured, cannot retrieve tag info" + ) + taginfo = self.pipeline_database.get_all_records( + self.pipeline_path, + ) + + if results is None or taginfo is None: + return None + + # hack - use polars for join as it can deal with complex data type + # TODO: convert the entire load logic to use polars with lazy evaluation + + joined_info = ( + pl.DataFrame(taginfo) + .join(pl.DataFrame(results), on=constants.PACKET_RECORD_ID, how="inner") + .to_arrow() + ) + + # joined_info = taginfo.join( + # results, + # constants.PACKET_RECORD_ID, + # join_type="inner", + # ) + + if not include_system_columns: + system_columns = [ + c + for c in joined_info.column_names + if c.startswith(constants.META_PREFIX) + or c.startswith(constants.DATAGRAM_PREFIX) + ] + joined_info = joined_info.drop(system_columns) + return joined_info diff --git a/src/orcapod/pipeline/pipeline.py b/src/orcapod/pipeline/pipeline.py deleted file mode 100644 index f160f2b..0000000 --- a/src/orcapod/pipeline/pipeline.py +++ /dev/null @@ -1,730 +0,0 @@ -import json -import logging -import pickle -import sys -import time -from abc import ABC, abstractmethod -from pathlib import Path -from typing import Any, Protocol, runtime_checkable - -import networkx as nx -import pandas as pd - -from orcapod.core.base import Invocation, Kernel -from orcapod.hashing import hash_to_hex -from orcapod.core.tracker import GraphTracker - -logger = logging.getLogger(__name__) - - -class SerializationError(Exception): - """Raised when pipeline cannot be serialized""" - - pass - - -class Pipeline(GraphTracker): - """ - Enhanced pipeline that tracks operations and provides queryable views. - Replaces the old Tracker with better persistence and view capabilities. - """ - - def __init__(self, name: str | None = None): - super().__init__() - self.name = name or f"pipeline_{id(self)}" - self._view_registry: dict[str, "PipelineView"] = {} - self._cache_dir = Path(".pipeline_cache") / self.name - self._cache_dir.mkdir(parents=True, exist_ok=True) - - # Core Pipeline Operations - def save(self, path: Path | str) -> None: - """Save complete pipeline state - named functions only""" - path = Path(path) - - # Validate serializability first - self._validate_serializable() - - state = { - "name": self.name, - "invocation_lut": self.invocation_lut, - "metadata": { - "created_at": time.time(), - "python_version": sys.version_info[:2], - "orcabridge_version": "0.1.0", # You can make this dynamic - }, - } - - # Atomic write - temp_path = path.with_suffix(".tmp") - try: - with open(temp_path, "wb") as f: - pickle.dump(state, f, protocol=pickle.HIGHEST_PROTOCOL) - temp_path.replace(path) - logger.info(f"Pipeline '{self.name}' saved to {path}") - except Exception: - if temp_path.exists(): - temp_path.unlink() - raise - - @classmethod - def load(cls, path: Path | str) -> "Pipeline": - """Load complete pipeline state""" - path = Path(path) - - with open(path, "rb") as f: - state = pickle.load(f) - - pipeline = cls(state["name"]) - pipeline.invocation_lut = state["invocation_lut"] - - logger.info(f"Pipeline '{pipeline.name}' loaded from {path}") - return pipeline - - def _validate_serializable(self) -> None: - """Ensure pipeline contains only serializable operations""" - issues = [] - - for operation, invocations in self.invocation_lut.items(): - # Check for lambda functions - if hasattr(operation, "function"): - func = getattr(operation, "function", None) - if func and hasattr(func, "__name__") and func.__name__ == "": - issues.append(f"Lambda function in {operation.__class__.__name__}") - - # Test actual serializability - try: - pickle.dumps(operation) - except Exception as e: - issues.append(f"Non-serializable operation {operation}: {e}") - - if issues: - raise SerializationError( - "Pipeline contains non-serializable elements:\n" - + "\n".join(f" - {issue}" for issue in issues) - + "\n\nOnly named functions are supported for serialization." - ) - - # View Management - def as_view( - self, renderer: "ViewRenderer", view_id: str | None = None, **kwargs - ) -> "PipelineView": - """Get a view of this pipeline using the specified renderer""" - view_id = ( - view_id - or f"{renderer.__class__.__name__.lower()}_{len(self._view_registry)}" - ) - - if view_id not in self._view_registry: - self._view_registry[view_id] = renderer.create_view( - self, view_id=view_id, **kwargs - ) - return self._view_registry[view_id] - - def as_dataframe(self, view_id: str = "default", **kwargs) -> "PandasPipelineView": - """Convenience method for pandas DataFrame view""" - return self.as_view(PandasViewRenderer(), view_id=view_id, **kwargs) - - def as_graph(self) -> nx.DiGraph: - """Get the computation graph""" - return self.generate_graph() - - # Combined save/load with views - def save_with_views(self, base_path: Path | str) -> dict[str, Path]: - """Save pipeline and all its views together""" - base_path = Path(base_path) - base_path.mkdir(parents=True, exist_ok=True) - - saved_files = {} - - # Save pipeline itself - pipeline_path = base_path / "pipeline.pkl" - self.save(pipeline_path) - saved_files["pipeline"] = pipeline_path - - # Save all views - for view_id, view in self._view_registry.items(): - view_path = base_path / f"view_{view_id}.pkl" - view.save(view_path, include_pipeline=False) - saved_files[f"view_{view_id}"] = view_path - - # Save manifest - manifest = { - "pipeline_file": "pipeline.pkl", - "views": { - view_id: f"view_{view_id}.pkl" for view_id in self._view_registry.keys() - }, - "created_at": time.time(), - "pipeline_name": self.name, - } - - manifest_path = base_path / "manifest.json" - with open(manifest_path, "w") as f: - json.dump(manifest, f, indent=2) - saved_files["manifest"] = manifest_path - - return saved_files - - @classmethod - def load_with_views( - cls, base_path: Path | str - ) -> tuple["Pipeline", dict[str, "PipelineView"]]: - """Load pipeline and all its views""" - base_path = Path(base_path) - - # Load manifest - manifest_path = base_path / "manifest.json" - with open(manifest_path, "r") as f: - manifest = json.load(f) - - # Load pipeline - pipeline_path = base_path / manifest["pipeline_file"] - pipeline = cls.load(pipeline_path) - - # Load views with appropriate renderers - renderers = { - "PandasPipelineView": PandasViewRenderer(), - "DataJointPipelineView": DataJointViewRenderer(None), # Would need schema - } - - views = {} - for view_id, view_file in manifest["views"].items(): - view_path = base_path / view_file - - # Load view data to determine type - with open(view_path, "rb") as f: - view_data = pickle.load(f) - - # Find appropriate renderer - view_type = view_data.get("view_type", "PandasPipelineView") - if view_type in renderers and renderers[view_type].can_load_view(view_data): - # Load with appropriate view class - if view_type == "PandasPipelineView": - view = PandasPipelineView.load(view_path, pipeline) - else: - view = DataJointPipelineView.load(view_path, pipeline) - else: - # Default to pandas view - view = PandasPipelineView.load(view_path, pipeline) - - views[view_id] = view - pipeline._view_registry[view_id] = view - - return pipeline, views - - def get_stats(self) -> dict[str, Any]: - """Get pipeline statistics""" - total_operations = len(self.invocation_lut) - total_invocations = sum(len(invs) for invs in self.invocation_lut.values()) - - operation_types = {} - for operation in self.invocation_lut.keys(): - op_type = operation.__class__.__name__ - operation_types[op_type] = operation_types.get(op_type, 0) + 1 - - return { - "name": self.name, - "total_operations": total_operations, - "total_invocations": total_invocations, - "operation_types": operation_types, - "views": list(self._view_registry.keys()), - } - - -# View Renderer Protocol -@runtime_checkable -class ViewRenderer(Protocol): - """Protocol for all view renderers - uses structural typing""" - - def create_view( - self, pipeline: "Pipeline", view_id: str, **kwargs - ) -> "PipelineView": - """Create a view for the given pipeline""" - ... - - def can_load_view(self, view_data: dict[str, Any]) -> bool: - """Check if this renderer can load the given view data""" - ... - - -class PandasViewRenderer: - """Renderer for pandas DataFrame views""" - - def create_view( - self, pipeline: "Pipeline", view_id: str, **kwargs - ) -> "PandasPipelineView": - return PandasPipelineView(pipeline, view_id=view_id, **kwargs) - - def can_load_view(self, view_data: dict[str, Any]) -> bool: - return view_data.get("view_type") == "PandasPipelineView" - - -class DataJointViewRenderer: - """Renderer for DataJoint views""" - - def __init__(self, schema): - self.schema = schema - - def create_view( - self, pipeline: "Pipeline", view_id: str, **kwargs - ) -> "DataJointPipelineView": - return DataJointPipelineView(pipeline, self.schema, view_id=view_id, **kwargs) - - def can_load_view(self, view_data: dict[str, Any]) -> bool: - return view_data.get("view_type") == "DataJointPipelineView" - - -# Base class for all views -class PipelineView(ABC): - """Base class for all pipeline views""" - - def __init__(self, pipeline: Pipeline, view_id: str): - self.pipeline = pipeline - self.view_id = view_id - self._cache_dir = pipeline._cache_dir / "views" - self._cache_dir.mkdir(parents=True, exist_ok=True) - - @abstractmethod - def save(self, path: Path | str, include_pipeline: bool = True) -> None: - """Save the view""" - pass - - @classmethod - @abstractmethod - def load(cls, path: Path | str, pipeline: Pipeline | None = None) -> "PipelineView": - """Load the view""" - pass - - def _compute_pipeline_hash(self) -> str: - """Compute hash of current pipeline state for validation""" - pipeline_state = [] - for operation, invocations in self.pipeline.invocation_lut.items(): - for invocation in invocations: - pipeline_state.append(invocation.content_hash()) - return hash_to_hex(sorted(pipeline_state)) - - -# Pandas DataFrame-like view -class PandasPipelineView(PipelineView): - """ - Provides a pandas DataFrame-like interface to pipeline metadata. - Focuses on tag information for querying and filtering. - """ - - def __init__( - self, - pipeline: Pipeline, - view_id: str = "pandas_view", - max_records: int = 10000, - sample_size: int = 100, - ): - super().__init__(pipeline, view_id) - self.max_records = max_records - self.sample_size = sample_size - self._cached_data: pd.DataFrame | None = None - self._build_options = {"max_records": max_records, "sample_size": sample_size} - self._hash_to_data_map: dict[str, Any] = {} - - @property - def df(self) -> pd.DataFrame: - """Access the underlying DataFrame, building if necessary""" - if self._cached_data is None: - # Try to load from cache first - cache_path = self._cache_dir / f"{self.view_id}.pkl" - if cache_path.exists(): - try: - loaded_view = self.load(cache_path, self.pipeline) - if self._is_cache_valid(loaded_view): - self._cached_data = loaded_view._cached_data - self._hash_to_data_map = loaded_view._hash_to_data_map - logger.info(f"Loaded view '{self.view_id}' from cache") - return self._cached_data - except Exception as e: - logger.warning(f"Failed to load cached view: {e}") - - # Build from scratch - logger.info(f"Building view '{self.view_id}' from pipeline") - self._cached_data = self._build_metadata() - - # Auto-save after building - try: - self.save(cache_path, include_pipeline=False) - except Exception as e: - logger.warning(f"Failed to cache view: {e}") - - return self._cached_data - - def _build_metadata(self) -> pd.DataFrame: - """Build the metadata DataFrame from pipeline operations""" - metadata_records = [] - total_records = 0 - - for operation, invocations in self.pipeline.invocation_lut.items(): - if total_records >= self.max_records: - logger.warning(f"Hit max_records limit ({self.max_records})") - break - - for invocation in invocations: - try: - # Get sample of outputs, not all - records = self._extract_metadata_from_invocation( - invocation, operation - ) - for record in records: - metadata_records.append(record) - total_records += 1 - if total_records >= self.max_records: - break - - if total_records >= self.max_records: - break - - except Exception as e: - logger.warning(f"Skipping {operation.__class__.__name__}: {e}") - # Create placeholder record - placeholder = self._create_placeholder_record(invocation, operation) - metadata_records.append(placeholder) - total_records += 1 - - if not metadata_records: - # Return empty DataFrame with basic structure - return pd.DataFrame( - columns=[ - "operation_name", - "operation_hash", - "invocation_id", - "created_at", - "packet_keys", - ] - ) - - return pd.DataFrame(metadata_records) - - def _extract_metadata_from_invocation( - self, invocation: Invocation, operation: Kernel - ) -> list[dict[str, Any]]: - """Extract metadata records from a single invocation""" - records = [] - - # Try to get sample outputs from the invocation - try: - # This is tricky - we need to reconstruct the output stream - # For now, we'll create a basic record from what we know - base_record = { - "operation_name": operation.label or operation.__class__.__name__, - "operation_hash": invocation.content_hash(), - "invocation_id": hash(invocation), - "created_at": time.time(), - "operation_type": operation.__class__.__name__, - } - - # Try to get tag and packet info from the operation - try: - tag_keys, packet_keys = invocation.keys() - base_record.update( - { - "tag_keys": list(tag_keys) if tag_keys else [], - "packet_keys": list(packet_keys) if packet_keys else [], - } - ) - except Exception: - base_record.update( - { - "tag_keys": [], - "packet_keys": [], - } - ) - - records.append(base_record) - - except Exception as e: - logger.debug(f"Could not extract detailed metadata from {operation}: {e}") - records.append(self._create_placeholder_record(invocation, operation)) - - return records - - def _create_placeholder_record( - self, invocation: Invocation, operation: Kernel - ) -> dict[str, Any]: - """Create a placeholder record when extraction fails""" - return { - "operation_name": operation.label or operation.__class__.__name__, - "operation_hash": invocation.content_hash(), - "invocation_id": hash(invocation), - "created_at": time.time(), - "operation_type": operation.__class__.__name__, - "tag_keys": [], - "packet_keys": [], - "is_placeholder": True, - } - - # DataFrame-like interface - def __getitem__(self, condition) -> "FilteredPipelineView": - """Enable pandas-like filtering: view[condition]""" - df = self.df - if isinstance(condition, pd.Series): - filtered_df = df[condition] - elif callable(condition): - filtered_df = df[condition(df)] - else: - filtered_df = df[condition] - - return FilteredPipelineView(self.pipeline, filtered_df, self._hash_to_data_map) - - def query(self, expr: str) -> "FilteredPipelineView": - """SQL-like querying: view.query('operation_name == "MyOperation"')""" - df = self.df - filtered_df = df.query(expr) - return FilteredPipelineView(self.pipeline, filtered_df, self._hash_to_data_map) - - def groupby(self, *args, **kwargs) -> "GroupedPipelineView": - """Group operations similar to pandas groupby""" - df = self.df - grouped = df.groupby(*args, **kwargs) - return GroupedPipelineView(self.pipeline, grouped, self._hash_to_data_map) - - def head(self, n: int = 5) -> pd.DataFrame: - """Return first n rows""" - return self.df.head(n) - - def info(self) -> None: - """Display DataFrame info""" - return self.df.info() - - def describe(self) -> pd.DataFrame: - """Generate descriptive statistics""" - return self.df.describe() - - # Persistence methods - def save(self, path: Path | str, include_pipeline: bool = True) -> None: - """Save view, optionally with complete pipeline state""" - path = Path(path) - - # Build the view data if not cached - df = self.df - - view_data = { - "view_id": self.view_id, - "view_type": self.__class__.__name__, - "dataframe": df, - "build_options": self._build_options, - "hash_to_data_map": self._hash_to_data_map, - "created_at": time.time(), - "pipeline_hash": self._compute_pipeline_hash(), - } - - if include_pipeline: - view_data["pipeline_state"] = { - "name": self.pipeline.name, - "invocation_lut": self.pipeline.invocation_lut, - } - view_data["has_pipeline"] = True - else: - view_data["pipeline_name"] = self.pipeline.name - view_data["has_pipeline"] = False - - with open(path, "wb") as f: - pickle.dump(view_data, f, protocol=pickle.HIGHEST_PROTOCOL) - - @classmethod - def load( - cls, path: Path | str, pipeline: Pipeline | None = None - ) -> "PandasPipelineView": - """Load view, reconstructing pipeline if needed""" - with open(path, "rb") as f: - view_data = pickle.load(f) - - # Handle pipeline reconstruction - if view_data["has_pipeline"]: - pipeline = Pipeline(view_data["pipeline_state"]["name"]) - pipeline.invocation_lut = view_data["pipeline_state"]["invocation_lut"] - elif pipeline is None: - raise ValueError( - "View was saved without pipeline state. " - "You must provide a pipeline parameter." - ) - - # Reconstruct view - build_options = view_data.get("build_options", {}) - view = cls( - pipeline, - view_id=view_data["view_id"], - max_records=build_options.get("max_records", 10000), - sample_size=build_options.get("sample_size", 100), - ) - view._cached_data = view_data["dataframe"] - view._hash_to_data_map = view_data.get("hash_to_data_map", {}) - - return view - - def _is_cache_valid(self, cached_view: "PandasPipelineView") -> bool: - """Check if cached view is still valid""" - try: - cached_hash = getattr(cached_view, "_pipeline_hash", None) - current_hash = self._compute_pipeline_hash() - return cached_hash == current_hash - except Exception: - return False - - def invalidate(self) -> None: - """Force re-rendering on next access""" - self._cached_data = None - cache_path = self._cache_dir / f"{self.view_id}.pkl" - if cache_path.exists(): - cache_path.unlink() - - -class FilteredPipelineView: - """Represents a filtered subset of pipeline metadata""" - - def __init__( - self, pipeline: Pipeline, filtered_df: pd.DataFrame, data_map: dict[str, Any] - ): - self.pipeline = pipeline - self.df = filtered_df - self._data_map = data_map - - def __getitem__(self, condition): - """Further filtering""" - further_filtered = self.df[condition] - return FilteredPipelineView(self.pipeline, further_filtered, self._data_map) - - def query(self, expr: str): - """Apply additional query""" - further_filtered = self.df.query(expr) - return FilteredPipelineView(self.pipeline, further_filtered, self._data_map) - - def to_pandas(self) -> pd.DataFrame: - """Convert to regular pandas DataFrame""" - return self.df.copy() - - def head(self, n: int = 5) -> pd.DataFrame: - """Return first n rows""" - return self.df.head(n) - - def __len__(self) -> int: - return len(self.df) - - def __repr__(self) -> str: - return f"FilteredPipelineView({len(self.df)} records)" - - -class GroupedPipelineView: - """Represents grouped pipeline metadata""" - - def __init__(self, pipeline: Pipeline, grouped_df, data_map: dict[str, Any]): - self.pipeline = pipeline - self.grouped = grouped_df - self._data_map = data_map - - def apply(self, func): - """Apply function to each group""" - return self.grouped.apply(func) - - def agg(self, *args, **kwargs): - """Aggregate groups""" - return self.grouped.agg(*args, **kwargs) - - def size(self): - """Get group sizes""" - return self.grouped.size() - - def get_group(self, name): - """Get specific group""" - group_df = self.grouped.get_group(name) - return FilteredPipelineView(self.pipeline, group_df, self._data_map) - - -# Basic DataJoint View (simplified implementation) -class DataJointPipelineView(PipelineView): - """ - Basic DataJoint view - creates tables for pipeline operations - This is a simplified version - you can expand based on your existing DJ code - """ - - def __init__(self, pipeline: Pipeline, schema, view_id: str = "dj_view"): - super().__init__(pipeline, view_id) - self.schema = schema - self._tables = {} - - def save(self, path: Path | str, include_pipeline: bool = True) -> None: - """Save DataJoint view metadata""" - view_data = { - "view_id": self.view_id, - "view_type": self.__class__.__name__, - "schema_database": self.schema.database, - "table_names": list(self._tables.keys()), - "created_at": time.time(), - } - - if include_pipeline: - view_data["pipeline_state"] = { - "name": self.pipeline.name, - "invocation_lut": self.pipeline.invocation_lut, - } - view_data["has_pipeline"] = True - - with open(path, "wb") as f: - pickle.dump(view_data, f) - - @classmethod - def load( - cls, path: Path | str, pipeline: Pipeline | None = None - ) -> "DataJointPipelineView": - """Load DataJoint view""" - with open(path, "rb") as f: - view_data = pickle.load(f) - - # This would need actual DataJoint schema reconstruction - # For now, return a basic instance - if pipeline is None: - raise ValueError("Pipeline required for DataJoint view loading") - - # You'd need to reconstruct the schema here - view = cls(pipeline, None, view_id=view_data["view_id"]) # schema=None for now - return view - - def generate_tables(self): - """Generate DataJoint tables from pipeline - placeholder implementation""" - # This would use your existing DataJoint generation logic - # from your dj/tracker.py file - pass - - -# Utility functions -def validate_pipeline_serializability(pipeline: Pipeline) -> None: - """Helper to check if pipeline can be saved""" - try: - pipeline._validate_serializable() - print("✅ Pipeline is ready for serialization") - - # Additional performance warnings - stats = pipeline.get_stats() - if stats["total_invocations"] > 1000: - print( - f"⚠️ Large pipeline ({stats['total_invocations']} invocations) - views may be slow to build" - ) - - except SerializationError as e: - print("❌ Pipeline cannot be serialized:") - print(str(e)) - print("\n💡 Convert lambda functions to named functions:") - print(" lambda x: x > 0.8 → def filter_func(x): return x > 0.8") - - -def create_example_pipeline() -> Pipeline: - """Create an example pipeline for testing""" - from orcapod import GlobSource, function_pod - - @function_pod - def example_function(input_file): - return f"processed_{input_file}" - - pipeline = Pipeline("example") - - with pipeline: - # This would need actual operations to be meaningful - # source = GlobSource('data', './test_data', '*.txt')() - # results = source >> example_function - pass - - return pipeline diff --git a/src/orcapod/pod/__init__.py b/src/orcapod/pod/__init__.py deleted file mode 100644 index 8567c2a..0000000 --- a/src/orcapod/pod/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from .core import Pod, FunctionPod, function_pod, TypedFunctionPod, typed_function_pod - -__all__ = [ - "Pod", - "FunctionPod", - "function_pod", - "TypedFunctionPod", - "typed_function_pod", -] diff --git a/src/orcapod/pod/core.py b/src/orcapod/pod/core.py deleted file mode 100644 index a82944f..0000000 --- a/src/orcapod/pod/core.py +++ /dev/null @@ -1,868 +0,0 @@ -import functools -import logging -import pickle -import warnings -from abc import abstractmethod -import pyarrow as pa -import sys -from collections.abc import Callable, Collection, Iterable, Iterator, Sequence -from typing import ( - Any, - Literal, -) - -from orcapod.types.registry import PacketConverter - -from orcapod.core.base import Kernel -from orcapod.hashing import ( - ObjectHasher, - ArrowHasher, - FunctionInfoExtractor, - get_function_signature, - hash_function, - get_default_object_hasher, - get_default_arrow_hasher, -) -from orcapod.core.operators import Join -from orcapod.store import DataStore, ArrowDataStore, NoOpDataStore -from orcapod.core.streams import SyncStream, SyncStreamFromGenerator -from orcapod.types import Packet, PathSet, PodFunction, Tag, TypeSpec - -from orcapod.types.default import default_registry -from orcapod.types.inference import ( - extract_function_data_types, - verify_against_typespec, - check_typespec_compatibility, -) -from orcapod.types.registry import is_packet_supported -import polars as pl - -logger = logging.getLogger(__name__) - - -def function_pod( - output_keys: Collection[str] | None = None, - function_name: str | None = None, - data_store: DataStore | None = None, - store_name: str | None = None, - function_hash_mode: Literal["signature", "content", "name", "custom"] = "name", - custom_hash: int | None = None, - force_computation: bool = False, - skip_memoization: bool = False, - error_handling: Literal["raise", "ignore", "warn"] = "raise", - **kwargs, -) -> Callable[..., "FunctionPod"]: - """ - Decorator that wraps a function in a FunctionPod instance. - - Args: - output_keys: Keys for the function output - force_computation: Whether to force computation - skip_memoization: Whether to skip memoization - - Returns: - FunctionPod instance wrapping the decorated function - """ - - def decorator(func) -> FunctionPod: - if func.__name__ == "": - raise ValueError("Lambda functions cannot be used with function_pod") - - if not hasattr(func, "__module__") or func.__module__ is None: - raise ValueError( - f"Function {func.__name__} must be defined at module level" - ) - - # Store the original function in the module for pickling purposes - # and make sure to change the name of the function - module = sys.modules[func.__module__] - base_function_name = func.__name__ - new_function_name = f"_original_{func.__name__}" - setattr(module, new_function_name, func) - # rename the function to be consistent and make it pickleable - setattr(func, "__name__", new_function_name) - setattr(func, "__qualname__", new_function_name) - - # Create the FunctionPod - pod = FunctionPod( - function=func, - output_keys=output_keys, - function_name=function_name or base_function_name, - data_store=data_store, - store_name=store_name, - function_hash_mode=function_hash_mode, - custom_hash=custom_hash, - force_computation=force_computation, - skip_memoization=skip_memoization, - error_handling=error_handling, - **kwargs, - ) - - return pod - - return decorator - - -class Pod(Kernel): - """ - An (abstract) base class for all pods. A pod can be seen as a special type of operation that - only operates on the packet content without reading tags. Consequently, no operation - of Pod can dependent on the tags of the packets. This is a design choice to ensure that - the pods act as pure functions which is a necessary condition to guarantee reproducibility. - """ - - def __init__( - self, error_handling: Literal["raise", "ignore", "warn"] = "raise", **kwargs - ): - super().__init__(**kwargs) - self.error_handling = error_handling - - def process_stream(self, *streams: SyncStream) -> list[SyncStream]: - """ - Prepare the incoming streams for execution in the pod. This default implementation - joins all the streams together and raises and error if no streams are provided. - """ - # if multiple streams are provided, join them - # otherwise, return as is - combined_streams = list(streams) - if len(streams) > 1: - stream = streams[0] - for next_stream in streams[1:]: - stream = Join()(stream, next_stream) - combined_streams = [stream] - return combined_streams - - def __call__(self, *streams: SyncStream, **kwargs) -> SyncStream: - stream = self.process_stream(*streams) - return super().__call__(*stream, **kwargs) - - def call(self, tag: Tag, packet: Packet) -> tuple[Tag, Packet]: ... - - def forward(self, *streams: SyncStream) -> SyncStream: - # if multiple streams are provided, join them - if len(streams) > 1: - raise ValueError("Multiple streams should be joined before calling forward") - if len(streams) == 0: - raise ValueError("No streams provided to forward") - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - n_computed = 0 - for tag, packet in stream: - try: - tag, output_packet = self.call(tag, packet) - n_computed += 1 - logger.info(f"Computed item {n_computed}") - yield tag, output_packet - - except Exception as e: - logger.error(f"Error processing packet {packet}: {e}") - if self.error_handling == "raise": - raise e - elif self.error_handling == "ignore": - continue - elif self.error_handling == "warn": - warnings.warn(f"Error processing packet {packet}: {e}") - continue - - return SyncStreamFromGenerator(generator) - - -class FunctionPod(Pod): - """ - A pod that wraps a function and allows it to be used as an operation in a stream. - This pod can be used to apply a function to the packets in a stream, with optional memoization - and caching of results. It can also handle multiple output keys and error handling. - The function should accept keyword arguments that correspond to the keys in the packets. - The output of the function should be a path or a collection of paths that correspond to the output keys.""" - - def __init__( - self, - function: PodFunction, - output_keys: Collection[str] | None = None, - function_name=None, - data_store: DataStore | None = None, - store_name: str | None = None, - function_hash_mode: Literal["signature", "content", "name", "custom"] = "name", - custom_hash: int | None = None, - label: str | None = None, - force_computation: bool = False, - skip_memoization_lookup: bool = False, - skip_memoization: bool = False, - error_handling: Literal["raise", "ignore", "warn"] = "raise", - _hash_function_kwargs: dict | None = None, - **kwargs, - ) -> None: - super().__init__(label=label, **kwargs) - self.function = function - self.output_keys = output_keys or [] - if function_name is None: - if hasattr(self.function, "__name__"): - function_name = getattr(self.function, "__name__") - else: - raise ValueError( - "function_name must be provided if function has no __name__ attribute" - ) - - self.function_name = function_name - self.data_store = data_store if data_store is not None else NoOpDataStore() - self.store_name = store_name or function_name - self.function_hash_mode = function_hash_mode - self.custom_hash = custom_hash - self.force_computation = force_computation - self.skip_memoization_lookup = skip_memoization_lookup - self.skip_memoization = skip_memoization - self.error_handling = error_handling - self._hash_function_kwargs = _hash_function_kwargs - - def __repr__(self) -> str: - func_sig = get_function_signature(self.function) - return f"FunctionPod:{func_sig} ⇒ {self.output_keys}" - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - stream = self.process_stream(*streams) - tag_keys, _ = stream[0].keys(trigger_run=trigger_run) - return tag_keys, tuple(self.output_keys) - - def is_memoized(self, packet: Packet) -> bool: - return self.retrieve_memoized(packet) is not None - - def retrieve_memoized(self, packet: Packet) -> Packet | None: - """ - Retrieve a memoized packet from the data store. - Returns None if no memoized packet is found. - """ - return self.data_store.retrieve_memoized( - self.store_name, - self.content_hash(char_count=16), - packet, - ) - - def memoize( - self, - packet: Packet, - output_packet: Packet, - ) -> Packet: - """ - Memoize the output packet in the data store. - Returns the memoized packet. - """ - return self.data_store.memoize( - self.store_name, - self.content_hash(char_count=16), # identity of this function pod - packet, - output_packet, - ) - - def forward(self, *streams: SyncStream) -> SyncStream: - # if multiple streams are provided, join them - if len(streams) > 1: - raise ValueError("Multiple streams should be joined before calling forward") - if len(streams) == 0: - raise ValueError("No streams provided to forward") - stream = streams[0] - - def generator() -> Iterator[tuple[Tag, Packet]]: - n_computed = 0 - for tag, packet in stream: - output_values: list["PathSet"] = [] - try: - if not self.skip_memoization_lookup: - memoized_packet = self.retrieve_memoized(packet) - else: - memoized_packet = None - if not self.force_computation and memoized_packet is not None: - logger.info("Memoized packet found, skipping computation") - yield tag, memoized_packet - continue - values = self.function(**packet) - - if len(self.output_keys) == 0: - output_values = [] - elif len(self.output_keys) == 1: - output_values = [values] # type: ignore - elif isinstance(values, Iterable): - output_values = list(values) # type: ignore - elif len(self.output_keys) > 1: - raise ValueError( - "Values returned by function must be a pathlike or a sequence of pathlikes" - ) - - if len(output_values) != len(self.output_keys): - raise ValueError( - f"Number of output keys {len(self.output_keys)}:{self.output_keys} does not match number of values returned by function {len(output_values)}" - ) - except Exception as e: - logger.error(f"Error processing packet {packet}: {e}") - if self.error_handling == "raise": - raise e - elif self.error_handling == "ignore": - continue - elif self.error_handling == "warn": - warnings.warn(f"Error processing packet {packet}: {e}") - continue - - output_packet: Packet = { - k: v for k, v in zip(self.output_keys, output_values) - } - - if not self.skip_memoization: - # output packet may be modified by the memoization process - # e.g. if the output is a file, the path may be changed - output_packet = self.memoize(packet, output_packet) # type: ignore - - n_computed += 1 - logger.info(f"Computed item {n_computed}") - yield tag, output_packet - - return SyncStreamFromGenerator(generator) - - def identity_structure(self, *streams) -> Any: - content_kwargs = self._hash_function_kwargs - if self.function_hash_mode == "content": - if content_kwargs is None: - content_kwargs = { - "include_name": False, - "include_module": False, - "include_declaration": False, - } - function_hash_value = hash_function( - self.function, - name_override=self.function_name, - function_hash_mode="content", - content_kwargs=content_kwargs, - ) - elif self.function_hash_mode == "signature": - function_hash_value = hash_function( - self.function, - name_override=self.function_name, - function_hash_mode="signature", - content_kwargs=content_kwargs, - ) - elif self.function_hash_mode == "name": - function_hash_value = hash_function( - self.function, - name_override=self.function_name, - function_hash_mode="name", - content_kwargs=content_kwargs, - ) - elif self.function_hash_mode == "custom": - if self.custom_hash is None: - raise ValueError("Custom hash function not provided") - function_hash_value = self.custom_hash - else: - raise ValueError( - f"Unknown function hash mode: {self.function_hash_mode}. " - "Must be one of 'content', 'signature', 'name', or 'custom'." - ) - - return ( - self.__class__.__name__, - function_hash_value, - tuple(self.output_keys), - ) + tuple(streams) - - -def typed_function_pod( - output_keys: str | Collection[str] | None = None, - function_name: str | None = None, - label: str | None = None, - result_store: ArrowDataStore | None = None, - tag_store: ArrowDataStore | None = None, - object_hasher: ObjectHasher | None = None, - arrow_hasher: ArrowHasher | None = None, - **kwargs, -) -> Callable[..., "TypedFunctionPod | CachedFunctionPod"]: - """ - Decorator that wraps a function in a FunctionPod instance. - - Args: - output_keys: Keys for the function output(s) - function_name: Name of the function pod; if None, defaults to the function name - **kwargs: Additional keyword arguments to pass to the FunctionPod constructor. Please refer to the FunctionPod documentation for details. - - Returns: - FunctionPod instance wrapping the decorated function - """ - - def decorator(func) -> TypedFunctionPod | CachedFunctionPod: - if func.__name__ == "": - raise ValueError("Lambda functions cannot be used with function_pod") - - if not hasattr(func, "__module__") or func.__module__ is None: - raise ValueError( - f"Function {func.__name__} must be defined at module level" - ) - - # Store the original function in the module for pickling purposes - # and make sure to change the name of the function - module = sys.modules[func.__module__] - base_function_name = func.__name__ - new_function_name = f"_original_{func.__name__}" - setattr(module, new_function_name, func) - # rename the function to be consistent and make it pickleable - setattr(func, "__name__", new_function_name) - setattr(func, "__qualname__", new_function_name) - - # Create a simple typed function pod - pod = TypedFunctionPod( - function=func, - output_keys=output_keys, - function_name=function_name or base_function_name, - label=label, - **kwargs, - ) - - if result_store is not None: - pod = CachedFunctionPod( - function_pod=pod, - object_hasher=object_hasher - if object_hasher is not None - else get_default_object_hasher(), - arrow_hasher=arrow_hasher - if arrow_hasher is not None - else get_default_arrow_hasher(), - result_store=result_store, - tag_store=tag_store, - ) - - return pod - - return decorator - - -class TypedFunctionPod(Pod): - """ - A type-aware pod that wraps a function and provides automatic type validation and inference. - - This pod extends the base Pod functionality by automatically extracting and validating - type information from function signatures and user-provided specifications. It ensures - type safety by verifying that both input and output types are supported by the - configured type registry before execution. - - The TypedFunctionPod analyzes the wrapped function's signature to determine: - - Parameter types (from annotations or user-provided input_types) - - Return value types (from annotations or user-provided output_types) - - Type compatibility with the packet type registry - - Key Features: - - Automatic type extraction from function annotations - - Type override support via input_types and output_types parameters - - Registry-based type validation ensuring data compatibility - - Memoization support with type-aware caching - - Multiple output key handling with proper type mapping - - Comprehensive error handling for type mismatches - - Type Resolution Priority: - 1. User-provided input_types/output_types override function annotations - 2. Function parameter annotations are used when available - 3. Function return annotations are parsed for output type inference - 4. Error raised if types cannot be determined or are unsupported - - Args: - function: The function to wrap. Must accept keyword arguments corresponding - to packet keys and return values compatible with output_keys. - output_keys: Collection of string keys for the function outputs. For functions - returning a single value, provide a single key. For multiple returns - (tuple/list), provide keys matching the number of return items. - function_name: Optional name for the function. Defaults to function.__name__. - input_types: Optional mapping of parameter names to their types. Overrides - function annotations for specified parameters. - output_types: Optional type specification for return values. Can be: - - A dict mapping output keys to types (TypeSpec) - - A sequence of types mapped to output_keys in order - These override inferred types from function return annotations. - data_store: DataStore instance for memoization. Defaults to NoOpDataStore. - function_hasher: Hasher function for creating function identity hashes. - Required parameter - no default implementation available. - label: Optional label for the pod instance. - skip_memoization_lookup: If True, skips checking for memoized results. - skip_memoization: If True, disables memoization entirely. - error_handling: How to handle execution errors: - - "raise": Raise exceptions (default) - - "ignore": Skip failed packets silently - - "warn": Issue warnings and continue - packet_type_registry: Registry for validating packet types. Defaults to - the default registry if None. - **kwargs: Additional arguments passed to the parent Pod class and above. - - Raises: - ValueError: When: - - function_name cannot be determined and is not provided - - Input types are not supported by the registry - - Output types are not supported by the registry - - Type extraction fails due to missing annotations/specifications - NotImplementedError: When function_hasher is None (required parameter). - - Examples: - Basic usage with annotated function: - - >>> def process_data(text: str, count: int) -> tuple[str, int]: - ... return text.upper(), count * 2 - >>> - >>> pod = TypedFunctionPod( - ... function=process_data, - ... output_keys=['upper_text', 'doubled_count'], - ... function_hasher=my_hasher - ... ) - - Override types for legacy function: - - >>> def legacy_func(x, y): # No annotations - ... return x + y - >>> - >>> pod = TypedFunctionPod( - ... function=legacy_func, - ... output_keys=['sum'], - ... input_types={'x': int, 'y': int}, - ... output_types={'sum': int}, - ... function_hasher=my_hasher - ... ) - - Multiple outputs with sequence override: - - >>> def analyze(data: list) -> tuple[int, float, str]: - ... return len(data), sum(data), str(data) - >>> - >>> pod = TypedFunctionPod( - ... function=analyze, - ... output_keys=['count', 'total', 'repr'], - ... output_types=[int, float, str], # Override with sequence - ... function_hasher=my_hasher - ... ) - - Attributes: - function: The wrapped function. - output_keys: List of output key names. - function_name: Name identifier for the function. - function_input_types: Resolved input type specification. - function_output_types: Resolved output type specification. - registry: Type registry for validation. - data_store: DataStore instance for memoization. - function_hasher: Function hasher for identity computation. - skip_memoization_lookup: Whether to skip memoization lookups. - skip_memoization: Whether to disable memoization entirely. - error_handling: Error handling strategy. - - Note: - The TypedFunctionPod requires a function_hasher to be provided as there - is no default implementation. This hasher is used to create stable - identity hashes for memoization and caching purposes. - - Type validation occurs during initialization, ensuring that any type - incompatibilities are caught early rather than during stream processing. - """ - - def __init__( - self, - function: Callable[..., Any], - output_keys: str | Collection[str] | None = None, - function_name=None, - input_types: TypeSpec | None = None, - output_types: TypeSpec | Sequence[type] | None = None, - label: str | None = None, - packet_type_registry=None, - function_info_extractor: FunctionInfoExtractor | None = None, - **kwargs, - ) -> None: - super().__init__(label=label, **kwargs) - self.function = function - if output_keys is None: - output_keys = [] - if isinstance(output_keys, str): - output_keys = [output_keys] - self.output_keys = output_keys - if function_name is None: - if hasattr(self.function, "__name__"): - function_name = getattr(self.function, "__name__") - else: - raise ValueError( - "function_name must be provided if function has no __name__ attribute" - ) - self.function_name = function_name - - if packet_type_registry is None: - packet_type_registry = default_registry - - self.registry = packet_type_registry - self.function_info_extractor = function_info_extractor - - # extract input and output types from the function signature - function_input_types, function_output_types = extract_function_data_types( - self.function, - self.output_keys, - input_types=input_types, - output_types=output_types, - ) - - self.function_input_types = function_input_types - self.function_output_types = function_output_types - - # TODO: include explicit check of support during PacketConverter creation - self.input_converter = PacketConverter(self.function_input_types, self.registry) - self.output_converter = PacketConverter( - self.function_output_types, self.registry - ) - - # TODO: prepare a separate str and repr methods - def __repr__(self) -> str: - func_sig = get_function_signature(self.function) - return f"FunctionPod:{func_sig} ⇒ {self.output_keys}" - - def call(self, tag, packet) -> tuple[Tag, Packet]: - output_values: list["PathSet"] = [] - - values = self.function(**packet) - - if len(self.output_keys) == 0: - output_values = [] - elif len(self.output_keys) == 1: - output_values = [values] # type: ignore - elif isinstance(values, Iterable): - output_values = list(values) # type: ignore - elif len(self.output_keys) > 1: - raise ValueError( - "Values returned by function must be a pathlike or a sequence of pathlikes" - ) - - if len(output_values) != len(self.output_keys): - raise ValueError( - f"Number of output keys {len(self.output_keys)}:{self.output_keys} does not match number of values returned by function {len(output_values)}" - ) - - output_packet: Packet = {k: v for k, v in zip(self.output_keys, output_values)} - return tag, output_packet - - def identity_structure(self, *streams) -> Any: - # construct identity structure for the function - # if function_info_extractor is available, use that but substitute the function_name - if self.function_info_extractor is not None: - function_info = self.function_info_extractor.extract_function_info( - self.function, - function_name=self.function_name, - input_types=self.function_input_types, - output_types=self.function_output_types, - ) - else: - # use basic information only - function_info = { - "name": self.function_name, - "input_types": self.function_input_types, - "output_types": self.function_output_types, - } - function_info["output_keys"] = tuple(self.output_keys) - - return ( - self.__class__.__name__, - function_info, - ) + tuple(streams) - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - stream = self.process_stream(*streams) - tag_keys, _ = stream[0].keys(trigger_run=trigger_run) - return tag_keys, tuple(self.output_keys) - - -class CachedFunctionPod(Pod): - def __init__( - self, - function_pod: TypedFunctionPod, - object_hasher: ObjectHasher, - arrow_hasher: ArrowHasher, - result_store: ArrowDataStore, - tag_store: ArrowDataStore | None = None, - label: str | None = None, - skip_memoization_lookup: bool = False, - skip_memoization: bool = False, - skip_tag_record: bool = False, - error_handling: Literal["raise", "ignore", "warn"] = "raise", - **kwargs, - ) -> None: - super().__init__(label=label, error_handling=error_handling, **kwargs) - self.function_pod = function_pod - - self.object_hasher = object_hasher - self.arrow_hasher = arrow_hasher - self.result_store = result_store - self.tag_store = tag_store - - self.skip_memoization_lookup = skip_memoization_lookup - self.skip_memoization = skip_memoization - self.skip_tag_record = skip_tag_record - - # TODO: consider making this dynamic - self.function_pod_hash = self.object_hasher.hash_to_hex(self.function_pod) - - def get_packet_key(self, packet: Packet) -> str: - return self.arrow_hasher.hash_table( - self.function_pod.input_converter.to_arrow_table(packet) - ) - - # TODO: prepare a separate str and repr methods - def __repr__(self) -> str: - return f"Cached:{self.function_pod}" - - def keys( - self, *streams: SyncStream, trigger_run: bool = False - ) -> tuple[Collection[str] | None, Collection[str] | None]: - return self.function_pod.keys(*streams, trigger_run=trigger_run) - - def is_memoized(self, packet: Packet) -> bool: - return self.retrieve_memoized(packet) is not None - - def add_tag_record(self, tag: Tag, packet: Packet) -> Tag: - """ - Record the tag for the packet in the record store. - This is used to keep track of the tags associated with memoized packets. - """ - - return self._add_tag_record_with_packet_key(tag, self.get_packet_key(packet)) - - def _add_tag_record_with_packet_key(self, tag: Tag, packet_key: str) -> Tag: - if self.tag_store is None: - raise ValueError("Recording of tag requires tag_store but none provided") - - tag = dict(tag) # ensure we don't modify the original tag - tag["__packet_key"] = packet_key - - # convert tag to arrow table - table = pa.Table.from_pylist([tag]) - - entry_hash = self.arrow_hasher.hash_table(table) - - # TODO: add error handling - # check if record already exists: - retrieved_table = self.tag_store.get_record( - self.function_pod.function_name, self.function_pod_hash, entry_hash - ) - if retrieved_table is None: - self.tag_store.add_record( - self.function_pod.function_name, - self.function_pod_hash, - entry_hash, - table, - ) - - return tag - - def retrieve_memoized(self, packet: Packet) -> Packet | None: - """ - Retrieve a memoized packet from the data store. - Returns None if no memoized packet is found. - """ - logger.info("Retrieving memoized packet") - return self._retrieve_memoized_by_hash(self.get_packet_key(packet)) - - def _retrieve_memoized_by_hash(self, packet_hash: str) -> Packet | None: - """ - Retrieve a memoized result packet from the data store, looking up by hash - Returns None if no memoized packet is found. - """ - logger.info(f"Retrieving memoized packet with hash {packet_hash}") - arrow_table = self.result_store.get_record( - self.function_pod.function_name, - self.function_pod_hash, - packet_hash, - ) - if arrow_table is None: - return None - packets = self.function_pod.output_converter.from_arrow_table(arrow_table) - # since memoizing single packet, it should only contain one packet - assert len(packets) == 1, ( - f"Memoizing single packet return {len(packets)} packets!" - ) - return packets[0] - - def memoize( - self, - packet: Packet, - output_packet: Packet, - ) -> Packet: - """ - Memoize the output packet in the data store. - Returns the memoized packet. - """ - logger.info("Memoizing packet") - return self._memoize_by_hash(self.get_packet_key(packet), output_packet) - - def _memoize_by_hash(self, packet_hash: str, output_packet: Packet) -> Packet: - """ - Memoize the output packet in the data store, looking up by hash. - Returns the memoized packet. - """ - logger.info(f"Memoizing packet with hash {packet_hash}") - packets = self.function_pod.output_converter.from_arrow_table( - self.result_store.add_record( - self.function_pod.function_name, - self.function_pod_hash, - packet_hash, - self.function_pod.output_converter.to_arrow_table(output_packet), - ) - ) - # since memoizing single packet, it should only contain one packet - assert len(packets) == 1, ( - f"Memoizing single packet return {len(packets)} packets!" - ) - return packets[0] - - def call(self, tag: Tag, packet: Packet) -> tuple[Tag, Packet]: - packet_key = "" - if ( - not self.skip_tag_record - or not self.skip_memoization_lookup - or not self.skip_memoization - ): - packet_key = self.get_packet_key(packet) - - if not self.skip_tag_record and self.tag_store is not None: - self._add_tag_record_with_packet_key(tag, packet_key) - - if not self.skip_memoization_lookup: - memoized_packet = self._retrieve_memoized_by_hash(packet_key) - else: - memoized_packet = None - if memoized_packet is not None: - logger.info("Memoized packet found, skipping computation") - return tag, memoized_packet - - tag, output_packet = self.function_pod.call(tag, packet) - - if not self.skip_memoization: - # output packet may be modified by the memoization process - # e.g. if the output is a file, the path may be changed - output_packet = self.memoize(packet, output_packet) # type: ignore - - return tag, output_packet - - def get_all_entries_with_tags(self) -> pl.LazyFrame | None: - """ - Retrieve all entries from the tag store with their associated tags. - Returns a DataFrame with columns for tag and packet key. - """ - if self.tag_store is None: - raise ValueError("Tag store is not set, cannot retrieve entries") - - tag_records = self.tag_store.get_all_records_as_polars( - self.function_pod.function_name, self.function_pod_hash - ) - if tag_records is None: - return None - result_packets = self.result_store.get_records_by_ids_as_polars( - self.function_pod.function_name, - self.function_pod_hash, - tag_records.collect()["__packet_key"], - preserve_input_order=True, - ) - if result_packets is None: - return None - - return pl.concat([tag_records, result_packets], how="horizontal").drop( - ["__packet_key"] - ) - - def identity_structure(self, *streams) -> Any: - return self.function_pod.identity_structure(*streams) diff --git a/src/orcapod/dj/__init__.py b/src/orcapod/protocols/__init__.py similarity index 100% rename from src/orcapod/dj/__init__.py rename to src/orcapod/protocols/__init__.py diff --git a/src/orcapod/protocols/core_protocols/__init__.py b/src/orcapod/protocols/core_protocols/__init__.py new file mode 100644 index 0000000..f9c711d --- /dev/null +++ b/src/orcapod/protocols/core_protocols/__init__.py @@ -0,0 +1,24 @@ +from .base import ExecutionEngine, PodFunction +from .datagrams import Datagram, Tag, Packet +from .streams import Stream, LiveStream +from .kernel import Kernel +from .pods import Pod, CachedPod +from .source import Source +from .trackers import Tracker, TrackerManager + + +__all__ = [ + "ExecutionEngine", + "PodFunction", + "Datagram", + "Tag", + "Packet", + "Stream", + "LiveStream", + "Kernel", + "Pod", + "CachedPod", + "Source", + "Tracker", + "TrackerManager", +] diff --git a/src/orcapod/protocols/core_protocols/base.py b/src/orcapod/protocols/core_protocols/base.py new file mode 100644 index 0000000..c44d52c --- /dev/null +++ b/src/orcapod/protocols/core_protocols/base.py @@ -0,0 +1,110 @@ +from collections.abc import Callable +from typing import Any, Protocol, runtime_checkable +from orcapod.types import DataValue + + +@runtime_checkable +class ExecutionEngine(Protocol): + @property + def name(self) -> str: ... + + def submit_sync(self, function: Callable, *args, **kwargs) -> Any: + """ + Run the given function with the provided arguments. + This method should be implemented by the execution engine. + """ + ... + + async def submit_async(self, function: Callable, *args, **kwargs) -> Any: + """ + Asynchronously run the given function with the provided arguments. + This method should be implemented by the execution engine. + """ + ... + + # TODO: consider adding batch submission + + +@runtime_checkable +class PodFunction(Protocol): + """ + A function suitable for use in a FunctionPod. + + PodFunctions define the computational logic that operates on individual + packets within a Pod. They represent pure functions that transform + data values without side effects. + + These functions are designed to be: + - Stateless: No dependency on external state + - Deterministic: Same inputs always produce same outputs + - Serializable: Can be cached and distributed + - Type-safe: Clear input/output contracts + + PodFunctions accept named arguments corresponding to packet fields + and return transformed data values. + """ + + def __call__(self, **kwargs: DataValue) -> None | DataValue: + """ + Execute the pod function with the given arguments. + + The function receives packet data as named arguments and returns + either transformed data or None (for filtering operations). + + Args: + **kwargs: Named arguments mapping packet fields to data values + + Returns: + None: Filter out this packet (don't include in output) + DataValue: Single transformed value + + Raises: + TypeError: If required arguments are missing + ValueError: If argument values are invalid + """ + ... + + +@runtime_checkable +class Labelable(Protocol): + """ + Protocol for objects that can have a human-readable label. + + Labels provide meaningful names for objects in the computational graph, + making debugging, visualization, and monitoring much easier. They serve + as human-friendly identifiers that complement the technical identifiers + used internally. + + Labels are optional but highly recommended for: + - Debugging complex computational graphs + - Visualization and monitoring tools + - Error messages and logging + - User interfaces and dashboards + """ + + @property + def label(self) -> str: + """ + Return the human-readable label for this object. + + Labels should be descriptive and help users understand the purpose + or role of the object in the computational graph. + + Returns: + str: Human-readable label for this object + None: No label is set (will use default naming) + """ + ... + + @label.setter + def label(self, label: str | None) -> None: + """ + Set the human-readable label for this object. + + Labels should be descriptive and help users understand the purpose + or role of the object in the computational graph. + + Args: + value (str): Human-readable label for this object + """ + ... diff --git a/src/orcapod/protocols/core_protocols/datagrams.py b/src/orcapod/protocols/core_protocols/datagrams.py new file mode 100644 index 0000000..a0f24d8 --- /dev/null +++ b/src/orcapod/protocols/core_protocols/datagrams.py @@ -0,0 +1,1104 @@ +from collections.abc import Collection, Iterator, Mapping +from typing import Any, Protocol, Self, TYPE_CHECKING, runtime_checkable +from orcapod.protocols.hashing_protocols import ContentIdentifiable +from orcapod.types import DataValue, PythonSchema + + +if TYPE_CHECKING: + import pyarrow as pa + + +@runtime_checkable +class Datagram(ContentIdentifiable, Protocol): + """ + Protocol for immutable datagram containers in Orcapod. + + Datagrams are the fundamental units of data that flow through the system. + They provide a unified interface for data access, conversion, and manipulation, + ensuring consistent behavior across different storage backends (dict, Arrow table, etc.). + + Each datagram contains: + - **Data columns**: The primary business data (user_id, name, etc.) + - **Meta columns**: Internal system metadata with {constants.META_PREFIX} (typically '__') prefixes (e.g. __processed_at, etc.) + - **Context column**: Data context information ({constants.CONTEXT_KEY}) + + Derivative of datagram (such as Packet or Tag) will also include some specific columns pertinent to the function of the specialized datagram: + - **Source info columns**: Data provenance with {constants.SOURCE_PREFIX} ('_source_') prefixes (_source_user_id, etc.) used in Packet + - **System tags**: Internal tags for system use, typically prefixed with {constants.SYSTEM_TAG_PREFIX} ('_system_') (_system_created_at, etc.) used in Tag + + All operations are by design immutable - methods return new datagram instances rather than modifying existing ones. + + Example: + >>> datagram = DictDatagram({"user_id": 123, "name": "Alice"}) + >>> updated = datagram.update(name="Alice Smith") + >>> filtered = datagram.select("user_id", "name") + >>> table = datagram.as_table() + """ + + # 1. Core Properties (Identity & Structure) + @property + def data_context_key(self) -> str: + """ + Return the data context key for this datagram. + + This key identifies a collection of system components that collectively controls + how information is serialized, hashed and represented, including the semantic type registry, + arrow data hasher, and other contextual information. Same piece of information (that is two datagrams + with an identical *logical* content) may bear distinct internal representation if they are + represented under two distinct data context, as signified by distinct data context keys. + + Returns: + str: Context key for proper datagram interpretation + """ + ... + + @property + def meta_columns(self) -> tuple[str, ...]: + """Return tuple of meta column names (with {constants.META_PREFIX} ('__') prefix).""" + ... + + # 2. Dict-like Interface (Data Access) + def __getitem__(self, key: str) -> DataValue: + """ + Get data column value by key. + + Provides dict-like access to data columns only. Meta columns + are not accessible through this method (use `get_meta_value()` instead). + + Args: + key: Data column name. + + Returns: + The value stored in the specified data column. + + Raises: + KeyError: If the column doesn't exist in data columns. + + Example: + >>> datagram["user_id"] + 123 + >>> datagram["name"] + 'Alice' + """ + ... + + def __contains__(self, key: str) -> bool: + """ + Check if data column exists. + + Args: + key: Column name to check. + + Returns: + True if column exists in data columns, False otherwise. + + Example: + >>> "user_id" in datagram + True + >>> "nonexistent" in datagram + False + """ + ... + + def __iter__(self) -> Iterator[str]: + """ + Iterate over data column names. + + Provides for-loop support over column names, enabling natural iteration + patterns without requiring conversion to dict. + + Yields: + Data column names in no particular order. + + Example: + >>> for column in datagram: + ... value = datagram[column] + ... print(f"{column}: {value}") + """ + ... + + def get(self, key: str, default: DataValue = None) -> DataValue: + """ + Get data column value with default fallback. + + Args: + key: Data column name. + default: Value to return if column doesn't exist. + + Returns: + Column value if exists, otherwise the default value. + + Example: + >>> datagram.get("user_id") + 123 + >>> datagram.get("missing", "default") + 'default' + """ + ... + + # 3. Structural Information + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> tuple[str, ...]: + """ + Return tuple of column names. + + Provides access to column names with filtering options for different + column types. Default returns only data column names. + + Args: + include_meta_columns: Controls meta column inclusion. + - False: Return only data column names (default) + - True: Include all meta column names + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context column. + + Returns: + Tuple of column names based on inclusion criteria. + + Example: + >>> datagram.keys() # Data columns only + ('user_id', 'name', 'email') + >>> datagram.keys(include_meta_columns=True) + ('user_id', 'name', 'email', f'{orcapod.META_PREFIX}processed_at', f'{orcapod.META_PREFIX}pipeline_version') + >>> datagram.keys(include_meta_columns=["pipeline"]) + ('user_id', 'name', 'email',f'{orcapod.META_PREFIX}pipeline_version') + >>> datagram.keys(include_context=True) + ('user_id', 'name', 'email', f'{orcapod.CONTEXT_KEY}') + """ + ... + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> PythonSchema: + """ + Return type specification mapping field names to Python types. + + The TypeSpec enables type checking and validation throughout the system. + + Args: + include_meta_columns: Controls meta column type inclusion. + - False: Exclude meta column types (default) + - True: Include all meta column types + - Collection[str]: Include meta column types matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context type. + + Returns: + TypeSpec mapping field names to their Python types. + + Example: + >>> datagram.types() + {'user_id': , 'name': } + """ + ... + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Schema": + """ + Return PyArrow schema representation. + + The schema provides structured field and type information for efficient + serialization and deserialization with PyArrow. + + Args: + include_meta_columns: Controls meta column schema inclusion. + - False: Exclude meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context column. + + Returns: + PyArrow Schema describing the datagram structure. + + Example: + >>> schema = datagram.arrow_schema() + >>> schema.names + ['user_id', 'name'] + """ + ... + + # 4. Format Conversions (Export) + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> dict[str, DataValue]: + """ + Convert datagram to dictionary format. + + Provides a simple key-value representation useful for debugging, + serialization, and interop with dict-based APIs. + + Args: + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include the context key. + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + + + Returns: + Dictionary with requested columns as key-value pairs. + + Example: + >>> data = datagram.as_dict() # {'user_id': 123, 'name': 'Alice'} + >>> full_data = datagram.as_dict( + ... include_meta_columns=True, + ... include_context=True + ... ) + """ + ... + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> "pa.Table": + """ + Convert datagram to PyArrow Table format. + + Provides a standardized columnar representation suitable for analysis, + processing, and interoperability with Arrow-based tools. + + Args: + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include the context column. + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + + Returns: + PyArrow Table with requested columns. + + Example: + >>> table = datagram.as_table() # Data columns only + >>> full_table = datagram.as_table( + ... include_meta_columns=True, + ... include_context=True + ... ) + >>> filtered = datagram.as_table(include_meta_columns=["pipeline"]) # same as passing f"{orcapod.META_PREFIX}pipeline" + """ + ... + + def as_arrow_compatible_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + ) -> dict[str, Any]: + """ + Return dictionary with values optimized for Arrow table conversion. + + This method returns a dictionary where values are in a form that can be + efficiently converted to Arrow format using pa.Table.from_pylist(). + + The key insight is that this avoids the expensive as_table() → concat pattern + by providing values that are "Arrow-ready" while remaining in dict format + for efficient batching. + + Implementation note: This may involve format conversions (e.g., Path objects + to strings, datetime objects to ISO strings, etc.) to ensure compatibility + with Arrow's expected input formats. + + Arrow table that results from pa.Table.from_pylist on the output of this should be accompanied + with arrow_schema(...) with the same argument options to ensure that the schema matches the table. + + Args: + include_all_info: Include all available information + include_meta_columns: Controls meta column inclusion + include_context: Whether to include context key + + Returns: + Dictionary with values optimized for Arrow conversion + + Example: + # Efficient batch conversion pattern + arrow_dicts = [datagram.as_arrow_compatible_dict() for datagram in datagrams] + schema = datagrams[0].arrow_schema() + table = pa.Table.from_pylist(arrow_dicts, schema=schema) + """ + ... + + # 5. Meta Column Operations + def get_meta_value(self, key: str, default: DataValue = None) -> DataValue: + """ + Get meta column value with optional default. + + Meta columns store operational metadata and use {orcapod.META_PREFIX} ('__') prefixes. + This method handles both prefixed and unprefixed key formats. + + Args: + key: Meta column key (with or without {orcapod.META_PREFIX} ('__') prefix). + default: Value to return if meta column doesn't exist. + + Returns: + Meta column value if exists, otherwise the default value. + + Example: + >>> datagram.get_meta_value("pipeline_version") # Auto-prefixed + 'v2.1.0' + >>> datagram.get_meta_value("__pipeline_version") # Already prefixed + 'v2.1.0' + >>> datagram.get_meta_value("missing", "default") + 'default' + """ + ... + + def with_meta_columns(self, **updates: DataValue) -> Self: + """ + Create new datagram with updated meta columns. + + Adds or updates operational metadata while preserving all data columns. + Keys are automatically prefixed with {orcapod.META_PREFIX} ('__') if needed. + + Args: + **updates: Meta column updates as keyword arguments. + + Returns: + New datagram instance with updated meta columns. + + Example: + >>> tracked = datagram.with_meta_columns( + ... processed_by="pipeline_v2", + ... timestamp="2024-01-15T10:30:00Z" + ... ) + """ + ... + + def drop_meta_columns(self, *keys: str, ignore_missing: bool = False) -> Self: + """ + Create new datagram with specified meta columns removed. + + Args: + *keys: Meta column keys to remove (prefixes optional). + ignore_missing: If True, ignore missing columns without raising an error. + + + Returns: + New datagram instance without specified meta columns. + + Raises: + KeryError: If any specified meta column to drop doesn't exist and ignore_missing=False. + + Example: + >>> cleaned = datagram.drop_meta_columns("old_source", "temp_debug") + """ + ... + + # 6. Data Column Operations + def select(self, *column_names: str) -> Self: + """ + Create new datagram with only specified data columns. + + Args: + *column_names: Data column names to keep. + + + Returns: + New datagram instance with only specified data columns. All other columns including + meta columns and context are preserved. + + Raises: + KeyError: If any specified column doesn't exist. + + Example: + >>> subset = datagram.select("user_id", "name", "email") + """ + ... + + def drop(self, *column_names: str, ignore_missing: bool = False) -> Self: + """ + Create new datagram with specified data columns removed. Note that this does not + remove meta columns or context column. Refer to `drop_meta_columns()` for dropping + specific meta columns. Context key column can never be dropped but a modified copy + can be created with a different context key using `with_data_context()`. + + Args: + *column_names: Data column names to remove. + ignore_missing: If True, ignore missing columns without raising an error. + + Returns: + New datagram instance without specified data columns. + + Raises: + KeryError: If any specified column to drop doesn't exist and ignore_missing=False. + + Example: + >>> filtered = datagram.drop("temp_field", "debug_info") + """ + ... + + def rename( + self, + column_mapping: Mapping[str, str], + ) -> Self: + """ + Create new datagram with data columns renamed. + + Args: + column_mapping: Mapping from old names to new names. + + Returns: + New datagram instance with renamed data columns. + + Example: + >>> renamed = datagram.rename( + ... {"old_id": "user_id", "old_name": "full_name"}, + ... column_types={"user_id": int} + ... ) + """ + ... + + def update(self, **updates: DataValue) -> Self: + """ + Create new datagram with existing column values updated. + + Updates values in existing data columns. Will error if any specified + column doesn't exist - use with_columns() to add new columns. + + Args: + **updates: Column names and their new values. + + Returns: + New datagram instance with updated values. + + Raises: + KeyError: If any specified column doesn't exist. + + Example: + >>> updated = datagram.update( + ... file_path="/new/absolute/path.txt", + ... status="processed" + ... ) + """ + ... + + def with_columns( + self, + column_types: Mapping[str, type] | None = None, + **updates: DataValue, + ) -> Self: + """ + Create new datagram with additional data columns. + + Adds new data columns to the datagram. Will error if any specified + column already exists - use update() to modify existing columns. + + Args: + column_types: Optional type specifications for new columns. If not provided, the column type is + inferred from the provided values. If value is None, the column type defaults to `str`. + **kwargs: New columns as keyword arguments. + + Returns: + New datagram instance with additional data columns. + + Raises: + ValueError: If any specified column already exists. + + Example: + >>> expanded = datagram.with_columns( + ... status="active", + ... score=95.5, + ... column_types={"score": float} + ... ) + """ + ... + + # 7. Context Operations + def with_context_key(self, new_context_key: str) -> Self: + """ + Create new datagram with different context key. + + Changes the semantic interpretation context while preserving all data. + The context key affects how columns are processed and converted. + + Args: + new_context_key: New context key string. + + Returns: + New datagram instance with updated context key. + + Note: + How the context is interpreted depends on the datagram implementation. + Semantic processing may be rebuilt for the new context. + + Example: + >>> financial_datagram = datagram.with_context_key("financial_v1") + """ + ... + + # 8. Utility Operations + def copy(self) -> Self: + """ + Create a shallow copy of the datagram. + + Returns a new datagram instance with the same data and cached values. + This is more efficient than reconstructing from scratch when you need + an identical datagram instance. + + Returns: + New datagram instance with copied data and caches. + + Example: + >>> copied = datagram.copy() + >>> copied is datagram # False - different instance + False + """ + ... + + # 9. String Representations + def __str__(self) -> str: + """ + Return user-friendly string representation. + + Shows the datagram as a simple dictionary for user-facing output, + messages, and logging. Only includes data columns for clean output. + + Returns: + Dictionary-style string representation of data columns only. + """ + ... + + def __repr__(self) -> str: + """ + Return detailed string representation for debugging. + + Shows the datagram type and comprehensive information for debugging. + + Returns: + Detailed representation with type and metadata information. + """ + ... + + +@runtime_checkable +class Tag(Datagram, Protocol): + """ + Metadata associated with each data item in a stream. + + Tags carry contextual information about data packets as they flow through + the computational graph. They are immutable and provide metadata that + helps with: + - Data lineage tracking + - Grouping and aggregation operations + - Temporal information (timestamps) + - Source identification + - Processing context + + Common examples include: + - Timestamps indicating when data was created/processed + - Source identifiers showing data origin + - Processing metadata like batch IDs or session information + - Grouping keys for aggregation operations + - Quality indicators or confidence scores + """ + + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> tuple[str, ...]: + """ + Return tuple of column names. + + Provides access to column names with filtering options for different + column types. Default returns only data column names. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column inclusion. + - False: Return only data column names (default) + - True: Include all meta column names + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context column. + include_source: Whether to include source info fields. + + + Returns: + Tuple of column names based on inclusion criteria. + + Example: + >>> datagram.keys() # Data columns only + ('user_id', 'name', 'email') + >>> datagram.keys(include_meta_columns=True) + ('user_id', 'name', 'email', f'{orcapod.META_PREFIX}processed_at', f'{orcapod.META_PREFIX}pipeline_version') + >>> datagram.keys(include_meta_columns=["pipeline"]) + ('user_id', 'name', 'email',f'{orcapod.META_PREFIX}pipeline_version') + >>> datagram.keys(include_context=True) + ('user_id', 'name', 'email', f'{orcapod.CONTEXT_KEY}') + """ + ... + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> PythonSchema: + """ + Return type specification mapping field names to Python types. + + The TypeSpec enables type checking and validation throughout the system. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column type inclusion. + - False: Exclude meta column types (default) + - True: Include all meta column types + - Collection[str]: Include meta column types matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context type. + include_source: Whether to include source info fields. + + Returns: + TypeSpec mapping field names to their Python types. + + Example: + >>> datagram.types() + {'user_id': , 'name': } + """ + ... + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> "pa.Schema": + """ + Return PyArrow schema representation. + + The schema provides structured field and type information for efficient + serialization and deserialization with PyArrow. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column schema inclusion. + - False: Exclude meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context column. + include_source: Whether to include source info fields. + + + Returns: + PyArrow Schema describing the datagram structure. + + Example: + >>> schema = datagram.arrow_schema() + >>> schema.names + ['user_id', 'name'] + """ + ... + + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> dict[str, DataValue]: + """ + Convert datagram to dictionary format. + + Provides a simple key-value representation useful for debugging, + serialization, and interop with dict-based APIs. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include the context key. + include_source: Whether to include source info fields. + + + Returns: + Dictionary with requested columns as key-value pairs. + + Example: + >>> data = datagram.as_dict() # {'user_id': 123, 'name': 'Alice'} + >>> full_data = datagram.as_dict( + ... include_meta_columns=True, + ... include_context=True + ... ) + """ + ... + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_system_tags: bool = False, + ) -> "pa.Table": + """ + Convert datagram to PyArrow Table format. + + Provides a standardized columnar representation suitable for analysis, + processing, and interoperability with Arrow-based tools. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include the context column. + include_source: Whether to include source info columns in the schema. + + Returns: + PyArrow Table with requested columns. + + Example: + >>> table = datagram.as_table() # Data columns only + >>> full_table = datagram.as_table( + ... include_meta_columns=True, + ... include_context=True + ... ) + >>> filtered = datagram.as_table(include_meta_columns=["pipeline"]) # same as passing f"{orcapod.META_PREFIX}pipeline" + """ + ... + + # TODO: add this back + # def as_arrow_compatible_dict( + # self, + # include_all_info: bool = False, + # include_meta_columns: bool | Collection[str] = False, + # include_context: bool = False, + # include_source: bool = False, + # ) -> dict[str, Any]: + # """Extended version with source info support.""" + # ... + + def as_datagram( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_system_tags: bool = False, + ) -> Datagram: + """ + Convert the packet to a Datagram. + + Args: + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + + Returns: + Datagram: Datagram representation of packet data + """ + ... + + def system_tags(self) -> dict[str, DataValue]: + """ + Return metadata about the packet's source/origin. + + Provides debugging and lineage information about where the packet + originated. May include information like: + - File paths for file-based sources + - Database connection strings + - API endpoints + - Processing pipeline information + + Returns: + dict[str, str | None]: Source information for each data column as key-value pairs. + """ + ... + + +@runtime_checkable +class Packet(Datagram, Protocol): + """ + The actual data payload in a stream. + + Packets represent the core data being processed through the computational + graph. Unlike Tags (which are metadata), Packets contain the actual + information that computations operate on. + + Packets extend Datagram with additional capabilities for: + - Source tracking and lineage + - Content-based hashing for caching + - Metadata inclusion for debugging + + The distinction between Tag and Packet is crucial for understanding + data flow: Tags provide context, Packets provide content. + """ + + def keys( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> tuple[str, ...]: + """ + Return tuple of column names. + + Provides access to column names with filtering options for different + column types. Default returns only data column names. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column inclusion. + - False: Return only data column names (default) + - True: Include all meta column names + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context column. + include_source: Whether to include source info fields. + + + Returns: + Tuple of column names based on inclusion criteria. + + Example: + >>> datagram.keys() # Data columns only + ('user_id', 'name', 'email') + >>> datagram.keys(include_meta_columns=True) + ('user_id', 'name', 'email', f'{orcapod.META_PREFIX}processed_at', f'{orcapod.META_PREFIX}pipeline_version') + >>> datagram.keys(include_meta_columns=["pipeline"]) + ('user_id', 'name', 'email',f'{orcapod.META_PREFIX}pipeline_version') + >>> datagram.keys(include_context=True) + ('user_id', 'name', 'email', f'{orcapod.CONTEXT_KEY}') + """ + ... + + def types( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> PythonSchema: + """ + Return type specification mapping field names to Python types. + + The TypeSpec enables type checking and validation throughout the system. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column type inclusion. + - False: Exclude meta column types (default) + - True: Include all meta column types + - Collection[str]: Include meta column types matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context type. + include_source: Whether to include source info fields. + + Returns: + TypeSpec mapping field names to their Python types. + + Example: + >>> datagram.types() + {'user_id': , 'name': } + """ + ... + + def arrow_schema( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> "pa.Schema": + """ + Return PyArrow schema representation. + + The schema provides structured field and type information for efficient + serialization and deserialization with PyArrow. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column schema inclusion. + - False: Exclude meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include context column. + include_source: Whether to include source info fields. + + + Returns: + PyArrow Schema describing the datagram structure. + + Example: + >>> schema = datagram.arrow_schema() + >>> schema.names + ['user_id', 'name'] + """ + ... + + def as_dict( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> dict[str, DataValue]: + """ + Convert datagram to dictionary format. + + Provides a simple key-value representation useful for debugging, + serialization, and interop with dict-based APIs. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include the context key. + include_source: Whether to include source info fields. + + + Returns: + Dictionary with requested columns as key-value pairs. + + Example: + >>> data = datagram.as_dict() # {'user_id': 123, 'name': 'Alice'} + >>> full_data = datagram.as_dict( + ... include_meta_columns=True, + ... include_context=True + ... ) + """ + ... + + def as_table( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_context: bool = False, + include_source: bool = False, + ) -> "pa.Table": + """ + Convert datagram to PyArrow Table format. + + Provides a standardized columnar representation suitable for analysis, + processing, and interoperability with Arrow-based tools. + + Args: + include_all_info: If True, include all available information. This option supersedes all other inclusion options. + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + include_context: Whether to include the context column. + include_source: Whether to include source info columns in the schema. + + Returns: + PyArrow Table with requested columns. + + Example: + >>> table = datagram.as_table() # Data columns only + >>> full_table = datagram.as_table( + ... include_meta_columns=True, + ... include_context=True + ... ) + >>> filtered = datagram.as_table(include_meta_columns=["pipeline"]) # same as passing f"{orcapod.META_PREFIX}pipeline" + """ + ... + + # TODO: add this back + # def as_arrow_compatible_dict( + # self, + # include_all_info: bool = False, + # include_meta_columns: bool | Collection[str] = False, + # include_context: bool = False, + # include_source: bool = False, + # ) -> dict[str, Any]: + # """Extended version with source info support.""" + # ... + + def as_datagram( + self, + include_all_info: bool = False, + include_meta_columns: bool | Collection[str] = False, + include_source: bool = False, + ) -> Datagram: + """ + Convert the packet to a Datagram. + + Args: + include_meta_columns: Controls meta column inclusion. + - False: Exclude all meta columns (default) + - True: Include all meta columns + - Collection[str]: Include meta columns matching these prefixes. If absent, + {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + + Returns: + Datagram: Datagram representation of packet data + """ + ... + + def source_info(self) -> dict[str, str | None]: + """ + Return metadata about the packet's source/origin. + + Provides debugging and lineage information about where the packet + originated. May include information like: + - File paths for file-based sources + - Database connection strings + - API endpoints + - Processing pipeline information + + Returns: + dict[str, str | None]: Source information for each data column as key-value pairs. + """ + ... + + def with_source_info( + self, + **source_info: str | None, + ) -> Self: + """ + Create new packet with updated source information. + + Adds or updates source metadata for the packet. This is useful for + tracking data provenance and lineage through the computational graph. + + Args: + **source_info: Source metadata as keyword arguments. + + Returns: + New packet instance with updated source information. + + Example: + >>> updated_packet = packet.with_source_info( + ... file_path="/new/path/to/file.txt", + ... source_id="source_123" + ... ) + """ + ... diff --git a/src/orcapod/protocols/core_protocols/kernel.py b/src/orcapod/protocols/core_protocols/kernel.py new file mode 100644 index 0000000..842d7af --- /dev/null +++ b/src/orcapod/protocols/core_protocols/kernel.py @@ -0,0 +1,201 @@ +from collections.abc import Collection +from datetime import datetime +from typing import Any, Protocol, runtime_checkable +from orcapod.protocols.hashing_protocols import ContentIdentifiable +from orcapod.types import PythonSchema +from orcapod.protocols.core_protocols.base import Labelable +from orcapod.protocols.core_protocols.streams import Stream, LiveStream + + +@runtime_checkable +class Kernel(ContentIdentifiable, Labelable, Protocol): + """ + The fundamental unit of computation in Orcapod. + + Kernels are the building blocks of computational graphs, transforming + zero, one, or more input streams into a single output stream. They + encapsulate computation logic while providing consistent interfaces + for validation, type checking, and execution. + + Key design principles: + - Immutable: Kernels don't change after creation + - Deterministic: Same inputs always produce same outputs + - Composable: Kernels can be chained and combined + - Trackable: All invocations are recorded for lineage + - Type-safe: Strong typing and validation throughout + + Execution modes: + - __call__(): Full-featured execution with tracking, returns LiveStream + - forward(): Pure computation without side effects, returns Stream + + The distinction between these modes enables both production use (with + full tracking) and testing/debugging (without side effects). + """ + + @property + def reference(self) -> tuple[str, ...]: + """ + Reference to the kernel + + The reference is used for caching/storage and tracking purposes. + As the name indicates, this is how data originating from the kernel will be referred to. + + + Returns: + tuple[str, ...]: Reference for this kernel + """ + ... + + @property + def data_context_key(self) -> str: + """ + Return the context key for this kernel's data processing. + + The context key is used to interpret how data columns should be + processed and converted. It provides semantic meaning to the data + being processed by this kernel. + + Returns: + str: Context key for this kernel's data processing + """ + ... + + @property + def last_modified(self) -> datetime | None: + """ + When the kernel was last modified. For most kernels, this is the timestamp + of the kernel creation. + """ + ... + + def __call__( + self, *streams: Stream, label: str | None = None, **kwargs + ) -> LiveStream: + """ + Main interface for kernel invocation with full tracking and guarantees. + + This is the primary way to invoke kernels in production. It provides + a complete execution pipeline: + 1. Validates input streams against kernel requirements + 2. Registers the invocation with the computational graph + 3. Calls forward() to perform the actual computation + 4. Ensures the result is a LiveStream that stays current + + The returned LiveStream automatically stays up-to-date with its + upstream dependencies, making it suitable for real-time processing + and reactive applications. + + Args: + *streams: Input streams to process (can be empty for source kernels) + label: Optional label for this invocation (overrides kernel.label) + **kwargs: Additional arguments for kernel configuration + + Returns: + LiveStream: Live stream that stays up-to-date with upstreams + + Raises: + ValidationError: If input streams are invalid for this kernel + TypeMismatchError: If stream types are incompatible + ValueError: If required arguments are missing + """ + ... + + def forward(self, *streams: Stream) -> Stream: + """ + Perform the actual computation without side effects. + + This method contains the core computation logic and should be + overridden by subclasses. It performs pure computation without: + - Registering with the computational graph + - Performing validation (caller's responsibility) + - Guaranteeing result type (may return static or live streams) + + The returned stream must be accurate at the time of invocation but + need not stay up-to-date with upstream changes. This makes forward() + suitable for: + - Testing and debugging + - Batch processing where currency isn't required + - Internal implementation details + + Args: + *streams: Input streams to process + + Returns: + Stream: Result of the computation (may be static or live) + """ + ... + + def output_types( + self, *streams: Stream, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Determine output types without triggering computation. + + This method performs type inference based on input stream types, + enabling efficient type checking and stream property queries. + It should be fast and not trigger any expensive computation. + + Used for: + - Pre-execution type validation + - Query planning and optimization + - Schema inference in complex pipelines + - IDE support and developer tooling + + Args: + *streams: Input streams to analyze + + Returns: + tuple[TypeSpec, TypeSpec]: (tag_types, packet_types) for output + + Raises: + ValidationError: If input types are incompatible + TypeError: If stream types cannot be processed + """ + ... + + def validate_inputs(self, *streams: Stream) -> None: + """ + Validate input streams, raising exceptions if incompatible. + + This method is called automatically by __call__ before computation + to provide fail-fast behavior. It should check: + - Number of input streams + - Stream types and schemas + - Any kernel-specific requirements + - Business logic constraints + + The goal is to catch errors early, before expensive computation + begins, and provide clear error messages for debugging. + + Args: + *streams: Input streams to validate + + Raises: + ValidationError: If streams are invalid for this kernel + TypeError: If stream types are incompatible + ValueError: If stream content violates business rules + """ + ... + + def identity_structure(self, streams: Collection[Stream] | None = None) -> Any: + """ + Generate a unique identity structure for this kernel and/or kernel invocation. + When invoked without streams, it should return a structure + that uniquely identifies the kernel itself (e.g., class name, parameters). + When invoked with streams, it should include the identity of the streams + to distinguish different invocations of the same kernel. + + This structure is used for: + - Caching and memoization + - Debugging and error reporting + - Tracking kernel invocations in computational graphs + + Args: + streams: Optional input streams for this invocation. If None, identity_structure is + based solely on the kernel. If streams are provided, they are included in the identity + to differentiate between different invocations of the same kernel. + + Returns: + Any: Unique identity structure (e.g., tuple of class name and stream identities) + """ + ... diff --git a/src/orcapod/protocols/core_protocols/pods.py b/src/orcapod/protocols/core_protocols/pods.py new file mode 100644 index 0000000..4546e36 --- /dev/null +++ b/src/orcapod/protocols/core_protocols/pods.py @@ -0,0 +1,220 @@ +from typing import TYPE_CHECKING, Protocol, runtime_checkable + +from orcapod.protocols.core_protocols.base import ExecutionEngine +from orcapod.protocols.core_protocols.datagrams import Packet, Tag +from orcapod.protocols.core_protocols.kernel import Kernel +from orcapod.types import PythonSchema + +if TYPE_CHECKING: + import pyarrow as pa + + +@runtime_checkable +class Pod(Kernel, Protocol): + """ + Specialized kernel for packet-level processing with advanced caching. + + Pods represent a different computational model from regular kernels: + - Process data one packet at a time (enabling fine-grained parallelism) + - Support just-in-time evaluation (computation deferred until needed) + - Provide stricter type contracts (clear input/output schemas) + - Enable advanced caching strategies (packet-level caching) + + The Pod abstraction is ideal for: + - Expensive computations that benefit from caching + - Operations that can be parallelized at the packet level + - Transformations with strict type contracts + - Processing that needs to be deferred until access time + - Functions that operate on individual data items + + Pods use a different execution model where computation is deferred + until results are actually needed, enabling efficient resource usage + and fine-grained caching. + """ + + @property + def version(self) -> str: ... + + def get_record_id(self, packet: Packet, execution_engine_hash: str) -> str: ... + + @property + def tiered_pod_id(self) -> dict[str, str]: + """ + Return a dictionary representation of the tiered pod's unique identifier. + The key is supposed to be ordered from least to most specific, allowing + for hierarchical identification of the pod. + + This is primarily used for tiered memoization/caching strategies. + + Returns: + dict[str, str]: Dictionary representation of the pod's ID + """ + ... + + def input_packet_types(self) -> PythonSchema: + """ + TypeSpec for input packets that this Pod can process. + + Defines the exact schema that input packets must conform to. + Pods are typically much stricter about input types than regular + kernels, requiring precise type matching for their packet-level + processing functions. + + This specification is used for: + - Runtime type validation + - Compile-time type checking + - Schema inference and documentation + - Input validation and error reporting + + Returns: + TypeSpec: Dictionary mapping field names to required packet types + """ + ... + + def output_packet_types(self) -> PythonSchema: + """ + TypeSpec for output packets that this Pod produces. + + Defines the schema of packets that will be produced by this Pod. + This is typically determined by the Pod's computational function + and is used for: + - Type checking downstream kernels + - Schema inference in complex pipelines + - Query planning and optimization + - Documentation and developer tooling + + Returns: + TypeSpec: Dictionary mapping field names to output packet types + """ + ... + + async def async_call( + self, + tag: Tag, + packet: Packet, + record_id: str | None = None, + execution_engine: ExecutionEngine | None = None, + ) -> tuple[Tag, Packet | None]: ... + + def call( + self, + tag: Tag, + packet: Packet, + record_id: str | None = None, + execution_engine: ExecutionEngine | None = None, + ) -> tuple[Tag, Packet | None]: + """ + Process a single packet with its associated tag. + + This is the core method that defines the Pod's computational behavior. + It processes one (tag, packet) pair at a time, enabling: + - Fine-grained caching at the packet level + - Parallelization opportunities + - Just-in-time evaluation + - Filtering operations (by returning None) + + The method signature supports: + - Tag transformation (modify metadata) + - Packet transformation (modify content) + - Filtering (return None to exclude packet) + - Pass-through (return inputs unchanged) + + Args: + tag: Metadata associated with the packet + packet: The data payload to process + + Returns: + tuple[Tag, Packet | None]: + - Tag: Output tag (may be modified from input) + - Packet: Processed packet, or None to filter it out + + Raises: + TypeError: If packet doesn't match input_packet_types + ValueError: If packet data is invalid for processing + """ + ... + + +@runtime_checkable +class CachedPod(Pod, Protocol): + async def async_call( + self, + tag: Tag, + packet: Packet, + record_id: str | None = None, + execution_engine: ExecutionEngine | None = None, + skip_cache_lookup: bool = False, + skip_cache_insert: bool = False, + ) -> tuple[Tag, Packet | None]: ... + + def call( + self, + tag: Tag, + packet: Packet, + record_id: str | None = None, + execution_engine: ExecutionEngine | None = None, + skip_cache_lookup: bool = False, + skip_cache_insert: bool = False, + ) -> tuple[Tag, Packet | None]: + """ + Process a single packet with its associated tag. + + This is the core method that defines the Pod's computational behavior. + It processes one (tag, packet) pair at a time, enabling: + - Fine-grained caching at the packet level + - Parallelization opportunities + - Just-in-time evaluation + - Filtering operations (by returning None) + + The method signature supports: + - Tag transformation (modify metadata) + - Packet transformation (modify content) + - Filtering (return None to exclude packet) + - Pass-through (return inputs unchanged) + + Args: + tag: Metadata associated with the packet + packet: The data payload to process + + Returns: + tuple[Tag, Packet | None]: + - Tag: Output tag (may be modified from input) + - Packet: Processed packet, or None to filter it out + + Raises: + TypeError: If packet doesn't match input_packet_types + ValueError: If packet data is invalid for processing + """ + ... + + def get_cached_output_for_packet(self, input_packet: Packet) -> Packet | None: + """ + Retrieve the cached output packet for a given input packet. + + Args: + input_packet: The input packet to look up in the cache + + Returns: + Packet | None: The cached output packet, or None if not found + """ + ... + + def get_all_cached_outputs( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + """ + Retrieve all packets processed by this Pod. + + This method returns a table containing all packets processed by the Pod, + including metadata and system columns if requested. It is useful for: + - Debugging and analysis + - Auditing and data lineage tracking + - Performance monitoring + + Args: + include_system_columns: Whether to include system columns in the output + + Returns: + pa.Table | None: A table containing all processed records, or None if no records are available + """ + ... diff --git a/src/orcapod/protocols/core_protocols/source.py b/src/orcapod/protocols/core_protocols/source.py new file mode 100644 index 0000000..e94f336 --- /dev/null +++ b/src/orcapod/protocols/core_protocols/source.py @@ -0,0 +1,33 @@ +from typing import Protocol, runtime_checkable + +from orcapod.protocols.core_protocols.kernel import Kernel +from orcapod.protocols.core_protocols.streams import Stream + + +@runtime_checkable +class Source(Kernel, Stream, Protocol): + """ + Entry point for data into the computational graph. + + Sources are special objects that serve dual roles: + - As Kernels: Can be invoked to produce streams + - As Streams: Directly provide data without upstream dependencies + + Sources represent the roots of computational graphs and typically + interface with external data sources. They bridge the gap between + the outside world and the Orcapod computational model. + + Common source types: + - File readers (CSV, JSON, Parquet, etc.) + - Database connections and queries + - API endpoints and web services + - Generated data sources (synthetic data) + - Manual data input and user interfaces + - Message queues and event streams + + Sources have unique properties: + - No upstream dependencies (upstreams is empty) + - Can be both invoked and iterated + - Serve as the starting point for data lineage + - May have their own refresh/update mechanisms + """ diff --git a/src/orcapod/protocols/core_protocols/streams.py b/src/orcapod/protocols/core_protocols/streams.py new file mode 100644 index 0000000..f7e692a --- /dev/null +++ b/src/orcapod/protocols/core_protocols/streams.py @@ -0,0 +1,583 @@ +from collections.abc import Collection, Iterator, Mapping +from datetime import datetime +from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable + +from orcapod.protocols.core_protocols.base import ExecutionEngine, Labelable +from orcapod.protocols.core_protocols.datagrams import Packet, Tag +from orcapod.protocols.hashing_protocols import ContentIdentifiable +from orcapod.types import PythonSchema + +if TYPE_CHECKING: + import polars as pl + import pyarrow as pa + import pandas as pd + from orcapod.protocols.core_protocols.kernel import Kernel + + +@runtime_checkable +class Stream(ContentIdentifiable, Labelable, Protocol): + """ + Base protocol for all streams in Orcapod. + + Streams represent sequences of (Tag, Packet) pairs flowing through the + computational graph. They are the fundamental data structure connecting + kernels and carrying both data and metadata. + + Streams can be either: + - Static: Immutable snapshots created at a specific point in time + - Live: Dynamic streams that stay current with upstream dependencies + + All streams provide: + - Iteration over (tag, packet) pairs + - Type information and schema access + - Lineage information (source kernel and upstream streams) + - Basic caching and freshness tracking + - Conversion to common formats (tables, dictionaries) + """ + + @property + def substream_identities(self) -> tuple[str, ...]: + """ + Unique identifiers for sub-streams within this stream. + + This property provides a way to identify and differentiate + sub-streams that may be part of a larger stream. It is useful + for tracking and managing complex data flows. + + Returns: + tuple[str, ...]: Unique identifiers for each sub-stream + """ + ... + + @property + def execution_engine(self) -> ExecutionEngine | None: + """ + The execution engine attached to this stream. By default, the stream + will use this execution engine whenever it needs to perform computation. + None means the stream is not attached to any execution engine and will default + to running natively. + """ + + @execution_engine.setter + def execution_engine(self, engine: ExecutionEngine | None) -> None: + """ + Set the execution engine for this stream. + + This allows the stream to use a specific execution engine for + computation, enabling optimized execution strategies and resource + management. + + Args: + engine: The execution engine to attach to this stream + """ + ... + + def get_substream(self, substream_id: str) -> "Stream": + """ + Retrieve a specific sub-stream by its identifier. + + This method allows access to individual sub-streams within the + main stream, enabling focused operations on specific data segments. + + Args: + substream_id: Unique identifier for the desired sub-stream. + + Returns: + Stream: The requested sub-stream if it exists + """ + ... + + @property + def source(self) -> "Kernel | None": + """ + The kernel that produced this stream. + + This provides lineage information for tracking data flow through + the computational graph. Root streams (like file sources) may + have no source kernel. + + Returns: + Kernel: The source kernel that created this stream + None: This is a root stream with no source kernel + """ + ... + + @property + def upstreams(self) -> tuple["Stream", ...]: + """ + Input streams used to produce this stream. + + These are the streams that were provided as input to the source + kernel when this stream was created. Used for dependency tracking + and cache invalidation. + + Returns: + tuple[Stream, ...]: Upstream dependency streams (empty for sources) + """ + ... + + def keys( + self, include_system_tags: bool = False + ) -> tuple[tuple[str, ...], tuple[str, ...]]: + """ + Available keys/fields in the stream content. + + Returns the field names present in both tags and packets. + This provides schema information without requiring type details, + useful for: + - Schema inspection and exploration + - Query planning and optimization + - Field validation and mapping + + Returns: + tuple[tuple[str, ...], tuple[str, ...]]: (tag_keys, packet_keys) + """ + ... + + def tag_keys(self, include_system_tags: bool = False) -> tuple[str, ...]: + """ + Return the keys used for the tag in the pipeline run records. + This is used to store the run-associated tag info. + """ + ... + + def packet_keys(self) -> tuple[str, ...]: + """ + Return the keys used for the packet in the pipeline run records. + This is used to store the run-associated packet info. + """ + ... + + def types( + self, include_system_tags: bool = False + ) -> tuple[PythonSchema, PythonSchema]: + """ + Type specifications for the stream content. + + Returns the type schema for both tags and packets in this stream. + This information is used for: + - Type checking and validation + - Schema inference and planning + - Compatibility checking between kernels + + Returns: + tuple[TypeSpec, TypeSpec]: (tag_types, packet_types) + """ + ... + + def tag_types(self, include_system_tags: bool = False) -> PythonSchema: + """ + Type specifications for the stream content. + + Returns the type schema for both tags and packets in this stream. + This information is used for: + - Type checking and validation + - Schema inference and planning + - Compatibility checking between kernels + + Returns: + tuple[TypeSpec, TypeSpec]: (tag_types, packet_types) + """ + ... + + def packet_types(self) -> PythonSchema: ... + + @property + def last_modified(self) -> datetime | None: + """ + When the stream's content was last modified. + + This property is crucial for caching decisions and dependency tracking: + - datetime: Content was last modified at this time (cacheable) + - None: Content is never stable, always recompute (some dynamic streams) + + Both static and live streams typically return datetime values, but + live streams update this timestamp whenever their content changes. + + Returns: + datetime: Timestamp of last modification for most streams + None: Stream content is never stable (some special dynamic streams) + """ + ... + + @property + def is_current(self) -> bool: + """ + Whether the stream is up-to-date with its dependencies. + + A stream is current if its content reflects the latest state of its + source kernel and upstream streams. This is used for cache validation + and determining when refresh is needed. + + For live streams, this should always return True since they stay + current automatically. For static streams, this indicates whether + the cached content is still valid. + + Returns: + bool: True if stream is up-to-date, False if refresh needed + """ + ... + + def __iter__(self) -> Iterator[tuple[Tag, Packet]]: + """ + Iterate over (tag, packet) pairs in the stream. + + This is the primary way to access stream data. The behavior depends + on the stream type: + - Static streams: Return cached/precomputed data + - Live streams: May trigger computation and always reflect current state + + Yields: + tuple[Tag, Packet]: Sequential (tag, packet) pairs + """ + ... + + def iter_packets( + self, execution_engine: ExecutionEngine | None = None + ) -> Iterator[tuple[Tag, Packet]]: + """ + Alias for __iter__ for explicit packet iteration. + + Provides a more explicit method name when the intent is to iterate + over packets specifically, improving code readability. + + This method must return an immutable iterator -- that is, the returned iterator + should not change and must consistently return identical tag,packet pairs across + multiple iterations of the iterator. + + Note that this is NOT to mean that multiple invocation of `iter_packets` must always + return an identical iterator. The iterator returned by `iter_packets` may change + between invocations, but the iterator itself must not change. Consequently, it should be understood + that the returned iterators may be a burden on memory if the stream is large or infinite. + + Yields: + tuple[Tag, Packet]: Sequential (tag, packet) pairs + """ + ... + + def run(self, execution_engine: ExecutionEngine | None = None) -> None: + """ + Execute the stream using the provided execution engine. + + This method triggers computation of the stream content based on its + source kernel and upstream streams. It returns a new stream instance + containing the computed (tag, packet) pairs. + + Args: + execution_engine: The execution engine to use for computation + + """ + ... + + async def run_async(self, execution_engine: ExecutionEngine | None = None) -> None: + """ + Asynchronously execute the stream using the provided execution engine. + + This method triggers computation of the stream content based on its + source kernel and upstream streams. It returns a new stream instance + containing the computed (tag, packet) pairs. + + Args: + execution_engine: The execution engine to use for computation + + """ + ... + + def as_df( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: ExecutionEngine | None = None, + ) -> "pl.DataFrame": + """ + Convert the entire stream to a Polars DataFrame. + """ + ... + + def as_lazy_frame( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: ExecutionEngine | None = None, + ) -> "pl.LazyFrame": + """ + Load the entire stream to a Polars LazyFrame. + """ + ... + + def as_polars_df( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: ExecutionEngine | None = None, + ) -> "pl.DataFrame": ... + + def as_pandas_df( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + index_by_tags: bool = True, + execution_engine: ExecutionEngine | None = None, + ) -> "pd.DataFrame": ... + + def as_table( + self, + include_data_context: bool = False, + include_source: bool = False, + include_system_tags: bool = False, + include_content_hash: bool | str = False, + sort_by_tags: bool = True, + execution_engine: ExecutionEngine | None = None, + ) -> "pa.Table": + """ + Convert the entire stream to a PyArrow Table. + + Materializes all (tag, packet) pairs into a single table for + analysis and processing. This operation may be expensive for + large streams or live streams that need computation. + + If include_content_hash is True, an additional column called "_content_hash" + containing the content hash of each packet is included. If include_content_hash + is a string, it is used as the name of the content hash column. + + Returns: + pa.Table: Complete stream data as a PyArrow Table + """ + ... + + def flow( + self, + execution_engine: ExecutionEngine | None = None, + ) -> Collection[tuple[Tag, Packet]]: + """ + Return the entire stream as a collection of (tag, packet) pairs. + + This method materializes the stream content into a list or similar + collection type. It is useful for small streams or when you need + to process all data at once. + + Args: + execution_engine: Optional execution engine to use for computation. + If None, the stream will use its default execution engine. + """ + ... + + def join(self, other_stream: "Stream", label: str | None = None) -> "Stream": + """ + Join this stream with another stream. + + Combines two streams into a single stream by merging their content. + The resulting stream contains all (tag, packet) pairs from both + streams, preserving their order. + + Args: + other_stream: The other stream to join with this one. + + Returns: + Self: New stream containing combined content from both streams. + """ + ... + + def semi_join(self, other_stream: "Stream", label: str | None = None) -> "Stream": + """ + Perform a semi-join with another stream. + + This operation filters this stream to only include packets that have + corresponding tags in the other stream. The resulting stream contains + all (tag, packet) pairs from this stream that match tags in the other. + + Args: + other_stream: The other stream to semi-join with this one. + + Returns: + Self: New stream containing filtered content based on the semi-join. + """ + ... + + def map_tags( + self, + name_map: Mapping[str, str], + drop_unmapped: bool = True, + label: str | None = None, + ) -> "Stream": + """ + Map tag names in this stream to new names based on the provided mapping. + """ + ... + + def map_packets( + self, + name_map: Mapping[str, str], + drop_unmapped: bool = True, + label: str | None = None, + ) -> "Stream": + """ + Map packet names in this stream to new names based on the provided mapping. + """ + ... + + def polars_filter( + self, + *predicates: Any, + constraint_map: Mapping[str, Any] | None = None, + label: str | None = None, + **constraints: Any, + ) -> "Stream": ... + + def select_tag_columns( + self, + tag_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> "Stream": + """ + Select the specified tag columns from the stream. A ValueError is raised + if one or more specified tag columns do not exist in the stream unless strict = False. + """ + ... + + def select_packet_columns( + self, + packet_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> "Stream": + """ + Select the specified tag columns from the stream. A ValueError is raised + if one or more specified tag columns do not exist in the stream unless strict = False. + """ + ... + + def drop_tag_columns( + self, + tag_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> "Stream": + """ + Drop the specified tag columns from the stream. A ValueError is raised + if one or more specified tag columns do not exist in the stream unless strict = False. + """ + ... + + # TODO: check to make sure source columns are also dropped + def drop_packet_columns( + self, + packet_columns: str | Collection[str], + strict: bool = True, + label: str | None = None, + ) -> "Stream": + """ + Drop the specified packet columns from the stream. A ValueError is raised + if one or more specified packet columns do not exist in the stream unless strict = False. + """ + ... + + def batch( + self, + batch_size: int = 0, + drop_partial_batch: bool = False, + label: str | None = None, + ) -> "Stream": + """ + Batch the stream into groups of the specified size. + + This operation groups (tag, packet) pairs into batches for more + efficient processing. Each batch is represented as a single (tag, packet) + pair where the tag is a list of tags and the packet is a list of packets. + + Args: + batch_size: Number of (tag, packet) pairs per batch. If 0, all + pairs are included in a single batch. + drop_partial_batch: If True, drop the last batch if it has fewer + than batch_size pairs. + + Returns: + Self: New stream containing batched (tag, packet) pairs. + """ + ... + + +@runtime_checkable +class LiveStream(Stream, Protocol): + """ + A stream that automatically stays up-to-date with its upstream dependencies. + + LiveStream extends the base Stream protocol with capabilities for "up-to-date" + data flow and reactive computation. Unlike static streams which represent + snapshots, LiveStreams provide the guarantee that their content always + reflects the current state of their dependencies. + + Key characteristics: + - Automatically refresh the stream if changes in the upstreams are detected + - Track last_modified timestamp when content changes + - Support manual refresh triggering and invalidation + - By design, LiveStream would return True for is_current except when auto-update fails. + + LiveStreams are always returned by Kernel.__call__() methods, ensuring + that normal kernel usage produces live, up-to-date results. + + Caching behavior: + - last_modified updates whenever content changes + - Can be cached based on dependency timestamps + - Invalidation happens automatically when upstreams change + + Use cases: + - Real-time data processing pipelines + - Reactive user interfaces + - Monitoring and alerting systems + - Dynamic dashboard updates + - Any scenario requiring current data + """ + + def refresh(self, force: bool = False) -> bool: + """ + Manually trigger a refresh of this stream's content. + + Forces the stream to check its upstream dependencies and update + its content if necessary. This is useful when: + - You want to ensure the latest data before a critical operation + - You need to force computation at a specific time + - You're debugging data flow issues + - You want to pre-compute results for performance + Args: + force: If True, always refresh even if the stream is current. + If False, only refresh if the stream is not current. + + Returns: + bool: True if the stream was refreshed, False if it was already current. + Note: LiveStream refreshes automatically on access, so this + method may be a no-op for some implementations. However, it's + always safe to call if you need to control when the cache is refreshed. + """ + ... + + def invalidate(self) -> None: + """ + Mark this stream as invalid, forcing a refresh on next access. + + This method is typically called when: + - Upstream dependencies have changed + - The source kernel has been modified + - External data sources have been updated + - Manual cache invalidation is needed + + The stream will automatically refresh its content the next time + it's accessed (via iteration, as_table(), etc.). + + This is more efficient than immediate refresh when you know the + data will be accessed later. + """ + ... diff --git a/src/orcapod/protocols/core_protocols/trackers.py b/src/orcapod/protocols/core_protocols/trackers.py new file mode 100644 index 0000000..7bc9a1e --- /dev/null +++ b/src/orcapod/protocols/core_protocols/trackers.py @@ -0,0 +1,213 @@ +from typing import Protocol, runtime_checkable +from contextlib import AbstractContextManager +from orcapod.protocols.core_protocols.kernel import Kernel +from orcapod.protocols.core_protocols.pods import Pod +from orcapod.protocols.core_protocols.source import Source +from orcapod.protocols.core_protocols.streams import Stream + + +@runtime_checkable +class Tracker(Protocol): + """ + Records kernel invocations and stream creation for computational graph tracking. + + Trackers are responsible for maintaining the computational graph by recording + relationships between kernels, streams, and invocations. They enable: + - Lineage tracking and data provenance + - Caching and memoization strategies + - Debugging and error analysis + - Performance monitoring and optimization + - Reproducibility and auditing + + Multiple trackers can be active simultaneously, each serving different + purposes (e.g., one for caching, another for debugging, another for + monitoring). This allows for flexible and composable tracking strategies. + + Trackers can be selectively activated/deactivated to control overhead + and focus on specific aspects of the computational graph. + """ + + def set_active(self, active: bool = True) -> None: + """ + Set the active state of the tracker. + + When active, the tracker will record all kernel invocations and + stream creations. When inactive, no recording occurs, reducing + overhead for performance-critical sections. + + Args: + active: True to activate recording, False to deactivate + """ + ... + + def is_active(self) -> bool: + """ + Check if the tracker is currently recording invocations. + + Returns: + bool: True if tracker is active and recording, False otherwise + """ + ... + + def record_kernel_invocation( + self, kernel: Kernel, upstreams: tuple[Stream, ...], label: str | None = None + ) -> None: + """ + Record a kernel invocation in the computational graph. + + This method is called whenever a kernel is invoked. The tracker + should record: + - The kernel and its properties + - The input streams that were used as input + - Timing and performance information + - Any relevant metadata + + Args: + kernel: The kernel that was invoked + upstreams: The input streams used for this invocation + """ + ... + + def record_source_invocation( + self, source: Source, label: str | None = None + ) -> None: + """ + Record a source invocation in the computational graph. + + This method is called whenever a source is invoked. The tracker + should record: + - The source and its properties + - Timing and performance information + - Any relevant metadata + + Args: + source: The source that was invoked + """ + ... + + def record_pod_invocation( + self, pod: Pod, upstreams: tuple[Stream, ...], label: str | None = None + ) -> None: + """ + Record a pod invocation in the computational graph. + + This method is called whenever a pod is invoked. The tracker + should record: + - The pod and its properties + - The upstream streams that were used as input + - Timing and performance information + - Any relevant metadata + + Args: + pod: The pod that was invoked + upstreams: The input streams used for this invocation + """ + ... + + +@runtime_checkable +class TrackerManager(Protocol): + """ + Manages multiple trackers and coordinates their activity. + + The TrackerManager provides a centralized way to: + - Register and manage multiple trackers + - Coordinate recording across all active trackers + - Provide a single interface for graph recording + - Enable dynamic tracker registration/deregistration + + This design allows for: + - Multiple concurrent tracking strategies + - Pluggable tracking implementations + - Easy testing and debugging (mock trackers) + - Performance optimization (selective tracking) + """ + + def get_active_trackers(self) -> list[Tracker]: + """ + Get all currently active trackers. + + Returns only trackers that are both registered and active, + providing the list of trackers that will receive recording events. + + Returns: + list[Tracker]: List of trackers that are currently recording + """ + ... + + def register_tracker(self, tracker: Tracker) -> None: + """ + Register a new tracker in the system. + + The tracker will be included in future recording operations + if it is active. Registration is separate from activation + to allow for dynamic control of tracking overhead. + + Args: + tracker: The tracker to register + """ + ... + + def deregister_tracker(self, tracker: Tracker) -> None: + """ + Remove a tracker from the system. + + The tracker will no longer receive recording notifications + even if it is still active. This is useful for: + - Cleaning up temporary trackers + - Removing failed or problematic trackers + - Dynamic tracker management + + Args: + tracker: The tracker to remove + """ + ... + + def record_kernel_invocation( + self, kernel: Kernel, upstreams: tuple[Stream, ...], label: str | None = None + ) -> None: + """ + Record a stream in all active trackers. + + This method broadcasts the stream recording to all currently + active and registered trackers. It provides a single point + of entry for recording events, simplifying kernel implementations. + + Args: + stream: The stream to record in all active trackers + """ + ... + + def record_source_invocation( + self, source: Source, label: str | None = None + ) -> None: + """ + Record a source invocation in the computational graph. + + This method is called whenever a source is invoked. The tracker + should record: + - The source and its properties + - Timing and performance information + - Any relevant metadata + + Args: + source: The source that was invoked + """ + ... + + def record_pod_invocation( + self, pod: Pod, upstreams: tuple[Stream, ...], label: str | None = None + ) -> None: + """ + Record a stream in all active trackers. + + This method broadcasts the stream recording to all currently` + active and registered trackers. It provides a single point + of entry for recording events, simplifying kernel implementations. + + Args: + stream: The stream to record in all active trackers + """ + ... + + def no_tracking(self) -> AbstractContextManager[None]: ... diff --git a/src/orcapod/protocols/database_protocols.py b/src/orcapod/protocols/database_protocols.py new file mode 100644 index 0000000..1bf9eac --- /dev/null +++ b/src/orcapod/protocols/database_protocols.py @@ -0,0 +1,88 @@ +from typing import Any, Protocol, TYPE_CHECKING +from collections.abc import Collection, Mapping + +if TYPE_CHECKING: + import pyarrow as pa + + +class ArrowDatabase(Protocol): + def add_record( + self, + record_path: tuple[str, ...], + record_id: str, + record: "pa.Table", + skip_duplicates: bool = False, + flush: bool = False, + ) -> None: ... + + def add_records( + self, + record_path: tuple[str, ...], + records: "pa.Table", + record_id_column: str | None = None, + skip_duplicates: bool = False, + flush: bool = False, + ) -> None: ... + + def get_record_by_id( + self, + record_path: tuple[str, ...], + record_id: str, + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": ... + + def get_all_records( + self, + record_path: tuple[str, ...], + record_id_column: str | None = None, + ) -> "pa.Table | None": + """Retrieve all records for a given path as a stream.""" + ... + + def get_records_by_ids( + self, + record_path: tuple[str, ...], + record_ids: Collection[str], + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": ... + + def get_records_with_column_value( + self, + record_path: tuple[str, ...], + column_values: Collection[tuple[str, Any]] | Mapping[str, Any], + record_id_column: str | None = None, + flush: bool = False, + ) -> "pa.Table | None": ... + + def flush(self) -> None: + """Flush any buffered writes to the underlying storage.""" + ... + + +class MetadataCapable(Protocol): + def set_metadata( + self, + record_path: tuple[str, ...], + metadata: Mapping[str, Any], + merge: bool = True, + ) -> None: ... + + def get_metadata( + self, + record_path: tuple[str, ...], + ) -> Mapping[str, Any]: ... + + def get_supported_metadata_schema(self) -> Mapping[str, type]: ... + + def validate_metadata( + self, + metadata: Mapping[str, Any], + ) -> Collection[str]: ... + + +class ArrowDatabaseWithMetadata(ArrowDatabase, MetadataCapable, Protocol): + """A protocol that combines ArrowDatabase with metadata capabilities.""" + + pass diff --git a/src/orcapod/protocols/hashing_protocols.py b/src/orcapod/protocols/hashing_protocols.py new file mode 100644 index 0000000..c47b322 --- /dev/null +++ b/src/orcapod/protocols/hashing_protocols.py @@ -0,0 +1,208 @@ +"""Hash strategy protocols for dependency injection.""" + +import uuid +from collections.abc import Callable +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable + +from orcapod.types import PathLike, PythonSchema + +if TYPE_CHECKING: + import pyarrow as pa + + +@dataclass(frozen=True, slots=True) +class ContentHash: + method: str + digest: bytes + + # TODO: make the default char count configurable + def to_hex(self, char_count: int | None = 20) -> str: + """Convert digest to hex string, optionally truncated.""" + hex_str = self.digest.hex() + return hex_str[:char_count] if char_count else hex_str + + def to_int(self, hexdigits: int = 20) -> int: + """ + Convert digest to integer representation. + + Args: + hexdigits: Number of hex digits to use (truncates if needed) + + Returns: + Integer representation of the hash + """ + hex_str = self.to_hex()[:hexdigits] + return int(hex_str, 16) + + def to_uuid(self, namespace: uuid.UUID = uuid.NAMESPACE_OID) -> uuid.UUID: + """ + Convert digest to UUID format. + + Args: + namespace: UUID namespace for uuid5 generation + + Returns: + UUID derived from this hash + """ + # Using uuid5 with the hex string ensures deterministic UUIDs + return uuid.uuid5(namespace, self.to_hex()) + + def to_base64(self) -> str: + """Convert digest to base64 string.""" + import base64 + + return base64.b64encode(self.digest).decode("ascii") + + def to_string(self, prefix_method: bool = True) -> str: + """Convert digest to a string representation.""" + if prefix_method: + return f"{self.method}:{self.to_hex()}" + return self.to_hex() + + def __str__(self) -> str: + return self.to_string() + + @classmethod + def from_string(cls, hash_string: str) -> "ContentHash": + """Parse 'method:hex_digest' format.""" + method, hex_digest = hash_string.split(":", 1) + return cls(method, bytes.fromhex(hex_digest)) + + def display_name(self, length: int = 8) -> str: + """Return human-friendly display like 'arrow_v2.1:1a2b3c4d'.""" + return f"{self.method}:{self.to_hex(length)}" + + +@runtime_checkable +class ContentIdentifiable(Protocol): + """Protocol for objects that can provide an identity structure.""" + + def identity_structure(self) -> Any: + """ + Return a structure that represents the identity of this object. + + Returns: + Any: A structure representing this object's content. + Should be deterministic and include all identity-relevant data. + Return None to indicate no custom identity is available. + """ + ... + + def content_hash(self) -> ContentHash: + """ + Compute a hash based on the content of this object. + + Returns: + bytes: A byte representation of the hash based on the content. + If no identity structure is provided, return None. + """ + ... + + def __eq__(self, other: object) -> bool: + """ + Equality check that compares the identity structures of two objects. + + Args: + other (object): The object to compare with. + + Returns: + bool: True if the identity structures are equal, False otherwise. + """ + ... + + def __hash__(self) -> int: + """ + Hash implementation that uses the identity structure if provided, + otherwise falls back to the default hash. + + Returns: + int: A hash value based on either content or identity. + """ + ... + + +class ObjectHasher(Protocol): + """Protocol for general object hashing.""" + + # TODO: consider more explicitly stating types of objects accepted + def hash_object(self, obj: Any) -> ContentHash: + """ + Hash an object to a byte representation. Object hasher must be + able to handle ContentIdentifiable objects to hash them based on their + identity structure. If compressed=True, the content identifiable object + is immediately replaced with its compressed string identity and used in the + computation of containing identity structure. + + Args: + obj (Any): The object to hash. + + Returns: + bytes: The byte representation of the hash. + """ + ... + + @property + def hasher_id(self) -> str: + """ + Returns a unique identifier/name assigned to the hasher + """ + ... + + +class FileContentHasher(Protocol): + """Protocol for file-related hashing.""" + + def hash_file(self, file_path: PathLike) -> ContentHash: ... + + +class ArrowHasher(Protocol): + """Protocol for hashing arrow packets.""" + + def get_hasher_id(self) -> str: ... + + def hash_table( + self, table: "pa.Table", prefix_hasher_id: bool = True + ) -> ContentHash: ... + + +class StringCacher(Protocol): + """Protocol for caching string key value pairs.""" + + def get_cached(self, cache_key: str) -> str | None: ... + def set_cached(self, cache_key: str, value: str) -> None: ... + def clear_cache(self) -> None: ... + + +class FunctionInfoExtractor(Protocol): + """Protocol for extracting function information.""" + + def extract_function_info( + self, + func: Callable[..., Any], + function_name: str | None = None, + input_typespec: PythonSchema | None = None, + output_typespec: PythonSchema | None = None, + exclude_function_signature: bool = False, + exclude_function_body: bool = False, + ) -> dict[str, Any]: ... + + +class SemanticTypeHasher(Protocol): + """Abstract base class for semantic type-specific hashers.""" + + @property + def hasher_id(self) -> str: + """Unique identifier for this semantic type hasher.""" + ... + + def hash_column( + self, + column: "pa.Array", + ) -> "pa.Array": + """Hash a column with this semantic type and return the hash bytes an an array""" + ... + + def set_cacher(self, cacher: StringCacher) -> None: + """Add a string cacher for caching hash values.""" + ... diff --git a/src/orcapod/protocols/legacy_data_protocols.py b/src/orcapod/protocols/legacy_data_protocols.py new file mode 100644 index 0000000..53a8657 --- /dev/null +++ b/src/orcapod/protocols/legacy_data_protocols.py @@ -0,0 +1,2278 @@ +# from collections.abc import Collection, Iterator, Mapping, Callable +# from datetime import datetime +# from typing import Any, ContextManager, Protocol, Self, TYPE_CHECKING, runtime_checkable +# from orcapod.protocols.hashing_protocols import ContentIdentifiable, ContentHash +# from orcapod.types import DataValue, TypeSpec + + +# if TYPE_CHECKING: +# import pyarrow as pa +# import polars as pl +# import pandas as pd + + +# @runtime_checkable +# class ExecutionEngine(Protocol): +# @property +# def name(self) -> str: ... + +# def submit_sync(self, function: Callable, *args, **kwargs) -> Any: +# """ +# Run the given function with the provided arguments. +# This method should be implemented by the execution engine. +# """ +# ... + +# async def submit_async(self, function: Callable, *args, **kwargs) -> Any: +# """ +# Asynchronously run the given function with the provided arguments. +# This method should be implemented by the execution engine. +# """ +# ... + +# # TODO: consider adding batch submission + + +# @runtime_checkable +# class Datagram(ContentIdentifiable, Protocol): +# """ +# Protocol for immutable datagram containers in Orcapod. + +# Datagrams are the fundamental units of data that flow through the system. +# They provide a unified interface for data access, conversion, and manipulation, +# ensuring consistent behavior across different storage backends (dict, Arrow table, etc.). + +# Each datagram contains: +# - **Data columns**: The primary business data (user_id, name, etc.) +# - **Meta columns**: Internal system metadata with {orcapod.META_PREFIX} (typically '__') prefixes (e.g. __processed_at, etc.) +# - **Context column**: Data context information ({orcapod.CONTEXT_KEY}) + +# Derivative of datagram (such as Packet or Tag) will also include some specific columns pertinent to the function of the specialized datagram: +# - **Source info columns**: Data provenance with {orcapod.SOURCE_PREFIX} ('_source_') prefixes (_source_user_id, etc.) used in Packet +# - **System tags**: Internal tags for system use, typically prefixed with {orcapod.SYSTEM_TAG_PREFIX} ('_system_') (_system_created_at, etc.) used in Tag + +# All operations are by design immutable - methods return new datagram instances rather than +# modifying existing ones. + +# Example: +# >>> datagram = DictDatagram({"user_id": 123, "name": "Alice"}) +# >>> updated = datagram.update(name="Alice Smith") +# >>> filtered = datagram.select("user_id", "name") +# >>> table = datagram.as_table() +# """ + +# # 1. Core Properties (Identity & Structure) +# @property +# def data_context_key(self) -> str: +# """ +# Return the data context key for this datagram. + +# This key identifies a collection of system components that collectively controls +# how information is serialized, hashed and represented, including the semantic type registry, +# arrow data hasher, and other contextual information. Same piece of information (that is two datagrams +# with an identical *logical* content) may bear distinct internal representation if they are +# represented under two distinct data context, as signified by distinct data context keys. + +# Returns: +# str: Context key for proper datagram interpretation +# """ +# ... + +# @property +# def meta_columns(self) -> tuple[str, ...]: +# """Return tuple of meta column names (with {orcapod.META_PREFIX} ('__') prefix).""" +# ... + +# # 2. Dict-like Interface (Data Access) +# def __getitem__(self, key: str) -> DataValue: +# """ +# Get data column value by key. + +# Provides dict-like access to data columns only. Meta columns +# are not accessible through this method (use `get_meta_value()` instead). + +# Args: +# key: Data column name. + +# Returns: +# The value stored in the specified data column. + +# Raises: +# KeyError: If the column doesn't exist in data columns. + +# Example: +# >>> datagram["user_id"] +# 123 +# >>> datagram["name"] +# 'Alice' +# """ +# ... + +# def __contains__(self, key: str) -> bool: +# """ +# Check if data column exists. + +# Args: +# key: Column name to check. + +# Returns: +# True if column exists in data columns, False otherwise. + +# Example: +# >>> "user_id" in datagram +# True +# >>> "nonexistent" in datagram +# False +# """ +# ... + +# def __iter__(self) -> Iterator[str]: +# """ +# Iterate over data column names. + +# Provides for-loop support over column names, enabling natural iteration +# patterns without requiring conversion to dict. + +# Yields: +# Data column names in no particular order. + +# Example: +# >>> for column in datagram: +# ... value = datagram[column] +# ... print(f"{column}: {value}") +# """ +# ... + +# def get(self, key: str, default: DataValue = None) -> DataValue: +# """ +# Get data column value with default fallback. + +# Args: +# key: Data column name. +# default: Value to return if column doesn't exist. + +# Returns: +# Column value if exists, otherwise the default value. + +# Example: +# >>> datagram.get("user_id") +# 123 +# >>> datagram.get("missing", "default") +# 'default' +# """ +# ... + +# # 3. Structural Information +# def keys( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# ) -> tuple[str, ...]: +# """ +# Return tuple of column names. + +# Provides access to column names with filtering options for different +# column types. Default returns only data column names. + +# Args: +# include_meta_columns: Controls meta column inclusion. +# - False: Return only data column names (default) +# - True: Include all meta column names +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context column. + +# Returns: +# Tuple of column names based on inclusion criteria. + +# Example: +# >>> datagram.keys() # Data columns only +# ('user_id', 'name', 'email') +# >>> datagram.keys(include_meta_columns=True) +# ('user_id', 'name', 'email', f'{orcapod.META_PREFIX}processed_at', f'{orcapod.META_PREFIX}pipeline_version') +# >>> datagram.keys(include_meta_columns=["pipeline"]) +# ('user_id', 'name', 'email',f'{orcapod.META_PREFIX}pipeline_version') +# >>> datagram.keys(include_context=True) +# ('user_id', 'name', 'email', f'{orcapod.CONTEXT_KEY}') +# """ +# ... + +# def types( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# ) -> TypeSpec: +# """ +# Return type specification mapping field names to Python types. + +# The TypeSpec enables type checking and validation throughout the system. + +# Args: +# include_meta_columns: Controls meta column type inclusion. +# - False: Exclude meta column types (default) +# - True: Include all meta column types +# - Collection[str]: Include meta column types matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context type. + +# Returns: +# TypeSpec mapping field names to their Python types. + +# Example: +# >>> datagram.types() +# {'user_id': , 'name': } +# """ +# ... + +# def arrow_schema( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# ) -> "pa.Schema": +# """ +# Return PyArrow schema representation. + +# The schema provides structured field and type information for efficient +# serialization and deserialization with PyArrow. + +# Args: +# include_meta_columns: Controls meta column schema inclusion. +# - False: Exclude meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context column. + +# Returns: +# PyArrow Schema describing the datagram structure. + +# Example: +# >>> schema = datagram.arrow_schema() +# >>> schema.names +# ['user_id', 'name'] +# """ +# ... + +# # 4. Format Conversions (Export) +# def as_dict( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# ) -> dict[str, DataValue]: +# """ +# Convert datagram to dictionary format. + +# Provides a simple key-value representation useful for debugging, +# serialization, and interop with dict-based APIs. + +# Args: +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include the context key. +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. + + +# Returns: +# Dictionary with requested columns as key-value pairs. + +# Example: +# >>> data = datagram.as_dict() # {'user_id': 123, 'name': 'Alice'} +# >>> full_data = datagram.as_dict( +# ... include_meta_columns=True, +# ... include_context=True +# ... ) +# """ +# ... + +# def as_table( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# ) -> "pa.Table": +# """ +# Convert datagram to PyArrow Table format. + +# Provides a standardized columnar representation suitable for analysis, +# processing, and interoperability with Arrow-based tools. + +# Args: +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include the context column. +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. + +# Returns: +# PyArrow Table with requested columns. + +# Example: +# >>> table = datagram.as_table() # Data columns only +# >>> full_table = datagram.as_table( +# ... include_meta_columns=True, +# ... include_context=True +# ... ) +# >>> filtered = datagram.as_table(include_meta_columns=["pipeline"]) # same as passing f"{orcapod.META_PREFIX}pipeline" +# """ +# ... + +# def as_arrow_compatible_dict( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# ) -> dict[str, Any]: +# """ +# Return dictionary with values optimized for Arrow table conversion. + +# This method returns a dictionary where values are in a form that can be +# efficiently converted to Arrow format using pa.Table.from_pylist(). + +# The key insight is that this avoids the expensive as_table() → concat pattern +# by providing values that are "Arrow-ready" while remaining in dict format +# for efficient batching. + +# Implementation note: This may involve format conversions (e.g., Path objects +# to strings, datetime objects to ISO strings, etc.) to ensure compatibility +# with Arrow's expected input formats. + +# Arrow table that results from pa.Table.from_pylist on the output of this should be accompanied +# with arrow_schema(...) with the same argument options to ensure that the schema matches the table. + +# Args: +# include_all_info: Include all available information +# include_meta_columns: Controls meta column inclusion +# include_context: Whether to include context key + +# Returns: +# Dictionary with values optimized for Arrow conversion + +# Example: +# # Efficient batch conversion pattern +# arrow_dicts = [datagram.as_arrow_compatible_dict() for datagram in datagrams] +# schema = datagrams[0].arrow_schema() +# table = pa.Table.from_pylist(arrow_dicts, schema=schema) +# """ +# ... + +# # 5. Meta Column Operations +# def get_meta_value(self, key: str, default: DataValue = None) -> DataValue: +# """ +# Get meta column value with optional default. + +# Meta columns store operational metadata and use {orcapod.META_PREFIX} ('__') prefixes. +# This method handles both prefixed and unprefixed key formats. + +# Args: +# key: Meta column key (with or without {orcapod.META_PREFIX} ('__') prefix). +# default: Value to return if meta column doesn't exist. + +# Returns: +# Meta column value if exists, otherwise the default value. + +# Example: +# >>> datagram.get_meta_value("pipeline_version") # Auto-prefixed +# 'v2.1.0' +# >>> datagram.get_meta_value("__pipeline_version") # Already prefixed +# 'v2.1.0' +# >>> datagram.get_meta_value("missing", "default") +# 'default' +# """ +# ... + +# def with_meta_columns(self, **updates: DataValue) -> Self: +# """ +# Create new datagram with updated meta columns. + +# Adds or updates operational metadata while preserving all data columns. +# Keys are automatically prefixed with {orcapod.META_PREFIX} ('__') if needed. + +# Args: +# **updates: Meta column updates as keyword arguments. + +# Returns: +# New datagram instance with updated meta columns. + +# Example: +# >>> tracked = datagram.with_meta_columns( +# ... processed_by="pipeline_v2", +# ... timestamp="2024-01-15T10:30:00Z" +# ... ) +# """ +# ... + +# def drop_meta_columns(self, *keys: str, ignore_missing: bool = False) -> Self: +# """ +# Create new datagram with specified meta columns removed. + +# Args: +# *keys: Meta column keys to remove (prefixes optional). +# ignore_missing: If True, ignore missing columns without raising an error. + + +# Returns: +# New datagram instance without specified meta columns. + +# Raises: +# KeryError: If any specified meta column to drop doesn't exist and ignore_missing=False. + +# Example: +# >>> cleaned = datagram.drop_meta_columns("old_source", "temp_debug") +# """ +# ... + +# # 6. Data Column Operations +# def select(self, *column_names: str) -> Self: +# """ +# Create new datagram with only specified data columns. + +# Args: +# *column_names: Data column names to keep. + + +# Returns: +# New datagram instance with only specified data columns. All other columns including +# meta columns and context are preserved. + +# Raises: +# KeyError: If any specified column doesn't exist. + +# Example: +# >>> subset = datagram.select("user_id", "name", "email") +# """ +# ... + +# def drop(self, *column_names: str, ignore_missing: bool = False) -> Self: +# """ +# Create new datagram with specified data columns removed. Note that this does not +# remove meta columns or context column. Refer to `drop_meta_columns()` for dropping +# specific meta columns. Context key column can never be dropped but a modified copy +# can be created with a different context key using `with_data_context()`. + +# Args: +# *column_names: Data column names to remove. +# ignore_missing: If True, ignore missing columns without raising an error. + +# Returns: +# New datagram instance without specified data columns. + +# Raises: +# KeryError: If any specified column to drop doesn't exist and ignore_missing=False. + +# Example: +# >>> filtered = datagram.drop("temp_field", "debug_info") +# """ +# ... + +# def rename( +# self, +# column_mapping: Mapping[str, str], +# ) -> Self: +# """ +# Create new datagram with data columns renamed. + +# Args: +# column_mapping: Mapping from old names to new names. + +# Returns: +# New datagram instance with renamed data columns. + +# Example: +# >>> renamed = datagram.rename( +# ... {"old_id": "user_id", "old_name": "full_name"}, +# ... column_types={"user_id": int} +# ... ) +# """ +# ... + +# def update(self, **updates: DataValue) -> Self: +# """ +# Create new datagram with existing column values updated. + +# Updates values in existing data columns. Will error if any specified +# column doesn't exist - use with_columns() to add new columns. + +# Args: +# **updates: Column names and their new values. + +# Returns: +# New datagram instance with updated values. + +# Raises: +# KeyError: If any specified column doesn't exist. + +# Example: +# >>> updated = datagram.update( +# ... file_path="/new/absolute/path.txt", +# ... status="processed" +# ... ) +# """ +# ... + +# def with_columns( +# self, +# column_types: Mapping[str, type] | None = None, +# **updates: DataValue, +# ) -> Self: +# """ +# Create new datagram with additional data columns. + +# Adds new data columns to the datagram. Will error if any specified +# column already exists - use update() to modify existing columns. + +# Args: +# column_types: Optional type specifications for new columns. If not provided, the column type is +# inferred from the provided values. If value is None, the column type defaults to `str`. +# **kwargs: New columns as keyword arguments. + +# Returns: +# New datagram instance with additional data columns. + +# Raises: +# ValueError: If any specified column already exists. + +# Example: +# >>> expanded = datagram.with_columns( +# ... status="active", +# ... score=95.5, +# ... column_types={"score": float} +# ... ) +# """ +# ... + +# # 7. Context Operations +# def with_context_key(self, new_context_key: str) -> Self: +# """ +# Create new datagram with different context key. + +# Changes the semantic interpretation context while preserving all data. +# The context key affects how columns are processed and converted. + +# Args: +# new_context_key: New context key string. + +# Returns: +# New datagram instance with updated context key. + +# Note: +# How the context is interpreted depends on the datagram implementation. +# Semantic processing may be rebuilt for the new context. + +# Example: +# >>> financial_datagram = datagram.with_context_key("financial_v1") +# """ +# ... + +# # 8. Utility Operations +# def copy(self) -> Self: +# """ +# Create a shallow copy of the datagram. + +# Returns a new datagram instance with the same data and cached values. +# This is more efficient than reconstructing from scratch when you need +# an identical datagram instance. + +# Returns: +# New datagram instance with copied data and caches. + +# Example: +# >>> copied = datagram.copy() +# >>> copied is datagram # False - different instance +# False +# """ +# ... + +# # 9. String Representations +# def __str__(self) -> str: +# """ +# Return user-friendly string representation. + +# Shows the datagram as a simple dictionary for user-facing output, +# messages, and logging. Only includes data columns for clean output. + +# Returns: +# Dictionary-style string representation of data columns only. +# """ +# ... + +# def __repr__(self) -> str: +# """ +# Return detailed string representation for debugging. + +# Shows the datagram type and comprehensive information for debugging. + +# Returns: +# Detailed representation with type and metadata information. +# """ +# ... + + +# @runtime_checkable +# class Tag(Datagram, Protocol): +# """ +# Metadata associated with each data item in a stream. + +# Tags carry contextual information about data packets as they flow through +# the computational graph. They are immutable and provide metadata that +# helps with: +# - Data lineage tracking +# - Grouping and aggregation operations +# - Temporal information (timestamps) +# - Source identification +# - Processing context + +# Common examples include: +# - Timestamps indicating when data was created/processed +# - Source identifiers showing data origin +# - Processing metadata like batch IDs or session information +# - Grouping keys for aggregation operations +# - Quality indicators or confidence scores +# """ + +# def keys( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_system_tags: bool = False, +# ) -> tuple[str, ...]: +# """ +# Return tuple of column names. + +# Provides access to column names with filtering options for different +# column types. Default returns only data column names. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column inclusion. +# - False: Return only data column names (default) +# - True: Include all meta column names +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context column. +# include_source: Whether to include source info fields. + + +# Returns: +# Tuple of column names based on inclusion criteria. + +# Example: +# >>> datagram.keys() # Data columns only +# ('user_id', 'name', 'email') +# >>> datagram.keys(include_meta_columns=True) +# ('user_id', 'name', 'email', f'{orcapod.META_PREFIX}processed_at', f'{orcapod.META_PREFIX}pipeline_version') +# >>> datagram.keys(include_meta_columns=["pipeline"]) +# ('user_id', 'name', 'email',f'{orcapod.META_PREFIX}pipeline_version') +# >>> datagram.keys(include_context=True) +# ('user_id', 'name', 'email', f'{orcapod.CONTEXT_KEY}') +# """ +# ... + +# def types( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_system_tags: bool = False, +# ) -> TypeSpec: +# """ +# Return type specification mapping field names to Python types. + +# The TypeSpec enables type checking and validation throughout the system. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column type inclusion. +# - False: Exclude meta column types (default) +# - True: Include all meta column types +# - Collection[str]: Include meta column types matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context type. +# include_source: Whether to include source info fields. + +# Returns: +# TypeSpec mapping field names to their Python types. + +# Example: +# >>> datagram.types() +# {'user_id': , 'name': } +# """ +# ... + +# def arrow_schema( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_system_tags: bool = False, +# ) -> "pa.Schema": +# """ +# Return PyArrow schema representation. + +# The schema provides structured field and type information for efficient +# serialization and deserialization with PyArrow. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column schema inclusion. +# - False: Exclude meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context column. +# include_source: Whether to include source info fields. + + +# Returns: +# PyArrow Schema describing the datagram structure. + +# Example: +# >>> schema = datagram.arrow_schema() +# >>> schema.names +# ['user_id', 'name'] +# """ +# ... + +# def as_dict( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_system_tags: bool = False, +# ) -> dict[str, DataValue]: +# """ +# Convert datagram to dictionary format. + +# Provides a simple key-value representation useful for debugging, +# serialization, and interop with dict-based APIs. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include the context key. +# include_source: Whether to include source info fields. + + +# Returns: +# Dictionary with requested columns as key-value pairs. + +# Example: +# >>> data = datagram.as_dict() # {'user_id': 123, 'name': 'Alice'} +# >>> full_data = datagram.as_dict( +# ... include_meta_columns=True, +# ... include_context=True +# ... ) +# """ +# ... + +# def as_table( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_system_tags: bool = False, +# ) -> "pa.Table": +# """ +# Convert datagram to PyArrow Table format. + +# Provides a standardized columnar representation suitable for analysis, +# processing, and interoperability with Arrow-based tools. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include the context column. +# include_source: Whether to include source info columns in the schema. + +# Returns: +# PyArrow Table with requested columns. + +# Example: +# >>> table = datagram.as_table() # Data columns only +# >>> full_table = datagram.as_table( +# ... include_meta_columns=True, +# ... include_context=True +# ... ) +# >>> filtered = datagram.as_table(include_meta_columns=["pipeline"]) # same as passing f"{orcapod.META_PREFIX}pipeline" +# """ +# ... + +# # TODO: add this back +# # def as_arrow_compatible_dict( +# # self, +# # include_all_info: bool = False, +# # include_meta_columns: bool | Collection[str] = False, +# # include_context: bool = False, +# # include_source: bool = False, +# # ) -> dict[str, Any]: +# # """Extended version with source info support.""" +# # ... + +# def as_datagram( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_system_tags: bool = False, +# ) -> Datagram: +# """ +# Convert the packet to a Datagram. + +# Args: +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + +# Returns: +# Datagram: Datagram representation of packet data +# """ +# ... + +# def system_tags(self) -> dict[str, DataValue]: +# """ +# Return metadata about the packet's source/origin. + +# Provides debugging and lineage information about where the packet +# originated. May include information like: +# - File paths for file-based sources +# - Database connection strings +# - API endpoints +# - Processing pipeline information + +# Returns: +# dict[str, str | None]: Source information for each data column as key-value pairs. +# """ +# ... + + +# @runtime_checkable +# class Packet(Datagram, Protocol): +# """ +# The actual data payload in a stream. + +# Packets represent the core data being processed through the computational +# graph. Unlike Tags (which are metadata), Packets contain the actual +# information that computations operate on. + +# Packets extend Datagram with additional capabilities for: +# - Source tracking and lineage +# - Content-based hashing for caching +# - Metadata inclusion for debugging + +# The distinction between Tag and Packet is crucial for understanding +# data flow: Tags provide context, Packets provide content. +# """ + +# def keys( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_source: bool = False, +# ) -> tuple[str, ...]: +# """ +# Return tuple of column names. + +# Provides access to column names with filtering options for different +# column types. Default returns only data column names. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column inclusion. +# - False: Return only data column names (default) +# - True: Include all meta column names +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context column. +# include_source: Whether to include source info fields. + + +# Returns: +# Tuple of column names based on inclusion criteria. + +# Example: +# >>> datagram.keys() # Data columns only +# ('user_id', 'name', 'email') +# >>> datagram.keys(include_meta_columns=True) +# ('user_id', 'name', 'email', f'{orcapod.META_PREFIX}processed_at', f'{orcapod.META_PREFIX}pipeline_version') +# >>> datagram.keys(include_meta_columns=["pipeline"]) +# ('user_id', 'name', 'email',f'{orcapod.META_PREFIX}pipeline_version') +# >>> datagram.keys(include_context=True) +# ('user_id', 'name', 'email', f'{orcapod.CONTEXT_KEY}') +# """ +# ... + +# def types( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_source: bool = False, +# ) -> TypeSpec: +# """ +# Return type specification mapping field names to Python types. + +# The TypeSpec enables type checking and validation throughout the system. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column type inclusion. +# - False: Exclude meta column types (default) +# - True: Include all meta column types +# - Collection[str]: Include meta column types matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context type. +# include_source: Whether to include source info fields. + +# Returns: +# TypeSpec mapping field names to their Python types. + +# Example: +# >>> datagram.types() +# {'user_id': , 'name': } +# """ +# ... + +# def arrow_schema( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_source: bool = False, +# ) -> "pa.Schema": +# """ +# Return PyArrow schema representation. + +# The schema provides structured field and type information for efficient +# serialization and deserialization with PyArrow. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column schema inclusion. +# - False: Exclude meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include context column. +# include_source: Whether to include source info fields. + + +# Returns: +# PyArrow Schema describing the datagram structure. + +# Example: +# >>> schema = datagram.arrow_schema() +# >>> schema.names +# ['user_id', 'name'] +# """ +# ... + +# def as_dict( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_source: bool = False, +# ) -> dict[str, DataValue]: +# """ +# Convert datagram to dictionary format. + +# Provides a simple key-value representation useful for debugging, +# serialization, and interop with dict-based APIs. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include the context key. +# include_source: Whether to include source info fields. + + +# Returns: +# Dictionary with requested columns as key-value pairs. + +# Example: +# >>> data = datagram.as_dict() # {'user_id': 123, 'name': 'Alice'} +# >>> full_data = datagram.as_dict( +# ... include_meta_columns=True, +# ... include_context=True +# ... ) +# """ +# ... + +# def as_table( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_context: bool = False, +# include_source: bool = False, +# ) -> "pa.Table": +# """ +# Convert datagram to PyArrow Table format. + +# Provides a standardized columnar representation suitable for analysis, +# processing, and interoperability with Arrow-based tools. + +# Args: +# include_all_info: If True, include all available information. This option supersedes all other inclusion options. +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. +# include_context: Whether to include the context column. +# include_source: Whether to include source info columns in the schema. + +# Returns: +# PyArrow Table with requested columns. + +# Example: +# >>> table = datagram.as_table() # Data columns only +# >>> full_table = datagram.as_table( +# ... include_meta_columns=True, +# ... include_context=True +# ... ) +# >>> filtered = datagram.as_table(include_meta_columns=["pipeline"]) # same as passing f"{orcapod.META_PREFIX}pipeline" +# """ +# ... + +# # TODO: add this back +# # def as_arrow_compatible_dict( +# # self, +# # include_all_info: bool = False, +# # include_meta_columns: bool | Collection[str] = False, +# # include_context: bool = False, +# # include_source: bool = False, +# # ) -> dict[str, Any]: +# # """Extended version with source info support.""" +# # ... + +# def as_datagram( +# self, +# include_all_info: bool = False, +# include_meta_columns: bool | Collection[str] = False, +# include_source: bool = False, +# ) -> Datagram: +# """ +# Convert the packet to a Datagram. + +# Args: +# include_meta_columns: Controls meta column inclusion. +# - False: Exclude all meta columns (default) +# - True: Include all meta columns +# - Collection[str]: Include meta columns matching these prefixes. If absent, +# {orcapod.META_PREFIX} ('__') prefix is prepended to each key. + +# Returns: +# Datagram: Datagram representation of packet data +# """ +# ... + +# def source_info(self) -> dict[str, str | None]: +# """ +# Return metadata about the packet's source/origin. + +# Provides debugging and lineage information about where the packet +# originated. May include information like: +# - File paths for file-based sources +# - Database connection strings +# - API endpoints +# - Processing pipeline information + +# Returns: +# dict[str, str | None]: Source information for each data column as key-value pairs. +# """ +# ... + +# def with_source_info( +# self, +# **source_info: str | None, +# ) -> Self: +# """ +# Create new packet with updated source information. + +# Adds or updates source metadata for the packet. This is useful for +# tracking data provenance and lineage through the computational graph. + +# Args: +# **source_info: Source metadata as keyword arguments. + +# Returns: +# New packet instance with updated source information. + +# Example: +# >>> updated_packet = packet.with_source_info( +# ... file_path="/new/path/to/file.txt", +# ... source_id="source_123" +# ... ) +# """ +# ... + + +# @runtime_checkable +# class PodFunction(Protocol): +# """ +# A function suitable for use in a FunctionPod. + +# PodFunctions define the computational logic that operates on individual +# packets within a Pod. They represent pure functions that transform +# data values without side effects. + +# These functions are designed to be: +# - Stateless: No dependency on external state +# - Deterministic: Same inputs always produce same outputs +# - Serializable: Can be cached and distributed +# - Type-safe: Clear input/output contracts + +# PodFunctions accept named arguments corresponding to packet fields +# and return transformed data values. +# """ + +# def __call__(self, **kwargs: DataValue) -> None | DataValue: +# """ +# Execute the pod function with the given arguments. + +# The function receives packet data as named arguments and returns +# either transformed data or None (for filtering operations). + +# Args: +# **kwargs: Named arguments mapping packet fields to data values + +# Returns: +# None: Filter out this packet (don't include in output) +# DataValue: Single transformed value + +# Raises: +# TypeError: If required arguments are missing +# ValueError: If argument values are invalid +# """ +# ... + + +# @runtime_checkable +# class Labelable(Protocol): +# """ +# Protocol for objects that can have a human-readable label. + +# Labels provide meaningful names for objects in the computational graph, +# making debugging, visualization, and monitoring much easier. They serve +# as human-friendly identifiers that complement the technical identifiers +# used internally. + +# Labels are optional but highly recommended for: +# - Debugging complex computational graphs +# - Visualization and monitoring tools +# - Error messages and logging +# - User interfaces and dashboards +# """ + +# @property +# def label(self) -> str | None: +# """ +# Return the human-readable label for this object. + +# Labels should be descriptive and help users understand the purpose +# or role of the object in the computational graph. + +# Returns: +# str: Human-readable label for this object +# None: No label is set (will use default naming) +# """ +# ... + + +# @runtime_checkable +# class Stream(ContentIdentifiable, Labelable, Protocol): +# """ +# Base protocol for all streams in Orcapod. + +# Streams represent sequences of (Tag, Packet) pairs flowing through the +# computational graph. They are the fundamental data structure connecting +# kernels and carrying both data and metadata. + +# Streams can be either: +# - Static: Immutable snapshots created at a specific point in time +# - Live: Dynamic streams that stay current with upstream dependencies + +# All streams provide: +# - Iteration over (tag, packet) pairs +# - Type information and schema access +# - Lineage information (source kernel and upstream streams) +# - Basic caching and freshness tracking +# - Conversion to common formats (tables, dictionaries) +# """ + +# @property +# def substream_identities(self) -> tuple[str, ...]: +# """ +# Unique identifiers for sub-streams within this stream. + +# This property provides a way to identify and differentiate +# sub-streams that may be part of a larger stream. It is useful +# for tracking and managing complex data flows. + +# Returns: +# tuple[str, ...]: Unique identifiers for each sub-stream +# """ +# ... + +# @property +# def execution_engine(self) -> ExecutionEngine | None: +# """ +# The execution engine attached to this stream. By default, the stream +# will use this execution engine whenever it needs to perform computation. +# None means the stream is not attached to any execution engine and will default +# to running natively. +# """ + +# @execution_engine.setter +# def execution_engine(self, engine: ExecutionEngine | None) -> None: +# """ +# Set the execution engine for this stream. + +# This allows the stream to use a specific execution engine for +# computation, enabling optimized execution strategies and resource +# management. + +# Args: +# engine: The execution engine to attach to this stream +# """ +# ... + +# def get_substream(self, substream_id: str) -> "Stream": +# """ +# Retrieve a specific sub-stream by its identifier. + +# This method allows access to individual sub-streams within the +# main stream, enabling focused operations on specific data segments. + +# Args: +# substream_id: Unique identifier for the desired sub-stream. + +# Returns: +# Stream: The requested sub-stream if it exists +# """ +# ... + +# @property +# def source(self) -> "Kernel | None": +# """ +# The kernel that produced this stream. + +# This provides lineage information for tracking data flow through +# the computational graph. Root streams (like file sources) may +# have no source kernel. + +# Returns: +# Kernel: The source kernel that created this stream +# None: This is a root stream with no source kernel +# """ +# ... + +# @property +# def upstreams(self) -> tuple["Stream", ...]: +# """ +# Input streams used to produce this stream. + +# These are the streams that were provided as input to the source +# kernel when this stream was created. Used for dependency tracking +# and cache invalidation. + +# Returns: +# tuple[Stream, ...]: Upstream dependency streams (empty for sources) +# """ +# ... + +# def keys(self) -> tuple[tuple[str, ...], tuple[str, ...]]: +# """ +# Available keys/fields in the stream content. + +# Returns the field names present in both tags and packets. +# This provides schema information without requiring type details, +# useful for: +# - Schema inspection and exploration +# - Query planning and optimization +# - Field validation and mapping + +# Returns: +# tuple[tuple[str, ...], tuple[str, ...]]: (tag_keys, packet_keys) +# """ +# ... + +# def types(self, include_system_tags: bool = False) -> tuple[TypeSpec, TypeSpec]: +# """ +# Type specifications for the stream content. + +# Returns the type schema for both tags and packets in this stream. +# This information is used for: +# - Type checking and validation +# - Schema inference and planning +# - Compatibility checking between kernels + +# Returns: +# tuple[TypeSpec, TypeSpec]: (tag_types, packet_types) +# """ +# ... + +# @property +# def last_modified(self) -> datetime | None: +# """ +# When the stream's content was last modified. + +# This property is crucial for caching decisions and dependency tracking: +# - datetime: Content was last modified at this time (cacheable) +# - None: Content is never stable, always recompute (some dynamic streams) + +# Both static and live streams typically return datetime values, but +# live streams update this timestamp whenever their content changes. + +# Returns: +# datetime: Timestamp of last modification for most streams +# None: Stream content is never stable (some special dynamic streams) +# """ +# ... + +# @property +# def is_current(self) -> bool: +# """ +# Whether the stream is up-to-date with its dependencies. + +# A stream is current if its content reflects the latest state of its +# source kernel and upstream streams. This is used for cache validation +# and determining when refresh is needed. + +# For live streams, this should always return True since they stay +# current automatically. For static streams, this indicates whether +# the cached content is still valid. + +# Returns: +# bool: True if stream is up-to-date, False if refresh needed +# """ +# ... + +# def __iter__(self) -> Iterator[tuple[Tag, Packet]]: +# """ +# Iterate over (tag, packet) pairs in the stream. + +# This is the primary way to access stream data. The behavior depends +# on the stream type: +# - Static streams: Return cached/precomputed data +# - Live streams: May trigger computation and always reflect current state + +# Yields: +# tuple[Tag, Packet]: Sequential (tag, packet) pairs +# """ +# ... + +# def iter_packets( +# self, execution_engine: ExecutionEngine | None = None +# ) -> Iterator[tuple[Tag, Packet]]: +# """ +# Alias for __iter__ for explicit packet iteration. + +# Provides a more explicit method name when the intent is to iterate +# over packets specifically, improving code readability. + +# This method must return an immutable iterator -- that is, the returned iterator +# should not change and must consistently return identical tag,packet pairs across +# multiple iterations of the iterator. + +# Note that this is NOT to mean that multiple invocation of `iter_packets` must always +# return an identical iterator. The iterator returned by `iter_packets` may change +# between invocations, but the iterator itself must not change. Consequently, it should be understood +# that the returned iterators may be a burden on memory if the stream is large or infinite. + +# Yields: +# tuple[Tag, Packet]: Sequential (tag, packet) pairs +# """ +# ... + +# def run(self, execution_engine: ExecutionEngine | None = None) -> None: +# """ +# Execute the stream using the provided execution engine. + +# This method triggers computation of the stream content based on its +# source kernel and upstream streams. It returns a new stream instance +# containing the computed (tag, packet) pairs. + +# Args: +# execution_engine: The execution engine to use for computation + +# """ +# ... + +# async def run_async(self, execution_engine: ExecutionEngine | None = None) -> None: +# """ +# Asynchronously execute the stream using the provided execution engine. + +# This method triggers computation of the stream content based on its +# source kernel and upstream streams. It returns a new stream instance +# containing the computed (tag, packet) pairs. + +# Args: +# execution_engine: The execution engine to use for computation + +# """ +# ... + +# def as_df( +# self, +# include_data_context: bool = False, +# include_source: bool = False, +# include_system_tags: bool = False, +# include_content_hash: bool | str = False, +# execution_engine: ExecutionEngine | None = None, +# ) -> "pl.DataFrame | None": +# """ +# Convert the entire stream to a Polars DataFrame. +# """ +# ... + +# def as_table( +# self, +# include_data_context: bool = False, +# include_source: bool = False, +# include_system_tags: bool = False, +# include_content_hash: bool | str = False, +# execution_engine: ExecutionEngine | None = None, +# ) -> "pa.Table": +# """ +# Convert the entire stream to a PyArrow Table. + +# Materializes all (tag, packet) pairs into a single table for +# analysis and processing. This operation may be expensive for +# large streams or live streams that need computation. + +# If include_content_hash is True, an additional column called "_content_hash" +# containing the content hash of each packet is included. If include_content_hash +# is a string, it is used as the name of the content hash column. + +# Returns: +# pa.Table: Complete stream data as a PyArrow Table +# """ +# ... + +# def flow( +# self, execution_engine: ExecutionEngine | None = None +# ) -> Collection[tuple[Tag, Packet]]: +# """ +# Return the entire stream as a collection of (tag, packet) pairs. + +# This method materializes the stream content into a list or similar +# collection type. It is useful for small streams or when you need +# to process all data at once. + +# Args: +# execution_engine: Optional execution engine to use for computation. +# If None, the stream will use its default execution engine. +# """ +# ... + +# def join(self, other_stream: "Stream") -> "Stream": +# """ +# Join this stream with another stream. + +# Combines two streams into a single stream by merging their content. +# The resulting stream contains all (tag, packet) pairs from both +# streams, preserving their order. + +# Args: +# other_stream: The other stream to join with this one. + +# Returns: +# Self: New stream containing combined content from both streams. +# """ +# ... + +# def semi_join(self, other_stream: "Stream") -> "Stream": +# """ +# Perform a semi-join with another stream. + +# This operation filters this stream to only include packets that have +# corresponding tags in the other stream. The resulting stream contains +# all (tag, packet) pairs from this stream that match tags in the other. + +# Args: +# other_stream: The other stream to semi-join with this one. + +# Returns: +# Self: New stream containing filtered content based on the semi-join. +# """ +# ... + +# def map_tags( +# self, name_map: Mapping[str, str], drop_unmapped: bool = True +# ) -> "Stream": +# """ +# Map tag names in this stream to new names based on the provided mapping. +# """ +# ... + +# def map_packets( +# self, name_map: Mapping[str, str], drop_unmapped: bool = True +# ) -> "Stream": +# """ +# Map packet names in this stream to new names based on the provided mapping. +# """ +# ... + + +# @runtime_checkable +# class LiveStream(Stream, Protocol): +# """ +# A stream that automatically stays up-to-date with its upstream dependencies. + +# LiveStream extends the base Stream protocol with capabilities for "up-to-date" +# data flow and reactive computation. Unlike static streams which represent +# snapshots, LiveStreams provide the guarantee that their content always +# reflects the current state of their dependencies. + +# Key characteristics: +# - Automatically refresh the stream if changes in the upstreams are detected +# - Track last_modified timestamp when content changes +# - Support manual refresh triggering and invalidation +# - By design, LiveStream would return True for is_current except when auto-update fails. + +# LiveStreams are always returned by Kernel.__call__() methods, ensuring +# that normal kernel usage produces live, up-to-date results. + +# Caching behavior: +# - last_modified updates whenever content changes +# - Can be cached based on dependency timestamps +# - Invalidation happens automatically when upstreams change + +# Use cases: +# - Real-time data processing pipelines +# - Reactive user interfaces +# - Monitoring and alerting systems +# - Dynamic dashboard updates +# - Any scenario requiring current data +# """ + +# def refresh(self, force: bool = False) -> bool: +# """ +# Manually trigger a refresh of this stream's content. + +# Forces the stream to check its upstream dependencies and update +# its content if necessary. This is useful when: +# - You want to ensure the latest data before a critical operation +# - You need to force computation at a specific time +# - You're debugging data flow issues +# - You want to pre-compute results for performance +# Args: +# force: If True, always refresh even if the stream is current. +# If False, only refresh if the stream is not current. + +# Returns: +# bool: True if the stream was refreshed, False if it was already current. +# Note: LiveStream refreshes automatically on access, so this +# method may be a no-op for some implementations. However, it's +# always safe to call if you need to control when the cache is refreshed. +# """ +# ... + +# def invalidate(self) -> None: +# """ +# Mark this stream as invalid, forcing a refresh on next access. + +# This method is typically called when: +# - Upstream dependencies have changed +# - The source kernel has been modified +# - External data sources have been updated +# - Manual cache invalidation is needed + +# The stream will automatically refresh its content the next time +# it's accessed (via iteration, as_table(), etc.). + +# This is more efficient than immediate refresh when you know the +# data will be accessed later. +# """ +# ... + + +# @runtime_checkable +# class Kernel(ContentIdentifiable, Labelable, Protocol): +# """ +# The fundamental unit of computation in Orcapod. + +# Kernels are the building blocks of computational graphs, transforming +# zero, one, or more input streams into a single output stream. They +# encapsulate computation logic while providing consistent interfaces +# for validation, type checking, and execution. + +# Key design principles: +# - Immutable: Kernels don't change after creation +# - Deterministic: Same inputs always produce same outputs +# - Composable: Kernels can be chained and combined +# - Trackable: All invocations are recorded for lineage +# - Type-safe: Strong typing and validation throughout + +# Execution modes: +# - __call__(): Full-featured execution with tracking, returns LiveStream +# - forward(): Pure computation without side effects, returns Stream + +# The distinction between these modes enables both production use (with +# full tracking) and testing/debugging (without side effects). +# """ + +# @property +# def kernel_id(self) -> tuple[str, ...]: +# """ +# Return a unique identifier for this Pod. + +# The pod_id is used for caching and tracking purposes. It should +# uniquely identify the Pod's computational logic, parameters, and +# any relevant metadata that affects its behavior. + +# Returns: +# tuple[str, ...]: Unique identifier for this Pod +# """ +# ... + +# @property +# def data_context_key(self) -> str: +# """ +# Return the context key for this kernel's data processing. + +# The context key is used to interpret how data columns should be +# processed and converted. It provides semantic meaning to the data +# being processed by this kernel. + +# Returns: +# str: Context key for this kernel's data processing +# """ +# ... + +# @property +# def last_modified(self) -> datetime | None: +# """ +# When the kernel was last modified. For most kernels, this is the timestamp +# of the kernel creation. +# """ +# ... + +# def __call__( +# self, *streams: Stream, label: str | None = None, **kwargs +# ) -> LiveStream: +# """ +# Main interface for kernel invocation with full tracking and guarantees. + +# This is the primary way to invoke kernels in production. It provides +# a complete execution pipeline: +# 1. Validates input streams against kernel requirements +# 2. Registers the invocation with the computational graph +# 3. Calls forward() to perform the actual computation +# 4. Ensures the result is a LiveStream that stays current + +# The returned LiveStream automatically stays up-to-date with its +# upstream dependencies, making it suitable for real-time processing +# and reactive applications. + +# Args: +# *streams: Input streams to process (can be empty for source kernels) +# label: Optional label for this invocation (overrides kernel.label) +# **kwargs: Additional arguments for kernel configuration + +# Returns: +# LiveStream: Live stream that stays up-to-date with upstreams + +# Raises: +# ValidationError: If input streams are invalid for this kernel +# TypeMismatchError: If stream types are incompatible +# ValueError: If required arguments are missing +# """ +# ... + +# def forward(self, *streams: Stream) -> Stream: +# """ +# Perform the actual computation without side effects. + +# This method contains the core computation logic and should be +# overridden by subclasses. It performs pure computation without: +# - Registering with the computational graph +# - Performing validation (caller's responsibility) +# - Guaranteeing result type (may return static or live streams) + +# The returned stream must be accurate at the time of invocation but +# need not stay up-to-date with upstream changes. This makes forward() +# suitable for: +# - Testing and debugging +# - Batch processing where currency isn't required +# - Internal implementation details + +# Args: +# *streams: Input streams to process + +# Returns: +# Stream: Result of the computation (may be static or live) +# """ +# ... + +# def output_types( +# self, *streams: Stream, include_system_tags: bool = False +# ) -> tuple[TypeSpec, TypeSpec]: +# """ +# Determine output types without triggering computation. + +# This method performs type inference based on input stream types, +# enabling efficient type checking and stream property queries. +# It should be fast and not trigger any expensive computation. + +# Used for: +# - Pre-execution type validation +# - Query planning and optimization +# - Schema inference in complex pipelines +# - IDE support and developer tooling + +# Args: +# *streams: Input streams to analyze + +# Returns: +# tuple[TypeSpec, TypeSpec]: (tag_types, packet_types) for output + +# Raises: +# ValidationError: If input types are incompatible +# TypeError: If stream types cannot be processed +# """ +# ... + +# def validate_inputs(self, *streams: Stream) -> None: +# """ +# Validate input streams, raising exceptions if incompatible. + +# This method is called automatically by __call__ before computation +# to provide fail-fast behavior. It should check: +# - Number of input streams +# - Stream types and schemas +# - Any kernel-specific requirements +# - Business logic constraints + +# The goal is to catch errors early, before expensive computation +# begins, and provide clear error messages for debugging. + +# Args: +# *streams: Input streams to validate + +# Raises: +# ValidationError: If streams are invalid for this kernel +# TypeError: If stream types are incompatible +# ValueError: If stream content violates business rules +# """ +# ... + +# def identity_structure(self, streams: Collection[Stream] | None = None) -> Any: +# """ +# Generate a unique identity structure for this kernel and/or kernel invocation. +# When invoked without streams, it should return a structure +# that uniquely identifies the kernel itself (e.g., class name, parameters). +# When invoked with streams, it should include the identity of the streams +# to distinguish different invocations of the same kernel. + +# This structure is used for: +# - Caching and memoization +# - Debugging and error reporting +# - Tracking kernel invocations in computational graphs + +# Args: +# streams: Optional input streams for this invocation. If None, identity_structure is +# based solely on the kernel. If streams are provided, they are included in the identity +# to differentiate between different invocations of the same kernel. + +# Returns: +# Any: Unique identity structure (e.g., tuple of class name and stream identities) +# """ +# ... + + +# @runtime_checkable +# class Pod(Kernel, Protocol): +# """ +# Specialized kernel for packet-level processing with advanced caching. + +# Pods represent a different computational model from regular kernels: +# - Process data one packet at a time (enabling fine-grained parallelism) +# - Support just-in-time evaluation (computation deferred until needed) +# - Provide stricter type contracts (clear input/output schemas) +# - Enable advanced caching strategies (packet-level caching) + +# The Pod abstraction is ideal for: +# - Expensive computations that benefit from caching +# - Operations that can be parallelized at the packet level +# - Transformations with strict type contracts +# - Processing that needs to be deferred until access time +# - Functions that operate on individual data items + +# Pods use a different execution model where computation is deferred +# until results are actually needed, enabling efficient resource usage +# and fine-grained caching. +# """ + +# @property +# def version(self) -> str: ... + +# def get_record_id(self, packet: Packet, execution_engine_hash: str) -> str: ... + +# @property +# def tiered_pod_id(self) -> dict[str, str]: +# """ +# Return a dictionary representation of the tiered pod's unique identifier. +# The key is supposed to be ordered from least to most specific, allowing +# for hierarchical identification of the pod. + +# This is primarily used for tiered memoization/caching strategies. + +# Returns: +# dict[str, str]: Dictionary representation of the pod's ID +# """ +# ... + +# def input_packet_types(self) -> TypeSpec: +# """ +# TypeSpec for input packets that this Pod can process. + +# Defines the exact schema that input packets must conform to. +# Pods are typically much stricter about input types than regular +# kernels, requiring precise type matching for their packet-level +# processing functions. + +# This specification is used for: +# - Runtime type validation +# - Compile-time type checking +# - Schema inference and documentation +# - Input validation and error reporting + +# Returns: +# TypeSpec: Dictionary mapping field names to required packet types +# """ +# ... + +# def output_packet_types(self) -> TypeSpec: +# """ +# TypeSpec for output packets that this Pod produces. + +# Defines the schema of packets that will be produced by this Pod. +# This is typically determined by the Pod's computational function +# and is used for: +# - Type checking downstream kernels +# - Schema inference in complex pipelines +# - Query planning and optimization +# - Documentation and developer tooling + +# Returns: +# TypeSpec: Dictionary mapping field names to output packet types +# """ +# ... + +# async def async_call( +# self, +# tag: Tag, +# packet: Packet, +# record_id: str | None = None, +# execution_engine: ExecutionEngine | None = None, +# ) -> tuple[Tag, Packet | None]: ... + +# def call( +# self, +# tag: Tag, +# packet: Packet, +# record_id: str | None = None, +# execution_engine: ExecutionEngine | None = None, +# ) -> tuple[Tag, Packet | None]: +# """ +# Process a single packet with its associated tag. + +# This is the core method that defines the Pod's computational behavior. +# It processes one (tag, packet) pair at a time, enabling: +# - Fine-grained caching at the packet level +# - Parallelization opportunities +# - Just-in-time evaluation +# - Filtering operations (by returning None) + +# The method signature supports: +# - Tag transformation (modify metadata) +# - Packet transformation (modify content) +# - Filtering (return None to exclude packet) +# - Pass-through (return inputs unchanged) + +# Args: +# tag: Metadata associated with the packet +# packet: The data payload to process + +# Returns: +# tuple[Tag, Packet | None]: +# - Tag: Output tag (may be modified from input) +# - Packet: Processed packet, or None to filter it out + +# Raises: +# TypeError: If packet doesn't match input_packet_types +# ValueError: If packet data is invalid for processing +# """ +# ... + + +# @runtime_checkable +# class CachedPod(Pod, Protocol): +# async def async_call( +# self, +# tag: Tag, +# packet: Packet, +# record_id: str | None = None, +# execution_engine: ExecutionEngine | None = None, +# skip_cache_lookup: bool = False, +# skip_cache_insert: bool = False, +# ) -> tuple[Tag, Packet | None]: ... + +# def call( +# self, +# tag: Tag, +# packet: Packet, +# record_id: str | None = None, +# execution_engine: ExecutionEngine | None = None, +# skip_cache_lookup: bool = False, +# skip_cache_insert: bool = False, +# ) -> tuple[Tag, Packet | None]: +# """ +# Process a single packet with its associated tag. + +# This is the core method that defines the Pod's computational behavior. +# It processes one (tag, packet) pair at a time, enabling: +# - Fine-grained caching at the packet level +# - Parallelization opportunities +# - Just-in-time evaluation +# - Filtering operations (by returning None) + +# The method signature supports: +# - Tag transformation (modify metadata) +# - Packet transformation (modify content) +# - Filtering (return None to exclude packet) +# - Pass-through (return inputs unchanged) + +# Args: +# tag: Metadata associated with the packet +# packet: The data payload to process + +# Returns: +# tuple[Tag, Packet | None]: +# - Tag: Output tag (may be modified from input) +# - Packet: Processed packet, or None to filter it out + +# Raises: +# TypeError: If packet doesn't match input_packet_types +# ValueError: If packet data is invalid for processing +# """ +# ... + +# def get_all_records( +# self, include_system_columns: bool = False +# ) -> "pa.Table | None": +# """ +# Retrieve all records processed by this Pod. + +# This method returns a table containing all packets processed by the Pod, +# including metadata and system columns if requested. It is useful for: +# - Debugging and analysis +# - Auditing and data lineage tracking +# - Performance monitoring + +# Args: +# include_system_columns: Whether to include system columns in the output + +# Returns: +# pa.Table | None: A table containing all processed records, or None if no records are available +# """ +# ... + + +# @runtime_checkable +# class Source(Kernel, Stream, Protocol): +# """ +# Entry point for data into the computational graph. + +# Sources are special objects that serve dual roles: +# - As Kernels: Can be invoked to produce streams +# - As Streams: Directly provide data without upstream dependencies + +# Sources represent the roots of computational graphs and typically +# interface with external data sources. They bridge the gap between +# the outside world and the Orcapod computational model. + +# Common source types: +# - File readers (CSV, JSON, Parquet, etc.) +# - Database connections and queries +# - API endpoints and web services +# - Generated data sources (synthetic data) +# - Manual data input and user interfaces +# - Message queues and event streams + +# Sources have unique properties: +# - No upstream dependencies (upstreams is empty) +# - Can be both invoked and iterated +# - Serve as the starting point for data lineage +# - May have their own refresh/update mechanisms +# """ + +# @property +# def tag_keys(self) -> tuple[str, ...]: +# """ +# Return the keys used for the tag in the pipeline run records. +# This is used to store the run-associated tag info. +# """ +# ... + +# @property +# def packet_keys(self) -> tuple[str, ...]: +# """ +# Return the keys used for the packet in the pipeline run records. +# This is used to store the run-associated packet info. +# """ +# ... + +# def get_all_records( +# self, include_system_columns: bool = False +# ) -> "pa.Table | None": +# """ +# Retrieve all records from the source. + +# Args: +# include_system_columns: Whether to include system columns in the output + +# Returns: +# pa.Table | None: A table containing all records, or None if no records are available +# """ +# ... + +# def as_lazy_frame(self, sort_by_tags: bool = False) -> "pl.LazyFrame | None": ... + +# def as_df(self, sort_by_tags: bool = True) -> "pl.DataFrame | None": ... + +# def as_polars_df(self, sort_by_tags: bool = False) -> "pl.DataFrame | None": ... + +# def as_pandas_df(self, sort_by_tags: bool = False) -> "pd.DataFrame | None": ... + + +# @runtime_checkable +# class Tracker(Protocol): +# """ +# Records kernel invocations and stream creation for computational graph tracking. + +# Trackers are responsible for maintaining the computational graph by recording +# relationships between kernels, streams, and invocations. They enable: +# - Lineage tracking and data provenance +# - Caching and memoization strategies +# - Debugging and error analysis +# - Performance monitoring and optimization +# - Reproducibility and auditing + +# Multiple trackers can be active simultaneously, each serving different +# purposes (e.g., one for caching, another for debugging, another for +# monitoring). This allows for flexible and composable tracking strategies. + +# Trackers can be selectively activated/deactivated to control overhead +# and focus on specific aspects of the computational graph. +# """ + +# def set_active(self, active: bool = True) -> None: +# """ +# Set the active state of the tracker. + +# When active, the tracker will record all kernel invocations and +# stream creations. When inactive, no recording occurs, reducing +# overhead for performance-critical sections. + +# Args: +# active: True to activate recording, False to deactivate +# """ +# ... + +# def is_active(self) -> bool: +# """ +# Check if the tracker is currently recording invocations. + +# Returns: +# bool: True if tracker is active and recording, False otherwise +# """ +# ... + +# def record_kernel_invocation( +# self, kernel: Kernel, upstreams: tuple[Stream, ...], label: str | None = None +# ) -> None: +# """ +# Record a kernel invocation in the computational graph. + +# This method is called whenever a kernel is invoked. The tracker +# should record: +# - The kernel and its properties +# - The input streams that were used as input +# - Timing and performance information +# - Any relevant metadata + +# Args: +# kernel: The kernel that was invoked +# upstreams: The input streams used for this invocation +# """ +# ... + +# def record_source_invocation( +# self, source: Source, label: str | None = None +# ) -> None: +# """ +# Record a source invocation in the computational graph. + +# This method is called whenever a source is invoked. The tracker +# should record: +# - The source and its properties +# - Timing and performance information +# - Any relevant metadata + +# Args: +# source: The source that was invoked +# """ +# ... + +# def record_pod_invocation( +# self, pod: Pod, upstreams: tuple[Stream, ...], label: str | None = None +# ) -> None: +# """ +# Record a pod invocation in the computational graph. + +# This method is called whenever a pod is invoked. The tracker +# should record: +# - The pod and its properties +# - The upstream streams that were used as input +# - Timing and performance information +# - Any relevant metadata + +# Args: +# pod: The pod that was invoked +# upstreams: The input streams used for this invocation +# """ +# ... + + +# @runtime_checkable +# class TrackerManager(Protocol): +# """ +# Manages multiple trackers and coordinates their activity. + +# The TrackerManager provides a centralized way to: +# - Register and manage multiple trackers +# - Coordinate recording across all active trackers +# - Provide a single interface for graph recording +# - Enable dynamic tracker registration/deregistration + +# This design allows for: +# - Multiple concurrent tracking strategies +# - Pluggable tracking implementations +# - Easy testing and debugging (mock trackers) +# - Performance optimization (selective tracking) +# """ + +# def get_active_trackers(self) -> list[Tracker]: +# """ +# Get all currently active trackers. + +# Returns only trackers that are both registered and active, +# providing the list of trackers that will receive recording events. + +# Returns: +# list[Tracker]: List of trackers that are currently recording +# """ +# ... + +# def register_tracker(self, tracker: Tracker) -> None: +# """ +# Register a new tracker in the system. + +# The tracker will be included in future recording operations +# if it is active. Registration is separate from activation +# to allow for dynamic control of tracking overhead. + +# Args: +# tracker: The tracker to register +# """ +# ... + +# def deregister_tracker(self, tracker: Tracker) -> None: +# """ +# Remove a tracker from the system. + +# The tracker will no longer receive recording notifications +# even if it is still active. This is useful for: +# - Cleaning up temporary trackers +# - Removing failed or problematic trackers +# - Dynamic tracker management + +# Args: +# tracker: The tracker to remove +# """ +# ... + +# def record_kernel_invocation( +# self, kernel: Kernel, upstreams: tuple[Stream, ...], label: str | None = None +# ) -> None: +# """ +# Record a stream in all active trackers. + +# This method broadcasts the stream recording to all currently +# active and registered trackers. It provides a single point +# of entry for recording events, simplifying kernel implementations. + +# Args: +# stream: The stream to record in all active trackers +# """ +# ... + +# def record_source_invocation( +# self, source: Source, label: str | None = None +# ) -> None: +# """ +# Record a source invocation in the computational graph. + +# This method is called whenever a source is invoked. The tracker +# should record: +# - The source and its properties +# - Timing and performance information +# - Any relevant metadata + +# Args: +# source: The source that was invoked +# """ +# ... + +# def record_pod_invocation( +# self, pod: Pod, upstreams: tuple[Stream, ...], label: str | None = None +# ) -> None: +# """ +# Record a stream in all active trackers. + +# This method broadcasts the stream recording to all currently` +# active and registered trackers. It provides a single point +# of entry for recording events, simplifying kernel implementations. + +# Args: +# stream: The stream to record in all active trackers +# """ +# ... + +# def no_tracking(self) -> ContextManager[None]: ... diff --git a/src/orcapod/protocols/pipeline_protocols.py b/src/orcapod/protocols/pipeline_protocols.py new file mode 100644 index 0000000..4e0ce73 --- /dev/null +++ b/src/orcapod/protocols/pipeline_protocols.py @@ -0,0 +1,37 @@ +# Protocols for pipeline and nodes +from typing import Protocol, runtime_checkable, TYPE_CHECKING +from orcapod.protocols.core_protocols.source import Source +from orcapod.protocols.core_protocols.pods import CachedPod + + +if TYPE_CHECKING: + import pyarrow as pa + + +class Node(Source, Protocol): + # def record_pipeline_outputs(self): + # pass + ... + + +@runtime_checkable +class PodNode(CachedPod, Protocol): + def get_all_records( + self, include_system_columns: bool = False + ) -> "pa.Table | None": + """ + Retrieve all tag and packet processed by this Pod. + + This method returns a table containing all packets processed by the Pod, + including metadata and system columns if requested. It is useful for: + - Debugging and analysis + - Auditing and data lineage tracking + - Performance monitoring + + Args: + include_system_columns: Whether to include system columns in the output + + Returns: + pa.Table | None: A table containing all processed records, or None if no records are available + """ + ... diff --git a/src/orcapod/protocols/semantic_types_protocols.py b/src/orcapod/protocols/semantic_types_protocols.py new file mode 100644 index 0000000..855f8a0 --- /dev/null +++ b/src/orcapod/protocols/semantic_types_protocols.py @@ -0,0 +1,106 @@ +from typing import TYPE_CHECKING, Any, Protocol +from collections.abc import Callable +from orcapod.types import PythonSchema, PythonSchemaLike + + +if TYPE_CHECKING: + import pyarrow as pa + + +class TypeConverter(Protocol): + def python_type_to_arrow_type(self, python_type: type) -> "pa.DataType": ... + + def python_schema_to_arrow_schema( + self, python_schema: PythonSchemaLike + ) -> "pa.Schema": ... + + def arrow_type_to_python_type(self, arrow_type: "pa.DataType") -> type: ... + + def arrow_schema_to_python_schema( + self, arrow_schema: "pa.Schema" + ) -> PythonSchema: ... + + def python_dicts_to_struct_dicts( + self, + python_dicts: list[dict[str, Any]], + python_schema: PythonSchemaLike | None = None, + ) -> list[dict[str, Any]]: ... + + def struct_dicts_to_python_dicts( + self, + struct_dict: list[dict[str, Any]], + arrow_schema: "pa.Schema", + ) -> list[dict[str, Any]]: ... + + def python_dicts_to_arrow_table( + self, + python_dicts: list[dict[str, Any]], + python_schema: PythonSchemaLike | None = None, + arrow_schema: "pa.Schema | None" = None, + ) -> "pa.Table": ... + + def arrow_table_to_python_dicts( + self, arrow_table: "pa.Table" + ) -> list[dict[str, Any]]: ... + + def get_python_to_arrow_converter( + self, python_type: type + ) -> "Callable[[Any], Any]": ... + + def get_arrow_to_python_converter( + self, arrow_type: "pa.DataType" + ) -> "Callable[[Any], Any]": ... + + +# Core protocols +class SemanticStructConverter(Protocol): + """Protocol for converting between Python objects and semantic structs.""" + + @property + def python_type(self) -> type: + """The Python type this converter can handle.""" + ... + + @property + def arrow_struct_type(self) -> "pa.StructType": + """The Arrow struct type this converter produces.""" + ... + + def python_to_struct_dict(self, value: Any) -> dict[str, Any]: + """Convert Python value to struct dictionary.""" + ... + + def struct_dict_to_python(self, struct_dict: dict[str, Any]) -> Any: + """Convert struct dictionary back to Python value.""" + ... + + def can_handle_python_type(self, python_type: type) -> bool: + """Check if this converter can handle the given Python type.""" + ... + + def can_handle_struct_type(self, struct_type: "pa.StructType") -> bool: + """Check if this converter can handle the given struct type.""" + ... + + def hash_struct_dict( + self, struct_dict: dict[str, Any], add_prefix: bool = False + ) -> str: + """ + Compute hash of the semantic type from its struct dictionary representation. + + Args: + struct_dict: Arrow struct dictionary representation + add_prefix: If True, prefix with semantic type and algorithm info + + Returns: + Hash string, optionally prefixed like "path:sha256:abc123..." + + Raises: + Exception: If hashing fails (e.g., file not found for path types) + """ + ... + + @property + def hasher_id(self) -> str: + """Identifier for this hasher (for debugging/versioning)""" + ... diff --git a/src/orcapod/semantic_types/__init__.py b/src/orcapod/semantic_types/__init__.py new file mode 100644 index 0000000..123777f --- /dev/null +++ b/src/orcapod/semantic_types/__init__.py @@ -0,0 +1,9 @@ +from .semantic_registry import SemanticTypeRegistry +from .universal_converter import UniversalTypeConverter +from .type_inference import infer_python_schema_from_pylist_data + +__all__ = [ + "SemanticTypeRegistry", + "UniversalTypeConverter", + "infer_python_schema_from_pylist_data", +] diff --git a/src/orcapod/semantic_types/precomputed_converters.py b/src/orcapod/semantic_types/precomputed_converters.py new file mode 100644 index 0000000..0fdedb8 --- /dev/null +++ b/src/orcapod/semantic_types/precomputed_converters.py @@ -0,0 +1,147 @@ +""" +Pre-computed column converters for efficient semantic type conversion. + +This module provides a way to pre-analyze schemas and create optimized +conversion functions for each column, eliminating runtime schema parsing +and type detection overhead. +""" + +from typing import Any, TYPE_CHECKING +from collections.abc import Callable +import pyarrow as pa +from orcapod.utils.lazy_module import LazyModule + + +from orcapod.semantic_types.universal_converter import UniversalTypeConverter + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class SemanticSchemaConverter: + def __init__( + self, + universal_converter: UniversalTypeConverter, + python_schema: dict[str, type] | None = None, + arrow_schema: pa.Schema | None = None, + ): + """ + self.universal_converter = universal_converter + """ + self.universal_converter = universal_converter + + if python_schema is not None: + self.python_schema = python_schema + self.arrow_schema = self.universal_converter.python_schema_to_arrow_schema( + python_schema + ) + elif arrow_schema is not None: + self.arrow_schema = arrow_schema + self.python_schema = self.universal_converter.arrow_schema_to_python_schema( + arrow_schema + ) + else: + raise ValueError( + "Either python_schema or arrow_schema must be provided to initialize SemanticSchemaConverter." + ) + + # Pre-compute converters for each field + self._python_to_arrow_converters: dict[str, Callable[[Any], Any]] = {} + self._arrow_to_python_converters: dict[str, Callable[[Any], Any]] = {} + + for field_name, python_type in self.python_schema.items(): + self._python_to_arrow_converters[field_name] = ( + self.universal_converter.get_python_to_arrow_converter(python_type) + ) + + for field in self.arrow_schema: + self._arrow_to_python_converters[field.name] = ( + self.universal_converter.get_arrow_to_python_converter(field.type) + ) + + def python_dict_to_struct_dict(self, record: dict[str, Any]) -> dict[str, Any]: + """ + Convert a single Python dictionary to an Arrow-compatible struct dictionary. + """ + if not record: + raise ValueError("Cannot convert empty record") + + return self.python_dicts_to_struct_dicts([record])[0] + + def python_dicts_to_struct_dicts( + self, data: list[dict[str, Any]] + ) -> list[dict[str, Any]]: + """ + Convert a list of Python dictionaries to a list of Arrow-compatible struct dictionaries. + """ + if not data: + raise ValueError("Cannot convert empty data list") + + converted_data = [] + for record in data: + converted_record = {} + for field_name, converter in self._python_to_arrow_converters.items(): + # TODO: test the case of None/missing value + value = record.get(field_name) + if value is None: + converted_record[field_name] = None + else: + # Convert using the pre-computed converter + converted_record[field_name] = converter(value) + converted_data.append(converted_record) + + return converted_data + + def struct_dicts_to_python_dicts( + self, struct_dicts: list[dict[str, Any]] + ) -> list[dict[str, Any]]: + """ + Convert a list of Arrow-compatible struct dictionaries back to Python dictionaries. + """ + if not struct_dicts: + raise ValueError("Cannot convert empty struct dicts list") + + # TODO: benchmark which approach of conversion would be faster + # 1) turn pylist to pydict and then convert each column at once + # 2) convert each row separately + converted_data = [] + for struct_dict in struct_dicts: + converted_record = {} + for field_name, converter in self._arrow_to_python_converters.items(): + value = struct_dict.get(field_name) + if value is None: + converted_record[field_name] = None + else: + # Convert using the pre-computed converter + converted_record[field_name] = converter(value) + converted_data.append(converted_record) + + return converted_data + + def struct_dict_to_python_dict(self, struct_dict: dict[str, Any]) -> dict[str, Any]: + """ + Convert a single Arrow-compatible struct dictionary back to a Python dictionary. + """ + if not struct_dict: + raise ValueError("Cannot convert empty struct dict") + + return self.struct_dicts_to_python_dicts([struct_dict])[0] + + def python_dicts_to_arrow_table(self, data: list[dict[str, Any]]) -> pa.Table: + """ + Convert a list of Python dictionaries to an Arrow table using pre-computed converters. + """ + struct_dicts = self.python_dicts_to_struct_dicts(data) + return pa.Table.from_pylist(struct_dicts, schema=self.arrow_schema) + + def arrow_table_to_python_dicts(self, table: pa.Table) -> list[dict[str, Any]]: + """ + Convert an Arrow table to a list of Python dictionaries using pre-computed converters. + """ + if not table: + raise ValueError("Cannot convert empty table") + + struct_dicts = table.to_pylist() + return self.struct_dicts_to_python_dicts(struct_dicts) diff --git a/src/orcapod/semantic_types/pydata_utils.py b/src/orcapod/semantic_types/pydata_utils.py new file mode 100644 index 0000000..5acc020 --- /dev/null +++ b/src/orcapod/semantic_types/pydata_utils.py @@ -0,0 +1,326 @@ +# A collection of utility functions for working with Python lists of dictionaries and +# dictionary of lists + +from types import UnionType +from typing import Any, Union, get_origin, get_args +from orcapod.types import PythonSchema + + +def pylist_to_pydict(pylist: list[dict]) -> dict: + """ + Convert a list of dictionaries to a dictionary of lists (columnar format). + + This function transforms row-based data (list of dicts) to column-based data + (dict of lists), similar to converting from records format to columnar format. + Missing keys in individual dictionaries are filled with None values. + + Args: + pylist: List of dictionaries representing rows of data + + Returns: + Dictionary where keys are column names and values are lists of column data + + Example: + >>> data = [{'a': 1, 'b': 2}, {'a': 3, 'c': 4}] + >>> pylist_to_pydict(data) + {'a': [1, 3], 'b': [2, None], 'c': [None, 4]} + """ + result = {} + known_keys = set() + for i, d in enumerate(pylist): + known_keys.update(d.keys()) + for k in known_keys: + result.setdefault(k, [None] * i).append(d.get(k, None)) + return result + + +def pydict_to_pylist(pydict: dict) -> list[dict]: + """ + Convert a dictionary of lists (columnar format) to a list of dictionaries. + + This function transforms column-based data (dict of lists) to row-based data + (list of dicts), similar to converting from columnar format to records format. + All arrays in the input dictionary must have the same length. + + Args: + pydict: Dictionary where keys are column names and values are lists of column data + + Returns: + List of dictionaries representing rows of data + + Raises: + ValueError: If arrays in the dictionary have inconsistent lengths + + Example: + >>> data = {'a': [1, 3], 'b': [2, None], 'c': [None, 4]} + >>> pydict_to_pylist(data) + [{'a': 1, 'b': 2, 'c': None}, {'a': 3, 'b': None, 'c': 4}] + """ + if not pydict: + return [] + + # Check all arrays have same length + lengths = [len(v) for v in pydict.values()] + if not all(length == lengths[0] for length in lengths): + raise ValueError( + f"Inconsistent array lengths: {dict(zip(pydict.keys(), lengths))}" + ) + + num_rows = lengths[0] + if num_rows == 0: + return [] + + result = [] + keys = pydict.keys() + for i in range(num_rows): + row = {k: pydict[k][i] for k in keys} + result.append(row) + return result + + +def infer_python_schema_from_pylist_data( + data: list[dict], + default_type: type = str, +) -> PythonSchema: + """ + Infer schema from sample data (best effort). + + Args: + data: List of sample dictionaries + default_type: Default type to use for fields with no values + + Returns: + Dictionary mapping field names to inferred Python types + + Note: This is best-effort inference and may not handle all edge cases. + For production use, explicit schemas are recommended. + """ + if not data: + return {} + + schema = {} + + # Get all possible field names + all_fields = [] + for record in data: + all_fields.extend(record.keys()) + + all_fields = list(dict.fromkeys(all_fields)) # Remove duplicates + + # Infer type for each field + for field_name in all_fields: + # Get all values for this field (including None) + all_field_values = [ + record.get(field_name) for record in data if field_name in record + ] + + # Separate None and non-None values + non_none_values = [v for v in all_field_values if v is not None] + # check if there is at least one None value + has_none = len(non_none_values) < len(all_field_values) + + if not non_none_values: + # Handle case where all values are None + schema[field_name] = default_type | None + continue + + # Infer type from non-None values + inferred_type = _infer_type_from_values(non_none_values) + + if inferred_type is None: + schema[field_name] = default_type | None + elif has_none: + # Wrap with Optional if None values present + schema[field_name] = inferred_type | None if inferred_type != Any else Any + else: + schema[field_name] = inferred_type + + return schema + + +def infer_python_schema_from_pydict_data( + data: dict[str, list[Any]], + default_type: type = str, +) -> PythonSchema: + """ + Infer schema from columnar sample data (best effort). + + Args: + data: Dictionary mapping field names to lists of values + default_type: Default type to use for fields with no values + + Returns: + Dictionary mapping field names to inferred Python types + + Note: This is best-effort inference and may not handle all edge cases. + For production use, explicit schemas are recommended. + """ + if not data: + return {} + + schema: PythonSchema = {} + + # Infer type for each field + for field_name, field_values in data.items(): + if not field_values: + # Handle case where field has empty list + schema[field_name] = default_type | None + continue + + # Separate None and non-None values + non_none_values = [v for v in field_values if v is not None] + has_none = len(non_none_values) < len(field_values) + + if not non_none_values: + # Handle case where all values are None + schema[field_name] = default_type | None + continue + + # Infer type from non-None values + inferred_type = _infer_type_from_values(non_none_values) + + if inferred_type is None: + schema[field_name] = default_type | None + elif has_none: + # Wrap with Optional if None values present + # TODO: consider the case of Any + schema[field_name] = inferred_type | None + else: + schema[field_name] = inferred_type + + return schema + + +# TODO: reconsider this type hint -- use of Any effectively renders this type hint useless +def _infer_type_from_values(values: list) -> type | UnionType | Any | None: + """Infer type from a list of non-None values.""" + if not values: + return None + + # Get types of all values + value_types = {type(v) for v in values} + + if len(value_types) == 1: + # All values have same type + value_type = next(iter(value_types)) + return _infer_container_type(value_type, values) + else: + # Mixed types - handle common cases + return _handle_mixed_types(value_types, values) + + +def _infer_container_type(value_type: type, values: list) -> type: + """Infer container type with element types.""" + if value_type is list: + return _infer_list_type(values) + elif value_type is tuple: + return _infer_tuple_type(values) + elif value_type in {set, frozenset}: + return _infer_set_type(values, value_type) + elif value_type is dict: + return _infer_dict_type(values) + else: + return value_type + + +def _infer_list_type(lists: list[list]) -> type: + """Infer list element type.""" + all_elements = [] + for lst in lists: + all_elements.extend(lst) + + if not all_elements: + return list[Any] + + element_type = _infer_type_from_values(all_elements) + return list[element_type] + + +def _infer_tuple_type(tuples: list[tuple]) -> type: + """Infer tuple element types.""" + if not tuples: + return tuple[Any, ...] + + # Check if all tuples have same length + lengths = {len(t) for t in tuples} + + if len(lengths) == 1: + # Fixed-length tuples - infer type for each position + tuple_length = next(iter(lengths)) + if tuple_length == 0: + return tuple[()] + + position_types = [] + for i in range(tuple_length): + position_values = [t[i] for t in tuples if len(t) > i] + position_type = _infer_type_from_values(position_values) + position_types.append(position_type) + + # Always use fixed-length notation for same-length tuples + return tuple[tuple(position_types)] + else: + # Variable-length tuples - infer common element type + all_elements = [] + for t in tuples: + all_elements.extend(t) + + if not all_elements: + return tuple[Any, ...] + + element_type = _infer_type_from_values(all_elements) + return tuple[element_type, ...] + + +def _infer_set_type(sets: list, set_type: type) -> type: + """Infer set element type.""" + all_elements = [] + for s in sets: + all_elements.extend(s) + + if not all_elements: + return set_type[Any] # type: ignore[return-value] + + element_type = _infer_type_from_values(all_elements) + return set_type[element_type] # type: ignore[return-value] + + +def _infer_dict_type(dicts: list[dict]) -> type: + """Infer dictionary key and value types.""" + all_keys = [] + all_values = [] + + for d in dicts: + all_keys.extend(d.keys()) + all_values.extend(d.values()) + + if not all_keys or not all_values: + return dict[Any, Any] + + key_type = _infer_type_from_values(all_keys) + value_type = _infer_type_from_values(all_values) + + return dict[key_type, value_type] + + +def _handle_mixed_types(value_types: set, values: list) -> UnionType | Any: + """Handle mixed types by creating appropriate Union types.""" + + # Handle common int/float mixing + if value_types == {int, float}: + return int | float + + # Handle numeric types with broader compatibility + numeric_types = {int, float, complex} + if value_types.issubset(numeric_types): + if complex in value_types: + return int | float | complex + else: + return int | float + + # For small number of types, create Union + if len(value_types) <= 4: # Arbitrary limit to avoid huge unions + sorted_types = sorted(value_types, key=lambda t: t.__name__) + return Union[tuple(sorted_types)] + + # Too many types, fall back to Any + return Any diff --git a/src/orcapod/semantic_types/semantic_registry.py b/src/orcapod/semantic_types/semantic_registry.py new file mode 100644 index 0000000..aa1c604 --- /dev/null +++ b/src/orcapod/semantic_types/semantic_registry.py @@ -0,0 +1,239 @@ +from typing import Any, TYPE_CHECKING +from collections.abc import Mapping +from orcapod.protocols.semantic_types_protocols import SemanticStructConverter +from orcapod.utils.lazy_module import LazyModule + +# from orcapod.semantic_types.type_inference import infer_python_schema_from_pylist_data +from orcapod.types import DataType, PythonSchema +from orcapod.semantic_types import pydata_utils + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class SemanticTypeRegistry: + """ + Registry that manages semantic type converters using struct signature recognition. + + This registry maps Python types to PyArrow struct signatures, enabling + automatic detection and conversion of semantic types based on their + struct schema alone. + """ + + @staticmethod + def infer_python_schema_from_pylist(data: list[dict[str, Any]]) -> PythonSchema: + """ + Infer Python schema from a list of dictionaries (pylist) + """ + return pydata_utils.infer_python_schema_from_pylist_data(data) + + @staticmethod + def infer_python_schema_from_pydict(data: dict[str, list[Any]]) -> PythonSchema: + # TODO: consider which data type is more efficient and use that pylist or pydict + return pydata_utils.infer_python_schema_from_pylist_data( + pydata_utils.pydict_to_pylist(data) + ) + + def __init__(self, converters: Mapping[str, SemanticStructConverter] | None = None): + # Bidirectional mappings between Python types and struct signatures + self._python_to_struct: dict[DataType, "pa.StructType"] = {} + self._struct_to_python: dict["pa.StructType", DataType] = {} + self._struct_to_converter: dict["pa.StructType", SemanticStructConverter] = {} + + # Name mapping for convenience + self._name_to_converter: dict[str, SemanticStructConverter] = {} + self._struct_to_name: dict["pa.StructType", str] = {} + + # If initialized with a list of converters, register them + if converters: + for semantic_type_name, converter in converters.items(): + self.register_converter(semantic_type_name, converter) + + def register_converter( + self, semantic_type_name: str, converter: SemanticStructConverter + ) -> None: + """ + Register a semantic type converter. + + This creates bidirectional mappings between: + - Python type ↔ Arrow struct signature + - Arrow struct signature ↔ converter instance + + Optionally, a semantic type name can be provided. + """ + python_type = converter.python_type + struct_signature = converter.arrow_struct_type + + # Check for conflicts + if python_type in self._python_to_struct: + existing_struct = self._python_to_struct[python_type] + if existing_struct != struct_signature: + raise ValueError( + f"Python type {python_type} already registered with different struct signature. " + f"Existing: {existing_struct}, New: {struct_signature}" + ) + + if struct_signature in self._struct_to_python: + existing_python = self._struct_to_python[struct_signature] + if existing_python != python_type: + raise ValueError( + f"Struct signature {struct_signature} already registered with different Python type. " + f"Existing: {existing_python}, New: {python_type}" + ) + + # catch case where a different converter is already registered with the semantic type name + if existing_converter := self.get_converter_for_semantic_type( + semantic_type_name + ): + if existing_converter != converter: + raise ValueError( + f"Semantic type name '{semantic_type_name}' is already registered to {existing_converter}" + ) + + # Register bidirectional mappings + self._python_to_struct[python_type] = struct_signature + self._struct_to_python[struct_signature] = python_type + self._struct_to_converter[struct_signature] = converter + + self._name_to_converter[semantic_type_name] = converter + self._struct_to_name[struct_signature] = semantic_type_name + + def get_converter_for_python_type( + self, python_type: DataType + ) -> SemanticStructConverter | None: + """Get converter registered to the Python type.""" + # Direct lookup first + struct_signature = self._python_to_struct.get(python_type) + if struct_signature: + return self._struct_to_converter[struct_signature] + + # Handle subclass relationships - add safety check + for registered_type, struct_signature in self._python_to_struct.items(): + try: + if ( + isinstance(registered_type, type) + and isinstance(python_type, type) + and issubclass(python_type, registered_type) + ): + return self._struct_to_converter[struct_signature] + except TypeError: + # Handle cases where issubclass fails (e.g., with generic types) + continue + + return None + + def get_converter_for_semantic_type( + self, semantic_type_name: str + ) -> SemanticStructConverter | None: + """Get converter registered to the semantic type name.""" + return self._name_to_converter.get(semantic_type_name) + + def get_converter_for_struct_signature( + self, struct_signature: "pa.StructType" + ) -> SemanticStructConverter | None: + """ + Get converter registered to the Arrow struct signature. + """ + return self._struct_to_converter.get(struct_signature) + + def get_python_type_for_semantic_struct_signature( + self, struct_signature: "pa.StructType" + ) -> DataType | None: + """ + Get Python type registered to the Arrow struct signature. + """ + return self._struct_to_python.get(struct_signature) + + def get_semantic_struct_signature_for_python_type( + self, python_type: type + ) -> "pa.StructType | None": + """Get Arrow struct signature registered to the Python type.""" + return self._python_to_struct.get(python_type) + + def has_semantic_type(self, semantic_type_name: str) -> bool: + """Check if the semantic type name is registered.""" + return semantic_type_name in self._name_to_converter + + def has_python_type(self, python_type: type) -> bool: + """Check if the Python type is registered.""" + return python_type in self._python_to_struct + + def has_semantic_struct_signature(self, struct_signature: "pa.StructType") -> bool: + """Check if the struct signature is registered.""" + return struct_signature in self._struct_to_python + + def list_semantic_types(self) -> list[str]: + """Get all registered semantic type names.""" + return list(self._name_to_converter.keys()) + + def list_python_types(self) -> list[DataType]: + """Get all registered Python types.""" + return list(self._python_to_struct.keys()) + + def list_struct_signatures(self) -> list["pa.StructType"]: + """Get all registered struct signatures.""" + return list(self._struct_to_python.keys()) + + def find_semantic_fields_in_schema(self, schema: "pa.Schema") -> dict[str, str]: + """ + Find all semantic type fields in a schema by struct signature recognition. + + Args: + schema: PyArrow schema to examine + + Returns: + Dictionary mapping field names to semantic type names + + Example: + schema with fields: + - name: string + - file_path: struct + - location: struct + + Returns: {"file_path": "path", "location": "geolocation"} + """ + semantic_fields = {} + for field in schema: + if pa.types.is_struct(field.type) and field.type in self._struct_to_name: + semantic_fields[field.name] = self._struct_to_name[field.type] + return semantic_fields + + def get_semantic_field_info(self, schema: "pa.Schema") -> dict[str, dict[str, Any]]: + """ + Get detailed information about semantic fields in a schema. + + Returns: + Dictionary with field names as keys and info dictionaries as values. + Each info dict contains: semantic_type, python_type, struct_signature + """ + semantic_info = {} + for field in schema: + if pa.types.is_struct(field.type): + converter = self.get_converter_for_struct_signature(field.type) + if converter: + semantic_info[field.name] = { + "python_type": converter.python_type, + "struct_signature": field.type, + "converter": converter, + } + return semantic_info + + def validate_struct_signature( + self, struct_signature: "pa.StructType", expected_python_type: type + ) -> bool: + """ + Validate that a struct signature matches the expected Python type. + + Args: + struct_signature: Arrow struct type to validate + expected_python_type: Expected Python type + + Returns: + True if the struct signature is registered for the Python type + """ + registered_type = self.get_python_type_for_semantic_struct_signature( + struct_signature + ) + return registered_type == expected_python_type diff --git a/src/orcapod/semantic_types/semantic_struct_converters.py b/src/orcapod/semantic_types/semantic_struct_converters.py new file mode 100644 index 0000000..3ba45f5 --- /dev/null +++ b/src/orcapod/semantic_types/semantic_struct_converters.py @@ -0,0 +1,174 @@ +""" +Struct-based semantic type system for OrcaPod. + +This replaces the metadata-based approach with explicit struct fields, +making semantic types visible in schemas and preserved through operations. +""" + +from typing import Any, TYPE_CHECKING +from pathlib import Path +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +class SemanticStructConverterBase: + """ + Base class providing common functionality for semantic struct converters. + + Subclasses only need to implement the abstract methods and can use + the common hashing infrastructure. + """ + + def __init__(self, semantic_type_name: str): + self._semantic_type_name = semantic_type_name + self._hasher_id = f"{self.semantic_type_name}_content_sha256" + + @property + def semantic_type_name(self) -> str: + """The name of the semantic type this converter handles.""" + return self._semantic_type_name + + @property + def hasher_id(self) -> str: + """Default hasher ID based on semantic type name""" + return self._hasher_id + + def _format_hash_string(self, hash_bytes: bytes, add_prefix: bool = False) -> str: + """ + Format hash bytes into the standard hash string format. + + Args: + hash_bytes: Raw hash bytes + add_prefix: Whether to add semantic type and algorithm prefix + + Returns: + Formatted hash string + """ + hash_hex = hash_bytes.hex() + if add_prefix: + return f"{self.semantic_type_name}:sha256:{hash_hex}" + else: + return hash_hex + + def _compute_content_hash(self, content: bytes) -> bytes: + """ + Compute SHA-256 hash of content bytes. + + Args: + content: Content to hash + + Returns: + SHA-256 hash bytes + """ + import hashlib + + return hashlib.sha256(content).digest() + + +# Path-specific implementation +class PathStructConverter(SemanticStructConverterBase): + """Converter for pathlib.Path objects to/from semantic structs.""" + + def __init__(self): + super().__init__("path") + self._python_type = Path + + # Define the Arrow struct type for paths + self._arrow_struct_type = pa.struct( + [ + pa.field("path", pa.large_string()), + ] + ) + + @property + def python_type(self) -> type: + return self._python_type + + @property + def arrow_struct_type(self) -> pa.StructType: + return self._arrow_struct_type + + def python_to_struct_dict(self, value: Path) -> dict[str, Any]: + """Convert Path to struct dictionary.""" + if not isinstance(value, Path): + raise TypeError(f"Expected Path, got {type(value)}") + + return { + "path": str(value), + } + + def struct_dict_to_python(self, struct_dict: dict[str, Any]) -> Path: + """Convert struct dictionary back to Path.""" + path_str = struct_dict.get("path") + if path_str is None: + raise ValueError("Missing 'path' field in struct") + + return Path(path_str) + + def can_handle_python_type(self, python_type: type) -> bool: + """Check if this converter can handle the given Python type.""" + return issubclass(python_type, Path) + + def can_handle_struct_type(self, struct_type: pa.StructType) -> bool: + """Check if this converter can handle the given struct type.""" + # Check if struct has the expected fields + field_names = [field.name for field in struct_type] + expected_fields = {"path"} + + if set(field_names) != expected_fields: + return False + + # Check field types + field_types = {field.name: field.type for field in struct_type} + + return field_types["path"] == pa.large_string() + + def is_semantic_struct(self, struct_dict: dict[str, Any]) -> bool: + """Check if a struct dictionary represents this semantic type.""" + return set(struct_dict.keys()) == {"path"} and isinstance( + struct_dict["path"], str + ) + + def hash_struct_dict( + self, struct_dict: dict[str, Any], add_prefix: bool = False + ) -> str: + """ + Compute hash of the file content pointed to by the path. + + Args: + struct_dict: Arrow struct dictionary with 'path' field + add_prefix: If True, prefix with semantic type and algorithm info + + Returns: + Hash string of the file content, optionally prefixed + + Raises: + FileNotFoundError: If the file doesn't exist + PermissionError: If the file can't be read + OSError: For other file system errors + """ + path_str = struct_dict.get("path") + if path_str is None: + raise ValueError("Missing 'path' field in struct") + + path = Path(path_str) + + try: + # TODO: replace with FileHasher implementation + # Read file content and compute hash + content = path.read_bytes() + hash_bytes = self._compute_content_hash(content) + return self._format_hash_string(hash_bytes, add_prefix) + + except FileNotFoundError: + raise FileNotFoundError(f"File not found: {path}") + except PermissionError: + raise PermissionError(f"Permission denied reading file: {path}") + except IsADirectoryError: + raise ValueError(f"Path is a directory, not a file: {path}") + except OSError as e: + raise OSError(f"Error reading file {path}: {e}") diff --git a/src/orcapod/semantic_types/type_inference.py b/src/orcapod/semantic_types/type_inference.py new file mode 100644 index 0000000..b51c267 --- /dev/null +++ b/src/orcapod/semantic_types/type_inference.py @@ -0,0 +1,310 @@ +from types import UnionType +from typing import Any, Union, get_origin, get_args +from collections.abc import Collection, Mapping + +from orcapod.types import PythonSchema + + +def infer_python_schema_from_pylist_data( + data: Collection[Mapping[str, Any]], + default_type: type = str, +) -> PythonSchema: + """ + Infer schema from sample data (best effort). + + Args: + data: List of sample dictionaries + default_type: Default type to use for fields with no values + + Returns: + Dictionary mapping field names to inferred Python types + + Note: This is best-effort inference and may not handle all edge cases. + For production use, explicit schemas are recommended. + """ + if not data: + return {} + + schema: PythonSchema = {} + + # Get all possible field names + all_fields = [] + for record in data: + all_fields.extend(record.keys()) + + all_fields = list(dict.fromkeys(all_fields)) # Remove duplicates + + # Infer type for each field + for field_name in all_fields: + # Get all values for this field (including None) + all_field_values = [ + record.get(field_name) for record in data if field_name in record + ] + + # Separate None and non-None values + non_none_values = [v for v in all_field_values if v is not None] + # check if there is at least one None value + has_none = len(non_none_values) < len(all_field_values) + + if not non_none_values: + # Handle case where all values are None + schema[field_name] = default_type | None + continue + + # Infer type from non-None values + inferred_type = _infer_type_from_values(non_none_values) + + if inferred_type is None: + schema[field_name] = default_type | None + elif has_none: + # Wrap with Optional if None values present + # TODO: consider the case of Any + schema[field_name] = inferred_type | None + else: + schema[field_name] = inferred_type + + return schema + + +def infer_python_schema_from_pydict_data( + data: dict[str, list[Any]], + default_type: type = str, +) -> PythonSchema: + """ + Infer schema from columnar sample data (best effort). + + Args: + data: Dictionary mapping field names to lists of values + default_type: Default type to use for fields with no values + + Returns: + Dictionary mapping field names to inferred Python types + + Note: This is best-effort inference and may not handle all edge cases. + For production use, explicit schemas are recommended. + """ + if not data: + return {} + + schema: PythonSchema = {} + + # Infer type for each field + for field_name, field_values in data.items(): + if not field_values: + # Handle case where field has empty list + schema[field_name] = default_type | None + continue + + # Separate None and non-None values + non_none_values = [v for v in field_values if v is not None] + has_none = len(non_none_values) < len(field_values) + + if not non_none_values: + # Handle case where all values are None + schema[field_name] = default_type | None + continue + + # Infer type from non-None values + inferred_type = _infer_type_from_values(non_none_values) + + if inferred_type is None: + schema[field_name] = default_type | None + elif has_none: + # Wrap with Optional if None values present + # TODO: consider the case of Any + schema[field_name] = inferred_type | None + else: + schema[field_name] = inferred_type + + return schema + + +# TODO: reconsider this type hint -- use of Any effectively renders this type hint useless +def _infer_type_from_values(values: list) -> type | UnionType | Any | None: + """Infer type from a list of non-None values.""" + if not values: + return None + + # Get types of all values + value_types = {type(v) for v in values} + + if len(value_types) == 1: + # All values have same type + value_type = next(iter(value_types)) + return _infer_container_type(value_type, values) + else: + # Mixed types - handle common cases + return _handle_mixed_types(value_types, values) + + +def _infer_container_type(value_type: type, values: list) -> type: + """Infer container type with element types.""" + if value_type is list: + return _infer_list_type(values) + elif value_type is tuple: + return _infer_tuple_type(values) + elif value_type in {set, frozenset}: + return _infer_set_type(values, value_type) + elif value_type is dict: + return _infer_dict_type(values) + else: + return value_type + + +def _infer_list_type(lists: list[list]) -> type: + """Infer list element type.""" + all_elements = [] + for lst in lists: + all_elements.extend(lst) + + if not all_elements: + return list[Any] + + element_type = _infer_type_from_values(all_elements) + return list[element_type] + + +def _infer_tuple_type(tuples: list[tuple]) -> type: + """Infer tuple element types.""" + if not tuples: + return tuple[Any, ...] + + # Check if all tuples have same length + lengths = {len(t) for t in tuples} + + if len(lengths) == 1: + # Fixed-length tuples - infer type for each position + tuple_length = next(iter(lengths)) + if tuple_length == 0: + return tuple[()] + + position_types = [] + for i in range(tuple_length): + position_values = [t[i] for t in tuples if len(t) > i] + position_type = _infer_type_from_values(position_values) + position_types.append(position_type) + + # Always use fixed-length notation for same-length tuples + return tuple[tuple(position_types)] + else: + # Variable-length tuples - infer common element type + all_elements = [] + for t in tuples: + all_elements.extend(t) + + if not all_elements: + return tuple[Any, ...] + + element_type = _infer_type_from_values(all_elements) + return tuple[element_type, ...] + + +def _infer_set_type(sets: list, set_type: type) -> type: + """Infer set element type.""" + all_elements = [] + for s in sets: + all_elements.extend(s) + + if not all_elements: + return set_type[Any] # type: ignore[return-value] + + element_type = _infer_type_from_values(all_elements) + return set_type[element_type] # type: ignore[return-value] + + +def _infer_dict_type(dicts: list[dict]) -> type: + """Infer dictionary key and value types.""" + all_keys = [] + all_values = [] + + for d in dicts: + all_keys.extend(d.keys()) + all_values.extend(d.values()) + + if not all_keys or not all_values: + return dict[Any, Any] + + key_type = _infer_type_from_values(all_keys) + value_type = _infer_type_from_values(all_values) + + return dict[key_type, value_type] + + +def _handle_mixed_types(value_types: set, values: list) -> UnionType | Any: + """Handle mixed types by creating appropriate Union types.""" + + # Handle common int/float mixing + if value_types == {int, float}: + return int | float + + # Handle numeric types with broader compatibility + numeric_types = {int, float, complex} + if value_types.issubset(numeric_types): + if complex in value_types: + return int | float | complex + else: + return int | float + + # For small number of types, create Union + if len(value_types) <= 4: # Arbitrary limit to avoid huge unions + sorted_types = sorted(value_types, key=lambda t: t.__name__) + return Union[tuple(sorted_types)] + + # Too many types, fall back to Any + return Any + + +# Example usage and test function +def test_schema_inference(): + """Test the improved schema inference function.""" + + test_data = [ + { + "id": 1, + "name": "Alice", + "scores": [85, 92, 78], + "coordinates": (10.5, 20.3), + "tags": {"python", "data"}, + "metadata": {"created": "2023-01-01", "version": 1}, + "optional_field": "present", + }, + { + "id": 2, + "name": "Bob", + "scores": [88, 91], + "coordinates": (15.2, 25.7), + "tags": {"java", "backend"}, + "metadata": {"created": "2023-01-02", "version": 2}, + "optional_field": None, + }, + { + "id": 3, + "name": "Charlie", + "scores": [95, 87, 89, 92], + "coordinates": (5.1, 30.9), + "tags": {"javascript", "frontend"}, + "metadata": {"created": "2023-01-03", "version": 1}, + "mixed_field": 42, + }, + { + "id": 4, + "name": "Diana", + "scores": [], + "coordinates": (0.0, 0.0), + "tags": set(), + "metadata": {}, + "mixed_field": "text", + }, + ] + + schema = infer_python_schema_from_pylist_data(test_data) + + print("Inferred Schema:") + for field, field_type in sorted(schema.items()): + print(f" {field}: {field_type}") + + return schema + + +if __name__ == "__main__": + test_schema_inference() diff --git a/src/orcapod/semantic_types/universal_converter.py b/src/orcapod/semantic_types/universal_converter.py new file mode 100644 index 0000000..c3ba97e --- /dev/null +++ b/src/orcapod/semantic_types/universal_converter.py @@ -0,0 +1,848 @@ +""" +Universal Type Conversion Engine for Python ↔ Arrow type bidirectional conversion. + +This provides a comprehensive, self-contained system that: +1. Converts Python type hints to Arrow types +2. Converts Arrow types back to Python type hints +3. Creates and caches conversion functions for optimal performance +4. Manages dynamic TypedDict creation for struct preservation +5. Integrates seamlessly with semantic type registries +""" + +import types +from typing import TypedDict, Any +import typing +from collections.abc import Callable, Mapping +import hashlib +import logging +from orcapod.contexts import DataContext, resolve_context +from orcapod.semantic_types.semantic_registry import SemanticTypeRegistry +from orcapod.semantic_types.type_inference import infer_python_schema_from_pylist_data + +# Handle generic types +from typing import get_origin, get_args + +from typing import TYPE_CHECKING +from orcapod.types import DataType, PythonSchemaLike +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +logger = logging.getLogger(__name__) + + +# Basic type mapping for Python -> Arrow conversion +_PYTHON_TO_ARROW_MAP = { + # Python built-ins + int: pa.int64(), + float: pa.float64(), + str: pa.large_string(), # Use large_string by default for Polars compatibility + bool: pa.bool_(), + bytes: pa.large_binary(), # Use large_binary by default for Polars compatibility + # String representations (for when we get type names as strings) + "int": pa.int64(), + "float": pa.float64(), + "str": pa.large_string(), + "bool": pa.bool_(), + "bytes": pa.large_binary(), + # Specific integer types + "int8": pa.int8(), + "int16": pa.int16(), + "int32": pa.int32(), + "int64": pa.int64(), + "uint8": pa.uint8(), + "uint16": pa.uint16(), + "uint32": pa.uint32(), + "uint64": pa.uint64(), + # Specific float types + "float32": pa.float32(), + "float64": pa.float64(), + # Date/time types + "date": pa.date32(), + "datetime": pa.timestamp("us"), + "timestamp": pa.timestamp("us"), +} + +# Add numpy types if available +try: + import numpy as np + + _PYTHON_TO_ARROW_MAP.update( + { + np.int8: pa.int8(), + np.int16: pa.int16(), + np.int32: pa.int32(), + np.int64: pa.int64(), + np.uint8: pa.uint8(), + np.uint16: pa.uint16(), + np.uint32: pa.uint32(), + np.uint64: pa.uint64(), + np.float32: pa.float32(), + np.float64: pa.float64(), + np.bool_: pa.bool_(), + } + ) +except ImportError: + pass + + +class UniversalTypeConverter: + """ + Universal engine for Python ↔ Arrow type conversion with cached conversion functions. + + This is a complete, self-contained system that handles: + - Python type hint → Arrow type conversion + - Arrow type → Python type hint conversion + - Dynamic TypedDict creation for struct field preservation + - Cached conversion function generation + - Integration with semantic type registries + """ + + def __init__(self, semantic_registry: SemanticTypeRegistry | None = None): + self.semantic_registry = semantic_registry + + # Cache for created TypedDict classes + self._struct_signature_to_typeddict: dict[pa.StructType, DataType] = {} + self._typeddict_to_struct_signature: dict[DataType, pa.StructType] = {} + self._created_type_names: set[str] = set() + + # Cache for conversion functions + self._python_to_arrow_converters: dict[DataType, Callable] = {} + self._arrow_to_python_converters: dict[pa.DataType, Callable] = {} + + # Cache for type mappings + self._python_to_arrow_types: dict[DataType, pa.DataType] = {} + self._arrow_to_python_types: dict[pa.DataType, DataType] = {} + + def python_type_to_arrow_type(self, python_type: DataType) -> pa.DataType: + """ + Convert Python type hint to Arrow type with caching. + + This is the main entry point for Python → Arrow type conversion. + Results are cached for performance. + """ + # Check cache first + if python_type in self._python_to_arrow_types: + return self._python_to_arrow_types[python_type] + + # Convert and cache result + arrow_type = self._convert_python_to_arrow(python_type) + self._python_to_arrow_types[python_type] = arrow_type + + return arrow_type + + def python_schema_to_arrow_schema( + self, python_schema: PythonSchemaLike + ) -> pa.Schema: + """ + Convert a Python schema (dict of field names to types) to an Arrow schema. + + This uses the main conversion logic and caches results for performance. + """ + fields = [] + for field_name, python_type in python_schema.items(): + arrow_type = self.python_type_to_arrow_type(python_type) + fields.append(pa.field(field_name, arrow_type)) + + return pa.schema(fields) + + def arrow_type_to_python_type(self, arrow_type: pa.DataType) -> DataType: + """ + Convert Arrow type to Python type hint with caching. + + This is the main entry point for Arrow → Python type conversion. + Results are cached for performance. + """ + # Check cache first + if arrow_type in self._arrow_to_python_types: + return self._arrow_to_python_types[arrow_type] + + # Convert and cache result + python_type = self._convert_arrow_to_python(arrow_type) + self._arrow_to_python_types[arrow_type] = python_type + + return python_type + + def arrow_schema_to_python_schema(self, arrow_schema: pa.Schema) -> dict[str, type]: + """ + Convert an Arrow schema to a Python schema (dict of field names to types). + + This uses the main conversion logic and caches results for performance. + """ + python_schema = {} + for field in arrow_schema: + python_type = self.arrow_type_to_python_type(field.type) + python_schema[field.name] = python_type + + return python_schema + + def python_dicts_to_struct_dicts( + self, + python_dicts: list[dict[str, Any]], + python_schema: PythonSchemaLike | None = None, + ) -> list[dict[str, Any]]: + """ + Convert a list of Python dictionaries to an Arrow table. + + This uses the main conversion logic and caches results for performance. + """ + if python_schema is None: + python_schema = infer_python_schema_from_pylist_data(python_dicts) + + converters = { + field_name: self.get_python_to_arrow_converter(python_type) + for field_name, python_type in python_schema.items() + } + + converted_data = [] + for record in python_dicts: + converted_record = {} + for field_name, converter in converters.items(): + if field_name in record: + converted_record[field_name] = converter(record[field_name]) + else: + converted_record[field_name] = None + converted_data.append(converted_record) + + return converted_data + + def struct_dict_to_python_dict( + self, + struct_dict: list[dict[str, Any]], + arrow_schema: pa.Schema, + ) -> list[dict[str, Any]]: + """ + Convert a list of Arrow structs to Python dictionaries. + + This uses the main conversion logic and caches results for performance. + """ + + converters = { + field.name: self.get_arrow_to_python_converter(field.type) + for field in arrow_schema + } + + converted_data = [] + for record in struct_dict: + converted_record = {} + for field_name, converter in converters.items(): + if field_name in record: + converted_record[field_name] = converter(record[field_name]) + else: + converted_record[field_name] = None + converted_data.append(converted_record) + + return converted_data + + def python_dicts_to_arrow_table( + self, + python_dicts: list[dict[str, Any]], + python_schema: PythonSchemaLike | None = None, + arrow_schema: "pa.Schema | None" = None, + ) -> pa.Table: + """ + Convert a list of Python dictionaries to an Arrow table. + + This uses the main conversion logic and caches results for performance. + """ + if python_schema is not None and arrow_schema is not None: + logger.warning( + "Both Python and Arrow schemas are provided. If they are not compatible, this may lead to unexpected behavior." + ) + if python_schema is None and arrow_schema is None: + # Infer schema from data if not provided + python_schema = infer_python_schema_from_pylist_data(python_dicts) + + if arrow_schema is None: + # Convert to Arrow schema + assert python_schema is not None, "Python schema should not be None here" + arrow_schema = self.python_schema_to_arrow_schema(python_schema) + + if python_schema is None: + assert arrow_schema is not None, ( + "Arrow schema should not be None if reaching here" + ) + python_schema = self.arrow_schema_to_python_schema(arrow_schema) + + struct_dicts = self.python_dicts_to_struct_dicts( + python_dicts, python_schema=python_schema + ) + + # TODO: add more helpful message here + return pa.Table.from_pylist(struct_dicts, schema=arrow_schema) + + def arrow_table_to_python_dicts( + self, arrow_table: pa.Table + ) -> list[dict[str, Any]]: + """ + Convert an Arrow table to a list of Python dictionaries. + + This uses the main conversion logic and caches results for performance. + """ + # Prepare converters for each field + converters = { + field.name: self.get_arrow_to_python_converter(field.type) + for field in arrow_table.schema + } + + python_dicts = [] + for row in arrow_table.to_pylist(): + python_dict = {} + for field_name, value in row.items(): + if value is not None: + python_dict[field_name] = converters[field_name](value) + else: + python_dict[field_name] = None + python_dicts.append(python_dict) + + return python_dicts + + def get_python_to_arrow_converter( + self, python_type: DataType + ) -> Callable[[Any], Any]: + """ + Get cached conversion function for Python value → Arrow value. + + This creates and caches conversion functions for optimal performance + during data conversion operations. + """ + if python_type in self._python_to_arrow_converters: + return self._python_to_arrow_converters[python_type] + + # Create conversion function + converter = self._create_python_to_arrow_converter(python_type) + self._python_to_arrow_converters[python_type] = converter + + return converter + + def get_arrow_to_python_converter( + self, arrow_type: pa.DataType + ) -> Callable[[Any], Any]: + """ + Get cached conversion function for Arrow value → Python value. + + This creates and caches conversion functions for optimal performance + during data conversion operations. + """ + if arrow_type in self._arrow_to_python_converters: + return self._arrow_to_python_converters[arrow_type] + + # Create conversion function + converter = self._create_arrow_to_python_converter(arrow_type) + self._arrow_to_python_converters[arrow_type] = converter + + return converter + + def _convert_python_to_arrow(self, python_type: DataType) -> pa.DataType: + """Core Python → Arrow type conversion logic.""" + + if python_type in _PYTHON_TO_ARROW_MAP: + return _PYTHON_TO_ARROW_MAP[python_type] + + # Check semantic registry for registered types + if self.semantic_registry: + converter = self.semantic_registry.get_converter_for_python_type( + python_type + ) + if converter: + return converter.arrow_struct_type + + # Handle typeddict look up + if python_type in self._typeddict_to_struct_signature: + return self._typeddict_to_struct_signature[python_type] + + # Check generic types + origin = get_origin(python_type) + args = get_args(python_type) + + if origin is None: + # Handle string type names + if hasattr(python_type, "__name__"): + type_name = getattr(python_type, "__name__") + if type_name in _PYTHON_TO_ARROW_MAP: + return _PYTHON_TO_ARROW_MAP[type_name] + raise ValueError(f"Unsupported Python type: {python_type}") + + # Handle list types + if origin is list: + if len(args) != 1: + raise ValueError( + f"list type must have exactly one type argument, got: {args}" + ) + element_type = self.python_type_to_arrow_type(args[0]) + return pa.large_list(element_type) + + # Handle tuple types + elif origin is tuple: + if len(args) == 0: + raise ValueError("Empty tuple type not supported") + + if len(set(args)) == 1: + # Homogeneous tuple → fixed-size list + element_type = self.python_type_to_arrow_type(args[0]) + return pa.list_(element_type, len(args)) + else: + # Heterogeneous tuple → struct with indexed fields + fields = [] + for i, arg_type in enumerate(args): + field_type = self.python_type_to_arrow_type(arg_type) + fields.append((f"f{i}", field_type)) + return pa.struct(fields) + + # Handle dict types + elif origin is dict: + if len(args) != 2: + raise ValueError( + f"dict type must have exactly two type arguments, got: {args}" + ) + key_type = self.python_type_to_arrow_type(args[0]) + value_type = self.python_type_to_arrow_type(args[1]) + key_value_struct = pa.struct([("key", key_type), ("value", value_type)]) + return pa.large_list(key_value_struct) + + # Handle Union/Optional types + elif origin is typing.Union or origin is types.UnionType: + if len(args) == 2 and type(None) in args: + # Optional[T] → just T (nullability handled at field level) + non_none_type = args[0] if args[1] is type(None) else args[1] + return self.python_type_to_arrow_type(non_none_type) + else: + # Complex unions → use first type as fallback + return self.python_type_to_arrow_type(args[0]) + + # Handle set types → lists + elif origin is set: + if len(args) != 1: + raise ValueError( + f"set type must have exactly one type argument, got: {args}" + ) + element_type = self.python_type_to_arrow_type(args[0]) + return pa.large_list(element_type) + + else: + raise ValueError(f"Unsupported generic type: {origin}") + + def _convert_arrow_to_python(self, arrow_type: pa.DataType) -> type | Any: + """Core Arrow → Python type conversion logic.""" + + # Handle basic types + if pa.types.is_integer(arrow_type): + return int + elif pa.types.is_floating(arrow_type): + return float + elif pa.types.is_string(arrow_type) or pa.types.is_large_string(arrow_type): + return str + elif pa.types.is_boolean(arrow_type): + return bool + elif pa.types.is_binary(arrow_type) or pa.types.is_large_binary(arrow_type): + return bytes + + # Handle struct types + elif pa.types.is_struct(arrow_type): + # Check if it's a registered semantic type first + if self.semantic_registry: + python_type = self.semantic_registry.get_python_type_for_semantic_struct_signature( + arrow_type + ) + if python_type: + return python_type + + # Check if it is heterogeneous tuple + if len(arrow_type) > 0 and all( + field.name.startswith("f") and field.name[1:].isdigit() + for field in arrow_type + ): + # This is likely a heterogeneous tuple, extract digits and ensure it + # is continuous + field_digits = [int(field.name[1:]) for field in arrow_type] + if field_digits == list(range(len(field_digits))): + return tuple[ + tuple( + self.arrow_type_to_python_type( + arrow_type.field(f"f{pos}").type + ) + for pos in range(len(arrow_type)) + ) + ] + else: + # Non-continuous field names, treat as dynamic TypedDict + logger.info( + "Detected heterogeneous tuple with non-continuous field names, " + "treating as dynamic TypedDict" + ) + + # Create dynamic TypedDict for unregistered struct + # TODO: add check for heterogeneous tuple checking each field starts with f + return self._get_or_create_typeddict_for_struct(arrow_type) + + # Handle list types + elif ( + pa.types.is_list(arrow_type) + or pa.types.is_large_list(arrow_type) + or pa.types.is_fixed_size_list(arrow_type) + ): + element_type = arrow_type.value_type + + # Check if this is a dict representation: list> + if pa.types.is_struct(element_type): + field_names = [field.name for field in element_type] + if set(field_names) == {"key", "value"}: + # This is a dict + key_field = next(f for f in element_type if f.name == "key") + value_field = next(f for f in element_type if f.name == "value") + + key_python_type = self.arrow_type_to_python_type(key_field.type) + value_python_type = self.arrow_type_to_python_type(value_field.type) + + return dict[key_python_type, value_python_type] + + # Regular list + element_python_type = self.arrow_type_to_python_type(element_type) + + if pa.types.is_fixed_size_list(arrow_type): + # Fixed-size list → homogeneous tuple + size = arrow_type.list_size + return tuple[tuple(element_python_type for _ in range(size))] + else: + # Variable-size list → list + return list[element_python_type] + + # Handle map types + elif pa.types.is_map(arrow_type): + key_python_type = self.arrow_type_to_python_type(arrow_type.key_type) + value_python_type = self.arrow_type_to_python_type(arrow_type.item_type) + return dict[key_python_type, value_python_type] + + # Handle union types + elif pa.types.is_union(arrow_type): + import typing + + child_types = [] + for i in range(arrow_type.num_fields): + child_field = arrow_type[i] + child_types.append(self.arrow_type_to_python_type(child_field.type)) + + if len(child_types) == 2 and type(None) in child_types: + # Optional[T] + non_none_type = next(t for t in child_types if t is not type(None)) + return typing.Optional[non_none_type] + else: + return typing.Union[tuple(child_types)] + + else: + # Default case for unsupported types + return Any + + def _get_or_create_typeddict_for_struct( + self, struct_type: pa.StructType + ) -> DataType: + """Get or create a TypedDict class for an Arrow struct type.""" + + # Check cache first + if struct_type in self._struct_signature_to_typeddict: + return self._struct_signature_to_typeddict[struct_type] + + # Create field specifications for TypedDict + field_specs: dict[str, DataType] = {} + for field in struct_type: + field_name = field.name + python_type = self.arrow_type_to_python_type(field.type) + field_specs[field_name] = python_type + + # Generate unique name + type_name = self._generate_unique_type_name(field_specs) + + # Create TypedDict dynamically + typeddict_class = TypedDict(type_name, field_specs) # type: ignore[call-arg] + + # Cache the mapping + self._struct_signature_to_typeddict[struct_type] = typeddict_class + self._typeddict_to_struct_signature[typeddict_class] = struct_type + + return typeddict_class + + # TODO: consider setting type of field_specs to PythonSchema + def _generate_unique_type_name(self, field_specs: Mapping[str, DataType]) -> str: + """Generate a unique name for TypedDict based on field specifications.""" + + # Create deterministic signature that includes both names and types + field_items = sorted(field_specs.items()) + signature_parts = [] + + for field_name, field_type in field_items: + type_name = getattr(field_type, "__name__", str(field_type)) + if type_name.startswith("typing."): + type_name = type_name[7:] + signature_parts.append(f"{field_name}_{type_name}") + + # Create base name from signature + if len(signature_parts) <= 2: + base_name = "Struct_" + "_".join(signature_parts) + else: + # Use hash-based approach for larger structs + signature_str = "_".join(signature_parts) + signature_hash = hashlib.md5(signature_str.encode()).hexdigest()[:8] + field_names = [item[0] for item in field_items] + + if len(field_names) <= 3: + base_name = f"Struct_{'_'.join(field_names)}_{signature_hash}" + else: + base_name = f"Struct_{len(field_names)}fields_{signature_hash}" + + # Clean up the name + base_name = ( + base_name.replace("[", "_") + .replace("]", "_") + .replace(",", "_") + .replace(" ", "") + ) + + self._created_type_names.add(base_name) + return base_name + + def _create_python_to_arrow_converter( + self, python_type: DataType + ) -> Callable[[Any], Any]: + """Create a cached conversion function for Python → Arrow values.""" + + # Get the Arrow type for this Python type + # TODO: check if this step is necessary + _ = self.python_type_to_arrow_type(python_type) + + # Check for semantic type first + if self.semantic_registry: + converter = self.semantic_registry.get_converter_for_python_type( + python_type + ) + if converter: + return converter.python_to_struct_dict + + # Create conversion function based on type + + origin = get_origin(python_type) + args = get_args(python_type) + + if python_type in {int, float, str, bool, bytes} or origin is None: + # Basic types - no conversion needed + return lambda value: value + + elif origin is list: + element_converter = self.get_python_to_arrow_converter(args[0]) + return ( + lambda value: [element_converter(item) for item in value] + if value + else [] + ) + + elif origin is dict: + key_converter = self.get_python_to_arrow_converter(args[0]) + value_converter = self.get_python_to_arrow_converter(args[1]) + return ( + lambda value: [ + {"key": key_converter(k), "value": value_converter(v)} + for k, v in value.items() + ] + if value + else [] + ) + + elif origin is tuple: + if len(set(args)) == 1: + # Homogeneous tuple + element_converter = self.get_python_to_arrow_converter(args[0]) + return lambda value: [element_converter(item) for item in value] + else: + # Heterogeneous tuple + converters = [self.get_python_to_arrow_converter(arg) for arg in args] + return lambda value: { + f"f{i}": converters[i](item) for i, item in enumerate(value) + } + + else: + # Default passthrough + return lambda value: value + + def _create_arrow_to_python_converter( + self, arrow_type: pa.DataType + ) -> Callable[[Any], Any]: + """Create a cached conversion function for Arrow → Python values.""" + + # Get the Python type for this Arrow type + python_type = self.arrow_type_to_python_type(arrow_type) + + # Check for semantic type first + if self.semantic_registry and pa.types.is_struct(arrow_type): + registered_python_type = ( + self.semantic_registry.get_python_type_for_semantic_struct_signature( + arrow_type + ) + ) + if registered_python_type: + converter = self.semantic_registry.get_converter_for_python_type( + registered_python_type + ) + if converter: + return converter.struct_dict_to_python + + # Handle basic types - no conversion needed + if ( + pa.types.is_integer(arrow_type) + or pa.types.is_floating(arrow_type) + or pa.types.is_boolean(arrow_type) + or pa.types.is_string(arrow_type) + or pa.types.is_large_string(arrow_type) + or pa.types.is_binary(arrow_type) + or pa.types.is_large_binary(arrow_type) + ): + return lambda value: value + + # Handle list types + elif ( + pa.types.is_list(arrow_type) + or pa.types.is_large_list(arrow_type) + or pa.types.is_fixed_size_list(arrow_type) + ): + element_type = arrow_type.value_type + + # Check if this is a dict representation + if pa.types.is_struct(element_type): + field_names = [field.name for field in element_type] + if set(field_names) == {"key", "value"}: + # Dict representation + key_field = next(f for f in element_type if f.name == "key") + value_field = next(f for f in element_type if f.name == "value") + + key_converter = self.get_arrow_to_python_converter(key_field.type) + value_converter = self.get_arrow_to_python_converter( + value_field.type + ) + + return ( + lambda value: { + key_converter(item["key"]): value_converter(item["value"]) + for item in value + if item is not None + } + if value + else {} + ) + + # Regular list + element_converter = self.get_arrow_to_python_converter(element_type) + + if pa.types.is_fixed_size_list(arrow_type): + # Fixed-size list → tuple + return ( + lambda value: tuple(element_converter(item) for item in value) + if value + else () + ) + else: + # Variable-size list → list + return ( + lambda value: [element_converter(item) for item in value] + if value + else [] + ) + + # Handle struct types - heterogeneous tuple or dynamic TypedDict + elif pa.types.is_struct(arrow_type): + # if python_type + if python_type is tuple or get_origin(python_type) is tuple: + n = len(get_args(python_type)) + # prepare list of converters + converters = [ + self.get_arrow_to_python_converter(arrow_type.field(f"f{i}").type) + for i in range(n) + ] + # this is a heterogeneous tuple + return lambda value: tuple( + converter(value[f"f{i}"]) for i, converter in enumerate(converters) + ) + + # Create converters for each field + field_converters = {} + for field in arrow_type: + field_converters[field.name] = self.get_arrow_to_python_converter( + field.type + ) + + return ( + lambda value: { + field_name: field_converters[field_name](value.get(field_name)) + for field_name in field_converters + } + if value + else {} + ) + + else: + # Default passthrough + return lambda value: value + + def is_dynamic_typeddict(self, python_type: type) -> bool: + """Check if a type is one of our dynamically created TypedDicts.""" + return python_type in self._typeddict_to_struct_signature + + def get_struct_signature_for_typeddict( + self, python_type: type + ) -> pa.StructType | None: + """Get the Arrow struct signature for a dynamically created TypedDict.""" + return self._typeddict_to_struct_signature.get(python_type) + + def clear_cache(self) -> None: + """Clear all caches (useful for testing or memory management).""" + self._struct_signature_to_typeddict.clear() + self._typeddict_to_struct_signature.clear() + self._created_type_names.clear() + self._python_to_arrow_converters.clear() + self._arrow_to_python_converters.clear() + self._python_to_arrow_types.clear() + self._arrow_to_python_types.clear() + + def get_cache_stats(self) -> dict[str, int]: + """Get statistics about cache usage (useful for debugging/optimization).""" + return { + "typeddict_count": len(self._struct_signature_to_typeddict), + "python_to_arrow_converters": len(self._python_to_arrow_converters), + "arrow_to_python_converters": len(self._arrow_to_python_converters), + "type_mappings": len(self._python_to_arrow_types) + + len(self._arrow_to_python_types), + } + + +# Public API functions +def python_type_to_arrow_type( + python_type: type, data_context: DataContext | str | None = None +) -> pa.DataType: + """Convert Python type to Arrow type using the global converter.""" + data_context = resolve_context(data_context) + converter = data_context.type_converter + return converter.python_type_to_arrow_type(python_type) + + +def arrow_type_to_python_type( + arrow_type: pa.DataType, data_context: DataContext | str | None = None +) -> type: + """Convert Arrow type to Python type using the global converter.""" + data_context = resolve_context(data_context) + converter = data_context.type_converter + return converter.arrow_type_to_python_type(arrow_type) + + +def get_conversion_functions( + python_type: type, data_context: DataContext | str | None = None +) -> tuple[Callable, Callable]: + """Get both conversion functions for a Python type.""" + data_context = resolve_context(data_context) + converter = data_context.type_converter + arrow_type = converter.python_type_to_arrow_type(python_type) + + python_to_arrow = converter.get_python_to_arrow_converter(python_type) + arrow_to_python = converter.get_arrow_to_python_converter(arrow_type) + + return python_to_arrow, arrow_to_python diff --git a/src/orcapod/store/__init__.py b/src/orcapod/store/__init__.py deleted file mode 100644 index f573c4d..0000000 --- a/src/orcapod/store/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from .types import DataStore, ArrowDataStore -from .core import DirDataStore, NoOpDataStore -from .safe_dir_data_store import SafeDirDataStore - -__all__ = [ - "DataStore", - "ArrowDataStore", - "DirDataStore", - "SafeDirDataStore", - "NoOpDataStore", -] diff --git a/src/orcapod/store/file.py b/src/orcapod/store/file.py deleted file mode 100644 index 0de8aff..0000000 --- a/src/orcapod/store/file.py +++ /dev/null @@ -1,159 +0,0 @@ -import builtins -import contextlib -import inspect -import os -from pathlib import Path -from typing import Callable, Collection, Dict, Optional, Tuple, Union - -from orcapod.types import Packet, PathSet - - -@contextlib.contextmanager -def redirect_open( - mapping: Union[Dict[str, str], Callable[[str], Optional[str]]], -): - """ - Context manager to intercept file opening operations. - - Args: - mapping: Either a dictionary mapping original paths to their replacements, - or a function that takes a path string and returns a replacement path - (or None to indicate the file should not be opened). - - Raises: - FileNotFoundError: If using a dictionary and the path is not found in it. - """ - # Track all places that might store an open() function - places_to_patch = [] - - # 1. Standard builtins.open - original_builtin_open = builtins.open - places_to_patch.append((builtins, "open", original_builtin_open)) - - # 2. __builtins__ (could be different in some contexts, especially IPython) - if isinstance(__builtins__, dict) and "open" in __builtins__: - places_to_patch.append((__builtins__, "open", __builtins__["open"])) - - # 3. Current module's globals (for the calling namespace) - current_frame = inspect.currentframe() - if current_frame is not None: - caller_globals = current_frame.f_back.f_globals if current_frame.f_back else {} - if "open" in caller_globals: - places_to_patch.append((caller_globals, "open", caller_globals["open"])) - - # 4. Check for IPython user namespace - try: - import IPython - - ip = IPython.get_ipython() # type: ignore - if ip and "open" in ip.user_ns: - places_to_patch.append((ip.user_ns, "open", ip.user_ns["open"])) - except (ImportError, AttributeError): - pass - - def patched_open(file, *args, **kwargs): - # Convert PathLike objects to string if needed - if hasattr(file, "__fspath__"): - file_path = os.fspath(file) - else: - file_path = str(file) - - if isinstance(mapping, dict): - if file_path in mapping: - redirected_path = mapping[file_path] - print(f"Redirecting '{file_path}' to '{redirected_path}'") - return original_builtin_open(redirected_path, *args, **kwargs) - else: - raise FileNotFoundError( - f"Path '{file_path}' not found in redirection mapping" - ) - else: # mapping is a function - redirected_path = mapping(file_path) - if redirected_path is not None: - print(f"Redirecting '{file_path}' to '{redirected_path}'") - return original_builtin_open(redirected_path, *args, **kwargs) - else: - raise FileNotFoundError(f"Path '{file_path}' could not be redirected") - - # Apply the patch to all places - for obj, attr, _ in places_to_patch: - if isinstance(obj, dict): - obj[attr] = patched_open - else: - setattr(obj, attr, patched_open) - - try: - yield - finally: - # Restore all original functions - for obj, attr, original in places_to_patch: - if isinstance(obj, dict): - obj[attr] = original - else: - setattr(obj, attr, original) - - -def virtual_mount( - packet: Packet, -) -> Tuple[Packet, Dict[str, str], Dict[str, str]]: - """ - Visit all pathset within the packet, and convert them to alternative path - representation. By default, full path is mapped to the file name. If two or - more paths have the same file name, the second one is suffixed with "_1", the - third one with "_2", etc. This is useful for creating a virtual mount point - for a set of files, where the original paths are not important, but the file - names can be used to identify the files. - """ - forward_lut = {} # mapping from original path to new path - reverse_lut = {} # mapping from new path to original path - new_packet = {} - - for key, value in packet.items(): - new_packet[key] = convert_pathset(value, forward_lut, reverse_lut) - - return new_packet, forward_lut, reverse_lut - - -# TODO: re-assess the structure of PathSet and consider making it recursive -def convert_pathset(pathset: PathSet, forward_lut, reverse_lut) -> PathSet: - """ - Convert a pathset to a new pathset. forward_lut and reverse_lut are updated - with the new paths. The new paths are created by replacing the original paths - with the new paths in the forward_lut. The reverse_lut is updated with the - original paths. If name already exists, a suffix is added to the new name to avoid - collisions. - """ - if isinstance(pathset, (str, bytes)): - new_name = Path(pathset).name - if new_name in reverse_lut: - # if the name already exists, add a suffix - i = 1 - while f"{new_name}_{i}" in reverse_lut: - i += 1 - new_name = f"{new_name}_{i}" - forward_lut[pathset] = new_name - reverse_lut[new_name] = pathset - return new_name - elif isinstance(pathset, Collection): - return [convert_pathset(p, forward_lut, reverse_lut) for p in pathset] # type: ignore - else: - raise ValueError( - f"Unsupported pathset type: {type(pathset)}. Expected str, bytes, or Collection." - ) - - -class WrappedPath: - def __init__(self, path, name=None): - self.path = Path(path) - if name is None: - name = self.path.name - self.name = name - - def __fspath__(self) -> Union[str, bytes]: - return self.path.__fspath__() - - def __str__(self) -> str: - return self.name - - def __repr__(self) -> str: - return f"WrappedPath({self.path}): {self.name}" diff --git a/src/orcapod/store/file_ops.py b/src/orcapod/store/file_ops.py deleted file mode 100644 index 0e34213..0000000 --- a/src/orcapod/store/file_ops.py +++ /dev/null @@ -1,278 +0,0 @@ -# file_ops.py - Atomic file operations module - -import logging -import os -from pathlib import Path - -from orcapod.types import PathLike - -logger = logging.getLogger(__name__) - - -def atomic_write(file_path: PathLike, content: str) -> Path: - """ - Atomically write content to a file. - - This function writes content to a temporary file and then atomically - renames it to the target file path, ensuring that other processes never - see a partially-written file. - - Args: - file_path: Target file path - content: Content to write - - Returns: - Path object to the written file - - Raises: - OSError: If the file cannot be written - """ - file_path = Path(file_path) - temp_path = file_path.with_name(f"{file_path.name}.tmp{os.getpid()}") - - # Ensure parent directory exists - file_path.parent.mkdir(parents=True, exist_ok=True) - - try: - # Write content to a temporary file - with open(temp_path, "w") as f: - f.write(content) - f.flush() - os.fsync(f.fileno()) # Force flush to disk - - # Atomic rename - os.rename(temp_path, file_path) - return file_path - except Exception as e: - logger.error(f"Error writing file {file_path}: {str(e)}") - raise - finally: - # Clean up the temporary file if it exists - if temp_path.exists(): - temp_path.unlink(missing_ok=True) - - -def atomic_write_bytes(file_path: PathLike, content: bytes) -> Path: - """ - Atomically write binary content to a file. - - This function writes binary content to a temporary file and then atomically - renames it to the target file path. - - Args: - file_path: Target file path - content: Binary content to write - - Returns: - Path object to the written file - - Raises: - OSError: If the file cannot be written - """ - file_path = Path(file_path) - temp_path = file_path.with_name(f"{file_path.name}.tmp{os.getpid()}") - - # Ensure parent directory exists - file_path.parent.mkdir(parents=True, exist_ok=True) - - try: - # Write content to a temporary file - with open(temp_path, "wb") as f: - f.write(content) - f.flush() - os.fsync(f.fileno()) # Force flush to disk - - # Atomic rename - os.rename(temp_path, file_path) - return file_path - except Exception as e: - logger.error(f"Error writing file {file_path}: {str(e)}") - raise - finally: - # Clean up the temporary file if it exists - if temp_path.exists(): - temp_path.unlink(missing_ok=True) - - -def atomic_copy(source_path: PathLike, dest_path: PathLike) -> Path: - """ - Atomically copy a file. - - This function copies a file to a temporary location and then atomically - renames it to the target path, ensuring that other processes never - see a partially-copied file. - - Args: - source_path: Source file path - dest_path: Destination file path - - Returns: - Path object to the copied file - - Raises: - OSError: If the file cannot be copied - FileNotFoundError: If the source file does not exist - """ - import shutil - - source_path = Path(source_path) - dest_path = Path(dest_path) - temp_path = dest_path.with_name(f"{dest_path.name}.tmp{os.getpid()}") - - # Check if source exists - if not source_path.exists(): - raise FileNotFoundError(f"Source file does not exist: {source_path}") - - # Ensure parent directory exists - dest_path.parent.mkdir(parents=True, exist_ok=True) - - try: - # Copy to temporary file - shutil.copy2(source_path, temp_path) - - # Ensure the data is written to disk - with open(temp_path, "a") as f: - os.fsync(f.fileno()) - - # Atomic rename - os.rename(temp_path, dest_path) - return dest_path - except Exception as e: - logger.error(f"Error copying file from {source_path} to {dest_path}: {str(e)}") - raise - finally: - # Clean up the temporary file if it exists - if temp_path.exists(): - temp_path.unlink(missing_ok=True) - - -def atomic_append(file_path: PathLike, content: str) -> Path: - """ - Atomically append content to a file. - - This function reads the existing content, appends the new content, - and then atomically writes the result back to the file. - - Args: - file_path: Target file path - content: Content to append - - Returns: - Path object to the appended file - - Raises: - OSError: If the file cannot be written - """ - file_path = Path(file_path) - - # Read existing content if file exists - existing_content = "" - if file_path.exists(): - try: - with open(file_path, "r") as f: - existing_content = f.read() - except Exception as e: - logger.error(f"Error reading file {file_path} for append: {str(e)}") - raise - - # Write the combined content atomically - return atomic_write(file_path, existing_content + content) - - -def atomic_replace( - file_path: PathLike, pattern: str, replacement: str, count: int = -1 -) -> tuple[Path, int]: - """ - Atomically replace text in a file. - - This function reads the existing content, performs the replacement, - and then atomically writes the result back to the file. - - Args: - file_path: Target file path - pattern: Pattern to replace - replacement: Replacement text - count: Maximum number of replacements (default: unlimited) - - Returns: - Tuple of (Path object to the file, number of replacements made) - - Raises: - OSError: If the file cannot be read or written - FileNotFoundError: If the file does not exist - """ - file_path = Path(file_path) - - # Check if file exists - if not file_path.exists(): - raise FileNotFoundError(f"File does not exist: {file_path}") - - # Read existing content - try: - with open(file_path, "r") as f: - existing_content = f.read() - except Exception as e: - logger.error(f"Error reading file {file_path} for replacement: {str(e)}") - raise - - # Perform replacement - new_content, num_replacements = existing_content, 0 - if count == -1: - # Replace all occurrences - new_content = existing_content.replace(pattern, replacement) - num_replacements = existing_content.count(pattern) - else: - # Replace only up to count occurrences - new_content = "" - remaining = existing_content - for _ in range(count): - if pattern not in remaining: - break - pos = remaining.find(pattern) - new_content += remaining[:pos] + replacement - remaining = remaining[pos + len(pattern) :] - num_replacements += 1 - new_content += remaining - - # Write the new content atomically - return atomic_write(file_path, new_content), num_replacements - - -def is_file_locked(file_path: PathLike) -> bool: - """ - Check if a file is locked. - - This function attempts to open the file for writing in non-blocking mode - and checks if it fails with a "resource temporarily unavailable" error. - - Args: - file_path: File path to check - - Returns: - True if the file is locked, False otherwise - """ - import errno - import fcntl - - file_path = Path(file_path) - - # If file doesn't exist, it's not locked - if not file_path.exists(): - return False - - try: - # Try to open the file and get an exclusive lock in non-blocking mode - with open(file_path, "r+") as f: - fcntl.flock(f, fcntl.LOCK_EX | fcntl.LOCK_NB) - # If we get here, the file is not locked - fcntl.flock(f, fcntl.LOCK_UN) - return False - except IOError as e: - if e.errno == errno.EAGAIN: - # Resource temporarily unavailable = the file is locked - return True - # Some other error - assume not locked - return False - except Exception: - # Any other exception - assume not locked - return False diff --git a/src/orcapod/types.py b/src/orcapod/types.py new file mode 100644 index 0000000..0f84d9c --- /dev/null +++ b/src/orcapod/types.py @@ -0,0 +1,37 @@ +from types import UnionType +from typing import TypeAlias +import os +from collections.abc import Collection, Mapping + +import logging + +logger = logging.getLogger(__name__) + +DataType: TypeAlias = type | UnionType | list[type] | tuple[type, ...] + +PythonSchema: TypeAlias = dict[str, DataType] # dict of parameter names to their types + +PythonSchemaLike: TypeAlias = Mapping[ + str, DataType +] # Mapping of parameter names to their types + +# Convenience alias for anything pathlike +PathLike = str | os.PathLike + +# an (optional) string or a collection of (optional) string values +# Note that TagValue can be nested, allowing for an arbitrary depth of nested lists +TagValue: TypeAlias = int | str | None | Collection["TagValue"] + +# a pathset is a path or an arbitrary depth of nested list of paths +PathSet: TypeAlias = PathLike | Collection[PathLike | None] + +# Simple data types that we support (with clear Polars correspondence) +SupportedNativePythonData: TypeAlias = str | int | float | bool | bytes + +ExtendedSupportedPythonData: TypeAlias = SupportedNativePythonData | PathSet + +# Extended data values that can be stored in packets +# Either the original PathSet or one of our supported simple data types +DataValue: TypeAlias = ExtendedSupportedPythonData | Collection["DataValue"] | None + +PacketLike: TypeAlias = Mapping[str, DataValue] diff --git a/src/orcapod/types/__init__.py b/src/orcapod/types/__init__.py deleted file mode 100644 index f372259..0000000 --- a/src/orcapod/types/__init__.py +++ /dev/null @@ -1,52 +0,0 @@ -# src/orcabridge/types.py -import os -from collections.abc import Collection, Mapping -from pathlib import Path -from typing import Any, Protocol -from typing_extensions import TypeAlias -from .core import TypeSpec, TypeHandler - - -SUPPORTED_PYTHON_TYPES = (str, int, float, bool, bytes) - -# Convenience alias for anything pathlike -PathLike = str | os.PathLike - -# an (optional) string or a collection of (optional) string values -# Note that TagValue can be nested, allowing for an arbitrary depth of nested lists -TagValue: TypeAlias = str | None | Collection["TagValue"] - -# the top level tag is a mapping from string keys to values that can be a string or -# an arbitrary depth of nested list of strings or None -Tag: TypeAlias = Mapping[str, TagValue] - -# a pathset is a path or an arbitrary depth of nested list of paths -PathSet: TypeAlias = PathLike | Collection[PathLike | None] - -# Simple data types that we support (with clear Polars correspondence) -SupportedNativePythonData: TypeAlias = str | int | float | bool | bytes - -ExtendedSupportedPythonData: TypeAlias = SupportedNativePythonData | PathLike - -# Extended data values that can be stored in packets -# Either the original PathSet or one of our supported simple data types -DataValue: TypeAlias = PathSet | SupportedNativePythonData | Collection["DataValue"] - - -# a packet is a mapping from string keys to data values -Packet: TypeAlias = Mapping[str, DataValue] - -# a batch is a tuple of a tag and a list of packets -Batch: TypeAlias = tuple[Tag, Collection[Packet]] - - -class PodFunction(Protocol): - """ - A function suitable to be used in a FunctionPod. - It takes one or more named arguments, each corresponding to either: - - A path to a file or directory (PathSet) - for backward compatibility - - A simple data value (str, int, float, bool, bytes, Path) - and returns either None, a single value, or a list of values - """ - - def __call__(self, **kwargs: DataValue) -> None | DataValue | list[DataValue]: ... diff --git a/src/orcapod/types/core.py b/src/orcapod/types/core.py deleted file mode 100644 index 5822f87..0000000 --- a/src/orcapod/types/core.py +++ /dev/null @@ -1,57 +0,0 @@ -from typing import Protocol, Any, TypeAlias, Mapping -import pyarrow as pa -from dataclasses import dataclass - - -# TODO: reconsider the need for this dataclass as its information is superfluous -# to the registration of the handler into the registry. -@dataclass -class TypeInfo: - python_type: type - arrow_type: pa.DataType - semantic_type: str | None # name under which the type is registered - handler: "TypeHandler" - - -DataType: TypeAlias = type - -TypeSpec: TypeAlias = Mapping[ - str, DataType -] # Mapping of parameter names to their types - - -class TypeHandler(Protocol): - """Protocol for handling conversion between Python types and underlying Arrow - data types used for storage. - - The handler itself IS the definition of a semantic type. The semantic type - name/identifier is provided by the registerer when registering the handler. - - TypeHandlers should clearly communicate what Python types they can handle, - and focus purely on conversion logic. - """ - - def python_types(self) -> type | tuple[type, ...]: - """Return the Python type(s) this handler can process. - - Returns: - Single Type or tuple of Types this handler supports - - Examples: - - PathHandler: return Path - - NumericHandler: return (int, float) - - CollectionHandler: return (list, tuple, set) - """ - ... - - def storage_type(self) -> pa.DataType: - """Return the Arrow DataType instance for schema definition.""" - ... - - def python_to_storage(self, value: Any) -> Any: - """Convert Python value to Arrow-compatible storage representation.""" - ... - - def storage_to_python(self, value: Any) -> Any: - """Convert storage representation back to Python object.""" - ... diff --git a/src/orcapod/types/default.py b/src/orcapod/types/default.py deleted file mode 100644 index d41e577..0000000 --- a/src/orcapod/types/default.py +++ /dev/null @@ -1,18 +0,0 @@ -from .registry import TypeRegistry -from .handlers import ( - PathHandler, - UUIDHandler, - SimpleMappingHandler, - DateTimeHandler, -) -import pyarrow as pa - -# Create default registry and register handlers -default_registry = TypeRegistry() - -# Register with semantic names - registry extracts supported types automatically -default_registry.register("path", PathHandler()) -default_registry.register("uuid", UUIDHandler()) -default_registry.register( - "datetime", DateTimeHandler() -) # Registers for datetime, date, time diff --git a/src/orcapod/types/handlers.py b/src/orcapod/types/handlers.py deleted file mode 100644 index ecbdfba..0000000 --- a/src/orcapod/types/handlers.py +++ /dev/null @@ -1,114 +0,0 @@ -from typing import Any -import pyarrow as pa -from pathlib import Path -from uuid import UUID -from decimal import Decimal -from datetime import datetime, date, time - - -class PathHandler: - """Handler for pathlib.Path objects, stored as strings.""" - - def python_types(self) -> type: - return Path - - def storage_type(self) -> pa.DataType: - return pa.string() - - def python_to_storage(self, value: Path) -> str: - return str(value) - - def storage_to_python(self, value: str) -> Path | None: - return Path(value) if value else None - - -class UUIDHandler: - """Handler for UUID objects, stored as strings.""" - - def python_types(self) -> type: - return UUID - - def storage_type(self) -> pa.DataType: - return pa.string() - - def python_to_storage(self, value: UUID) -> str: - return str(value) - - def storage_to_python(self, value: str) -> UUID | None: - return UUID(value) if value else None - - -class DecimalHandler: - """Handler for Decimal objects, stored as strings.""" - - def python_types(self) -> type: - return Decimal - - def storage_type(self) -> pa.DataType: - return pa.string() - - def python_to_storage(self, value: Decimal) -> str: - return str(value) - - def storage_to_python(self, value: str) -> Decimal | None: - return Decimal(value) if value else None - - -class SimpleMappingHandler: - """Handler for basic types that map directly to Arrow.""" - - def __init__(self, python_type: type, arrow_type: pa.DataType): - self._python_type = python_type - self._arrow_type = arrow_type - - def python_types(self) -> type: - return self._python_type - - def storage_type(self) -> pa.DataType: - return self._arrow_type - - def python_to_storage(self, value: Any) -> Any: - return value # Direct mapping - - def storage_to_python(self, value: Any) -> Any: - return value # Direct mapping - - -class DirectArrowHandler: - """Handler for types that map directly to Arrow without conversion.""" - - def __init__(self, arrow_type: pa.DataType): - self._arrow_type = arrow_type - - def python_types(self) -> type: - return self._arrow_type - - def storage_type(self) -> pa.DataType: - return self._arrow_type - - def python_to_storage(self, value: Any) -> Any: - return value # Direct mapping - - def storage_to_python(self, value: Any) -> Any: - return value # Direct mapping - - -class DateTimeHandler: - """Handler for datetime objects.""" - - def python_types(self) -> tuple[type, ...]: - return (datetime, date, time) # Handles multiple related types - - def storage_type(self) -> pa.DataType: - return pa.timestamp("us") # Store everything as timestamp - - def python_to_storage(self, value: datetime | date | time) -> Any: - if isinstance(value, datetime): - return value - elif isinstance(value, date): - return datetime.combine(value, time.min) - elif isinstance(value, time): - return datetime.combine(date.today(), value) - - def storage_to_python(self, value: datetime) -> datetime: - return value # Could add logic to restore original type if needed diff --git a/src/orcapod/types/registry.py b/src/orcapod/types/registry.py deleted file mode 100644 index 0dafda5..0000000 --- a/src/orcapod/types/registry.py +++ /dev/null @@ -1,439 +0,0 @@ -from collections.abc import Callable, Collection, Sequence -import logging -from optparse import Values -from typing import Any -import pyarrow as pa -from orcapod.types import Packet -from .core import TypeHandler, TypeInfo, TypeSpec - -# This mapping is expected to be stable -# Be sure to test this assumption holds true -DEFAULT_ARROW_TYPE_LUT = { - int: pa.int64(), - float: pa.float64(), - str: pa.string(), - bool: pa.bool_(), -} - -logger = logging.getLogger(__name__) - - -class TypeRegistry: - """Registry that manages type handlers with semantic type names.""" - - def __init__(self): - self._handlers: dict[ - type, tuple[TypeHandler, str] - ] = {} # Type -> (Handler, semantic_name) - self._semantic_handlers: dict[str, TypeHandler] = {} # semantic_name -> Handler - - def register( - self, - semantic_name: str, - handler: TypeHandler, - explicit_types: type | tuple[type, ...] | None = None, - override: bool = False, - ): - """Register a handler with a semantic type name. - - Args: - semantic_name: Identifier for this semantic type (e.g., 'path', 'uuid') - handler: The type handler instance - explicit_types: Optional override of types to register for (if different from handler's supported_types) - override: If True, allow overriding existing registration for the same semantic name and Python type(s) - """ - # Determine which types to register for - if explicit_types is not None: - types_to_register = ( - explicit_types - if isinstance(explicit_types, tuple) - else (explicit_types,) - ) - else: - supported = handler.python_types() - types_to_register = ( - supported if isinstance(supported, tuple) else (supported,) - ) - - # Register handler for each type - for python_type in types_to_register: - if python_type in self._handlers and not override: - existing_semantic = self._handlers[python_type][1] - # TODO: handle overlapping registration more gracefully - raise ValueError( - f"Type {python_type} already registered with semantic type '{existing_semantic}'" - ) - - self._handlers[python_type] = (handler, semantic_name) - - # Register by semantic name - if semantic_name in self._semantic_handlers and not override: - raise ValueError(f"Semantic type '{semantic_name}' already registered") - - self._semantic_handlers[semantic_name] = handler - - def get_handler(self, python_type: type) -> TypeHandler | None: - """Get handler for a Python type.""" - handler_info = self._handlers.get(python_type) - return handler_info[0] if handler_info else None - - def get_semantic_name(self, python_type: type) -> str | None: - """Get semantic name for a Python type.""" - handler_info = self._handlers.get(python_type) - return handler_info[1] if handler_info else None - - def get_type_info(self, python_type: type) -> TypeInfo | None: - """Get TypeInfo for a Python type.""" - handler = self.get_handler(python_type) - if handler is None: - return None - semantic_name = self.get_semantic_name(python_type) - return TypeInfo( - python_type=python_type, - arrow_type=handler.storage_type(), - semantic_type=semantic_name, - handler=handler, - ) - - def get_handler_by_semantic_name(self, semantic_name: str) -> TypeHandler | None: - """Get handler by semantic name.""" - return self._semantic_handlers.get(semantic_name) - - def __contains__(self, python_type: type) -> bool: - """Check if a Python type is registered.""" - return python_type in self._handlers - - -class PacketConverter: - def __init__(self, python_type_spec: TypeSpec, registry: TypeRegistry): - self.python_type_spec = python_type_spec - self.registry = registry - - # Lookup handlers and type info for fast access - self.handlers: dict[str, TypeHandler] = {} - self.storage_type_info: dict[str, TypeInfo] = {} - - self.expected_key_set = set(python_type_spec.keys()) - - # prepare the corresponding arrow table schema with metadata - self.keys_with_handlers, self.schema = create_schema_from_python_type_info( - python_type_spec, registry - ) - - self.semantic_type_lut = get_metadata_from_schema(self.schema, b"semantic_type") - - def _check_key_consistency(self, keys): - """Check if the provided keys match the expected keys.""" - keys_set = set(keys) - if keys_set != self.expected_key_set: - missing_keys = self.expected_key_set - keys_set - extra_keys = keys_set - self.expected_key_set - error_parts = [] - if missing_keys: - error_parts.append(f"Missing keys: {missing_keys}") - if extra_keys: - error_parts.append(f"Extra keys: {extra_keys}") - - raise KeyError(f"Keys don't match expected keys. {'; '.join(error_parts)}") - - def _to_storage_packet(self, packet: Packet) -> dict[str, Any]: - """Convert packet to storage representation. - - Args: - packet: Dictionary mapping parameter names to Python values - - Returns: - Dictionary with same keys but values converted to storage format - - Raises: - KeyError: If packet keys don't match the expected type_info keys - TypeError: If value type doesn't match expected type - ValueError: If conversion fails - """ - # Validate packet keys - packet_keys = set(packet.keys()) - - self._check_key_consistency(packet_keys) - - # Convert each value - storage_packet: dict[str, Any] = ( - packet.copy() - ) # Start with a copy of the packet - - for key, handler in self.keys_with_handlers: - try: - storage_packet[key] = handler.python_to_storage(storage_packet[key]) - except Exception as e: - raise ValueError(f"Failed to convert value for '{key}': {e}") from e - - return storage_packet - - def _from_storage_packet(self, storage_packet: dict[str, Any]) -> Packet: - """Convert storage packet back to Python packet. - - Args: - storage_packet: Dictionary with values in storage format - - Returns: - Packet with values converted back to Python types - - Raises: - KeyError: If storage packet keys don't match the expected type_info keys - TypeError: If value type doesn't match expected type - ValueError: If conversion fails - """ - # Validate storage packet keys - storage_keys = set(storage_packet.keys()) - - self._check_key_consistency(storage_keys) - - # Convert each value back to Python type - packet: Packet = storage_packet.copy() - - for key, handler in self.keys_with_handlers: - try: - packet[key] = handler.storage_to_python(storage_packet[key]) - except Exception as e: - raise ValueError(f"Failed to convert value for '{key}': {e}") from e - - return packet - - def to_arrow_table(self, packet: Packet | Sequence[Packet]) -> pa.Table: - """Convert packet to PyArrow Table with field metadata. - - Args: - packet: Dictionary mapping parameter names to Python values - - Returns: - PyArrow Table with the packet data as a single row - """ - # Convert packet to storage format - if not isinstance(packet, Sequence): - packets = [packet] - else: - packets = packet - - storage_packets = [self._to_storage_packet(p) for p in packets] - - # Create arrays - arrays = [] - for field in self.schema: - values = [p[field.name] for p in storage_packets] - array = pa.array(values, type=field.type) - arrays.append(array) - - return pa.Table.from_arrays(arrays, schema=self.schema) - - def from_arrow_table( - self, table: pa.Table, verify_semantic_equivalence: bool = True - ) -> list[Packet]: - """Convert Arrow table to packet with field metadata. - - Args: - table: PyArrow Table with metadata - - Returns: - List of packets converted from the Arrow table - """ - # Check for consistency in the semantic type mapping: - semantic_type_info = get_metadata_from_schema(table.schema, b"semantic_type") - - if semantic_type_info != self.semantic_type_lut: - if not verify_semantic_equivalence: - logger.warning( - "Arrow table semantic types do not match expected type registry. " - f"Expected: {self.semantic_type_lut}, got: {semantic_type_info}" - ) - else: - raise ValueError( - "Arrow table semantic types do not match expected type registry. " - f"Expected: {self.semantic_type_lut}, got: {semantic_type_info}" - ) - - # Create packets from the Arrow table - # TODO: make this more efficient - storage_packets: list[Packet] = arrow_to_dicts(table) # type: ignore - if not self.keys_with_handlers: - # no special handling required - return storage_packets - - return [self._from_storage_packet(packet) for packet in storage_packets] - - -def arrow_to_dicts(table: pa.Table) -> list[dict[str, Any]]: - """ - Convert Arrow table to dictionary or list of dictionaries. - By default returns a list of dictionaries (one per row) with column names as keys. - If `collapse_singleton` is True, return a single dictionary for single-row tables. - Args: - table: PyArrow Table to convert - collapse_singleton: If True, return a single dictionary for single-row tables. Defaults to False. - Returns: - A dictionary if singleton and collapse_singleton=True. Otherwise, list of dictionaries for multi-row tables. - """ - if len(table) == 0: - return [] - - # Multiple rows: return list of dicts (one per row) - return [ - {col_name: table.column(col_name)[i].as_py() for col_name in table.column_names} - for i in range(len(table)) - ] - - -def get_metadata_from_schema( - schema: pa.Schema, metadata_field: bytes -) -> dict[str, str]: - """ - Extract metadata from Arrow schema fields. Metadata value will be utf-8 decoded. - Args: - schema: PyArrow Schema to extract metadata from - metadata_field: Metadata field to extract (e.g., b'semantic_type') - Returns: - Dictionary mapping field names to their metadata values - """ - metadata = {} - for field in schema: - if field.metadata and metadata_field in field.metadata: - metadata[field.name] = field.metadata[metadata_field].decode("utf-8") - return metadata - - -def create_schema_from_python_type_info( - python_type_spec: TypeSpec, - registry: TypeRegistry, - arrow_type_lut: dict[type, pa.DataType] | None = None, -) -> tuple[list[tuple[str, TypeHandler]], pa.Schema]: - if arrow_type_lut is None: - arrow_type_lut = DEFAULT_ARROW_TYPE_LUT - keys_with_handlers: list[tuple[str, TypeHandler]] = [] - schema_fields = [] - for key, python_type in python_type_spec.items(): - type_info = registry.get_type_info(python_type) - - field_metadata = {} - if type_info and type_info.semantic_type: - field_metadata["semantic_type"] = type_info.semantic_type - keys_with_handlers.append((key, type_info.handler)) - arrow_type = type_info.arrow_type - else: - arrow_type = arrow_type_lut.get(python_type) - if arrow_type is None: - raise ValueError( - f"Direct support for Python type {python_type} is not provided. Register a handler to work with {python_type}" - ) - - schema_fields.append(pa.field(key, arrow_type, metadata=field_metadata)) - return keys_with_handlers, pa.schema(schema_fields) - - -def arrow_table_to_packets( - table: pa.Table, - registry: TypeRegistry, -) -> list[Packet]: - """Convert Arrow table to packet with field metadata. - - Args: - packet: Dictionary mapping parameter names to Python values - - Returns: - PyArrow Table with the packet data as a single row - """ - packets: list[Packet] = [] - - # prepare converter for each field - - def no_op(x) -> Any: - return x - - converter_lut = {} - for field in table.schema: - if field.metadata and b"semantic_type" in field.metadata: - semantic_type = field.metadata[b"semantic_type"].decode("utf-8") - if semantic_type: - handler = registry.get_handler_by_semantic_name(semantic_type) - if handler is None: - raise ValueError( - f"No handler registered for semantic type '{semantic_type}'" - ) - converter_lut[field.name] = handler.storage_to_python - - # Create packets from the Arrow table - # TODO: make this more efficient - for row in range(table.num_rows): - packet: Packet = {} - for field in table.schema: - value = table.column(field.name)[row].as_py() - packet[field.name] = converter_lut.get(field.name, no_op)(value) - packets.append(packet) - - return packets - - -def is_packet_supported( - python_type_info: TypeSpec, registry: TypeRegistry, type_lut: dict | None = None -) -> bool: - """Check if all types in the packet are supported by the registry or known to the default lut.""" - if type_lut is None: - type_lut = {} - return all( - python_type in registry or python_type in type_lut - for python_type in python_type_info.values() - ) - - -def create_arrow_table_with_meta( - storage_packet: dict[str, Any], type_info: dict[str, TypeInfo] -): - """Create an Arrow table with metadata from a storage packet. - - Args: - storage_packet: Dictionary with values in storage format - type_info: Dictionary mapping parameter names to TypeInfo objects - - Returns: - PyArrow Table with metadata - """ - schema_fields = [] - for key, type_info_obj in type_info.items(): - field_metadata = {} - if type_info_obj.semantic_type: - field_metadata["semantic_type"] = type_info_obj.semantic_type - - field = pa.field(key, type_info_obj.arrow_type, metadata=field_metadata) - schema_fields.append(field) - - schema = pa.schema(schema_fields) - - arrays = [] - for field in schema: - value = storage_packet[field.name] - array = pa.array([value], type=field.type) - arrays.append(array) - - return pa.Table.from_arrays(arrays, schema=schema) - - -def retrieve_storage_packet_from_arrow_with_meta( - arrow_table: pa.Table, -) -> dict[str, Any]: - """Retrieve storage packet from Arrow table with metadata. - - Args: - arrow_table: PyArrow Table with metadata - - Returns: - Dictionary representing the storage packet - """ - storage_packet = {} - for field in arrow_table.schema: - # Extract value from Arrow array - array = arrow_table.column(field.name) - if array.num_chunks > 0: - value = array.chunk(0).as_py()[0] # Get first value - else: - value = None # Handle empty arrays - - storage_packet[field.name] = value - - return storage_packet diff --git a/src/orcapod/types/utils.py b/src/orcapod/types/utils.py deleted file mode 100644 index 5393492..0000000 --- a/src/orcapod/types/utils.py +++ /dev/null @@ -1,62 +0,0 @@ -# TODO: move these functions to util -def escape_with_postfix(field: str, postfix=None, separator="_") -> str: - """ - Escape the field string by doubling separators and optionally append a postfix. - This function takes a field string and escapes any occurrences of the separator - by doubling them, then optionally appends a postfix with a separator prefix. - - Args: - field (str): The input string containing to be escaped. - postfix (str, optional): An optional postfix to append to the escaped string. - If None, no postfix is added. Defaults to None. - separator (str, optional): The separator character to escape and use for - prefixing the postfix. Defaults to "_". - Returns: - str: The escaped string with optional postfix. Returns empty string if - fields is provided but postfix is None. - Examples: - >>> escape_with_postfix("field1_field2", "suffix") - 'field1__field2_suffix' - >>> escape_with_postfix("name_age_city", "backup", "_") - 'name__age__city_backup' - >>> escape_with_postfix("data-info", "temp", "-") - 'data--info-temp' - >>> escape_with_postfix("simple", None) - 'simple' - >>> escape_with_postfix("no_separators", "end") - 'no__separators_end' - """ - - return field.replace(separator, separator * 2) + (f"_{postfix}" if postfix else "") - - -def unescape_with_postfix(field: str, separator="_") -> tuple[str, str | None]: - """ - Unescape a string by converting double separators back to single separators and extract postfix metadata. - This function reverses the escaping process where single separators were doubled to avoid - conflicts with metadata delimiters. It splits the input on double separators, then extracts - any postfix metadata from the last part. - - Args: - field (str): The escaped string containing doubled separators and optional postfix metadata - separator (str, optional): The separator character used for escaping. Defaults to "_" - Returns: - tuple[str, str | None]: A tuple containing: - - The unescaped string with single separators restored - - The postfix metadata if present, None otherwise - Examples: - >>> unescape_with_postfix("field1__field2__field3") - ('field1_field2_field3', None) - >>> unescape_with_postfix("field1__field2_metadata") - ('field1_field2', 'metadata') - >>> unescape_with_postfix("simple") - ('simple', None) - >>> unescape_with_postfix("field1--field2", separator="-") - ('field1-field2', None) - >>> unescape_with_postfix("field1--field2-meta", separator="-") - ('field1-field2', 'meta') - """ - - parts = field.split(separator * 2) - parts[-1], *meta = parts[-1].split("_", 1) - return separator.join(parts), meta[0] if meta else None diff --git a/src/orcapod/utils/arrow_utils.py b/src/orcapod/utils/arrow_utils.py new file mode 100644 index 0000000..deeabcb --- /dev/null +++ b/src/orcapod/utils/arrow_utils.py @@ -0,0 +1,733 @@ +# TODO: move this to a separate module + +from collections import defaultdict +from collections.abc import Mapping, Collection +from typing import Any + + +from typing import TYPE_CHECKING +from orcapod.utils.lazy_module import LazyModule + +if TYPE_CHECKING: + import pyarrow as pa +else: + pa = LazyModule("pyarrow") + + +def schema_select( + arrow_schema: "pa.Schema", + column_names: Collection[str], + ignore_missing: bool = False, +) -> "pa.Schema": + """ + Select a subset of columns from a PyArrow schema. + + Args: + arrow_schema: The original PyArrow schema. + column_names: A collection of column names to select. + + Returns: + A new PyArrow schema containing only the selected columns. + """ + if not ignore_missing: + existing_columns = set(field.name for field in arrow_schema) + missing_columns = set(column_names) - existing_columns + if missing_columns: + raise KeyError(f"Missing columns in Arrow schema: {missing_columns}") + selected_fields = [field for field in arrow_schema if field.name in column_names] + return pa.schema(selected_fields) + + +def schema_drop( + arrow_schema: "pa.Schema", + column_names: Collection[str], + ignore_missing: bool = False, +) -> "pa.Schema": + """ + Drop a subset of columns from a PyArrow schema. + + Args: + arrow_schema: The original PyArrow schema. + column_names: A collection of column names to drop. + + Returns: + A new PyArrow schema containing only the remaining columns. + """ + if not ignore_missing: + existing_columns = set(field.name for field in arrow_schema) + missing_columns = set(column_names) - existing_columns + if missing_columns: + raise KeyError(f"Missing columns in Arrow schema: {missing_columns}") + remaining_fields = [ + field for field in arrow_schema if field.name not in column_names + ] + return pa.schema(remaining_fields) + + +def normalize_to_large_types(arrow_type: "pa.DataType") -> "pa.DataType": + """ + Recursively convert Arrow types to their large variants where available. + + This ensures consistent schema representation regardless of the original + type choices (e.g., string vs large_string, binary vs large_binary). + + Args: + arrow_type: Arrow data type to normalize + + Returns: + Arrow data type with large variants substituted + + Examples: + >>> normalize_to_large_types(pa.string()) + large_string + + >>> normalize_to_large_types(pa.list_(pa.string())) + large_list + + >>> normalize_to_large_types(pa.struct([pa.field("name", pa.string())])) + struct + """ + # Handle primitive types that have large variants + if pa.types.is_null(arrow_type): + # TODO: make this configurable + return pa.large_string() + if pa.types.is_string(arrow_type): + return pa.large_string() + elif pa.types.is_binary(arrow_type): + return pa.large_binary() + elif pa.types.is_list(arrow_type): + # Regular list -> large_list with normalized element type + element_type = normalize_to_large_types(arrow_type.value_type) + return pa.large_list(element_type) + + # Large variants and fixed-size lists stay as-is (already normalized or no large variant) + elif pa.types.is_large_string(arrow_type) or pa.types.is_large_binary(arrow_type): + return arrow_type + elif pa.types.is_large_list(arrow_type): + # Still need to normalize the element type + element_type = normalize_to_large_types(arrow_type.value_type) + return pa.large_list(element_type) + elif pa.types.is_fixed_size_list(arrow_type): + # Fixed-size lists don't have large variants, but normalize element type + element_type = normalize_to_large_types(arrow_type.value_type) + return pa.list_(element_type, arrow_type.list_size) + + # Handle struct types recursively + elif pa.types.is_struct(arrow_type): + normalized_fields = [] + for field in arrow_type: + normalized_field_type = normalize_to_large_types(field.type) + normalized_fields.append( + pa.field( + field.name, + normalized_field_type, + nullable=field.nullable, + metadata=field.metadata, + ) + ) + return pa.struct(normalized_fields) + + # Handle map types (key and value types) + elif pa.types.is_map(arrow_type): + normalized_key_type = normalize_to_large_types(arrow_type.key_type) + normalized_value_type = normalize_to_large_types(arrow_type.item_type) + return pa.map_(normalized_key_type, normalized_value_type) + + # Handle union types + elif pa.types.is_union(arrow_type): + # Union types contain multiple child types + normalized_child_types = [] + for i in range(arrow_type.num_fields): + child_field = arrow_type[i] + normalized_child_type = normalize_to_large_types(child_field.type) + normalized_child_types.append( + pa.field(child_field.name, normalized_child_type) + ) + + # Reconstruct union with normalized child types + if isinstance(arrow_type, pa.SparseUnionType): + return pa.sparse_union(normalized_child_types) + else: # dense union + return pa.dense_union(normalized_child_types) + + # Handle dictionary types + elif pa.types.is_dictionary(arrow_type): + # Normalize the value type (dictionary values), keep index type as-is + normalized_value_type = normalize_to_large_types(arrow_type.value_type) + return pa.dictionary(arrow_type.index_type, normalized_value_type) # type: ignore + + # All other types (int, float, bool, date, timestamp, etc.) don't have large variants + else: + return arrow_type + + +def normalize_schema_to_large_types(schema: "pa.Schema") -> "pa.Schema": + """ + Convert a schema to use large variants of data types. + + This normalizes schemas so that string -> large_string, binary -> large_binary, + list -> large_list, etc., handling nested structures recursively. + + Args: + schema: Arrow schema to normalize + + Returns: + New schema with large type variants, or same schema if no changes needed + + Examples: + >>> schema = pa.schema([ + ... pa.field("name", pa.string()), + ... pa.field("files", pa.list_(pa.string())), + ... ]) + >>> normalize_schema_to_large_types(schema) + name: large_string + files: large_list + """ + normalized_fields = [] + schema_changed = False + + for field in schema: + normalized_type = normalize_to_large_types(field.type) + + # Check if the type actually changed + if normalized_type != field.type: + schema_changed = True + + normalized_field = pa.field( + field.name, + normalized_type, + nullable=field.nullable, + metadata=field.metadata, + ) + normalized_fields.append(normalized_field) + + # Only create new schema if something actually changed + if schema_changed: + return pa.schema(normalized_fields, metadata=schema.metadata) # type: ignore + else: + return schema + + +def normalize_table_to_large_types(table: "pa.Table") -> "pa.Table": + """ + Normalize table schema to use large type variants. + + Uses cast() which should be zero-copy for large variant conversions + since they have identical binary representations, but ensures proper + type validation and handles any edge cases safely. + + Args: + table: Arrow table to normalize + + Returns: + Table with normalized schema, or same table if no changes needed + + Examples: + >>> table = pa.table({"name": ["Alice", "Bob"], "age": [25, 30]}) + >>> normalized = normalize_table_to_large_types(table) + >>> normalized.schema + name: large_string + age: int64 + """ + normalized_schema = normalize_schema_to_large_types(table.schema) + + # If schema didn't change, return original table + if normalized_schema is table.schema: + return table + + # Use cast() for safety - should be zero-copy for large variant conversions + # but handles Arrow's internal type validation and any edge cases properly + return table.cast(normalized_schema) + + +def pylist_to_pydict(pylist: list[dict]) -> dict: + """ + Convert a list of dictionaries to a dictionary of lists (columnar format). + + This function transforms row-based data (list of dicts) to column-based data + (dict of lists), similar to converting from records format to columnar format. + Missing keys in individual dictionaries are filled with None values. + + Args: + pylist: List of dictionaries representing rows of data + + Returns: + Dictionary where keys are column names and values are lists of column data + + Example: + >>> data = [{'a': 1, 'b': 2}, {'a': 3, 'c': 4}] + >>> pylist_to_pydict(data) + {'a': [1, 3], 'b': [2, None], 'c': [None, 4]} + """ + result = {} + known_keys = set() + for i, d in enumerate(pylist): + known_keys.update(d.keys()) + for k in known_keys: + result.setdefault(k, [None] * i).append(d.get(k, None)) + return result + + +def pydict_to_pylist(pydict: dict) -> list[dict]: + """ + Convert a dictionary of lists (columnar format) to a list of dictionaries. + + This function transforms column-based data (dict of lists) to row-based data + (list of dicts), similar to converting from columnar format to records format. + All arrays in the input dictionary must have the same length. + + Args: + pydict: Dictionary where keys are column names and values are lists of column data + + Returns: + List of dictionaries representing rows of data + + Raises: + ValueError: If arrays in the dictionary have inconsistent lengths + + Example: + >>> data = {'a': [1, 3], 'b': [2, None], 'c': [None, 4]} + >>> pydict_to_pylist(data) + [{'a': 1, 'b': 2, 'c': None}, {'a': 3, 'b': None, 'c': 4}] + """ + if not pydict: + return [] + + # Check all arrays have same length + lengths = [len(v) for v in pydict.values()] + if not all(length == lengths[0] for length in lengths): + raise ValueError( + f"Inconsistent array lengths: {dict(zip(pydict.keys(), lengths))}" + ) + + num_rows = lengths[0] + if num_rows == 0: + return [] + + result = [] + keys = pydict.keys() + for i in range(num_rows): + row = {k: pydict[k][i] for k in keys} + result.append(row) + return result + + +def join_arrow_schemas(*schemas: "pa.Schema") -> "pa.Schema": + """Join multiple Arrow schemas into a single schema, ensuring compatibility of fields. In particular, + no field names should collide.""" + merged_fields = [] + for schema in schemas: + merged_fields.extend(schema) + return pa.schema(merged_fields) + + +def select_table_columns_with_prefixes( + table: "pa.Table", prefix: str | Collection[str] +) -> "pa.Table": + """ + Select columns from a PyArrow table that start with a specific prefix. + + Args: + table (pa.Table): The original table. + prefix (str): The prefix to filter column names. + + Returns: + pa.Table: New table containing only the columns with the specified prefix. + """ + if isinstance(prefix, str): + prefix = [prefix] + selected_columns = [ + col for col in table.column_names if any(col.startswith(p) for p in prefix) + ] + return table.select(selected_columns) + + +def select_schema_columns_with_prefixes( + schema: "pa.Schema", prefix: str | Collection[str] +) -> "pa.Schema": + """ + Select columns from an Arrow schema that start with a specific prefix. + + Args: + schema (pa.Schema): The original schema. + prefix (str): The prefix to filter column names. + + Returns: + pa.Schema: New schema containing only the columns with the specified prefix. + """ + if isinstance(prefix, str): + prefix = [prefix] + selected_fields = [ + field for field in schema if any(field.name.startswith(p) for p in prefix) + ] + return pa.schema(selected_fields) + + +def select_arrow_schema(schema: "pa.Schema", columns: Collection[str]) -> "pa.Schema": + """ + Select specific columns from an Arrow schema. + + Args: + schema (pa.Schema): The original schema. + columns (Collection[str]): List of column names to select. + + Returns: + pa.Schema: New schema containing only the specified columns. + """ + selected_fields = [field for field in schema if field.name in columns] + return pa.schema(selected_fields) + + +def hstack_tables(*tables: "pa.Table") -> "pa.Table": + """ + Horizontally stack multiple PyArrow tables by concatenating their columns. + + All input tables must have the same number of rows and unique column names. + + Args: + *tables: Variable number of PyArrow tables to stack horizontally + + Returns: + Combined PyArrow table with all columns from input tables + + Raises: + ValueError: If no tables provided, tables have different row counts, + or duplicate column names are found + """ + if len(tables) == 0: + raise ValueError("At least one table is required for horizontal stacking.") + if len(tables) == 1: + return tables[0] + + N = len(tables[0]) + for table in tables[1:]: + if len(table) != N: + raise ValueError( + "All tables must have the same number of rows for horizontal stacking." + ) + + # create combined schema + all_fields = [] + all_names = set() + for table in tables: + for field in table.schema: + if field.name in all_names: + raise ValueError( + f"Duplicate column name '{field.name}' found in input tables." + ) + all_fields.append(field) + all_names.add(field.name) + combined_schmea = pa.schema(all_fields) + + # create combined columns + all_columns = [] + for table in tables: + all_columns += table.columns + + return pa.Table.from_arrays(all_columns, schema=combined_schmea) + + +def check_arrow_schema_compatibility( + incoming_schema: "pa.Schema", target_schema: "pa.Schema", strict: bool = False +) -> tuple[bool, list[str]]: + # TODO: add strict comparison + """ + Check if incoming schema is compatible with current schema. + + Args: + incoming_schema: Schema to validate + target_schema: Expected schema to match against + strict: If True, requires exact match of field names and types. If False (default), + incoming_schema can have additional fields or different types as long as they are compatible. + + Returns: + Tuple of (is_compatible, list_of_errors) + """ + errors = [] + + # Create lookup dictionaries for efficient access + incoming_fields = {field.name: field for field in incoming_schema} + target_fields = {field.name: field for field in target_schema} + + # Check each field in target_schema + for field_name, target_field in target_fields.items(): + if field_name not in incoming_fields: + errors.append(f"Missing field '{field_name}' in incoming schema") + continue + + incoming_field = incoming_fields[field_name] + + # Check data type compatibility + if not target_field.type.equals(incoming_field.type): + # TODO: if not strict, allow type coercion + errors.append( + f"Type mismatch for field '{field_name}': " + f"expected {target_field.type}, got {incoming_field.type}" + ) + + # Check semantic_type metadata if present in current schema + current_metadata = target_field.metadata or {} + incoming_metadata = incoming_field.metadata or {} + + if b"semantic_type" in current_metadata: + expected_semantic_type = current_metadata[b"semantic_type"] + + if b"semantic_type" not in incoming_metadata: + errors.append( + f"Missing 'semantic_type' metadata for field '{field_name}'" + ) + elif incoming_metadata[b"semantic_type"] != expected_semantic_type: + errors.append( + f"Semantic type mismatch for field '{field_name}': " + f"expected {expected_semantic_type.decode()}, " + f"got {incoming_metadata[b'semantic_type'].decode()}" + ) + elif b"semantic_type" in incoming_metadata: + errors.append( + f"Unexpected 'semantic_type' metadata for field '{field_name}': " + f"{incoming_metadata[b'semantic_type'].decode()}" + ) + + # If strict mode, check for additional fields in incoming schema + if strict: + for field_name in incoming_fields: + if field_name not in target_fields: + errors.append(f"Unexpected field '{field_name}' in incoming schema") + + return len(errors) == 0, errors + + +def split_by_column_groups( + table, + *column_groups: Collection[str], +) -> tuple["pa.Table | None", ...]: + """ + Split the table into multiple tables based on the provided column groups. + Each group is a collection of column names that should be included in the same table. + The remaining columns that are not part of any group will be returned as the first table/None. + """ + if not column_groups: + return (table,) + + tables = [] + remaining_columns = set(table.column_names) + + for group in column_groups: + group_columns = [col for col in group if col in remaining_columns] + if group_columns: + tables.append(table.select(group_columns)) + remaining_columns.difference_update(group_columns) + else: + tables.append(None) + + remaining_table = None + if remaining_columns: + ordered_remaining_columns = [ + col for col in table.column_names if col in remaining_columns + ] + remaining_table = table.select(ordered_remaining_columns) + return (remaining_table, *tables) + + +def prepare_prefixed_columns( + table: "pa.Table | pa.RecordBatch", + prefix_info: Collection[str] + | Mapping[str, Any | None] + | Mapping[str, Mapping[str, Any | None]], + exclude_columns: Collection[str] = (), + exclude_prefixes: Collection[str] = (), +) -> tuple["pa.Table", dict[str, "pa.Table"]]: + """ """ + all_prefix_info = {} + if isinstance(prefix_info, Mapping): + for prefix, info in prefix_info.items(): + if isinstance(info, Mapping): + all_prefix_info[prefix] = info + else: + all_prefix_info[prefix] = info + elif isinstance(prefix_info, Collection): + for prefix in prefix_info: + all_prefix_info[prefix] = {} + else: + raise TypeError( + "prefix_group must be a Collection of strings or a Mapping of string to string or None." + ) + + # split column into prefix groups + data_column_names = [] + data_columns = [] + existing_prefixed_columns = defaultdict(list) + + for col_name in table.column_names: + prefix_found = False + for prefix in all_prefix_info: + if col_name.startswith(prefix): + # Remove the prefix from the column name + base_name = col_name.removeprefix(prefix) + existing_prefixed_columns[prefix].append(base_name) + prefix_found = True + if not prefix_found: + # if no prefix found, consider this as a data column + data_column_names.append(col_name) + data_columns.append(table[col_name]) + + # Create source_info columns for each regular column + num_rows = table.num_rows + + prefixed_column_names = defaultdict(list) + prefixed_columns = defaultdict(list) + + target_column_names = [ + c + for c in data_column_names + if not any(c.startswith(prefix) for prefix in exclude_prefixes) + and c not in exclude_columns + ] + + for prefix, value_lut in all_prefix_info.items(): + target_prefixed_column_names = prefixed_column_names[prefix] + target_prefixed_columns = prefixed_columns[prefix] + + for col_name in target_column_names: + prefixed_col_name = f"{prefix}{col_name}" + existing_columns = existing_prefixed_columns[prefix] + + if isinstance(value_lut, Mapping): + value = value_lut.get(col_name) + else: + value = value_lut + + if value is not None: + # Use value from source_info dictionary + # TODO: clean up the logic here + if not isinstance(value, str) and isinstance(value, Collection): + # TODO: this won't work other data types!!! + column_values = pa.array( + [value] * num_rows, type=pa.list_(pa.large_string()) + ) + else: + column_values = pa.array([value] * num_rows, type=pa.large_string()) + # if col_name is in existing_source_info, use that column + elif col_name in existing_columns: + # Use existing prefixed column, but convert to large_string + existing_col = table[prefixed_col_name] + + if existing_col.type == pa.string(): + # Convert to large_string + column_values = pa.compute.cast(existing_col, pa.large_string()) # type: ignore + else: + column_values = existing_col + else: + # Use null values + column_values = pa.array([None] * num_rows, type=pa.large_string()) + target_prefixed_column_names.append(prefixed_col_name) + target_prefixed_columns.append(column_values) + + # Step 3: Create the final table + data_table: pa.Table = pa.Table.from_arrays(data_columns, names=data_column_names) + result_tables = {} + for prefix in all_prefix_info: + prefix_table = pa.Table.from_arrays( + prefixed_columns[prefix], names=prefixed_column_names[prefix] + ) + result_tables[prefix] = normalize_table_to_large_types(prefix_table) + + return data_table, result_tables + + +def drop_schema_columns(schema: "pa.Schema", columns: Collection[str]) -> "pa.Schema": + """ + Drop specified columns from a PyArrow schema. + + Args: + schema (pa.Schema): The original schema. + columns (list[str]): List of column names to drop. + + Returns: + pa.Schema: New schema with specified columns removed. + """ + return pa.schema([field for field in schema if field.name not in columns]) + + +# Test function to demonstrate usage +def test_schema_normalization(): + """Test the schema normalization functions.""" + print("=== Testing Arrow Schema Normalization ===\n") + + # Test basic types + print("1. Basic type normalization:") + basic_types = [ + pa.string(), + pa.binary(), + pa.list_(pa.string()), + pa.large_string(), # Should stay the same + pa.int64(), # Should stay the same + ] + + for arrow_type in basic_types: + normalized = normalize_to_large_types(arrow_type) + print(f" {arrow_type} -> {normalized}") + + print("\n2. Complex nested type normalization:") + complex_types = [ + pa.struct( + [pa.field("name", pa.string()), pa.field("files", pa.list_(pa.binary()))] + ), + pa.map_(pa.string(), pa.list_(pa.string())), + pa.large_list( + pa.struct([pa.field("id", pa.int64()), pa.field("path", pa.string())]) + ), + ] + + for arrow_type in complex_types: + normalized = normalize_to_large_types(arrow_type) + print(f" {arrow_type}") + print(f" -> {normalized}") + print() + + print("3. Schema normalization:") + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("name", pa.string()), + pa.field("files", pa.list_(pa.string())), + pa.field( + "metadata", + pa.struct( + [pa.field("created", pa.string()), pa.field("size", pa.int64())] + ), + ), + ] + ) + + print("Original schema:") + print(schema) + + normalized_schema = normalize_schema_to_large_types(schema) + print("\nNormalized schema:") + print(normalized_schema) + + print("\n4. Table normalization:") + table_data = { + "name": ["Alice", "Bob", "Charlie"], + "files": [ + ["file1.txt", "file2.txt"], + ["data.csv"], + ["config.json", "output.log"], + ], + } + + table = pa.table(table_data) + print("Original table schema:") + print(table.schema) + + normalized_table = normalize_table_to_large_types(table) + print("\nNormalized table schema:") + print(normalized_table.schema) + + # Verify data is preserved + print(f"\nData preserved: {table.to_pydict() == normalized_table.to_pydict()}") + + +if __name__ == "__main__": + test_schema_normalization() diff --git a/src/orcapod/utils/function_info.py b/src/orcapod/utils/function_info.py new file mode 100644 index 0000000..c76f8e6 --- /dev/null +++ b/src/orcapod/utils/function_info.py @@ -0,0 +1,320 @@ +import ast +import inspect +from collections.abc import Callable + + +def _is_in_string(line: str, pos: int) -> bool: + """Helper to check if a position in a line is inside a string literal.""" + # This is a simplified check - would need proper parsing for robust handling + in_single = False + in_double = False + for i in range(pos): + if line[i] == "'" and not in_double and (i == 0 or line[i - 1] != "\\"): + in_single = not in_single + elif line[i] == '"' and not in_single and (i == 0 or line[i - 1] != "\\"): + in_double = not in_double + return in_single or in_double + + +class SourceProcessor: + """Handles AST-based and fallback source code processing.""" + + # @staticmethod + # def remove_docstrings_and_comments_ast( + # source: str, remove_docstrings: bool = True, remove_comments: bool = True + # ) -> str: + # """Remove docstrings and comments using AST parsing.""" + # try: + # tree = ast.parse(source) + + # if remove_docstrings: + # SourceProcessor._remove_docstrings_from_ast(tree) + + # # Convert back to source + # import astor # Note: This would require astor package + + # processed = astor.to_source(tree) + + # if remove_comments: + # # AST doesn't preserve comments, so we still need line-by-line processing + # processed = SourceProcessor._remove_comments_fallback(processed) + + # return processed + + # except (ImportError, SyntaxError, TypeError): + # # Fall back to string-based processing + # return SourceProcessor._remove_docstrings_and_comments_fallback( + # source, remove_docstrings, remove_comments + # ) + + @staticmethod + def _remove_docstrings_from_ast(node: ast.AST) -> None: + """Remove docstring nodes from AST.""" + for child in ast.walk(node): + if isinstance(child, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)): + if ( + child.body + and isinstance(child.body[0], ast.Expr) + and isinstance(child.body[0].value, ast.Constant) + and isinstance(child.body[0].value.value, str) + ): + child.body.pop(0) + + @staticmethod + def _remove_docstrings_and_comments_fallback( + source: str, remove_docstrings: bool = True, remove_comments: bool = True + ) -> str: + """Fallback string-based processing.""" + if remove_comments: + lines = source.split("\n") + for i, line in enumerate(lines): + comment_pos = line.find("#") + if comment_pos >= 0 and not _is_in_string(line, comment_pos): + lines[i] = line[:comment_pos].rstrip() + source = "\n".join(lines) + + # Simplified docstring removal (keeping original logic) + if remove_docstrings: + # This is basic - the AST approach above is more robust + pass + + return source + + @staticmethod + def _remove_comments_fallback(source: str) -> str: + """Remove comments using line-by-line parsing.""" + lines = source.split("\n") + for i, line in enumerate(lines): + comment_pos = line.find("#") + if comment_pos >= 0 and not _is_in_string(line, comment_pos): + lines[i] = line[:comment_pos].rstrip() + return "\n".join(lines) + + +def extract_decorators(func: Callable) -> list[str]: + """Extract decorator information from function source.""" + decorators = [] + try: + source = inspect.getsource(func) + tree = ast.parse(source) + + for node in ast.walk(tree): + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): + for decorator in node.decorator_list: + if isinstance(decorator, ast.Name): + decorators.append(decorator.id) + elif isinstance(decorator, ast.Attribute): + decorators.append(ast.unparse(decorator)) + elif isinstance(decorator, ast.Call): + decorators.append(ast.unparse(decorator)) + break # Only process the first function found + + except (ImportError, SyntaxError, TypeError, IOError): + # Fallback: try to extract decorators from source lines + try: + source_lines = inspect.getsourcelines(func)[0] + for line in source_lines: + stripped = line.strip() + if stripped.startswith("@"): + decorators.append(stripped[1:]) + elif stripped.startswith("def "): + break + except (IOError, TypeError): + pass + + return decorators + + +def get_function_components( + func: Callable, + name_override: str | None = None, + include_name: bool = True, + include_module: bool = True, + include_declaration: bool = True, + include_docstring: bool = True, + include_comments: bool = True, + preserve_whitespace: bool = True, + include_annotations: bool = True, + include_code_properties: bool = True, + include_defaults: bool = True, + include_custom_attributes: bool = True, + include_closure_info: bool = True, + include_decorators: bool = True, + include_extended_code_props: bool = True, + use_ast_parsing: bool = True, + skip_source_for_performance: bool = False, +) -> list[str]: + """ + Extract the components of a function that determine its identity for hashing. + + Args: + func: The function to process + name_override: Override for function name + include_name: Whether to include the function name + include_module: Whether to include the module name + include_declaration: Whether to include the function declaration line + include_docstring: Whether to include the function's docstring + include_comments: Whether to include comments in the function body + preserve_whitespace: Whether to preserve original whitespace/indentation + include_annotations: Whether to include function type annotations + include_code_properties: Whether to include basic code object properties + include_defaults: Whether to include default parameter values + include_custom_attributes: Whether to include custom function attributes + include_closure_info: Whether to include closure variable information + include_decorators: Whether to include decorator information + include_extended_code_props: Whether to include extended code properties + use_ast_parsing: Whether to use AST-based parsing for robust processing + skip_source_for_performance: Skip expensive source code operations + + Returns: + A list of string components + """ + components = [] + + # Add function name + if include_name: + components.append(f"name:{name_override or func.__name__}") + + # Add module + if include_module and hasattr(func, "__module__"): + components.append(f"module:{func.__module__}") + + # Add decorators + if include_decorators and not skip_source_for_performance: + try: + decorators = extract_decorators(func) + if decorators: + components.append(f"decorators:{';'.join(decorators)}") + except Exception: + pass # Don't fail if decorator extraction fails + + # Process source code + if not skip_source_for_performance: + try: + source = inspect.getsource(func) + + # Handle whitespace preservation + if not preserve_whitespace: + source = inspect.cleandoc(source) + + # Remove declaration if requested + if not include_declaration: + lines = source.split("\n") + for i, line in enumerate(lines): + if line.strip().startswith(("def ", "async def ")): + lines.pop(i) + break + source = "\n".join(lines) + + # Process docstrings and comments + if not include_docstring or not include_comments: + # if use_ast_parsing: + # source = SourceProcessor.remove_docstrings_and_comments_ast( + # source, + # remove_docstrings=not include_docstring, + # remove_comments=not include_comments, + # ) + # else: + source = SourceProcessor._remove_docstrings_and_comments_fallback( + source, + remove_docstrings=not include_docstring, + remove_comments=not include_comments, + ) + + components.append(f"source:{source}") + + except (IOError, TypeError, OSError): + # Handle special function types + if hasattr(func, "__name__"): + if func.__name__ == "": + components.append("function_type:lambda") + else: + components.append("function_type:dynamic_or_builtin") + + # Fall back to signature if available + try: + sig = inspect.signature(func) + components.append(f"signature:{str(sig)}") + except (ValueError, TypeError): + components.append("builtin:True") + + # Add function annotations + if ( + include_annotations + and hasattr(func, "__annotations__") + and func.__annotations__ + ): + sorted_annotations = sorted(func.__annotations__.items()) + annotations_str = ";".join(f"{k}:{v}" for k, v in sorted_annotations) + components.append(f"annotations:{annotations_str}") + + # Add default parameter values + if include_defaults: + defaults_info = [] + + if hasattr(func, "__defaults__") and func.__defaults__: + defaults_info.append(f"defaults:{func.__defaults__}") + + if hasattr(func, "__kwdefaults__") and func.__kwdefaults__: + sorted_kwdefaults = sorted(func.__kwdefaults__.items()) + kwdefaults_str = ";".join(f"{k}:{v}" for k, v in sorted_kwdefaults) + defaults_info.append(f"kwdefaults:{kwdefaults_str}") + + if defaults_info: + components.extend(defaults_info) + + # Add custom function attributes + if include_custom_attributes and hasattr(func, "__dict__") and func.__dict__: + # Filter out common built-in attributes + custom_attrs = {k: v for k, v in func.__dict__.items() if not k.startswith("_")} + if custom_attrs: + sorted_attrs = sorted(custom_attrs.items()) + attrs_str = ";".join(f"{k}:{v}" for k, v in sorted_attrs) + components.append(f"custom_attributes:{attrs_str}") + + # Add closure information + if include_closure_info and hasattr(func, "__closure__") and func.__closure__: + # Be careful with closure - it can contain dynamic data + # We'll just include the variable names, not values + try: + closure_vars = ( + func.__code__.co_freevars if hasattr(func, "__code__") else () + ) + if closure_vars: + components.append(f"closure_vars:{closure_vars}") + except AttributeError: + pass + + # Add basic code object properties + if include_code_properties and hasattr(func, "__code__"): + code = func.__code__ + stable_code_props = { + "co_argcount": code.co_argcount, + "co_kwonlyargcount": getattr(code, "co_kwonlyargcount", 0), + "co_nlocals": code.co_nlocals, + "co_varnames": code.co_varnames[: code.co_argcount], + } + components.append(f"code_properties:{stable_code_props}") + + # Add extended code object properties + if include_extended_code_props and hasattr(func, "__code__"): + code = func.__code__ + extended_props = {} + + # Add code flags (generator, coroutine, etc.) + if hasattr(code, "co_flags"): + extended_props["co_flags"] = code.co_flags + + # Add free variables (closure) + if hasattr(code, "co_freevars") and code.co_freevars: + extended_props["co_freevars"] = code.co_freevars + + # Add global names referenced + if hasattr(code, "co_names") and code.co_names: + # Limit to avoid too much noise - maybe first 10 names + extended_props["co_names"] = code.co_names[:10] + + if extended_props: + components.append(f"extended_code_properties:{extended_props}") + + return components diff --git a/src/orcapod/utils/lazy_module.py b/src/orcapod/utils/lazy_module.py new file mode 100644 index 0000000..75cf057 --- /dev/null +++ b/src/orcapod/utils/lazy_module.py @@ -0,0 +1,155 @@ +import importlib +from types import ModuleType +from typing import Any, Optional + + +class LazyModule: + """ + A wrapper that lazily loads a module only when its attributes are first accessed. + + Example: + # Instead of: import expensive_module + expensive_module = LazyModule('expensive_module') + + # Module is only loaded when you access something: + result = expensive_module.some_function() # Now it imports + """ + + def __init__(self, module_name: str, package: str | None = None): + """ + Initialize lazy module loader. + + Args: + module_name: Name of the module to import + package: Package for relative imports (same as importlib.import_module) + """ + self._module_name = module_name + self._package = package + self._module: ModuleType | None = None + self._loaded = False + + def _load_module(self) -> ModuleType: + """Load the module if not already loaded.""" + if not self._loaded: + self._module = importlib.import_module(self._module_name, self._package) + self._loaded = True + assert self._module is not None, ( + f"Module '{self._module_name}' could not be loaded. " + "This should not happen if the module exists." + ) + return self._module + + def __getattr__(self, name: str) -> Any: + """Get attribute from the wrapped module, loading it if necessary.""" + if name.startswith("_"): + # Avoid infinite recursion for internal attributes + raise AttributeError( + f"'{self.__class__.__name__}' object has no attribute '{name}'" + ) + + module = self._load_module() + return getattr(module, name) + + def __setattr__(self, name: str, value: Any) -> None: + """Set attribute on the wrapped module or on this instance.""" + if name.startswith("_") or not self._loaded: + # Set on this instance for internal attributes or before loading + super().__setattr__(name, value) + else: + # Set on the wrapped module + setattr(self._load_module(), name, value) + + def __delattr__(self, name: str) -> None: + """Delete attribute from the wrapped module.""" + if name.startswith("_"): + super().__delattr__(name) + else: + delattr(self._load_module(), name) + + def __dir__(self) -> list[str]: + """Return directory of the wrapped module.""" + if self._loaded: + return dir(self._module) + else: + # Return empty list or basic attributes before loading + return [] + + def __repr__(self) -> str: + """String representation.""" + if self._loaded: + return f"" + else: + return f"" + + def __str__(self) -> str: + """String representation.""" + return self.__repr__() + + # Support for callable modules (modules with __call__) + def __call__(self, *args, **kwargs): + """Call the module if it's callable.""" + module = self._load_module() + return module(*args, **kwargs) # type: ignore + + # Support for iteration if the module is iterable + def __iter__(self): + """Iterate over the module if it's iterable.""" + module = self._load_module() + return iter(module) # type: ignore + + def __len__(self): + """Get length of the module if it supports len().""" + module = self._load_module() + return len(module) # type: ignore + + def __getitem__(self, key): + """Get item from the module if it supports indexing.""" + module = self._load_module() + return module[key] # type: ignore + + def __setitem__(self, key, value): + """Set item on the module if it supports item assignment.""" + module = self._load_module() + module[key] = value # type: ignore + + def __contains__(self, item): + """Check if item is in the module if it supports 'in' operator.""" + module = self._load_module() + return item in module + + @property + def is_loaded(self) -> bool: + """Check if the module has been loaded.""" + return self._loaded + + @property + def module_name(self) -> str: + """Get the module name.""" + return self._module_name + + def force_load(self) -> ModuleType: + """Force load the module and return it.""" + return self._load_module() + + +# Convenience function for creating lazy modules +def lazy_import(module_name: str, package: Optional[str] = None) -> LazyModule: + """ + Create a lazy module loader. + + Args: + module_name: Name of the module to import + package: Package for relative imports + + Returns: + LazyModule instance that will load the module on first access + + Example: + np = lazy_import('numpy') + pd = lazy_import('pandas') + + # Modules are only imported when you use them: + array = np.array([1, 2, 3]) # numpy imported here + df = pd.DataFrame({'a': [1, 2]}) # pandas imported here + """ + return LazyModule(module_name, package) diff --git a/src/orcapod/utils/name.py b/src/orcapod/utils/name.py index ba2c4f0..2211ef6 100644 --- a/src/orcapod/utils/name.py +++ b/src/orcapod/utils/name.py @@ -5,6 +5,70 @@ import re +# TODO: move these functions to util +def escape_with_postfix(field: str, postfix=None, separator="_") -> str: + """ + Escape the field string by doubling separators and optionally append a postfix. + This function takes a field string and escapes any occurrences of the separator + by doubling them, then optionally appends a postfix with a separator prefix. + + Args: + field (str): The input string containing to be escaped. + postfix (str, optional): An optional postfix to append to the escaped string. + If None, no postfix is added. Defaults to None. + separator (str, optional): The separator character to escape and use for + prefixing the postfix. Defaults to "_". + Returns: + str: The escaped string with optional postfix. Returns empty string if + fields is provided but postfix is None. + Examples: + >>> escape_with_postfix("field1_field2", "suffix") + 'field1__field2_suffix' + >>> escape_with_postfix("name_age_city", "backup", "_") + 'name__age__city_backup' + >>> escape_with_postfix("data-info", "temp", "-") + 'data--info-temp' + >>> escape_with_postfix("simple", None) + 'simple' + >>> escape_with_postfix("no_separators", "end") + 'no__separators_end' + """ + + return field.replace(separator, separator * 2) + (f"_{postfix}" if postfix else "") + + +def unescape_with_postfix(field: str, separator="_") -> tuple[str, str | None]: + """ + Unescape a string by converting double separators back to single separators and extract postfix metadata. + This function reverses the escaping process where single separators were doubled to avoid + conflicts with metadata delimiters. It splits the input on double separators, then extracts + any postfix metadata from the last part. + + Args: + field (str): The escaped string containing doubled separators and optional postfix metadata + separator (str, optional): The separator character used for escaping. Defaults to "_" + Returns: + tuple[str, str | None]: A tuple containing: + - The unescaped string with single separators restored + - The postfix metadata if present, None otherwise + Examples: + >>> unescape_with_postfix("field1__field2__field3") + ('field1_field2_field3', None) + >>> unescape_with_postfix("field1__field2_metadata") + ('field1_field2', 'metadata') + >>> unescape_with_postfix("simple") + ('simple', None) + >>> unescape_with_postfix("field1--field2", separator="-") + ('field1-field2', None) + >>> unescape_with_postfix("field1--field2-meta", separator="-") + ('field1-field2', 'meta') + """ + + parts = field.split(separator * 2) + parts[-1], *meta = parts[-1].split("_", 1) + return separator.join(parts), meta[0] if meta else None + + def find_noncolliding_name(name: str, lut: dict) -> str: """ Generate a unique name that does not collide with existing keys in a lookup table (lut). diff --git a/src/orcapod/utils/object_spec.py b/src/orcapod/utils/object_spec.py new file mode 100644 index 0000000..8ecfd0a --- /dev/null +++ b/src/orcapod/utils/object_spec.py @@ -0,0 +1,105 @@ +import importlib +from typing import Any +from weakref import ref + + +def parse_objectspec( + obj_spec: Any, + ref_lut: dict[str, Any] | None = None, + validate: bool = True, +) -> Any: + """Enhanced ObjectSpec with better error handling and validation.""" + if ref_lut is None: + ref_lut = {} + + if isinstance(obj_spec, dict): + if "_class" in obj_spec: + return _create_instance_from_spec(obj_spec, ref_lut, validate) + elif "_ref" in obj_spec: + ref_key = obj_spec["_ref"] + if ref_key in ref_lut: + return ref_lut[ref_key] + else: + raise ValueError(f"Unknown reference: {ref_key}") + else: + # Recursively process dict + return { + k: parse_objectspec(v, ref_lut, validate) for k, v in obj_spec.items() + } + + elif isinstance(obj_spec, (list, tuple)): + processed = [parse_objectspec(item, ref_lut, validate) for item in obj_spec] + return tuple(processed) if isinstance(obj_spec, tuple) else processed + + else: + return obj_spec + + +def _create_instance_from_spec( + spec: dict[str, Any], ref_lut: dict[str, Any], validate: bool +) -> Any: + """Create instance with better error handling.""" + try: + class_path = spec["_class"] + config = spec.get("_config", {}) + + # Import and validate class exists + module_name, class_name = class_path.rsplit(".", 1) + module = importlib.import_module(module_name) + cls = getattr(module, class_name) + + # Process config recursively + processed_config = parse_objectspec(config, ref_lut, validate) + + # Optional: validate config matches class signature + if validate: + _validate_config_for_class(cls, processed_config) + + return cls(**processed_config) + + except Exception as e: + raise ValueError(f"Failed to create instance from spec {spec}: {e}") from e + + +def _validate_config_for_class(cls: type, config: dict[str, Any]) -> None: + """Optional validation that config matches class signature.""" + import inspect + + try: + sig = inspect.signature(cls.__init__) + valid_params = set(sig.parameters.keys()) - {"self"} + invalid_params = set(config.keys()) - valid_params + + if invalid_params: + raise ValueError(f"Invalid parameters for {cls.__name__}: {invalid_params}") + + except Exception: + # Skip validation if introspection fails + pass + + +# def parse_objectspec(obj_spec: Any) -> Any: +# if isinstance(obj_spec, dict): +# if "_class" in obj_spec: +# # if _class is specified, treat the dict as an object specification, looking for +# # _config key to extract configuration parameters +# module_name, class_name = obj_spec["_class"].rsplit(".", 1) +# module = importlib.import_module(module_name) +# cls = getattr(module, class_name) +# configs = parse_objectspec(obj_spec.get("_config", {})) +# return cls(**configs) +# else: +# # otherwise, parse through the dictionary recursively +# parsed_object = obj_spec +# for k, v in obj_spec.items(): +# parsed_object[k] = parse_objectspec(v) +# return parsed_object +# elif isinstance(obj_spec, list): +# # if it's a list, parse each item in the list +# return [parse_objectspec(item) for item in obj_spec] +# elif isinstance(obj_spec, tuple): +# # if it's a tuple, parse each item in the tuple +# return tuple(parse_objectspec(item) for item in obj_spec) +# else: +# # if it's neither a dict nor a list, return it as is +# return obj_spec diff --git a/src/orcapod/utils/stream_utils.py b/src/orcapod/utils/stream_utils.py deleted file mode 100644 index 51d46c1..0000000 --- a/src/orcapod/utils/stream_utils.py +++ /dev/null @@ -1,148 +0,0 @@ -""" -Utility functions for handling tags -""" - -from collections.abc import Collection, Mapping -from typing import TypeVar, Hashable, Any - -from orcapod.types import Packet, Tag, TypeSpec - - -K = TypeVar("K", bound=Hashable) -V = TypeVar("V") - - -def get_typespec(dict: Mapping) -> TypeSpec: - """ - Returns a TypeSpec for the given dictionary. - The TypeSpec is a mapping from field name to Python type. - """ - return {key: type(value) for key, value in dict.items()} - - -def get_compatible_type(type1: Any, type2: Any) -> Any: - if type1 is type2: - return type1 - if issubclass(type1, type2): - return type2 - if issubclass(type2, type1): - return type1 - raise TypeError(f"Types {type1} and {type2} are not compatible") - - -def merge_dicts(left: dict[K, V], right: dict[K, V]) -> dict[K, V]: - merged = left.copy() - for key, right_value in right.items(): - if key in merged: - if merged[key] != right_value: - raise ValueError( - f"Conflicting values for key '{key}': {merged[key]} vs {right_value}" - ) - else: - merged[key] = right_value - return merged - - -def merge_typespecs(left: TypeSpec | None, right: TypeSpec | None) -> TypeSpec | None: - if left is None: - return right - if right is None: - return left - # Merge the two TypeSpecs but raise an error if conflicts in types are found - merged = dict(left) - for key, right_type in right.items(): - merged[key] = ( - get_compatible_type(merged[key], right_type) - if key in merged - else right_type - ) - return merged - - -def common_elements(*values) -> Collection[str]: - """ - Returns the common keys between all lists of values. The identified common elements are - order preserved with respect to the first list of values - """ - if len(values) == 0: - return [] - common_keys = set(values[0]) - for tag in values[1:]: - common_keys.intersection_update(tag) - # Preserve the order of the first list of values - common_keys = [k for k in values[0] if k in common_keys] - return common_keys - - -def join_tags(tag1: Mapping[K, V], tag2: Mapping[K, V]) -> dict[K, V] | None: - """ - Joins two tags together. If the tags have the same key, the value must be the same or None will be returned. - """ - # create a dict copy of tag1 - joined_tag = dict(tag1) - for k, v in tag2.items(): - if k in joined_tag and joined_tag[k] != v: - # Detected a mismatch in the tags, return None - return None - else: - joined_tag[k] = v - return joined_tag - - -def check_packet_compatibility(packet1: Packet, packet2: Packet) -> bool: - """ - Checks if two packets are compatible. If the packets have the same key, the value must be the same or False will be returned. - If the packets have different keys, they are compatible. - """ - for k in packet1.keys(): - if k in packet2 and packet1[k] != packet2[k]: - return False - return True - - -def batch_tags(all_tags: Collection[Tag]) -> Tag: - """ - Batches the tags together. Grouping values under the same key into a list. - """ - all_keys: set[str] = set() - for tag in all_tags: - all_keys.update(tag.keys()) - batch_tag = {key: [] for key in all_keys} # Initialize batch_tag with all keys - for tag in all_tags: - for k in all_keys: - batch_tag[k].append( - tag.get(k, None) - ) # Append the value or None if the key is not present - return batch_tag - - -def batch_packet( - all_packets: Collection[Packet], drop_missing_keys: bool = True -) -> Packet: - """ - Batches the packets together. Grouping values under the same key into a list. - If all packets do not have the same key, raise an error unless drop_missing_keys is True - """ - all_keys: set[str] = set() - for p in all_packets: - all_keys.update(p.keys()) - batch_packet = {key: [] for key in all_keys} - for p in all_packets: - for k in all_keys: - if k not in p: - if drop_missing_keys: - continue - else: - raise KeyError(f"Packet {p} does not have key {k}") - batch_packet[k].append(p[k]) - return batch_packet - - -def fill_missing(dict, keys, default=None): - """ - Fill the missing keys in the dictionary with the specified default value. - """ - for key in keys: - if key not in dict: - dict[key] = default - return dict diff --git a/src/orcapod/types/inference.py b/src/orcapod/utils/types_utils.py similarity index 58% rename from src/orcapod/types/inference.py rename to src/orcapod/utils/types_utils.py index 2f18f39..5c25d03 100644 --- a/src/orcapod/types/inference.py +++ b/src/orcapod/utils/types_utils.py @@ -1,20 +1,19 @@ -# Library of functions for inferring types for FunctionPod input and output parameters. +# Library of functions for working with TypeSpecs and for extracting TypeSpecs from a function's signature - -from collections.abc import Callable, Collection, Sequence -from typing import get_origin, get_args, TypeAlias -from .core import TypeSpec +from collections.abc import Callable, Collection, Sequence, Mapping +from typing import get_origin, get_args, Any +from orcapod.types import PythonSchema, PythonSchemaLike import inspect import logging - - +import sys logger = logging.getLogger(__name__) -def verify_against_typespec(packet: dict, typespec: TypeSpec) -> bool: +def verify_against_typespec(packet: dict, typespec: PythonSchema) -> bool: """Verify that the dictionary's types match the expected types in the typespec.""" from beartype.door import is_bearable + # verify that packet contains no keys not in typespec if set(packet.keys()) - set(typespec.keys()): logger.warning( @@ -37,9 +36,10 @@ def verify_against_typespec(packet: dict, typespec: TypeSpec) -> bool: # TODO: is_subhint does not handle invariance properly # so when working with mutable types, we have to make sure to perform deep copy def check_typespec_compatibility( - incoming_types: TypeSpec, receiving_types: TypeSpec + incoming_types: PythonSchema, receiving_types: PythonSchema ) -> bool: from beartype.door import is_subhint + for key, type_info in incoming_types.items(): if key not in receiving_types: logger.warning(f"Key '{key}' not found in parameter types.") @@ -52,12 +52,12 @@ def check_typespec_compatibility( return True -def extract_function_data_types( +def extract_function_typespecs( func: Callable, output_keys: Collection[str], - input_types: TypeSpec | None = None, - output_types: TypeSpec | Sequence[type] | None = None, -) -> tuple[TypeSpec, TypeSpec]: + input_typespec: PythonSchemaLike | None = None, + output_typespec: PythonSchemaLike | Sequence[type] | None = None, +) -> tuple[PythonSchema, PythonSchema]: """ Extract input and output data types from a function signature. @@ -136,24 +136,24 @@ def extract_function_data_types( >>> output_types {'count': , 'total': , 'repr': } """ - verified_output_types: TypeSpec = {} - if output_types is not None: - if isinstance(output_types, dict): - verified_output_types = output_types - elif isinstance(output_types, Sequence): + verified_output_types: PythonSchema = {} + if output_typespec is not None: + if isinstance(output_typespec, dict): + verified_output_types = output_typespec + elif isinstance(output_typespec, Sequence): # If output_types is a collection, convert it to a dict with keys from return_keys - if len(output_types) != len(output_keys): + if len(output_typespec) != len(output_keys): raise ValueError( - f"Output types collection length {len(output_types)} does not match return keys length {len(output_keys)}." + f"Output types collection length {len(output_typespec)} does not match return keys length {len(output_keys)}." ) - verified_output_types = {k: v for k, v in zip(output_keys, output_types)} + verified_output_types = {k: v for k, v in zip(output_keys, output_typespec)} signature = inspect.signature(func) - param_info: TypeSpec = {} + param_info: PythonSchema = {} for name, param in signature.parameters.items(): - if input_types and name in input_types: - param_info[name] = input_types[name] + if input_typespec and name in input_typespec: + param_info[name] = input_typespec[name] else: # check if the parameter has annotation if param.annotation is not inspect.Signature.empty: @@ -164,7 +164,7 @@ def extract_function_data_types( ) return_annot = signature.return_annotation - inferred_output_types: TypeSpec = {} + inferred_output_types: PythonSchema = {} if return_annot is not inspect.Signature.empty and return_annot is not None: output_item_types = [] if len(output_keys) == 0: @@ -212,3 +212,155 @@ def extract_function_data_types( f"Type for return item '{key}' is not specified in output_types and has no type annotation in function signature." ) return param_info, inferred_output_types + + +def get_typespec_from_dict( + data: Mapping, typespec: PythonSchema | None = None, default=str +) -> PythonSchema: + """ + Returns a TypeSpec for the given dictionary. + The TypeSpec is a mapping from field name to Python type. If typespec is provided, then + it is used as a base when inferring types for the fields in dict + """ + if typespec is None: + typespec = {} + return { + key: typespec.get(key, type(value) if value is not None else default) + for key, value in data.items() + } + + +# def get_compatible_type(type1: Any, type2: Any) -> Any: +# if type1 is type2: +# return type1 +# if issubclass(type1, type2): +# return type2 +# if issubclass(type2, type1): +# return type1 +# raise TypeError(f"Types {type1} and {type2} are not compatible") + + +def get_compatible_type(type1: Any, type2: Any) -> Any: + # Handle identical types + if type1 is type2: + return type1 + + # Handle None/NoneType + if type1 is type(None) or type2 is type(None): + # You might want to handle Optional types here + if type1 is type(None): + return type2 + return type1 + + # Get origins for generic types (e.g., list from list[int]) + origin1 = get_origin(type1) or type1 + origin2 = get_origin(type2) or type2 + + # If origins are different, check basic subclass relationship + if origin1 != origin2: + try: + if issubclass(origin1, origin2): + return type2 + if issubclass(origin2, origin1): + return type1 + except TypeError: + # issubclass fails on some special forms + pass + raise TypeError(f"Types {type1} and {type2} are not compatible") + + # Same origin - check type arguments + args1 = get_args(type1) + args2 = get_args(type2) + + # If no type arguments, return the origin + if not args1 and not args2: + return origin1 + + # If only one has type arguments, prefer the more specific one + if not args1: + return type2 + if not args2: + return type1 + + # Both have type arguments - recursively check compatibility + if len(args1) != len(args2): + raise TypeError(f"Types {type1} and {type2} have incompatible argument counts") + + compatible_args = [] + for arg1, arg2 in zip(args1, args2): + try: + compatible_args.append(get_compatible_type(arg1, arg2)) + except TypeError: + raise TypeError( + f"Types {type1} and {type2} have incompatible type arguments" + ) + + # Reconstruct the generic type + if sys.version_info >= (3, 9): + return origin1[tuple(compatible_args)] + else: + # For Python < 3.9, you might need to use typing._GenericAlias + from typing import _GenericAlias + + return _GenericAlias(origin1, tuple(compatible_args)) + + +def union_typespecs(*typespecs: PythonSchema) -> PythonSchema: + # Merge the two TypeSpecs but raise an error if conflicts in types are found + merged = dict(typespecs[0]) + for typespec in typespecs[1:]: + for key, right_type in typespec.items(): + merged[key] = ( + get_compatible_type(merged[key], right_type) + if key in merged + else right_type + ) + return merged + + +def intersection_typespecs(*typespecs: PythonSchema) -> PythonSchema: + """ + Returns the intersection of all TypeSpecs, only returning keys that are present in all typespecs. + If a key is present in both TypeSpecs, the type must be the same. + """ + + # Find common keys and ensure types match + + common_keys = set(typespecs[0].keys()) + for typespec in typespecs[1:]: + common_keys.intersection_update(typespec.keys()) + + intersection = {k: typespecs[0][k] for k in common_keys} + for typespec in typespecs[1:]: + for key in common_keys: + try: + intersection[key] = get_compatible_type( + intersection[key], typespec[key] + ) + except TypeError: + # If types are not compatible, raise an error + raise TypeError( + f"Type conflict for key '{key}': {intersection[key]} vs {typespec[key]}" + ) + return intersection + + +# def intersection_typespecs(left: TypeSpec, right: TypeSpec) -> TypeSpec: +# """ +# Returns the intersection of two TypeSpecs, only returning keys that are present in both. +# If a key is present in both TypeSpecs, the type must be the same. +# """ + +# # Find common keys and ensure types match +# common_keys = set(left.keys()).intersection(set(right.keys())) +# intersection = {} +# for key in common_keys: +# try: +# intersection[key] = get_compatible_type(left[key], right[key]) +# except TypeError: +# # If types are not compatible, raise an error +# raise TypeError( +# f"Type conflict for key '{key}': {left[key]} vs {right[key]}" +# ) + +# return intersection diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_data/__init__.py b/tests/test_data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_data/test_datagrams/__init__.py b/tests/test_data/test_datagrams/__init__.py new file mode 100644 index 0000000..94f78e2 --- /dev/null +++ b/tests/test_data/test_datagrams/__init__.py @@ -0,0 +1 @@ +# Test package for datagrams diff --git a/tests/test_data/test_datagrams/test_arrow_datagram.py b/tests/test_data/test_datagrams/test_arrow_datagram.py new file mode 100644 index 0000000..d23a4fd --- /dev/null +++ b/tests/test_data/test_datagrams/test_arrow_datagram.py @@ -0,0 +1,1128 @@ +""" +Comprehensive tests for ArrowDatagram class. + +This module tests all functionality of the ArrowDatagram class including: +- Initialization and validation +- Dict-like interface operations +- Structural information methods +- Format conversion methods +- Meta column operations +- Data column operations +- Context operations +- Utility operations +""" +# Verified by Edgar Y. Walker + +from typing import cast +import pytest +import pyarrow as pa +from datetime import datetime, date + +from orcapod.core.datagrams import ArrowDatagram +from orcapod.core.system_constants import constants +from orcapod.protocols.core_protocols import Datagram +from orcapod.protocols.hashing_protocols import ContentHash + + +class TestArrowDatagramInitialization: + def test_basic_initialization(self): + """Test basic initialization with PyArrow table.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5]} + ) + + datagram = ArrowDatagram(table) + + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + assert datagram["score"] == 85.5 + + def test_initialization_multiple_rows_fails(self): + """Test initialization with multiple rows fails.""" + table = pa.Table.from_pydict({"user_id": [123, 456], "name": ["Alice", "Bob"]}) + + with pytest.raises(ValueError, match="exactly one row"): + ArrowDatagram(table) + + def test_initialization_empty_table_fails(self): + """Test initialization with empty table fails.""" + table = pa.Table.from_pydict({"user_id": [], "name": []}) + + with pytest.raises(ValueError, match="exactly one row"): + ArrowDatagram(table) + + def test_string_type_initialization(self) -> None: + """Initializing with pa.string() table should yield table with pa.large_string()""" + table = pa.Table.from_pydict({"name": ["John"]}) + datagram = ArrowDatagram(table) + # TODO: fix this type annotation mistake in the pyi of pyarrow-stubs + assert datagram._data_table.schema[0].type == pa.large_string() # type: ignore + + def test_initialization_with_meta_info(self): + """Test initialization with meta information.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + meta_info = {"pipeline_version": "v1.0", "timestamp": "2024-01-01"} + + datagram = ArrowDatagram(table, meta_info=meta_info) + + assert datagram["user_id"] == 123 + assert datagram.get_meta_value("pipeline_version") == "v1.0" + assert datagram.get_meta_value("timestamp") == "2024-01-01" + assert [ + f"{constants.META_PREFIX}pipeline_version" + in datagram.as_table(include_meta_columns=True).column_names + ] + + def test_initialization_with_context_in_table(self): + """Test initialization when context is included in table.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + constants.CONTEXT_KEY: ["v0.1"], + } + ) + + datagram = ArrowDatagram(table) + + assert datagram.data_context_key == "std:v0.1:default" + assert constants.CONTEXT_KEY not in datagram._data_table.column_names + + def test_initialization_with_meta_columns_in_table(self): + """Test initialization when meta columns are included in table.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.META_PREFIX}version": ["1.0"], + f"{constants.META_PREFIX}timestamp": ["2024-01-01"], + } + ) + + datagram = ArrowDatagram(table) + + assert datagram["user_id"] == 123 + assert datagram.get_meta_value("version") == "1.0" + assert datagram.get_meta_value("timestamp") == "2024-01-01" + + def test_initialization_with_explicit_context(self): + """Test initialization with explicit data context.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + + datagram = ArrowDatagram(table, data_context="std:v0.1:default") + + assert datagram.data_context_key == "std:v0.1:default" + + def test_initialization_no_data_columns_fails(self): + """Test initialization with no data columns fails.""" + table = pa.Table.from_pydict( + { + f"{constants.META_PREFIX}version": ["1.0"], + constants.CONTEXT_KEY: ["std:v0.1:default"], + } + ) + + with pytest.raises(ValueError, match="at least one data column"): + ArrowDatagram(table) + + +class TestArrowDatagramDictInterface: + """Test dict-like interface operations.""" + + @pytest.fixture + def sample_datagram(self): + """Create a sample datagram for testing.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + "score": [85.5], + "active": [True], + f"{constants.META_PREFIX}version": ["1.0"], + } + ) + return ArrowDatagram(table) + + def test_getitem(self, sample_datagram): + """Test __getitem__ method.""" + assert sample_datagram["user_id"] == 123 + assert sample_datagram["name"] == "Alice" + assert sample_datagram["score"] == 85.5 + assert sample_datagram["active"] is True + + def test_getitem_missing_key(self, sample_datagram): + """Test __getitem__ with missing key raises KeyError.""" + with pytest.raises(KeyError): + _ = sample_datagram["nonexistent"] + + def test_contains(self, sample_datagram): + """Test __contains__ method.""" + assert "user_id" in sample_datagram + assert "name" in sample_datagram + assert "nonexistent" not in sample_datagram + + def test_iter(self, sample_datagram): + """Test __iter__ method.""" + keys = list(sample_datagram) + # this should not include the meta column + expected_keys = ["user_id", "name", "score", "active"] + assert set(keys) == set(expected_keys) + + def test_get(self, sample_datagram): + """Test get method with and without default.""" + assert sample_datagram.get("user_id") == 123 + assert sample_datagram.get("nonexistent") is None + assert sample_datagram.get("nonexistent", "default") == "default" + + +class TestArrowDatagramProtocolAdherance: + @pytest.fixture + def basic_datagram(self) -> ArrowDatagram: + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + "score": [85.5], + "active": [True], + f"{constants.META_PREFIX}version": ["1.0"], + } + ) + return ArrowDatagram(table) + + def test_is_instance_of_datagram(self, basic_datagram): + # ArrowDatagram should ben an instance of Datagram protocol + assert isinstance(basic_datagram, Datagram) + + # verify that it is NOT possible to check for inheritance + with pytest.raises(TypeError): + issubclass(ArrowDatagram, Datagram) + + +class TestArrowDatagramStructuralInfo: + """Test structural information methods.""" + + @pytest.fixture + def datagram_with_meta(self): + """Create a datagram with meta columns.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.META_PREFIX}version": ["1.0"], + f"{constants.META_PREFIX}pipeline_id": ["test_pipeline"], + } + ) + return ArrowDatagram(table) + + def test_keys_data_only(self, datagram_with_meta): + """Test keys method with data columns only.""" + keys = datagram_with_meta.keys() + expected = ("user_id", "name") + assert set(keys) == set(expected) + + def test_keys_with_meta_columns(self, datagram_with_meta): + """Test keys method including meta columns.""" + keys = datagram_with_meta.keys(include_meta_columns=True) + expected = ( + "user_id", + "name", + f"{constants.META_PREFIX}version", + f"{constants.META_PREFIX}pipeline_id", + ) + assert set(keys) == set(expected) + + def test_keys_with_context(self, datagram_with_meta): + """Test keys method including context.""" + keys = datagram_with_meta.keys(include_context=True) + expected = ("user_id", "name", constants.CONTEXT_KEY) + assert set(keys) == set(expected) + + def test_keys_with_all_info(self, datagram_with_meta): + """Test keys method including all information.""" + keys = datagram_with_meta.keys(include_all_info=True) + expected = ( + "user_id", + "name", + f"{constants.META_PREFIX}version", + f"{constants.META_PREFIX}pipeline_id", + constants.CONTEXT_KEY, + ) + assert set(keys) == set(expected) + + def test_keys_with_specific_meta_prefix(self, datagram_with_meta): + """Test keys method with specific meta column prefixes.""" + keys = datagram_with_meta.keys( + include_meta_columns=[f"{constants.META_PREFIX}version"] + ) + expected = ("user_id", "name", f"{constants.META_PREFIX}version") + assert set(keys) == set(expected) + + def test_keys_with_nonexistent_meta_prefix(self, datagram_with_meta): + """Test keys methods when called with non-existent meta column prefixes""" + # non-existing prefix should be ignored + keys = datagram_with_meta.keys( + include_meta_columns=[ + f"{constants.META_PREFIX}nonexistent", + f"{constants.META_PREFIX}version", + ] + ) + expected = ("user_id", "name", f"{constants.META_PREFIX}version") + assert set(keys) == set(expected) + + def test_types_data_only(self, datagram_with_meta): + """Test types method with data columns only.""" + types = datagram_with_meta.types() + expected_keys = {"user_id", "name"} + assert set(types.keys()) == expected_keys + assert types["user_id"] is int + assert types["name"] is str + + def test_types_with_meta_columns(self, datagram_with_meta): + """Test types method including meta columns.""" + types = datagram_with_meta.types(include_meta_columns=True) + expected_keys = { + "user_id", + "name", + f"{constants.META_PREFIX}version", + f"{constants.META_PREFIX}pipeline_id", + } + assert set(types.keys()) == expected_keys + + def test_types_with_context(self, datagram_with_meta): + """Test types method including context.""" + types = datagram_with_meta.types(include_context=True) + expected_keys = {"user_id", "name", constants.CONTEXT_KEY} + assert set(types.keys()) == expected_keys + assert types[constants.CONTEXT_KEY] is str + + def test_arrow_schema_data_only(self, datagram_with_meta): + """Test arrow_schema method with data columns only.""" + schema = datagram_with_meta.arrow_schema() + expected_names = {"user_id", "name"} + assert set(schema.names) == expected_names + + def test_arrow_schema_with_meta_columns(self, datagram_with_meta): + """Test arrow_schema method including meta columns.""" + schema = datagram_with_meta.arrow_schema(include_meta_columns=True) + expected_names = { + "user_id", + "name", + f"{constants.META_PREFIX}version", + f"{constants.META_PREFIX}pipeline_id", + } + assert set(schema.names) == expected_names + + def test_arrow_schema_with_context(self, datagram_with_meta): + """Test arrow_schema method including context.""" + schema = datagram_with_meta.arrow_schema(include_context=True) + expected_names = {"user_id", "name", constants.CONTEXT_KEY} + assert set(schema.names) == expected_names + + def test_content_hash(self, datagram_with_meta): + """Test content hash calculation.""" + hash1 = datagram_with_meta.content_hash() + hash2 = datagram_with_meta.content_hash() + + # Hash should be consistent + assert hash1 == hash2 + assert isinstance(hash1, ContentHash) + assert len(hash1.digest) > 0 + + def test_content_hash_same_data_different_meta_data(self): + """Test that the content hash is the same for identical data with different meta data.""" + table1 = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + "__version": ["1.0"], + "__pipeline_id": ["pipeline_1"], + } + ) + table2 = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + "__version": ["1.1"], + "__pipeline_id": ["pipeline_2"], + } + ) + datagram1 = ArrowDatagram(table1) + datagram2 = ArrowDatagram(table2) + hash1 = datagram1.content_hash() + hash2 = datagram2.content_hash() + + assert hash1 == hash2 + + def test_content_hash_different_data(self): + """Test that different data produces different hashes.""" + table1 = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + table2 = pa.Table.from_pydict({"user_id": [456], "name": ["Bob"]}) + + datagram1 = ArrowDatagram(table1) + datagram2 = ArrowDatagram(table2) + + hash1 = datagram1.content_hash() + hash2 = datagram2.content_hash() + + assert hash1 != hash2 + + +class TestArrowDatagramFormatConversions: + """Test format conversion methods.""" + + @pytest.fixture + def datagram_with_all(self): + """Create a datagram with data, meta, and context.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.META_PREFIX}version": ["1.0"], + constants.CONTEXT_KEY: ["std:v0.1:default"], + } + ) + return ArrowDatagram(table) + + def test_as_dict_data_only(self, datagram_with_all): + """Test as_dict method with data columns only.""" + result = datagram_with_all.as_dict() + expected = {"user_id": 123, "name": "Alice"} + assert result == expected + + def test_as_dict_with_meta_columns(self, datagram_with_all): + """Test as_dict method including meta columns.""" + result = datagram_with_all.as_dict(include_meta_columns=True) + expected = { + "user_id": 123, + "name": "Alice", + f"{constants.META_PREFIX}version": "1.0", + } + assert result == expected + + def test_as_dict_with_context(self, datagram_with_all): + """Test as_dict method including context.""" + result = datagram_with_all.as_dict(include_context=True) + expected = { + "user_id": 123, + "name": "Alice", + constants.CONTEXT_KEY: "std:v0.1:default", + } + assert result == expected + + def test_as_dict_with_all_info(self, datagram_with_all): + """Test as_dict method including all information.""" + result = datagram_with_all.as_dict(include_all_info=True) + all_placed = datagram_with_all.as_dict( + include_meta_columns=True, include_context=True + ) + expected = { + "user_id": 123, + "name": "Alice", + f"{constants.META_PREFIX}version": "1.0", + constants.CONTEXT_KEY: "std:v0.1:default", + } + assert result == expected + assert result == all_placed + + def test_as_table_data_only(self, datagram_with_all): + """Test as_table method with data columns only.""" + table = datagram_with_all.as_table() + + assert len(table) == 1 + assert set(table.column_names) == {"user_id", "name"} + assert table["user_id"].to_pylist()[0] == 123 + assert table["name"].to_pylist()[0] == "Alice" + + def test_as_table_with_meta_columns(self, datagram_with_all): + """Test as_table method including meta columns.""" + table = datagram_with_all.as_table(include_meta_columns=True) + + assert len(table) == 1 + expected_columns = {"user_id", "name", f"{constants.META_PREFIX}version"} + assert set(table.column_names) == expected_columns + assert table[f"{constants.META_PREFIX}version"].to_pylist() == ["1.0"] + + def test_as_table_with_context(self, datagram_with_all): + """Test as_table method including context.""" + table = datagram_with_all.as_table(include_context=True) + + assert len(table) == 1 + expected_columns = {"user_id", "name", constants.CONTEXT_KEY} + assert set(table.column_names) == expected_columns + assert table[constants.CONTEXT_KEY].to_pylist() == ["std:v0.1:default"] + + def test_as_arrow_compatible_dict(self, datagram_with_all): + """Test as_arrow_compatible_dict method.""" + result = datagram_with_all.as_arrow_compatible_dict() + # TODO: add test case including complex data types + + # Should have same keys as as_dict + dict_result = datagram_with_all.as_dict() + assert set(result.keys()) == set(dict_result.keys()) + + +class TestArrowDatagramMetaOperations: + """Test meta column operations.""" + + @pytest.fixture + def datagram_with_meta(self): + """Create a datagram with meta columns.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.META_PREFIX}version": ["1.0"], + f"{constants.META_PREFIX}pipeline_id": ["test"], + } + ) + return ArrowDatagram(table) + + def test_meta_columns_property(self, datagram_with_meta): + """Test meta_columns property.""" + meta_cols = datagram_with_meta.meta_columns + expected = ( + f"{constants.META_PREFIX}version", + f"{constants.META_PREFIX}pipeline_id", + ) + assert set(meta_cols) == set(expected) + + def test_get_meta_value(self, datagram_with_meta): + """Test get_meta_value method.""" + # With prefix + assert ( + datagram_with_meta.get_meta_value(f"{constants.META_PREFIX}version") + == "1.0" + ) + + # Without prefix + assert datagram_with_meta.get_meta_value("version") == "1.0" + + # With default + assert datagram_with_meta.get_meta_value("nonexistent", "default") == "default" + + def test_with_meta_columns(self, datagram_with_meta): + """Test with_meta_columns method.""" + updated = datagram_with_meta.with_meta_columns( + version="2.0", # Update existing + new_meta=3.5, # Add new + ) + + # Original should be unchanged + assert datagram_with_meta.get_meta_value("version") == "1.0" + + # Updated should have new values + assert updated.get_meta_value("version") == "2.0" + assert updated.get_meta_value("new_meta") == 3.5 + + # meta data should be available as meta-prefixed column + table_with_meta = updated.as_table(include_meta_columns=True) + assert table_with_meta[f"{constants.META_PREFIX}version"].to_pylist() == ["2.0"] + assert table_with_meta[f"{constants.META_PREFIX}new_meta"].to_pylist() == [3.5] + + assert ( + table_with_meta[f"{constants.META_PREFIX}version"].type == pa.large_string() + ) + + # Data should be preserved + assert updated["user_id"] == 123 + assert updated["name"] == "Alice" + + def test_with_meta_columns_prefixed_keys(self, datagram_with_meta): + """Test with_meta_columns method with prefixed keys.""" + updated = datagram_with_meta.with_meta_columns( + **{f"{constants.META_PREFIX}version": "2.0"} + ) + + assert updated.get_meta_value("version") == "2.0" + + def test_drop_meta_columns(self, datagram_with_meta): + """Test drop_meta_columns method.""" + updated = datagram_with_meta.drop_meta_columns("version") + + # Original should be unchanged + assert datagram_with_meta.get_meta_value("version") == "1.0" + + # Updated should not have dropped other metadata columns + assert updated.get_meta_value("version") is None + assert updated.get_meta_value("pipeline_id") == "test" + + # Data should be preserved + assert updated["user_id"] == 123 + + def test_drop_meta_columns_prefixed(self, datagram_with_meta): + """Test drop_meta_columns method with prefixed keys.""" + updated = datagram_with_meta.drop_meta_columns( + f"{constants.META_PREFIX}version" + ) + + assert updated.get_meta_value("version") is None + + def test_drop_meta_columns_multiple(self, datagram_with_meta): + """Test dropping multiple meta columns.""" + updated = datagram_with_meta.drop_meta_columns("version", "pipeline_id") + + # original should not be modified + assert datagram_with_meta.get_meta_value("version") == "1.0" + assert datagram_with_meta.get_meta_value("pipeline_id") == "test" + + assert updated.get_meta_value("version") is None + assert updated.get_meta_value("pipeline_id") is None + + # Data should be preserved + assert updated["user_id"] == 123 + + def test_drop_meta_columns_missing_key(self, datagram_with_meta): + """Test drop_meta_columns with missing key raises KeyError.""" + with pytest.raises(KeyError): + datagram_with_meta.drop_meta_columns("nonexistent") + + def test_drop_meta_columns_ignore_missing(self, datagram_with_meta): + """Test drop_meta_columns with ignore_missing=True.""" + updated = datagram_with_meta.drop_meta_columns( + "version", "nonexistent", ignore_missing=True + ) + + assert updated.get_meta_value("version") is None + assert updated.get_meta_value("pipeline_id") == "test" + + +class TestArrowDatagramDataOperations: + """Test data column operations.""" + + @pytest.fixture + def sample_datagram(self): + """Create a sample datagram for testing.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + "score": [85.5], + "active": [True], + f"{constants.META_PREFIX}version": ["1.0"], + f"{constants.META_PREFIX}pipeline_id": ["test"], + } + ) + return ArrowDatagram(table) + + def test_select(self, sample_datagram: ArrowDatagram): + """Test select method.""" + selected = sample_datagram.select("user_id", "name") + + assert set(selected.keys()) == {"user_id", "name"} + assert selected["user_id"] == 123 + assert selected["name"] == "Alice" + # meta values should be copied over + assert selected.get_meta_value("version") == "1.0" + assert selected.get_meta_value("pipeline_id") == "test" + + # context should be preserved + assert selected.data_context_key == sample_datagram.data_context_key + + # Original should be unchanged + assert set(sample_datagram.keys()) == {"user_id", "name", "score", "active"} + + def test_select_single_column(self, sample_datagram: ArrowDatagram): + """Test select method with single column.""" + selected = sample_datagram.select("user_id") + + assert set(selected.keys()) == {"user_id"} + assert selected["user_id"] == 123 + + def test_select_missing_column(self, sample_datagram): + """Test select method with missing column raises ValueError.""" + with pytest.raises(ValueError): + sample_datagram.select("user_id", "nonexistent") + + def test_drop(self, sample_datagram: ArrowDatagram): + """Test drop method.""" + dropped = sample_datagram.drop("score", "active") + + assert set(dropped.keys()) == {"user_id", "name"} + assert dropped["user_id"] == 123 + assert dropped["name"] == "Alice" + + # drop should preserve context and meta values + assert dropped.get_meta_value("version") == "1.0" + assert dropped.get_meta_value("pipeline_id") == "test" + assert dropped.data_context_key == sample_datagram.data_context_key + + # Original should be unchanged + assert set(sample_datagram.keys()) == {"user_id", "name", "score", "active"} + + def test_drop_single_column(self, sample_datagram: ArrowDatagram): + """Test drop method with single column.""" + dropped = sample_datagram.drop("score") + # drop should preserve context and meta values + assert dropped.get_meta_value("version") == "1.0" + assert dropped.get_meta_value("pipeline_id") == "test" + assert dropped.data_context_key == sample_datagram.data_context_key + + assert set(dropped.keys()) == {"user_id", "name", "active"} + + def test_drop_missing_column(self, sample_datagram: ArrowDatagram): + """Test drop method with missing column raises KeyError.""" + with pytest.raises(KeyError): + sample_datagram.drop("nonexistent") + + def test_drop_ignore_missing(self, sample_datagram: ArrowDatagram): + """Test drop method with ignore_missing=True.""" + dropped = sample_datagram.drop("score", "nonexistent", ignore_missing=True) + + assert set(dropped.keys()) == {"user_id", "name", "active"} + + def test_rename(self, sample_datagram: ArrowDatagram): + """Test rename method.""" + renamed = sample_datagram.rename({"user_id": "id", "name": "username"}) + + expected_keys = {"id", "username", "score", "active"} + assert set(renamed.keys()) == expected_keys + assert renamed["id"] == 123 + assert renamed["username"] == "Alice" + assert renamed["score"] == 85.5 + + # meta and context should be unaffected + assert renamed.get_meta_value("version") == "1.0" + assert renamed.get_meta_value("pipeline_id") == "test" + assert renamed.data_context_key == sample_datagram.data_context_key + + # Original should be unchanged + assert "user_id" in sample_datagram + assert "id" not in sample_datagram + + def test_rename_empty_mapping(self, sample_datagram: ArrowDatagram): + """Test rename method with empty mapping.""" + renamed = sample_datagram.rename({}) + + # Should be identical + assert set(renamed.keys()) == set(sample_datagram.keys()) + assert renamed["user_id"] == sample_datagram["user_id"] + + def test_update(self, sample_datagram: ArrowDatagram): + """Test update method.""" + updated = sample_datagram.update(score=95.0, active=False) + + # Original should be unchanged + assert sample_datagram["score"] == 85.5 + assert sample_datagram["active"] is True + + # Updated should have new values + assert updated["score"] == 95.0 + assert not updated["active"] + assert updated["user_id"] == 123 # Unchanged columns preserved + + def test_update_missing_column(self, sample_datagram: ArrowDatagram): + """Test update method with missing column raises KeyError.""" + with pytest.raises(KeyError): + sample_datagram.update(nonexistent="value") + + def test_update_empty(self, sample_datagram: ArrowDatagram): + """Test update method with no updates returns same instance.""" + updated = sample_datagram.update() + + # Should return the same instance + # TODO: reconsider if this behavior is what is specified by the protocol + assert updated is sample_datagram + + def test_with_columns(self, sample_datagram: ArrowDatagram): + """Test with_columns method.""" + new_datagram = sample_datagram.with_columns( + department="Engineering", salary=75000 + ) + + # Original should be unchanged + assert "department" not in sample_datagram + assert "salary" not in sample_datagram + + # New datagram should have additional columns + expected_keys = {"user_id", "name", "score", "active", "department", "salary"} + assert set(new_datagram.keys()) == expected_keys + assert new_datagram["department"] == "Engineering" + assert new_datagram["salary"] == 75000 + + def test_with_columns_with_types(self, sample_datagram: ArrowDatagram): + """Test with_columns method with explicit types.""" + new_datagram = sample_datagram.with_columns( + column_types={"salary": int, "rate": float}, salary=75000, rate=85.5 + ) + + types = new_datagram.types() + assert types["salary"] is int + assert types["rate"] is float + + def test_with_columns_existing_column_fails(self, sample_datagram): + """Test with_columns method with existing column raises ValueError.""" + with pytest.raises(ValueError): + sample_datagram.with_columns(user_id=456) + + def test_with_columns_empty(self, sample_datagram): + """Test with_columns method with no columns returns same instance.""" + new_datagram = sample_datagram.with_columns() + + # TODO: again consider if this behavior is what's specified by protocol + assert new_datagram is sample_datagram + + +class TestArrowDatagramContextOperations: + """Test context operations.""" + + def test_with_context_key(self): + """Test with_context_key method.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + original_datagram = ArrowDatagram(table, data_context="std:v0.1:default") + + new_datagram = original_datagram.with_context_key("std:v0.1:default") + + # Original should be unchanged + assert original_datagram.data_context_key == "std:v0.1:default" + + # New should have updated context + assert new_datagram.data_context_key == "std:v0.1:default" + + # Data should be preserved + assert new_datagram["user_id"] == 123 + assert new_datagram["name"] == "Alice" + + +class TestArrowDatagramUtilityOperations: + """Test utility operations.""" + + @pytest.fixture + def sample_datagram(self): + """Create a sample datagram for testing.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.META_PREFIX}version": ["1.0"], + } + ) + return ArrowDatagram(table) + + def test_copy_with_cache(self, sample_datagram): + """Test copy method with cache included.""" + # Force cache creation + _ = sample_datagram.as_dict() + + copied = sample_datagram.copy(include_cache=True) + + # Should be different instances + assert copied is not sample_datagram + + # Should have same data + assert copied["user_id"] == sample_datagram["user_id"] + assert copied["name"] == sample_datagram["name"] + + # Should share cached values + assert copied._cached_python_dict is sample_datagram._cached_python_dict + + def test_copy_without_cache(self, sample_datagram): + """Test copy method without cache.""" + # Force cache creation + _ = sample_datagram.as_dict() + + copied = sample_datagram.copy(include_cache=False) + + # Should be different instances + assert copied is not sample_datagram + + # Should have same data + assert copied["user_id"] == sample_datagram["user_id"] + + # Should not share cached values + assert copied._cached_python_dict is None + + def test_str_representation(self, sample_datagram): + """Test string representation.""" + str_repr = str(sample_datagram) + + # Should contain data values + assert "123" in str_repr + assert "Alice" in str_repr + + # Should not contain meta columns + assert f"{constants.META_PREFIX}version" not in str_repr + + def test_repr_representation(self, sample_datagram): + """Test repr representation.""" + repr_str = repr(sample_datagram) + + # Should contain data values + assert "123" in repr_str + assert "Alice" in repr_str + + +class TestArrowDatagramEdgeCases: + """Test edge cases and error conditions.""" + + def test_none_values(self): + """Test handling of None values.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": [None], "optional": [None]} + ) + datagram = ArrowDatagram(table) + + assert datagram["user_id"] == 123 + assert datagram["name"] is None + assert datagram["optional"] is None + + def test_complex_data_types(self): + """Test handling of complex Arrow data types.""" + # Create table with various Arrow types + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array(["Alice"], type=pa.string()), + pa.array([85.5], type=pa.float64()), + pa.array([True], type=pa.bool_()), + pa.array([[1, 2, 3]], type=pa.list_(pa.int32())), + ], + names=["id", "name", "score", "active", "numbers"], + ) + + datagram = ArrowDatagram(table) + + assert datagram["id"] == 123 + assert datagram["name"] == "Alice" + assert datagram["score"] == 85.5 + assert datagram["active"] is True + assert datagram["numbers"] == [1, 2, 3] + + def test_large_string_types(self): + """Test handling of large string types.""" + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array(["A very long string " * 100], type=pa.large_string()), + ], + names=["id", "text"], + ) + + datagram = ArrowDatagram(table) + + assert datagram["id"] == 123 + assert len(cast(str, datagram["text"])) > 1000 + + def test_timestamp_types(self): + """Test handling of timestamp types.""" + now = datetime.now() + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array([now], type=pa.timestamp("ns")), + ], + names=["id", "timestamp"], + ) + + datagram = ArrowDatagram(table) + + assert datagram["id"] == 123 + # Arrow timestamps are returned as pandas Timestamp objects + assert datagram["timestamp"] is not None + + def test_date_types(self): + """Test handling of date types.""" + today = date.today() + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array([today], type=pa.date32()), + ], + names=["id", "date"], + ) + + datagram = ArrowDatagram(table) + + assert datagram["id"] == 123 + assert datagram["date"] is not None + + def test_duplicate_operations(self): + """Test operations that shouldn't change anything.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + datagram = ArrowDatagram(table) + + # Select all columns + selected = datagram.select("user_id", "name") + assert set(selected.keys()) == set(datagram.keys()) + + # Update with same values + updated = datagram.update(user_id=123, name="Alice") + assert updated["user_id"] == datagram["user_id"] + assert updated["name"] == datagram["name"] + + # Rename with identity mapping + renamed = datagram.rename({"user_id": "user_id", "name": "name"}) + assert set(renamed.keys()) == set(datagram.keys()) + + def test_conversion_to_large_types(self): + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int8()), + pa.array(["A very long string " * 100], type=pa.string()), + ], + names=["id", "text"], + ) + + datagram = ArrowDatagram(table) + + returned_table = datagram.as_table() + + # integer should be preserved but string should become large_string + assert returned_table["id"].type == pa.int8() + assert returned_table["text"].type == pa.large_string() + + +class TestArrowDatagramIntegration: + """Test integration between different operations.""" + + def test_chained_operations(self): + """Test chaining multiple operations.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "first_name": ["Alice"], + "last_name": ["Smith"], + "score": [85.5], + "active": [True], + f"{constants.META_PREFIX}version": ["1.0"], + } + ) + original_keys = set(table.column_names) - {f"{constants.META_PREFIX}version"} + + datagram = ArrowDatagram(table) + + # Chain operations + result = ( + datagram.with_columns(full_name="Alice Smith") + .drop("first_name", "last_name") + .update(score=90.0) + .with_meta_columns(version="2.0") + ) + + # verify original is not modified + assert set(datagram.keys()) == original_keys + assert datagram["first_name"] == "Alice" + assert datagram["score"] == 85.5 + + # Verify final state + assert set(result.keys()) == {"user_id", "score", "active", "full_name"} + assert result["full_name"] == "Alice Smith" + assert result["score"] == 90.0 + assert result.get_meta_value("version") == "2.0" + + def test_dict_roundtrip(self): + """Test conversion to dict and back preserves data.""" + + # TODO: perform this test but using semantic types + + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5]} + ) + original = ArrowDatagram(table) + + # Convert to dict + data_dict = original.as_dict() + + # Create new table from dict + new_table = pa.Table.from_pylist([data_dict]) + reconstructed = ArrowDatagram(new_table) + + # Should have same data + assert reconstructed["user_id"] == original["user_id"] + assert reconstructed["name"] == original["name"] + assert reconstructed["score"] == original["score"] + + def test_mixed_include_options(self): + """Test various combinations of include options.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.META_PREFIX}version": ["1.0"], + f"{constants.META_PREFIX}pipeline": ["test"], + } + ) + + datagram = ArrowDatagram(table) + + # Test different combinations + dict1 = datagram.as_dict(include_meta_columns=True, include_context=True) + dict2 = datagram.as_dict(include_all_info=True) + + # Should be equivalent + assert dict1 == dict2 + + # Test specific meta prefixes + dict3 = datagram.as_dict( + include_meta_columns=[f"{constants.META_PREFIX}version"] + ) + expected_keys = {"user_id", "name", f"{constants.META_PREFIX}version"} + assert set(dict3.keys()) == expected_keys + + def test_arrow_table_schema_preservation(self): + """Test that Arrow table schemas are preserved through operations.""" + # Create table with specific Arrow types + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int32()), # Specific int type + pa.array(["Alice"], type=pa.large_string()), # Large string + pa.array([85.5], type=pa.float32()), # Specific float type + ], + names=["id", "name", "score"], + ) + + datagram = ArrowDatagram(table) + + # Get schema + schema = datagram.arrow_schema() + + # Types should be preserved + assert schema.field("id").type == pa.int32() + assert schema.field("name").type == pa.large_string() + assert schema.field("score").type == pa.float32() + + # Operations should preserve types + updated = datagram.update(score=90.0) + updated_schema = updated.arrow_schema() + assert updated_schema.field("score").type == pa.float32() + + +class TestArrowDatagramPerformance: + """Test performance-related aspects.""" + + def test_caching_behavior(self): + """Test that caching works as expected.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + datagram = ArrowDatagram(table) + + # First call should populate cache + dict1 = datagram.as_dict() + assert datagram._cached_python_dict is not None + cached_dict_id = id(datagram._cached_python_dict) + + # Second call should use same cache (not create new one) + dict2 = datagram.as_dict() + assert id(datagram._cached_python_dict) == cached_dict_id # Same cached object + # Returned dicts are copies for safety, so they're not identical + assert dict1 == dict2 # Same content + assert dict1 is not dict2 # Different objects (copies) + + # Operations should invalidate cache + updated = datagram.update(name="Bob") + assert updated._cached_python_dict is None + + def test_lazy_evaluation(self): + """Test that expensive operations are performed lazily.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + datagram = ArrowDatagram(table) + + # Hash should not be calculated until requested + assert datagram._cached_content_hash is None + + # First hash call should calculate + hash1 = datagram.content_hash() + assert datagram._cached_content_hash is not None + + # Second call should use cache + hash2 = datagram.content_hash() + assert hash1 == hash2 + assert hash1 is hash2 # Should be same object diff --git a/tests/test_data/test_datagrams/test_arrow_tag_packet.py b/tests/test_data/test_datagrams/test_arrow_tag_packet.py new file mode 100644 index 0000000..3154bdc --- /dev/null +++ b/tests/test_data/test_datagrams/test_arrow_tag_packet.py @@ -0,0 +1,1070 @@ +""" +Comprehensive tests for ArrowTag and ArrowPacket classes. + +This module tests all functionality of the Arrow-based tag and packet classes including: +- Tag-specific functionality (system tags) +- Packet-specific functionality (source info) +- Integration with Arrow datagram functionality +- Conversion operations +- Arrow-specific optimizations +""" + +import pytest +import pyarrow as pa +from datetime import datetime, date + +from orcapod.core.datagrams import ArrowTag, ArrowPacket +from orcapod.core.system_constants import constants + + +class TestArrowTagInitialization: + """Test ArrowTag initialization and basic properties.""" + + def test_basic_initialization(self): + """Test basic initialization with PyArrow table.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5]} + ) + + tag = ArrowTag(table) + + assert tag["user_id"] == 123 + assert tag["name"] == "Alice" + assert tag["score"] == 85.5 + + def test_initialization_multiple_rows_fails(self): + """Test initialization with multiple rows fails.""" + table = pa.Table.from_pydict({"user_id": [123, 456], "name": ["Alice", "Bob"]}) + + with pytest.raises(ValueError, match="single row"): + ArrowTag(table) + + def test_initialization_with_system_tags(self): + """Test initialization with system tags.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + system_tags = {"tag_type": "user", "created_by": "system"} + + tag = ArrowTag(table, system_tags=system_tags) + + assert tag["user_id"] == 123 + system_tag_dict = tag.system_tags() + assert system_tag_dict["tag_type"] == "user" + assert system_tag_dict["created_by"] == "system" + + def test_initialization_with_system_tags_in_table(self): + """Test initialization when system tags are included in table.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.SYSTEM_TAG_PREFIX}tag_type": ["user"], + f"{constants.SYSTEM_TAG_PREFIX}version": ["1.0"], + } + ) + + tag = ArrowTag(table) + + assert tag["user_id"] == 123 + assert tag["name"] == "Alice" + + system_tags = tag.system_tags() + assert system_tags[f"{constants.SYSTEM_TAG_PREFIX}tag_type"] == "user" + assert system_tags[f"{constants.SYSTEM_TAG_PREFIX}version"] == "1.0" + + def test_initialization_mixed_system_tags(self): + """Test initialization with both embedded and explicit system tags.""" + table = pa.Table.from_pydict( + {"user_id": [123], f"{constants.SYSTEM_TAG_PREFIX}embedded": ["value1"]} + ) + system_tags = {"explicit": "value2"} + + tag = ArrowTag(table, system_tags=system_tags) + + system_tag_dict = tag.system_tags() + assert system_tag_dict[f"{constants.SYSTEM_TAG_PREFIX}embedded"] == "value1" + assert system_tag_dict["explicit"] == "value2" + + +class TestArrowTagSystemTagOperations: + """Test system tag specific operations.""" + + @pytest.fixture + def sample_tag(self): + """Create a sample tag for testing.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + system_tags = {"tag_type": "user", "version": "1.0"} + return ArrowTag(table, system_tags=system_tags) + + def test_system_tags_method(self, sample_tag): + """Test system_tags method.""" + system_tags = sample_tag.system_tags() + + assert isinstance(system_tags, dict) + assert system_tags["tag_type"] == "user" + assert system_tags["version"] == "1.0" + + def test_keys_with_system_tags(self, sample_tag): + """Test keys method including system tags.""" + keys_data_only = sample_tag.keys() + keys_with_system = sample_tag.keys(include_system_tags=True) + + assert "user_id" in keys_data_only + assert "name" in keys_data_only + assert len(keys_with_system) > len(keys_data_only) + assert "tag_type" in keys_with_system + assert "version" in keys_with_system + + def test_types_with_system_tags(self, sample_tag): + """Test types method including system tags.""" + types_data_only = sample_tag.types() + types_with_system = sample_tag.types(include_system_tags=True) + + assert len(types_with_system) > len(types_data_only) + assert "tag_type" in types_with_system + assert "version" in types_with_system + + def test_arrow_schema_with_system_tags(self, sample_tag): + """Test arrow_schema method including system tags.""" + schema_data_only = sample_tag.arrow_schema() + schema_with_system = sample_tag.arrow_schema(include_system_tags=True) + + assert len(schema_with_system) > len(schema_data_only) + assert "tag_type" in schema_with_system.names + assert "version" in schema_with_system.names + + def test_as_dict_with_system_tags(self, sample_tag): + """Test as_dict method including system tags.""" + dict_data_only = sample_tag.as_dict() + dict_with_system = sample_tag.as_dict(include_system_tags=True) + + assert "user_id" in dict_data_only + assert "name" in dict_data_only + assert "tag_type" not in dict_data_only + + assert "user_id" in dict_with_system + assert "tag_type" in dict_with_system + assert "version" in dict_with_system + + def test_as_table_with_system_tags(self, sample_tag): + """Test as_table method including system tags.""" + table_data_only = sample_tag.as_table() + table_with_system = sample_tag.as_table(include_system_tags=True) + + assert len(table_with_system.column_names) > len(table_data_only.column_names) + assert "tag_type" in table_with_system.column_names + assert "version" in table_with_system.column_names + + def test_as_datagram_conversion(self, sample_tag): + """Test conversion to datagram.""" + datagram = sample_tag.as_datagram() + + # Should preserve data + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + + # Should not include system tags by default + assert "tag_type" not in datagram.keys() + + def test_as_datagram_with_system_tags(self, sample_tag): + """Test conversion to datagram including system tags.""" + datagram = sample_tag.as_datagram(include_system_tags=True) + + # Should preserve data and include system tags + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + assert "tag_type" in datagram.keys() + + +class TestArrowTagDataOperations: + """Test that system tags are preserved across all data operations.""" + + @pytest.fixture + def sample_tag_with_system_tags(self): + """Create a sample tag with system tags for testing operations.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5], "active": [True]} + ) + system_tags = { + "tag_type": "user", + "version": "1.0", + "created_by": "system", + "priority": "high", + } + return ArrowTag(table, system_tags=system_tags) + + def test_select_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that select operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Select subset of columns + selected = sample_tag_with_system_tags.select("user_id", "name") + + # System tags should be preserved + assert selected.system_tags() == original_system_tags + assert selected.system_tags()["tag_type"] == "user" + assert selected.system_tags()["version"] == "1.0" + assert selected.system_tags()["created_by"] == "system" + assert selected.system_tags()["priority"] == "high" + + # Only selected data columns should remain + assert "user_id" in selected.keys() + assert "name" in selected.keys() + assert "score" not in selected.keys() + assert "active" not in selected.keys() + + def test_drop_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that drop operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Drop some columns + dropped = sample_tag_with_system_tags.drop("score", "active") + + # System tags should be preserved + assert dropped.system_tags() == original_system_tags + assert dropped.system_tags()["tag_type"] == "user" + assert dropped.system_tags()["version"] == "1.0" + + # Dropped columns should be gone, others should remain + assert "user_id" in dropped.keys() + assert "name" in dropped.keys() + assert "score" not in dropped.keys() + assert "active" not in dropped.keys() + + def test_rename_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that rename operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Rename columns + renamed = sample_tag_with_system_tags.rename( + {"user_id": "id", "name": "username"} + ) + + # System tags should be preserved + assert renamed.system_tags() == original_system_tags + assert renamed.system_tags()["tag_type"] == "user" + assert renamed.system_tags()["version"] == "1.0" + + # Data columns should be renamed + assert "id" in renamed.keys() + assert "username" in renamed.keys() + assert "user_id" not in renamed.keys() + assert "name" not in renamed.keys() + + def test_update_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that update operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Update some column values + updated = sample_tag_with_system_tags.update(name="Alice Smith", score=92.0) + + # System tags should be preserved + assert updated.system_tags() == original_system_tags + assert updated.system_tags()["tag_type"] == "user" + assert updated.system_tags()["version"] == "1.0" + + # Updated values should be reflected + assert updated["name"] == "Alice Smith" + assert updated["score"] == 92.0 + assert updated["user_id"] == 123 # Unchanged + + def test_with_columns_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that with_columns operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Add new columns + with_new_cols = sample_tag_with_system_tags.with_columns( + email="alice@example.com", age=30, department="engineering" + ) + + # System tags should be preserved + assert with_new_cols.system_tags() == original_system_tags + assert with_new_cols.system_tags()["tag_type"] == "user" + assert with_new_cols.system_tags()["version"] == "1.0" + assert with_new_cols.system_tags()["created_by"] == "system" + assert with_new_cols.system_tags()["priority"] == "high" + + # New columns should be added + assert with_new_cols["email"] == "alice@example.com" + assert with_new_cols["age"] == 30 + assert with_new_cols["department"] == "engineering" + + # Original columns should remain + assert with_new_cols["user_id"] == 123 + assert with_new_cols["name"] == "Alice" + + def test_with_meta_columns_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that with_meta_columns operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Add meta columns + with_meta = sample_tag_with_system_tags.with_meta_columns( + pipeline_version="v2.1.0", processed_at="2024-01-01" + ) + + # System tags should be preserved + assert with_meta.system_tags() == original_system_tags + assert with_meta.system_tags()["tag_type"] == "user" + assert with_meta.system_tags()["version"] == "1.0" + + # Meta columns should be added + assert with_meta.get_meta_value("pipeline_version") == "v2.1.0" + assert with_meta.get_meta_value("processed_at") == "2024-01-01" + + def test_drop_meta_columns_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that drop_meta_columns operation preserves system tags.""" + # First add some meta columns + with_meta = sample_tag_with_system_tags.with_meta_columns( + pipeline_version="v2.1.0", processed_at="2024-01-01" + ) + original_system_tags = with_meta.system_tags() + + # Drop meta columns + dropped_meta = with_meta.drop_meta_columns("pipeline_version") + + # System tags should be preserved + assert dropped_meta.system_tags() == original_system_tags + assert dropped_meta.system_tags()["tag_type"] == "user" + assert dropped_meta.system_tags()["version"] == "1.0" + + # Meta column should be dropped + assert dropped_meta.get_meta_value("pipeline_version") is None + assert dropped_meta.get_meta_value("processed_at") == "2024-01-01" + + def test_with_context_key_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that with_context_key operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Change context key (note: "test" will resolve to "default" but that's expected) + new_context = sample_tag_with_system_tags.with_context_key("std:v0.1:test") + + # System tags should be preserved + assert new_context.system_tags() == original_system_tags + assert new_context.system_tags()["tag_type"] == "user" + assert new_context.system_tags()["version"] == "1.0" + + # Context should be different from original (even if resolved to default) + # The important thing is that the operation worked and system tags are preserved + assert new_context.data_context_key.startswith("std:v0.1:") + # Verify that this is a different object + assert new_context is not sample_tag_with_system_tags + + def test_copy_preserves_system_tags(self, sample_tag_with_system_tags): + """Test that copy operation preserves system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Copy with cache + copied_with_cache = sample_tag_with_system_tags.copy(include_cache=True) + + # Copy without cache + copied_without_cache = sample_tag_with_system_tags.copy(include_cache=False) + + # System tags should be preserved in both cases + assert copied_with_cache.system_tags() == original_system_tags + assert copied_without_cache.system_tags() == original_system_tags + + # Verify all system tags are present + for copy_obj in [copied_with_cache, copied_without_cache]: + assert copy_obj.system_tags()["tag_type"] == "user" + assert copy_obj.system_tags()["version"] == "1.0" + assert copy_obj.system_tags()["created_by"] == "system" + assert copy_obj.system_tags()["priority"] == "high" + + def test_chained_operations_preserve_system_tags(self, sample_tag_with_system_tags): + """Test that chained operations preserve system tags.""" + original_system_tags = sample_tag_with_system_tags.system_tags() + + # Chain multiple operations + result = ( + sample_tag_with_system_tags.with_columns( + full_name="Alice Smith", department="eng" + ) + .drop("score") + .update(active=False) + .rename({"user_id": "id"}) + .with_meta_columns(processed=True) + ) + + # System tags should be preserved through all operations + assert result.system_tags() == original_system_tags + assert result.system_tags()["tag_type"] == "user" + assert result.system_tags()["version"] == "1.0" + assert result.system_tags()["created_by"] == "system" + assert result.system_tags()["priority"] == "high" + + # Verify the chained operations worked + assert result["full_name"] == "Alice Smith" + assert result["department"] == "eng" + assert "score" not in result.keys() + assert result["active"] is False + assert "id" in result.keys() + assert "user_id" not in result.keys() + assert result.get_meta_value("processed") is True + + +class TestArrowPacketInitialization: + """Test ArrowPacket initialization and basic properties.""" + + def test_basic_initialization(self): + """Test basic initialization with PyArrow table.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5]} + ) + + packet = ArrowPacket(table) + + assert packet["user_id"] == 123 + assert packet["name"] == "Alice" + assert packet["score"] == 85.5 + + def test_initialization_multiple_rows_fails(self): + """Test initialization with multiple rows fails.""" + table = pa.Table.from_pydict({"user_id": [123, 456], "name": ["Alice", "Bob"]}) + + with pytest.raises(ValueError, match="single row"): + ArrowPacket(table) + + def test_initialization_with_source_info(self): + """Test initialization with source info.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + source_info = {"user_id": "database", "name": "user_input"} + + packet = ArrowPacket(table, source_info=source_info) + + assert packet["user_id"] == 123 + source_dict = packet.source_info() + assert source_dict["user_id"] == "database" + assert source_dict["name"] == "user_input" + + def test_initialization_with_source_info_in_table(self): + """Test initialization when source info is included in table.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.SOURCE_PREFIX}user_id": ["database"], + f"{constants.SOURCE_PREFIX}name": ["user_input"], + } + ) + + packet = ArrowPacket(table) + + assert packet["user_id"] == 123 + assert packet["name"] == "Alice" + + source_info = packet.source_info() + assert source_info["user_id"] == "database" + assert source_info["name"] == "user_input" + + def test_initialization_mixed_source_info(self): + """Test initialization with both embedded and explicit source info.""" + table = pa.Table.from_pydict( + { + "user_id": [123], + "name": ["Alice"], + f"{constants.SOURCE_PREFIX}user_id": ["embedded_source"], + } + ) + source_info = {"name": "explicit_source"} + + packet = ArrowPacket(table, source_info=source_info) + + source_dict = packet.source_info() + assert source_dict["user_id"] == "embedded_source" + assert source_dict["name"] == "explicit_source" + + def test_initialization_with_recordbatch(self): + """Test initialization with RecordBatch instead of Table.""" + batch = pa.RecordBatch.from_pydict({"user_id": [123], "name": ["Alice"]}) + + packet = ArrowPacket(batch) + + assert packet["user_id"] == 123 + assert packet["name"] == "Alice" + + +class TestArrowPacketSourceInfoOperations: + """Test source info specific operations.""" + + @pytest.fixture + def sample_packet(self): + """Create a sample packet for testing.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5]} + ) + source_info = { + "user_id": "database", + "name": "user_input", + "score": "calculation", + } + return ArrowPacket(table, source_info=source_info) + + def test_source_info_method(self, sample_packet): + """Test source_info method.""" + source_info = sample_packet.source_info() + + assert isinstance(source_info, dict) + assert source_info["user_id"] == "database" + assert source_info["name"] == "user_input" + assert source_info["score"] == "calculation" + + def test_source_info_with_missing_keys(self): + """Test source_info method when some keys are missing.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5]} + ) + source_info = {"user_id": "database"} # Only partial source info + + packet = ArrowPacket(table, source_info=source_info) + full_source_info = packet.source_info() + + assert full_source_info["user_id"] == "database" + assert full_source_info["name"] is None + assert full_source_info["score"] is None + + def test_with_source_info(self, sample_packet): + """Test with_source_info method.""" + updated = sample_packet.with_source_info( + user_id="new_database", name="new_input" + ) + + # Original should be unchanged + original_source = sample_packet.source_info() + assert original_source["user_id"] == "database" + + # Updated should have new values + updated_source = updated.source_info() + assert updated_source["user_id"] == "new_database" + assert updated_source["name"] == "new_input" + assert updated_source["score"] == "calculation" # Unchanged + + def test_keys_with_source_info(self, sample_packet): + """Test keys method including source info.""" + keys_data_only = sample_packet.keys() + keys_with_source = sample_packet.keys(include_source=True) + + assert "user_id" in keys_data_only + assert "name" in keys_data_only + assert len(keys_with_source) > len(keys_data_only) + + # Should include prefixed source columns + source_keys = [ + k for k in keys_with_source if k.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_keys) > 0 + + def test_types_with_source_info(self, sample_packet): + """Test types method including source info.""" + types_data_only = sample_packet.types() + types_with_source = sample_packet.types(include_source=True) + + assert len(types_with_source) > len(types_data_only) + + # Source columns should be string type + source_keys = [ + k for k in types_with_source.keys() if k.startswith(constants.SOURCE_PREFIX) + ] + for key in source_keys: + assert types_with_source[key] is str + + def test_arrow_schema_with_source_info(self, sample_packet): + """Test arrow_schema method including source info.""" + schema_data_only = sample_packet.arrow_schema() + schema_with_source = sample_packet.arrow_schema(include_source=True) + + assert len(schema_with_source) > len(schema_data_only) + + source_columns = [ + name + for name in schema_with_source.names + if name.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_columns) > 0 + + def test_as_dict_with_source_info(self, sample_packet): + """Test as_dict method including source info.""" + dict_data_only = sample_packet.as_dict() + dict_with_source = sample_packet.as_dict(include_source=True) + + assert "user_id" in dict_data_only + assert "name" in dict_data_only + assert not any( + k.startswith(constants.SOURCE_PREFIX) for k in dict_data_only.keys() + ) + + assert "user_id" in dict_with_source + source_keys = [ + k for k in dict_with_source.keys() if k.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_keys) > 0 + + def test_as_table_with_source_info(self, sample_packet): + """Test as_table method including source info.""" + table_data_only = sample_packet.as_table() + table_with_source = sample_packet.as_table(include_source=True) + + assert len(table_with_source.column_names) > len(table_data_only.column_names) + + source_columns = [ + name + for name in table_with_source.column_names + if name.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_columns) > 0 + + def test_as_datagram_conversion(self, sample_packet): + """Test conversion to datagram.""" + datagram = sample_packet.as_datagram() + + # Should preserve data + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + + # Should not include source info by default + assert not any(k.startswith(constants.SOURCE_PREFIX) for k in datagram.keys()) + + def test_as_datagram_with_source_info(self, sample_packet): + """Test conversion to datagram including source info.""" + datagram = sample_packet.as_datagram(include_source=True) + + # Should preserve data and include source info + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + source_keys = [ + k for k in datagram.keys() if k.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_keys) > 0 + + +class TestArrowPacketDataOperations: + """Test data operations specific to packets.""" + + @pytest.fixture + def sample_packet(self): + """Create a sample packet for testing.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "score": [85.5]} + ) + source_info = { + "user_id": "database", + "name": "user_input", + "score": "calculation", + } + return ArrowPacket(table, source_info=source_info) + + def test_rename_preserves_source_info(self, sample_packet): + """Test that rename operation preserves source info mapping.""" + renamed = sample_packet.rename({"user_id": "id", "name": "username"}) + + # Data should be renamed + assert "id" in renamed.keys() + assert "username" in renamed.keys() + assert "user_id" not in renamed.keys() + assert "name" not in renamed.keys() + + # Source info should follow the rename + source_info = renamed.source_info() + assert source_info["id"] == "database" + assert source_info["username"] == "user_input" + assert source_info["score"] == "calculation" + + def test_with_columns_creates_source_info_columns(self, sample_packet): + """Test that with_columns() creates corresponding source info columns with correct data types.""" + # Add new columns + updated = sample_packet.with_columns( + full_name="Alice Smith", age=30, is_active=True + ) + + # Verify new data columns exist + assert "full_name" in updated.keys() + assert "age" in updated.keys() + assert "is_active" in updated.keys() + assert updated["full_name"] == "Alice Smith" + assert updated["age"] == 30 + assert updated["is_active"] is True + + # Verify corresponding source info columns are created + source_info = updated.source_info() + assert "full_name" in source_info + assert "age" in source_info + assert "is_active" in source_info + + # New source info columns should be initialized as None + assert source_info["full_name"] is None + assert source_info["age"] is None + assert source_info["is_active"] is None + + # Verify existing source info is preserved + assert source_info["user_id"] == "database" + assert source_info["name"] == "user_input" + assert source_info["score"] == "calculation" + + # Verify Arrow schema has correct data types for source info columns + schema = updated.arrow_schema(include_source=True) + + # All source info columns should be large_string type + source_columns = [col for col in schema if col.name.startswith("_source_")] + assert len(source_columns) == 6 # 3 original + 3 new + + for field in source_columns: + assert field.type == pa.large_string(), ( + f"Source column {field.name} should be large_string, got {field.type}" + ) + + # Verify we can set source info for new columns + with_source = updated.with_source_info( + full_name="calculated", age="user_input", is_active="default" + ) + + final_source_info = with_source.source_info() + assert final_source_info["full_name"] == "calculated" + assert final_source_info["age"] == "user_input" + assert final_source_info["is_active"] == "default" + + +class TestArrowTagPacketIntegration: + """Test integration between tags, packets, and base functionality.""" + + def test_tag_to_packet_conversion(self): + """Test converting a tag to a packet-like structure.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + system_tags = {"tag_type": "user", "version": "1.0"} + tag = ArrowTag(table, system_tags=system_tags) + + # Convert to full dictionary + full_dict = tag.as_dict(include_all_info=True) + + # Should include data, system tags, meta columns, and context + assert "user_id" in full_dict + assert "tag_type" in full_dict + assert constants.CONTEXT_KEY in full_dict + + def test_packet_comprehensive_dict(self): + """Test packet with all information types.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "__meta_field": ["meta_value"]} + ) + source_info = {"user_id": "database", "name": "user_input"} + + packet = ArrowPacket(table, source_info=source_info) + + # Get comprehensive dictionary + full_dict = packet.as_dict(include_all_info=True) + + # Should include data, source info, meta columns, and context + assert "user_id" in full_dict + assert f"{constants.SOURCE_PREFIX}user_id" in full_dict + assert "__meta_field" in full_dict + assert constants.CONTEXT_KEY in full_dict + + def test_chained_operations_tag(self): + """Test chaining operations on tags.""" + table = pa.Table.from_pydict( + {"user_id": [123], "first_name": ["Alice"], "last_name": ["Smith"]} + ) + system_tags = {"tag_type": "user"} + + tag = ArrowTag(table, system_tags=system_tags) + + # Chain operations + result = ( + tag.with_columns(full_name="Alice Smith") + .drop("first_name", "last_name") + .update(user_id=456) + ) + + # Verify final state + assert set(result.keys()) == {"user_id", "full_name"} + assert result["user_id"] == 456 + assert result["full_name"] == "Alice Smith" + + # System tags should be preserved + system_tags = result.system_tags() + assert system_tags["tag_type"] == "user" + + def test_chained_operations_packet(self): + """Test chaining operations on packets.""" + table = pa.Table.from_pydict( + {"user_id": [123], "first_name": ["Alice"], "last_name": ["Smith"]} + ) + source_info = {"user_id": "database", "first_name": "form", "last_name": "form"} + + packet = ArrowPacket(table, source_info=source_info) + + # Chain operations + result = ( + packet.with_columns(full_name="Alice Smith") + .drop("first_name", "last_name") + .update(user_id=456) + .with_source_info(full_name="calculated") + ) + + # Verify final state + assert set(result.keys()) == {"user_id", "full_name"} + assert result["user_id"] == 456 + assert result["full_name"] == "Alice Smith" + + # Source info should be updated + source_info = result.source_info() + assert source_info["user_id"] == "database" + assert source_info["full_name"] == "calculated" + + def test_copy_operations(self): + """Test copy operations preserve all information.""" + # Test tag copy + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + system_tags = {"tag_type": "user"} + tag = ArrowTag(table, system_tags=system_tags) + + tag_copy = tag.copy() + assert tag_copy is not tag + assert tag_copy["user_id"] == tag["user_id"] + assert tag_copy.system_tags() == tag.system_tags() + + # Test packet copy + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + source_info = {"user_id": "database"} + packet = ArrowPacket(table, source_info=source_info) + + packet_copy = packet.copy() + assert packet_copy is not packet + assert packet_copy["user_id"] == packet["user_id"] + assert packet_copy.source_info() == packet.source_info() + + +class TestArrowTagPacketArrowSpecific: + """Test Arrow-specific functionality and optimizations.""" + + def test_tag_arrow_schema_preservation(self): + """Test that Arrow schemas are preserved in tags.""" + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int32()), + pa.array(["Alice"], type=pa.large_string()), + ], + names=["id", "name"], + ) + + tag = ArrowTag(table) + + schema = tag.arrow_schema() + assert schema.field("id").type == pa.int32() + assert schema.field("name").type == pa.large_string() + + def test_packet_arrow_schema_preservation(self): + """Test that Arrow schemas are preserved in packets.""" + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array([85.5], type=pa.float32()), + ], + names=["id", "score"], + ) + + packet = ArrowPacket(table) + + schema = packet.arrow_schema() + assert schema.field("id").type == pa.int64() + assert schema.field("score").type == pa.float32() + + def test_tag_complex_arrow_types(self): + """Test tags with complex Arrow data types.""" + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array([[1, 2, 3]], type=pa.list_(pa.int32())), + pa.array( + [{"nested": "value"}], + type=pa.struct([pa.field("nested", pa.string())]), + ), + ], + names=["id", "numbers", "struct_field"], + ) + + tag = ArrowTag(table) + + assert tag["id"] == 123 + assert tag["numbers"] == [1, 2, 3] + assert tag["struct_field"]["nested"] == "value" # type: ignore + + def test_packet_complex_arrow_types(self): + """Test packets with complex Arrow data types.""" + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array([[1, 2, 3]], type=pa.list_(pa.int32())), + pa.array( + [{"nested": "value"}], + type=pa.struct([pa.field("nested", pa.string())]), + ), + ], + names=["id", "numbers", "struct_field"], + ) + + packet = ArrowPacket(table) + + assert packet["id"] == 123 + assert packet["numbers"] == [1, 2, 3] + assert packet["struct_field"]["nested"] == "value" # type: ignore + + def test_tag_timestamp_handling(self): + """Test tag handling of timestamp types.""" + now = datetime.now() + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array([now], type=pa.timestamp("ns")), + ], + names=["id", "timestamp"], + ) + + tag = ArrowTag(table) + + assert tag["id"] == 123 + assert tag["timestamp"] is not None + + def test_packet_date_handling(self): + """Test packet handling of date types.""" + today = date.today() + table = pa.Table.from_arrays( + [ + pa.array([123], type=pa.int64()), + pa.array([today], type=pa.date32()), + ], + names=["id", "date"], + ) + + packet = ArrowPacket(table) + + assert packet["id"] == 123 + assert packet["date"] is not None + + def test_tag_arrow_memory_efficiency(self): + """Test that tags share Arrow memory efficiently.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + + tag = ArrowTag(table) + + # The important thing is that underlying arrays are shared for memory efficiency + # Whether the table object itself is the same depends on whether system tag columns needed extraction + original_array = table["user_id"] + tag_array = tag._data_table["user_id"] + assert tag_array.to_pylist() == original_array.to_pylist() + + # Test with a table that has system tag columns to ensure processing works + table_with_system = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "_system_tag_type": ["user"]} + ) + tag_with_system = ArrowTag(table_with_system) + # This should create a different table since system columns are extracted + assert tag_with_system._data_table is not table_with_system + assert set(tag_with_system._data_table.column_names) == {"user_id", "name"} + + def test_packet_arrow_memory_efficiency(self): + """Test that packets handle Arrow memory efficiently.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + + packet = ArrowPacket(table) + + # Should efficiently handle memory + assert ( + packet._data_table is not table + ) # Different due to source info processing + + # But data should be preserved + assert packet["user_id"] == 123 + assert packet["name"] == "Alice" + + +class TestArrowTagPacketEdgeCases: + """Test edge cases and error conditions.""" + + def test_tag_empty_system_tags(self): + """Test tag with empty system tags.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + tag = ArrowTag(table, system_tags={}) + + assert tag["user_id"] == 123 + assert tag.system_tags() == {} + + def test_packet_empty_source_info(self): + """Test packet with empty source info.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + packet = ArrowPacket(table, source_info={}) + + assert packet["user_id"] == 123 + source_info = packet.source_info() + assert all(v is None for v in source_info.values()) + + def test_tag_none_system_tags(self): + """Test tag with None system tags.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + tag = ArrowTag(table, system_tags=None) + + assert tag["user_id"] == 123 + assert tag.system_tags() == {} + + def test_packet_none_source_info(self): + """Test packet with None source info.""" + table = pa.Table.from_pydict({"user_id": [123], "name": ["Alice"]}) + packet = ArrowPacket(table, source_info=None) + + assert packet["user_id"] == 123 + source_info = packet.source_info() + assert all(v is None for v in source_info.values()) + + def test_tag_with_meta_and_system_tags(self): + """Test tag with both meta columns and system tags.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "__meta_field": ["meta_value"]} + ) + system_tags = {"tag_type": "user"} + + tag = ArrowTag(table, system_tags=system_tags) + + # All information should be accessible + full_dict = tag.as_dict(include_all_info=True) + assert "user_id" in full_dict + assert "__meta_field" in full_dict + assert "tag_type" in full_dict + assert constants.CONTEXT_KEY in full_dict + + def test_packet_with_meta_and_source_info(self): + """Test packet with both meta columns and source info.""" + table = pa.Table.from_pydict( + {"user_id": [123], "name": ["Alice"], "__meta_field": ["meta_value"]} + ) + source_info = {"user_id": "database"} + + packet = ArrowPacket(table, source_info=source_info) + + # All information should be accessible + full_dict = packet.as_dict(include_all_info=True) + assert "user_id" in full_dict + assert "__meta_field" in full_dict + assert f"{constants.SOURCE_PREFIX}user_id" in full_dict + assert constants.CONTEXT_KEY in full_dict + + def test_tag_large_system_tags(self): + """Test tag with many system tags.""" + table = pa.Table.from_pydict({"user_id": [123]}) + system_tags = {f"tag_{i}": f"value_{i}" for i in range(100)} + + tag = ArrowTag(table, system_tags=system_tags) + + assert tag["user_id"] == 123 + retrieved_tags = tag.system_tags() + assert len(retrieved_tags) == 100 + assert retrieved_tags["tag_50"] == "value_50" + + def test_packet_large_source_info(self): + """Test packet with source info for many columns.""" + data = {f"col_{i}": [i] for i in range(50)} + table = pa.Table.from_pydict(data) + source_info = {f"col_{i}": f"source_{i}" for i in range(50)} + + packet = ArrowPacket(table, source_info=source_info) + + assert packet["col_25"] == 25 + retrieved_source = packet.source_info() + assert len(retrieved_source) == 50 + assert retrieved_source["col_25"] == "source_25" diff --git a/tests/test_data/test_datagrams/test_base_integration.py b/tests/test_data/test_datagrams/test_base_integration.py new file mode 100644 index 0000000..896a60f --- /dev/null +++ b/tests/test_data/test_datagrams/test_base_integration.py @@ -0,0 +1,594 @@ +""" +Comprehensive tests for base datagram functionality and integration tests. + +This module tests: +- Base datagram abstract interface +- Integration between different datagram implementations +- Cross-format conversions +- Performance and memory considerations +""" + +import pytest +import pyarrow as pa + +from orcapod.core.datagrams import ( + DictDatagram, + ArrowDatagram, + DictTag, + DictPacket, + ArrowTag, + ArrowPacket, +) +from orcapod.core.datagrams.base import ( + BaseDatagram, + ImmutableDict, + contains_prefix_from, +) +from orcapod.core.system_constants import constants + + +class TestImmutableDict: + """Test ImmutableDict utility class.""" + + def test_basic_functionality(self): + """Test basic ImmutableDict operations.""" + data = {"a": 1, "b": 2, "c": 3} + immutable = ImmutableDict(data) + + assert immutable["a"] == 1 + assert immutable["b"] == 2 + assert immutable["c"] == 3 + assert len(immutable) == 3 + + def test_iteration(self): + """Test iteration over ImmutableDict.""" + data = {"a": 1, "b": 2, "c": 3} + immutable = ImmutableDict(data) + + keys = list(immutable) + assert set(keys) == {"a", "b", "c"} + + items = list(immutable.items()) + assert set(items) == {("a", 1), ("b", 2), ("c", 3)} + + def test_merge_operation(self): + """Test merge operation with | operator.""" + data1 = {"a": 1, "b": 2} + data2 = {"c": 3, "d": 4} + + immutable1 = ImmutableDict(data1) + immutable2 = ImmutableDict(data2) + + merged = immutable1 | immutable2 + + assert len(merged) == 4 + assert merged["a"] == 1 + assert merged["c"] == 3 + + def test_merge_with_dict(self): + """Test merge operation with regular dict.""" + data1 = {"a": 1, "b": 2} + data2 = {"c": 3, "d": 4} + + immutable = ImmutableDict(data1) + merged = immutable | data2 + + assert len(merged) == 4 + assert merged["a"] == 1 + assert merged["c"] == 3 + + def test_string_representations(self): + """Test string representations.""" + data = {"a": 1, "b": 2} + immutable = ImmutableDict(data) + + str_repr = str(immutable) + repr_str = repr(immutable) + + assert "a" in str_repr and "1" in str_repr + assert "a" in repr_str and "1" in repr_str + + +class TestUtilityFunctions: + """Test utility functions.""" + + def test_contains_prefix_from(self): + """Test contains_prefix_from function.""" + prefixes = ["__", "_source_", "_system_"] + + assert contains_prefix_from("__version", prefixes) + assert contains_prefix_from("_source_file", prefixes) + assert contains_prefix_from("_system_tag", prefixes) + assert not contains_prefix_from("regular_column", prefixes) + assert not contains_prefix_from("_other_prefix", prefixes) + + def test_contains_prefix_from_empty(self): + """Test contains_prefix_from with empty prefixes.""" + assert not contains_prefix_from("any_column", []) + + def test_contains_prefix_from_edge_cases(self): + """Test contains_prefix_from edge cases.""" + prefixes = ["__"] + + assert contains_prefix_from("__", prefixes) + assert not contains_prefix_from("_", prefixes) + assert not contains_prefix_from("", prefixes) + + +class TestBaseDatagram: + """Test BaseDatagram abstract interface.""" + + def test_is_abstract(self): + """Test that BaseDatagram cannot be instantiated directly.""" + try: + # This should raise TypeError for abstract class + BaseDatagram() # type: ignore + pytest.fail("Expected TypeError for abstract class instantiation") + except TypeError as e: + # Expected behavior - BaseDatagram is abstract + assert "abstract" in str(e).lower() or "instantiate" in str(e).lower() + + def test_abstract_methods(self): + """Test that all abstract methods are defined.""" + # Get all abstract methods + abstract_methods = BaseDatagram.__abstractmethods__ + + # Verify key abstract methods exist + expected_methods = { + "__getitem__", + "__contains__", + "__iter__", + "get", + "keys", + "types", + "arrow_schema", + "content_hash", + "as_dict", + "as_table", + "meta_columns", + "get_meta_value", + "with_meta_columns", + "drop_meta_columns", + "select", + "drop", + "rename", + "update", + "with_columns", + } + + assert expected_methods.issubset(abstract_methods) + + +class TestCrossFormatConversions: + """Test conversions between different datagram formats.""" + + @pytest.fixture + def sample_data(self): + """Sample data for conversion tests.""" + return { + "user_id": 123, + "name": "Alice", + "score": 85.5, + "active": True, + "__version": "1.0", + "__pipeline": "test", + } + + def test_dict_to_arrow_conversion(self, sample_data): + """Test converting DictDatagram to ArrowDatagram.""" + dict_datagram = DictDatagram(sample_data) + + # Convert via table + table = dict_datagram.as_table(include_all_info=True) + arrow_datagram = ArrowDatagram(table) + + # Data should be preserved + assert arrow_datagram["user_id"] == dict_datagram["user_id"] + assert arrow_datagram["name"] == dict_datagram["name"] + assert arrow_datagram["score"] == dict_datagram["score"] + assert arrow_datagram["active"] == dict_datagram["active"] + + # Meta columns should be preserved + assert arrow_datagram.get_meta_value("version") == dict_datagram.get_meta_value( + "version" + ) + assert arrow_datagram.get_meta_value( + "pipeline" + ) == dict_datagram.get_meta_value("pipeline") + + def test_arrow_to_dict_conversion(self, sample_data): + """Test converting ArrowDatagram to DictDatagram.""" + table = pa.Table.from_pylist([sample_data]) + arrow_datagram = ArrowDatagram(table) + + # Convert via dict + data_dict = arrow_datagram.as_dict(include_all_info=True) + dict_datagram = DictDatagram(data_dict) + + # Data should be preserved + assert dict_datagram["user_id"] == arrow_datagram["user_id"] + assert dict_datagram["name"] == arrow_datagram["name"] + assert dict_datagram["score"] == arrow_datagram["score"] + assert dict_datagram["active"] == arrow_datagram["active"] + + # Meta columns should be preserved + assert dict_datagram.get_meta_value("version") == arrow_datagram.get_meta_value( + "version" + ) + + def test_tag_conversions(self): + """Test conversions between tag formats.""" + data = {"user_id": 123, "name": "Alice"} + system_tags = {"tag_type": "user", "version": "1.0"} + + # Dict to Arrow tag + dict_tag = DictTag(data, system_tags=system_tags) + table = dict_tag.as_table(include_all_info=True) + arrow_tag = ArrowTag(table) + + # Data and system tags should be preserved + assert arrow_tag["user_id"] == dict_tag["user_id"] + assert arrow_tag["name"] == dict_tag["name"] + + # Arrow to Dict tag + full_dict = arrow_tag.as_dict(include_all_info=True) + reconstructed_dict_tag = DictTag(full_dict) + + assert reconstructed_dict_tag["user_id"] == arrow_tag["user_id"] + assert reconstructed_dict_tag["name"] == arrow_tag["name"] + + def test_packet_conversions(self): + """Test conversions between packet formats.""" + data = {"user_id": 123, "name": "Alice"} + source_info = {"user_id": "database", "name": "user_input"} + + # Dict to Arrow packet + dict_packet = DictPacket(data, source_info=source_info) + table = dict_packet.as_table(include_all_info=True) + arrow_packet = ArrowPacket(table) + + # Data and source info should be preserved + assert arrow_packet["user_id"] == dict_packet["user_id"] + assert arrow_packet["name"] == dict_packet["name"] + + # Arrow to Dict packet + full_dict = arrow_packet.as_dict(include_all_info=True) + reconstructed_dict_packet = DictPacket(full_dict) + + assert reconstructed_dict_packet["user_id"] == arrow_packet["user_id"] + assert reconstructed_dict_packet["name"] == arrow_packet["name"] + + +class TestDatagramIntegration: + """Test integration between different datagram types.""" + + def test_mixed_operations(self): + """Test operations that mix different datagram types.""" + # Start with dict datagram + dict_data = {"user_id": 123, "name": "Alice", "score": 85.5} + dict_datagram = DictDatagram(dict_data) + + # Convert to arrow + table = dict_datagram.as_table() + arrow_datagram = ArrowDatagram(table) + + # Perform operations on arrow datagram + modified_arrow = arrow_datagram.update(score=90.0).with_columns(grade="A") + + # Convert back to dict + modified_dict = DictDatagram(modified_arrow.as_dict()) + + # Verify final state + assert modified_dict["user_id"] == 123 + assert modified_dict["score"] == 90.0 + assert modified_dict["grade"] == "A" + + def test_tag_packet_interoperability(self): + """Test interoperability between tags and packets.""" + # Create a tag + tag_data = {"entity_id": "user_123", "entity_type": "user"} + system_tags = {"created_by": "system", "version": "1.0"} + tag = DictTag(tag_data, system_tags=system_tags) + + # Convert tag to packet-like structure + tag_as_dict = tag.as_dict(include_system_tags=True) + packet = DictPacket(tag_as_dict, source_info={"entity_id": "tag_system"}) + + # Verify data preservation + assert packet["entity_id"] == tag["entity_id"] + assert packet["entity_type"] == tag["entity_type"] + + # Source info should be available + source_info = packet.source_info() + assert source_info["entity_id"] == "tag_system" + + def test_comprehensive_roundtrip(self): + """Test comprehensive roundtrip through all formats.""" + original_data = { + "user_id": 123, + "name": "Alice", + "score": 85.5, + "active": True, + "__version": "1.0", + constants.CONTEXT_KEY: "v0.1", + } + + # Start with DictDatagram + dict_datagram = DictDatagram(original_data) + + # Convert to ArrowDatagram + table = dict_datagram.as_table(include_all_info=True) + arrow_datagram = ArrowDatagram(table) + + # Convert to DictTag with some system tags + tag_dict = arrow_datagram.as_dict(include_all_info=True) + dict_tag = DictTag(tag_dict, system_tags={"tag_type": "test", "version": "1.0"}) + + # Convert to ArrowTag + tag_table = dict_tag.as_table(include_all_info=True) + arrow_tag = ArrowTag(tag_table) + + # Convert to DictPacket with some source info + packet_dict = arrow_tag.as_dict(include_all_info=True) + dict_packet = DictPacket( + packet_dict, source_info={"source": "test", "timestamp": "2024-01-01"} + ) + + # Convert to ArrowPacket + packet_table = dict_packet.as_table(include_all_info=True) + arrow_packet = ArrowPacket(packet_table) + + # Convert back to DictDatagram + final_dict = arrow_packet.as_dict(include_all_info=True) + final_datagram = DictDatagram(final_dict) + + # Verify data preservation through the entire journey + assert final_datagram["user_id"] == original_data["user_id"] + assert final_datagram["name"] == original_data["name"] + assert final_datagram["score"] == original_data["score"] + assert final_datagram["active"] == original_data["active"] + assert final_datagram.get_meta_value("version") == "1.0" + assert final_datagram.data_context_key == "std:v0.1:default" + + +class TestDatagramConsistency: + """Test consistency across different datagram implementations.""" + + @pytest.fixture + def equivalent_datagrams(self): + """Create equivalent datagrams in different formats.""" + data = { + "user_id": 123, + "name": "Alice", + "score": 85.5, + "active": True, + "__version": "1.0", + } + + dict_datagram = DictDatagram(data) + table = pa.Table.from_pylist([data]) + arrow_datagram = ArrowDatagram(table) + + return dict_datagram, arrow_datagram + + def test_consistent_dict_interface(self, equivalent_datagrams): + """Test that dict-like interface is consistent.""" + dict_dg, arrow_dg = equivalent_datagrams + + # __getitem__ + assert dict_dg["user_id"] == arrow_dg["user_id"] + assert dict_dg["name"] == arrow_dg["name"] + assert dict_dg["score"] == arrow_dg["score"] + assert dict_dg["active"] == arrow_dg["active"] + + # __contains__ + assert ("user_id" in dict_dg) == ("user_id" in arrow_dg) + assert ("nonexistent" in dict_dg) == ("nonexistent" in arrow_dg) + + # get + assert dict_dg.get("user_id") == arrow_dg.get("user_id") + assert dict_dg.get("nonexistent", "default") == arrow_dg.get( + "nonexistent", "default" + ) + + def test_consistent_structural_info(self, equivalent_datagrams): + """Test that structural information is consistent.""" + dict_dg, arrow_dg = equivalent_datagrams + + # keys + assert set(dict_dg.keys()) == set(arrow_dg.keys()) + assert set(dict_dg.keys(include_meta_columns=True)) == set( + arrow_dg.keys(include_meta_columns=True) + ) + + # meta_columns + assert set(dict_dg.meta_columns) == set(arrow_dg.meta_columns) + + # types (basic structure, not exact types due to inference differences) + dict_types = dict_dg.types() + arrow_types = arrow_dg.types() + assert set(dict_types.keys()) == set(arrow_types.keys()) + + def test_consistent_meta_operations(self, equivalent_datagrams): + """Test that meta operations are consistent.""" + dict_dg, arrow_dg = equivalent_datagrams + + # get_meta_value + assert dict_dg.get_meta_value("version") == arrow_dg.get_meta_value("version") + assert dict_dg.get_meta_value( + "nonexistent", "default" + ) == arrow_dg.get_meta_value("nonexistent", "default") + + def test_consistent_data_operations(self, equivalent_datagrams): + """Test that data operations produce consistent results.""" + dict_dg, arrow_dg = equivalent_datagrams + + # select + dict_selected = dict_dg.select("user_id", "name") + arrow_selected = arrow_dg.select("user_id", "name") + + assert set(dict_selected.keys()) == set(arrow_selected.keys()) + assert dict_selected["user_id"] == arrow_selected["user_id"] + assert dict_selected["name"] == arrow_selected["name"] + + # update + dict_updated = dict_dg.update(score=95.0) + arrow_updated = arrow_dg.update(score=95.0) + + assert dict_updated["score"] == arrow_updated["score"] + assert dict_updated["user_id"] == arrow_updated["user_id"] # Unchanged + + def test_consistent_format_conversions(self, equivalent_datagrams): + """Test that format conversions are consistent.""" + dict_dg, arrow_dg = equivalent_datagrams + + # as_dict + dict_as_dict = dict_dg.as_dict() + arrow_as_dict = arrow_dg.as_dict() + + assert dict_as_dict == arrow_as_dict + + # as_table + dict_as_table = dict_dg.as_table() + arrow_as_table = arrow_dg.as_table() + + assert dict_as_table.column_names == arrow_as_table.column_names + assert len(dict_as_table) == len(arrow_as_table) + + +class TestDatagramPerformance: + """Test performance characteristics of different implementations.""" + + def test_memory_efficiency(self): + """Test memory efficiency considerations.""" + # Create large-ish data + n_cols = 100 + data = {f"col_{i}": [i * 1.5] for i in range(n_cols)} + + # Dict implementation + dict_datagram = DictDatagram(data) + + # Arrow implementation - get data in correct format from dict datagram + arrow_data = dict_datagram.as_dict() + # Convert scalar values to single-element lists for PyArrow + arrow_data_lists = {k: [v] for k, v in arrow_data.items()} + table = pa.Table.from_pydict(arrow_data_lists) + arrow_datagram = ArrowDatagram(table) + + # Both should handle the data efficiently + assert len(dict_datagram.keys()) == n_cols + assert len(arrow_datagram.keys()) == n_cols + + # Verify data integrity - both should have consistent data + # Note: The original data has lists, so both implementations should handle lists consistently + assert dict_datagram["col_50"] == [ + 75.0 + ] # DictDatagram preserves list structure + assert arrow_datagram["col_50"] == [ + 75.0 + ] # ArrowDatagram also preserves list structure + + def test_caching_behavior(self): + """Test caching behavior across implementations.""" + data = {"user_id": [123], "name": ["Alice"]} # Lists for PyArrow + + # Test dict caching + dict_datagram = DictDatagram(data) + dict1 = dict_datagram.as_dict() + dict2 = dict_datagram.as_dict() + # Dict implementation may or may not cache, but should be consistent + assert dict1 == dict2 + + # Test arrow caching + table = pa.Table.from_pydict(data) + arrow_datagram = ArrowDatagram(table) + arrow_dict1 = arrow_datagram.as_dict() + arrow_dict2 = arrow_datagram.as_dict() + # Arrow implementation should cache + assert arrow_dict1 == arrow_dict2 # Same content + # Note: ArrowDatagram returns copies for safety, not identical objects + + def test_operation_efficiency(self): + """Test efficiency of common operations.""" + # Create moderately sized data + data = {f"col_{i}": [i] for i in range(50)} # Lists for PyArrow + + dict_datagram = DictDatagram(data) + table = pa.Table.from_pydict(data) + arrow_datagram = ArrowDatagram(table) + + # Select operations should be efficient + dict_selected = dict_datagram.select("col_0", "col_25", "col_49") + arrow_selected = arrow_datagram.select("col_0", "col_25", "col_49") + + assert len(dict_selected.keys()) == 3 + assert len(arrow_selected.keys()) == 3 + + # Update operations should be efficient + dict_updated = dict_datagram.update(col_25=999) + arrow_updated = arrow_datagram.update(col_25=999) + + assert dict_updated["col_25"] == 999 + assert arrow_updated["col_25"] == 999 + + +class TestDatagramErrorHandling: + """Test error handling consistency across implementations.""" + + def test_consistent_key_errors(self): + """Test that KeyError handling is consistent.""" + data = {"user_id": [123], "name": ["Alice"]} # Lists for PyArrow + + dict_datagram = DictDatagram(data) + table = pa.Table.from_pydict(data) + arrow_datagram = ArrowDatagram(table) + + # Both should raise KeyError for missing keys + with pytest.raises(KeyError): + _ = dict_datagram["nonexistent"] + + with pytest.raises(KeyError): + _ = arrow_datagram["nonexistent"] + + def test_consistent_operation_errors(self): + """Test that operation errors are consistent.""" + data = {"user_id": [123], "name": ["Alice"]} # Lists for PyArrow + + dict_datagram = DictDatagram(data) + table = pa.Table.from_pydict(data) + arrow_datagram = ArrowDatagram(table) + + # Both should raise appropriate errors for invalid operations + with pytest.raises((KeyError, ValueError)): + dict_datagram.select("nonexistent") + + with pytest.raises((KeyError, ValueError)): + arrow_datagram.select("nonexistent") + + with pytest.raises(KeyError): + dict_datagram.update(nonexistent="value") + + with pytest.raises(KeyError): + arrow_datagram.update(nonexistent="value") + + def test_consistent_validation(self): + """Test that validation is consistent.""" + data = {"user_id": 123, "name": "Alice"} # Lists for PyArrow + + dict_datagram = DictDatagram(data) + table = pa.Table.from_pylist([data]) + arrow_datagram = ArrowDatagram(table) + + # Both should handle edge cases consistently + # Test that empty select behavior is consistent (may select all or raise error) + try: + dict_result = dict_datagram.select() + arrow_result = arrow_datagram.select() + # If both succeed, they should have the same keys + assert set(dict_result.keys()) == set(arrow_result.keys()) + except (ValueError, TypeError): + # If one raises an error, both should raise similar errors + with pytest.raises((ValueError, TypeError)): + arrow_datagram.select() + + # Note: The important thing is that both behave the same way diff --git a/tests/test_data/test_datagrams/test_dict_datagram.py b/tests/test_data/test_datagrams/test_dict_datagram.py new file mode 100644 index 0000000..5538d59 --- /dev/null +++ b/tests/test_data/test_datagrams/test_dict_datagram.py @@ -0,0 +1,765 @@ +""" +Comprehensive tests for DictDatagram class. + +This module tests all functionality of the DictDatagram class including: +- Initialization and validation +- Dict-like interface operations +- Structural information methods +- Format conversion methods +- Meta column operations +- Data column operations +- Context operations +- Utility operations +""" + +import pytest +import pyarrow as pa + +from orcapod.core.datagrams import DictDatagram +from orcapod.core.system_constants import constants + + +class TestDictDatagramInitialization: + """Test DictDatagram initialization and basic properties.""" + + def test_basic_initialization(self): + """Test basic initialization with simple data.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + datagram = DictDatagram(data) + + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + assert datagram["score"] == 85.5 + + def test_initialization_with_meta_info(self): + """Test initialization with meta information.""" + data = {"user_id": 123, "name": "Alice"} + meta_info = {"__pipeline_version": "v1.0", "__timestamp": "2024-01-01"} + + datagram = DictDatagram(data, meta_info=meta_info) + + assert datagram["user_id"] == 123 + assert datagram.get_meta_value("pipeline_version") == "v1.0" + assert datagram.get_meta_value("timestamp") == "2024-01-01" + + def test_initialization_with_context_in_data(self): + """Test initialization when context is included in data.""" + data = {"user_id": 123, "name": "Alice", constants.CONTEXT_KEY: "v0.1"} + + datagram = DictDatagram(data) + + # The context key is transformed to include full context path + assert "v0.1" in datagram.data_context_key + assert constants.CONTEXT_KEY not in datagram._data + + def test_initialization_with_meta_columns_in_data(self): + """Test initialization when meta columns are included in data.""" + data = { + "user_id": 123, + "name": "Alice", + "__version": "1.0", + "__timestamp": "2024-01-01", + } + + datagram = DictDatagram(data) + + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + assert datagram.get_meta_value("version") == "1.0" + assert datagram.get_meta_value("timestamp") == "2024-01-01" + # Meta columns should not be in regular data + assert "__version" not in datagram._data + assert "__timestamp" not in datagram._data + + def test_initialization_with_python_schema(self): + """Test initialization with explicit Python schema.""" + data = {"user_id": "123", "score": "85.5"} # String values + python_schema = {"user_id": int, "score": float} + + datagram = DictDatagram(data, python_schema=python_schema) + + # Data should be stored as provided (conversion happens during export) + assert datagram["user_id"] == "123" + assert datagram["score"] == "85.5" + + def test_empty_data_initialization(self): + """Test initialization with empty data succeeds.""" + # Empty data should be allowed in OrcaPod + data = {} + datagram = DictDatagram(data) + + assert len(datagram.keys()) == 0 + assert datagram.as_dict() == {} + + +class TestDictDatagramDictInterface: + """Test dict-like interface methods.""" + + @pytest.fixture + def sample_datagram(self): + """Create a sample datagram for testing.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5, "active": True} + return DictDatagram(data) + + def test_getitem(self, sample_datagram): + """Test __getitem__ method.""" + assert sample_datagram["user_id"] == 123 + assert sample_datagram["name"] == "Alice" + assert sample_datagram["score"] == 85.5 + assert sample_datagram["active"] is True + + def test_getitem_missing_key(self, sample_datagram): + """Test __getitem__ with missing key raises KeyError.""" + with pytest.raises(KeyError): + _ = sample_datagram["nonexistent"] + + def test_contains(self, sample_datagram): + """Test __contains__ method.""" + assert "user_id" in sample_datagram + assert "name" in sample_datagram + assert "nonexistent" not in sample_datagram + + def test_iter(self, sample_datagram): + """Test __iter__ method.""" + keys = list(sample_datagram) + expected_keys = {"user_id", "name", "score", "active"} + assert set(keys) == expected_keys + + def test_get(self, sample_datagram): + """Test get method.""" + assert sample_datagram.get("user_id") == 123 + assert sample_datagram.get("nonexistent") is None + assert sample_datagram.get("nonexistent", "default") == "default" + + +class TestDictDatagramStructuralInfo: + """Test structural information methods.""" + + @pytest.fixture + def datagram_with_meta(self): + """Create a datagram with meta data for testing.""" + data = { + "user_id": 123, + "name": "Alice", + "__version": "1.0", + "__pipeline_id": "test_pipeline", + } + return DictDatagram(data, data_context="v0.1") + + def test_keys_data_only(self, datagram_with_meta): + """Test keys method with data columns only.""" + keys = datagram_with_meta.keys() + expected_keys = {"user_id", "name"} + assert set(keys) == expected_keys + + def test_keys_with_meta_columns(self, datagram_with_meta): + """Test keys method including meta columns.""" + keys = datagram_with_meta.keys(include_meta_columns=True) + expected_keys = {"user_id", "name", "__version", "__pipeline_id"} + assert set(keys) == expected_keys + + def test_keys_with_context(self, datagram_with_meta): + """Test keys method including context.""" + keys = datagram_with_meta.keys(include_context=True) + expected_keys = {"user_id", "name", constants.CONTEXT_KEY} + assert set(keys) == expected_keys + + def test_keys_with_all_info(self, datagram_with_meta): + """Test keys method including all information.""" + keys = datagram_with_meta.keys(include_meta_columns=True, include_context=True) + expected_keys = { + "user_id", + "name", + "__version", + "__pipeline_id", + constants.CONTEXT_KEY, + } + assert set(keys) == expected_keys + + def test_keys_with_specific_meta_prefix(self, datagram_with_meta): + """Test keys method with specific meta columns.""" + # Test selecting specific meta columns by getting all first + all_keys_with_meta = datagram_with_meta.keys(include_meta_columns=True) + + # Should include data columns and meta columns + expected_keys = {"user_id", "name", "__version", "__pipeline_id"} + assert set(all_keys_with_meta) == expected_keys + + def test_types_data_only(self, datagram_with_meta): + """Test types method with data columns only.""" + types = datagram_with_meta.types() + expected_keys = {"user_id", "name"} + assert set(types.keys()) == expected_keys + assert types["user_id"] is int + assert types["name"] is str + + def test_types_with_meta_columns(self, datagram_with_meta): + """Test types method including meta columns.""" + types = datagram_with_meta.types(include_meta_columns=True) + expected_keys = {"user_id", "name", "__version", "__pipeline_id"} + assert set(types.keys()) == expected_keys + assert types["__version"] is str + assert types["__pipeline_id"] is str + + def test_types_with_context(self, datagram_with_meta): + """Test types method including context.""" + types = datagram_with_meta.types(include_context=True) + expected_keys = {"user_id", "name", constants.CONTEXT_KEY} + assert set(types.keys()) == expected_keys + assert types[constants.CONTEXT_KEY] is str + + def test_arrow_schema_data_only(self, datagram_with_meta): + """Test arrow_schema method with data columns only.""" + schema = datagram_with_meta.arrow_schema() + expected_names = {"user_id", "name"} + assert set(schema.names) == expected_names + # Access field by name, not index + assert schema.field("user_id").type == pa.int64() + assert schema.field("name").type == pa.large_string() + + def test_arrow_schema_with_meta_columns(self, datagram_with_meta): + """Test arrow_schema method including meta columns.""" + schema = datagram_with_meta.arrow_schema(include_meta_columns=True) + expected_names = {"user_id", "name", "__version", "__pipeline_id"} + assert set(schema.names) == expected_names + assert schema.field("__version").type == pa.large_string() + assert schema.field("__pipeline_id").type == pa.large_string() + + def test_arrow_schema_with_context(self, datagram_with_meta): + """Test arrow_schema method including context.""" + schema = datagram_with_meta.arrow_schema(include_context=True) + expected_names = {"user_id", "name", constants.CONTEXT_KEY} + assert set(schema.names) == expected_names + + def test_content_hash(self, datagram_with_meta): + """Test content hash calculation.""" + hash1 = datagram_with_meta.content_hash().to_hex() + hash2 = datagram_with_meta.content_hash().to_hex() + + # Hash should be consistent + assert hash1 == hash2 + assert isinstance(hash1, str) + assert len(hash1) > 0 + + def test_content_hash_different_data(self): + """Test content hash is different for different data.""" + datagram1 = DictDatagram({"user_id": 123, "name": "Alice"}) + datagram2 = DictDatagram({"user_id": 456, "name": "Bob"}) + + hash1 = datagram1.content_hash() + hash2 = datagram2.content_hash() + + assert hash1 != hash2 + + +class TestDictDatagramFormatConversions: + """Test format conversion methods.""" + + @pytest.fixture + def datagram_with_all(self): + """Create a datagram with data, meta, and context.""" + data = { + "user_id": 123, + "name": "Alice", + "__version": "1.0", + constants.CONTEXT_KEY: "v0.1", + } + return DictDatagram(data) + + def test_as_dict_data_only(self, datagram_with_all): + """Test as_dict method with data columns only.""" + result = datagram_with_all.as_dict() + expected = {"user_id": 123, "name": "Alice"} + assert result == expected + + def test_as_dict_with_meta_columns(self, datagram_with_all): + """Test as_dict method including meta columns.""" + result = datagram_with_all.as_dict(include_meta_columns=True) + expected = {"user_id": 123, "name": "Alice", "__version": "1.0"} + assert result == expected + + def test_as_dict_with_context(self, datagram_with_all): + """Test as_dict method including context.""" + result = datagram_with_all.as_dict(include_context=True) + expected_user_id = 123 + expected_name = "Alice" + + assert result["user_id"] == expected_user_id + assert result["name"] == expected_name + # Context key should be present but value might be transformed + assert constants.CONTEXT_KEY in result + + def test_as_dict_with_all_info(self, datagram_with_all): + """Test as_dict method including all information.""" + result = datagram_with_all.as_dict( + include_meta_columns=True, include_context=True + ) + + assert result["user_id"] == 123 + assert result["name"] == "Alice" + assert result["__version"] == "1.0" + # Context key should be present but value might be transformed + assert constants.CONTEXT_KEY in result + + def test_as_table_data_only(self, datagram_with_all): + """Test as_table method with data columns only.""" + table = datagram_with_all.as_table() + + assert table.num_rows == 1 + assert set(table.column_names) == {"user_id", "name"} + assert table["user_id"].to_pylist() == [123] + assert table["name"].to_pylist() == ["Alice"] + + def test_as_table_with_meta_columns(self, datagram_with_all): + """Test as_table method including meta columns.""" + table = datagram_with_all.as_table(include_meta_columns=True) + + assert table.num_rows == 1 + expected_columns = {"user_id", "name", "__version"} + assert set(table.column_names) == expected_columns + assert table["__version"].to_pylist() == ["1.0"] + + def test_as_table_with_context(self, datagram_with_all): + """Test as_table method including context.""" + table = datagram_with_all.as_table(include_context=True) + + assert table.num_rows == 1 + expected_columns = {"user_id", "name", constants.CONTEXT_KEY} + assert set(table.column_names) == expected_columns + # Context value might be transformed, just check it exists + assert len(table[constants.CONTEXT_KEY].to_pylist()) == 1 + + def test_as_arrow_compatible_dict(self, datagram_with_all): + """Test as_arrow_compatible_dict method.""" + result = datagram_with_all.as_arrow_compatible_dict() + + # Should be dict with list values suitable for PyArrow + assert isinstance(result, dict) + # The method returns single values, not lists for single-row data + assert result["user_id"] == 123 + assert result["name"] == "Alice" + + +class TestDictDatagramMetaOperations: + """Test meta column operations.""" + + @pytest.fixture + def datagram_with_meta(self): + """Create a datagram with meta columns.""" + data = { + "user_id": 123, + "name": "Alice", + "__version": "1.0", + "__pipeline_id": "test_pipeline", + "__timestamp": "2024-01-01", + } + return DictDatagram(data) + + def test_meta_columns_property(self, datagram_with_meta): + """Test meta_columns property.""" + meta_columns = datagram_with_meta.meta_columns + expected = {"__version", "__pipeline_id", "__timestamp"} + assert set(meta_columns) == expected + + def test_get_meta_value(self, datagram_with_meta): + """Test get_meta_value method.""" + assert datagram_with_meta.get_meta_value("version") == "1.0" + assert datagram_with_meta.get_meta_value("pipeline_id") == "test_pipeline" + assert datagram_with_meta.get_meta_value("timestamp") == "2024-01-01" + assert datagram_with_meta.get_meta_value("nonexistent") is None + assert datagram_with_meta.get_meta_value("nonexistent", "default") == "default" + + def test_with_meta_columns(self, datagram_with_meta): + """Test with_meta_columns method.""" + new_datagram = datagram_with_meta.with_meta_columns( + new_meta="new_value", updated_version="2.0" + ) + + # Original should be unchanged + assert datagram_with_meta.get_meta_value("version") == "1.0" + assert datagram_with_meta.get_meta_value("new_meta") is None + + # New datagram should have updates + assert new_datagram.get_meta_value("version") == "1.0" # unchanged + assert new_datagram.get_meta_value("updated_version") == "2.0" # new + assert new_datagram.get_meta_value("new_meta") == "new_value" # new + + def test_with_meta_columns_prefixed_keys(self, datagram_with_meta): + """Test with_meta_columns method with already prefixed keys.""" + new_datagram = datagram_with_meta.with_meta_columns( + **{"__direct_meta": "direct_value"} + ) + + assert new_datagram.get_meta_value("direct_meta") == "direct_value" + + def test_drop_meta_columns(self, datagram_with_meta): + """Test drop_meta_columns method.""" + new_datagram = datagram_with_meta.drop_meta_columns("version", "timestamp") + + # Original should be unchanged + assert datagram_with_meta.get_meta_value("version") == "1.0" + assert datagram_with_meta.get_meta_value("timestamp") == "2024-01-01" + + # New datagram should have dropped columns + assert new_datagram.get_meta_value("version") is None + assert new_datagram.get_meta_value("timestamp") is None + assert ( + new_datagram.get_meta_value("pipeline_id") == "test_pipeline" + ) # unchanged + + def test_drop_meta_columns_prefixed(self, datagram_with_meta): + """Test drop_meta_columns method with prefixed keys.""" + new_datagram = datagram_with_meta.drop_meta_columns("__version") + + assert new_datagram.get_meta_value("version") is None + assert ( + new_datagram.get_meta_value("pipeline_id") == "test_pipeline" + ) # unchanged + + def test_drop_meta_columns_multiple(self, datagram_with_meta): + """Test dropping multiple meta columns.""" + new_datagram = datagram_with_meta.drop_meta_columns("version", "pipeline_id") + + assert new_datagram.get_meta_value("version") is None + assert new_datagram.get_meta_value("pipeline_id") is None + assert new_datagram.get_meta_value("timestamp") == "2024-01-01" # unchanged + + def test_drop_meta_columns_missing_key(self, datagram_with_meta): + """Test drop_meta_columns with missing key raises KeyError.""" + with pytest.raises(KeyError): + datagram_with_meta.drop_meta_columns("nonexistent") + + def test_drop_meta_columns_ignore_missing(self, datagram_with_meta): + """Test drop_meta_columns with ignore_missing=True.""" + new_datagram = datagram_with_meta.drop_meta_columns( + "version", "nonexistent", ignore_missing=True + ) + + assert new_datagram.get_meta_value("version") is None + assert ( + new_datagram.get_meta_value("pipeline_id") == "test_pipeline" + ) # unchanged + + +class TestDictDatagramDataOperations: + """Test data column operations.""" + + @pytest.fixture + def sample_datagram(self): + """Create a sample datagram for testing.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5, "active": True} + return DictDatagram(data) + + def test_select(self, sample_datagram): + """Test select method.""" + new_datagram = sample_datagram.select("user_id", "name") + + assert set(new_datagram.keys()) == {"user_id", "name"} + assert new_datagram["user_id"] == 123 + assert new_datagram["name"] == "Alice" + + # Original should be unchanged + assert len(sample_datagram.keys()) == 4 + + def test_select_single_column(self, sample_datagram): + """Test select method with single column.""" + new_datagram = sample_datagram.select("user_id") + + assert list(new_datagram.keys()) == ["user_id"] + assert new_datagram["user_id"] == 123 + + def test_select_missing_column(self, sample_datagram): + """Test select method with missing column raises KeyError.""" + with pytest.raises(KeyError): + sample_datagram.select("user_id", "nonexistent") + + def test_drop(self, sample_datagram): + """Test drop method.""" + new_datagram = sample_datagram.drop("score", "active") + + assert set(new_datagram.keys()) == {"user_id", "name"} + assert new_datagram["user_id"] == 123 + assert new_datagram["name"] == "Alice" + + # Original should be unchanged + assert len(sample_datagram.keys()) == 4 + + def test_drop_single_column(self, sample_datagram): + """Test drop method with single column.""" + new_datagram = sample_datagram.drop("score") + + expected_keys = {"user_id", "name", "active"} + assert set(new_datagram.keys()) == expected_keys + + def test_drop_missing_column(self, sample_datagram): + """Test drop method with missing column raises KeyError.""" + with pytest.raises(KeyError): + sample_datagram.drop("nonexistent") + + def test_drop_ignore_missing(self, sample_datagram): + """Test drop method with ignore_missing=True.""" + new_datagram = sample_datagram.drop("score", "nonexistent", ignore_missing=True) + + expected_keys = {"user_id", "name", "active"} + assert set(new_datagram.keys()) == expected_keys + + def test_drop_all_columns_fails(self, sample_datagram): + """Test dropping all columns raises appropriate error.""" + with pytest.raises(ValueError): + sample_datagram.drop("user_id", "name", "score", "active") + + def test_rename(self, sample_datagram): + """Test rename method.""" + new_datagram = sample_datagram.rename({"user_id": "id", "name": "full_name"}) + + expected_keys = {"id", "full_name", "score", "active"} + assert set(new_datagram.keys()) == expected_keys + assert new_datagram["id"] == 123 + assert new_datagram["full_name"] == "Alice" + + # Original should be unchanged + assert "user_id" in sample_datagram.keys() + assert "name" in sample_datagram.keys() + + def test_rename_empty_mapping(self, sample_datagram): + """Test rename method with empty mapping returns new instance.""" + new_datagram = sample_datagram.rename({}) + + # Should return new instance with same data + assert new_datagram is not sample_datagram + assert new_datagram.as_dict() == sample_datagram.as_dict() + + def test_update(self, sample_datagram): + """Test update method.""" + new_datagram = sample_datagram.update(score=95.0, active=False) + + assert new_datagram["score"] == 95.0 + assert new_datagram["active"] is False + assert new_datagram["user_id"] == 123 # unchanged + assert new_datagram["name"] == "Alice" # unchanged + + # Original should be unchanged + assert sample_datagram["score"] == 85.5 + assert sample_datagram["active"] is True + + def test_update_missing_column(self, sample_datagram): + """Test update method with missing column raises KeyError.""" + with pytest.raises(KeyError): + sample_datagram.update(nonexistent="value") + + def test_update_empty(self, sample_datagram): + """Test update method with no updates returns same instance.""" + new_datagram = sample_datagram.update() + + assert new_datagram is sample_datagram + + def test_with_columns(self, sample_datagram): + """Test with_columns method.""" + new_datagram = sample_datagram.with_columns(grade="A", rank=1) + + expected_keys = {"user_id", "name", "score", "active", "grade", "rank"} + assert set(new_datagram.keys()) == expected_keys + assert new_datagram["grade"] == "A" + assert new_datagram["rank"] == 1 + assert new_datagram["user_id"] == 123 # unchanged + + # Original should be unchanged + assert len(sample_datagram.keys()) == 4 + + def test_with_columns_with_types(self, sample_datagram): + """Test with_columns method with type specification.""" + new_datagram = sample_datagram.with_columns( + grade="A", rank=1, python_schema={"grade": str, "rank": int} + ) + + assert new_datagram["grade"] == "A" + assert new_datagram["rank"] == 1 + + def test_with_columns_existing_column_fails(self, sample_datagram): + """Test with_columns method with existing column raises ValueError.""" + with pytest.raises(ValueError): + sample_datagram.with_columns(user_id=456) + + def test_with_columns_empty(self, sample_datagram): + """Test with_columns method with no columns returns same instance.""" + new_datagram = sample_datagram.with_columns() + + assert new_datagram is sample_datagram + + +class TestDictDatagramContextOperations: + """Test context operations.""" + + def test_with_context_key(self): + """Test with_context_key method.""" + data = {"user_id": 123, "name": "Alice"} + original_datagram = DictDatagram(data, data_context="v0.1") + + new_datagram = original_datagram.with_context_key("v0.1") + + # Both should have the full context key + assert "v0.1" in original_datagram.data_context_key + assert "v0.1" in new_datagram.data_context_key + assert new_datagram["user_id"] == 123 # data unchanged + + +class TestDictDatagramUtilityOperations: + """Test utility operations.""" + + @pytest.fixture + def sample_datagram(self): + """Create a sample datagram for testing.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + return DictDatagram(data) + + def test_copy_with_cache(self, sample_datagram): + """Test copy method preserves cache.""" + # Access something to populate cache + _ = sample_datagram.as_dict() + + copied = sample_datagram.copy() + + assert copied is not sample_datagram + assert copied.as_dict() == sample_datagram.as_dict() + + def test_copy_without_cache(self, sample_datagram): + """Test copy method without cache.""" + copied = sample_datagram.copy() + + assert copied is not sample_datagram + assert copied.as_dict() == sample_datagram.as_dict() + + def test_str_representation(self, sample_datagram): + """Test string representation.""" + str_repr = str(sample_datagram) + + # The string representation might be the dict itself + assert "user_id" in str_repr + assert "123" in str_repr + + def test_repr_representation(self, sample_datagram): + """Test repr representation.""" + repr_str = repr(sample_datagram) + + # The repr might be the dict itself + assert "user_id" in repr_str + assert "123" in repr_str + + +class TestDictDatagramEdgeCases: + """Test edge cases and error conditions.""" + + def test_none_values(self): + """Test handling of None values.""" + data = {"user_id": 123, "name": None, "score": 85.5} + datagram = DictDatagram(data) + + assert datagram["user_id"] == 123 + assert datagram["name"] is None + assert datagram["score"] == 85.5 + + def test_complex_data_types(self): + """Test handling of complex data types.""" + data = { + "user_id": 123, + "tags": ["tag1", "tag2"], + "metadata": {"key": "value"}, + "score": 85.5, + } + datagram = DictDatagram(data) + + assert datagram["user_id"] == 123 + assert datagram["tags"] == ["tag1", "tag2"] + assert datagram["metadata"] == {"key": "value"} + + def test_unicode_strings(self): + """Test handling of Unicode strings.""" + data = {"user_id": 123, "name": "Алиса", "emoji": "😊"} + datagram = DictDatagram(data) + + assert datagram["name"] == "Алиса" + assert datagram["emoji"] == "😊" + + def test_large_numbers(self): + """Test handling of large numbers.""" + data = { + "user_id": 123, + "large_int": 9223372036854775807, # Max int64 + "large_float": 1.7976931348623157e308, # Near max float64 + } + datagram = DictDatagram(data) + + assert datagram["large_int"] == 9223372036854775807 + assert datagram["large_float"] == 1.7976931348623157e308 + + def test_duplicate_operations(self): + """Test that duplicate operations are idempotent.""" + data = {"user_id": 123, "name": "Alice"} + datagram = DictDatagram(data) + + # Multiple selects should be the same + selected1 = datagram.select("user_id") + selected2 = datagram.select("user_id") + + assert selected1.as_dict() == selected2.as_dict() + + +class TestDictDatagramIntegration: + """Test integration with other components.""" + + def test_chained_operations(self): + """Test chaining multiple operations.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5, "active": True} + datagram = DictDatagram(data) + + result = ( + datagram.update(score=95.0) + .with_columns(grade="A") + .drop("active") + .rename({"user_id": "id"}) + ) + + expected_keys = {"id", "name", "score", "grade"} + assert set(result.keys()) == expected_keys + assert result["id"] == 123 + assert result["score"] == 95.0 + assert result["grade"] == "A" + + def test_arrow_roundtrip(self): + """Test conversion to Arrow and back.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + original = DictDatagram(data) + + # Convert to Arrow table and back + table = original.as_table() + arrow_dict = table.to_pydict() + + # Convert dict format back to DictDatagram compatible format + converted_dict = {k: v[0] for k, v in arrow_dict.items()} + reconstructed = DictDatagram(converted_dict) + + # Should preserve data + assert reconstructed["user_id"] == original["user_id"] + assert reconstructed["name"] == original["name"] + assert reconstructed["score"] == original["score"] + + def test_mixed_include_options(self): + """Test various combinations of include options.""" + data = { + "user_id": 123, + "name": "Alice", + "__version": "1.0", + constants.CONTEXT_KEY: "v0.1", + } + datagram = DictDatagram(data) + + # Test all combinations + data_only = datagram.as_dict() + with_meta = datagram.as_dict(include_meta_columns=True) + with_context = datagram.as_dict(include_context=True) + with_all = datagram.as_dict(include_meta_columns=True, include_context=True) + + assert len(data_only) == 2 # user_id, name + assert len(with_meta) == 3 # + __version + assert len(with_context) == 3 # + context + assert len(with_all) == 4 # + both diff --git a/tests/test_data/test_datagrams/test_dict_tag_packet.py b/tests/test_data/test_datagrams/test_dict_tag_packet.py new file mode 100644 index 0000000..a255f79 --- /dev/null +++ b/tests/test_data/test_datagrams/test_dict_tag_packet.py @@ -0,0 +1,566 @@ +""" +Comprehensive tests for DictTag and DictPacket classes. + +This module tests all functionality of the dictionary-based tag and packet classes including: +- Tag-specific functionality (system tags) +- Packet-specific functionality (source info) +- Integration with base datagram functionality +- Conversion operations +""" + +import pytest + +from orcapod.core.datagrams import DictTag, DictPacket +from orcapod.core.system_constants import constants + + +class TestDictTagInitialization: + """Test DictTag initialization and basic properties.""" + + def test_basic_initialization(self): + """Test basic initialization with simple data.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + tag = DictTag(data) + + assert tag["user_id"] == 123 + assert tag["name"] == "Alice" + assert tag["score"] == 85.5 + + def test_initialization_with_system_tags(self): + """Test initialization with system tags.""" + data = {"user_id": 123, "name": "Alice"} + system_tags = {"tag_type": "user", "created_by": "system"} + + tag = DictTag(data, system_tags=system_tags) + + assert tag["user_id"] == 123 + system_tag_dict = tag.system_tags() + assert system_tag_dict["tag_type"] == "user" + assert system_tag_dict["created_by"] == "system" + + def test_initialization_with_system_tags_in_data(self): + """Test initialization when system tags are included in data.""" + data = { + "user_id": 123, + "name": "Alice", + f"{constants.SYSTEM_TAG_PREFIX}tag_type": "user", + f"{constants.SYSTEM_TAG_PREFIX}version": "1.0", + } + + tag = DictTag(data) + + assert tag["user_id"] == 123 + assert tag["name"] == "Alice" + + system_tags = tag.system_tags() + assert system_tags[f"{constants.SYSTEM_TAG_PREFIX}tag_type"] == "user" + assert system_tags[f"{constants.SYSTEM_TAG_PREFIX}version"] == "1.0" + + def test_initialization_mixed_system_tags(self): + """Test initialization with both embedded and explicit system tags.""" + data = {"user_id": 123, f"{constants.SYSTEM_TAG_PREFIX}embedded": "value1"} + system_tags = {"explicit": "value2"} + + tag = DictTag(data, system_tags=system_tags) + + system_tag_dict = tag.system_tags() + assert system_tag_dict[f"{constants.SYSTEM_TAG_PREFIX}embedded"] == "value1" + assert system_tag_dict["explicit"] == "value2" + + +class TestDictTagSystemTagOperations: + """Test system tag specific operations.""" + + @pytest.fixture + def sample_tag(self): + """Create a sample tag for testing.""" + data = {"user_id": 123, "name": "Alice"} + system_tags = {"tag_type": "user", "version": "1.0"} + return DictTag(data, system_tags=system_tags) + + def test_system_tags_method(self, sample_tag): + """Test system_tags method.""" + system_tags = sample_tag.system_tags() + + assert isinstance(system_tags, dict) + assert system_tags["tag_type"] == "user" + assert system_tags["version"] == "1.0" + + def test_keys_with_system_tags(self, sample_tag): + """Test keys method including system tags.""" + keys_data_only = sample_tag.keys() + keys_with_system = sample_tag.keys(include_system_tags=True) + + assert "user_id" in keys_data_only + assert "name" in keys_data_only + assert len(keys_with_system) > len(keys_data_only) + assert "tag_type" in keys_with_system + assert "version" in keys_with_system + + def test_types_with_system_tags(self, sample_tag): + """Test types method including system tags.""" + types_data_only = sample_tag.types() + types_with_system = sample_tag.types(include_system_tags=True) + + assert len(types_with_system) > len(types_data_only) + assert "tag_type" in types_with_system + assert "version" in types_with_system + + def test_arrow_schema_with_system_tags(self, sample_tag): + """Test arrow_schema method including system tags.""" + schema_data_only = sample_tag.arrow_schema() + schema_with_system = sample_tag.arrow_schema(include_system_tags=True) + + assert len(schema_with_system) > len(schema_data_only) + assert "tag_type" in schema_with_system.names + assert "version" in schema_with_system.names + + def test_as_dict_with_system_tags(self, sample_tag): + """Test as_dict method including system tags.""" + dict_data_only = sample_tag.as_dict() + dict_with_system = sample_tag.as_dict(include_system_tags=True) + + assert "user_id" in dict_data_only + assert "name" in dict_data_only + assert "tag_type" not in dict_data_only + + assert "user_id" in dict_with_system + assert "tag_type" in dict_with_system + assert "version" in dict_with_system + + def test_as_table_with_system_tags(self, sample_tag): + """Test as_table method including system tags.""" + table_data_only = sample_tag.as_table() + table_with_system = sample_tag.as_table(include_system_tags=True) + + assert len(table_with_system.column_names) > len(table_data_only.column_names) + assert "tag_type" in table_with_system.column_names + assert "version" in table_with_system.column_names + + def test_as_datagram_conversion(self, sample_tag): + """Test conversion to datagram.""" + datagram = sample_tag.as_datagram() + + # Should preserve data + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + + # Should not include system tags by default + assert "tag_type" not in datagram.keys() + + def test_as_datagram_with_system_tags(self, sample_tag): + """Test conversion to datagram including system tags.""" + datagram = sample_tag.as_datagram(include_system_tags=True) + + # Should preserve data and include system tags + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + assert "tag_type" in datagram.keys() + + +class TestDictPacketInitialization: + """Test DictPacket initialization and basic properties.""" + + def test_basic_initialization(self): + """Test basic initialization with simple data.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + packet = DictPacket(data) + + assert packet["user_id"] == 123 + assert packet["name"] == "Alice" + assert packet["score"] == 85.5 + + def test_initialization_with_source_info(self): + """Test initialization with source info.""" + data = {"user_id": 123, "name": "Alice"} + source_info = {"user_id": "database", "name": "user_input"} + + packet = DictPacket(data, source_info=source_info) + + assert packet["user_id"] == 123 + source_dict = packet.source_info() + assert source_dict["user_id"] == "database" + assert source_dict["name"] == "user_input" + + def test_initialization_with_source_info_in_data(self): + """Test initialization when source info is included in data.""" + data = { + "user_id": 123, + "name": "Alice", + f"{constants.SOURCE_PREFIX}user_id": "database", + f"{constants.SOURCE_PREFIX}name": "user_input", + } + + packet = DictPacket(data) + + assert packet["user_id"] == 123 + assert packet["name"] == "Alice" + + source_info = packet.source_info() + assert source_info["user_id"] == "database" + assert source_info["name"] == "user_input" + + def test_initialization_mixed_source_info(self): + """Test initialization with both embedded and explicit source info.""" + data = { + "user_id": 123, + "name": "Alice", + f"{constants.SOURCE_PREFIX}user_id": "embedded_source", + } + source_info = {"name": "explicit_source"} + + packet = DictPacket(data, source_info=source_info) + + source_dict = packet.source_info() + assert source_dict["user_id"] == "embedded_source" + assert source_dict["name"] == "explicit_source" + + +class TestDictPacketSourceInfoOperations: + """Test source info specific operations.""" + + @pytest.fixture + def sample_packet(self): + """Create a sample packet for testing.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + source_info = { + "user_id": "database", + "name": "user_input", + "score": "calculation", + } + return DictPacket(data, source_info=source_info) + + def test_source_info_method(self, sample_packet): + """Test source_info method.""" + source_info = sample_packet.source_info() + + assert isinstance(source_info, dict) + assert source_info["user_id"] == "database" + assert source_info["name"] == "user_input" + assert source_info["score"] == "calculation" + + def test_source_info_with_missing_keys(self): + """Test source_info method when some keys are missing.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + source_info = {"user_id": "database"} # Only partial source info + + packet = DictPacket(data, source_info=source_info) + full_source_info = packet.source_info() + + assert full_source_info["user_id"] == "database" + assert full_source_info["name"] is None + assert full_source_info["score"] is None + + def test_with_source_info(self, sample_packet): + """Test with_source_info method.""" + updated = sample_packet.with_source_info( + user_id="new_database", name="new_input" + ) + + # Original should be unchanged + original_source = sample_packet.source_info() + assert original_source["user_id"] == "database" + + # Updated should have new values + updated_source = updated.source_info() + assert updated_source["user_id"] == "new_database" + assert updated_source["name"] == "new_input" + assert updated_source["score"] == "calculation" # Unchanged + + def test_keys_with_source_info(self, sample_packet): + """Test keys method including source info.""" + keys_data_only = sample_packet.keys() + keys_with_source = sample_packet.keys(include_source=True) + + assert "user_id" in keys_data_only + assert "name" in keys_data_only + assert len(keys_with_source) > len(keys_data_only) + + # Should include prefixed source columns + source_keys = [ + k for k in keys_with_source if k.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_keys) > 0 + + def test_types_with_source_info(self, sample_packet): + """Test types method including source info.""" + types_data_only = sample_packet.types() + types_with_source = sample_packet.types(include_source=True) + + assert len(types_with_source) > len(types_data_only) + + # Source columns should be string type + source_keys = [ + k for k in types_with_source.keys() if k.startswith(constants.SOURCE_PREFIX) + ] + for key in source_keys: + assert types_with_source[key] is str + + def test_arrow_schema_with_source_info(self, sample_packet): + """Test arrow_schema method including source info.""" + schema_data_only = sample_packet.arrow_schema() + schema_with_source = sample_packet.arrow_schema(include_source=True) + + assert len(schema_with_source) > len(schema_data_only) + + source_columns = [ + name + for name in schema_with_source.names + if name.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_columns) > 0 + + def test_as_dict_with_source_info(self, sample_packet): + """Test as_dict method including source info.""" + dict_data_only = sample_packet.as_dict() + dict_with_source = sample_packet.as_dict(include_source=True) + + assert "user_id" in dict_data_only + assert "name" in dict_data_only + assert not any( + k.startswith(constants.SOURCE_PREFIX) for k in dict_data_only.keys() + ) + + assert "user_id" in dict_with_source + source_keys = [ + k for k in dict_with_source.keys() if k.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_keys) > 0 + + def test_as_table_with_source_info(self, sample_packet): + """Test as_table method including source info.""" + table_data_only = sample_packet.as_table() + table_with_source = sample_packet.as_table(include_source=True) + + assert len(table_with_source.column_names) > len(table_data_only.column_names) + + source_columns = [ + name + for name in table_with_source.column_names + if name.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_columns) > 0 + + def test_as_datagram_conversion(self, sample_packet): + """Test conversion to datagram.""" + datagram = sample_packet.as_datagram() + + # Should preserve data + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + + # Should not include source info by default + assert not any(k.startswith(constants.SOURCE_PREFIX) for k in datagram.keys()) + + def test_as_datagram_with_source_info(self, sample_packet): + """Test conversion to datagram including source info.""" + datagram = sample_packet.as_datagram(include_source=True) + + # Should preserve data and include source info + assert datagram["user_id"] == 123 + assert datagram["name"] == "Alice" + source_keys = [ + k for k in datagram.keys() if k.startswith(constants.SOURCE_PREFIX) + ] + assert len(source_keys) > 0 + + +class TestDictPacketDataOperations: + """Test data operations specific to packets.""" + + @pytest.fixture + def sample_packet(self): + """Create a sample packet for testing.""" + data = {"user_id": 123, "name": "Alice", "score": 85.5} + source_info = { + "user_id": "database", + "name": "user_input", + "score": "calculation", + } + return DictPacket(data, source_info=source_info) + + def test_rename_preserves_source_info(self, sample_packet): + """Test that rename operation preserves source info mapping.""" + renamed = sample_packet.rename({"user_id": "id", "name": "username"}) + + # Data should be renamed + assert "id" in renamed.keys() + assert "username" in renamed.keys() + assert "user_id" not in renamed.keys() + assert "name" not in renamed.keys() + + # Source info should follow the rename + source_info = renamed.source_info() + assert source_info["id"] == "database" + assert source_info["username"] == "user_input" + assert source_info["score"] == "calculation" + + +class TestDictTagPacketIntegration: + """Test integration between tags, packets, and base functionality.""" + + def test_tag_to_packet_conversion(self): + """Test converting a tag to a packet-like structure.""" + data = {"user_id": 123, "name": "Alice"} + system_tags = {"tag_type": "user", "version": "1.0"} + tag = DictTag(data, system_tags=system_tags) + + # Convert to full dictionary + full_dict = tag.as_dict(include_all_info=True) + + # Should include data, system tags, meta columns, and context + assert "user_id" in full_dict + assert "tag_type" in full_dict + assert constants.CONTEXT_KEY in full_dict + + def test_packet_comprehensive_dict(self): + """Test packet with all information types.""" + data = {"user_id": 123, "name": "Alice", "__meta_field": "meta_value"} + source_info = {"user_id": "database", "name": "user_input"} + + packet = DictPacket(data, source_info=source_info) + + # Get comprehensive dictionary + full_dict = packet.as_dict(include_all_info=True) + + # Should include data, source info, meta columns, and context + assert "user_id" in full_dict + assert f"{constants.SOURCE_PREFIX}user_id" in full_dict + assert "__meta_field" in full_dict + assert constants.CONTEXT_KEY in full_dict + + def test_chained_operations_tag(self): + """Test chaining operations on tags.""" + data = {"user_id": 123, "first_name": "Alice", "last_name": "Smith"} + system_tags = {"tag_type": "user"} + + tag = DictTag(data, system_tags=system_tags) + + # Chain operations + result = ( + tag.with_columns(full_name="Alice Smith") + .drop("first_name", "last_name") + .update(user_id=456) + ) + + # Verify final state + assert set(result.keys()) == {"user_id", "full_name"} + assert result["user_id"] == 456 + assert result["full_name"] == "Alice Smith" + + # System tags should be preserved + system_tags = result.system_tags() + assert system_tags["tag_type"] == "user" + + def test_chained_operations_packet(self): + """Test chaining operations on packets.""" + data = {"user_id": 123, "first_name": "Alice", "last_name": "Smith"} + source_info = {"user_id": "database", "first_name": "form", "last_name": "form"} + + packet = DictPacket(data, source_info=source_info) + + # Chain operations + result = ( + packet.with_columns(full_name="Alice Smith") + .drop("first_name", "last_name") + .update(user_id=456) + .with_source_info(full_name="calculated") + ) + + # Verify final state + assert set(result.keys()) == {"user_id", "full_name"} + assert result["user_id"] == 456 + assert result["full_name"] == "Alice Smith" + + # Source info should be updated + source_info = result.source_info() + assert source_info["user_id"] == "database" + assert source_info["full_name"] == "calculated" + + def test_copy_operations(self): + """Test copy operations preserve all information.""" + # Test tag copy + tag_data = {"user_id": 123, "name": "Alice"} + system_tags = {"tag_type": "user"} + tag = DictTag(tag_data, system_tags=system_tags) + + tag_copy = tag.copy() + assert tag_copy is not tag + assert tag_copy["user_id"] == tag["user_id"] + assert tag_copy.system_tags() == tag.system_tags() + + # Test packet copy + packet_data = {"user_id": 123, "name": "Alice"} + source_info = {"user_id": "database"} + packet = DictPacket(packet_data, source_info=source_info) + + packet_copy = packet.copy() + assert packet_copy is not packet + assert packet_copy["user_id"] == packet["user_id"] + assert packet_copy.source_info() == packet.source_info() + + +class TestDictTagPacketEdgeCases: + """Test edge cases and error conditions.""" + + def test_tag_empty_system_tags(self): + """Test tag with empty system tags.""" + data = {"user_id": 123, "name": "Alice"} + tag = DictTag(data, system_tags={}) + + assert tag["user_id"] == 123 + assert tag.system_tags() == {} + + def test_packet_empty_source_info(self): + """Test packet with empty source info.""" + data = {"user_id": 123, "name": "Alice"} + packet = DictPacket(data, source_info={}) + + assert packet["user_id"] == 123 + source_info = packet.source_info() + assert all(v is None for v in source_info.values()) + + def test_tag_none_system_tags(self): + """Test tag with None system tags.""" + data = {"user_id": 123, "name": "Alice"} + tag = DictTag(data, system_tags=None) + + assert tag["user_id"] == 123 + assert tag.system_tags() == {} + + def test_packet_none_source_info(self): + """Test packet with None source info.""" + data = {"user_id": 123, "name": "Alice"} + packet = DictPacket(data, source_info=None) + + assert packet["user_id"] == 123 + source_info = packet.source_info() + assert all(v is None for v in source_info.values()) + + def test_tag_with_meta_and_system_tags(self): + """Test tag with both meta columns and system tags.""" + data = {"user_id": 123, "name": "Alice", "__meta_field": "meta_value"} + system_tags = {"tag_type": "user"} + + tag = DictTag(data, system_tags=system_tags) + + # All information should be accessible + full_dict = tag.as_dict(include_all_info=True) + assert "user_id" in full_dict + assert "__meta_field" in full_dict + assert "tag_type" in full_dict + assert constants.CONTEXT_KEY in full_dict + + def test_packet_with_meta_and_source_info(self): + """Test packet with both meta columns and source info.""" + data = {"user_id": 123, "name": "Alice", "__meta_field": "meta_value"} + source_info = {"user_id": "database"} + + packet = DictPacket(data, source_info=source_info) + + # All information should be accessible + full_dict = packet.as_dict(include_all_info=True) + assert "user_id" in full_dict + assert "__meta_field" in full_dict + assert f"{constants.SOURCE_PREFIX}user_id" in full_dict + assert constants.CONTEXT_KEY in full_dict diff --git a/tests/test_hashing/generate_file_hashes.py b/tests/test_hashing/generate_file_hashes.py index 1002b7f..0beb66c 100644 --- a/tests/test_hashing/generate_file_hashes.py +++ b/tests/test_hashing/generate_file_hashes.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/generate_file_hashes.py """ Generate sample files with random content and record their hashes. @@ -14,7 +13,7 @@ from datetime import datetime from pathlib import Path -# Add the parent directory to the path to import orcabridge +# Add the parent directory to the path to import orcapod sys.path.append(str(Path(__file__).parent.parent.parent)) from orcapod.hashing import hash_file diff --git a/tests/test_hashing/generate_pathset_packet_hashes.py b/tests/test_hashing/generate_pathset_packet_hashes.py index 61a36eb..edd804d 100644 --- a/tests/test_hashing/generate_pathset_packet_hashes.py +++ b/tests/test_hashing/generate_pathset_packet_hashes.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/generate_pathset_packet_hashes.py """ Generate sample pathsets and packets and record their hashes. @@ -11,7 +10,7 @@ import sys from pathlib import Path -# Add the parent directory to the path to import orcabridge +# Add the parent directory to the path to import orcapod sys.path.append(str(Path(__file__).parent.parent.parent)) from orcapod.hashing import hash_packet, hash_pathset diff --git a/tests/test_hashing/test_basic_composite_hasher.py b/tests/test_hashing/test_basic_composite_hasher.py index d2c5361..a2d35a6 100644 --- a/tests/test_hashing/test_basic_composite_hasher.py +++ b/tests/test_hashing/test_basic_composite_hasher.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/test_default_file_hasher.py """ Test DefaultFileHasher functionality. @@ -13,7 +12,7 @@ import pytest -from orcapod.hashing.file_hashers import PathLikeHasherFactory +from orcapod.hashing.file_hashers import LegacyPathLikeHasherFactory def load_hash_lut(): @@ -83,7 +82,7 @@ def verify_path_exists(rel_path): def test_default_file_hasher_file_hash_consistency(): """Test that DefaultFileHasher.hash_file produces consistent results for the sample files.""" hash_lut = load_hash_lut() - hasher = PathLikeHasherFactory.create_basic_composite() + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite() for filename, info in hash_lut.items(): rel_path = info["file"] @@ -105,7 +104,7 @@ def test_default_file_hasher_file_hash_consistency(): def test_default_file_hasher_pathset_hash_consistency(): """Test that DefaultFileHasher.hash_pathset produces consistent results for the sample pathsets.""" hash_lut = load_pathset_hash_lut() - hasher = PathLikeHasherFactory.create_basic_composite() + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite() for name, info in hash_lut.items(): paths_rel = info["paths"] @@ -138,7 +137,7 @@ def test_default_file_hasher_pathset_hash_consistency(): def test_default_file_hasher_packet_hash_consistency(): """Test that DefaultFileHasher.hash_packet produces consistent results for the sample packets.""" hash_lut = load_packet_hash_lut() - hasher = PathLikeHasherFactory.create_basic_composite() + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite() for name, info in hash_lut.items(): structure = info["structure"] @@ -182,7 +181,9 @@ def test_default_file_hasher_file_hash_algorithm_parameters(): for algorithm in algorithms: try: - hasher = PathLikeHasherFactory.create_basic_composite(algorithm=algorithm) + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( + algorithm=algorithm + ) hash1 = hasher.hash_file(file_path) hash2 = hasher.hash_file(file_path) assert hash1 == hash2, f"Hash inconsistent for algorithm {algorithm}" @@ -194,7 +195,9 @@ def test_default_file_hasher_file_hash_algorithm_parameters(): buffer_sizes = [1024, 4096, 16384, 65536] for buffer_size in buffer_sizes: - hasher = PathLikeHasherFactory.create_basic_composite(buffer_size=buffer_size) + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( + buffer_size=buffer_size + ) hash1 = hasher.hash_file(file_path) hash2 = hasher.hash_file(file_path) assert hash1 == hash2, f"Hash inconsistent for buffer size {buffer_size}" @@ -223,7 +226,9 @@ def test_default_file_hasher_pathset_hash_algorithm_parameters(): for algorithm in algorithms: try: - hasher = PathLikeHasherFactory.create_basic_composite(algorithm=algorithm) + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( + algorithm=algorithm + ) hash1 = hasher.hash_pathset(pathset) hash2 = hasher.hash_pathset(pathset) assert hash1 == hash2, f"Hash inconsistent for algorithm {algorithm}" @@ -235,7 +240,9 @@ def test_default_file_hasher_pathset_hash_algorithm_parameters(): buffer_sizes = [1024, 4096, 16384, 65536] for buffer_size in buffer_sizes: - hasher = PathLikeHasherFactory.create_basic_composite(buffer_size=buffer_size) + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( + buffer_size=buffer_size + ) hash1 = hasher.hash_pathset(pathset) hash2 = hasher.hash_pathset(pathset) assert hash1 == hash2, f"Hash inconsistent for buffer size {buffer_size}" @@ -267,7 +274,9 @@ def test_default_file_hasher_packet_hash_algorithm_parameters(): for algorithm in algorithms: try: - hasher = PathLikeHasherFactory.create_basic_composite(algorithm=algorithm) + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( + algorithm=algorithm + ) hash1 = hasher.hash_packet(packet) hash2 = hasher.hash_packet(packet) @@ -286,7 +295,9 @@ def test_default_file_hasher_packet_hash_algorithm_parameters(): buffer_sizes = [1024, 4096, 16384, 65536] for buffer_size in buffer_sizes: - hasher = PathLikeHasherFactory.create_basic_composite(buffer_size=buffer_size) + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( + buffer_size=buffer_size + ) hash1 = hasher.hash_packet(packet) hash2 = hasher.hash_packet(packet) assert hash1 == hash2, f"Hash inconsistent for buffer size {buffer_size}" diff --git a/tests/test_hashing/test_basic_hashing.py b/tests/test_hashing/test_basic_hashing.py index df90a1a..c67723a 100644 --- a/tests/test_hashing/test_basic_hashing.py +++ b/tests/test_hashing/test_basic_hashing.py @@ -1,4 +1,4 @@ -from orcapod.hashing.core import ( +from orcapod.hashing.legacy_core import ( HashableMixin, hash_to_hex, hash_to_int, diff --git a/tests/test_hashing/test_cached_file_hasher.py b/tests/test_hashing/test_cached_file_hasher.py index 3307628..8b9ce30 100644 --- a/tests/test_hashing/test_cached_file_hasher.py +++ b/tests/test_hashing/test_cached_file_hasher.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/test_cached_file_hasher.py """Tests for CachedFileHasher implementation.""" import json @@ -11,11 +10,11 @@ import pytest from orcapod.hashing.file_hashers import ( - BasicFileHasher, - CachedFileHasher, + LegacyDefaultFileHasher, + LegacyCachedFileHasher, ) from orcapod.hashing.string_cachers import InMemoryCacher -from orcapod.hashing.types import FileHasher, StringCacher +from orcapod.hashing.types import LegacyFileHasher, StringCacher def verify_path_exists(rel_path): @@ -74,15 +73,15 @@ def load_packet_hash_lut(): def test_cached_file_hasher_construction(): """Test that CachedFileHasher can be constructed with various parameters.""" # Test with default parameters - file_hasher = BasicFileHasher() + file_hasher = LegacyDefaultFileHasher() string_cacher = InMemoryCacher() - cached_hasher1 = CachedFileHasher(file_hasher, string_cacher) + cached_hasher1 = LegacyCachedFileHasher(file_hasher, string_cacher) assert cached_hasher1.file_hasher == file_hasher assert cached_hasher1.string_cacher == string_cacher # Test that CachedFileHasher implements FileHasher protocol - assert isinstance(cached_hasher1, FileHasher) + assert isinstance(cached_hasher1, LegacyFileHasher) def test_cached_file_hasher_file_caching(): @@ -100,8 +99,8 @@ def test_cached_file_hasher_file_caching(): mock_string_cacher = MagicMock(spec=StringCacher) mock_string_cacher.get_cached.return_value = None # Initially no cached value - file_hasher = BasicFileHasher() - cached_hasher = CachedFileHasher(file_hasher, mock_string_cacher) + file_hasher = LegacyDefaultFileHasher() + cached_hasher = LegacyCachedFileHasher(file_hasher, mock_string_cacher) # First call should compute the hash and cache it result1 = cached_hasher.hash_file(file_path) @@ -137,14 +136,14 @@ def test_cached_file_hasher_call_counts(): try: # Mock the file_hasher to track calls - mock_file_hasher = MagicMock(spec=FileHasher) + mock_file_hasher = MagicMock(spec=LegacyFileHasher) mock_file_hasher.hash_file.return_value = "mock_file_hash" # Real cacher string_cacher = InMemoryCacher() # Create the cached file hasher with all caching enabled - cached_hasher = CachedFileHasher( + cached_hasher = LegacyCachedFileHasher( mock_file_hasher, string_cacher, ) @@ -182,11 +181,11 @@ def test_cached_file_hasher_performance(): file_path = verify_path_exists(info["file"]) # Setup non-cached hasher - file_hasher = BasicFileHasher() + file_hasher = LegacyDefaultFileHasher() # Setup cached hasher string_cacher = InMemoryCacher() - cached_hasher = CachedFileHasher(file_hasher, string_cacher) + cached_hasher = LegacyCachedFileHasher(file_hasher, string_cacher) # Measure time for multiple hash operations with non-cached hasher start_time = time.time() @@ -222,11 +221,11 @@ def test_cached_file_hasher_with_different_cachers(): try: file_path = temp_file.name - file_hasher = BasicFileHasher() + file_hasher = LegacyDefaultFileHasher() # Test with InMemoryCacher mem_cacher = InMemoryCacher(max_size=10) - cached_hasher1 = CachedFileHasher(file_hasher, mem_cacher) + cached_hasher1 = LegacyCachedFileHasher(file_hasher, mem_cacher) # First hash call hash1 = cached_hasher1.hash_file(file_path) @@ -250,7 +249,7 @@ def clear_cache(self) -> None: self.storage.clear() custom_cacher = CustomCacher() - cached_hasher2 = CachedFileHasher(file_hasher, custom_cacher) + cached_hasher2 = LegacyCachedFileHasher(file_hasher, custom_cacher) # Get hash with custom cacher hash2 = cached_hasher2.hash_file(file_path) diff --git a/tests/test_hashing/test_file_hashes.py b/tests/test_hashing/test_file_hashes.py index 66ed987..afcaaad 100644 --- a/tests/test_hashing/test_file_hashes.py +++ b/tests/test_hashing/test_file_hashes.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/test_file_hashes.py """ Test file hash consistency. @@ -12,8 +11,8 @@ import pytest -# Add the parent directory to the path to import orcabridge -from orcapod.hashing import hash_file +# Add the parent directory to the path to import orcapod +from orcapod.hashing.legacy_core import hash_file def load_hash_lut(): diff --git a/tests/test_hashing/test_hash_samples.py b/tests/test_hashing/test_hash_samples.py index cfb3e35..1e536cb 100644 --- a/tests/test_hashing/test_hash_samples.py +++ b/tests/test_hashing/test_hash_samples.py @@ -12,7 +12,7 @@ import pytest -from orcapod.hashing import hash_to_hex, hash_to_int, hash_to_uuid +from orcapod.hashing.legacy_core import hash_to_hex, hash_to_int, hash_to_uuid def get_latest_hash_samples(): diff --git a/tests/test_hashing/test_hasher_factory.py b/tests/test_hashing/test_hasher_factory.py index afd2392..68daa3a 100644 --- a/tests/test_hashing/test_hasher_factory.py +++ b/tests/test_hashing/test_hasher_factory.py @@ -5,9 +5,9 @@ from pathlib import Path from orcapod.hashing.file_hashers import ( - BasicFileHasher, - CachedFileHasher, - PathLikeHasherFactory, + LegacyDefaultFileHasher, + LegacyCachedFileHasher, + LegacyPathLikeHasherFactory, ) from orcapod.hashing.string_cachers import FileCacher, InMemoryCacher @@ -17,11 +17,11 @@ class TestPathLikeHasherFactoryCreateFileHasher: def test_create_file_hasher_without_cacher(self): """Test creating a file hasher without string cacher (returns BasicFileHasher).""" - hasher = PathLikeHasherFactory.create_file_hasher() + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher() - # Should return BasicFileHasher - assert isinstance(hasher, BasicFileHasher) - assert not isinstance(hasher, CachedFileHasher) + # Should return LegacyDefaultFileHasher + assert isinstance(hasher, LegacyDefaultFileHasher) + assert not isinstance(hasher, LegacyCachedFileHasher) # Check default parameters assert hasher.algorithm == "sha256" @@ -30,60 +30,67 @@ def test_create_file_hasher_without_cacher(self): def test_create_file_hasher_with_cacher(self): """Test creating a file hasher with string cacher (returns CachedFileHasher).""" cacher = InMemoryCacher() - hasher = PathLikeHasherFactory.create_file_hasher(string_cacher=cacher) + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( + string_cacher=cacher + ) - # Should return CachedFileHasher - assert isinstance(hasher, CachedFileHasher) + # Should return LegacyCachedFileHasher + assert isinstance(hasher, LegacyCachedFileHasher) assert hasher.string_cacher is cacher - # The underlying file hasher should be BasicFileHasher with defaults - assert isinstance(hasher.file_hasher, BasicFileHasher) + # The underlying file hasher should be LegacyDefaultFileHasher with defaults + assert isinstance(hasher.file_hasher, LegacyDefaultFileHasher) assert hasher.file_hasher.algorithm == "sha256" assert hasher.file_hasher.buffer_size == 65536 def test_create_file_hasher_custom_algorithm(self): """Test creating file hasher with custom algorithm.""" # Without cacher - hasher = PathLikeHasherFactory.create_file_hasher(algorithm="md5") - assert isinstance(hasher, BasicFileHasher) + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher(algorithm="md5") + assert isinstance(hasher, LegacyDefaultFileHasher) assert hasher.algorithm == "md5" assert hasher.buffer_size == 65536 # With cacher cacher = InMemoryCacher() - hasher = PathLikeHasherFactory.create_file_hasher( + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=cacher, algorithm="sha512" ) - assert isinstance(hasher, CachedFileHasher) + assert isinstance(hasher, LegacyCachedFileHasher) + assert isinstance(hasher.file_hasher, LegacyDefaultFileHasher) assert hasher.file_hasher.algorithm == "sha512" assert hasher.file_hasher.buffer_size == 65536 def test_create_file_hasher_custom_buffer_size(self): """Test creating file hasher with custom buffer size.""" # Without cacher - hasher = PathLikeHasherFactory.create_file_hasher(buffer_size=32768) - assert isinstance(hasher, BasicFileHasher) + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( + buffer_size=32768 + ) + assert isinstance(hasher, LegacyDefaultFileHasher) assert hasher.algorithm == "sha256" assert hasher.buffer_size == 32768 # With cacher cacher = InMemoryCacher() - hasher = PathLikeHasherFactory.create_file_hasher( + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=cacher, buffer_size=8192 ) - assert isinstance(hasher, CachedFileHasher) + assert isinstance(hasher, LegacyCachedFileHasher) + assert isinstance(hasher.file_hasher, LegacyDefaultFileHasher) assert hasher.file_hasher.algorithm == "sha256" assert hasher.file_hasher.buffer_size == 8192 def test_create_file_hasher_all_custom_parameters(self): """Test creating file hasher with all custom parameters.""" cacher = InMemoryCacher(max_size=500) - hasher = PathLikeHasherFactory.create_file_hasher( + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=cacher, algorithm="blake2b", buffer_size=16384 ) - assert isinstance(hasher, CachedFileHasher) + assert isinstance(hasher, LegacyCachedFileHasher) assert hasher.string_cacher is cacher + assert isinstance(hasher.file_hasher, LegacyDefaultFileHasher) assert hasher.file_hasher.algorithm == "blake2b" assert hasher.file_hasher.buffer_size == 16384 @@ -91,17 +98,19 @@ def test_create_file_hasher_different_cacher_types(self): """Test creating file hasher with different types of string cachers.""" # InMemoryCacher memory_cacher = InMemoryCacher() - hasher1 = PathLikeHasherFactory.create_file_hasher(string_cacher=memory_cacher) - assert isinstance(hasher1, CachedFileHasher) + hasher1 = LegacyPathLikeHasherFactory.create_legacy_file_hasher( + string_cacher=memory_cacher + ) + assert isinstance(hasher1, LegacyCachedFileHasher) assert hasher1.string_cacher is memory_cacher # FileCacher with tempfile.NamedTemporaryFile(delete=False) as tmp_file: file_cacher = FileCacher(tmp_file.name) - hasher2 = PathLikeHasherFactory.create_file_hasher( + hasher2 = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=file_cacher ) - assert isinstance(hasher2, CachedFileHasher) + assert isinstance(hasher2, LegacyCachedFileHasher) assert hasher2.string_cacher is file_cacher # Clean up @@ -109,7 +118,7 @@ def test_create_file_hasher_different_cacher_types(self): def test_create_file_hasher_functional_without_cache(self): """Test that created file hasher actually works for hashing files.""" - hasher = PathLikeHasherFactory.create_file_hasher( + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( algorithm="sha256", buffer_size=1024 ) @@ -136,7 +145,7 @@ def test_create_file_hasher_functional_without_cache(self): def test_create_file_hasher_functional_with_cache(self): """Test that created cached file hasher works and caches results.""" cacher = InMemoryCacher() - hasher = PathLikeHasherFactory.create_file_hasher( + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=cacher, algorithm="sha256" ) @@ -164,44 +173,55 @@ def test_create_file_hasher_functional_with_cache(self): def test_create_file_hasher_none_cacher_explicit(self): """Test explicitly passing None for string_cacher.""" - hasher = PathLikeHasherFactory.create_file_hasher( + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=None, algorithm="sha1", buffer_size=4096 ) - assert isinstance(hasher, BasicFileHasher) - assert not isinstance(hasher, CachedFileHasher) + assert isinstance(hasher, LegacyDefaultFileHasher) + assert not isinstance(hasher, LegacyCachedFileHasher) assert hasher.algorithm == "sha1" assert hasher.buffer_size == 4096 def test_create_file_hasher_parameter_edge_cases(self): """Test edge cases for parameters.""" # Very small buffer size - hasher1 = PathLikeHasherFactory.create_file_hasher(buffer_size=1) + hasher1 = LegacyPathLikeHasherFactory.create_legacy_file_hasher(buffer_size=1) + assert isinstance(hasher1, LegacyDefaultFileHasher) assert hasher1.buffer_size == 1 # Large buffer size - hasher2 = PathLikeHasherFactory.create_file_hasher(buffer_size=1024 * 1024) + hasher2 = LegacyPathLikeHasherFactory.create_legacy_file_hasher( + buffer_size=1024 * 1024 + ) + assert isinstance(hasher2, LegacyDefaultFileHasher) assert hasher2.buffer_size == 1024 * 1024 # Different algorithms for algorithm in ["md5", "sha1", "sha224", "sha256", "sha384", "sha512"]: - hasher = PathLikeHasherFactory.create_file_hasher(algorithm=algorithm) + hasher = LegacyPathLikeHasherFactory.create_legacy_file_hasher( + algorithm=algorithm + ) + assert isinstance(hasher, LegacyDefaultFileHasher) assert hasher.algorithm == algorithm def test_create_file_hasher_cache_independence(self): """Test that different cached hashers with same cacher are independent.""" cacher = InMemoryCacher() - hasher1 = PathLikeHasherFactory.create_file_hasher( + hasher1 = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=cacher, algorithm="sha256" ) - hasher2 = PathLikeHasherFactory.create_file_hasher( + hasher2 = LegacyPathLikeHasherFactory.create_legacy_file_hasher( string_cacher=cacher, algorithm="md5" ) # Both should use the same cacher but be different instances + assert isinstance(hasher1, LegacyCachedFileHasher) + assert isinstance(hasher2, LegacyCachedFileHasher) assert hasher1.string_cacher is cacher assert hasher2.string_cacher is cacher assert hasher1 is not hasher2 assert hasher1.file_hasher is not hasher2.file_hasher + assert isinstance(hasher1.file_hasher, LegacyDefaultFileHasher) + assert isinstance(hasher2.file_hasher, LegacyDefaultFileHasher) assert hasher1.file_hasher.algorithm != hasher2.file_hasher.algorithm diff --git a/tests/test_hashing/test_hasher_parity.py b/tests/test_hashing/test_hasher_parity.py index fb83afb..a278a92 100644 --- a/tests/test_hashing/test_hasher_parity.py +++ b/tests/test_hashing/test_hasher_parity.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/test_hasher_parity.py """ Test parity between DefaultFileHasher and core hashing functions. @@ -14,8 +13,8 @@ import pytest -from orcapod.hashing.core import hash_file, hash_packet, hash_pathset -from orcapod.hashing.file_hashers import PathLikeHasherFactory +from orcapod.hashing.legacy_core import hash_file, hash_packet, hash_pathset +from orcapod.hashing.file_hashers import LegacyPathLikeHasherFactory def load_hash_lut(): @@ -74,7 +73,7 @@ def verify_path_exists(rel_path): def test_hasher_core_parity_file_hash(): """Test that BasicFileHasher.hash_file produces the same results as hash_file.""" hash_lut = load_hash_lut() - hasher = PathLikeHasherFactory.create_basic_composite() + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite() # Test all sample files for filename, info in hash_lut.items(): @@ -103,7 +102,7 @@ def test_hasher_core_parity_file_hash(): for buffer_size in buffer_sizes: try: # Create a hasher with specific parameters - hasher = PathLikeHasherFactory.create_basic_composite( + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( algorithm=algorithm, buffer_size=buffer_size ) @@ -148,7 +147,7 @@ def test_hasher_core_parity_pathset_hash(): for buffer_size in buffer_sizes: for char_count in char_counts: # Create a hasher with specific parameters - hasher = PathLikeHasherFactory.create_basic_composite( + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( algorithm=algorithm, buffer_size=buffer_size, char_count=char_count, @@ -202,7 +201,7 @@ def test_hasher_core_parity_packet_hash(): for buffer_size in buffer_sizes: for char_count in char_counts: # Create a hasher with specific parameters - hasher = PathLikeHasherFactory.create_basic_composite( + hasher = LegacyPathLikeHasherFactory.create_basic_legacy_composite( algorithm=algorithm, buffer_size=buffer_size, char_count=char_count, diff --git a/tests/test_hashing/test_composite_hasher.py b/tests/test_hashing/test_legacy_composite_hasher.py similarity index 81% rename from tests/test_hashing/test_composite_hasher.py rename to tests/test_hashing/test_legacy_composite_hasher.py index 1cbe386..f234bb7 100644 --- a/tests/test_hashing/test_composite_hasher.py +++ b/tests/test_hashing/test_legacy_composite_hasher.py @@ -5,9 +5,16 @@ import pytest -from orcapod.hashing.core import hash_to_hex -from orcapod.hashing.file_hashers import BasicFileHasher, DefaultCompositeFileHasher -from orcapod.hashing.types import FileHasher, PacketHasher, PathSetHasher +from orcapod.hashing.legacy_core import hash_to_hex +from orcapod.hashing.file_hashers import ( + LegacyDefaultFileHasher, + LegacyDefaultCompositeFileHasher, +) +from orcapod.hashing.types import ( + LegacyFileHasher, + LegacyPacketHasher, + LegacyPathSetHasher, +) # Custom implementation of hash_file for tests that doesn't check for file existence @@ -89,9 +96,11 @@ def mock_hash_packet( def patch_hash_functions(): """Patch the hash functions in the core module for all tests.""" with ( - patch("orcapod.hashing.core.hash_file", side_effect=mock_hash_file), - patch("orcapod.hashing.core.hash_pathset", side_effect=mock_hash_pathset), - patch("orcapod.hashing.core.hash_packet", side_effect=mock_hash_packet), + patch("orcapod.hashing.legacy_core.hash_file", side_effect=mock_hash_file), + patch( + "orcapod.hashing.legacy_core.hash_pathset", side_effect=mock_hash_pathset + ), + patch("orcapod.hashing.legacy_core.hash_packet", side_effect=mock_hash_packet), ): yield @@ -99,15 +108,15 @@ def patch_hash_functions(): def test_default_composite_hasher_implements_all_protocols(): """Test that CompositeFileHasher implements all three protocols.""" # Create a basic file hasher to be used within the composite hasher - file_hasher = BasicFileHasher() + file_hasher = LegacyDefaultFileHasher() # Create the composite hasher - composite_hasher = DefaultCompositeFileHasher(file_hasher) + composite_hasher = LegacyDefaultCompositeFileHasher(file_hasher) # Verify it implements all three protocols - assert isinstance(composite_hasher, FileHasher) - assert isinstance(composite_hasher, PathSetHasher) - assert isinstance(composite_hasher, PacketHasher) + assert isinstance(composite_hasher, LegacyFileHasher) + assert isinstance(composite_hasher, LegacyPathSetHasher) + assert isinstance(composite_hasher, LegacyPacketHasher) def test_default_composite_hasher_file_hashing(): @@ -121,7 +130,7 @@ def hash_file(self, file_path): return mock_hash_file(file_path) file_hasher = MockFileHasher() - composite_hasher = DefaultCompositeFileHasher(file_hasher) + composite_hasher = LegacyDefaultCompositeFileHasher(file_hasher) # Get hash from the composite hasher and directly from the file hasher direct_hash = file_hasher.hash_file(file_path) @@ -136,11 +145,11 @@ def test_default_composite_hasher_pathset_hashing(): # Create a custom mock file hasher that doesn't check for file existence class MockFileHasher: - def hash_file(self, file_path): + def hash_file(self, file_path) -> str: return mock_hash_file(file_path) file_hasher = MockFileHasher() - composite_hasher = DefaultCompositeFileHasher(file_hasher) + composite_hasher = LegacyDefaultCompositeFileHasher(file_hasher) # Simple path set with non-existent paths pathset = ["/path/to/file1.txt", "/path/to/file2.txt"] diff --git a/tests/test_hashing/test_packet_hasher.py b/tests/test_hashing/test_packet_hasher.py index f9d519d..80a16ed 100644 --- a/tests/test_hashing/test_packet_hasher.py +++ b/tests/test_hashing/test_packet_hasher.py @@ -1,14 +1,13 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/test_packet_hasher.py """Tests for the PacketHasher protocol implementation.""" import pytest -from orcapod.hashing.file_hashers import DefaultPacketHasher -from orcapod.hashing.types import PathSetHasher +from orcapod.hashing.file_hashers import LegacyDefaultPacketHasher +from orcapod.hashing.types import LegacyPathSetHasher -class MockPathSetHasher(PathSetHasher): +class MockPathSetHasher(LegacyPathSetHasher): """Simple mock PathSetHasher for testing.""" def __init__(self, hash_value="mock_hash"): @@ -20,10 +19,10 @@ def hash_pathset(self, pathset): return f"{self.hash_value}_{pathset}" -def test_default_packet_hasher_empty_packet(): - """Test DefaultPacketHasher with an empty packet.""" +def test_legacy_packet_hasher_empty_packet(): + """Test LegacyPacketHasher with an empty packet.""" pathset_hasher = MockPathSetHasher() - packet_hasher = DefaultPacketHasher(pathset_hasher) + packet_hasher = LegacyDefaultPacketHasher(pathset_hasher) # Test with empty packet packet = {} @@ -37,10 +36,10 @@ def test_default_packet_hasher_empty_packet(): assert isinstance(result, str) -def test_default_packet_hasher_single_entry(): - """Test DefaultPacketHasher with a packet containing a single entry.""" +def test_legacy_packet_hasher_single_entry(): + """Test LegacyPacketHasher with a packet containing a single entry.""" pathset_hasher = MockPathSetHasher() - packet_hasher = DefaultPacketHasher(pathset_hasher) + packet_hasher = LegacyDefaultPacketHasher(pathset_hasher) # Test with a single entry packet = {"input": "/path/to/file.txt"} @@ -55,10 +54,10 @@ def test_default_packet_hasher_single_entry(): assert isinstance(result, str) -def test_default_packet_hasher_multiple_entries(): - """Test DefaultPacketHasher with a packet containing multiple entries.""" +def test_legacy_packet_hasher_multiple_entries(): + """Test LegacyPacketHasher with a packet containing multiple entries.""" pathset_hasher = MockPathSetHasher() - packet_hasher = DefaultPacketHasher(pathset_hasher) + packet_hasher = LegacyDefaultPacketHasher(pathset_hasher) # Test with multiple entries packet = { @@ -79,10 +78,10 @@ def test_default_packet_hasher_multiple_entries(): assert isinstance(result, str) -def test_default_packet_hasher_nested_structure(): - """Test DefaultPacketHasher with a deeply nested packet structure.""" +def test_legacy_packet_hasher_nested_structure(): + """Test LegacyPacketHasher with a deeply nested packet structure.""" pathset_hasher = MockPathSetHasher() - packet_hasher = DefaultPacketHasher(pathset_hasher) + packet_hasher = LegacyDefaultPacketHasher(pathset_hasher) # Test with nested packet structure packet = { @@ -104,16 +103,16 @@ def test_default_packet_hasher_nested_structure(): assert isinstance(result, str) -def test_default_packet_hasher_with_char_count(): - """Test DefaultPacketHasher with different char_count values.""" +def test_legacy_packet_hasher_with_char_count(): + """Test LegacyPacketHasher with different char_count values.""" pathset_hasher = MockPathSetHasher() # Test with default char_count (32) - default_hasher = DefaultPacketHasher(pathset_hasher) + default_hasher = LegacyDefaultPacketHasher(pathset_hasher) default_result = default_hasher.hash_packet({"input": "/path/to/file.txt"}) # Test with custom char_count - custom_hasher = DefaultPacketHasher(pathset_hasher, char_count=16) + custom_hasher = LegacyDefaultPacketHasher(pathset_hasher, char_count=16) custom_result = custom_hasher.hash_packet({"input": "/path/to/file.txt"}) # Results should be different based on char_count diff --git a/tests/test_hashing/test_path_set_hasher.py b/tests/test_hashing/test_path_set_hasher.py index 999cc2a..c235eb0 100644 --- a/tests/test_hashing/test_path_set_hasher.py +++ b/tests/test_hashing/test_path_set_hasher.py @@ -8,12 +8,12 @@ import pytest -import orcapod.hashing.core -from orcapod.hashing.file_hashers import DefaultPathsetHasher -from orcapod.hashing.types import FileHasher +import orcapod.hashing.legacy_core +from orcapod.hashing.file_hashers import LegacyDefaultPathsetHasher +from orcapod.hashing.types import LegacyFileHasher -class MockFileHasher(FileHasher): +class MockFileHasher(LegacyFileHasher): """Simple mock FileHasher for testing.""" def __init__(self, hash_value="mock_hash"): @@ -35,7 +35,7 @@ def create_temp_file(content="test content"): # Store original function for restoration -original_hash_pathset = orcapod.hashing.core.hash_pathset +original_hash_pathset = orcapod.hashing.legacy_core.hash_pathset # Custom implementation of hash_pathset for tests that doesn't check for file existence @@ -46,7 +46,7 @@ def mock_hash_pathset( from collections.abc import Collection from os import PathLike - from orcapod.hashing.core import hash_to_hex + from orcapod.hashing.legacy_core import hash_to_hex from orcapod.utils.name import find_noncolliding_name # If file_hasher is None, we'll need to handle it differently @@ -86,14 +86,16 @@ def mock_hash_pathset( @pytest.fixture(autouse=True) def patch_hash_pathset(): """Patch the hash_pathset function in the hashing module for all tests.""" - with patch("orcapod.hashing.core.hash_pathset", side_effect=mock_hash_pathset): + with patch( + "orcapod.hashing.legacy_core.hash_pathset", side_effect=mock_hash_pathset + ): yield -def test_default_pathset_hasher_single_file(): - """Test DefaultPathsetHasher with a single file path.""" +def test_legacy_pathset_hasher_single_file(): + """Test LegacyPathsetHasher with a single file path.""" file_hasher = MockFileHasher() - pathset_hasher = DefaultPathsetHasher(file_hasher) + pathset_hasher = LegacyDefaultPathsetHasher(file_hasher) # Create a real file for testing file_path = create_temp_file() @@ -116,7 +118,7 @@ def test_default_pathset_hasher_single_file(): def test_default_pathset_hasher_multiple_files(): """Test DefaultPathsetHasher with multiple files in a list.""" file_hasher = MockFileHasher() - pathset_hasher = DefaultPathsetHasher(file_hasher) + pathset_hasher = LegacyDefaultPathsetHasher(file_hasher) # Create real files for testing file_paths = [create_temp_file(f"content {i}") for i in range(3)] @@ -195,7 +197,7 @@ def test_default_pathset_hasher_nested_paths(): def test_default_pathset_hasher_with_nonexistent_files(): """Test DefaultPathsetHasher with both existent and non-existent files.""" file_hasher = MockFileHasher() - pathset_hasher = DefaultPathsetHasher(file_hasher) + pathset_hasher = LegacyDefaultPathsetHasher(file_hasher) # Reset the file_hasher's call list file_hasher.file_hash_calls = [] @@ -225,7 +227,8 @@ def custom_hash_nonexistent(pathset, **kwargs): # Patch hash_pathset just for this test with patch( - "orcapod.hashing.core.hash_pathset", side_effect=custom_hash_nonexistent + "orcapod.hashing.legacy_core.hash_pathset", + side_effect=custom_hash_nonexistent, ): result = pathset_hasher.hash_pathset(pathset) @@ -249,14 +252,14 @@ def test_default_pathset_hasher_with_char_count(): try: # Test with default char_count (32) - default_hasher = DefaultPathsetHasher(file_hasher) + default_hasher = LegacyDefaultPathsetHasher(file_hasher) default_result = default_hasher.hash_pathset(file_path) # Reset call list file_hasher.file_hash_calls = [] # Test with custom char_count - custom_hasher = DefaultPathsetHasher(file_hasher, char_count=16) + custom_hasher = LegacyDefaultPathsetHasher(file_hasher, char_count=16) custom_result = custom_hasher.hash_pathset(file_path) # Both should have called the file_hasher once diff --git a/tests/test_hashing/test_pathset_and_packet.py b/tests/test_hashing/test_pathset_and_packet.py index 6b7eb6f..cde79da 100644 --- a/tests/test_hashing/test_pathset_and_packet.py +++ b/tests/test_hashing/test_pathset_and_packet.py @@ -1,7 +1,6 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/test_pathset_and_packet.py """ -Test the hash_pathset and hash_packet functions from orcabridge.hashing. +Test the hash_pathset and hash_packet functions from orcapod.hashing. This module contains tests to verify the correct behavior of hash_pathset and hash_packet functions with various input types and configurations. @@ -14,7 +13,7 @@ import pytest -from orcapod.hashing import hash_file, hash_packet, hash_pathset +from orcapod.hashing.legacy_core import hash_file, hash_packet, hash_pathset logger = logging.getLogger(__name__) diff --git a/tests/test_hashing/test_pathset_packet_hashes.py b/tests/test_hashing/test_pathset_packet_hashes.py index 49e2d0c..7df740d 100644 --- a/tests/test_hashing/test_pathset_packet_hashes.py +++ b/tests/test_hashing/test_pathset_packet_hashes.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_hashing/test_pathset_packet_hashes.py """ Test pathset and packet hash consistency. @@ -12,8 +11,8 @@ import pytest -# Add the parent directory to the path to import orcabridge -from orcapod.hashing import hash_packet, hash_pathset +# Add the parent directory to the path to import orcapod +from orcapod.hashing.legacy_core import hash_packet, hash_pathset def load_pathset_hash_lut(): diff --git a/tests/test_hashing/test_process_structure.py b/tests/test_hashing/test_process_structure.py index 933e2dc..2967ed4 100644 --- a/tests/test_hashing/test_process_structure.py +++ b/tests/test_hashing/test_process_structure.py @@ -3,7 +3,7 @@ from pathlib import Path from typing import Any -from orcapod.hashing.core import HashableMixin, hash_to_hex, process_structure +from orcapod.hashing.legacy_core import HashableMixin, hash_to_hex, process_structure # Define a simple HashableMixin class for testing diff --git a/tests/test_hashing/test_string_cacher/test_redis_cacher.py b/tests/test_hashing/test_string_cacher/test_redis_cacher.py index 3ef49e1..eef7c43 100644 --- a/tests/test_hashing/test_string_cacher/test_redis_cacher.py +++ b/tests/test_hashing/test_string_cacher/test_redis_cacher.py @@ -68,21 +68,21 @@ def keys(self, pattern): return [key for key in self.data.keys() if key.startswith(prefix)] return [key for key in self.data.keys() if key == pattern] + class MockRedisModule: ConnectionError = MockConnectionError RedisError = MockRedisError Redis = MagicMock(return_value=MockRedis()) # Simple one-liner! - def mock_get_redis(): return MockRedisModule + def mock_no_redis(): return None - class TestRedisCacher: """Test cases for RedisCacher with mocked Redis.""" diff --git a/tests/test_semantic_types/test_path_struct_converter.py b/tests/test_semantic_types/test_path_struct_converter.py new file mode 100644 index 0000000..4e539d3 --- /dev/null +++ b/tests/test_semantic_types/test_path_struct_converter.py @@ -0,0 +1,115 @@ +from typing import cast +import pytest +from pathlib import Path +from unittest.mock import patch +from orcapod.semantic_types.semantic_struct_converters import PathStructConverter + + +def test_path_to_struct_and_back(): + converter = PathStructConverter() + path_obj = Path("/tmp/test.txt") + struct_dict = converter.python_to_struct_dict(path_obj) + assert struct_dict["path"] == str(path_obj) + restored = converter.struct_dict_to_python(struct_dict) + assert restored == path_obj + + +def test_path_to_struct_invalid_type(): + converter = PathStructConverter() + with pytest.raises(TypeError): + converter.python_to_struct_dict("not_a_path") # type: ignore + + +def test_struct_to_python_missing_field(): + converter = PathStructConverter() + with pytest.raises(ValueError): + converter.struct_dict_to_python({}) + + +def test_can_handle_python_type(): + converter = PathStructConverter() + assert converter.can_handle_python_type(Path) + assert not converter.can_handle_python_type(str) + + +def test_can_handle_struct_type(): + converter = PathStructConverter() + struct_type = converter.arrow_struct_type + assert converter.can_handle_struct_type(struct_type) + + # Should fail for wrong fields + class FakeField: + def __init__(self, name, type): + self.name = name + self.type = type + + class FakeStructType(list): + pass + + import pyarrow as pa + + fake_struct = cast( + pa.StructType, FakeStructType([FakeField("wrong", struct_type[0].type)]) + ) + assert not converter.can_handle_struct_type(fake_struct) + + +def test_is_semantic_struct(): + converter = PathStructConverter() + assert converter.is_semantic_struct({"path": "/tmp/test.txt"}) + assert not converter.is_semantic_struct({"not_path": "value"}) + assert not converter.is_semantic_struct({"path": 123}) + + +def test_hash_struct_dict_file_not_found(tmp_path): + converter = PathStructConverter() + struct_dict = {"path": str(tmp_path / "does_not_exist.txt")} + with pytest.raises(FileNotFoundError): + converter.hash_struct_dict(struct_dict) + + +def test_hash_struct_dict_permission_error(tmp_path): + converter = PathStructConverter() + file_path = tmp_path / "file.txt" + file_path.write_text("data") + with patch("pathlib.Path.read_bytes", side_effect=PermissionError): + struct_dict = {"path": str(file_path)} + with pytest.raises(PermissionError): + converter.hash_struct_dict(struct_dict) + + +def test_hash_struct_dict_is_directory(tmp_path): + converter = PathStructConverter() + struct_dict = {"path": str(tmp_path)} + with pytest.raises(ValueError): + converter.hash_struct_dict(struct_dict) + + +def test_hash_struct_dict_content_based(tmp_path): + converter = PathStructConverter() + file1 = tmp_path / "file1.txt" + file2 = tmp_path / "file2.txt" + content = "identical content" + file1.write_text(content) + file2.write_text(content) + struct_dict1 = {"path": str(file1)} + struct_dict2 = {"path": str(file2)} + hash1 = converter.hash_struct_dict(struct_dict1) + hash2 = converter.hash_struct_dict(struct_dict2) + assert hash1 == hash2 + + +def test_hash_path_objects_content_based(tmp_path): + converter = PathStructConverter() + file1 = tmp_path / "fileA.txt" + file2 = tmp_path / "fileB.txt" + content = "same file content" + file1.write_text(content) + file2.write_text(content) + path_obj1 = Path(file1) + path_obj2 = Path(file2) + struct_dict1 = converter.python_to_struct_dict(path_obj1) + struct_dict2 = converter.python_to_struct_dict(path_obj2) + hash1 = converter.hash_struct_dict(struct_dict1) + hash2 = converter.hash_struct_dict(struct_dict2) + assert hash1 == hash2 diff --git a/tests/test_semantic_types/test_pydata_utils.py b/tests/test_semantic_types/test_pydata_utils.py new file mode 100644 index 0000000..622a8fc --- /dev/null +++ b/tests/test_semantic_types/test_pydata_utils.py @@ -0,0 +1,116 @@ +import pytest +from pathlib import Path, PosixPath +from orcapod.semantic_types import pydata_utils + + +def test_pylist_to_pydict_typical(): + data = [{"a": 1, "b": 2}, {"a": 3, "c": 4}] + result = pydata_utils.pylist_to_pydict(data) + assert result == {"a": [1, 3], "b": [2, None], "c": [None, 4]} + + +def test_pylist_to_pydict_missing_keys(): + data = [{"a": 1}, {"b": 2}, {"a": 3, "b": 4}] + result = pydata_utils.pylist_to_pydict(data) + assert result == {"a": [1, None, 3], "b": [None, 2, 4]} + + +def test_pylist_to_pydict_empty(): + assert pydata_utils.pylist_to_pydict([]) == {} + + +def test_pylist_to_pydict_empty_dicts(): + data = [{}, {}, {}] + assert pydata_utils.pylist_to_pydict(data) == {} + + +def test_pydict_to_pylist_typical(): + data = {"a": [1, 3], "b": [2, None], "c": [None, 4]} + result = pydata_utils.pydict_to_pylist(data) + assert result == [{"a": 1, "b": 2, "c": None}, {"a": 3, "b": None, "c": 4}] + + +def test_pydict_to_pylist_uneven_lengths(): + data = {"a": [1, 2], "b": [3]} + with pytest.raises(ValueError): + pydata_utils.pydict_to_pylist(data) + + +def test_pydict_to_pylist_empty(): + assert pydata_utils.pydict_to_pylist({}) == [] + + +def test_pydict_to_pylist_empty_lists(): + data = {"a": [], "b": []} + assert pydata_utils.pydict_to_pylist(data) == [] + + +def test_infer_python_schema_from_pylist_data_typical(): + data = [{"a": 1, "b": 2.0}, {"a": 3, "b": None}] + schema = pydata_utils.infer_python_schema_from_pylist_data(data) + assert schema["a"] in (int, int | None) + assert schema["b"] in (float | None, float) + + +def test_infer_python_schema_from_pylist_data_complex(): + data = [ + {"path": Path("/tmp/file1"), "size": 123}, + {"path": Path("/tmp/file2"), "size": None}, + ] + schema = pydata_utils.infer_python_schema_from_pylist_data(data) + assert schema["path"] in (Path, PosixPath) + assert schema["size"] == int | None + + +def test_infer_python_schema_from_pylist_data_empty(): + assert pydata_utils.infer_python_schema_from_pylist_data([]) == {} + + +def test_infer_python_schema_from_pylist_data_mixed_types(): + data = [{"a": 1}, {"a": "x"}, {"a": 2.5}] + schema = pydata_utils.infer_python_schema_from_pylist_data(data) + # Should be Union[int, float, str] or Any + assert "a" in schema + + +def test_infer_python_schema_from_pydict_data_typical(): + data = {"a": [1, 2], "b": [None, 3.5]} + schema = pydata_utils.infer_python_schema_from_pydict_data(data) + assert schema["a"] in (int, int | None) + assert schema["b"] in (float | None, float) + + +def test_infer_python_schema_from_pydict_data_empty(): + assert pydata_utils.infer_python_schema_from_pydict_data({}) == {} + + +def test_infer_python_schema_from_pydict_data_empty_lists(): + data = {"a": [], "b": []} + schema = pydata_utils.infer_python_schema_from_pydict_data(data) + assert schema["a"] == str | None + assert schema["b"] == str | None + + +def test_infer_python_schema_from_pydict_data_mixed_types(): + data = {"a": [1, "x", 2.5]} + schema = pydata_utils.infer_python_schema_from_pydict_data(data) + assert "a" in schema + + +def test_round_trip_pylist_pydict(): + data = [{"a": 1, "b": 2}, {"a": 3, "c": 4}] + pydict = pydata_utils.pylist_to_pydict(data) + pylist = pydata_utils.pydict_to_pylist(pydict) + # Should be equivalent to original data (order of keys may differ) + for orig, roundtrip in zip(data, pylist): + # Compare dicts for value equality, ignoring key order and missing keys + for k in orig: + assert orig[k] == roundtrip[k] + + +def test_round_trip_pydict_pylist(): + data = {"a": [1, 3], "b": [2, None], "c": [None, 4]} + pylist = pydata_utils.pydict_to_pylist(data) + pydict = pydata_utils.pylist_to_pydict(pylist) + for k in data: + assert pydict[k] == data[k] diff --git a/tests/test_semantic_types/test_semantic_registry.py b/tests/test_semantic_types/test_semantic_registry.py new file mode 100644 index 0000000..4c387a1 --- /dev/null +++ b/tests/test_semantic_types/test_semantic_registry.py @@ -0,0 +1,200 @@ +import pytest +from unittest.mock import Mock +from orcapod.semantic_types import semantic_registry + + +def test_registry_initialization(): + registry = semantic_registry.SemanticTypeRegistry() + assert registry.list_semantic_types() == [] + assert registry.list_python_types() == [] + assert registry.list_struct_signatures() == [] + + +def test_register_and_retrieve_converter(): + registry = semantic_registry.SemanticTypeRegistry() + python_type = Mock(name="PythonType") + struct_type = Mock(name="StructType") + converter = Mock() + converter.python_type = python_type + converter.arrow_struct_type = struct_type + registry.register_converter("mock_type", converter) + # Retrieve by semantic type name + assert registry.get_converter_for_semantic_type("mock_type") is converter + # Retrieve by python type + assert registry.get_converter_for_python_type(python_type) is converter + # Retrieve by struct signature + assert registry.get_converter_for_struct_signature(struct_type) is converter + + +def test_register_duplicate_semantic_type_raises(): + registry = semantic_registry.SemanticTypeRegistry() + python_type = Mock(name="PythonType") + struct_type = Mock(name="StructType") + converter1 = Mock() + converter1.python_type = python_type + converter1.arrow_struct_type = struct_type + registry.register_converter("mock_type", converter1) + converter2 = Mock() + converter2.python_type = python_type + converter2.arrow_struct_type = struct_type + with pytest.raises(ValueError): + registry.register_converter("mock_type", converter2) + + +def test_register_conflicting_python_type_raises(): + registry = semantic_registry.SemanticTypeRegistry() + python_type = Mock(name="PythonType") + struct_type1 = Mock(name="StructType1") + struct_type2 = Mock(name="StructType2") + converter1 = Mock() + converter1.python_type = python_type + converter1.arrow_struct_type = struct_type1 + registry.register_converter("mock_type1", converter1) + converter2 = Mock() + converter2.python_type = python_type + converter2.arrow_struct_type = struct_type2 + with pytest.raises(ValueError): + registry.register_converter("mock_type2", converter2) + + +def test_register_conflicting_struct_signature_raises(): + registry = semantic_registry.SemanticTypeRegistry() + python_type1 = Mock(name="PythonType1") + python_type2 = Mock(name="PythonType2") + struct_type = Mock(name="StructType") + converter1 = Mock() + converter1.python_type = python_type1 + converter1.arrow_struct_type = struct_type + registry.register_converter("mock_type1", converter1) + converter2 = Mock() + converter2.python_type = python_type2 + converter2.arrow_struct_type = struct_type + with pytest.raises(ValueError): + registry.register_converter("mock_type2", converter2) + + +def test_get_nonexistent_returns_none(): + registry = semantic_registry.SemanticTypeRegistry() + python_type = Mock(name="PythonType") + struct_type = Mock(name="StructType") + assert registry.get_converter_for_semantic_type("not_present") is None + assert registry.get_converter_for_python_type(python_type) is None + assert registry.get_converter_for_struct_signature(struct_type) is None + + +def test_list_registered_types(): + registry = semantic_registry.SemanticTypeRegistry() + python_type1 = Mock(name="PythonType1") + struct_type1 = Mock(name="StructType1") + converter1 = Mock() + converter1.python_type = python_type1 + converter1.arrow_struct_type = struct_type1 + registry.register_converter("mock_type1", converter1) + + python_type2 = Mock(name="PythonType2") + struct_type2 = Mock(name="StructType2") + converter2 = Mock() + converter2.python_type = python_type2 + converter2.arrow_struct_type = struct_type2 + registry.register_converter("mock_type2", converter2) + + assert set(registry.list_semantic_types()) == {"mock_type1", "mock_type2"} + assert set(registry.list_python_types()) == {python_type1, python_type2} + assert set(registry.list_struct_signatures()) == {struct_type1, struct_type2} + + +def test_has_methods(): + registry = semantic_registry.SemanticTypeRegistry() + python_type = Mock(name="PythonType") + struct_type = Mock(name="StructType") + converter = Mock() + converter.python_type = python_type + converter.arrow_struct_type = struct_type + registry.register_converter("mock_type", converter) + assert registry.has_semantic_type("mock_type") + assert registry.has_python_type(python_type) + assert registry.has_semantic_struct_signature(struct_type) + + +def test_integration_with_converter(): + registry = semantic_registry.SemanticTypeRegistry() + python_type = Mock(name="PythonType") + struct_type = Mock(name="StructType") + converter = Mock() + converter.python_type = python_type + converter.arrow_struct_type = struct_type + registry.register_converter("mock_type", converter) + retrieved = registry.get_converter_for_semantic_type("mock_type") + assert retrieved is converter + + +# Comprehensive unregister tests for future implementation +# Uncomment when unregister methods are implemented +# +# def test_unregister_by_semantic_type_name(): +# registry = semantic_registry.SemanticTypeRegistry() +# python_type = Mock(name="PythonType") +# struct_type = Mock(name="StructType") +# converter = Mock() +# converter.python_type = python_type +# converter.arrow_struct_type = struct_type +# registry.register_converter("mock_type", converter) +# result = registry.unregister_by_semantic_type_name("mock_type") +# assert result == {"mock_type": converter} +# assert not registry.has_semantic_type("mock_type") +# assert not registry.has_python_type(python_type) +# assert not registry.has_semantic_struct_signature(struct_type) +# assert registry.get_converter_for_semantic_type("mock_type") is None +# assert registry.get_converter_for_python_type(python_type) is None +# assert registry.get_converter_for_struct_signature(struct_type) is None +# +# def test_unregister_by_converter(): +# registry = semantic_registry.SemanticTypeRegistry() +# python_type = Mock(name="PythonType") +# struct_type = Mock(name="StructType") +# converter = Mock() +# converter.python_type = python_type +# converter.arrow_struct_type = struct_type +# registry.register_converter("mock_type", converter) +# result = registry.unregister_by_converter(converter) +# assert result == {"mock_type": converter} +# assert not registry.has_semantic_type("mock_type") +# assert not registry.has_python_type(python_type) +# assert not registry.has_semantic_struct_signature(struct_type) +# assert registry.get_converter_for_semantic_type("mock_type") is None +# assert registry.get_converter_for_python_type(python_type) is None +# assert registry.get_converter_for_struct_signature(struct_type) is None +# +# def test_unregister_by_python_type(): +# registry = semantic_registry.SemanticTypeRegistry() +# python_type = Mock(name="PythonType") +# struct_type = Mock(name="StructType") +# converter = Mock() +# converter.python_type = python_type +# converter.arrow_struct_type = struct_type +# registry.register_converter("mock_type", converter) +# result = registry.unregister_by_python_type(python_type) +# assert result == {"mock_type": converter} +# assert not registry.has_semantic_type("mock_type") +# assert not registry.has_python_type(python_type) +# assert not registry.has_semantic_struct_signature(struct_type) +# assert registry.get_converter_for_semantic_type("mock_type") is None +# assert registry.get_converter_for_python_type(python_type) is None +# assert registry.get_converter_for_struct_signature(struct_type) is None +# +# def test_unregister_by_struct_signature(): +# registry = semantic_registry.SemanticTypeRegistry() +# python_type = Mock(name="PythonType") +# struct_type = Mock(name="StructType") +# converter = Mock() +# converter.python_type = python_type +# converter.arrow_struct_type = struct_type +# registry.register_converter("mock_type", converter) +# result = registry.unregister_by_struct_signature(struct_type) +# assert result == {"mock_type": converter} +# assert not registry.has_semantic_type("mock_type") +# assert not registry.has_python_type(python_type) +# assert not registry.has_semantic_struct_signature(struct_type) +# assert registry.get_converter_for_semantic_type("mock_type") is None +# assert registry.get_converter_for_python_type(python_type) is None +# assert registry.get_converter_for_struct_signature(struct_type) is None diff --git a/tests/test_semantic_types/test_semantic_struct_converters.py b/tests/test_semantic_types/test_semantic_struct_converters.py new file mode 100644 index 0000000..66eab14 --- /dev/null +++ b/tests/test_semantic_types/test_semantic_struct_converters.py @@ -0,0 +1,116 @@ +from orcapod.semantic_types.semantic_struct_converters import ( + SemanticStructConverterBase, +) + + +class DummyConverter(SemanticStructConverterBase): + def __init__(self): + super().__init__("dummy") + self._python_type = dict + self._arrow_struct_type = "dummy_struct" + + @property + def python_type(self): + return self._python_type + + @property + def arrow_struct_type(self): + return self._arrow_struct_type + + def python_to_struct_dict(self, value): + return value + + def struct_dict_to_python(self, struct_dict): + return struct_dict + + def can_handle_python_type(self, python_type): + return python_type is dict + + def can_handle_struct_type(self, struct_type): + return struct_type == "dummy_struct" + + def is_semantic_struct(self, struct_dict): + return isinstance(struct_dict, dict) + + def hash_struct_dict(self, struct_dict, add_prefix=False): + return "dummyhash" + + +# --- SemanticStructConverterBase tests --- +def test_semantic_struct_converter_base_properties(): + converter = DummyConverter() + assert converter.semantic_type_name == "dummy" + assert converter.hasher_id == "dummy_content_sha256" + + +def test_format_hash_string(): + converter = DummyConverter() + hash_bytes = b"\x01\x02" + assert converter._format_hash_string(hash_bytes, add_prefix=False) == "0102" + assert ( + converter._format_hash_string(hash_bytes, add_prefix=True) + == "dummy:sha256:0102" + ) + + +def test_compute_content_hash(): + converter = DummyConverter() + data = b"abc" + result = converter._compute_content_hash(data) + import hashlib + + assert result == hashlib.sha256(data).digest() + + +# --- PathStructConverter tests --- + + +def test_extensibility_with_new_converter(): + class NewConverter(SemanticStructConverterBase): + def __init__(self): + super().__init__("newtype") + self._python_type = list + self._arrow_struct_type = "new_struct" + + @property + def python_type(self): + return self._python_type + + @property + def arrow_struct_type(self): + return self._arrow_struct_type + + def python_to_struct_dict(self, value): + return {"data": value} + + def struct_dict_to_python(self, struct_dict): + return struct_dict["data"] + + def can_handle_python_type(self, python_type): + return python_type is list + + def can_handle_struct_type(self, struct_type): + return struct_type == "new_struct" + + def is_semantic_struct(self, struct_dict): + return "data" in struct_dict + + def hash_struct_dict(self, struct_dict, add_prefix=False): + return "newhash" + + converter = NewConverter() + assert converter.semantic_type_name == "newtype" + assert converter.python_to_struct_dict([1, 2, 3]) == {"data": [1, 2, 3]} + assert converter.struct_dict_to_python({"data": [1, 2, 3]}) == [1, 2, 3] + assert converter.can_handle_python_type(list) + assert converter.can_handle_struct_type("new_struct") + assert converter.is_semantic_struct({"data": [1, 2, 3]}) + assert converter.hash_struct_dict({"data": [1, 2, 3]}) == "newhash" + + +# --- Edge cases --- +def test_dummy_converter_edge_cases(): + converter = DummyConverter() + assert converter.is_semantic_struct({}) + assert not converter.is_semantic_struct(None) + assert converter.hash_struct_dict({}) == "dummyhash" diff --git a/tests/test_semantic_types/test_universal_converter.py b/tests/test_semantic_types/test_universal_converter.py new file mode 100644 index 0000000..375a119 --- /dev/null +++ b/tests/test_semantic_types/test_universal_converter.py @@ -0,0 +1,300 @@ +from typing import cast +import pytest +import pyarrow as pa +import numpy as np +from pathlib import Path +from orcapod.semantic_types import universal_converter +from orcapod.contexts import get_default_context + + +def test_python_type_to_arrow_type_basic(): + assert universal_converter.python_type_to_arrow_type(int) == pa.int64() + assert universal_converter.python_type_to_arrow_type(float) == pa.float64() + assert universal_converter.python_type_to_arrow_type(str) == pa.large_string() + assert universal_converter.python_type_to_arrow_type(bool) == pa.bool_() + assert universal_converter.python_type_to_arrow_type(bytes) == pa.large_binary() + + +def test_python_type_to_arrow_type_numpy(): + assert universal_converter.python_type_to_arrow_type(np.int32) == pa.int32() + assert universal_converter.python_type_to_arrow_type(np.float64) == pa.float64() + assert universal_converter.python_type_to_arrow_type(np.bool_) == pa.bool_() + + +def test_python_type_to_arrow_type_custom(): + arrow_type = universal_converter.python_type_to_arrow_type(Path) + # Should be a StructType with field 'path' of type large_string + assert isinstance(arrow_type, pa.StructType) + assert len(arrow_type) == 1 + field = arrow_type[0] + assert field.name == "path" + assert field.type == pa.large_string() + + +def test_python_type_to_arrow_type_context(): + ctx = get_default_context() + assert universal_converter.python_type_to_arrow_type(int, ctx) == pa.int64() + + +def test_python_type_to_arrow_type_unsupported(): + class CustomType: + pass + + with pytest.raises(Exception): + universal_converter.python_type_to_arrow_type(CustomType) + + +def test_arrow_type_to_python_type_basic(): + assert universal_converter.arrow_type_to_python_type(pa.int64()) is int + assert universal_converter.arrow_type_to_python_type(pa.float64()) is float + assert universal_converter.arrow_type_to_python_type(pa.large_string()) is str + assert universal_converter.arrow_type_to_python_type(pa.bool_()) is bool + assert universal_converter.arrow_type_to_python_type(pa.large_binary()) is bytes + + +def test_arrow_type_to_python_type_context(): + ctx = get_default_context() + assert universal_converter.arrow_type_to_python_type(pa.int64(), ctx) is int + + +def test_arrow_type_to_python_type_unsupported(): + class FakeArrowType: + pass + + with pytest.raises(Exception): + universal_converter.arrow_type_to_python_type( + cast(pa.DataType, FakeArrowType()) + ) + + +def test_get_conversion_functions_basic(): + to_arrow, to_python = universal_converter.get_conversion_functions(int) + assert callable(to_arrow) + assert callable(to_python) + assert to_arrow(42) == 42 + assert to_python(42) == 42 + + +def test_get_conversion_functions_custom(): + to_arrow, to_python = universal_converter.get_conversion_functions(str) + assert to_arrow("abc") == "abc" + assert to_python("abc") == "abc" + + +def test_get_conversion_functions_context(): + ctx = get_default_context() + to_arrow, to_python = universal_converter.get_conversion_functions(float, ctx) + assert to_arrow(1.5) == 1.5 + assert to_python(1.5) == 1.5 + + +def test_python_type_to_arrow_type_list(): + # Unparameterized list should raise ValueError + with pytest.raises(ValueError): + universal_converter.python_type_to_arrow_type(list) + + +def test_python_type_to_arrow_type_dict(): + # Unparameterized dict should raise ValueError + with pytest.raises(ValueError): + universal_converter.python_type_to_arrow_type(dict) + + +def test_python_type_to_arrow_type_list_of_dict(): + # For list[dict[str, int]], expect LargeListType of LargeListType of StructType + arrow_type = universal_converter.python_type_to_arrow_type(list[dict[str, int]]) + # Should be LargeListType + assert arrow_type.__class__.__name__.endswith("ListType") + # Next level should also be LargeListType + arrow_type = cast(pa.ListType, arrow_type) + inner_list = arrow_type.value_type + assert inner_list.__class__.__name__.endswith("ListType") + # Innermost should be StructType + struct_type = inner_list.value_type + assert isinstance(struct_type, pa.StructType) + assert struct_type[0].name == "key" + assert struct_type[0].type == pa.large_string() + assert struct_type[1].name == "value" + assert struct_type[1].type == pa.int64() + + +def test_python_type_to_arrow_type_dict_of_list(): + # dict[str, list[int]] should be a LargeListType of StructType, with value field as LargeListType + arrow_type = universal_converter.python_type_to_arrow_type(dict[str, list[int]]) + assert arrow_type.__class__.__name__.endswith("ListType") + arrow_type = cast(pa.ListType, arrow_type) + struct_type = arrow_type.value_type + assert isinstance(struct_type, pa.StructType) + assert struct_type[0].name == "key" + assert struct_type[0].type == pa.large_string() + assert struct_type[1].name == "value" + value_type = struct_type[1].type + assert value_type.__class__.__name__.endswith("ListType") + assert value_type.value_type == pa.int64() + + +def test_python_type_to_arrow_type_list_of_list(): + arrow_type = universal_converter.python_type_to_arrow_type(list[list[int]]) + assert arrow_type.__class__.__name__.endswith("ListType") + arrow_type = cast(pa.ListType, arrow_type) + inner_list = arrow_type.value_type + assert inner_list.__class__.__name__.endswith("ListType") + assert inner_list.value_type == pa.int64() + + +def test_python_type_to_arrow_type_deeply_nested(): + # dict[str, list[list[dict[str, float]]]] + complex_type = dict[str, list[list[dict[str, float]]]] + arrow_type = universal_converter.python_type_to_arrow_type(complex_type) + # Should be a LargeListType of StructType + assert arrow_type.__class__.__name__.endswith("ListType") + arrow_type = cast(pa.ListType, arrow_type) + struct_type = arrow_type.value_type + assert isinstance(struct_type, pa.StructType) + assert struct_type[0].name == "key" + assert struct_type[0].type == pa.large_string() + assert struct_type[1].name == "value" + outer_list = struct_type[1].type + assert outer_list.__class__.__name__.endswith("ListType") + inner_list = outer_list.value_type + assert inner_list.__class__.__name__.endswith("ListType") + inner_struct_list = inner_list.value_type + assert inner_struct_list.__class__.__name__.endswith("ListType") + inner_struct = inner_struct_list.value_type + assert isinstance(inner_struct, pa.StructType) + assert inner_struct[0].name == "key" + assert inner_struct[0].type == pa.large_string() + assert inner_struct[1].name == "value" + assert inner_struct[1].type == pa.float64() + + +# Roundtrip tests for complex types +def test_roundtrip_list_of_int(): + py_val = [1, 2, 3, 4] + to_arrow, to_python = universal_converter.get_conversion_functions(list[int]) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + assert py_val == py_val2 + + +def test_roundtrip_dict_str_int(): + py_val = {"a": 1, "b": 2} + to_arrow, to_python = universal_converter.get_conversion_functions(dict[str, int]) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + # dict roundtrip may come back as dict or list of pairs + if isinstance(py_val2, dict): + assert py_val == py_val2 + else: + # Accept list of pairs + assert sorted(py_val.items()) == sorted( + [(d["key"], d["value"]) for d in py_val2] + ) + + +def test_roundtrip_list_of_list_of_float(): + py_val = [[1.1, 2.2], [3.3, 4.4]] + to_arrow, to_python = universal_converter.get_conversion_functions( + list[list[float]] + ) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + assert py_val == py_val2 + + +def test_roundtrip_set_of_int(): + py_val = {1, 2, 3} + to_arrow, to_python = universal_converter.get_conversion_functions(set[int]) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + # set will come back as list + assert py_val != py_val2 + assert set(py_val) == set(py_val2) + + +def test_roundtrip_various_complex_types(): + cases = [ + ([1, 2, 3], list[int]), + ([["a", "b"], ["c"]], list[list[str]]), + ({"a": 1, "b": 2}, dict[str, int]), + ([{"x": 1.1, "y": 2.2}, {"x": 3.3, "y": 4.4}], list[dict[str, float]]), + ({"a": [1, 2], "b": [3]}, dict[str, list[int]]), + ( + [{"a": [1, 2]}, {"b": [3], "c": [4, 5, 6]}], + list[dict[str, list[int]]], + ), + ( + [[{"k": "a", "v": 1.1}, {"k": "b", "v": 2.2}], [{"k": "c", "v": 3.3}]], + list[list[dict[str, float]]], + ), + ( + {"outer": [{"inner": [1, 2]}, {"inner": [3, 4]}]}, + dict[str, list[dict[str, list[int]]]], + ), + ({"a": {"b": {"c": 42}}}, dict[str, dict[str, dict[str, int]]]), + ({"a": None, "b": 2}, dict[str, int]), + ( + [{"x": [1, 2], "y": [3, 4]}, {"x": [5], "y": [6, 7]}], + list[dict[str, list[int]]], + ), + ] + for py_val, typ in cases: + to_arrow, to_python = universal_converter.get_conversion_functions(typ) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + assert py_val == py_val2, f"Failed roundtrip for type {typ} with value {py_val}" + + +def test_incomplete_roundtrip_types(): + cases = [({"a": {1, 2}, "b": {3}}, dict[str, set[int]], {"a": [1, 2], "b": [3]})] + + for py_val, typ, expected_return in cases: + to_arrow, to_python = universal_converter.get_conversion_functions(typ) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + assert py_val2 == expected_return, ( + f"Failed roundtrip for type {typ} with value {py_val}" + ) + + +def test_roundtrip_minimal_key_list_issue(): + py_val = [{"test": [1, 2, 3], "next": [3, 4]}] + typ = list[dict[str, list[int]]] + to_arrow, to_python = universal_converter.get_conversion_functions(typ) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + print("Original:", py_val) + print("Roundtrip:", py_val2) + assert py_val == py_val2 + + +def test_roundtrip_simpler_key_issue_dict_str_list(): + py_val = {"a": [1, 2]} + typ = dict[str, list[int]] + to_arrow, to_python = universal_converter.get_conversion_functions(typ) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + print("Original dict[str, list[int]]:", py_val) + print("Roundtrip:", py_val2) + assert py_val == py_val2 + + +def test_roundtrip_simpler_key_issue_list_dict_str_int(): + py_val = [{"key": "a", "value": 1}] + typ = list[dict[str, int]] + to_arrow, to_python = universal_converter.get_conversion_functions(typ) + arr = to_arrow(py_val) + py_val2 = to_python(arr) + print("Original list[dict[str, int]]:", py_val) + print("Roundtrip:", py_val2) + assert py_val == py_val2 + + +def test_inspect_arrow_schema_dict_str_list(): + py_val = {"test": [1, 2]} + typ = dict[str, list[int]] + arrow_type = universal_converter.python_type_to_arrow_type(typ) + print("Arrow type for dict[str, list[int]]:", arrow_type) + to_arrow_struct, to_python = universal_converter.get_conversion_functions(typ) + arr = to_arrow_struct(py_val) + assert arr == [{"key": "test", "value": [1, 2]}] diff --git a/tests/test_store/conftest.py b/tests/test_store/conftest.py index 77ca9f9..6b8aa6f 100644 --- a/tests/test_store/conftest.py +++ b/tests/test_store/conftest.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_store/conftest.py """Common test fixtures for store tests.""" import shutil diff --git a/tests/test_store/test_dir_data_store.py b/tests/test_store/test_dir_data_store.py index c07f141..1e91272 100644 --- a/tests/test_store/test_dir_data_store.py +++ b/tests/test_store/test_dir_data_store.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_store/test_dir_data_store.py """Tests for DirDataStore.""" import json @@ -9,15 +8,15 @@ import pytest from orcapod.hashing.types import ( - CompositeFileHasher, - FileHasher, - PacketHasher, - PathSetHasher, + LegacyCompositeFileHasher, + LegacyFileHasher, + LegacyPacketHasher, + LegacyPathSetHasher, ) -from orcapod.store.core import DirDataStore +from orcapod.databases.legacy.dict_data_stores import DirDataStore -class MockFileHasher(FileHasher): +class MockFileHasher(LegacyFileHasher): """Mock FileHasher for testing.""" def __init__(self, hash_value="mock_hash"): @@ -29,19 +28,19 @@ def hash_file(self, file_path): return f"{self.hash_value}_file" -class MockPathSetHasher(PathSetHasher): +class MockPathSetHasher(LegacyPathSetHasher): """Mock PathSetHasher for testing.""" def __init__(self, hash_value="mock_hash"): self.hash_value = hash_value self.pathset_hash_calls = [] - def hash_pathset(self, pathset): + def hash_pathset(self, pathset) -> str: self.pathset_hash_calls.append(pathset) return f"{self.hash_value}_pathset" -class MockPacketHasher(PacketHasher): +class MockPacketHasher(LegacyPacketHasher): """Mock PacketHasher for testing.""" def __init__(self, hash_value="mock_hash"): @@ -53,7 +52,7 @@ def hash_packet(self, packet): return f"{self.hash_value}_packet" -class MockCompositeHasher(CompositeFileHasher): +class MockCompositeHasher(LegacyCompositeFileHasher): """Mock CompositeHasher that implements all three hash protocols.""" def __init__(self, hash_value="mock_hash"): @@ -62,15 +61,15 @@ def __init__(self, hash_value="mock_hash"): self.pathset_hash_calls = [] self.packet_hash_calls = [] - def hash_file(self, file_path): + def hash_file_content(self, file_path): self.file_hash_calls.append(file_path) return f"{self.hash_value}_file" - def hash_pathset(self, pathset): + def hash_pathset(self, pathset) -> str: self.pathset_hash_calls.append(pathset) return f"{self.hash_value}_pathset" - def hash_packet(self, packet): + def hash_packet(self, packet) -> str: self.packet_hash_calls.append(packet) return f"{self.hash_value}_packet" @@ -87,7 +86,7 @@ def test_dir_data_store_init_default_hasher(temp_dir): assert store_dir.is_dir() # Verify the default PacketHasher is used - assert isinstance(store.packet_hasher, PacketHasher) + assert isinstance(store.packet_hasher, LegacyPacketHasher) # Check default parameters assert store.copy_files is True @@ -462,7 +461,7 @@ def test_dir_data_store_with_default_packet_hasher(temp_dir, sample_files): store = DirDataStore(store_dir=store_dir) # Verify that default PacketHasher was created - assert isinstance(store.packet_hasher, PacketHasher) + assert isinstance(store.packet_hasher, LegacyPacketHasher) # Test memoization and retrieval packet = {"input_file": sample_files["input"]["file1"]} @@ -500,7 +499,7 @@ def test_dir_data_store_legacy_mode_compatibility(temp_dir, sample_files): output_packet = {"output_file": sample_files["output"]["output1"]} # Get the hash values directly for comparison - from orcapod.hashing import hash_packet + from orcapod.hashing.legacy_core import hash_packet legacy_hash = hash_packet(packet, algorithm="sha256") assert store_default.packet_hasher is not None, ( @@ -611,7 +610,7 @@ def test_dir_data_store_hash_equivalence(temp_dir, sample_files): output_packet = {"output_file": sample_files["output"]["output1"]} # First compute hashes directly - from orcapod.hashing import hash_packet + from orcapod.hashing.legacy_core import hash_packet from orcapod.hashing.defaults import get_default_composite_file_hasher legacy_hash = hash_packet(packet, algorithm="sha256") diff --git a/tests/test_store/test_integration.py b/tests/test_store/test_integration.py index 023e6e6..88c081b 100644 --- a/tests/test_store/test_integration.py +++ b/tests/test_store/test_integration.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_store/test_integration.py """Integration tests for the store module.""" import os @@ -10,10 +9,10 @@ from orcapod.hashing.file_hashers import ( BasicFileHasher, CachedFileHasher, - DefaultCompositeFileHasher, + LegacyDefaultCompositeFileHasher, ) from orcapod.hashing.string_cachers import InMemoryCacher -from orcapod.store.core import DirDataStore, NoOpDataStore +from orcapod.databases.legacy.dict_data_stores import DirDataStore, NoOpDataStore def test_integration_with_cached_file_hasher(temp_dir, sample_files): @@ -29,7 +28,7 @@ def test_integration_with_cached_file_hasher(temp_dir, sample_files): ) # Create a CompositeFileHasher that will use the CachedFileHasher - composite_hasher = DefaultCompositeFileHasher(file_hasher) + composite_hasher = LegacyDefaultCompositeFileHasher(file_hasher) # Create the store with CompositeFileHasher store = DirDataStore(store_dir=store_dir, packet_hasher=composite_hasher) diff --git a/tests/test_store/test_noop_data_store.py b/tests/test_store/test_noop_data_store.py index 0da82c7..4091d7f 100644 --- a/tests/test_store/test_noop_data_store.py +++ b/tests/test_store/test_noop_data_store.py @@ -1,10 +1,9 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_store/test_noop_data_store.py """Tests for NoOpDataStore.""" import pytest -from orcapod.store.core import NoOpDataStore +from orcapod.databases.legacy.dict_data_stores import NoOpDataStore def test_noop_data_store_memoize(): @@ -44,7 +43,7 @@ def test_noop_data_store_retrieve_memoized(): def test_noop_data_store_is_data_store_subclass(): """Test that NoOpDataStore is a subclass of DataStore.""" - from orcapod.store.core import DataStore + from orcapod.databases import DataStore store = NoOpDataStore() assert isinstance(store, DataStore) diff --git a/tests/test_store/test_transfer_data_store.py b/tests/test_store/test_transfer_data_store.py index 85d0a87..036825d 100644 --- a/tests/test_store/test_transfer_data_store.py +++ b/tests/test_store/test_transfer_data_store.py @@ -1,18 +1,16 @@ #!/usr/bin/env python -# filepath: /home/eywalker/workspace/orcabridge/tests/test_store/test_transfer_data_store.py """Tests for TransferDataStore.""" -import json from pathlib import Path import pytest -from orcapod.hashing.types import PacketHasher -from orcapod.store.core import DirDataStore, NoOpDataStore -from orcapod.store.transfer import TransferDataStore +from orcapod.hashing.types import LegacyPacketHasher +from orcapod.databases.legacy.dict_data_stores import DirDataStore, NoOpDataStore +from orcapod.databases.legacy.dict_transfer_data_store import TransferDataStore -class MockPacketHasher(PacketHasher): +class MockPacketHasher(LegacyPacketHasher): """Mock PacketHasher for testing.""" def __init__(self, hash_value="mock_hash"): diff --git a/tests/test_types/__init__.py b/tests/test_types/__init__.py index aa691b1..2be2a50 100644 --- a/tests/test_types/__init__.py +++ b/tests/test_types/__init__.py @@ -1 +1 @@ -# Test package for orcabridge types module +# Test package for orcapod types module diff --git a/tests/test_types/test_inference/__init__.py b/tests/test_types/test_inference/__init__.py index 45e6baf..ae4cff0 100644 --- a/tests/test_types/test_inference/__init__.py +++ b/tests/test_types/test_inference/__init__.py @@ -1 +1 @@ -# Test package for orcabridge types inference module +# Test package for orcapod types inference module diff --git a/tests/test_types/test_inference/test_extract_function_data_types.py b/tests/test_types/test_inference/test_extract_function_data_types.py index a357bb0..dc20b90 100644 --- a/tests/test_types/test_inference/test_extract_function_data_types.py +++ b/tests/test_types/test_inference/test_extract_function_data_types.py @@ -1,5 +1,5 @@ """ -Unit tests for the extract_function_data_types function. +Unit tests for the extract_function_typespecs function. This module tests the function type extraction functionality, covering: - Type inference from function annotations @@ -11,11 +11,11 @@ import pytest from collections.abc import Collection -from orcapod.types.inference import extract_function_data_types +from orcapod.utils.types_utils import extract_function_typespecs class TestExtractFunctionDataTypes: - """Test cases for extract_function_data_types function.""" + """Test cases for extract_function_typespecs function.""" def test_simple_annotated_function(self): """Test function with simple type annotations.""" @@ -23,7 +23,7 @@ def test_simple_annotated_function(self): def add(x: int, y: int) -> int: return x + y - input_types, output_types = extract_function_data_types(add, ["result"]) + input_types, output_types = extract_function_typespecs(add, ["result"]) assert input_types == {"x": int, "y": int} assert output_types == {"result": int} @@ -34,7 +34,7 @@ def test_multiple_return_values_tuple(self): def process(data: str) -> tuple[int, str]: return len(data), data.upper() - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( process, ["length", "upper_data"] ) @@ -54,7 +54,7 @@ def split_data(data: str) -> tuple[str, str]: # Note: This tests the case where we have multiple output keys # but the return type is list[str] (homogeneous) - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( split_data, ["first_word", "second_word"] ) @@ -71,7 +71,7 @@ def mystery_func(x: int): ValueError, match="Type for return item 'number' is not specified in output_types", ): - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( mystery_func, ["number", "text"], ) @@ -82,7 +82,7 @@ def test_input_types_override(self): def legacy_func(x, y) -> int: # No annotations return x + y - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( legacy_func, ["sum"], input_types={"x": int, "y": int} ) @@ -95,7 +95,7 @@ def test_partial_input_types_override(self): def mixed_func(x: int, y) -> int: # One annotated, one not return x + y - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( mixed_func, ["sum"], input_types={"y": float} ) @@ -108,7 +108,7 @@ def test_output_types_dict_override(self): def mystery_func(x: int) -> str: return str(x) - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( mystery_func, ["result"], output_types={"result": float} ) @@ -121,7 +121,7 @@ def test_output_types_sequence_override(self): def multi_return(data: list) -> tuple[int, float, str]: return len(data), sum(data), str(data) - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( multi_return, ["count", "total", "repr"], output_types=[int, float, str] ) @@ -134,7 +134,7 @@ def test_complex_types(self): def complex_func(x: str | None, y: int | float) -> tuple[bool, list[str]]: return bool(x), [x] if x else [] - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( complex_func, ["is_valid", "items"] ) @@ -147,7 +147,7 @@ def test_none_return_annotation(self): def side_effect_func(x: int) -> None: print(x) - input_types, output_types = extract_function_data_types(side_effect_func, []) + input_types, output_types = extract_function_typespecs(side_effect_func, []) assert input_types == {"x": int} assert output_types == {} @@ -158,7 +158,7 @@ def test_empty_parameters(self): def get_constant() -> int: return 42 - input_types, output_types = extract_function_data_types(get_constant, ["value"]) + input_types, output_types = extract_function_typespecs(get_constant, ["value"]) assert input_types == {} assert output_types == {"value": int} @@ -172,7 +172,7 @@ def bad_func(x, y: int): return x + y with pytest.raises(ValueError, match="Parameter 'x' has no type annotation"): - extract_function_data_types(bad_func, ["result"]) + extract_function_typespecs(bad_func, ["result"]) def test_return_annotation_but_no_output_keys_error(self): """Test error when function has return annotation but no output keys.""" @@ -184,7 +184,7 @@ def func_with_return(x: int) -> str: ValueError, match="Function has a return type annotation, but no return keys were specified", ): - extract_function_data_types(func_with_return, []) + extract_function_typespecs(func_with_return, []) def test_none_return_with_output_keys_error(self): """Test error when function returns None but output keys provided.""" @@ -196,7 +196,7 @@ def side_effect_func(x: int) -> None: ValueError, match="Function provides explicit return type annotation as None", ): - extract_function_data_types(side_effect_func, ["result"]) + extract_function_typespecs(side_effect_func, ["result"]) def test_single_return_multiple_keys_error(self): """Test error when single return type but multiple output keys.""" @@ -208,7 +208,7 @@ def single_return(x: int) -> str: ValueError, match="Multiple return keys were specified but return type annotation .* is not a sequence type", ): - extract_function_data_types(single_return, ["first", "second"]) + extract_function_typespecs(single_return, ["first", "second"]) def test_unparameterized_sequence_type_error(self): """Test error when return type is sequence but not parameterized.""" @@ -219,7 +219,7 @@ def bad_return(x: int) -> tuple: # tuple without types with pytest.raises( ValueError, match="is a Sequence type but does not specify item types" ): - extract_function_data_types(bad_return, ["number", "text"]) + extract_function_typespecs(bad_return, ["number", "text"]) def test_mismatched_return_types_count_error(self): """Test error when return type count doesn't match output keys count.""" @@ -230,7 +230,7 @@ def three_returns(x: int) -> tuple[int, str, float]: with pytest.raises( ValueError, match="has 3 items, but output_keys has 2 items" ): - extract_function_data_types(three_returns, ["first", "second"]) + extract_function_typespecs(three_returns, ["first", "second"]) def test_mismatched_output_types_sequence_length_error(self): """Test error when output_types sequence length doesn't match output_keys.""" @@ -242,7 +242,7 @@ def func(x: int) -> tuple[int, str]: ValueError, match="Output types collection length .* does not match return keys length", ): - extract_function_data_types( + extract_function_typespecs( func, ["first", "second"], output_types=[int, str, float], # Wrong length @@ -258,7 +258,7 @@ def no_return_annotation(x: int): ValueError, match="Type for return item 'first' is not specified in output_types", ): - extract_function_data_types(no_return_annotation, ["first", "second"]) + extract_function_typespecs(no_return_annotation, ["first", "second"]) # Edge cases @@ -268,7 +268,7 @@ def test_callable_with_args_kwargs(self): def flexible_func(x: int, *args: str, **kwargs: float) -> bool: return True - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( flexible_func, ["success"] ) @@ -284,7 +284,7 @@ def test_mixed_override_scenarios(self): def complex_func(a, b: str) -> tuple[int, str]: return len(b), b.upper() - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( complex_func, ["length", "upper"], input_types={"a": float}, @@ -300,7 +300,7 @@ def test_generic_types(self): def generic_func(data: list[int]) -> dict[str, int]: return {str(i): i for i in data} - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( generic_func, ["mapping"] ) @@ -316,7 +316,7 @@ def list_func( return str(x), x # This tests the sequence detection logic - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( list_func, ["text", "number"] ) @@ -330,7 +330,7 @@ def collection_func(x: int) -> Collection[str]: return [str(x)] # Single output key with Collection type - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( collection_func, ["result"] ) @@ -347,7 +347,7 @@ def test_empty_function(self): def empty_func(): pass - input_types, output_types = extract_function_data_types(empty_func, []) + input_types, output_types = extract_function_typespecs(empty_func, []) assert input_types == {} assert output_types == {} @@ -364,7 +364,7 @@ class Container(Generic[T]): def generic_container_func(x: Container[int]) -> Container[str]: return Container() - input_types, output_types = extract_function_data_types( + input_types, output_types = extract_function_typespecs( generic_container_func, ["result"] ) @@ -377,7 +377,7 @@ 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