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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,6 @@ public class ColumnType extends BaseTypeSignature<ValueType>
*/
public static final ColumnType FLOAT = new ColumnType(ValueType.FLOAT, null, null);
// currently, arrays only come from expressions or aggregators
// and there are no native float expressions (or aggs which produce float arrays)
/**
* An array of Strings. Values will be represented as Object[]
* @see ValueType#ARRAY
Expand All @@ -84,6 +83,12 @@ public class ColumnType extends BaseTypeSignature<ValueType>
* @see ValueType#DOUBLE
*/
public static final ColumnType DOUBLE_ARRAY = new ColumnType(ValueType.ARRAY, null, DOUBLE);
/**
* An array of Floats. Values will be represented as Object[] or float[].
* @see ValueType#ARRAY
* @see ValueType#FLOAT
*/
public static final ColumnType FLOAT_ARRAY = new ColumnType(ValueType.ARRAY, null, FLOAT);
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@clintropolis what are your thoughts on the implications of adding float array? Do we need to update any other code?

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Float arrays are allowed to exist in the type system, we just didn't have anything producing them so I didn't make a constant for it. The type system even allows nested arrays if druid.expressions.allowNestedArrays is true (but it is off by default), so there should be no problem with it. I will ensure that float[] is handled correctly in the expression system when I make the other adjustments.

/**
* Placeholder type for an "unknown" complex, which is used when the complex type name was "lost" or unavailable for
* whatever reason, to indicate an opaque type that cannot be generically handled with normal complex type handling
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1 change: 1 addition & 0 deletions docs/development/extensions-core/datasketches-extension.md
Original file line number Diff line number Diff line change
Expand Up @@ -36,4 +36,5 @@ The following modules are available:
* [Theta sketch](datasketches-theta.md) - approximate distinct counting with set operations (union, intersection and set difference).
* [Tuple sketch](datasketches-tuple.md) - extension of Theta sketch to support values associated with distinct keys (arrays of numeric values in this specialized implementation).
* [Quantiles sketch](datasketches-quantiles.md) - approximate distribution of comparable values to obtain ranks, quantiles and histograms. This is a specialized implementation for numeric values.
* [KLL Quantiles sketch](datasketches-kll.md) - approximate distribution of comparable values to obtain ranks, quantiles and histograms. This is a specialized implementation for numeric values. This is a more advanced algorithm compared to the classic quantiles above, sketches are more compact for the same accuracy, or more accurate for the same size.
* [HLL sketch](datasketches-hll.md) - approximate distinct counting using very compact HLL sketch.
138 changes: 138 additions & 0 deletions docs/development/extensions-core/datasketches-kll.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,138 @@
---
id: datasketches-kll
title: "DataSketches KLL Sketch module"
---

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~ to you under the Apache License, Version 2.0 (the
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~
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~ KIND, either express or implied. See the License for the
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-->


This module provides Apache Druid aggregators based on numeric quantiles KllFloatsSketch and KllDoublesSketch from [Apache DataSketches](https://datasketches.apache.org/) library. KLL quantiles sketch is a mergeable streaming algorithm to estimate the distribution of values, and approximately answer queries about the rank of a value, probability mass function of the distribution (PMF) or histogram, cumulative distribution function (CDF), and quantiles (median, min, max, 95th percentile and such). See [Quantiles Sketch Overview](https://datasketches.apache.org/docs/Quantiles/QuantilesOverview). This document applies to both KllFloatsSketch and KllDoublesSketch. Only one of them will be used in the examples.

There are three major modes of operation:

1. Ingesting sketches built outside of Druid (say, with Pig or Hive)
2. Building sketches from raw data during ingestion
3. Building sketches from raw data at query time

To use this aggregator, make sure you [include](../../development/extensions.md#loading-extensions) the extension in your config file:

```
druid.extensions.loadList=["druid-datasketches"]
```

### Aggregator

The result of the aggregation is a KllFloatsSketch or KllDoublesSketch that is the union of all sketches either built from raw data or read from the segments.

```json
{
"type" : "KllDoublesSketch",
"name" : <output_name>,
"fieldName" : <metric_name>,
"k": <parameter that controls size and accuracy>
}
```

|property|description|required?|
|--------|-----------|---------|
|type|This String should be "KllFloatsSketch" or "KllDoublesSketch"|yes|
|name|A String for the output (result) name of the calculation.|yes|
|fieldName|A String for the name of the input field (can contain sketches or raw numeric values).|yes|
|k|Parameter that determines the accuracy and size of the sketch. Higher k means higher accuracy but more space to store sketches. Must be from 8 to 65535. See [KLL Sketch Accuracy and Size](https://datasketches.apache.org/docs/KLL/KLLAccuracyAndSize.html).|no, defaults to 200|
|maxStreamLength|This parameter defines the number of items that can be presented to each sketch before it may need to move from off-heap to on-heap memory. This is relevant to query types that use off-heap memory, including [TopN](../../querying/topnquery.md) and [GroupBy](../../querying/groupbyquery.md). Ideally, should be set high enough such that most sketches can stay off-heap.|no, defaults to 1000000000|

### Post Aggregators

#### Quantile

This returns an approximation to the value that would be preceded by a given fraction of a hypothetical sorted version of the input stream.

```json
{
"type" : "KllDoublesSketchToQuantile",
"name": <output name>,
"field" : <post aggregator that refers to a KllDoublesSketch (fieldAccess or another post aggregator)>,
"fraction" : <fractional position in the hypothetical sorted stream, number from 0 to 1 inclusive>
}
```

#### Quantiles

This returns an array of quantiles corresponding to a given array of fractions

```json
{
"type" : "KllDoublesSketchToQuantiles",
"name": <output name>,
"field" : <post aggregator that refers to a KllDoublesSketch (fieldAccess or another post aggregator)>,
"fractions" : <array of fractional positions in the hypothetical sorted stream, number from 0 to 1 inclusive>
}
```

#### Histogram

This returns an approximation to the histogram given an array of split points that define the histogram bins or a number of bins (not both). An array of <i>m</i> unique, monotonically increasing split points divide the real number line into <i>m+1</i> consecutive disjoint intervals. The definition of an interval is inclusive of the left split point and exclusive of the right split point. If the number of bins is specified instead of split points, the interval between the minimum and maximum values is divided into the given number of equally-spaced bins.

```json
{
"type" : "KllDoublesSketchToHistogram",
"name": <output name>,
"field" : <post aggregator that refers to a KllDoublesSketch (fieldAccess or another post aggregator)>,
"splitPoints" : <array of split points (optional)>,
"numBins" : <number of bins (optional, defaults to 10)>
}
```

#### Rank

This returns an approximation to the rank of a given value that is the fraction of the distribution less than that value.

```json
{
"type" : "KllDoublesSketchToRank",
"name": <output name>,
"field" : <post aggregator that refers to a KllDoublesSketch (fieldAccess or another post aggregator)>,
"value" : <value>
}
```
#### CDF

This returns an approximation to the Cumulative Distribution Function given an array of split points that define the edges of the bins. An array of <i>m</i> unique, monotonically increasing split points divide the real number line into <i>m+1</i> consecutive disjoint intervals. The definition of an interval is inclusive of the left split point and exclusive of the right split point. The resulting array of fractions can be viewed as ranks of each split point with one additional rank that is always 1.

```json
{
"type" : "KllDoublesSketchToCDF",
"name": <output name>,
"field" : <post aggregator that refers to a KllDoublesSketch (fieldAccess or another post aggregator)>,
"splitPoints" : <array of split points>
}
```

#### Sketch Summary

This returns a summary of the sketch that can be used for debugging. This is the result of calling toString() method.

```json
{
"type" : "KllDoublesSketchToString",
"name": <output name>,
"field" : <post aggregator that refers to a KllDoublesSketch (fieldAccess or another post aggregator)>
}
```
2 changes: 1 addition & 1 deletion docs/development/extensions-core/datasketches-quantiles.md
Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ The result of the aggregation is a DoublesSketch that is the union of all sketch
|name|A String for the output (result) name of the calculation.|yes|
|fieldName|A String for the name of the input field (can contain sketches or raw numeric values).|yes|
|k|Parameter that determines the accuracy and size of the sketch. Higher k means higher accuracy but more space to store sketches. Must be a power of 2 from 2 to 32768. See [accuracy information](https://datasketches.apache.org/docs/Quantiles/OrigQuantilesSketch) in the DataSketches documentation for details.|no, defaults to 128|
|maxStreamLength|This parameter is a temporary solution to avoid a [known issue](https://github.com/apache/druid/issues/11544). It may be removed in a future release after the bug is fixed. This parameter defines the maximum number of items to store in each sketch. If a sketch reaches the limit, the query can throw `IllegalStateException`. To workaround this issue, increase the maximum stream length. See [accuracy information](https://datasketches.apache.org/docs/Quantiles/OrigQuantilesSketch) in the DataSketches documentation for how many bytes are required per stream length.|no, defaults to 1000000000|
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Yeah, good call. Documenting something as a temporary solution is a great way to ensure it sticks around forever 🙂

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Yes, the parameter was introduced to temporarily work around a bug, but after some discussion it was decided that it might be useful regardless

|maxStreamLength|This parameter defines the number of items that can be presented to each sketch before it may need to move from off-heap to on-heap memory. This is relevant to query types that use off-heap memory, including [TopN](../../querying/topnquery.md) and [GroupBy](../../querying/groupbyquery.md). Ideally, should be set high enough such that most sketches can stay off-heap.|no, defaults to 1000000000|

### Post Aggregators

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