fix(namespace): support nested types in convert_json_arrow_type#6300
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…ABLES When DuckDB runs SHOW ALL TABLES, the lance extension calls GetDefaultEntries (list_tables) then CreateDefaultEntry (describe_table) for each table sequentially. With ~1800 tables, this took ~600s. Changes: - list_tables_inner now concurrently describes all tables (10 at a time) to prefetch their JSON schemas alongside the table names - GetDefaultEntries parses the prefetched schemas into a cache - CreateDefaultEntry checks the cache before making HTTP calls - Add tmp_schema module with convert_json_arrow_type that supports nested types (list, struct, large_binary, etc.) — the upstream lance-namespace only handles scalars (fix pending in lance-format/lance#6300) Result: SHOW ALL TABLES drops from ~600s to ~21s (~28x speedup). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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`convert_json_arrow_type` only handled scalar types (int/float/utf8/binary), causing deserialization failures for any schema containing list, struct, large_binary, large_utf8, fixed_size_list, map, decimal, or date/time types. This made `arrow_type_to_json` and `convert_json_arrow_type` asymmetric: serialization worked for all types but deserialization rejected most of them with "Unsupported Arrow type". In practice this broke the DuckDB lance extension's fast schema-from-REST path — tables with list/struct columns fell back to opening the S3 dataset for every DESCRIBE, making SHOW ALL TABLES ~20x slower than necessary. Add support for: float16, large_utf8, large_binary, fixed_size_binary, decimal32/64/128/256, date32, date64, timestamp, duration, list, large_list, fixed_size_list, struct, and map. Add a roundtrip test covering all supported types. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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BubbleCal
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| let encoded = json_type.length.unwrap_or(0); | ||
| Ok(DataType::Decimal32( | ||
| (encoded / 1000) as u8, | ||
| (encoded % 1000) as i8, |
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for decimal the scale can be negative, so we need to restore precision and scale in such way:
let precision = ((encoded + 128) / 1000) as u8;
let scale = (encoded - precision as i64 * 1000) as i8;
need tests for:
- Decimal32(10, -2)
- Decimal128(9, -2)
- Decimal256(38, 10)
…improve test coverage Fix decimal type deserialization to correctly handle negative scale values. The previous decode logic (encoded / 1000, encoded % 1000) produced wrong results for negative scales. Use ((encoded + 128) / 1000) to recover precision, then derive scale from the remainder. Add tests for: decimal types with negative scale, schema/field metadata roundtrip, dictionary type unwrapping, map keys_sorted error, unsupported type errors (RunEndEncoded, ListView, Utf8View, BinaryView), and nested list with field metadata. schema.rs line coverage: 83.87% -> 97.54% Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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convert_json_arrow_typeonly handled scalar types (int/float/utf8/binary), causing deserialization failures for any schema containing list, struct, large_binary, large_utf8, fixed_size_list, map, decimal, or date/time types.This made
arrow_type_to_jsonandconvert_json_arrow_typeasymmetric: serialization worked for all types but deserialization rejected most of them with "Unsupported Arrow type".In practice this broke the DuckDB lance extension's fast schema-from-REST path — tables with list/struct columns fell back to opening the S3 dataset for every DESCRIBE, making SHOW ALL TABLES ~20x slower than necessary.
Add support for: float16, large_utf8, large_binary, fixed_size_binary, decimal32/64/128/256, date32, date64, timestamp, duration, list, large_list, fixed_size_list, struct, and map.
Add a roundtrip test covering all supported types.