Kafka connect shredding#16369
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This commit adds automatic variant shredding support for Kafka Connect and other tools using generic Record types. Variant shredding optimizes Parquet storage by extracting frequently-occurring fields from variant data into typed columns for better compression and query performance. Key Changes: - Add RecordVariantShreddingAnalyzer for generic Record types - Update GenericFormatModels to enable variant shredding with buffering - Add comprehensive documentation for configuration and usage Implementation Details: - Buffers first N rows (default: 100) for schema analysis - Automatically selects fields appearing in ≥10% of rows - Limits shredding to 300 fields and 50 nesting levels - Uses GenericRecord::copy for safe row buffering Configuration: - Enable: write.parquet.shred-variants=true - Buffer size: write.parquet.variant-inference-buffer-size=100 Benefits: - Better Parquet compression through typed columns - Faster queries with predicate pushdown - Lower storage costs - Zero code changes required in Kafka Connect Memory Considerations: - Memory usage: buffer_size × concurrent_writers × row_size - Recommended: Keep buffer small (100-500) for production - Increase target file size to reduce concurrent writers Limitations: - No explicit field control (automatic selection only) - Per-file analysis (schemas may vary across files) - Requires buffering (adds latency for first N rows) Future Enhancements: - Schema hints for zero-buffering production deployments - Global sampling with schema caching - Field whitelisting/blacklisting See KAFKA_CONNECT_VARIANT_SHREDDING.md for detailed documentation. Co-authored-by: Cursor <cursoragent@cursor.com>
Register RecordVariantShreddingAnalyzer and Record::copy on the generic Parquet format model, and resolve VARIANT column indices from Iceberg schema order so analysis works with Void engine schemas (Connect path). Co-authored-by: Cursor <cursoragent@cursor.com>
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