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- Add max_parallel_downloads configuration to Cache class (default: 4) - Extract download logic into _download_and_read_parquet helper function - Implement parallel downloads using ThreadPoolExecutor - Maintain file ordering by extracting index from filenames - Keep serial processing for single files or when max_workers=1 - Update progress bar to track completion of parallel downloads Co-authored-by: jhamon <jhamon@pinecone.io>
- Add unit tests for parallel download configuration - Test file index extraction from filenames - Verify correct ordering of files after parallel downloads - Test both serial and parallel processing paths - Verify max_workers limits are respected - All 8 new tests pass Co-authored-by: jhamon <jhamon@pinecone.io>
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Co-authored-by: jhamon <jhamon@pinecone.io>
- Remove unused imports (Mock, call, pytest) - Fix import ordering - Replace typing.Tuple with builtin tuple for Python 3.10+ compatibility - Update .gitignore to exclude __pycache__ directories Co-authored-by: jhamon <jhamon@pinecone.io>
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Implement parallel downloading for multi-file datasets to significantly improve loading performance.
Previously, datasets with multiple parquet files downloaded serially, leading to long load times (e.g., 70-80 seconds for 10 files). This change introduces
ThreadPoolExecutorto download files concurrently, reducing load times by 3-4x and better utilizing network bandwidth. It includes configurable parallel workers, maintains existing error handling, and ensures correct file ordering.Linear Issue: SDK-326