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From Disk Pages to Columnar Memory: Why Apache Arrow?
Apache Arrow and Parquet serve different masters: Parquet is for disk storage, while Arrow is designed for high-performance, in-memory analytical processing.
What: Apache Arrow provides a standardized, columnar memory format that allows different engines like DuckDB, Spark, and pandas to share data without costly serialization overhead.
Why it matters: In-memory analytics are often bottlenecked by CPU cache misses and the time spent serializing data between tools; Arrow removes this friction by enforcing memory locality.
Deep dive
- Parquet is optimized for block storage, compression, and efficient I/O.
- Arrow is optimized for cache-friendly scan performance and SIMD (Single Instruction, Multiple Data) operations.
- Arrow eliminates expensive 'pickling' or serialization steps when passing datasets between libraries in Python or C++.
- Contiguous memory layouts in Arrow allow for zero-copy reads, significantly speeding up cross-tool pipelines.
Decoder
- Serialization: The process of translating data structures or object state into a format that can be stored or transmitted.
- SIMD: An instruction set that allows a single CPU command to perform the same operation on multiple data points simultaneously.
Original article
Apache Arrow addresses a different problem than Parquet: fast in-memory analytics rather than compact storage. By keeping data in contiguous typed buffers instead of heap objects, Arrow preserves column locality for cache-friendly scans and SIMD execution, and it also reduces repeated serialization between engines like Spark, pandas, and DuckDB.
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