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deepmind.google·6d ago
AI Engineering
Lobsters·Jun 30·5 min read
Benchmarking Hardwood 1.0 on a Threadripper 9980X
TL;DR
Reading Parquet files efficiently is crucial for data processing, but existing libraries may not fully utilize modern hardware. Hardwood 1.0 is a Java library designed for this purpose, featuring row-reader and columnar-reader APIs.
✦ Why It Matters
Engineers can optimize data processing performance by selecting the right library and thread configuration for their hardware.
Key Takeaways
- Explore Hardwood 1.0 for efficient Parquet file reading.
- Test different decoder-thread counts for optimal performance.
- Consider hardware capabilities when benchmarking libraries.
- Stay updated on future writing capabilities of Hardwood.
Full Summary
Efficiently reading Parquet files, a popular columnar storage format, is essential for data analytics and processing. Hardwood 1.0, a minimal-dependency Java library, provides APIs for reading these files, with plans for writing capabilities in the future.
Initial benchmarks by Gunnar Morling on an m7i.2xlarge instance showed performance metrics for Hardwood's row and column readers. To explore performance on more powerful hardware, benchmarks were conducted on a Threadripper 9980X, which has 64 cores and 128 threads.
The modified tests included fixed decoder-thread counts of 1, 4, and 8. Results indicated how scaling the number of decoder threads affected read speeds, providing insights into optimal configurations for high-performance data processing.
These findings can guide engineers in selecting appropriate tools and configurations for their data workloads.