Hudi’s Column Stats Index and Data Skipping feature help speed up queries by an orders of magnitude!
Blog post from Onehouse
Hudi 0.11 introduces advanced data layout optimization techniques, such as Z-order and Hilbert Space Filling curves, to enhance data skipping in large, frequently queried tables. Data skipping, a method of using indexes to bypass irrelevant data during queries, reduces data scanning and improves execution time. The new version incorporates Multi-modal Indexes like bloom-filter and column stats index within the Metadata Table, providing robust support and efficient implementation. The column stats index stores min/max values and other metrics in a compact format using HFile, enabling efficient data retrieval by avoiding unnecessary full-index scans. This approach significantly boosts performance for large datasets, as demonstrated with the Amazon Reviews dataset, and offers substantial cost savings for cloud-based data lakes. Future improvements are planned to extend data skipping support to Merge-On-Read tables, add caching, and further optimize performance.
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