erikbogado-nstech commented on PR #4744: URL: https://github.com/apache/datafusion-comet/pull/4744#issuecomment-5555003000
Posting benchmark numbers for this PR from the suite I mentioned on the dev list (https://github.com/ErikBPF/ndc — derived from TPC-H over nested layouts; answers pinned, row parity enforced per query). **Setup:** Spark 4.1.3 on a single 28-core host with local NVMe. 24 queries × 3 runs, page cache dropped between runs, medians. Built `main` (75fdddc92) and this PR's head from source with identical flags (`-Prelease` jars, packaged native lib). Caveat: the PR head no longer rebases cleanly over `native/core/src/execution/planner.rs`, so numbers are directional w.r.t. the diff. **Results (Parquet, speedup vs vanilla Spark, 24 queries):** | | main | this PR | |--------------|-------|---------| | built-in (scan+agg) | ~2.3x | ~2.2x | | depth 1–8 | ~1.1x | ~1.1x | | extended | ~1.08x | ~1.31x | | **total** | 1.26x | **1.42x** | The two queries that lose to vanilla on `main` recover largely here: - `e21_nested` (correlated quantifiers as boolean HOF `filter`, no explode): **2735 → 567 ms (−79%)** - `e19_nested_array` (IN-lists + arithmetic inside `aggregate`): −33% A few non-lambda extended queries regress on the PR head (e14 +54%, e07 +41%, e13 +26%) — base drift or lambda-planner adoption, worth a look on rebase. Raw records (per-query medians, native/fallback share, parity flags) happy to share. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
