yaooqinn opened a new pull request, #47929: URL: https://github.com/apache/spark/pull/47929
### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> This PR adds (java.util.zip.)CRC32C to `spark.shuffle.checksum.algorithm`. CRC32C has been supported by JDK since 9. > /* * This CRC-32C implementation uses the 'slicing-by-8' algorithm described * in the paper "A Systematic Approach to Building High Performance * Software-Based CRC Generators" by Michael E. Kounavis and Frank L. Berry, * Intel Research and Development */ ### Why are the changes needed? CRC32C performs better on some SIMD CPU instruction sets ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as the documentation fix. If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master. If no, write 'No'. --> Yes, spark.shuffle.checksum.algorithm can be set to CRC32C. ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. If benchmark tests were added, please run the benchmarks in GitHub Actions for the consistent environment, and the instructions could accord to: https://spark.apache.org/developer-tools.html#github-workflow-benchmarks. --> I tested this via benchmark - On my MAC M2, Adler32 is fastest ``` ================================================================================================ Benchmark Checksum Algorithms ================================================================================================ OpenJDK 64-Bit Server VM 17.0.12+0 on Mac OS X 14.6.1 Apple M2 Max Checksum Algorithms: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ CRC32 4145 4190 46 0.0 4047834.9 1.0X CRC32C 4115 4155 35 0.0 4018904.7 1.0X Adler32 1961 1972 16 0.0 1914619.1 2.1X PureJavaCrc32C 18115 18322 245 0.0 17690350.5 0.2X ``` - On GitHub Action Runner, CRC32C is the best ### Was this patch authored or co-authored using generative AI tooling? <!-- If generative AI tooling has been used in the process of authoring this patch, please include the phrase: 'Generated-by: ' followed by the name of the tool and its version. If no, write 'No'. Please refer to the [ASF Generative Tooling Guidance](https://www.apache.org/legal/generative-tooling.html) for details. --> no -- 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: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org