chenminghua8 opened a new pull request, #38381: URL: https://github.com/apache/spark/pull/38381
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If there is a discussion in the mailing list, please add the link. --> This RP modifies the "computeStats" method of the "LogicalRelation" class: when the external table does not perform pre-analysis to generate statistics, it calculates the space size of the HDFS file corresponding to the table as the return value and uses this value to update the external table Statistics's sizeInBytes value for lazy calculation of external table statistics. This solves the problem that Row-level Runtime Filtering cannot be applied when the external table does not have pre-performed analysis to generate statistics. ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> When using external tables, Row-level Runtime Filtering cannot be applied without pre-performing analysis on the table to generate statistics. In actual use, external tables usually do not have pre-executed analysis to generate statistics, but it is also hoped that Row-level Runtime Filtering can be used to improve execution efficiency. The reason why Row-level Runtime Filtering cannot be enabled is that the "InjectRuntimeFilter" class calls the "satisfyByteSizeRequirement" method to determine whether the Bloom filter application side plan's aggregated scan size meets the requirements, and this value is finally calculated by the "computeStats" method of the "LogicalRelation" class. The current implementation of the 'computeStats' method first determines whether there is statistics in the external table, and if not, returns the value of 'spark.sql.defaultSizeInBytes', which results in that the calculated value returned when no pre-analysis is performed on the table to generate stat istics cannot be satisfied Requirements for applying Row-level Runtime Filtering. ### 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'. --> No ### 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. --> New unit tests. -- 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. 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