sitegui opened a new pull request #26029: [SPARK-29336][SQL] Fix the implementation of QuantileSummaries.merge (guarantee that the relativeError will be respected) URL: https://github.com/apache/spark/pull/26029 <!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### 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. --> Reimplement `org.apache.spark.sql.catalyst.util.QuantileSummaries#merge` and add a test-case showing the previous bug. ### 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. --> The original Greenwald-Khanna paper, from which the algorithm behind `approxQuantile` was taken, does not cover how to merge the result of multiple parallel QuantileSummaries. The current implementation violates some invariants and therefore the effective error can be larger than the specified. ### Does this PR introduce any user-facing change? <!-- 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 no, write 'No'. --> Yes, for same cases, the results from `approxQuantile` (`percentile_approx` in SQL) will now be within the expected error margin. For example: ```scala var values = (1 to 100).toArray val all_quantiles = values.indices.map(i => (i+1).toDouble / values.length).toArray for (n <- 0 until 5) { var df = spark.sparkContext.makeRDD(values).toDF("value").repartition(5) val all_answers = df.stat.approxQuantile("value", all_quantiles, 0.1) val all_answered_ranks = all_answers.map(ans => values.indexOf(ans)).toArray val error = all_answered_ranks.zipWithIndex.map({ case (answer, expected) => Math.abs(expected - answer) }).toArray val max_error = error.max print(max_error + "\n") } ``` In the current build it returns: ``` 16 12 10 11 17 ``` I couldn't run the code with this patch applied to double check the implementation. Can someone please confirm it now outputs at most `10`, please? ### 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. --> A new unit test was added to uncover the previous bug.
---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
