Cancel jobs only in PRs #28
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HyukjinKwon
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Jan 11, 2021
… Actions ### What changes were proposed in this pull request? This is kind of a followup of #31104 but I decided to track it separately with a separate JIRA. Currently the jobs are being canceled in main repo branches. If a commit is merged, for example, to master branch before the test finishes, it cancels the previous builds. This is a problem because we cannot, for example, detect logical conflict properly. We should only cancel the jobs in PRs:  This PR proposes to don't do this in the main repo branch commits but only do it in PRs. ### Why are the changes needed? - To keep the test coverage - To run the test in the synced master branch instead of relying on the builds made in each PR with an outdated master branch - To detect test failures from logical conflicts from merging two conflicting PRs at the same time. ### Does this PR introduce _any_ user-facing change? No, dev-only. ### How was this patch tested? I manually tested in - HyukjinKwon#27 - HyukjinKwon#28 I added Yi Wu as a co-author since he helped verifying the current fix in the PR above. I checked that it does not cancel in the main repo branch:  I checked it cancels in PRs:  Closes #31121 from HyukjinKwon/SPARK-34065. Lead-authored-by: hyukjinkwon <[email protected]> Co-authored-by: yi.wu <[email protected]> Co-authored-by: HyukjinKwon <[email protected]> Signed-off-by: HyukjinKwon <[email protected]>
HyukjinKwon
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Apr 22, 2023
…onnect ### What changes were proposed in this pull request? Implement Arrow-optimized Python UDFs in Spark Connect. Please see apache#39384 for motivation and performance improvements of Arrow-optimized Python UDFs. ### Why are the changes needed? Parity with vanilla PySpark. ### Does this PR introduce _any_ user-facing change? Yes. In Spark Connect Python Client, users can: 1. Set `useArrow` parameter True to enable Arrow optimization for a specific Python UDF. ```sh >>> df = spark.range(2) >>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).show() +------------+ |<lambda>(id)| +------------+ | 1| | 2| +------------+ # ArrowEvalPython indicates Arrow optimization >>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).explain() == Physical Plan == *(2) Project [pythonUDF0#18 AS <lambda>(id)#16] +- ArrowEvalPython [<lambda>(id#14L)#15], [pythonUDF0#18], 200 +- *(1) Range (0, 2, step=1, splits=1) ``` 2. Enable `spark.sql.execution.pythonUDF.arrow.enabled` Spark Conf to make all Python UDFs Arrow-optimized. ```sh >>> spark.conf.set("spark.sql.execution.pythonUDF.arrow.enabled", True) >>> df.select(udf(lambda x : x + 1)('id')).show() +------------+ |<lambda>(id)| +------------+ | 1| | 2| +------------+ # ArrowEvalPython indicates Arrow optimization >>> df.select(udf(lambda x : x + 1)('id')).explain() == Physical Plan == *(2) Project [pythonUDF0#30 AS <lambda>(id)#28] +- ArrowEvalPython [<lambda>(id#26L)#27], [pythonUDF0#30], 200 +- *(1) Range (0, 2, step=1, splits=1) ``` ### How was this patch tested? Parity unit tests. Closes apache#40725 from xinrong-meng/connect_arrow_py_udf. Authored-by: Xinrong Meng <[email protected]> Signed-off-by: Hyukjin Kwon <[email protected]>
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