[GitHub] spark issue #19147: [WIP][SPARK-21190][SQL][PYTHON] Vectorized UDFs in Pytho...
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Github user ueshin commented on the issue: https://github.com/apache/spark/pull/19147 The test failure above should be fixed by #19158. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user ueshin commented on the issue: https://github.com/apache/spark/pull/19147 @felixcheung Thank you for your comment. We already support data type in string form. I'll add a test to confirm it. As for decorator name, we can have a more generic decorator name but we need the format name or something to know the format anyway to know what users want, and also what if some format needs additional options. IMO, it's ok to have the format specific decorators for users to understand each format spec. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user felixcheung commented on the issue: https://github.com/apache/spark/pull/19147 relaying my questions from the dev@ thread: +1 for type in string form. Would it be correct to assume there will be data type check, for example the returned pandas data frame column data types match what are specified. We have seen quite a bit of issues/confusions with that in R. Would it make sense to have a more generic decorator name so that it could also be useable for other efficient vectorized format in the future? Or do we anticipate the decorator to be format specific and will have more in the future? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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