Comments (4)
Yeah, we should avoid. With Spark 3.0, we can avoid a shuffle with mapInPandas
API.
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There are some problems to use it directly.
-
Current Scalar UDF doesn't support aggregation across whole data. If we do the same thing, it will aggregate each batch unit that's internally used (arrow batch).
-
If we restrict it's 1 to 1 then we can consider
transform
instead. But we shouldn't forget we should check if the input & output length are same. -
When
axis
is different, it will need a different execution style to row by row, which will probably be like d397c1a way.
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Actually, I think we could hack this via df.groupby(F.spark_partition_id()).apply(pands_udf)
. Let me take a look soon.
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Would groupby-apply introduce exchange/shuffle on every apply
? If so, seems like that would be an expensive operation.
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Related Issues (20)
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