[SPARK-31915][SQL][PYTHON] Resolve the grouping column properly per the case sensitivity in grouped and cogrouped pandas UDFs #28777
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…ped and cogrouped pandas UDFs
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Test build #123737 has finished for PR 28777 at commit
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retest this please |
cloud-fan
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Jun 10, 2020
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Test build #123759 has finished for PR 28777 at commit
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viirya
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Jun 10, 2020
BryanCutler
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LGTM, this approach looks good
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merged to master, thanks @HyukjinKwon ! |
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Thanks guys! |
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Actually, I believe this is a pretty conservative fix bug fix .. let me port this back to 3.0 as well... if requested, I don't mind porting back to 2.4 as well. |
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Jun 11, 2020
…he case sensitivity in grouped and cogrouped pandas UDFs ### What changes were proposed in this pull request? This is another approach to fix the issue. See the previous try #28745. It was too invasive so I took more conservative approach. This PR proposes to resolve grouping attributes separately first so it can be properly referred when `FlatMapGroupsInPandas` and `FlatMapCoGroupsInPandas` are resolved without ambiguity. Previously, ```python from pyspark.sql.functions import * df = spark.createDataFrame([[1, 1]], ["column", "Score"]) pandas_udf("column integer, Score float", PandasUDFType.GROUPED_MAP) def my_pandas_udf(pdf): return pdf.assign(Score=0.5) df.groupby('COLUMN').apply(my_pandas_udf).show() ``` was failed as below: ``` pyspark.sql.utils.AnalysisException: "Reference 'COLUMN' is ambiguous, could be: COLUMN, COLUMN.;" ``` because the unresolved `COLUMN` in `FlatMapGroupsInPandas` doesn't know which reference to take from the child projection. After this fix, it resolves the child projection first with grouping keys and pass, to `FlatMapGroupsInPandas`, the attribute as a grouping key from the child projection that is positionally selected. ### Why are the changes needed? To resolve grouping keys correctly. ### Does this PR introduce _any_ user-facing change? Yes, ```python from pyspark.sql.functions import * df = spark.createDataFrame([[1, 1]], ["column", "Score"]) pandas_udf("column integer, Score float", PandasUDFType.GROUPED_MAP) def my_pandas_udf(pdf): return pdf.assign(Score=0.5) df.groupby('COLUMN').apply(my_pandas_udf).show() ``` ```python df1 = spark.createDataFrame([(1, 1)], ("column", "value")) df2 = spark.createDataFrame([(1, 1)], ("column", "value")) df1.groupby("COLUMN").cogroup( df2.groupby("COLUMN") ).applyInPandas(lambda r, l: r + l, df1.schema).show() ``` Before: ``` pyspark.sql.utils.AnalysisException: Reference 'COLUMN' is ambiguous, could be: COLUMN, COLUMN.; ``` ``` pyspark.sql.utils.AnalysisException: cannot resolve '`COLUMN`' given input columns: [COLUMN, COLUMN, value, value];; 'FlatMapCoGroupsInPandas ['COLUMN], ['COLUMN], <lambda>(column#9L, value#10L, column#13L, value#14L), [column#22L, value#23L] :- Project [COLUMN#9L, column#9L, value#10L] : +- LogicalRDD [column#9L, value#10L], false +- Project [COLUMN#13L, column#13L, value#14L] +- LogicalRDD [column#13L, value#14L], false ``` After: ``` +------+-----+ |column|Score| +------+-----+ | 1| 0.5| +------+-----+ ``` ``` +------+-----+ |column|value| +------+-----+ | 2| 2| +------+-----+ ``` ### How was this patch tested? Unittests were added and manually tested. Closes #28777 from HyukjinKwon/SPARK-31915-another. Authored-by: HyukjinKwon <[email protected]> Signed-off-by: Bryan Cutler <[email protected]>
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Merged to branch-3.0 as well! |
holdenk
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Jun 25, 2020
…he case sensitivity in grouped and cogrouped pandas UDFs ### What changes were proposed in this pull request? This is another approach to fix the issue. See the previous try apache#28745. It was too invasive so I took more conservative approach. This PR proposes to resolve grouping attributes separately first so it can be properly referred when `FlatMapGroupsInPandas` and `FlatMapCoGroupsInPandas` are resolved without ambiguity. Previously, ```python from pyspark.sql.functions import * df = spark.createDataFrame([[1, 1]], ["column", "Score"]) pandas_udf("column integer, Score float", PandasUDFType.GROUPED_MAP) def my_pandas_udf(pdf): return pdf.assign(Score=0.5) df.groupby('COLUMN').apply(my_pandas_udf).show() ``` was failed as below: ``` pyspark.sql.utils.AnalysisException: "Reference 'COLUMN' is ambiguous, could be: COLUMN, COLUMN.;" ``` because the unresolved `COLUMN` in `FlatMapGroupsInPandas` doesn't know which reference to take from the child projection. After this fix, it resolves the child projection first with grouping keys and pass, to `FlatMapGroupsInPandas`, the attribute as a grouping key from the child projection that is positionally selected. ### Why are the changes needed? To resolve grouping keys correctly. ### Does this PR introduce _any_ user-facing change? Yes, ```python from pyspark.sql.functions import * df = spark.createDataFrame([[1, 1]], ["column", "Score"]) pandas_udf("column integer, Score float", PandasUDFType.GROUPED_MAP) def my_pandas_udf(pdf): return pdf.assign(Score=0.5) df.groupby('COLUMN').apply(my_pandas_udf).show() ``` ```python df1 = spark.createDataFrame([(1, 1)], ("column", "value")) df2 = spark.createDataFrame([(1, 1)], ("column", "value")) df1.groupby("COLUMN").cogroup( df2.groupby("COLUMN") ).applyInPandas(lambda r, l: r + l, df1.schema).show() ``` Before: ``` pyspark.sql.utils.AnalysisException: Reference 'COLUMN' is ambiguous, could be: COLUMN, COLUMN.; ``` ``` pyspark.sql.utils.AnalysisException: cannot resolve '`COLUMN`' given input columns: [COLUMN, COLUMN, value, value];; 'FlatMapCoGroupsInPandas ['COLUMN], ['COLUMN], <lambda>(column#9L, value#10L, column#13L, value#14L), [column#22L, value#23L] :- Project [COLUMN#9L, column#9L, value#10L] : +- LogicalRDD [column#9L, value#10L], false +- Project [COLUMN#13L, column#13L, value#14L] +- LogicalRDD [column#13L, value#14L], false ``` After: ``` +------+-----+ |column|Score| +------+-----+ | 1| 0.5| +------+-----+ ``` ``` +------+-----+ |column|value| +------+-----+ | 2| 2| +------+-----+ ``` ### How was this patch tested? Unittests were added and manually tested. Closes apache#28777 from HyukjinKwon/SPARK-31915-another. Authored-by: HyukjinKwon <[email protected]> Signed-off-by: Bryan Cutler <[email protected]>
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What changes were proposed in this pull request?
This is another approach to fix the issue. See the previous try #28745. It was too invasive so I took more conservative approach.
This PR proposes to resolve grouping attributes separately first so it can be properly referred when
FlatMapGroupsInPandasandFlatMapCoGroupsInPandasare resolved without ambiguity.Previously,
was failed as below:
because the unresolved
COLUMNinFlatMapGroupsInPandasdoesn't know which reference to take from the child projection.After this fix, it resolves the child projection first with grouping keys and pass, to
FlatMapGroupsInPandas, the attribute as a grouping key from the child projection that is positionally selected.Why are the changes needed?
To resolve grouping keys correctly.
Does this PR introduce any user-facing change?
Yes,
Before:
After:
How was this patch tested?
Unittests were added and manually tested.