3 commits
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HyukjinKwon
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00d06cad56 |
[SPARK-31915][SQL][PYTHON] Resolve the grouping column properly per the 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 https://github.com/apache/spark/pull/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 <gurwls223@apache.org> Signed-off-by: Bryan Cutler <cutlerb@gmail.com> |
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Bryan Cutler
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07a9885f27 |
[SPARK-30777][PYTHON][TESTS] Fix test failures for Pandas >= 1.0.0
### What changes were proposed in this pull request? Fix PySpark test failures for using Pandas >= 1.0.0. ### Why are the changes needed? Pandas 1.0.0 has recently been released and has API changes that result in PySpark test failures, this PR fixes the broken tests. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Manually tested with Pandas 1.0.1 and PyArrow 0.16.0 Closes #27529 from BryanCutler/pandas-fix-tests-1.0-SPARK-30777. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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HyukjinKwon
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ab0890bdb1 |
[SPARK-28264][PYTHON][SQL] Support type hints in pandas UDF and rename/move inconsistent pandas UDF types
### What changes were proposed in this pull request? This PR proposes to redesign pandas UDFs as described in [the proposal](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit?usp=sharing). ```python from pyspark.sql.functions import pandas_udf import pandas as pd pandas_udf("long") def plug_one(s: pd.Series) -> pd.Series: return s + 1 spark.range(10).select(plug_one("id")).show() ``` ``` +------------+ |plug_one(id)| +------------+ | 1| | 2| | 3| | 4| | 5| | 6| | 7| | 8| | 9| | 10| +------------+ ``` Note that, this PR address one of the future improvements described [here](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit#heading=h.h3ncjpk6ujqu), "A couple of less-intuitive pandas UDF types" (by zero323) together. In short, - Adds new way with type hints as an alternative and experimental way. ```python pandas_udf(schema='...') def func(c1: Series, c2: Series) -> DataFrame: pass ``` - Replace and/or add an alias for three types below from UDF, and make them as separate standalone APIs. So, `pandas_udf` is now consistent with regular `udf`s and other expressions. `df.mapInPandas(udf)` -replace-> `df.mapInPandas(f, schema)` `df.groupby.apply(udf)` -alias-> `df.groupby.applyInPandas(f, schema)` `df.groupby.cogroup.apply(udf)` -replace-> `df.groupby.cogroup.applyInPandas(f, schema)` *`df.groupby.apply` was added from 2.3 while the other were added in the master only. - No deprecation for the existing ways for now. ```python pandas_udf(schema='...', functionType=PandasUDFType.SCALAR) def func(c1, c2): pass ``` If users are happy with this, I plan to deprecate the existing way and declare using type hints is not experimental anymore. One design goal in this PR was that, avoid touching the internal (since we didn't deprecate the old ways for now), but supports type hints with a minimised changes only at the interface. - Once we deprecate or remove the old ways, I think it requires another refactoring for the internal in the future. At the very least, we should rename internal pandas evaluation types. - If users find this experimental type hints isn't quite helpful, we should simply revert the changes at the interface level. ### Why are the changes needed? In order to address old design issues. Please see [the proposal](https://docs.google.com/document/d/1-kV0FS_LF2zvaRh_GhkV32Uqksm_Sq8SvnBBmRyxm30/edit?usp=sharing). ### Does this PR introduce any user-facing change? For behaviour changes, No. It adds new ways to use pandas UDFs by using type hints. See below. **SCALAR**: ```python pandas_udf(schema='...') def func(c1: Series, c2: DataFrame) -> Series: pass # DataFrame represents a struct column ``` **SCALAR_ITER**: ```python pandas_udf(schema='...') def func(iter: Iterator[Tuple[Series, DataFrame, ...]]) -> Iterator[Series]: pass # Same as SCALAR but wrapped by Iterator ``` **GROUPED_AGG**: ```python pandas_udf(schema='...') def func(c1: Series, c2: DataFrame) -> int: pass # DataFrame represents a struct column ``` **GROUPED_MAP**: This was added in Spark 2.3 as of SPARK-20396. As described above, it keeps the existing behaviour. Additionally, we now have a new alias `groupby.applyInPandas` for `groupby.apply`. See the example below: ```python def func(pdf): return pdf df.groupby("...").applyInPandas(func, schema=df.schema) ``` **MAP_ITER**: this is not a pandas UDF anymore This was added in Spark 3.0 as of SPARK-28198; and this PR replaces the usages. See the example below: ```python def func(iter): for df in iter: yield df df.mapInPandas(func, df.schema) ``` **COGROUPED_MAP**: this is not a pandas UDF anymore This was added in Spark 3.0 as of SPARK-27463; and this PR replaces the usages. See the example below: ```python def asof_join(left, right): return pd.merge_asof(left, right, on="...", by="...") df1.groupby("...").cogroup(df2.groupby("...")).applyInPandas(asof_join, schema="...") ``` ### How was this patch tested? Unittests added and tested against Python 2.7, 3.6 and 3.7. Closes #27165 from HyukjinKwon/revisit-pandas. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
Renamed from python/pyspark/sql/tests/test_pandas_udf_grouped_map.py (Browse further)