Source code for pyspark.sql.cogroup

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import sys

from pyspark import since
from pyspark.rdd import PythonEvalType
from pyspark.sql.column import Column
from pyspark.sql.dataframe import DataFrame


[docs]class CoGroupedData(object): """ A logical grouping of two :class:`GroupedData`, created by :func:`GroupedData.cogroup`. .. note:: Experimental .. versionadded:: 3.0 """ def __init__(self, gd1, gd2): self._gd1 = gd1 self._gd2 = gd2 self.sql_ctx = gd1.sql_ctx
[docs] @since(3.0) def apply(self, udf): """ Applies a function to each cogroup using a pandas udf and returns the result as a `DataFrame`. The user-defined function should take two `pandas.DataFrame` and return another `pandas.DataFrame`. For each side of the cogroup, all columns are passed together as a `pandas.DataFrame` to the user-function and the returned `pandas.DataFrame` are combined as a :class:`DataFrame`. The returned `pandas.DataFrame` can be of arbitrary length and its schema must match the returnType of the pandas udf. .. note:: This function requires a full shuffle. All the data of a cogroup will be loaded into memory, so the user should be aware of the potential OOM risk if data is skewed and certain groups are too large to fit in memory. .. note:: Experimental :param udf: a cogrouped map user-defined function returned by :func:`pyspark.sql.functions.pandas_udf`. >>> from pyspark.sql.functions import pandas_udf, PandasUDFType >>> df1 = spark.createDataFrame( ... [(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)], ... ("time", "id", "v1")) >>> df2 = spark.createDataFrame( ... [(20000101, 1, "x"), (20000101, 2, "y")], ... ("time", "id", "v2")) >>> @pandas_udf("time int, id int, v1 double, v2 string", ... PandasUDFType.COGROUPED_MAP) # doctest: +SKIP ... def asof_join(l, r): ... return pd.merge_asof(l, r, on="time", by="id") >>> df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show() # doctest: +SKIP +--------+---+---+---+ | time| id| v1| v2| +--------+---+---+---+ |20000101| 1|1.0| x| |20000102| 1|3.0| x| |20000101| 2|2.0| y| |20000102| 2|4.0| y| +--------+---+---+---+ Alternatively, the user can define a function that takes three arguments. In this case, the grouping key(s) will be passed as the first argument and the data will be passed as the second and third arguments. The grouping key(s) will be passed as a tuple of numpy data types, e.g., `numpy.int32` and `numpy.float64`. The data will still be passed in as two `pandas.DataFrame` containing all columns from the original Spark DataFrames. >>> @pandas_udf("time int, id int, v1 double, v2 string", ... PandasUDFType.COGROUPED_MAP) # doctest: +SKIP ... def asof_join(k, l, r): ... if k == (1,): ... return pd.merge_asof(l, r, on="time", by="id") ... else: ... return pd.DataFrame(columns=['time', 'id', 'v1', 'v2']) >>> df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show() # doctest: +SKIP +--------+---+---+---+ | time| id| v1| v2| +--------+---+---+---+ |20000101| 1|1.0| x| |20000102| 1|3.0| x| +--------+---+---+---+ .. seealso:: :meth:`pyspark.sql.functions.pandas_udf` """ # Columns are special because hasattr always return True if isinstance(udf, Column) or not hasattr(udf, 'func') \ or udf.evalType != PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF: raise ValueError("Invalid udf: the udf argument must be a pandas_udf of type " "COGROUPED_MAP.") all_cols = self._extract_cols(self._gd1) + self._extract_cols(self._gd2) udf_column = udf(*all_cols) jdf = self._gd1._jgd.flatMapCoGroupsInPandas(self._gd2._jgd, udf_column._jc.expr()) return DataFrame(jdf, self.sql_ctx)
@staticmethod def _extract_cols(gd): df = gd._df return [df[col] for col in df.columns]
def _test(): import doctest from pyspark.sql import SparkSession import pyspark.sql.cogroup globs = pyspark.sql.cogroup.__dict__.copy() spark = SparkSession.builder\ .master("local[4]")\ .appName("sql.cogroup tests")\ .getOrCreate() globs['spark'] = spark (failure_count, test_count) = doctest.testmod( pyspark.sql.cogroup, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()