39d244d921
## What changes were proposed in this pull request? This PR proposes to deprecate `register*` for UDFs in `SQLContext` and `Catalog` in Spark 2.3.0. These are inconsistent with Scala / Java APIs and also these basically do the same things with `spark.udf.register*`. Also, this PR moves the logcis from `[sqlContext|spark.catalog].register*` to `spark.udf.register*` and reuse the docstring. This PR also handles minor doc corrections. It also includes https://github.com/apache/spark/pull/20158 ## How was this patch tested? Manually tested, manually checked the API documentation and tests added to check if deprecated APIs call the aliases correctly. Author: hyukjinkwon <gurwls223@gmail.com> Closes #20288 from HyukjinKwon/deprecate-udf.
284 lines
10 KiB
Python
284 lines
10 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from pyspark import since
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from pyspark.rdd import ignore_unicode_prefix, PythonEvalType
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from pyspark.sql.column import Column, _to_seq, _to_java_column, _create_column_from_literal
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from pyspark.sql.dataframe import DataFrame
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from pyspark.sql.udf import UserDefinedFunction
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from pyspark.sql.types import *
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__all__ = ["GroupedData"]
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def dfapi(f):
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def _api(self):
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name = f.__name__
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jdf = getattr(self._jgd, name)()
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return DataFrame(jdf, self.sql_ctx)
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_api.__name__ = f.__name__
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_api.__doc__ = f.__doc__
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return _api
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def df_varargs_api(f):
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def _api(self, *cols):
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name = f.__name__
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jdf = getattr(self._jgd, name)(_to_seq(self.sql_ctx._sc, cols))
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return DataFrame(jdf, self.sql_ctx)
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_api.__name__ = f.__name__
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_api.__doc__ = f.__doc__
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return _api
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class GroupedData(object):
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"""
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A set of methods for aggregations on a :class:`DataFrame`,
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created by :func:`DataFrame.groupBy`.
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.. note:: Experimental
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.. versionadded:: 1.3
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"""
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def __init__(self, jgd, df):
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self._jgd = jgd
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self._df = df
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self.sql_ctx = df.sql_ctx
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@ignore_unicode_prefix
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@since(1.3)
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def agg(self, *exprs):
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"""Compute aggregates and returns the result as a :class:`DataFrame`.
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The available aggregate functions are `avg`, `max`, `min`, `sum`, `count`.
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If ``exprs`` is a single :class:`dict` mapping from string to string, then the key
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is the column to perform aggregation on, and the value is the aggregate function.
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Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions.
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:param exprs: a dict mapping from column name (string) to aggregate functions (string),
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or a list of :class:`Column`.
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>>> gdf = df.groupBy(df.name)
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>>> sorted(gdf.agg({"*": "count"}).collect())
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[Row(name=u'Alice', count(1)=1), Row(name=u'Bob', count(1)=1)]
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>>> from pyspark.sql import functions as F
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>>> sorted(gdf.agg(F.min(df.age)).collect())
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[Row(name=u'Alice', min(age)=2), Row(name=u'Bob', min(age)=5)]
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"""
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assert exprs, "exprs should not be empty"
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if len(exprs) == 1 and isinstance(exprs[0], dict):
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jdf = self._jgd.agg(exprs[0])
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else:
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# Columns
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assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
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jdf = self._jgd.agg(exprs[0]._jc,
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_to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]]))
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return DataFrame(jdf, self.sql_ctx)
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@dfapi
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@since(1.3)
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def count(self):
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"""Counts the number of records for each group.
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>>> sorted(df.groupBy(df.age).count().collect())
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[Row(age=2, count=1), Row(age=5, count=1)]
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"""
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@df_varargs_api
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@since(1.3)
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def mean(self, *cols):
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"""Computes average values for each numeric columns for each group.
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:func:`mean` is an alias for :func:`avg`.
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:param cols: list of column names (string). Non-numeric columns are ignored.
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>>> df.groupBy().mean('age').collect()
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[Row(avg(age)=3.5)]
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>>> df3.groupBy().mean('age', 'height').collect()
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[Row(avg(age)=3.5, avg(height)=82.5)]
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"""
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@df_varargs_api
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@since(1.3)
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def avg(self, *cols):
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"""Computes average values for each numeric columns for each group.
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:func:`mean` is an alias for :func:`avg`.
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:param cols: list of column names (string). Non-numeric columns are ignored.
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>>> df.groupBy().avg('age').collect()
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[Row(avg(age)=3.5)]
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>>> df3.groupBy().avg('age', 'height').collect()
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[Row(avg(age)=3.5, avg(height)=82.5)]
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"""
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@df_varargs_api
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@since(1.3)
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def max(self, *cols):
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"""Computes the max value for each numeric columns for each group.
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>>> df.groupBy().max('age').collect()
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[Row(max(age)=5)]
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>>> df3.groupBy().max('age', 'height').collect()
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[Row(max(age)=5, max(height)=85)]
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"""
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@df_varargs_api
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@since(1.3)
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def min(self, *cols):
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"""Computes the min value for each numeric column for each group.
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:param cols: list of column names (string). Non-numeric columns are ignored.
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>>> df.groupBy().min('age').collect()
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[Row(min(age)=2)]
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>>> df3.groupBy().min('age', 'height').collect()
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[Row(min(age)=2, min(height)=80)]
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"""
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@df_varargs_api
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@since(1.3)
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def sum(self, *cols):
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"""Compute the sum for each numeric columns for each group.
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:param cols: list of column names (string). Non-numeric columns are ignored.
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>>> df.groupBy().sum('age').collect()
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[Row(sum(age)=7)]
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>>> df3.groupBy().sum('age', 'height').collect()
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[Row(sum(age)=7, sum(height)=165)]
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"""
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@since(1.6)
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def pivot(self, pivot_col, values=None):
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"""
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Pivots a column of the current :class:`DataFrame` and perform the specified aggregation.
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There are two versions of pivot function: one that requires the caller to specify the list
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of distinct values to pivot on, and one that does not. The latter is more concise but less
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efficient, because Spark needs to first compute the list of distinct values internally.
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:param pivot_col: Name of the column to pivot.
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:param values: List of values that will be translated to columns in the output DataFrame.
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# Compute the sum of earnings for each year by course with each course as a separate column
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>>> df4.groupBy("year").pivot("course", ["dotNET", "Java"]).sum("earnings").collect()
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[Row(year=2012, dotNET=15000, Java=20000), Row(year=2013, dotNET=48000, Java=30000)]
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# Or without specifying column values (less efficient)
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>>> df4.groupBy("year").pivot("course").sum("earnings").collect()
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[Row(year=2012, Java=20000, dotNET=15000), Row(year=2013, Java=30000, dotNET=48000)]
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"""
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if values is None:
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jgd = self._jgd.pivot(pivot_col)
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else:
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jgd = self._jgd.pivot(pivot_col, values)
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return GroupedData(jgd, self._df)
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@since(2.3)
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def apply(self, udf):
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"""
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Maps each group of the current :class:`DataFrame` using a pandas udf and returns the result
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as a `DataFrame`.
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The user-defined function should take a `pandas.DataFrame` and return another
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`pandas.DataFrame`. For each group, all columns are passed together as a `pandas.DataFrame`
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to the user-function and the returned `pandas.DataFrame`s are combined as a
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:class:`DataFrame`.
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The returned `pandas.DataFrame` can be of arbitrary length and its schema must match the
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returnType of the pandas udf.
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This function does not support partial aggregation, and requires shuffling all the data in
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the :class:`DataFrame`.
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:param udf: a group map user-defined function returned by
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:meth:`pyspark.sql.functions.pandas_udf`.
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>>> from pyspark.sql.functions import pandas_udf, PandasUDFType
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>>> df = spark.createDataFrame(
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... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
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... ("id", "v"))
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>>> @pandas_udf("id long, v double", PandasUDFType.GROUP_MAP) # doctest: +SKIP
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... def normalize(pdf):
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... v = pdf.v
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... return pdf.assign(v=(v - v.mean()) / v.std())
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>>> df.groupby("id").apply(normalize).show() # doctest: +SKIP
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+---+-------------------+
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| id| v|
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+---+-------------------+
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| 1|-0.7071067811865475|
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| 1| 0.7071067811865475|
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| 2|-0.8320502943378437|
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| 2|-0.2773500981126146|
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| 2| 1.1094003924504583|
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+---+-------------------+
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.. seealso:: :meth:`pyspark.sql.functions.pandas_udf`
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"""
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# Columns are special because hasattr always return True
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if isinstance(udf, Column) or not hasattr(udf, 'func') \
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or udf.evalType != PythonEvalType.SQL_PANDAS_GROUP_MAP_UDF:
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raise ValueError("Invalid udf: the udf argument must be a pandas_udf of type "
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"GROUP_MAP.")
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df = self._df
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udf_column = udf(*[df[col] for col in df.columns])
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jdf = self._jgd.flatMapGroupsInPandas(udf_column._jc.expr())
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return DataFrame(jdf, self.sql_ctx)
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def _test():
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import doctest
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from pyspark.sql import Row, SparkSession
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import pyspark.sql.group
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globs = pyspark.sql.group.__dict__.copy()
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spark = SparkSession.builder\
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.master("local[4]")\
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.appName("sql.group tests")\
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.getOrCreate()
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sc = spark.sparkContext
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globs['sc'] = sc
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globs['spark'] = spark
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globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \
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.toDF(StructType([StructField('age', IntegerType()),
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StructField('name', StringType())]))
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globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80),
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Row(name='Bob', age=5, height=85)]).toDF()
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globs['df4'] = sc.parallelize([Row(course="dotNET", year=2012, earnings=10000),
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Row(course="Java", year=2012, earnings=20000),
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Row(course="dotNET", year=2012, earnings=5000),
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Row(course="dotNET", year=2013, earnings=48000),
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Row(course="Java", year=2013, earnings=30000)]).toDF()
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(failure_count, test_count) = doctest.testmod(
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pyspark.sql.group, globs=globs,
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optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
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spark.stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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