d766ea2ff2
## What changes were proposed in this pull request? Add docstring to clarify default window frame boundaries with and without orderBy clause ## How was this patch tested? Manually generate doc and check. Author: Li Jin <ice.xelloss@gmail.com> Closes #20978 from icexelloss/SPARK-23861-window-doc.
276 lines
11 KiB
Python
276 lines
11 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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import sys
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if sys.version >= '3':
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long = int
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from pyspark import since, SparkContext
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from pyspark.sql.column import Column, _to_seq, _to_java_column
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__all__ = ["Window", "WindowSpec"]
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def _to_java_cols(cols):
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sc = SparkContext._active_spark_context
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if len(cols) == 1 and isinstance(cols[0], list):
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cols = cols[0]
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return _to_seq(sc, cols, _to_java_column)
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class Window(object):
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"""
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Utility functions for defining window in DataFrames.
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For example:
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>>> # ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
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>>> window = Window.orderBy("date").rowsBetween(Window.unboundedPreceding, Window.currentRow)
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>>> # PARTITION BY country ORDER BY date RANGE BETWEEN 3 PRECEDING AND 3 FOLLOWING
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>>> window = Window.orderBy("date").partitionBy("country").rangeBetween(-3, 3)
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.. note:: When ordering is not defined, an unbounded window frame (rowFrame,
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unboundedPreceding, unboundedFollowing) is used by default. When ordering is defined,
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a growing window frame (rangeFrame, unboundedPreceding, currentRow) is used by default.
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.. note:: Experimental
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.. versionadded:: 1.4
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"""
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_JAVA_MIN_LONG = -(1 << 63) # -9223372036854775808
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_JAVA_MAX_LONG = (1 << 63) - 1 # 9223372036854775807
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_PRECEDING_THRESHOLD = max(-sys.maxsize, _JAVA_MIN_LONG)
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_FOLLOWING_THRESHOLD = min(sys.maxsize, _JAVA_MAX_LONG)
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unboundedPreceding = _JAVA_MIN_LONG
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unboundedFollowing = _JAVA_MAX_LONG
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currentRow = 0
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@staticmethod
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@since(1.4)
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def partitionBy(*cols):
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"""
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Creates a :class:`WindowSpec` with the partitioning defined.
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"""
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sc = SparkContext._active_spark_context
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jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols))
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return WindowSpec(jspec)
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@staticmethod
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@since(1.4)
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def orderBy(*cols):
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"""
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Creates a :class:`WindowSpec` with the ordering defined.
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"""
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sc = SparkContext._active_spark_context
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jspec = sc._jvm.org.apache.spark.sql.expressions.Window.orderBy(_to_java_cols(cols))
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return WindowSpec(jspec)
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@staticmethod
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@since(2.1)
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def rowsBetween(start, end):
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"""
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Creates a :class:`WindowSpec` with the frame boundaries defined,
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from `start` (inclusive) to `end` (inclusive).
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Both `start` and `end` are relative positions from the current row.
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For example, "0" means "current row", while "-1" means the row before
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the current row, and "5" means the fifth row after the current row.
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We recommend users use ``Window.unboundedPreceding``, ``Window.unboundedFollowing``,
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and ``Window.currentRow`` to specify special boundary values, rather than using integral
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values directly.
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:param start: boundary start, inclusive.
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The frame is unbounded if this is ``Window.unboundedPreceding``, or
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any value less than or equal to -9223372036854775808.
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:param end: boundary end, inclusive.
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The frame is unbounded if this is ``Window.unboundedFollowing``, or
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any value greater than or equal to 9223372036854775807.
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"""
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if start <= Window._PRECEDING_THRESHOLD:
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start = Window.unboundedPreceding
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if end >= Window._FOLLOWING_THRESHOLD:
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end = Window.unboundedFollowing
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sc = SparkContext._active_spark_context
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jspec = sc._jvm.org.apache.spark.sql.expressions.Window.rowsBetween(start, end)
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return WindowSpec(jspec)
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@staticmethod
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@since(2.1)
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def rangeBetween(start, end):
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"""
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Creates a :class:`WindowSpec` with the frame boundaries defined,
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from `start` (inclusive) to `end` (inclusive).
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Both `start` and `end` are relative from the current row. For example,
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"0" means "current row", while "-1" means one off before the current row,
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and "5" means the five off after the current row.
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We recommend users use ``Window.unboundedPreceding``, ``Window.unboundedFollowing``,
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``Window.currentRow``, ``pyspark.sql.functions.unboundedPreceding``,
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``pyspark.sql.functions.unboundedFollowing`` and ``pyspark.sql.functions.currentRow``
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to specify special boundary values, rather than using integral values directly.
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:param start: boundary start, inclusive.
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The frame is unbounded if this is ``Window.unboundedPreceding``,
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a column returned by ``pyspark.sql.functions.unboundedPreceding``, or
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any value less than or equal to max(-sys.maxsize, -9223372036854775808).
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:param end: boundary end, inclusive.
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The frame is unbounded if this is ``Window.unboundedFollowing``,
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a column returned by ``pyspark.sql.functions.unboundedFollowing``, or
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any value greater than or equal to min(sys.maxsize, 9223372036854775807).
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>>> from pyspark.sql import functions as F, SparkSession, Window
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>>> spark = SparkSession.builder.getOrCreate()
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>>> df = spark.createDataFrame(
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... [(1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b")], ["id", "category"])
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>>> window = Window.orderBy("id").partitionBy("category").rangeBetween(
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... F.currentRow(), F.lit(1))
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>>> df.withColumn("sum", F.sum("id").over(window)).show()
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+---+--------+---+
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| id|category|sum|
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+---+--------+---+
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| 1| b| 3|
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| 2| b| 5|
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| 3| b| 3|
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| 1| a| 4|
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| 1| a| 4|
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| 2| a| 2|
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+---+--------+---+
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"""
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if isinstance(start, (int, long)) and isinstance(end, (int, long)):
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if start <= Window._PRECEDING_THRESHOLD:
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start = Window.unboundedPreceding
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if end >= Window._FOLLOWING_THRESHOLD:
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end = Window.unboundedFollowing
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elif isinstance(start, Column) and isinstance(end, Column):
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start = start._jc
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end = end._jc
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sc = SparkContext._active_spark_context
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jspec = sc._jvm.org.apache.spark.sql.expressions.Window.rangeBetween(start, end)
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return WindowSpec(jspec)
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class WindowSpec(object):
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"""
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A window specification that defines the partitioning, ordering,
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and frame boundaries.
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Use the static methods in :class:`Window` to create a :class:`WindowSpec`.
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.. note:: Experimental
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.. versionadded:: 1.4
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"""
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def __init__(self, jspec):
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self._jspec = jspec
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@since(1.4)
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def partitionBy(self, *cols):
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"""
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Defines the partitioning columns in a :class:`WindowSpec`.
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:param cols: names of columns or expressions
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"""
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return WindowSpec(self._jspec.partitionBy(_to_java_cols(cols)))
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@since(1.4)
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def orderBy(self, *cols):
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"""
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Defines the ordering columns in a :class:`WindowSpec`.
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:param cols: names of columns or expressions
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"""
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return WindowSpec(self._jspec.orderBy(_to_java_cols(cols)))
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@since(1.4)
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def rowsBetween(self, start, end):
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"""
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Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
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Both `start` and `end` are relative positions from the current row.
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For example, "0" means "current row", while "-1" means the row before
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the current row, and "5" means the fifth row after the current row.
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We recommend users use ``Window.unboundedPreceding``, ``Window.unboundedFollowing``,
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and ``Window.currentRow`` to specify special boundary values, rather than using integral
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values directly.
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:param start: boundary start, inclusive.
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The frame is unbounded if this is ``Window.unboundedPreceding``, or
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any value less than or equal to max(-sys.maxsize, -9223372036854775808).
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:param end: boundary end, inclusive.
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The frame is unbounded if this is ``Window.unboundedFollowing``, or
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any value greater than or equal to min(sys.maxsize, 9223372036854775807).
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"""
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if start <= Window._PRECEDING_THRESHOLD:
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start = Window.unboundedPreceding
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if end >= Window._FOLLOWING_THRESHOLD:
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end = Window.unboundedFollowing
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return WindowSpec(self._jspec.rowsBetween(start, end))
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@since(1.4)
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def rangeBetween(self, start, end):
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"""
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Defines the frame boundaries, from `start` (inclusive) to `end` (inclusive).
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Both `start` and `end` are relative from the current row. For example,
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"0" means "current row", while "-1" means one off before the current row,
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and "5" means the five off after the current row.
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We recommend users use ``Window.unboundedPreceding``, ``Window.unboundedFollowing``,
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``Window.currentRow``, ``pyspark.sql.functions.unboundedPreceding``,
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``pyspark.sql.functions.unboundedFollowing`` and ``pyspark.sql.functions.currentRow``
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to specify special boundary values, rather than using integral values directly.
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:param start: boundary start, inclusive.
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The frame is unbounded if this is ``Window.unboundedPreceding``,
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a column returned by ``pyspark.sql.functions.unboundedPreceding``, or
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any value less than or equal to max(-sys.maxsize, -9223372036854775808).
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:param end: boundary end, inclusive.
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The frame is unbounded if this is ``Window.unboundedFollowing``,
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a column returned by ``pyspark.sql.functions.unboundedFollowing``, or
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any value greater than or equal to min(sys.maxsize, 9223372036854775807).
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"""
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if isinstance(start, (int, long)) and isinstance(end, (int, long)):
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if start <= Window._PRECEDING_THRESHOLD:
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start = Window.unboundedPreceding
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if end >= Window._FOLLOWING_THRESHOLD:
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end = Window.unboundedFollowing
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elif isinstance(start, Column) and isinstance(end, Column):
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start = start._jc
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end = end._jc
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return WindowSpec(self._jspec.rangeBetween(start, end))
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def _test():
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import doctest
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SparkContext('local[4]', 'PythonTest')
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(failure_count, test_count) = doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
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if failure_count:
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sys.exit(-1)
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if __name__ == "__main__":
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_test()
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