# Source code for pyspark.sql.window

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import sys

from pyspark import since, SparkContext
from pyspark.sql.column import _to_seq, _to_java_column

__all__ = ["Window", "WindowSpec"]

def _to_java_cols(cols):
sc = SparkContext._active_spark_context
if len(cols) == 1 and isinstance(cols[0], list):
cols = cols[0]
return _to_seq(sc, cols, _to_java_column)

[docs]class Window(object): """ Utility functions for defining window in DataFrames. .. versionadded:: 1.4 Notes ----- When ordering is not defined, an unbounded window frame (rowFrame, unboundedPreceding, unboundedFollowing) is used by default. When ordering is defined, a growing window frame (rangeFrame, unboundedPreceding, currentRow) is used by default. Examples -------- >>> # ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW >>> window = Window.orderBy("date").rowsBetween(Window.unboundedPreceding, Window.currentRow) >>> # PARTITION BY country ORDER BY date RANGE BETWEEN 3 PRECEDING AND 3 FOLLOWING >>> window = Window.orderBy("date").partitionBy("country").rangeBetween(-3, 3) """ _JAVA_MIN_LONG = -(1 << 63) # -9223372036854775808 _JAVA_MAX_LONG = (1 << 63) - 1 # 9223372036854775807 _PRECEDING_THRESHOLD = max(-sys.maxsize, _JAVA_MIN_LONG) _FOLLOWING_THRESHOLD = min(sys.maxsize, _JAVA_MAX_LONG) unboundedPreceding = _JAVA_MIN_LONG unboundedFollowing = _JAVA_MAX_LONG currentRow = 0
[docs] @staticmethod @since(1.4) def partitionBy(*cols): """ Creates a :class:WindowSpec with the partitioning defined. """ sc = SparkContext._active_spark_context jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols)) return WindowSpec(jspec)
[docs] @staticmethod @since(1.4) def orderBy(*cols): """ Creates a :class:WindowSpec with the ordering defined. """ sc = SparkContext._active_spark_context jspec = sc._jvm.org.apache.spark.sql.expressions.Window.orderBy(_to_java_cols(cols)) return WindowSpec(jspec)
[docs] @staticmethod def rowsBetween(start, end): """ Creates a :class:WindowSpec with the frame boundaries defined, from start (inclusive) to end (inclusive). Both start and end are relative positions from the current row. For example, "0" means "current row", while "-1" means the row before the current row, and "5" means the fifth row after the current row. We recommend users use Window.unboundedPreceding, Window.unboundedFollowing, and Window.currentRow to specify special boundary values, rather than using integral values directly. A row based boundary is based on the position of the row within the partition. An offset indicates the number of rows above or below the current row, the frame for the current row starts or ends. For instance, given a row based sliding frame with a lower bound offset of -1 and a upper bound offset of +2. The frame for row with index 5 would range from index 4 to index 7. .. versionadded:: 2.1.0 Parameters ---------- start : int boundary start, inclusive. The frame is unbounded if this is Window.unboundedPreceding, or any value less than or equal to -9223372036854775808. end : int boundary end, inclusive. The frame is unbounded if this is Window.unboundedFollowing, or any value greater than or equal to 9223372036854775807. Examples -------- >>> from pyspark.sql import Window >>> from pyspark.sql import functions as func >>> from pyspark.sql import SQLContext >>> sc = SparkContext.getOrCreate() >>> sqlContext = SQLContext(sc) >>> tup = [(1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b")] >>> df = sqlContext.createDataFrame(tup, ["id", "category"]) >>> window = Window.partitionBy("category").orderBy("id").rowsBetween(Window.currentRow, 1) >>> df.withColumn("sum", func.sum("id").over(window)).sort("id", "category", "sum").show() +---+--------+---+ | id|category|sum| +---+--------+---+ | 1| a| 2| | 1| a| 3| | 1| b| 3| | 2| a| 2| | 2| b| 5| | 3| b| 3| +---+--------+---+ """ if start <= Window._PRECEDING_THRESHOLD: start = Window.unboundedPreceding if end >= Window._FOLLOWING_THRESHOLD: end = Window.unboundedFollowing sc = SparkContext._active_spark_context jspec = sc._jvm.org.apache.spark.sql.expressions.Window.rowsBetween(start, end) return WindowSpec(jspec)
[docs] @staticmethod def rangeBetween(start, end): """ Creates a :class:WindowSpec with the frame boundaries defined, from start (inclusive) to end (inclusive). Both start and end are relative from the current row. For example, "0" means "current row", while "-1" means one off before the current row, and "5" means the five off after the current row. We recommend users use Window.unboundedPreceding, Window.unboundedFollowing, and Window.currentRow to specify special boundary values, rather than using integral values directly. A range-based boundary is based on the actual value of the ORDER BY expression(s). An offset is used to alter the value of the ORDER BY expression, for instance if the current ORDER BY expression has a value of 10 and the lower bound offset is -3, the resulting lower bound for the current row will be 10 - 3 = 7. This however puts a number of constraints on the ORDER BY expressions: there can be only one expression and this expression must have a numerical data type. An exception can be made when the offset is unbounded, because no value modification is needed, in this case multiple and non-numeric ORDER BY expression are allowed. .. versionadded:: 2.1.0 Parameters ---------- start : int boundary start, inclusive. The frame is unbounded if this is Window.unboundedPreceding, or any value less than or equal to max(-sys.maxsize, -9223372036854775808). end : int boundary end, inclusive. The frame is unbounded if this is Window.unboundedFollowing, or any value greater than or equal to min(sys.maxsize, 9223372036854775807). Examples -------- >>> from pyspark.sql import Window >>> from pyspark.sql import functions as func >>> from pyspark.sql import SQLContext >>> sc = SparkContext.getOrCreate() >>> sqlContext = SQLContext(sc) >>> tup = [(1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b")] >>> df = sqlContext.createDataFrame(tup, ["id", "category"]) >>> window = Window.partitionBy("category").orderBy("id").rangeBetween(Window.currentRow, 1) >>> df.withColumn("sum", func.sum("id").over(window)).sort("id", "category").show() +---+--------+---+ | id|category|sum| +---+--------+---+ | 1| a| 4| | 1| a| 4| | 1| b| 3| | 2| a| 2| | 2| b| 5| | 3| b| 3| +---+--------+---+ """ if start <= Window._PRECEDING_THRESHOLD: start = Window.unboundedPreceding if end >= Window._FOLLOWING_THRESHOLD: end = Window.unboundedFollowing sc = SparkContext._active_spark_context jspec = sc._jvm.org.apache.spark.sql.expressions.Window.rangeBetween(start, end) return WindowSpec(jspec)
class WindowSpec(object): """ A window specification that defines the partitioning, ordering, and frame boundaries. Use the static methods in :class:Window to create a :class:WindowSpec. .. versionadded:: 1.4.0 """ def __init__(self, jspec): self._jspec = jspec
[docs] def partitionBy(self, *cols): """ Defines the partitioning columns in a :class:WindowSpec. .. versionadded:: 1.4.0 Parameters ---------- cols : str, :class:Column or list names of columns or expressions """ return WindowSpec(self._jspec.partitionBy(_to_java_cols(cols)))
[docs] def orderBy(self, *cols): """ Defines the ordering columns in a :class:WindowSpec. .. versionadded:: 1.4.0 Parameters ---------- cols : str, :class:Column or list names of columns or expressions """ return WindowSpec(self._jspec.orderBy(_to_java_cols(cols)))
[docs] def rowsBetween(self, start, end): """ Defines the frame boundaries, from start (inclusive) to end (inclusive). Both start and end are relative positions from the current row. For example, "0" means "current row", while "-1" means the row before the current row, and "5" means the fifth row after the current row. We recommend users use Window.unboundedPreceding, Window.unboundedFollowing, and Window.currentRow to specify special boundary values, rather than using integral values directly. .. versionadded:: 1.4.0 Parameters ---------- start : int boundary start, inclusive. The frame is unbounded if this is Window.unboundedPreceding, or any value less than or equal to max(-sys.maxsize, -9223372036854775808). end : int boundary end, inclusive. The frame is unbounded if this is Window.unboundedFollowing, or any value greater than or equal to min(sys.maxsize, 9223372036854775807). """ if start <= Window._PRECEDING_THRESHOLD: start = Window.unboundedPreceding if end >= Window._FOLLOWING_THRESHOLD: end = Window.unboundedFollowing return WindowSpec(self._jspec.rowsBetween(start, end))
[docs] def rangeBetween(self, start, end): """ Defines the frame boundaries, from start (inclusive) to end (inclusive). Both start and end are relative from the current row. For example, "0" means "current row", while "-1" means one off before the current row, and "5" means the five off after the current row. We recommend users use Window.unboundedPreceding, Window.unboundedFollowing, and Window.currentRow to specify special boundary values, rather than using integral values directly. .. versionadded:: 1.4.0 Parameters ---------- start : int boundary start, inclusive. The frame is unbounded if this is Window.unboundedPreceding, or any value less than or equal to max(-sys.maxsize, -9223372036854775808). end : int boundary end, inclusive. The frame is unbounded if this is Window.unboundedFollowing, or any value greater than or equal to min(sys.maxsize, 9223372036854775807). """ if start <= Window._PRECEDING_THRESHOLD: start = Window.unboundedPreceding if end >= Window._FOLLOWING_THRESHOLD: end = Window.unboundedFollowing return WindowSpec(self._jspec.rangeBetween(start, end))
def _test(): import doctest import pyspark.sql.window SparkContext('local[4]', 'PythonTest') globs = pyspark.sql.window.__dict__.copy() (failure_count, test_count) = doctest.testmod( pyspark.sql.window, globs=globs, optionflags=doctest.NORMALIZE_WHITESPACE) if failure_count: sys.exit(-1) if __name__ == "__main__": _test()