eb037a8180
### What changes were proposed in this pull request? The Experimental and Evolving annotations are both (like Unstable) used to express that a an API may change. However there are many things in the code that have been marked that way since even Spark 1.x. Per the dev thread, anything introduced at or before Spark 2.3.0 is pretty much 'stable' in that it would not change without a deprecation cycle. Therefore I'd like to remove most of these annotations. And, remove the `:: Experimental ::` scaladoc tag too. And likewise for Python, R. The changes below can be summarized as: - Generally, anything introduced at or before Spark 2.3.0 has been unmarked as neither Evolving nor Experimental - Obviously experimental items like DSv2, Barrier mode, ExperimentalMethods are untouched - I _did_ unmark a few MLlib classes introduced in 2.4, as I am quite confident they're not going to change (e.g. KolmogorovSmirnovTest, PowerIterationClustering) It's a big change to review, so I'd suggest scanning the list of _files_ changed to see if any area seems like it should remain partly experimental and examine those. ### Why are the changes needed? Many of these annotations are incorrect; the APIs are de facto stable. Leaving them also makes legitimate usages of the annotations less meaningful. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? Existing tests. Closes #25558 from srowen/SPARK-28855. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
297 lines
12 KiB
Python
297 lines
12 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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from pyspark import since, SparkContext
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from pyspark.sql.column import _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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.. 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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A row based boundary is based on the position of the row within the partition.
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An offset indicates the number of rows above or below the current row, the frame for the
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current row starts or ends. For instance, given a row based sliding frame with a lower bound
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offset of -1 and a upper bound offset of +2. The frame for row with index 5 would range from
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index 4 to index 7.
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>>> from pyspark.sql import Window
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>>> from pyspark.sql import functions as func
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>>> from pyspark.sql import SQLContext
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>>> sc = SparkContext.getOrCreate()
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>>> sqlContext = SQLContext(sc)
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>>> tup = [(1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b")]
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>>> df = sqlContext.createDataFrame(tup, ["id", "category"])
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>>> window = Window.partitionBy("category").orderBy("id").rowsBetween(Window.currentRow, 1)
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>>> df.withColumn("sum", func.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| 2|
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| 1| a| 3|
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| 2| a| 2|
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+---+--------+---+
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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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and ``Window.currentRow`` to specify special boundary values, rather than using integral
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values directly.
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A range-based boundary is based on the actual value of the ORDER BY
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expression(s). An offset is used to alter the value of the ORDER BY expression, for
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instance if the current ORDER BY expression has a value of 10 and the lower bound offset
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is -3, the resulting lower bound for the current row will be 10 - 3 = 7. This however puts a
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number of constraints on the ORDER BY expressions: there can be only one expression and this
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expression must have a numerical data type. An exception can be made when the offset is
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unbounded, because no value modification is needed, in this case multiple and non-numeric
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ORDER BY expression are allowed.
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>>> from pyspark.sql import Window
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>>> from pyspark.sql import functions as func
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>>> from pyspark.sql import SQLContext
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>>> sc = SparkContext.getOrCreate()
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>>> sqlContext = SQLContext(sc)
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>>> tup = [(1, "a"), (1, "a"), (2, "a"), (1, "b"), (2, "b"), (3, "b")]
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>>> df = sqlContext.createDataFrame(tup, ["id", "category"])
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>>> window = Window.partitionBy("category").orderBy("id").rangeBetween(Window.currentRow, 1)
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>>> df.withColumn("sum", func.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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: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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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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.. 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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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.rangeBetween(start, end))
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def _test():
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import doctest
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import pyspark.sql.window
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SparkContext('local[4]', 'PythonTest')
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globs = pyspark.sql.window.__dict__.copy()
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(failure_count, test_count) = doctest.testmod(
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pyspark.sql.window, globs=globs,
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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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