86100df54b
## What changes were proposed in this pull request? This PR implements a new feature - window aggregation Pandas UDF for bounded window. #### Doc: https://docs.google.com/document/d/14EjeY5z4-NC27-SmIP9CsMPCANeTcvxN44a7SIJtZPc/edit#heading=h.c87w44wcj3wj #### Example: ``` from pyspark.sql.functions import pandas_udf, PandasUDFType from pyspark.sql.window import Window df = spark.range(0, 10, 2).toDF('v') w1 = Window.partitionBy().orderBy('v').rangeBetween(-2, 4) w2 = Window.partitionBy().orderBy('v').rowsBetween(-2, 2) pandas_udf('double', PandasUDFType.GROUPED_AGG) def avg(v): return v.mean() df.withColumn('v_mean', avg(df['v']).over(w1)).show() # +---+------+ # | v|v_mean| # +---+------+ # | 0| 1.0| # | 2| 2.0| # | 4| 4.0| # | 6| 6.0| # | 8| 7.0| # +---+------+ df.withColumn('v_mean', avg(df['v']).over(w2)).show() # +---+------+ # | v|v_mean| # +---+------+ # | 0| 2.0| # | 2| 3.0| # | 4| 4.0| # | 6| 5.0| # | 8| 6.0| # +---+------+ ``` #### High level changes: This PR modifies the existing WindowInPandasExec physical node to deal with unbounded (growing, shrinking and sliding) windows. * `WindowInPandasExec` now share the same base class as `WindowExec` and share utility functions. See `WindowExecBase` * `WindowFunctionFrame` now has two new functions `currentLowerBound` and `currentUpperBound` - to return the lower and upper window bound for the current output row. It is also modified to allow `AggregateProcessor` == null. Null aggregator processor is used for `WindowInPandasExec` where we don't have an aggregator and only uses lower and upper bound functions from `WindowFunctionFrame` * The biggest change is in `WindowInPandasExec`, where it is modified to take `currentLowerBound` and `currentUpperBound` and write those values together with the input data to the python process for rolling window aggregation. See `WindowInPandasExec` for more details. #### Discussion In benchmarking, I found numpy variant of the rolling window UDF is much faster than the pandas version: Spark SQL window function: 20s Pandas variant: ~80s Numpy variant: 10s Numpy variant with numba: 4s Allowing numpy variant of the vectorized UDFs is something I want to discuss because of the performance improvement, but doesn't have to be in this PR. ## How was this patch tested? New tests Closes #22305 from icexelloss/SPARK-24561-bounded-window-udf. Authored-by: Li Jin <ice.xelloss@gmail.com> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
362 lines
13 KiB
Python
362 lines
13 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 unittest
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from pyspark.sql.utils import AnalysisException
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from pyspark.sql.functions import array, explode, col, lit, mean, min, max, rank, \
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udf, pandas_udf, PandasUDFType
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from pyspark.sql.window import Window
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from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
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pandas_requirement_message, pyarrow_requirement_message
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from pyspark.testing.utils import QuietTest
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@unittest.skipIf(
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not have_pandas or not have_pyarrow,
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pandas_requirement_message or pyarrow_requirement_message)
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class WindowPandasUDFTests(ReusedSQLTestCase):
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@property
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def data(self):
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return self.spark.range(10).toDF('id') \
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.withColumn("vs", array([lit(i * 1.0) + col('id') for i in range(20, 30)])) \
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.withColumn("v", explode(col('vs'))) \
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.drop('vs') \
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.withColumn('w', lit(1.0))
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@property
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def python_plus_one(self):
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return udf(lambda v: v + 1, 'double')
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@property
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def pandas_scalar_time_two(self):
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return pandas_udf(lambda v: v * 2, 'double')
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@property
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def pandas_agg_count_udf(self):
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from pyspark.sql.functions import pandas_udf, PandasUDFType
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@pandas_udf('long', PandasUDFType.GROUPED_AGG)
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def count(v):
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return len(v)
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return count
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@property
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def pandas_agg_mean_udf(self):
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@pandas_udf('double', PandasUDFType.GROUPED_AGG)
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def avg(v):
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return v.mean()
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return avg
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@property
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def pandas_agg_max_udf(self):
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@pandas_udf('double', PandasUDFType.GROUPED_AGG)
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def max(v):
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return v.max()
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return max
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@property
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def pandas_agg_min_udf(self):
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@pandas_udf('double', PandasUDFType.GROUPED_AGG)
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def min(v):
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return v.min()
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return min
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@property
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def unbounded_window(self):
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return Window.partitionBy('id') \
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.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing).orderBy('v')
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@property
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def ordered_window(self):
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return Window.partitionBy('id').orderBy('v')
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@property
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def unpartitioned_window(self):
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return Window.partitionBy()
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@property
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def sliding_row_window(self):
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return Window.partitionBy('id').orderBy('v').rowsBetween(-2, 1)
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@property
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def sliding_range_window(self):
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return Window.partitionBy('id').orderBy('v').rangeBetween(-2, 4)
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@property
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def growing_row_window(self):
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return Window.partitionBy('id').orderBy('v').rowsBetween(Window.unboundedPreceding, 3)
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@property
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def growing_range_window(self):
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return Window.partitionBy('id').orderBy('v') \
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.rangeBetween(Window.unboundedPreceding, 4)
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@property
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def shrinking_row_window(self):
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return Window.partitionBy('id').orderBy('v').rowsBetween(-2, Window.unboundedFollowing)
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@property
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def shrinking_range_window(self):
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return Window.partitionBy('id').orderBy('v') \
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.rangeBetween(-3, Window.unboundedFollowing)
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def test_simple(self):
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df = self.data
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w = self.unbounded_window
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.withColumn('mean_v', mean_udf(df['v']).over(w))
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expected1 = df.withColumn('mean_v', mean(df['v']).over(w))
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result2 = df.select(mean_udf(df['v']).over(w))
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expected2 = df.select(mean(df['v']).over(w))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
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def test_multiple_udfs(self):
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df = self.data
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w = self.unbounded_window
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result1 = df.withColumn('mean_v', self.pandas_agg_mean_udf(df['v']).over(w)) \
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.withColumn('max_v', self.pandas_agg_max_udf(df['v']).over(w)) \
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.withColumn('min_w', self.pandas_agg_min_udf(df['w']).over(w))
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expected1 = df.withColumn('mean_v', mean(df['v']).over(w)) \
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.withColumn('max_v', max(df['v']).over(w)) \
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.withColumn('min_w', min(df['w']).over(w))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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def test_replace_existing(self):
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df = self.data
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w = self.unbounded_window
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result1 = df.withColumn('v', self.pandas_agg_mean_udf(df['v']).over(w))
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expected1 = df.withColumn('v', mean(df['v']).over(w))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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def test_mixed_sql(self):
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df = self.data
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w = self.unbounded_window
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.withColumn('v', mean_udf(df['v'] * 2).over(w) + 1)
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expected1 = df.withColumn('v', mean(df['v'] * 2).over(w) + 1)
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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def test_mixed_udf(self):
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df = self.data
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w = self.unbounded_window
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plus_one = self.python_plus_one
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time_two = self.pandas_scalar_time_two
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.withColumn(
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'v2',
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plus_one(mean_udf(plus_one(df['v'])).over(w)))
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expected1 = df.withColumn(
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'v2',
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plus_one(mean(plus_one(df['v'])).over(w)))
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result2 = df.withColumn(
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'v2',
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time_two(mean_udf(time_two(df['v'])).over(w)))
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expected2 = df.withColumn(
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'v2',
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time_two(mean(time_two(df['v'])).over(w)))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
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def test_without_partitionBy(self):
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df = self.data
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w = self.unpartitioned_window
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.withColumn('v2', mean_udf(df['v']).over(w))
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expected1 = df.withColumn('v2', mean(df['v']).over(w))
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result2 = df.select(mean_udf(df['v']).over(w))
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expected2 = df.select(mean(df['v']).over(w))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
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def test_mixed_sql_and_udf(self):
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df = self.data
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w = self.unbounded_window
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ow = self.ordered_window
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max_udf = self.pandas_agg_max_udf
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min_udf = self.pandas_agg_min_udf
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result1 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min_udf(df['v']).over(w))
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expected1 = df.withColumn('v_diff', max(df['v']).over(w) - min(df['v']).over(w))
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# Test mixing sql window function and window udf in the same expression
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result2 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min(df['v']).over(w))
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expected2 = expected1
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# Test chaining sql aggregate function and udf
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result3 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
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.withColumn('min_v', min(df['v']).over(w)) \
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.withColumn('v_diff', col('max_v') - col('min_v')) \
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.drop('max_v', 'min_v')
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expected3 = expected1
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# Test mixing sql window function and udf
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result4 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
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.withColumn('rank', rank().over(ow))
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expected4 = df.withColumn('max_v', max(df['v']).over(w)) \
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.withColumn('rank', rank().over(ow))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
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self.assertPandasEqual(expected3.toPandas(), result3.toPandas())
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self.assertPandasEqual(expected4.toPandas(), result4.toPandas())
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def test_array_type(self):
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df = self.data
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w = self.unbounded_window
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array_udf = pandas_udf(lambda x: [1.0, 2.0], 'array<double>', PandasUDFType.GROUPED_AGG)
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result1 = df.withColumn('v2', array_udf(df['v']).over(w))
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self.assertEquals(result1.first()['v2'], [1.0, 2.0])
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def test_invalid_args(self):
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df = self.data
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w = self.unbounded_window
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with QuietTest(self.sc):
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with self.assertRaisesRegexp(
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AnalysisException,
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'.*not supported within a window function'):
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foo_udf = pandas_udf(lambda x: x, 'v double', PandasUDFType.GROUPED_MAP)
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df.withColumn('v2', foo_udf(df['v']).over(w))
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def test_bounded_simple(self):
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from pyspark.sql.functions import mean, max, min, count
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df = self.data
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w1 = self.sliding_row_window
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w2 = self.shrinking_range_window
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plus_one = self.python_plus_one
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count_udf = self.pandas_agg_count_udf
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mean_udf = self.pandas_agg_mean_udf
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max_udf = self.pandas_agg_max_udf
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min_udf = self.pandas_agg_min_udf
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result1 = df.withColumn('mean_v', mean_udf(plus_one(df['v'])).over(w1)) \
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.withColumn('count_v', count_udf(df['v']).over(w2)) \
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.withColumn('max_v', max_udf(df['v']).over(w2)) \
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.withColumn('min_v', min_udf(df['v']).over(w1))
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expected1 = df.withColumn('mean_v', mean(plus_one(df['v'])).over(w1)) \
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.withColumn('count_v', count(df['v']).over(w2)) \
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.withColumn('max_v', max(df['v']).over(w2)) \
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.withColumn('min_v', min(df['v']).over(w1))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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def test_growing_window(self):
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from pyspark.sql.functions import mean
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df = self.data
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w1 = self.growing_row_window
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w2 = self.growing_range_window
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
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.withColumn('m2', mean_udf(df['v']).over(w2))
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expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
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.withColumn('m2', mean(df['v']).over(w2))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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def test_sliding_window(self):
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from pyspark.sql.functions import mean
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df = self.data
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w1 = self.sliding_row_window
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w2 = self.sliding_range_window
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
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.withColumn('m2', mean_udf(df['v']).over(w2))
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expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
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.withColumn('m2', mean(df['v']).over(w2))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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def test_shrinking_window(self):
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from pyspark.sql.functions import mean
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df = self.data
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w1 = self.shrinking_row_window
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w2 = self.shrinking_range_window
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mean_udf = self.pandas_agg_mean_udf
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result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
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.withColumn('m2', mean_udf(df['v']).over(w2))
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expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
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.withColumn('m2', mean(df['v']).over(w2))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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def test_bounded_mixed(self):
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from pyspark.sql.functions import mean, max
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df = self.data
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w1 = self.sliding_row_window
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w2 = self.unbounded_window
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mean_udf = self.pandas_agg_mean_udf
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max_udf = self.pandas_agg_max_udf
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result1 = df.withColumn('mean_v', mean_udf(df['v']).over(w1)) \
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.withColumn('max_v', max_udf(df['v']).over(w2)) \
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.withColumn('mean_unbounded_v', mean_udf(df['v']).over(w1))
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expected1 = df.withColumn('mean_v', mean(df['v']).over(w1)) \
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.withColumn('max_v', max(df['v']).over(w2)) \
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.withColumn('mean_unbounded_v', mean(df['v']).over(w1))
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self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
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if __name__ == "__main__":
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from pyspark.sql.tests.test_pandas_udf_window import *
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try:
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import xmlrunner
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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