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spark-instrumented-optimizer/python/pyspark/sql/tests/test_pandas_udf_window.py
Li Jin 86100df54b [SPARK-24561][SQL][PYTHON] User-defined window aggregation functions with Pandas UDF (bounded window) 
## 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>
2018-12-18 09:15:21 +08:00

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#
# 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
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# 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 unittest
from pyspark.sql.utils import AnalysisException
from pyspark.sql.functions import array, explode, col, lit, mean, min, max, rank, \
udf, pandas_udf, PandasUDFType
from pyspark.sql.window import Window
from pyspark.testing.sqlutils import ReusedSQLTestCase, have_pandas, have_pyarrow, \
pandas_requirement_message, pyarrow_requirement_message
from pyspark.testing.utils import QuietTest
@unittest.skipIf(
not have_pandas or not have_pyarrow,
pandas_requirement_message or pyarrow_requirement_message)
class WindowPandasUDFTests(ReusedSQLTestCase):
@property
def data(self):
return self.spark.range(10).toDF('id') \
.withColumn("vs", array([lit(i * 1.0) + col('id') for i in range(20, 30)])) \
.withColumn("v", explode(col('vs'))) \
.drop('vs') \
.withColumn('w', lit(1.0))
@property
def python_plus_one(self):
return udf(lambda v: v + 1, 'double')
@property
def pandas_scalar_time_two(self):
return pandas_udf(lambda v: v * 2, 'double')
@property
def pandas_agg_count_udf(self):
from pyspark.sql.functions import pandas_udf, PandasUDFType
@pandas_udf('long', PandasUDFType.GROUPED_AGG)
def count(v):
return len(v)
return count
@property
def pandas_agg_mean_udf(self):
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
def avg(v):
return v.mean()
return avg
@property
def pandas_agg_max_udf(self):
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
def max(v):
return v.max()
return max
@property
def pandas_agg_min_udf(self):
@pandas_udf('double', PandasUDFType.GROUPED_AGG)
def min(v):
return v.min()
return min
@property
def unbounded_window(self):
return Window.partitionBy('id') \
.rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing).orderBy('v')
@property
def ordered_window(self):
return Window.partitionBy('id').orderBy('v')
@property
def unpartitioned_window(self):
return Window.partitionBy()
@property
def sliding_row_window(self):
return Window.partitionBy('id').orderBy('v').rowsBetween(-2, 1)
@property
def sliding_range_window(self):
return Window.partitionBy('id').orderBy('v').rangeBetween(-2, 4)
@property
def growing_row_window(self):
return Window.partitionBy('id').orderBy('v').rowsBetween(Window.unboundedPreceding, 3)
@property
def growing_range_window(self):
return Window.partitionBy('id').orderBy('v') \
.rangeBetween(Window.unboundedPreceding, 4)
@property
def shrinking_row_window(self):
return Window.partitionBy('id').orderBy('v').rowsBetween(-2, Window.unboundedFollowing)
@property
def shrinking_range_window(self):
return Window.partitionBy('id').orderBy('v') \
.rangeBetween(-3, Window.unboundedFollowing)
def test_simple(self):
df = self.data
w = self.unbounded_window
mean_udf = self.pandas_agg_mean_udf
result1 = df.withColumn('mean_v', mean_udf(df['v']).over(w))
expected1 = df.withColumn('mean_v', mean(df['v']).over(w))
result2 = df.select(mean_udf(df['v']).over(w))
expected2 = df.select(mean(df['v']).over(w))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
def test_multiple_udfs(self):
df = self.data
w = self.unbounded_window
result1 = df.withColumn('mean_v', self.pandas_agg_mean_udf(df['v']).over(w)) \
.withColumn('max_v', self.pandas_agg_max_udf(df['v']).over(w)) \
.withColumn('min_w', self.pandas_agg_min_udf(df['w']).over(w))
expected1 = df.withColumn('mean_v', mean(df['v']).over(w)) \
.withColumn('max_v', max(df['v']).over(w)) \
.withColumn('min_w', min(df['w']).over(w))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
def test_replace_existing(self):
df = self.data
w = self.unbounded_window
result1 = df.withColumn('v', self.pandas_agg_mean_udf(df['v']).over(w))
expected1 = df.withColumn('v', mean(df['v']).over(w))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
def test_mixed_sql(self):
df = self.data
w = self.unbounded_window
mean_udf = self.pandas_agg_mean_udf
result1 = df.withColumn('v', mean_udf(df['v'] * 2).over(w) + 1)
expected1 = df.withColumn('v', mean(df['v'] * 2).over(w) + 1)
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
def test_mixed_udf(self):
df = self.data
w = self.unbounded_window
plus_one = self.python_plus_one
time_two = self.pandas_scalar_time_two
mean_udf = self.pandas_agg_mean_udf
result1 = df.withColumn(
'v2',
plus_one(mean_udf(plus_one(df['v'])).over(w)))
expected1 = df.withColumn(
'v2',
plus_one(mean(plus_one(df['v'])).over(w)))
result2 = df.withColumn(
'v2',
time_two(mean_udf(time_two(df['v'])).over(w)))
expected2 = df.withColumn(
'v2',
time_two(mean(time_two(df['v'])).over(w)))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
def test_without_partitionBy(self):
df = self.data
w = self.unpartitioned_window
mean_udf = self.pandas_agg_mean_udf
result1 = df.withColumn('v2', mean_udf(df['v']).over(w))
expected1 = df.withColumn('v2', mean(df['v']).over(w))
result2 = df.select(mean_udf(df['v']).over(w))
expected2 = df.select(mean(df['v']).over(w))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
def test_mixed_sql_and_udf(self):
df = self.data
w = self.unbounded_window
ow = self.ordered_window
max_udf = self.pandas_agg_max_udf
min_udf = self.pandas_agg_min_udf
result1 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min_udf(df['v']).over(w))
expected1 = df.withColumn('v_diff', max(df['v']).over(w) - min(df['v']).over(w))
# Test mixing sql window function and window udf in the same expression
result2 = df.withColumn('v_diff', max_udf(df['v']).over(w) - min(df['v']).over(w))
expected2 = expected1
# Test chaining sql aggregate function and udf
result3 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
.withColumn('min_v', min(df['v']).over(w)) \
.withColumn('v_diff', col('max_v') - col('min_v')) \
.drop('max_v', 'min_v')
expected3 = expected1
# Test mixing sql window function and udf
result4 = df.withColumn('max_v', max_udf(df['v']).over(w)) \
.withColumn('rank', rank().over(ow))
expected4 = df.withColumn('max_v', max(df['v']).over(w)) \
.withColumn('rank', rank().over(ow))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
self.assertPandasEqual(expected2.toPandas(), result2.toPandas())
self.assertPandasEqual(expected3.toPandas(), result3.toPandas())
self.assertPandasEqual(expected4.toPandas(), result4.toPandas())
def test_array_type(self):
df = self.data
w = self.unbounded_window
array_udf = pandas_udf(lambda x: [1.0, 2.0], 'array<double>', PandasUDFType.GROUPED_AGG)
result1 = df.withColumn('v2', array_udf(df['v']).over(w))
self.assertEquals(result1.first()['v2'], [1.0, 2.0])
def test_invalid_args(self):
df = self.data
w = self.unbounded_window
with QuietTest(self.sc):
with self.assertRaisesRegexp(
AnalysisException,
'.*not supported within a window function'):
foo_udf = pandas_udf(lambda x: x, 'v double', PandasUDFType.GROUPED_MAP)
df.withColumn('v2', foo_udf(df['v']).over(w))
def test_bounded_simple(self):
from pyspark.sql.functions import mean, max, min, count
df = self.data
w1 = self.sliding_row_window
w2 = self.shrinking_range_window
plus_one = self.python_plus_one
count_udf = self.pandas_agg_count_udf
mean_udf = self.pandas_agg_mean_udf
max_udf = self.pandas_agg_max_udf
min_udf = self.pandas_agg_min_udf
result1 = df.withColumn('mean_v', mean_udf(plus_one(df['v'])).over(w1)) \
.withColumn('count_v', count_udf(df['v']).over(w2)) \
.withColumn('max_v', max_udf(df['v']).over(w2)) \
.withColumn('min_v', min_udf(df['v']).over(w1))
expected1 = df.withColumn('mean_v', mean(plus_one(df['v'])).over(w1)) \
.withColumn('count_v', count(df['v']).over(w2)) \
.withColumn('max_v', max(df['v']).over(w2)) \
.withColumn('min_v', min(df['v']).over(w1))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
def test_growing_window(self):
from pyspark.sql.functions import mean
df = self.data
w1 = self.growing_row_window
w2 = self.growing_range_window
mean_udf = self.pandas_agg_mean_udf
result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
.withColumn('m2', mean_udf(df['v']).over(w2))
expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
.withColumn('m2', mean(df['v']).over(w2))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
def test_sliding_window(self):
from pyspark.sql.functions import mean
df = self.data
w1 = self.sliding_row_window
w2 = self.sliding_range_window
mean_udf = self.pandas_agg_mean_udf
result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
.withColumn('m2', mean_udf(df['v']).over(w2))
expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
.withColumn('m2', mean(df['v']).over(w2))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
def test_shrinking_window(self):
from pyspark.sql.functions import mean
df = self.data
w1 = self.shrinking_row_window
w2 = self.shrinking_range_window
mean_udf = self.pandas_agg_mean_udf
result1 = df.withColumn('m1', mean_udf(df['v']).over(w1)) \
.withColumn('m2', mean_udf(df['v']).over(w2))
expected1 = df.withColumn('m1', mean(df['v']).over(w1)) \
.withColumn('m2', mean(df['v']).over(w2))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
def test_bounded_mixed(self):
from pyspark.sql.functions import mean, max
df = self.data
w1 = self.sliding_row_window
w2 = self.unbounded_window
mean_udf = self.pandas_agg_mean_udf
max_udf = self.pandas_agg_max_udf
result1 = df.withColumn('mean_v', mean_udf(df['v']).over(w1)) \
.withColumn('max_v', max_udf(df['v']).over(w2)) \
.withColumn('mean_unbounded_v', mean_udf(df['v']).over(w1))
expected1 = df.withColumn('mean_v', mean(df['v']).over(w1)) \
.withColumn('max_v', max(df['v']).over(w2)) \
.withColumn('mean_unbounded_v', mean(df['v']).over(w1))
self.assertPandasEqual(expected1.toPandas(), result1.toPandas())
if __name__ == "__main__":
from pyspark.sql.tests.test_pandas_udf_window import *
try:
import xmlrunner
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
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