Repository: spark
Updated Branches:
refs/heads/branch-2.4 443d12dbb -> 0763b758d
[SPARK-25601][PYTHON] Register Grouped aggregate UDF Vectorized UDFs for SQL
Statement
## What changes were proposed in this pull request?
This PR proposes to register Grouped aggregate UDF Vectorized UDFs for SQL
Statement, for instance:
```python
from pyspark.sql.functions import pandas_udf, PandasUDFType
pandas_udf("integer", PandasUDFType.GROUPED_AGG)
def sum_udf(v):
return v.sum()
spark.udf.register("sum_udf", sum_udf)
q = "SELECT v2, sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2)
GROUP BY v2"
spark.sql(q).show()
```
```
+---+-----------+
| v2|sum_udf(v1)|
+---+-----------+
| 1| 1|
| 0| 5|
+---+-----------+
```
## How was this patch tested?
Manual test and unit test.
Closes #22620 from HyukjinKwon/SPARK-25601.
Authored-by: hyukjinkwon <gurwls...@apache.org>
Signed-off-by: hyukjinkwon <gurwls...@apache.org>
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/0763b758
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/0763b758
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/0763b758
Branch: refs/heads/branch-2.4
Commit: 0763b758de55fd14d7da4832d01b5713e582b257
Parents: 443d12d
Author: hyukjinkwon <gurwls...@apache.org>
Authored: Thu Oct 4 09:36:23 2018 +0800
Committer: hyukjinkwon <gurwls...@apache.org>
Committed: Thu Oct 4 09:43:42 2018 +0800
----------------------------------------------------------------------
python/pyspark/sql/tests.py | 20 ++++++++++++++++++--
python/pyspark/sql/udf.py | 15 +++++++++++++--
2 files changed, 31 insertions(+), 4 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/0763b758/python/pyspark/sql/tests.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 690035a..e991032 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -5595,8 +5595,9 @@ class GroupedMapPandasUDFTests(ReusedSQLTestCase):
foo_udf = pandas_udf(lambda x: x, "id long", PandasUDFType.GROUPED_MAP)
with QuietTest(self.sc):
- with self.assertRaisesRegexp(ValueError, 'f must be either
SQL_BATCHED_UDF or '
- 'SQL_SCALAR_PANDAS_UDF'):
+ with self.assertRaisesRegexp(
+ ValueError,
+
'f.*SQL_BATCHED_UDF.*SQL_SCALAR_PANDAS_UDF.*SQL_GROUPED_AGG_PANDAS_UDF.*'):
self.spark.catalog.registerFunction("foo_udf", foo_udf)
def test_decorator(self):
@@ -6412,6 +6413,21 @@ class GroupedAggPandasUDFTests(ReusedSQLTestCase):
'mixture.*aggregate function.*group aggregate pandas UDF'):
df.groupby(df.id).agg(mean_udf(df.v), mean(df.v)).collect()
+ def test_register_vectorized_udf_basic(self):
+ from pyspark.sql.functions import pandas_udf
+ from pyspark.rdd import PythonEvalType
+
+ sum_pandas_udf = pandas_udf(
+ lambda v: v.sum(), "integer",
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
+
+ self.assertEqual(sum_pandas_udf.evalType,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
+ group_agg_pandas_udf = self.spark.udf.register("sum_pandas_udf",
sum_pandas_udf)
+ self.assertEqual(group_agg_pandas_udf.evalType,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
+ q = "SELECT sum_pandas_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1)
tbl(v1, v2) GROUP BY v2"
+ actual = sorted(map(lambda r: r[0], self.spark.sql(q).collect()))
+ expected = [1, 5]
+ self.assertEqual(actual, expected)
+
@unittest.skipIf(
not _have_pandas or not _have_pyarrow,
http://git-wip-us.apache.org/repos/asf/spark/blob/0763b758/python/pyspark/sql/udf.py
----------------------------------------------------------------------
diff --git a/python/pyspark/sql/udf.py b/python/pyspark/sql/udf.py
index 9dbe49b..58f4e0d 100644
--- a/python/pyspark/sql/udf.py
+++ b/python/pyspark/sql/udf.py
@@ -298,6 +298,15 @@ class UDFRegistration(object):
>>> spark.sql("SELECT add_one(id) FROM range(3)").collect() #
doctest: +SKIP
[Row(add_one(id)=1), Row(add_one(id)=2), Row(add_one(id)=3)]
+ >>> @pandas_udf("integer", PandasUDFType.GROUPED_AGG) # doctest:
+SKIP
+ ... def sum_udf(v):
+ ... return v.sum()
+ ...
+ >>> _ = spark.udf.register("sum_udf", sum_udf) # doctest: +SKIP
+ >>> q = "SELECT sum_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1)
tbl(v1, v2) GROUP BY v2"
+ >>> spark.sql(q).collect() # doctest: +SKIP
+ [Row(sum_udf(v1)=1), Row(sum_udf(v1)=5)]
+
.. note:: Registration for a user-defined function (case 2.) was
added from
Spark 2.3.0.
"""
@@ -310,9 +319,11 @@ class UDFRegistration(object):
"Invalid returnType: data type can not be specified when f
is"
"a user-defined function, but got %s." % returnType)
if f.evalType not in [PythonEvalType.SQL_BATCHED_UDF,
- PythonEvalType.SQL_SCALAR_PANDAS_UDF]:
+ PythonEvalType.SQL_SCALAR_PANDAS_UDF,
+ PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF]:
raise ValueError(
- "Invalid f: f must be either SQL_BATCHED_UDF or
SQL_SCALAR_PANDAS_UDF")
+ "Invalid f: f must be SQL_BATCHED_UDF,
SQL_SCALAR_PANDAS_UDF or "
+ "SQL_GROUPED_AGG_PANDAS_UDF")
register_udf = UserDefinedFunction(f.func,
returnType=f.returnType, name=name,
evalType=f.evalType,
deterministic=f.deterministic)
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