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Posted to reviews@spark.apache.org by "ueshin (via GitHub)" <gi...@apache.org> on 2023/07/26 01:50:31 UTC
[GitHub] [spark] ueshin opened a new pull request, #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
ueshin opened a new pull request, #42161:
URL: https://github.com/apache/spark/pull/42161
### What changes were proposed in this pull request?
Fixes `ArrowStreamPandasUDFSerializer` to accept no-column pandas DataFrame.
```py
>>> def _scalar_f(id):
... return pd.DataFrame(index=id)
...
>>> scalar_f = pandas_udf(_scalar_f, returnType=StructType())
>>> df = spark.range(3).withColumn("f", scalar_f(col("id")))
>>> df.printSchema()
root
|-- id: long (nullable = false)
|-- f: struct (nullable = true)
>>> df.show()
+---+---+
| id| f|
+---+---+
| 0| {}|
| 1| {}|
| 2| {}|
+---+---+
```
### Why are the changes needed?
The above query fails with the following error:
```py
>>> df.show()
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
...
ValueError: not enough values to unpack (expected 2, got 0)
```
### Does this PR introduce _any_ user-facing change?
Yes, Pandas UDF will accept no-column pandas DataFrame.
### How was this patch tested?
Added related tests.
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[GitHub] [spark] ueshin commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1275516715
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -932,8 +940,7 @@ def eval(self, *args):
StructType().add("col0", IntegerType()).add("col1", StringType()),
[Row(a=1, b="x")],
),
- # TODO(SPARK-44479): Support Python UDTFs with empty schema
- # (func(), StructType(), [Row()]),
+ (func(), StructType(), [Row()]),
Review Comment:
https://github.com/apache/spark/pull/42176
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -932,8 +940,7 @@ def eval(self, *args):
StructType().add("col0", IntegerType()).add("col1", StringType()),
[Row(a=1, b="x")],
),
- # TODO(SPARK-44479): Support Python UDTFs with empty schema
- # (func(), StructType(), [Row()]),
+ (func(), StructType(), [Row()]),
Review Comment:
For 3.5: https://github.com/apache/spark/pull/42176
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[GitHub] [spark] ueshin commented on pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on PR #42161:
URL: https://github.com/apache/spark/pull/42161#issuecomment-1650850836
cc @HyukjinKwon @allisonwang-db
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[GitHub] [spark] ueshin commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1275448295
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -932,8 +940,7 @@ def eval(self, *args):
StructType().add("col0", IntegerType()).add("col1", StringType()),
[Row(a=1, b="x")],
),
- # TODO(SPARK-44479): Support Python UDTFs with empty schema
- # (func(), StructType(), [Row()]),
+ (func(), StructType(), [Row()]),
Review Comment:
We should backport this PR to 3.5 except for this change.
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[GitHub] [spark] ueshin commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1274361664
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -385,37 +385,28 @@ def _create_struct_array(self, df, arrow_struct_type):
"""
import pyarrow as pa
- # Input partition and result pandas.DataFrame empty, make empty Arrays with struct
- if len(df) == 0 and len(df.columns) == 0:
- arrs_names = [
- (pa.array([], type=field.type), field.name) for field in arrow_struct_type
- ]
+ if len(df.columns) == 0:
+ return pa.array([{}] * len(df), arrow_struct_type)
Review Comment:
For example:
```py
>>> pdf = pd.DataFrame(index=[1,2,3])
>>> pdf
Empty DataFrame
Columns: []
Index: [1, 2, 3]
>>> len(pdf)
3
>>> len(pdf.columns)
0
```
or
```py
>>> pd.DataFrame([tuple(), tuple(), tuple()])
Empty DataFrame
Columns: []
Index: [0, 1, 2]
```
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[GitHub] [spark] ueshin commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1275448295
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -932,8 +940,7 @@ def eval(self, *args):
StructType().add("col0", IntegerType()).add("col1", StringType()),
[Row(a=1, b="x")],
),
- # TODO(SPARK-44479): Support Python UDTFs with empty schema
- # (func(), StructType(), [Row()]),
+ (func(), StructType(), [Row()]),
Review Comment:
We should backport this PR except for this line.
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[GitHub] [spark] ueshin commented on pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on PR #42161:
URL: https://github.com/apache/spark/pull/42161#issuecomment-1652751432
Thanks! merging to master.
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[GitHub] [spark] allisonwang-db commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1275240003
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -487,6 +487,14 @@ def eval(self, x: int):
self.assertEqual(TestUDTF(lit(1)).collect(), [Row(x={1: "1"})])
+ def test_udtf_with_empty_output_types(self):
+ @udtf(returnType=StructType())
Review Comment:
This might be a related error:
```
pyspark.errors.exceptions.connect.SparkConnectGrpcException: (org.apache.spark.sql.connect.common.InvalidPlanInput) Does not support convert KIND_NOT_SET to catalyst types.
```
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[GitHub] [spark] ueshin closed pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin closed pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
URL: https://github.com/apache/spark/pull/42161
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[GitHub] [spark] ueshin commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1274361664
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -385,37 +385,28 @@ def _create_struct_array(self, df, arrow_struct_type):
"""
import pyarrow as pa
- # Input partition and result pandas.DataFrame empty, make empty Arrays with struct
- if len(df) == 0 and len(df.columns) == 0:
- arrs_names = [
- (pa.array([], type=field.type), field.name) for field in arrow_struct_type
- ]
+ if len(df.columns) == 0:
+ return pa.array([{}] * len(df), arrow_struct_type)
Review Comment:
For example:
```py
>>> pdf = pd.DataFrame(index=[1,2,3])
>>> pdf
Empty DataFrame
Columns: []
Index: [1, 2, 3]
>>> len(pdf)
3
>>> len(pdf.columns)
0
```
--
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[GitHub] [spark] ueshin commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1275448791
##########
python/pyspark/sql/connect/types.py:
##########
@@ -170,14 +170,16 @@ def pyspark_types_to_proto_types(data_type: DataType) -> pb2.DataType:
ret.year_month_interval.start_field = data_type.startField
ret.year_month_interval.end_field = data_type.endField
elif isinstance(data_type, StructType):
+ struct = pb2.DataType.Struct()
for field in data_type.fields:
struct_field = pb2.DataType.StructField()
struct_field.name = field.name
struct_field.data_type.CopyFrom(pyspark_types_to_proto_types(field.dataType))
struct_field.nullable = field.nullable
if field.metadata is not None and len(field.metadata) > 0:
struct_field.metadata = json.dumps(field.metadata)
- ret.struct.fields.append(struct_field)
+ struct.fields.append(struct_field)
+ ret.struct.CopyFrom(struct)
Review Comment:
We might want to backport this part to 3.4, too.
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -932,8 +940,7 @@ def eval(self, *args):
StructType().add("col0", IntegerType()).add("col1", StringType()),
[Row(a=1, b="x")],
),
- # TODO(SPARK-44479): Support Python UDTFs with empty schema
- # (func(), StructType(), [Row()]),
+ (func(), StructType(), [Row()]),
Review Comment:
We should backport this PR to 3.5 except for this line.
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[GitHub] [spark] allisonwang-db commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1274354197
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -385,37 +385,28 @@ def _create_struct_array(self, df, arrow_struct_type):
"""
import pyarrow as pa
- # Input partition and result pandas.DataFrame empty, make empty Arrays with struct
- if len(df) == 0 and len(df.columns) == 0:
- arrs_names = [
- (pa.array([], type=field.type), field.name) for field in arrow_struct_type
- ]
+ if len(df.columns) == 0:
+ return pa.array([{}] * len(df), arrow_struct_type)
Review Comment:
Under what scenario would it be possible for a DataFrame to have rows but no columns? i.e len(df.columns) == 0 but len(df) > 0?
##########
python/pyspark/sql/tests/test_udtf.py:
##########
@@ -487,6 +487,14 @@ def eval(self, x: int):
self.assertEqual(TestUDTF(lit(1)).collect(), [Row(x={1: "1"})])
+ def test_udtf_with_empty_output_types(self):
+ @udtf(returnType=StructType())
+ class TestUDTF:
+ def eval(self):
+ yield tuple()
+
+ self.assertEqual(TestUDTF().collect(), [Row()])
Review Comment:
Let's use assertDataFrameEqual :)
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -385,37 +385,28 @@ def _create_struct_array(self, df, arrow_struct_type):
"""
import pyarrow as pa
- # Input partition and result pandas.DataFrame empty, make empty Arrays with struct
- if len(df) == 0 and len(df.columns) == 0:
- arrs_names = [
- (pa.array([], type=field.type), field.name) for field in arrow_struct_type
- ]
+ if len(df.columns) == 0:
+ return pa.array([{}] * len(df), arrow_struct_type)
# Assign result columns by schema name if user labeled with strings
elif self._assign_cols_by_name and any(isinstance(name, str) for name in df.columns):
Review Comment:
```suggestion
if self._assign_cols_by_name and any(isinstance(name, str) for name in df.columns):
```
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[GitHub] [spark] allisonwang-db commented on a diff in pull request #42161: [SPARK-44479][PYTHON] Fix ArrowStreamPandasUDFSerializer to accept no-column pandas DataFrame
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #42161:
URL: https://github.com/apache/spark/pull/42161#discussion_r1275479798
##########
python/pyspark/sql/connect/types.py:
##########
@@ -170,14 +170,16 @@ def pyspark_types_to_proto_types(data_type: DataType) -> pb2.DataType:
ret.year_month_interval.start_field = data_type.startField
ret.year_month_interval.end_field = data_type.endField
elif isinstance(data_type, StructType):
+ struct = pb2.DataType.Struct()
for field in data_type.fields:
struct_field = pb2.DataType.StructField()
struct_field.name = field.name
struct_field.data_type.CopyFrom(pyspark_types_to_proto_types(field.dataType))
struct_field.nullable = field.nullable
if field.metadata is not None and len(field.metadata) > 0:
struct_field.metadata = json.dumps(field.metadata)
- ret.struct.fields.append(struct_field)
+ struct.fields.append(struct_field)
+ ret.struct.CopyFrom(struct)
Review Comment:
Good catch!
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