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[SPARK-48142][PYTHON][CONNECT][TESTS] Enable CogroupedApplyInPandasTests.test_wrong_args #46397

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[SPARK-48142][PYTHON][CONNECT][TESTS] Enable CogroupedApplyInPandasTests.test_wrong_args #46397

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@zhengruifeng zhengruifeng commented May 6, 2024 •
edited

What changes were proposed in this pull request?

Enable CogroupedApplyInPandasTests.test_wrong_args by including a missing check

Why are the changes needed?

for test coverage

Does this PR introduce any user-facing change?

no

How was this patch tested?

ci

Was this patch authored or co-authored using generative AI tooling?

no

@zhengruifeng zhengruifeng changed the title [MINOR][DOCS] Make a dataframe.join doctest deterministic [WIP] Enable CogroupedApplyInPandasTests.test_wrong_args May 6, 2024
@zhengruifeng zhengruifeng changed the title [WIP] Enable CogroupedApplyInPandasTests.test_wrong_args [WIP] Enable CogroupedApplyInPandasTests.test_wrong_args May 6, 2024
@zhengruifeng zhengruifeng changed the title [WIP] Enable CogroupedApplyInPandasTests.test_wrong_args [SPARK-48142][PYTHON][CONNECT][TESTS] Enable CogroupedApplyInPandasTests.test_wrong_args May 6, 2024
zhengruifeng
zhengruifeng commented May 6, 2024
View reviewed changes
python/pyspark/sql/connect/group.py
func,
schema,
PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF,
)

udf_obj = UserDefinedFunction(
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PySpark Classic directly uses the pandas_udf method which include this check

spark/python/pyspark/sql/pandas/group_ops.py

Lines 617 to 620 in c74f584

# The usage of the pandas_udf is internal so type checking is disabled.
udf = pandas_udf(
func, returnType=schema, functionType=PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF
) # type: ignore[call-overload]

while in Spark Connect, it cannot use the output of pandas_udf method here and requires a UserDefinedFunction object.

So I factor the check to a separate method and invoke it here.

@zhengruifeng
nit
4304b9b 
nit

nit
@xinrong-meng
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LGTM thank you!

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thanks @xinrong-meng

merged to master

@zhengruifeng zhengruifeng deleted the fix_pandas_udf_check branch May 7, 2024 01:16
@zhengruifeng zhengruifeng mentioned this pull request May 10, 2024
Closed
dongjoon-hyun pushed a commit that referenced this pull request May 10, 2024
@zhengruifeng @dongjoon-hyun
[SPARK-48228][PYTHON][CONNECT] Implement the missing function validat…
9bb15db 
…ion in ApplyInXXX

### What changes were proposed in this pull request?
Implement the missing function validation in ApplyInXXX

#46397 fixed this issue for `Cogrouped.ApplyInPandas`, this PR fix remaining methods.

### Why are the changes needed?
for better error message:

```
In [12]: df1 = spark.range(11)

In [13]: df2 = df1.groupby("id").applyInPandas(lambda: 1, StructType([StructField("d", DoubleType())]))

In [14]: df2.show()
```

before this PR, an invalid function causes weird execution errors:
```
24/05/10 11:37:36 ERROR Executor: Exception in task 0.0 in stage 10.0 (TID 36)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1834, in main
    process()
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1826, in process
    serializer.dump_stream(out_iter, outfile)
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 531, in dump_stream
    return ArrowStreamSerializer.dump_stream(self, init_stream_yield_batches(), stream)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 104, in dump_stream
    for batch in iterator:
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 524, in init_stream_yield_batches
    for series in iterator:
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1610, in mapper
    return f(keys, vals)
           ^^^^^^^^^^^^^
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 488, in <lambda>
    return lambda k, v: [(wrapped(k, v), to_arrow_type(return_type))]
                          ^^^^^^^^^^^^^
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 483, in wrapped
    result, return_type, _assign_cols_by_name, truncate_return_schema=False
    ^^^^^^
UnboundLocalError: cannot access local variable 'result' where it is not associated with a value

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:523)
	at org.apache.spark.sql.execution.python.PythonArrowOutput$$anon$1.read(PythonArrowOutput.scala:117)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:479)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:601)
	at scala.collection.Iterator$$anon$9.hasNext(Iterator.scala:583)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvaluator$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:50)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:896)

	...
```

After this PR, the error happens before execution, which is consistent with Spark Classic, and
 much clear
```
PySparkValueError: [INVALID_PANDAS_UDF] Invalid function: pandas_udf with function type GROUPED_MAP or the function in groupby.applyInPandas must take either one argument (data) or two arguments (key, data).

```

### Does this PR introduce _any_ user-facing change?
yes, error message changes

### How was this patch tested?
added tests

### Was this patch authored or co-authored using generative AI tooling?
no

Closes #46519 from zhengruifeng/missing_check_in_group.

Authored-by: Ruifeng Zheng <ruifengz@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
JacobZheng0927 pushed a commit to JacobZheng0927/spark that referenced this pull request May 11, 2024
@zhengruifeng @JacobZheng0927
[SPARK-48142][PYTHON][CONNECT][TESTS] Enable `CogroupedApplyInPandasT…
9ae157e 
…ests.test_wrong_args`

### What changes were proposed in this pull request?
Enable `CogroupedApplyInPandasTests.test_wrong_args` by including a missing check

### Why are the changes needed?
for test coverage

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?
ci

### Was this patch authored or co-authored using generative AI tooling?
no

Closes apache#46397 from zhengruifeng/fix_pandas_udf_check.

Authored-by: Ruifeng Zheng <ruifengz@apache.org>
Signed-off-by: Ruifeng Zheng <ruifengz@apache.org>
JacobZheng0927 pushed a commit to JacobZheng0927/spark that referenced this pull request May 11, 2024
@zhengruifeng @JacobZheng0927
[SPARK-48228][PYTHON][CONNECT] Implement the missing function validat…
c5da268 
…ion in ApplyInXXX

### What changes were proposed in this pull request?
Implement the missing function validation in ApplyInXXX

apache#46397 fixed this issue for `Cogrouped.ApplyInPandas`, this PR fix remaining methods.

### Why are the changes needed?
for better error message:

```
In [12]: df1 = spark.range(11)

In [13]: df2 = df1.groupby("id").applyInPandas(lambda: 1, StructType([StructField("d", DoubleType())]))

In [14]: df2.show()
```

before this PR, an invalid function causes weird execution errors:
```
24/05/10 11:37:36 ERROR Executor: Exception in task 0.0 in stage 10.0 (TID 36)
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1834, in main
    process()
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1826, in process
    serializer.dump_stream(out_iter, outfile)
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 531, in dump_stream
    return ArrowStreamSerializer.dump_stream(self, init_stream_yield_batches(), stream)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 104, in dump_stream
    for batch in iterator:
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/sql/pandas/serializers.py", line 524, in init_stream_yield_batches
    for series in iterator:
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 1610, in mapper
    return f(keys, vals)
           ^^^^^^^^^^^^^
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 488, in <lambda>
    return lambda k, v: [(wrapped(k, v), to_arrow_type(return_type))]
                          ^^^^^^^^^^^^^
  File "/Users/ruifeng.zheng/Dev/spark/python/lib/pyspark.zip/pyspark/worker.py", line 483, in wrapped
    result, return_type, _assign_cols_by_name, truncate_return_schema=False
    ^^^^^^
UnboundLocalError: cannot access local variable 'result' where it is not associated with a value

	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:523)
	at org.apache.spark.sql.execution.python.PythonArrowOutput$$anon$1.read(PythonArrowOutput.scala:117)
	at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:479)
	at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
	at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:601)
	at scala.collection.Iterator$$anon$9.hasNext(Iterator.scala:583)
	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
	at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
	at org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvaluator$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:50)
	at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)
	at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:896)

	...
```

After this PR, the error happens before execution, which is consistent with Spark Classic, and
 much clear
```
PySparkValueError: [INVALID_PANDAS_UDF] Invalid function: pandas_udf with function type GROUPED_MAP or the function in groupby.applyInPandas must take either one argument (data) or two arguments (key, data).

```

### Does this PR introduce _any_ user-facing change?
yes, error message changes

### How was this patch tested?
added tests

### Was this patch authored or co-authored using generative AI tooling?
no

Closes apache#46519 from zhengruifeng/missing_check_in_group.

Authored-by: Ruifeng Zheng <ruifengz@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
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@xinrong-meng xinrong-meng xinrong-meng approved these changes

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