[SPARK-44559][PYTHON][3.5] Improve error messages for Python UDTF arrow cast#42290
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[SPARK-44559][PYTHON][3.5] Improve error messages for Python UDTF arrow cast#42290allisonwang-db wants to merge 1 commit intoapache:branch-3.5from
allisonwang-db wants to merge 1 commit intoapache:branch-3.5from
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This PR improves error messages when the output of an arrow-optimized Python UDTF cannot be casted to the specified return schema of the UDTF.
To make Python UDTFs more user-friendly.
Yes, before this PR, when the output of a UDTF fails to cast to the desired schema, Spark will throw this confusing error message:
```python
udtf(returnType="x: int")
class TestUDTF:
def eval(self):
yield [1, 2],
TestUDTF().collect()
```
```
File "pyarrow/array.pxi", line 1044, in pyarrow.lib.Array.from_pandas
File "pyarrow/array.pxi", line 316, in pyarrow.lib.array
File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Could not convert [1, 2] with type list: tried to convert to int32
```
Now, after this PR, the error message will look like this:
`pyspark.errors.exceptions.base.PySparkRuntimeError: [UDTF_ARROW_TYPE_CAST_ERROR] Cannot convert the output value of the column 'x' with type 'object' to the specified return type of the column: 'int32'. Please check if the data types match and try again.
`
New unit tests
Closes apache#42191 from allisonwang-db/spark-44559-arrow-cast.
Authored-by: allisonwang-db <allison.wang@databricks.com>
Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
(cherry picked from commit 5384f46)
Signed-off-by: allisonwang-db <allison.wang@databricks.com>
HyukjinKwon
approved these changes
Aug 2, 2023
ueshin
approved these changes
Aug 2, 2023
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Thanks! merging to 3.5. |
ueshin
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Aug 2, 2023
…ow cast ### What changes were proposed in this pull request? This PR cherry-picks 5384f46. It improves error messages when the output of an arrow-optimized Python UDTF cannot be casted to the specified return schema of the UDTF. ### Why are the changes needed? To make Python UDTFs more user-friendly. ### Does this PR introduce _any_ user-facing change? Yes, before this PR, when the output of a UDTF fails to cast to the desired schema, Spark will throw this confusing error message: ```python udtf(returnType="x: int") class TestUDTF: def eval(self): yield [1, 2], TestUDTF().collect() ``` ``` File "pyarrow/array.pxi", line 1044, in pyarrow.lib.Array.from_pandas File "pyarrow/array.pxi", line 316, in pyarrow.lib.array File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Could not convert [1, 2] with type list: tried to convert to int32 ``` Now, after this PR, the error message will look like this: `pyspark.errors.exceptions.base.PySparkRuntimeError: [UDTF_ARROW_TYPE_CAST_ERROR] Cannot convert the output value of the column 'x' with type 'object' to the specified return type of the column: 'int32'. Please check if the data types match and try again. ` ### How was this patch tested? New unit tests Closes #42290 from allisonwang-db/spark-44559-3.5. Authored-by: allisonwang-db <allison.wang@databricks.com> Signed-off-by: Takuya UESHIN <ueshin@databricks.com>
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What changes were proposed in this pull request?
This PR cherry-picks 5384f46. It improves error messages when the output of an arrow-optimized Python UDTF cannot be casted to the specified return schema of the UDTF.
Why are the changes needed?
To make Python UDTFs more user-friendly.
Does this PR introduce any user-facing change?
Yes, before this PR, when the output of a UDTF fails to cast to the desired schema, Spark will throw this confusing error message:
Now, after this PR, the error message will look like this:
pyspark.errors.exceptions.base.PySparkRuntimeError: [UDTF_ARROW_TYPE_CAST_ERROR] Cannot convert the output value of the column 'x' with type 'object' to the specified return type of the column: 'int32'. Please check if the data types match and try again.How was this patch tested?
New unit tests