allisonwang-db opened a new pull request, #42290:
URL: https://github.com/apache/spark/pull/42290
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### What changes were proposed in this pull request?
This PR cherry-picks
https://github.com/apache/spark/commit/5384f4601a4ba8daba76d67e945eaa6fc2b70b2c.
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?
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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?
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New unit tests
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