ueshin opened a new pull request, #40998:
URL: https://github.com/apache/spark/pull/40998
### What changes were proposed in this pull request?
Fixes `DataFrame.toPandas` with Arrow enabled to handle exceptions properly.
```py
>>> spark.conf.set("spark.sql.ansi.enabled", True)
>>> spark.conf.set('spark.sql.execution.arrow.pyspark.enabled', True)
>>> spark.sql("select 1/0").toPandas()
...
Traceback (most recent call last):
...
pyspark.errors.exceptions.captured.ArithmeticException: [DIVIDE_BY_ZERO]
Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL
instead. If necessary set "spark.sql.ansi.enabled" to "false" to bypass this
error.
== SQL(line 1, position 8) ==
select 1/0
^^^
```
### Why are the changes needed?
Currently `DataFrame.toPandas` doesn't capture exceptions happened in Spark
properly.
```py
>>> spark.conf.set("spark.sql.ansi.enabled", True)
>>> spark.conf.set('spark.sql.execution.arrow.pyspark.enabled', True)
>>> spark.sql("select 1/0").toPandas()
...
An error occurred while calling o53.getResult.
: org.apache.spark.SparkException: Exception thrown in awaitResult:
at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:322)
...
```
because `jsocket_auth_server.getResult()` always wraps the thrown exceptions
with `SparkException` that won't be captured.
Whereas without Arrow:
```py
>>> spark.conf.set("spark.sql.ansi.enabled", True)
>>> spark.conf.set('spark.sql.execution.arrow.pyspark.enabled', False)
>>> spark.sql("select 1/0").toPandas()
Traceback (most recent call last):
...
pyspark.errors.exceptions.captured.ArithmeticException: [DIVIDE_BY_ZERO]
Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL
instead. If necessary set "spark.sql.ansi.enabled" to "false" to bypass this
error.
== SQL(line 1, position 8) ==
select 1/0
^^^
```
### Does this PR introduce _any_ user-facing change?
`DataFrame.toPandas` with Arrow enabled will show a proper exception.
### How was this patch tested?
Added the related test.
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