[
https://issues.apache.org/jira/browse/SPARK-33073?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17208523#comment-17208523
]
Apache Spark commented on SPARK-33073:
--------------------------------------
User 'BryanCutler' has created a pull request for this issue:
https://github.com/apache/spark/pull/29951
> Improve error handling on Pandas to Arrow conversion failures
> -------------------------------------------------------------
>
> Key: SPARK-33073
> URL: https://issues.apache.org/jira/browse/SPARK-33073
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 3.0.1
> Reporter: Bryan Cutler
> Priority: Major
>
> Currently, when converting from Pandas to Arrow for Pandas UDF return values
> or from createDataFrame(), PySpark will catch all ArrowExceptions and display
> info on how to disable the safe conversion config. This is displayed with the
> original error as a tuple:
> {noformat}
> ('Exception thrown when converting pandas.Series (object) to Arrow Array
> (int32). It can be caused by overflows or other unsafe conversions warned by
> Arrow. Arrow safe type check can be disabled by using SQL config
> `spark.sql.execution.pandas.convertToArrowArraySafely`.', ArrowInvalid('Could
> not convert a with type str: tried to convert to int'))
> {noformat}
> The problem is that this is meant mainly for thing like float truncation or
> overflow, but will also show if the user has an invalid schema with types
> that are incompatible. The extra information is confusing in this case and
> the real error is buried.
> This could be improved by only printing the extra info on how to disable safe
> checking if the config is actually set and using exception chaining to better
> show the original error. Also, any safe failures would be a ValueError, which
> ArrowInvaildError is a subclass, so the catch could be made more narrow.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
