Github user icexelloss commented on the issue:
https://github.com/apache/spark/pull/21650
@BryanCutler I think your suggestion would change the behavior. Using
ArrowEvalExec and BatchEvalExec are still different when it comes to corner
cases, for example, type coercion (ArrowEvalExec supports type coercion but
BatchEvalExec doesn't) and timestamp type (regular UDF expects Python datetime
for timestamp and pandas UDF expects pd.Timestamp)
I think this is probably a good future improvement but not great for this
Jira because of the behavior change. WDYT?
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