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https://issues.apache.org/jira/browse/SPARK-22010?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16166097#comment-16166097
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Hyukjin Kwon commented on SPARK-22010:
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I don't think this is worth fixing for now. The improvement looks quite trivial
but it sounds we should reinvent the wheel. Do you know a simple and well-known
workaround or any measurement between the custom fix and the current status?
Otherwise, I'd close this as {{Won't Fix}}.
> Slow fromInternal conversion for TimestampType
> ----------------------------------------------
>
> Key: SPARK-22010
> URL: https://issues.apache.org/jira/browse/SPARK-22010
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.2.0
> Reporter: Maciej Bryński
>
> To convert timestamp type to python we are using
> `datetime.datetime.fromtimestamp(ts // 1000000).replace(microsecond=ts %
> 1000000)`
> code.
> {code}
> In [34]: %%timeit
> ...:
> datetime.datetime.fromtimestamp(1505383647).replace(microsecond=12344)
> ...:
> 4.2 µs ± 558 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
> {code}
> It's slow, because:
> # we're trying to get TZ on every conversion
> # we're using replace method
> Proposed solution: custom datetime conversion and move calculation of TZ to
> module
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