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https://issues.apache.org/jira/browse/SPARK-30788?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17503127#comment-17503127
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Max Gekk edited comment on SPARK-30788 at 3/8/22, 6:55 PM:
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It was merged to 3.0
https://github.com/apache/spark/commit/2a059e65bae93ddb61f7154d81da3fa0c2dcb669
was (Author: maxgekk):
It was merged to 3.0.0
https://github.com/apache/spark/commit/2a059e65bae93ddb61f7154d81da3fa0c2dcb669
> Support `SimpleDateFormat` and `FastDateFormat` as legacy date/timestamp
> formatters
> -----------------------------------------------------------------------------------
>
> Key: SPARK-30788
> URL: https://issues.apache.org/jira/browse/SPARK-30788
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Max Gekk
> Assignee: Max Gekk
> Priority: Major
> Fix For: 3.0.0
>
>
> To be absolutely sure that Spark 3.0 is compatible with 2.4 when
> spark.sql.legacy.timeParser.enabled is set to true, need to support
> SimpleDateFormat and FastDateFormat as legacy parsers/formatters in
> TimestampFormatter.
> Spark 2.4.x uses the following parsers for parsing/formatting date/timestamp
> strings:
> # DateTimeFormat in CSV/JSON datasource
> # SimpleDateFormat - is used in JDBC datasource, in partitions parsing.
> # SimpleDateFormat in strong mode (lenient = false). It is used by the
> date_format, from_unixtime, unix_timestamp and to_unix_timestamp functions.
> Spark 3.0 should use the same parsers in those cases.
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