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https://issues.apache.org/jira/browse/SPARK-39731?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17564744#comment-17564744
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Apache Spark commented on SPARK-39731:
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User 'sadikovi' has created a pull request for this issue:
https://github.com/apache/spark/pull/37147
> Correctness issue when parsing dates with yyyyMMdd format in CSV
> ----------------------------------------------------------------
>
> Key: SPARK-39731
> URL: https://issues.apache.org/jira/browse/SPARK-39731
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.4.0
> Reporter: Ivan Sadikov
> Priority: Major
>
> In Spark 3.x, when reading CSV data like this:
> {code:java}
> name,mydate
> 1,2020011
> 2,20201203{code}
> and specifying date pattern as "yyyyMMdd", dates are not parsed correctly
> with CORRECTED time parser policy.
> For example,
> {code:java}
> val df = spark.read.schema("name string, mydate date").option("dateFormat",
> "yyyyMMdd").option("header", "true").csv("file:/tmp/test.csv")
> df.show(false){code}
> Returns:
> {code:java}
> +----+--------------+
> |name|mydate |
> +----+--------------+
> |1 |+2020011-01-01|
> |2 |2020-12-03 |
> +----+--------------+ {code}
> and it used to return null instead of the invalid date in Spark 3.2 or below.
>
> The issue appears to be caused by this PR:
> [https://github.com/apache/spark/pull/32959].
>
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