Github user HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/15147 To continue the discussion of JIRA, I think the issue you faced is to read those in CSV? Whether it is intended or not in `FastDateFormat`, the default pattern `"yyyy-MM-dd'T'HH:mm:ss.SSSZZ"` covers this. it seems I can't reproduce the issue you met in the JIRA. Have you tried the problematic codes in the master branch? That would not ran in Spark 2.0 but we made a change. ```scala val path = "/tmp/timestamps" val textDf = Seq( "time", "2015-07-20T15:09:23.736-0500", "2015-07-20T15:10:51.687-0500", "2015-11-21T23:15:01.499-0600").toDF() textDf.coalesce(1).write.text(path) val df = spark.read.format("csv") .option("header", "true") .option("inferSchema", "true") .load(path) df.show() df.printSchema() ``` works fine ``` +--------------------+ | time| +--------------------+ |2015-07-20 13:09:...| |2015-07-20 13:10:...| |2015-11-21 21:15:...| +--------------------+ root |-- time: timestamp (nullable = true) ```
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