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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