[GitHub] [spark] MaxGekk commented on a diff in pull request #41078: [SPARK-39280][SQL] Speed up Timestamp type inference with user-provided format in JSON/CSV data source

["MaxGekk (via GitHub)" <gi...@apache.org>]

MaxGekk commented on code in PR #41078:
URL: https://github.com/apache/spark/pull/41078#discussion_r1192102884


##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TimestampFormatter.scala:
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@@ -163,27 +165,66 @@ class Iso8601TimestampFormatter(
   protected lazy val legacyFormatter = TimestampFormatter.getLegacyFormatter(
     pattern, zoneId, locale, legacyFormat)
 
+  override def parseOptional(s: String): Option[Long] = {
+    try {
+      val parsed = formatter.parseUnresolved(s, new ParsePosition(0))

Review Comment:
   `parseUnresolved()` doesn't validate correctness of result, see its docs:
   ```
   The result of this method is TemporalAccessor which represents the data as seen in the input. Values are not validated, thus parsing a date string of '2012-00-65' would result in a temporal with three fields - year of '2012', month of '0' and day-of-month of '65'.
   ```
   
   I think you should check the result yourself somewhere.
   
   Could you re-check the example from the docs: '2012-00-65' and add a test, please.



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