Hisoka-X commented on code in PR #43243:
URL: https://github.com/apache/spark/pull/43243#discussion_r1454478275
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/csv/CSVInferSchema.scala:
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@@ -202,8 +202,11 @@ class CSVInferSchema(val options: CSVOptions) extends
Serializable {
// We can only parse the value as TimestampNTZType if it does not have
zone-offset or
// time-zone component and can be parsed with the timestamp formatter.
// Otherwise, it is likely to be a timestamp with timezone.
- if (timestampNTZFormatter.parseWithoutTimeZoneOptional(field,
false).isDefined) {
- SQLConf.get.timestampType
+ val timestampType = SQLConf.get.timestampType
+ if ((SQLConf.get.legacyTimeParserPolicy == LegacyBehaviorPolicy.LEGACY ||
+ timestampType == TimestampNTZType) &&
+ timestampNTZFormatter.parseWithoutTimeZoneOptional(field,
false).isDefined) {
Review Comment:
I did some test use old version which before this PR merged. When
`SQLConf.get.timestampType == TIMESTAMP_LTZ`, it used `timestampNTZFormatter`
to parsed timestamp and passed. You can use
`CSVLegacyTimeParserSuite.SPARK-37326: Timestamp type inference for a column
with TIMESTAMP_NTZ values` to reproduce this behavior.
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