HyukjinKwon commented on a change in pull request #28993:
URL: https://github.com/apache/spark/pull/28993#discussion_r451234819
##########
File path:
sql/catalyst/src/test/scala/org/apache/spark/sql/connector/InMemoryTable.scala
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@@ -78,10 +92,44 @@ class InMemoryTable(
throw new IllegalArgumentException(s"Unsupported type,
${dataType.simpleString}")
}
} else {
- value
+ (value, schema(index).dataType)
}
}
- partCols.map(fieldNames => extractor(fieldNames, schema, row))
+
+ partitioning.map {
+ case IdentityTransform(ref) =>
+ extractor(ref.fieldNames, schema, row)._1
+ case YearsTransform(ref) =>
+ extractor(ref.fieldNames, schema, row) match {
+ case (days: Int, DateType) =>
+ ChronoUnit.YEARS.between(EPOCH_LOCAL_DATE,
DateTimeUtils.daysToLocalDate(days))
+ case (micros: Long, TimestampType) =>
+ val localDate =
DateTimeUtils.microsToInstant(micros).atZone(UTC).toLocalDate
+ ChronoUnit.YEARS.between(EPOCH_LOCAL_DATE, localDate)
Review comment:
Hm .. @rdblue, will this be the default behaviour for these partitioning
expressions? I wonder what happens if other datasources implement these in a
different implementation. For example, some expressions might respect Spark
session timezone.
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