[GitHub] [spark] cloud-fan commented on a change in pull request #28993: [SPARK-32168][SQL] Fix hidden partitioning correctness bug in SQL overwrite

[GitBox <gi...@apache.org>]

cloud-fan commented on a change in pull request #28993:
URL: https://github.com/apache/spark/pull/28993#discussion_r451280565



##########
File path: sql/catalyst/src/test/scala/org/apache/spark/sql/connector/InMemoryTable.scala
##########
@@ -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:
       It's a testing implementation. There is no default behavior. Partitioning expression just indicates how the scan can be faster with specific pushed filters. 




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