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

2020-07-08 Thread GitBox 


HyukjinKwon commented on a change in pull request #28993:
URL: https://github.com/apache/spark/pull/28993#discussion_r451898804



##
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:
   Sure, sorry it was clearly my misunderstanding. LGTM.





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

2020-07-07 Thread GitBox 


HyukjinKwon commented on a change in pull request #28993:
URL: https://github.com/apache/spark/pull/28993#discussion_r451292098



##
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:
   Oh! It was in the test!





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

2020-07-07 Thread GitBox 


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
##
@@ -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 way, for example, some expressions might respect Spark session 
timezone.





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

2020-07-07 Thread GitBox 


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
##
@@ -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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