namrathamyske commented on code in PR #8042:
URL: https://github.com/apache/iceberg/pull/8042#discussion_r1277927973
##########
spark/v3.3/spark-extensions/src/main/scala/org/apache/spark/sql/execution/datasources/v2/ExtendedDistributionAndOrderingUtils.scala:
##########
@@ -57,10 +61,34 @@ object ExtendedDistributionAndOrderingUtils {
} else {
conf.numShufflePartitions
}
- // the conversion to catalyst expressions above produces SortOrder
expressions
- // for OrderedDistribution and generic expressions for
ClusteredDistribution
- // this allows RepartitionByExpression to pick either range or hash
partitioning
- RepartitionByExpression(ArraySeq.unsafeWrapArray(distribution), query,
finalNumPartitions)
+
+ val tableProperties = table match {
+ case d : RowLevelOperationTable => d.table.properties()
+ case _ : Table => table.properties()
+ }
+
+ val isHashDistributionMode = write.requiredDistribution match {
+ case _ : ClusteredDistribution => true
+ case _ => false
+ }
+
+ val strictDistributionMode = tableProperties
+ .getOrDefault(TableProperties.STRICT_TABLE_DISTRIBUTION_AND_ORDERING,
+ TableProperties.STRICT_TABLE_DISTRIBUTION_AND_ORDERING_DEFAULT)
+
+ if (strictDistributionMode.equals("false") && isHashDistributionMode) {
+ // if strict distribution mode is not enabled, then we fallback to
spark AQE
+ // to determine the number of partitions by colaesceing and
un-skewing partitions
+ // Also to note, Rebalance is only supported for hash distribution
mode till spark 3.3
Review Comment:
@aokolnychyi in spark 3.4 both hash, range is supported for rebalance
operator. But for spark 3.3 only hash is supported.I will change the statement
to `in spark 3.3`. Do we we want to fall back to rebalance even for none
distribution mode (aka round robin partitioning)
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