cloud-fan commented on code in PR #42194:
URL: https://github.com/apache/spark/pull/42194#discussion_r1305073226
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sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:
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@@ -1500,6 +1500,16 @@ object SQLConf {
.booleanConf
.createWithDefault(false)
+ val V2_BUCKETING_SHUFFLE_ENABLED =
+ buildConf("spark.sql.sources.v2.bucketing.shuffle.enabled")
+ .doc("During a storage-partitioned join, whether to allow to shuffle
only one side." +
+ "When only one side is KeyGroupedPartitioning, if the conditions are
met, spark will " +
+ "only shuffle the other side. This optimization will reduce the amount
of data that " +
Review Comment:
shall we make the algorithm smarter? If the other side is large, doing a
`KeyGroupedPartitioning` may lead to skew and it's still better to shuffle both
sides with hash partitioning.
Let's think of an extreme case: one side reports `KeyGroupedPartitioning`
with only one partition, with this optimization, we end up with doing the join
using a single thread.
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