parthchandra commented on code in PR #1424:
URL: https://github.com/apache/datafusion-comet/pull/1424#discussion_r1964726051
##########
spark/src/main/scala/org/apache/comet/rules/RewriteJoin.scala:
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@@ -67,4 +78,21 @@ object RewriteJoin extends JoinSelectionHelper {
}
case _ => plan
}
+
+ def getOptimalBuildSide(join: Join): BuildSide = {
+ val leftSize = join.left.stats.sizeInBytes
+ val rightSize = join.right.stats.sizeInBytes
+ val leftRowCount = join.left.stats.rowCount
+ val rightRowCount = join.right.stats.rowCount
+ if (leftSize == rightSize && rightRowCount.isDefined &&
leftRowCount.isDefined) {
+ if (rightRowCount.get <= leftRowCount.get) {
Review Comment:
> Do we have any sort of cardinality stats (possibly with some catalog
implementations)?
With cbo enabled, we might have cardinality information. Column stats
require a catalog to keep the metadata and stats so with just plain Parquet
files, we may not have the column stats information. With iceberg, there is
some work that has been done to incorporate column stats into the iceberg table
but I'm not sure where the effort to use them in Spark is.
Ref:
https://github.com/apache/spark/blob/f37be893d01884461ac515c8b197fb30d9ba68ff/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/Statistics.scala#L101
> I'm envisioning a scenario where the right table has fewer rows but much
higher cardinality so the resulting hash table is bigger.
The hash table might have more keys but total size is still a very good
metric simply because a larger size hash table might not fit into memory. Also,
depending on the implementation, the larger the size of the data in the hash
table, the less cache friendly it might be and we may end up with _slower_
performance.
For reference, Spark uses
https://github.com/apache/spark/blob/master/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
as the fallback hash table implementation (afaik).
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