[SPARK-11704] Optimize the Cartesian Join

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Component/s: SQL
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: Zhan Zhang
• Created: 12/Nov/15 18:22
• Updated: 21/May/19 04:37
• Resolved: 21/May/19 04:37

Description

Currently CartesianProduct relies on RDD.cartesian, in which the computation is realized as follows

override def compute(split: Partition, context: TaskContext): Iterator[(T, U)] =

{ val currSplit = split.asInstanceOf[CartesianPartition] for (x <- rdd1.iterator(currSplit.s1, context); y <- rdd2.iterator(currSplit.s2, context)) yield (x, y) }

From the above loop, if rdd1.count is n, rdd2 needs to be recomputed n times. Which is really heavy and may never finished if n is large, especially when rdd2 is coming from ShuffleRDD.

We should have some optimization on CartesianProduct by caching rightResults. The problem is that we don’t have cleanup hook to unpersist rightResults AFAIK. I think we should have some cleanup hook after query execution.
With the hook available, we can easily optimize such Cartesian join. I believe such cleanup hook may also benefit other query optimizations.

Issue Links

• duplicates: SPARK-11982 Improve performance of CartesianProduct