Zhan Zhang created SPARK-11704:
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Summary: Optimize the Cartesian Join
Key: SPARK-11704
URL: https://issues.apache.org/jira/browse/SPARK-11704
Project: Spark
Issue Type: Bug
Components: SQL
Reporter: Zhan Zhang
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.
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