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https://issues.apache.org/jira/browse/SPARK-11704?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Zhan Zhang updated SPARK-11704:
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Issue Type: Improvement (was: Bug)
> Optimize the Cartesian Join
> ---------------------------
>
> Key: SPARK-11704
> URL: https://issues.apache.org/jira/browse/SPARK-11704
> Project: Spark
> Issue Type: Improvement
> 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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