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From "Zhan Zhang (JIRA)" <>
Subject [jira] [Commented] (SPARK-11704) Optimize the Cartesian Join
Date Sun, 15 Nov 2015 00:29:10 GMT


Zhan Zhang commented on SPARK-11704:

[~maropu] You are right. I mean fetching from network is a big overhead. Feel free to work
on it.

> Optimize the Cartesian Join
> ---------------------------
>                 Key: SPARK-11704
>                 URL:
>             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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