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https://issues.apache.org/jira/browse/SPARK-4818?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14242064#comment-14242064
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Apache Spark commented on SPARK-4818:
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User 'zsxwing' has created a pull request for this issue:
https://github.com/apache/spark/pull/3671
> Join operation should use iterator/lazy evaluation
> --------------------------------------------------
>
> Key: SPARK-4818
> URL: https://issues.apache.org/jira/browse/SPARK-4818
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 1.1.1
> Reporter: Johannes Simon
> Priority: Minor
>
> The current implementation of the join operation does not use an iterator
> (i.e. lazy evaluation), causing it to explicitly evaluate the co-grouped
> values. In big data applications, these value collections can be very large.
> This causes the *cartesian product of all co-grouped values* for a specific
> key of both RDDs to be kept in memory during the flatMapValues operation,
> resulting in an *O(size(pair._1)*size(pair._2))* memory consumption instead
> of *O(1)*. Very large value collections will therefore cause "GC overhead
> limit exceeded" exceptions and fail the task, or at least slow down execution
> dramatically.
> {code:title=PairRDDFunctions.scala|borderStyle=solid}
> //...
> def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] =
> {
> this.cogroup(other, partitioner).flatMapValues( pair =>
> for (v <- pair._1; w <- pair._2) yield (v, w)
> )
> }
> //...
> {code}
> Since cogroup returns an Iterable instance of an Array, the join
> implementation could be changed to the following, which uses lazy evaluation
> instead, and has almost no memory overhead:
> {code:title=PairRDDFunctions.scala|borderStyle=solid}
> //...
> def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] =
> {
> this.cogroup(other, partitioner).flatMapValues( pair =>
> for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w)
> )
> }
> //...
> {code}
> Alternatively, if the current implementation is intentionally not using lazy
> evaluation for some reason, there could be a *lazyJoin()* method next to the
> original join implementation that utilizes lazy evaluation. This of course
> applies to other join operations as well.
> Thanks! :)
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