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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2015/04/07 12:22:12 UTC
[jira] [Resolved] (SPARK-6695) Add an external iterator: a
hadoop-like output collector
[ https://issues.apache.org/jira/browse/SPARK-6695?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-6695.
------------------------------
Resolution: Won't Fix
I suppose my problem with that is that it would be duplicating Spark's spill mechanism and leaves open the questions I put forth above about cleanup. Spark functions aren't "supposed" to need a huge amount of memory all at once, and so I imagine the solution in every case is to redesign the method.
> Add an external iterator: a hadoop-like output collector
> --------------------------------------------------------
>
> Key: SPARK-6695
> URL: https://issues.apache.org/jira/browse/SPARK-6695
> Project: Spark
> Issue Type: New Feature
> Components: Spark Core
> Reporter: uncleGen
>
> In practical use, we usually need to create a big iterator, which means too big in `memory usage` or too long in `array size`. On the one hand, it leads to too much memory consumption. On the other hand, one `Array` may not hold all the elements, as java array indices are of type 'int' (4 bytes or 32 bits). So, IMHO, we may provide a `collector`, which has a buffer, 100MB or any others, and could spill data into disk. The use case may like:
> {code: borderStyle=solid}
> rdd.mapPartition { it =>
> ...
> val collector = new ExternalCollector()
> collector.collect(a)
> ...
> collector.iterator
> }
>
> {code}
> I have done some related works, and I need your opinions, thanks!
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