[
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!
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
