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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/06/13 14:00:26 UTC
[jira] [Resolved] (SPARK-15904) High Memory Pressure using MLlib
K-means
[ https://issues.apache.org/jira/browse/SPARK-15904?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-15904.
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Resolution: Not A Problem
Memory and disk still means it's also persisting in memory. I think you'll see the physical memory used by the JVM is much more than 10GB. Because it works fine with _less_ RAM, this really has to be the issue. You should never be swapping.
> High Memory Pressure using MLlib K-means
> ----------------------------------------
>
> Key: SPARK-15904
> URL: https://issues.apache.org/jira/browse/SPARK-15904
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.6.1
> Environment: Mac OS X 10.11.6beta on Macbook Pro 13" mid-2012. 16GB of RAM.
> Reporter: Alessio
> Priority: Minor
>
> Running MLlib K-Means on a ~400MB dataset (12 partitions), persisted on Memory and Disk.
> Everything's fine, although at the end of K-Means, after the number of iterations, the cost function value and the running time there's a nice "Removing RDD <idx> from persistent list" stage. However, during this stage there's a high memory pressure. Weird, since RDDs are about to be removed. Full log of this stage:
> 16/06/12 20:37:33 INFO clustering.KMeans: Run 0 finished in 14 iterations
> 16/06/12 20:37:33 INFO clustering.KMeans: Iterations took 694.544 seconds.
> 16/06/12 20:37:33 INFO clustering.KMeans: KMeans converged in 14 iterations.
> 16/06/12 20:37:33 INFO clustering.KMeans: The cost for the best run is 49784.87126751288.
> 16/06/12 20:37:33 INFO rdd.MapPartitionsRDD: Removing RDD 781 from persistence list
> 16/06/12 20:37:33 INFO storage.BlockManager: Removing RDD 781
> 16/06/12 20:37:33 INFO rdd.MapPartitionsRDD: Removing RDD 780 from persistence list
> 16/06/12 20:37:33 INFO storage.BlockManager: Removing RDD 780
> I'm running this K-Means on a 16GB machine, with Spark Context as local[*]. My machine has an i5 hyperthreaded dual-core, thus [*] means 4.
> I'm launching this application though spark-submit with --driver-memory 9G
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