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https://issues.apache.org/jira/browse/SPARK-6334?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Antony Mayi updated SPARK-6334:
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Attachment: gc.png
> spark-local dir not getting cleared during ALS
> ----------------------------------------------
>
> Key: SPARK-6334
> URL: https://issues.apache.org/jira/browse/SPARK-6334
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Antony Mayi
> Attachments: als-diskusage.png, gc.png
>
>
> when running bigger ALS training spark spills loads of temp data into the
> local-dir (in my case yarn/local/usercache/antony.mayi/appcache/... - running
> on YARN from cdh 5.3.2) eventually causing all the disks of all nodes running
> out of space (in my case I have 12TB of available disk capacity before
> kicking off the ALS but it all gets used (and yarn kills the containers when
> reaching 90%).
> even with all recommended options (configuring checkpointing and forcing GC
> when possible) it still doesn't get cleared.
> here is my (pseudo)code (pyspark):
> {code}
> sc.setCheckpointDir('/tmp')
> training =
> sc.pickleFile('/tmp/dataset').repartition(768).persist(StorageLevel.MEMORY_AND_DISK)
> model = ALS.trainImplicit(training, 50, 15, lambda_=0.1, blocks=-1, alpha=40)
> sc._jvm.System.gc()
> {code}
> the training RDD has about 3.5 billions of items (~60GB on disk). after about
> 6 hours the ALS will consume all 12TB of disk space in local-dir data and
> gets killed. my cluster has 192 cores, 1.5TB RAM and for this task I am using
> 37 executors of 4 cores/28+4GB RAM each.
> this is the graph of disk consumption pattern showing the space being all
> eaten from 7% to 90% during the ALS (90% is when YARN kills the container):
> !als-diskusage.png!
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