Antony Mayi created SPARK-6334:
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Summary: 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
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 disk 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:python}
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. if possible I'll try attaching the graph
of disk consumption pattern showing the space being all eaten from 7% to 90%
during the ALS.
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