Antony Mayi created SPARK-6334: ---------------------------------- 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. -- 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