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https://issues.apache.org/jira/browse/SPARK-13909?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-13909:
---------------------------------
Labels: bulk-closed dataframe (was: dataframe)
> DataFrames DISK_ONLY persistence leads to OOME
> ----------------------------------------------
>
> Key: SPARK-13909
> URL: https://issues.apache.org/jira/browse/SPARK-13909
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.6.0
> Environment: debian:jessie, java 1.8, hadoop 2.6.0, current zeppelin
> snapshot
> Reporter: Jakub Liska
> Priority: Major
> Labels: bulk-closed, dataframe
>
> Hey, I migrated to 1.6.0, and suddenly `persist` behaves as if it was
> `MEMORY_ONLY` instead of `DISK_ONLY` so that it eventually ends with OOME.
> However if I remove `persist` it works fine. I'm calling this snippet from
> Zeppelin notebook :
> {code}
> val coreRdd =
> sc.textFile("s3n://gwiq-views-p/external/core/tsv/*.tsv").map(_.split("\t")).map(
> fields => Row(fields:_*) )
> val coreDataFrame = sqlContext.createDataFrame(coreRdd, schema)
> coreDataFrame.registerTempTable("core")
> coreDataFrame.persist(StorageLevel.DISK_ONLY)
> {code}
> {code}
> SELECT COUNT(*) FROM core
> {code}
> {code}
> ------ Create new SparkContext spark://master:7077 -------
> Exception in thread "pool-1-thread-5" java.lang.OutOfMemoryError: GC overhead
> limit exceeded
> at
> com.google.gson.internal.bind.ObjectTypeAdapter.read(ObjectTypeAdapter.java:66)
> at
> com.google.gson.internal.bind.ObjectTypeAdapter.read(ObjectTypeAdapter.java:69)
> at
> com.google.gson.internal.bind.TypeAdapterRuntimeTypeWrapper.read(TypeAdapterRuntimeTypeWrapper.java:40)
> at
> com.google.gson.internal.bind.MapTypeAdapterFactory$Adapter.read(MapTypeAdapterFactory.java:188)
> at
> com.google.gson.internal.bind.MapTypeAdapterFactory$Adapter.read(MapTypeAdapterFactory.java:146)
> at com.google.gson.Gson.fromJson(Gson.java:791)
> at com.google.gson.Gson.fromJson(Gson.java:757)
> at com.google.gson.Gson.fromJson(Gson.java:706)
> at
> org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer.convert(RemoteInterpreterServer.java:417)
> at
> org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer.getProgress(RemoteInterpreterServer.java:384)
> at
> org.apache.zeppelin.interpreter.thrift.RemoteInterpreterService$Processor$getProgress.getResult(RemoteInterpreterService.java:1376)
> at
> org.apache.zeppelin.interpreter.thrift.RemoteInterpreterService$Processor$getProgress.getResult(RemoteInterpreterService.java:1361)
> at org.apache.thrift.ProcessFunction.process(ProcessFunction.java:39)
> at org.apache.thrift.TBaseProcessor.process(TBaseProcessor.java:39)
> at
> org.apache.thrift.server.TThreadPoolServer$WorkerProcess.run(TThreadPoolServer.java:285)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> {code}
> I'm using this https://github.com/gettyimages/docker-spark setup with a
> zeppelin docker container...
> {code}
> ZEPPELIN_JAVA_OPTS: -Dspark.executor.memory=16g
> -Dspark.serializer=org.apache.spark.serializer.KryoSerializer
> -Dspark.app.id=zeppelin
> SPARK_SUBMIT_OPTIONS: --driver-memory 1g --repositories
> https://oss.sonatype.org/content/repositories/snapshots --packages
> com.viagraphs:spark-extensions_2.10:1.04-SNAPSHOT
> --jars=file:/usr/spark-1.6.0-bin-hadoop2.6/lib/aws-java-sdk-1.7.14.jar,file:/usr/spark-1.6.0-bin-hadoop2.6/lib/hadoop-aws-2.6.0.jar,file:/usr/spark-1.6.0-bin-hadoop2.6/lib/google-collections-1.0.jar,file:/usr/spark-1.6.0-bin-hadoop2.6/lib/joda-time-2.8.2.jar
> SPARK_WORKER_CORES: 8
> SPARK_WORKER_MEMORY: 16g
> {code}
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