org.apache.spark.SparkException: Job aborted due to stage failure: Task 98 in stage 11.0 failed 4 times

Question

I am using Google Cloud Dataproc to do spark job and my editor is Zepplin. I was trying to write json data into gcp bucket. It succeeded before when I tried 10MB file. But failed with 10GB file. My dataproc has 1 master with 4CPU, 26GB memory, 500GB disk. 5 workers with same config. I guess it should've been able to handle 10GB data.

My command is toDatabase.repartition(10).write.json("gs://mypath")

Error is

org.apache.spark.SparkException: Job aborted.
  at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:224)
  at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:154)
  at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
  at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
  at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
  at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
  at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
  at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
  at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)
  at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:656)
  at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
  at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:656)
  at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:273)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:267)
  at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:225)
  at org.apache.spark.sql.DataFrameWriter.json(DataFrameWriter.scala:528)
  ... 54 elided
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 98 in stage 11.0 failed 4 times, most recent failure: Lost task 98.3 in stage 11.0 (TID 3895, etl-w-2.us-east1-b.c.team-etl-234919.internal, executor 294): ExecutorLostFailure (executor 294 exited caused by one of the running tasks) Reason: Container marked as failed: container_1554684028327_0001_01_000307 on host: etl-w-2.us-east1-b.c.team-etl-234919.internal. Exit status: 143. Diagnostics: [2019-04-08 01:50:14.153]Container killed on request. Exit code is 143
[2019-04-08 01:50:14.153]Container exited with a non-zero exit code 143.
[2019-04-08 01:50:14.154]Killed by external signal

Driver stacktrace:
  at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1651)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1639)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1638)
  at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
  at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1638)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
  at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
  at scala.Option.foreach(Option.scala:257)
  at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1872)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1821)
  at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1810)
  at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
  at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
  at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
  at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:194)
  ... 74 more

Any idea about why?

Answer 1

Most likely you're running into out-of-memory limits on Spark workers if it runs on the smaller data set but not the larger one. The per-worker memory issues will be more of a function of your partitioning and per-executor settings rather than total cluster-wide memory available (so creating a larger cluster would not help that type of issue).

You can try any combination of the following:

1. Repartition into a larger number of partitions for output instead of 10
2. Create cluster with highmem instead of standard machines
3. Create cluster with spark memory settings that change the ratio of memory to CPU: gcloud dataproc clusters create --properties spark:spark.executor.cores=1 for example will change each executor to only run one task at a time with the same amount of memory, whereas Dataproc normally runs 2 executors per machine and divides CPUs accordingly. On 4-core machines, you normally have 2 executors and each executor allows 2 cores. This setting would then only give each of those 2 executors 1 core while still using half a machine worth of memory.