CODEWITHSUNDEEP
apache-sparkapache-spark-mlgoogle-cloud-dataproc
Stijn
Stijn

Reputation: 459

Executor heartbeat timed out Spark on DataProc

I am trying to fit a ml model in Spark (2.0.0) on a Google DataProc Cluster. When fitting the model I receive an Executor heartbeat timed out error. How can I resolve this?

Other solutions indicate this is probably due to Out of Memory of (one of) the executors. I read as solutions: Set the right setting, repartition, cache, and get a bigger cluster. What can I do, preferably without setting up a larger cluster? (Make more/less partitions? Cache less? Adjust settings?)

My setting:

Spark 2.0.0 on a Google DataProc Cluster: 1 Master and 2 workers all with the same specs: n1-highmem-8 -> 8 vCPUs, 52.0 GB memory - 500GB disk

Settings:

spark\:spark.executor.cores=1
distcp\:mapreduce.map.java.opts=-Xmx2457m
spark\:spark.driver.maxResultSize=1920m
mapred\:mapreduce.map.java.opts=-Xmx2457m
yarn\:yarn.nodemanager.resource.memory-mb=6144
mapred\:mapreduce.reduce.memory.mb=6144
spark\:spark.yarn.executor.memoryOverhead=384
mapred\:mapreduce.map.cpu.vcores=1
distcp\:mapreduce.reduce.memory.mb=6144
mapred\:yarn.app.mapreduce.am.resource.mb=6144
mapred\:mapreduce.reduce.java.opts=-Xmx4915m
yarn\:yarn.scheduler.maximum-allocation-mb=6144
dataproc\:dataproc.scheduler.max-concurrent-jobs=11
dataproc\:dataproc.heartbeat.master.frequency.sec=30
mapred\:mapreduce.reduce.cpu.vcores=2
distcp\:mapreduce.reduce.java.opts=-Xmx4915m
distcp\:mapreduce.map.memory.mb=3072
spark\:spark.driver.memory=3840m
mapred\:mapreduce.map.memory.mb=3072
yarn\:yarn.scheduler.minimum-allocation-mb=512
mapred\:yarn.app.mapreduce.am.resource.cpu-vcores=2
spark\:spark.yarn.am.memoryOverhead=384
spark\:spark.executor.memory=2688m
spark\:spark.yarn.am.memory=2688m
mapred\:yarn.app.mapreduce.am.command-opts=-Xmx4915m

Full Error:

Py4JJavaError: An error occurred while calling o4973.fit. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 151 in stage 16964.0 failed 4 times, most recent failure: Lost task 151.3 in stage 16964.0 (TID 779444, reco-test-w-0.c.datasetredouteasvendor.internal): ExecutorLostFailure (executor 14 exited caused by one of the running tasks) Reason: Executor heartbeat timed out after 175122 ms Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897) at org.apache.spark.SparkContext.runJob(SparkContext.scala:1911) at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:893) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:358) at org.apache.spark.rdd.RDD.collect(RDD.scala:892) at org.apache.spark.rdd.PairRDDFunctions$$anonfun$countByKey$1.apply(PairRDDFunctions.scala:372) at org.apache.spark.rdd.PairRDDFunctions$$anonfun$countByKey$1.apply(PairRDDFunctions.scala:372) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:358) at org.apache.spark.rdd.PairRDDFunctions.countByKey(PairRDDFunctions.scala:371) at org.apache.spark.rdd.RDD$$anonfun$countByValue$1.apply(RDD.scala:1156) at org.apache.spark.rdd.RDD$$anonfun$countByValue$1.apply(RDD.scala:1156) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:358) at org.apache.spark.rdd.RDD.countByValue(RDD.scala:1155) at org.apache.spark.ml.feature.StringIndexer.fit(StringIndexer.scala:91) at org.apache.spark.ml.feature.StringIndexer.fit(StringIndexer.scala:66) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:280) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:211) at java.lang.Thread.run(Thread.java:745)

Upvotes: 0

Views: 7417

Answers (1)

Adam
Adam

Reputation: 6015

As this question doesn't have an answer, to summarize the issue appears to have been related to spark.executor.memory being set too low, causing occasional out-of-memory errors on an executor.

The suggested fix was to first try starting with the default Dataproc config, which tries to fully use all cores and memory available on the instance. If issues continue, then adjust spark.executor.memory and spark.executor.cores to increase the amount of memory available per task (essentially spark.executor.memory / spark.executor.cores).

Dennis also gives more details about the Spark memory config on Dataproc in the following answer:
Google Cloud Dataproc configuration issues

Upvotes: 1

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