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https://issues.apache.org/jira/browse/SPARK-2243?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14392616#comment-14392616
]
sam commented on SPARK-2243:
----------------------------
[~srowen]
The real issue here and use case, is to be able to change configuration during
execution without serialization concerns. It's common for different steps of a
job to require different amounts of various caches, e.g. part one caches an RDD
into memory to reduce cost of iterating over it, part two takes the result and
does some kind of big shuffle. The second part needs a big shuffle memory
fraction while the first part might need a large cache memory fraction. In
fact I have jobs where I need 0.8 for a big shuffle, 0.8 for some caching, then
set both to 0 for a part that requires a ton of heap.
Currently the only work around is to write out the results of each part to disk
(causing serialization faff), and wrap the application in a bash script to run
it repeatedly with different configuration and parameters.
Creating new SparkContexts would not necessarily solve this since as pointed
out one would need to be able to share RDDs across contexts.
> Support multiple SparkContexts in the same JVM
> ----------------------------------------------
>
> Key: SPARK-2243
> URL: https://issues.apache.org/jira/browse/SPARK-2243
> Project: Spark
> Issue Type: New Feature
> Components: Block Manager, Spark Core
> Affects Versions: 0.7.0, 1.0.0, 1.1.0
> Reporter: Miguel Angel Fernandez Diaz
>
> We're developing a platform where we create several Spark contexts for
> carrying out different calculations. Is there any restriction when using
> several Spark contexts? We have two contexts, one for Spark calculations and
> another one for Spark Streaming jobs. The next error arises when we first
> execute a Spark calculation and, once the execution is finished, a Spark
> Streaming job is launched:
> {code}
> 14/06/23 16:40:08 ERROR executor.Executor: Exception in task ID 0
> java.io.FileNotFoundException: http://172.19.0.215:47530/broadcast_0
> at
> sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1624)
> at
> org.apache.spark.broadcast.HttpBroadcast$.read(HttpBroadcast.scala:156)
> at
> org.apache.spark.broadcast.HttpBroadcast.readObject(HttpBroadcast.scala:56)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at
> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017)
> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893)
> at
> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
> at
> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
> at
> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
> at
> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:40)
> at
> org.apache.spark.scheduler.ResultTask$.deserializeInfo(ResultTask.scala:63)
> at
> org.apache.spark.scheduler.ResultTask.readExternal(ResultTask.scala:139)
> at
> java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1837)
> at
> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
> at
> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:40)
> at
> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:62)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:193)
> at
> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:45)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:176)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> 14/06/23 16:40:08 WARN scheduler.TaskSetManager: Lost TID 0 (task 0.0:0)
> 14/06/23 16:40:08 WARN scheduler.TaskSetManager: Loss was due to
> java.io.FileNotFoundException
> java.io.FileNotFoundException: http://172.19.0.215:47530/broadcast_0
> at
> sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1624)
> at
> org.apache.spark.broadcast.HttpBroadcast$.read(HttpBroadcast.scala:156)
> at
> org.apache.spark.broadcast.HttpBroadcast.readObject(HttpBroadcast.scala:56)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at
> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017)
> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893)
> at
> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
> at
> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990)
> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1915)
> at
> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
> at
> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:40)
> at
> org.apache.spark.scheduler.ResultTask$.deserializeInfo(ResultTask.scala:63)
> at
> org.apache.spark.scheduler.ResultTask.readExternal(ResultTask.scala:139)
> at
> java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1837)
> at
> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796)
> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350)
> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370)
> at
> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:40)
> at
> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:62)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:193)
> at
> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:45)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:176)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> 14/06/23 16:40:08 ERROR scheduler.TaskSetManager: Task 0.0:0 failed 1 times;
> aborting job
> 14/06/23 16:40:08 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0,
> whose tasks have all completed, from pool
> 14/06/23 16:40:08 INFO scheduler.DAGScheduler: Failed to run runJob at
> NetworkInputTracker.scala:182
> [WARNING]
> org.apache.spark.SparkException: Job aborted: Task 0.0:0 failed 1 times (most
> recent failure: Exception failure: java.io.FileNotFoundException:
> http://172.19.0.215:47530/broadcast_0)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1020)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1018)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1018)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604)
> at scala.Option.foreach(Option.scala:236)
> at
> org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:604)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:190)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
> at akka.actor.ActorCell.invoke(ActorCell.scala:456)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
> at akka.dispatch.Mailbox.run(Mailbox.scala:219)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:385)
> at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> at
> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
> at
> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
> at
> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
> 14/06/23 16:40:09 INFO dstream.ForEachDStream: metadataCleanupDelay = 3600
> {code}
> So far, we are working on localhost. Any clue about where this error is
> coming from? Any workaround to solve the issue?
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