2015-11-25 46 views
2

我在下面写了简单的spark程序,使用spark的StreamingContext和SQLContext。Apache Spark:线程驱动心跳中的未捕获异常

注意:即使没有streamingContext,问题也是可重复的。更新程序以反映相同。

注意:将spark版本降级到1.4.1(我使用的是1.5.2)似乎已经解决了我的问题。火花也是1.5.1这个问题我们可以重现。

def main(args: Array[String]) { 
    val sc = new SparkContext("local[*]", "test") 
    val sqc = new SQLContext(sc) 

    val dataFrame = sqc.read.json(sc.textFile("<dir>")) 
    println(dataFrame.groupBy("Product.SerialNumber").count().count()) 
    sc.stop() 
} 

这是在开始时给出以下例外但执行正在进行并打印结果。

15/11/25 15:48:55 ERROR Utils: Uncaught exception in thread driver-heartbeater 
java.io.IOException: java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.HashMap$SerializationProxy to field org.apache.spark.executor.TaskMetrics._accumulatorUpdates of type scala.collection.immutable.Map in instance of org.apache.spark.executor.TaskMetrics 
     at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1163) 
     at org.apache.spark.executor.TaskMetrics.readObject(TaskMetrics.scala:219) 
     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:497) 
     at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) 
     at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1900) 
     at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801) 
     at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351) 
     at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371) 
     at org.apache.spark.util.Utils$.deserialize(Utils.scala:91) 
     at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1$$anonfun$apply$6.apply(Executor.scala:440) 
     at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1$$anonfun$apply$6.apply(Executor.scala:430) 
     at scala.Option.foreach(Option.scala:257) 
     at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1.apply(Executor.scala:430) 
     at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$reportHeartBeat$1.apply(Executor.scala:428) 
     at scala.collection.Iterator$class.foreach(Iterator.scala:742) 
     at scala.collection.AbstractIterator.foreach(Iterator.scala:1194) 
     at scala.collection.IterableLike$class.foreach(IterableLike.scala:72) 
     at scala.collection.AbstractIterable.foreach(Iterable.scala:54) 
     at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$reportHeartBeat(Executor.scala:428) 
     at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply$mcV$sp(Executor.scala:472) 
     at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:472) 
     at org.apache.spark.executor.Executor$$anon$1$$anonfun$run$1.apply(Executor.scala:472) 
     at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699) 
     at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:472) 
     at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) 
     at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308) 
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180) 
     at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294) 
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) 
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) 
     at java.lang.Thread.run(Thread.java:745) 
Caused by: java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.HashMap$SerializationProxy to field org.apache.spark.executor.TaskMetrics._accumulatorUpdates of type scala.collection.immutable.Map in instance of org.apache.spark.executor.TaskMetrics 
     at java.io.ObjectStreamClass$FieldReflector.setObjFieldValues(ObjectStreamClass.java:2089) 
     at java.io.ObjectStreamClass.setObjFieldValues(ObjectStreamClass.java:1261) 
     at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2006) 
     at java.io.ObjectInputStream.defaultReadObject(ObjectInputStream.java:501) 
     at org.apache.spark.executor.TaskMetrics$$anonfun$readObject$1.apply$mcV$sp(TaskMetrics.scala:220) 
     at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1160) 
     ... 33 more 

2分钟后,发生以下异常,执行结束。直到两分钟,执行完美无缺,并且没有报告问题/异常。

15/11/25 15:51:44 WARN HeartbeatReceiver: Removing executor driver with no recent heartbeats: 179219 ms exceeds timeout 120000 ms^M 
15/11/25 15:51:44 ERROR TaskSchedulerImpl: Lost executor driver on localhost: Executor heartbeat timed out after 179219 ms^M 
15/11/25 15:51:44 WARN TaskSetManager: Lost task 4.0 in stage 193.0 (TID 7688, localhost): ExecutorLostFailure (executor driver lost)^M 
15/11/25 15:51:44 ERROR TaskSetManager: Task 4 in stage 193.0 failed 1 times; aborting job^M 
15/11/25 15:51:44 WARN TaskSetManager: Lost task 7.0 in stage 193.0 (TID 7691, localhost): ExecutorLostFailure (executor driver lost)^M 
15/11/25 15:51:44 WARN TaskSetManager: Lost task 6.0 in stage 193.0 (TID 7690, localhost): ExecutorLostFailure (executor driver lost)^M 
15/11/25 15:51:44 WARN TaskSetManager: Lost task 5.0 in stage 193.0 (TID 7689, localhost): ExecutorLostFailure (executor driver lost)^M 
15/11/25 15:51:44 WARN SparkContext: Killing executors is only supported in coarse-grained mode^M 
15/11/25 15:51:45 ERROR JobScheduler: Error running job streaming job 1448446890000 ms.0^M 
org.apache.spark.SparkException: Job aborted due to stage failure: Task 4 in stage 193.0 failed 1 times, most recent failure: Lost task 4.0 in stage 193.0 (TID 7688, localhost): ExecutorLostFailure (executor driver lost)^M 
Driver stacktrace:^M 
     at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1283)^M 
     at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1271)^M 
     at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1270)^M 
     at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)^M 
     at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)^M 
     at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1270)^M 
     at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)^M 
     at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:697)^M 
     at scala.Option.foreach(Option.scala:257)^M 
     at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:697)^M 
     at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1496)^M 
     at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1458)^M 
     at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1447)^M 
     at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)^M 
     at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)^M 
     at org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)^M 
     at org.apache.spark.SparkContext.runJob(SparkContext.scala:1837)^M 
     at org.apache.spark.SparkContext.runJob(SparkContext.scala:1850)^M 
     at org.apache.spark.SparkContext.runJob(SparkContext.scala:1921)^M 
     at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:909)^M 
     at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)^M 
     at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)^M 
     at org.apache.spark.rdd.RDD.withScope(RDD.scala:310)^M 
     at org.apache.spark.rdd.RDD.collect(RDD.scala:908)^M 
     at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:177)^M 
     at org.apache.spark.sql.DataFrame$$anonfun$collect$1.apply(DataFrame.scala:1385)^M 
     at org.apache.spark.sql.DataFrame$$anonfun$collect$1.apply(DataFrame.scala:1385)^M 
     at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)^M 
     at org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:1903)^M 
     at org.apache.spark.sql.DataFrame.collect(DataFrame.scala:1384)^M 
     at org.apache.spark.sql.DataFrame.count(DataFrame.scala:1402)^M 
     at main$$anonfun$main$1.apply(Main.scala:72)^M 
     at main$$anonfun$main$1.apply(Main.scala:68)^M 
     at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:631)^M 
     at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:631)^M 
     at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:42)^M 
     at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:40)^M 
     at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:40)^M 
     at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:399)^M 
     at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:40)^M 
     at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)^M 
     at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:40)^M 
     at scala.util.Try$.apply(Try.scala:192)^M 
     at org.apache.spark.streaming.scheduler.Job.run(Job.scala:34)^M 
     at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:218)^M 
     at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:218)^M 
     at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:218)^M 
     at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)^M 
     at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:217)^M 
     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)^M 
     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)^M 
     at java.lang.Thread.run(Thread.java:745)^M 
[error] (run-main-0) org.apache.spark.SparkException: Job aborted due to stage failure: Task 4 in stage 193.0 failed 1 times, most recent failure: Lost task 4.0 in stage 193.0 (TID 7688, localhost): ExecutorLostFailure (executor driver lost)^M 

回答

2

当您提交spark工作时,您可能会忘记添加一些依赖关系jar。 尝试组装项目(使所有依赖被包括在内)提交之前引发:

sbt assembly 

BTW,当我运行

sbt console 

和运行斯卡拉解释器的命令,我将有和你一样的问题。但是,如果我先组装起来,并通过

spark submit --class className target/scala-2.10/xxx-assembly-0.1.0.jar someArgs 

运行作业工程:)

裁判:Apache Spark 1.5 with Cassandra : Class cast exception

+0

以前我是用“SBT跑”运行。当我用spark-submit运行时,还有其他问题,如http://stackoverflow.com/questions/34038913/spark-streaming-throwing-java-net-connectexception – rakesh

-1

尝试做

val dataFrame = sqc.read.json(sc.textFile("<dir>")).cache() 

我有这个同样的确切的问题;在同一个数据帧上多次运行.count()操作会导致此错误。

如果不帮忙,试试这个:

val dataFrame = sqc.read.json(sc.textFile("<dir>")) 
val serialNumberDF = dataFrame.groupBy("Product.SerialNumber").cache() 
println(serialNumberDF.count().count()) 

我的猜测是,不得不一遍又一遍地重新评估数据帧(因为dataframes懒洋洋地评估)的地方导致了错误。而且,如果数据量较大,在多个地方使用数据框可能会导致性能相当高昂,而无需缓存。

+0

downvote的任何特定原因? –

0

在我们的案例(Spark 1.6.1)中,这些相同的错误在通过sbt运行测试时有些随机出现。这个问题实际上似乎是sbt issue。该变通(在上面的链接提到)是叉形JVM运行测试:

fork in test := true

相关问题