接收TimeoutException的可能原因是:使用Spark时,[n秒]之后的期货超时

Question

我正在研究Spark SQL程序,我收到以下异常:

16/11/07 15:58:25 ERROR yarn.ApplicationMaster: User class threw exception: java.util.concurrent.TimeoutException: Futures timed out after [3000 seconds]
java.util.concurrent.TimeoutException: Futures timed out after [3000 seconds]
    at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
    at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
    at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190)
    at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
    at scala.concurrent.Await$.result(package.scala:190)
    at org.apache.spark.sql.execution.joins.BroadcastHashJoin.doExecute(BroadcastHashJoin.scala:107)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.Union$$anonfun$doExecute$1.apply(basicOperators.scala:144)
    at org.apache.spark.sql.execution.Union$$anonfun$doExecute$1.apply(basicOperators.scala:144)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:245)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.Union.doExecute(basicOperators.scala:144)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation.buildBuffers(InMemoryColumnarTableScan.scala:129)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation.<init>(InMemoryColumnarTableScan.scala:118)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$.apply(InMemoryColumnarTableScan.scala:41)
    at org.apache.spark.sql.execution.CacheManager$$anonfun$cacheQuery$1.apply(CacheManager.scala:93)
    at org.apache.spark.sql.execution.CacheManager.writeLock(CacheManager.scala:60)
    at org.apache.spark.sql.execution.CacheManager.cacheQuery(CacheManager.scala:84)
    at org.apache.spark.sql.DataFrame.persist(DataFrame.scala:1581)
    at org.apache.spark.sql.DataFrame.cache(DataFrame.scala:1590)
    at com.somecompany.ml.modeling.NewModel.getTrainingSet(FlowForNewModel.scala:56)
    at com.somecompany.ml.modeling.NewModel.generateArtifacts(FlowForNewModel.scala:32)
    at com.somecompany.ml.modeling.Flow$class.run(Flow.scala:52)
    at com.somecompany.ml.modeling.lowForNewModel.run(FlowForNewModel.scala:15)
    at com.somecompany.ml.Main$$anonfun$2.apply(Main.scala:54)
    at com.somecompany.ml.Main$$anonfun$2.apply(Main.scala:54)
    at scala.Option.getOrElse(Option.scala:121)
    at com.somecompany.ml.Main$.main(Main.scala:46)
    at com.somecompany.ml.Main.main(Main.scala)
    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 org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:542)
16/11/07 15:58:25 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 15, (reason: User class threw exception: java.util.concurrent.TimeoutException: Futures timed out after [3000 seconds])

我从堆栈跟踪中识别出的代码的最后一部分是com.somecompany.ml.modeling.NewModel.getTrainingSet(FlowForNewModel.scala:56)让我profilesDF.cache() 走到这一行:在缓存之前,我在两个数据帧之间执行联合.我见过一个答案约持续两个加入前dataframes 这里我还需要缓存联合在一起数据框,因为我在我的几个变换使用它

我想知道是什么原因导致这个异常被抛出？搜索它让我找到了一个处理rpc超时异常或一些安全问题的链接,这不是我的问题如果你对如何解决它有任何想法我很明显很欣赏它但即使只是理解问题也会帮我解决它

提前致谢

Answer 1

问题:我想知道什么可能导致抛出此异常？

答案:

spark.sql.broadcastTimeout 300广播连接中广播等待时间的超时秒数

spark.network.timeout所有网络交互120s的默认超时.. spark.network.timeout (spark.rpc.askTimeout),spark.sql.broadcastTimeout, spark.kryoserializer.buffer.max(如果你使用KRYO序列化)等,与比默认的较大值,以处理复杂的查询调整.您可以从这些值开始,并根据您的SQL工作负载进行相应调整.

注意:Doc说

以下选项(请参阅spark.sql.属性)也可用于调整查询执行的性能.由于会自动执行更多优化,因此在将来的版本中可能会弃用这些选项.*

此外,为了更好地理解,您可以看到BroadCastHashJoin,其中execute方法是上述堆栈跟踪的触发点.

protected override def doExecute(): RDD[Row] = {
    val broadcastRelation = Await.result(broadcastFuture, timeout)

    streamedPlan.execute().mapPartitions { streamedIter =>
      hashJoin(streamedIter, broadcastRelation.value)
    }
  }