内部加入海量数据导致黑名单问题

问题描述

当我尝试对3个或更多表的大型数据进行内部联接时，我得到的错误率低于此值。

Py4JJavaError：调用o1093.count时发生错误。 ：org.apache.spark.SparkException：由于阶段失败，作业中止了： 由于任务4（分区4）而中止TaskSet 12.0 由于节点和执行者黑名单而无法在任何地方运行。 最近失败： 在阶段12.0（TID 109，te-163l.de.net.com，执行者7）中丢失任务4.0：java.io.IOException：流已损坏 在net.jpountz.lz4.LZ4BlockInputStream.refill（LZ4BlockInputStream.java:202） 在net.jpountz.lz4.LZ4BlockInputStream.refill（LZ4BlockInputStream.java:228） 在net.jpountz.lz4.LZ4BlockInputStream.read（LZ4BlockInputStream.java:157） 在org.apache.spark.io.ReadAheadInputStream $ 1.run（ReadAheadInputStream.java:168） 在java.util.concurrent.ThreadPoolExecutor.runWorker（ThreadPoolExecutor.java:1149） 在java.util.concurrent.ThreadPoolExecutor $ Worker.run（ThreadPoolExecutor.java:624） 在java.lang.Thread.run（Thread.java:748）

Blacklisting behavior can be configured via spark.blacklist.*.

    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1890)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1877)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:929)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:929)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2111)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2060)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2049)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:740)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2081)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2102)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2121)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2146)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:944)
    at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:299)
    at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2830)
    at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2829)
    at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363)
    at org.apache.spark.sql.Dataset.count(Dataset.scala:2829)
    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:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)

我什至尝试设置“ spark.unsafe.sorter.spill.read.ahead.enabled”的配置属性，“ false”仍然遇到相同的错误。

解决方法

我知道这已经有一段时间了，但是对于需要它的人，您不能尝试配置 "spark.blacklist.enabled,False" 来禁用黑名单或 "spark.blacklist.killBlacklistedExecutors,True" 在它被列入黑名单时尝试移动到另一个执行程序