scala - 在大型数据集上运行 Spark 时出现 "sparkContext was shut down"

Question

当在超过一定数据大小(~2,5gb)的集群上运行sparkJob时，我收到“作业被取消，因为SparkContext被关闭”或“执行程序丢失”。当查看yarn gui时，我发现被杀死的工作是成功的。运行500mb的数据没有问题。我正在寻找解决方案并发现: - “似乎 yarn 杀死了一些执行器，因为它们请求的内存比预期更多。”

有什么建议如何调试吗？

命令我提交我的 Spark 作业:

/opt/spark-1.5.0-bin-hadoop2.4/bin/spark-submit  --driver-memory 22g --driver-cores 4 --num-executors 15 --executor-memory 6g --executor-cores 6  --class sparkTesting.Runner   --master yarn-client myJar.jar jarArguments

和sparkContext设置

val sparkConf = (new SparkConf()
    .set("spark.driver.maxResultSize", "21g")
    .set("spark.akka.frameSize", "2011")
    .set("spark.eventLog.enabled", "true")
    .set("spark.eventLog.enabled", "true")
    .set("spark.eventLog.dir", configVar.sparkLogDir)
    )

失败的简化代码看起来像这样

 val hc = new org.apache.spark.sql.hive.HiveContext(sc)
val broadcastParser = sc.broadcast(new Parser())

val featuresRdd = hc.sql("select "+ configVar.columnName + " from " + configVar.Table +" ORDER BY RAND() LIMIT " + configVar.Articles)
val myRdd : org.apache.spark.rdd.RDD[String] = featuresRdd.map(doSomething(_,broadcastParser))

val allWords= featuresRdd
  .flatMap(line => line.split(" "))
  .count

val wordQuantiles= featuresRdd
  .flatMap(line => line.split(" "))
  .map(word => (word, 1))
  .reduceByKey(_ + _)
  .map(pair => (pair._2 , pair._2))
  .reduceByKey(_+_)
  .sortBy(_._1)
  .collect
  .scanLeft((0,0.0)) ( (res,add) => (add._1, res._2+add._2) )
  .map(entry => (entry._1,entry._2/allWords))

val dictionary = featuresRdd
  .flatMap(line => line.split(" "))
  .map(word => (word, 1))
  .reduceByKey(_ + _) // here I have Rdd of word,count tuples
  .filter(_._2 >= moreThan)
  .filter(_._2 <= lessThan)
  .filter(_._1.trim!=(""))
  .map(_._1)
  .zipWithIndex
  .collect
  .toMap

和错误堆栈

Exception in thread "main" org.apache.spark.SparkException: Job cancelled because SparkContext was shut down
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:703)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:702)
at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
at org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:702)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1511)
at org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)
at org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1435)
at org.apache.spark.SparkContext$$anonfun$stop$7.apply$mcV$sp(SparkContext.scala:1715)
at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1185)
at org.apache.spark.SparkContext.stop(SparkContext.scala:1714)
at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$MonitorThread.run(YarnClientSchedulerBackend.scala:146)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1813)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1826)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1839)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1910)
at org.apache.spark.rdd.RDD.count(RDD.scala:1121)
at sparkTesting.InputGenerationAndDictionaryComputations$.createDictionary(InputGenerationAndDictionaryComputations.scala:50)
at sparkTesting.Runner$.main(Runner.scala:133)
at sparkTesting.Runner.main(Runner.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:483)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:672)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Answer 1

找到答案了。

我的表被保存为 20gb avro 文件。当执行者试图打开它时。他们每个人都必须将 20GB 加载到内存中。通过使用 csv 而不是 avro 解决了这个问题