"sparkContext被关闭",同时在大型数据集上运行spark

Question

当在群集上运行sparkJob超过某个数据大小(~2,5GB)时,我得到"因为SparkContext被关闭而取消了作业"或"执行者丢失".看着纱桂,我看到被杀的工作是成功的.运行500mb的数据时没有问题.我正在寻找一个解决方案,并发现:"似乎纱线杀死了一些执行者,因为他们要求的内存超出预期."

有什么建议怎么调试呢？

命令我提交我的火花作业:

/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那样

 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

症状是执行程序任务之一中出现 OutOfMemory 错误的典型症状。启动作业时尝试增加执行器的内存。参见spark-submit、spark-shell等参数--executor-memory。默认值为1G

Answer 2

找到了答案。

我的表格另存为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解决了它