scala - 激发多个上下文

Question

总之 :

EC2集群:1主3从

Spark 版本:1.3.1

我希望使用选项 spark.driver.allowMultipleContexts ，一个本地上下文(仅主)和一个集群(主和从)。

我收到此堆栈跟踪错误(第 29 行是我调用初始化第二个 sparkcontext 的对象的地方):

fr.entry.Main.main(Main.scala)
   at org.apache.spark.SparkContext$$anonfun$assertNoOtherContextIsRunning$1$$anonfun$apply$10.apply(SparkContext.scala:1812)
   at org.apache.spark.SparkContext$$anonfun$assertNoOtherContextIsRunning$1$$anonfun$apply$10.apply(SparkContext.scala:1808)
   at scala.Option.foreach(Option.scala:236)
   at org.apache.spark.SparkContext$$anonfun$assertNoOtherContextIsRunning$1.apply(SparkContext.scala:1808)
   at org.apache.spark.SparkContext$$anonfun$assertNoOtherContextIsRunning$1.apply(SparkContext.scala:1795)
   at scala.Option.foreach(Option.scala:236)
   at org.apache.spark.SparkContext$.assertNoOtherContextIsRunning(SparkContext.scala:1795)
   at org.apache.spark.SparkContext$.setActiveContext(SparkContext.scala:1847)
   at org.apache.spark.SparkContext.<init>(SparkContext.scala:1754)
   at fr.entry.cluster$.<init>(Main.scala:79)
   at fr.entry.cluster$.<clinit>(Main.scala)
   at fr.entry.Main$delayedInit$body.apply(Main.scala:29)
   at scala.Function0$class.apply$mcV$sp(Function0.scala:40)
   at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
   at scala.App$$anonfun$main$1.apply(App.scala:71)
   at scala.App$$anonfun$main$1.apply(App.scala:71)
   at scala.collection.immutable.List.foreach(List.scala:318)
   at scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:32)
   at scala.App$class.main(App.scala:71)
   at fr.entry.Main$.main(Main.scala:14)
   at fr.entry.Main.main(Main.scala)
15/09/28 15:33:30 INFO AppClient$ClientActor: Executor updated: app-  20150928153330-0036/2 is now LOADING
15/09/28 15:33:30 INFO AppClient$ClientActor: Executor updated: app-    20150928153330-0036/0 is now RUNNING
15/09/28 15:33:30 INFO AppClient$ClientActor: Executor updated: app-20150928153330-0036/1 is now RUNNING
15/09/28 15:33:30 INFO SparkContext: Starting job: sum at Main.scala:29
15/09/28 15:33:30 INFO DAGScheduler: Got job 0 (sum at Main.scala:29) with 2 output partitions (allowLocal=false)
15/09/28 15:33:30 INFO DAGScheduler: Final stage: Stage 0(sum at Main.scala:29)
15/09/28 15:33:30 INFO DAGScheduler: Parents of final stage: List()
15/09/28 15:33:30 INFO DAGScheduler: Missing parents: List()
15/09/28 15:33:30 INFO DAGScheduler: Submitting Stage 0 (MapPartitionsRDD[2] at numericRDDToDoubleRDDFunctions at Main.scala:29), which has no missing parents
15/09/28 15:33:30 INFO MemoryStore: ensureFreeSpace(2264) called with curMem=0, maxMem=55566516879
15/09/28 15:33:30 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 2.2 KB, free 51.8 GB)
15/09/28 15:33:30 INFO MemoryStore: ensureFreeSpace(1656) called with curMem=2264, maxMem=55566516879
15/09/28 15:33:30 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 1656.0 B, free 51.8 GB)
15/09/28 15:33:30 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:40476 (size: 1656.0 B, free: 51.8 GB)
15/09/28 15:33:30 INFO BlockManagerMaster: Updated info of block broadcast_0_piece0
15/09/28 15:33:30 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:839
15/09/28 15:33:30 INFO AppClient$ClientActor: Executor updated: app-20150928153330-0036/2 is now RUNNING
15/09/28 15:33:30 INFO DAGScheduler: Submitting 2 missing tasks from Stage 0 (MapPartitionsRDD[2] at numericRDDToDoubleRDDFunctions at Main.scala:29)
15/09/28 15:33:30 INFO TaskSchedulerImpl: Adding task set 0.0 with 2 tasks
15/09/28 15:33:45 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
15/09/28 15:34:00 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources

更多详情:

我想运行一个程序来做两件事。首先，我有一个本地 sparkContext(仅在 master 上)，我创建了一个 RDD 并执行了一些操作。其次，我有第二个 sparkContext 初始化，其中包含一个主节点和 3 个从节点，它们也创建一个 RDD 并执行一些操作。
因此，在第一种情况下，我想使用主设备的 16 个内核，而第二种情况下，我想使用从设备的 8 核 x 3。

简单示例:

val arr = Array(Array(1, 2, 3, 4, 5, 6, 7, 8), Array(1, 2, 3, 4, 5, 6, 7, 8))
println(local.sparkContext.makeRDD(arr).count()) 
println(cluster.sparkContext.makeRDD(arr).map(l => l.sum).sum)

我的两个 SparkContexts :

object local {

  val project = "test"
  val version = "1.0"

  val sc = new SparkConf()
    .setMaster("local[16]")
    .setAppName("Local")
    .set("spark.local.dir", "/mnt")
    .setJars(Seq("target/scala-2.10/" + project + "-assembly-" + version + ".jar", "target/scala-2.10/" + project + "_2.10-" + version + "-tests.jar"))
    .setSparkHome("/root/spark")
    .set("spark.driver.allowMultipleContexts", "true")
    .set("spark.executor.memory", "45g")

  val sparkContext = new SparkContext(sc)
}

object cluster {

  val project = "test"
  val version = "1.0"

  val sc = new SparkConf()
   .setMaster(masterURL)  // ec2-XX-XXX-XXX-XX.compute-1.amazonaws.com
   .setAppName("Cluster")
   .set("spark.local.dir", "/mnt")
   .setJars(Seq("target/scala-2.10/" + project + "-assembly-" + version + ".jar", "target/scala-2.10/" + project + "_2.10-" + version + "-tests.jar") ++ otherJars)
   .setSparkHome("/root/spark")
   .set("spark.driver.allowMultipleContexts", "true")
   .set("spark.executor.memory", "35g")

  val sparkContext = new SparkContext(sc)
}

我怎样才能解决这个问题？

Answer 1

尽管配置选项 spark.driver.allowMultipleContexts 存在，但它具有误导性，因为不鼓励使用多个 Spark 上下文。此选项仅用于 Spark 内部测试，不应在用户程序中使用。在单个 JVM 中运行多个 Spark 上下文时，您可能会得到意想不到的结果。