scala – Spark多个上下文

EC2集群：1个主3个从属

Spark版本：1.3.1

我希望使用选项spark.driver.allowMultipleContexts,一个上下文本地(仅限主)和一个集群(主服务器和从服务器).

我得到这个stacktrace错误(第29行是我调用初始化第二个sparkcontext的对象)：

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

更多细节 ：

我想运行一个做两件事的程序.首先我有一个sparkContext本地(仅在master上),我做一个RDD并做一些操作.其次,我有一个第二个sparkContext,它使用一个主服务器和3个从服务器进行初始化,这也是一个RDD并进行一些操作.
因此,在第一种情况下,我想使用主机的16个核心,第二种情况我想使用从机的8核x 3.

简单示例：

val arr = Array(Array(1,2,3,4,5,6,7,8),Array(1,8))
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？