火花殼牌錯誤：「spark.dynamicAllocation {最小/最大}執行人必須在Cloudera的快速啓動VM我提示以下錯誤：建立星火1.2.1後置

Question

我試圖啓動火花外殼。 。尋找解決此問題的幫助感激有這方面的快速幫助解決該問題的錯誤日誌中提到如下：

16/03/03 09:40:37 INFO EventLoggingListener: Logging events to hdfs://quickstart.cloudera:8020/user/spark/applicationHistory/local-1457026830824 
org.apache.spark.SparkException: spark.dynamicAllocation.{min/max}Executors must be set! 
    at org.apache.spark.ExecutorAllocationManager.validateSettings(ExecutorAllocationManager.scala:135) 
    at org.apache.spark.ExecutorAllocationManager.<init>(ExecutorAllocationManager.scala:98) 
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:377) 
    at org.apache.spark.repl.SparkILoop.createSparkContext(SparkILoop.scala:986) 
    at $iwC$$iwC.<init>(<console>:9) 
    at $iwC.<init>(<console>:18) 
    at <init>(<console>:20) 
    at .<init>(<console>:24) 
    at .<clinit>(<console>) 
    at .<init>(<console>:7) 
    at .<clinit>(<console>) 
    at $print(<console>) 
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) 
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) 
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) 
    at java.lang.reflect.Method.invoke(Method.java:606) 
    at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852) 
    at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125) 
    at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674) 
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705) 
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669) 
    at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:828) 
    at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:873) 
    at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:785) 
    at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:123) 
    at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:122) 
    at org.apache.spark.repl.SparkIMain.beQuietDuring(SparkIMain.scala:270) 
    at org.apache.spark.repl.SparkILoopInit$class.initializeSpark(SparkILoopInit.scala:122) 
    at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:60) 
    at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$apply$mcZ$sp$5.apply$mcV$sp(SparkILoop.scala:945) 
    at org.apache.spark.repl.SparkILoopInit$class.runThunks(SparkILoopInit.scala:147) 
    at org.apache.spark.repl.SparkILoop.runThunks(SparkILoop.scala:60) 
    at org.apache.spark.repl.SparkILoopInit$class.postInitialization(SparkILoopInit.scala:106) 
    at org.apache.spark.repl.SparkILoop.postInitialization(SparkILoop.scala:60) 
    at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:962) 
    at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916) 
    at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:916) 
    at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135) 
    at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:916) 
    at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1011) 
    at org.apache.spark.repl.Main$.main(Main.scala:31) 
    at org.apache.spark.repl.Main.main(Main.scala) 
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) 
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) 
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) 
    at java.lang.reflect.Method.invoke(Method.java:606) 
    at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:358) 
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75) 
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala) 


scala> 

Answer 1

唯一的例外是很清楚看來你已經設置的spark.dynamicAllocation.enabled物業給true，但未能設置spark.dynamicAllocation.minExecutors和spark.dynamicAllocation.maxExecutors。spark 1.2.1 documentation明確指出這一點（從spark.dynamicAllocation.enabled說明，重點是我的）：

This requires the following configurations to be set: spark.dynamicAllocation.minExecutors, spark.dynamicAllocation.maxExecutors, and spark.shuffle.service.enabled

如果你看一下1.2 branch of Spark，你會發現，如果你不指定這些值，默認推遲到-1：

// Lower and upper bounds on the number of executors. These are required. 
private val minNumExecutors = conf.getInt("spark.dynamicAllocation.minExecutors", -1) 
private val maxNumExecutors = conf.getInt("spark.dynamicAllocation.maxExecutors", -1) 

此行爲已改變。如果你看一下updated 1.6 branch of Spark，你會看到，他們分別推遲到0和Integer.MAX_VALUE，：

// Lower and upper bounds on the number of executors. 
private val minNumExecutors = conf.getInt("spark.dynamicAllocation.minExecutors", 0) 
private val maxNumExecutors = conf.getInt("spark.dynamicAllocation.maxExecutors", 
              Integer.MAX_VALUE) 

這只是意味着，你需要添加這些無論對SparkConf設置，或者以任何其他配置文件，你'提供給火花外殼：

val sparkConf = new SparkConf() 
    .set("spark.dynamicAllocation.minExecutors", minExecutors) 
    .set("spark.dynamicAllocation.maxExecutors", maxExecutors)