作业提交在星火作业服务器提供的NullPointerException

Question

失败

我使用星火作业服务器0.6.2与1.6.0星火并在一些工作的提交尝试，我得到以下异常：

[ERROR] 2016-11-16 08:01:59,595 spark.jobserver.context.DefaultSparkContextFactory$$anon$1 logError - Error initializing SparkContext. 
java.lang.NullPointerException 
at org.apache.spark.scheduler.TaskSchedulerImpl.<init>(TaskSchedulerImpl.scala:106) 
at org.apache.spark.scheduler.TaskSchedulerImpl.<init>(TaskSchedulerImpl.scala:60) 
at org.apache.spark.SparkContext$.org$apache$spark$SparkContext$$createTaskScheduler(SparkContext.scala:2630) 
at org.apache.spark.SparkContext.<init>(SparkContext.scala:522) 
at spark.jobserver.context.DefaultSparkContextFactory$$anon$1.<init>(SparkContextFactory.scala:53) 
at spark.jobserver.context.DefaultSparkContextFactory.makeContext(SparkContextFactory.scala:53) 
at spark.jobserver.context.DefaultSparkContextFactory.makeContext(SparkContextFactory.scala:48) 
at spark.jobserver.context.SparkContextFactory$class.makeContext(SparkContextFactory.scala:37) 
at spark.jobserver.context.DefaultSparkContextFactory.makeContext(SparkContextFactory.scala:48) 
at spark.jobserver.JobManagerActor.createContextFromConfig(JobManagerActor.scala:378) 
at spark.jobserver.JobManagerActor$$anonfun$wrappedReceive$1.applyOrElse(JobManagerActor.scala:122) 

可能是什么原因在哪里？

Answer 1

貌似jobserver无法找到你的配置文件火花上下文配置。请使用有效的配置文件。 实施例：

spark { 
    # spark.master will be passed to each job's JobContext 
    # master = "local[4]" 
    # master = "mesos://vm28-hulk-pub:5050" 
    master = "yarn-client" 

    # Default # of CPUs for jobs to use for Spark standalone cluster 
    job-number-cpus = 2 

    jobserver { 
    port = 8090 
    jar-store-rootdir = /opt/test/jobserver/jars 

    jobdao = spark.jobserver.io.JobFileDAO 

    filedao { 
     rootdir = /opt/test/jobserver/data 
    } 
    } 

    # predefined Spark contexts 
    contexts { 
    # my-low-latency-context { 
    #  num-cpu-cores = 1   # Number of cores to allocate. Required. 
    #  memory-per-node = 512m   # Executor memory per node, -Xmx style eg 512m, 1G, etc. 
    # } 

    # define additional contexts here 
    shared { 
     num-cpu-cores = 1 # shared tasks work best in parallel.                                  
     memory-per-node = 1024M # trial-and-error discovered memory per nodes 
    spark.yarn.executor.memoryOverhead = 512 
     spark.yarn.am.memory = 1024m 
     spark.yarn.am.memoryOverhead = 512 

     spark.executor.instances = 14 # 4 r3.xlarge instances with 4 cores each = 16 + 1 master                         
     spark.scheduler.mode = "FAIR"  
    } 

    } 

    # universal context configuration. These settings can be overridden, see README.md 
    context-settings { 
    num-cpu-cores = 2   # Number of cores to allocate. Required. 
    memory-per-node = 512m   # Executor memory per node, -Xmx style eg 512m, #1G, etc. 

    # in case spark distribution should be accessed from HDFS (as opposed to being installed on every mesos slave) 
    # spark.executor.uri = "hdfs://namenode:8020/apps/spark/spark.tgz" 

    # uris of jars to be loaded into the classpath for this context. Uris is a string list, or a string separated by commas ',' 
    # dependent-jar-uris = ["file:///some/path/present/in/each/mesos/slave/somepackage.jar"] 

    # If you wish to pass any settings directly to the sparkConf as-is, add them here in passthrough, 
    # such as hadoop connection settings that don't use the "spark." prefix 
    passthrough { 
     #es.nodes = "192.1.1.1" 
    } 
    } 

    # This needs to match SPARK_HOME for cluster SparkContexts to be created successfully 
    # home = "/home/spark/spark" 
}