Apache spark spark2+；纱线-准备AM容器时出现空点异常

我想跑

pyspark --master yarn

• Spark版本：2.0.0
• Hadoop版本：2.7.2
• Hadoop纱线web界面是 成功启动

情况就是这样：

16/08/15 10:00:12 DEBUG Client: Using the default MR application classpath: $HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*,$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*
16/08/15 10:00:12 INFO Client: Preparing resources for our AM container
16/08/15 10:00:12 DEBUG Client: 
16/08/15 10:00:12 DEBUG DFSClient: /user/mispp/.sparkStaging/application_1471254869164_0006: masked=rwxr-xr-x
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp sending #8
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp got value #8
16/08/15 10:00:12 DEBUG ProtobufRpcEngine: Call: mkdirs took 14ms
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp sending #9
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp got value #9
16/08/15 10:00:12 DEBUG ProtobufRpcEngine: Call: setPermission took 10ms
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp sending #10
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp got value #10
16/08/15 10:00:12 DEBUG ProtobufRpcEngine: Call: getFileInfo took 2ms
16/08/15 10:00:12 INFO Client: Deleting staging directory hdfs://sm/user/mispp/.sparkStaging/application_1471254869164_0006
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp sending #11
16/08/15 10:00:12 DEBUG Client: IPC Client (1933573135) connection to sm/192.168.29.71:8020 from mispp got value #11
16/08/15 10:00:12 DEBUG ProtobufRpcEngine: Call: delete took 14ms
16/08/15 10:00:12 ERROR SparkContext: Error initializing SparkContext.
java.lang.NullPointerException
        at scala.collection.mutable.ArrayOps$ofRef$.newBuilder$extension(ArrayOps.scala:190)
        at scala.collection.mutable.ArrayOps$ofRef.newBuilder(ArrayOps.scala:186)
        at scala.collection.TraversableLike$class.filterImpl(TraversableLike.scala:246)
        at scala.collection.TraversableLike$class.filter(TraversableLike.scala:259)
        at scala.collection.mutable.ArrayOps$ofRef.filter(ArrayOps.scala:186)
        at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$6.apply(Client.scala:484)
        at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources$6.apply(Client.scala:480)
        at scala.collection.mutable.ArraySeq.foreach(ArraySeq.scala:74)
        at org.apache.spark.deploy.yarn.Client.prepareLocalResources(Client.scala:480)
        at org.apache.spark.deploy.yarn.Client.createContainerLaunchContext(Client.scala:834)
        at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:167)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
        at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:149)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:500)
        at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
        at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
        at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
        at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
        at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
        at py4j.Gateway.invoke(Gateway.java:236)
        at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
        at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
        at py4j.GatewayConnection.run(GatewayConnection.java:211)
        at java.lang.Thread.run(Thread.java:745)
16/08/15 10:00:12 DEBUG AbstractLifeCycle: stopping org.spark_project.jetty.server.Server@69e507eb
16/08/15 10:00:12 DEBUG Server: Graceful shutdown org.spark_project.jetty.server.Server@69e507eb by 

知道为什么会这样吗？ 它安装在3个LXD容器（主计算机+两台计算机）中，安装在一台16GB ram的服务器上。

当您看到

错误SparkContext:初始化SparkContext时出错

当使用纱线时，这意味着Spark应用程序无法启动，因为它无法获得足够的资源（通常是内存）。所以这是你需要检查的第一件事

您可以将

spark defaults.conf

粘贴到此处。或者如果您没有注意到，

spark.executor.memory

的默认值是

1g

。您可以尝试覆盖此值，例如

pyspark --executor-memory 256m

看看它是否开始

---

另外，

warn site.xml

中没有资源配置（例如，

warn.nodemanager.resource.memory mb

），因此您可能没有为warn分配足够的资源。考虑到机器的大小，最好对这些值进行解释。

考虑到Spark 2.0.0代码中错误的位置：

---

我怀疑发生错误的原因是

spark.warn.jars

的配置错误。根据位于的文档，我将再次检查您的设置中此配置的值是否正确。

我刚刚添加了@tinfoiled答案，但我想在这里对

spark.warn.jars

（它以“s”结尾）属性的语法进行评论，因为我花了相当长的时间才弄清楚

正确的语法（OP已经知道）是-

实际上，我没有在最后放入*.jar，结果导致“无法加载ApplicationMaster”。我尝试了各种组合，但都不起作用。事实上，我在SOF上发布了一个关于同一问题的问题

---

我甚至不确定我所做的是否正确，但OP的问题和@tinfoiled的回答给了我一些信心，我终于能够利用这个属性。

是的，纱线引起了问题。hdfs上包括了什么。这是没有回应的。

export HADOOP_PREFIX=/home/mispp/hadoop-2.7.2
export PATH=$PATH:$HADOOP_PREFIX/bin
export HADOOP_HOME=$HADOOP_PREFIX
export HADOOP_COMMON_HOME=$HADOOP_PREFIX
export HADOOP_YARN_HOME=$HADOOP_PREFIX
export HADOOP_HDFS_HOME=$HADOOP_PREFIX
export HADOOP_MAPRED_HOME=$HADOOP_PREFIX
export HADOOP_CONF_DIR=$HADOOP_PREFIX/etc/hadoop
export YARN_CONF_DIR=$HADOOP_PREFIX/etc/hadoop

pyspark --executor-memory 256m

spark.yarn.jars=hdfs://xxx:9000/user/spark/share/lib/*.jar