apache-spark - spark2 + yarn - 准备 AM 容器时出现空指针异常

Question

我在努力奔跑

pyspark --master yarn

• 星火版本:2.0.0
• Hadoop版本:2.7.2
• Hadoop yarn 网络界面是成功启动

是这样的:

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 

yarn 站点.xml:(最后一个属性是我在网上找到的，所以就试了一下是否可行)

<configuration>

<!-- Site specific YARN configuration properties -->
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>
        <value>org.apache.hadoop.mapred.ShuffleHandler</value>
    </property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.address</name>
        <value>sm:8025</value>
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>sm:8030</value>
    </property>
    <property>
        <name>yarn.resourcemanager.address</name>
        <value>sm:8050</value>
    </property>
    <property>
        <name>yarn.application.classpath</name>
        <value>/home/mispp/hadoop-2.7.2/share/hadoop/yarn</value>
    </property>
</configuration>

.bashrc:

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

知道为什么会这样吗？它在具有 16GB 内存的服务器上设置在 3 个 LXD 容器(主 + 两个计算)中。

Answer 1

给出错误在Spark 2.0.0代码中的位置:

https://github.com/apache/spark/blob/v2.0.0/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala#L480

我怀疑错误是由于 spark.yarn.jars 配置错误造成的。根据 http://spark.apache.org/docs/2.0.0/running-on-yarn.html#spark-properties 上的文档，我会仔细检查您设置中此配置的值是否正确。 .