spark2 + yarn - 准备 AM 容器时出现空指针异常

Question

我正在尝试运行

pyspark --master yarn

Spark 版本：2.0.0
Hadoop 版本：2.7.2
Hadoop yarn Web 界面是成功启动

事情是这样的：

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$$anonfun$prepareLocalResources.apply(Client.scala:484)
        at org.apache.spark.deploy.yarn.Client$$anonfun$prepareLocalResources.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

纱线-site.xml：（最后一个属性是我在网上找到的，所以试了一下是否可行）

<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 3 个 LXD 容器（主 + 两个计算）中设置，在具有 16GB 内存的服务器上。

Answer 1

通常（显然不总是）当你看到

ERROR SparkContext: Error initializing SparkContext.

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

您可以在此处粘贴您的 spark-defaults.conf。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

看是否启动。

此外，您的 yarn-site.xml 中没有资源配置（例如 yarn.nodemanager.resource.memory-mb），因此您可能没有为 Yarn 分配足够的资源。考虑到你的机器的大小，你最好明确这些值。

Answer 2

鉴于Spark 2.0.0代码中的错误位置：

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

Answer 3

我刚刚提高了@tinfoiled 的回答，但我想在这里评论 spark.yarn.jars（以 's' 结尾）属性的语法，因为我花了很多时间弄清楚.

正确的语法（哪个 OP 已经知道了）是 -

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

其实我最后没有放*.jar，结果变成了"not being able to load ApplicationMaster"。我尝试了各种组合，但没有用。事实上，我在

上针对同样的问题在 SOF 上发布了一个问题

我什至不确定我正在做的事情是否正确，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.

spark2 + yarn - 准备 AM 容器时出现空指针异常

spark2 + yarn - nullpointerexception while preparing AM container

hadoop-yarn

apache-spark

hadoop2

pyspark