Java及Web程序调用hadoop2.6

时间：2015-01-11 06:15:09 阅读：307 评论：0 收藏：0 [点我收藏+]

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1. hadoop集群：

1.1 系统及硬件配置：

hadoop版本：2.6 ；三台虚拟机：node101(192.168.0.101)、node102(192.168.0.102)、node103(192.168.0.103)；每台机器2G内存、1个CPU核；

node101: NodeManager、 NameNode、ResourceManager、DataNode；

node102: NodeManager、DataNode 、SecondaryNameNode、JobHistoryServer；

node103: NodeManager 、DataNode；

1.2 配置过程中遇到的问题：

1） NodeManager启动不了；

最开始配置的虚拟机配置的是512M内存，所以在yarn-site.xml 中的“yarn.nodemanager.resource.memory-mb”配置为512（其默认配置是1024），查看日志，报错：

org.apache.hadoop.yarn.exceptions.YarnRuntimeException: Recieved SHUTDOWN signal from Resourcemanager ,Registration of NodeManager failed, Message from ResourceManager: NodeManager from  node101 doesn‘t satisfy minimum allocations, Sending SHUTDOWN signal to the NodeManager.

把它改为1024或者以上就可以正常启动NodeManager了，我设置的是2048；

2）任务可以提交，但是不会继续运行

a. 由于这里每个虚拟机只配置了一个核，但是yarn-site.xml里面的“yarn.nodemanager.resource.cpu-vcores”默认配置是8，这样在分配资源的时候会有问题，所以把这个参数配置为1；

b. 出现下面的错误：

is running beyond virtual memory limits. Current usage: 96.6 MB of 1.5 GB physical memory used; 1.6 GB of 1.5 GB virtual memory used. Killing container.

这个应该是map、reduce、NodeManager的资源配置没有配置好，大小配置不正确导致的，但是我改了好久，感觉应该是没问题的，但是一直报这个错，最后没办法，把这个检查去掉了，即把yarn-site.xml 中的“yarn.nodemanager.vmem-check-enabled”配置为false；这样就可以提交任务了。

1.3 配置文件（希望有高人可以指点下资源配置情况，可以不出现上面b的错误，而不是使用去掉检查的方法）：

1）hadoop-env.sh 和yarn-env.sh 中配置jdk，同时HADOOP_HEAPSIZE和YARN_HEAPSIZE配置为512；

2）hdfs-site.xml 配置数据存储路径和secondaryname所在节点：

<configuration>
<property>
  <name>dfs.namenode.name.dir</name>
  <value>file:////data/hadoop/hdfs/name</value>
  <description>Determines where on the local filesystem the DFS name node
      should store the name table(fsimage).  If this is a comma-delimited list
      of directories then the name table is replicated in all of the
      directories, for redundancy. </description>
</property>
<property>
  <name>dfs.datanode.data.dir</name>
  <value>file:///data/hadoop/hdfs/data</value>
  <description>Determines where on the local filesystem an DFS data node
  should store its blocks.  If this is a comma-delimited
  list of directories, then data will be stored in all named
  directories, typically on different devices.
  Directories that do not exist are ignored.
  </description>
</property>
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>node102:50090</value>
</property>
</configuration>

3）core-site.xml 配置namenode：

<configuration>
<property>
<name>fs.defaultFS</name>
  <value>hdfs://node101:8020</value>
</property>
</configuration>

4） mapred-site.xml 配置map和reduce的资源：

<configuration>
<property>
  <name>mapreduce.framework.name</name>
  <value>yarn</value>
  <description>The runtime framework for executing MapReduce jobs.
  Can be one of local, classic or yarn.
  </description>
</property>

<!-- jobhistory properties -->
<property>
  <name>mapreduce.jobhistory.address</name>
  <value>node102:10020</value>
  <description>MapReduce JobHistory Server IPC host:port</description>
</property>


<property>
<name>mapreduce.map.memory.mb</name>
<value>1024</value>
</property>
<property>
<name>mapreduce.reduce.memory.mb</name>
<value>1024</value>
</property>
<property>
<name>mapreduce.map.java.opts</name>
<value>-Xmx512m</value>
</property>
<property>
<name>mapreduce.reduce.java.opts</name>
<value>-Xmx512m</value>
</property>
</configuration>

5）yarn-site.xml 配置resourcemanager及相关资源：

<configuration>

 <property>
    <description>The hostname of the RM.</description>
    <name>yarn.resourcemanager.hostname</name>
    <value>node101</value>
  </property>    
  
  <property>
    <description>The address of the applications manager interface in the RM.</description>
    <name>yarn.resourcemanager.address</name>
    <value>${yarn.resourcemanager.hostname}:8032</value>
  </property>

  <property>
    <description>The address of the scheduler interface.</description>
    <name>yarn.resourcemanager.scheduler.address</name>
    <value>${yarn.resourcemanager.hostname}:8030</value>
  </property>

  <property>
    <description>The http address of the RM web application.</description>
    <name>yarn.resourcemanager.webapp.address</name>
    <value>${yarn.resourcemanager.hostname}:8088</value>
  </property>

  <property>
    <description>The https adddress of the RM web application.</description>
    <name>yarn.resourcemanager.webapp.https.address</name>
    <value>${yarn.resourcemanager.hostname}:8090</value>
  </property>

  <property>
    <name>yarn.resourcemanager.resource-tracker.address</name>
    <value>${yarn.resourcemanager.hostname}:8031</value>
  </property>

  <property>
    <description>The address of the RM admin interface.</description>
    <name>yarn.resourcemanager.admin.address</name>
    <value>${yarn.resourcemanager.hostname}:8033</value>
  </property>

  <property>
    <description>List of directories to store localized files in. An 
      application‘s localized file directory will be found in:
      ${yarn.nodemanager.local-dirs}/usercache/${user}/appcache/application_${appid}.
      Individual containers‘ work directories, called container_${contid}, will
      be subdirectories of this.
   </description>
    <name>yarn.nodemanager.local-dirs</name>
    <value>/data/hadoop/yarn/local</value>
  </property>

  <property>
    <description>Whether to enable log aggregation</description>
    <name>yarn.log-aggregation-enable</name>
    <value>true</value>
  </property>

  <property>
    <description>Where to aggregate logs to.</description>
    <name>yarn.nodemanager.remote-app-log-dir</name>
    <value>/data/tmp/logs</value>
  </property>

  <property>
    <description>Amount of physical memory, in MB, that can be allocated 
    for containers.</description>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>2048</value>
  </property>
<property>
	<name>yarn.scheduler.minimum-allocation-mb</name>
	<value>512</value>
</property>
<property>
	<name>yarn.nodemanager.vmem-pmem-ratio</name>
	<value>1.0</value>
</property>
<property>
	<name>yarn.nodemanager.vmem-check-enabled</name>
	<value>false</value>
</property>
<!--	
 <property>
     <description>The class to use as the resource scheduler.</description>
         <name>yarn.resourcemanager.scheduler.class</name>
             <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
               </property>

  <property>
      <description>fair-scheduler conf location</description>
          <name>yarn.scheduler.fair.allocation.file</name>
              <value>${yarn.home.dir}/etc/hadoop/fairscheduler.xml</value>
                </property>
-->
<property>
	<name>yarn.nodemanager.resource.cpu-vcores</name>
	<value>1</value>
</property>
  <property>
    <description>the valid service name should only contain a-zA-Z0-9_ and can not start with numbers</description>
    <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>
</configuration>

2. Java调用Hadoop2.6 ，运行MR程序：

需修改下面两个地方：

1）调用主程序的Configuration需要配置：

Configuration conf = new Configuration();
    
    conf.setBoolean("mapreduce.app-submission.cross-platform", true);// 配置使用跨平台提交任务
    conf.set("fs.defaultFS", "hdfs://node101:8020");//指定namenode  
    conf.set("mapreduce.framework.name", "yarn");  // 指定使用yarn框架
    conf.set("yarn.resourcemanager.address", "node101:8032"); // 指定resourcemanager
    conf.set("yarn.resourcemanager.scheduler.address", "node101:8030");// 指定资源分配器

2）添加下面的类到classpath：

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