[综合]Apache Hadoop 2.2.0单节点集群安装[翻译]

Mapreduce Tarball

你需要有MapReduce tarball，如果没有的话那么你需要从源文件中创建一个，执行命令如下：

$ mvn clean install -DskipTests
$ cd hadoop-mapreduce-project
$ mvn clean install assembly:assembly -Pnative

注意：你需要安装protoc 2.5.0 。

如果需要忽略本地化编译那么你可以去掉在maven中去掉 -Pnative参数，Tarball会在target/ 目录中生成。

 

设置环境变量

假定你已经安装了hadoop-common/hadoop-hdfs，并且已经设置了$HADOOP_COMMON_HOME/$HADOOP_HDFS_HOME，解压hadoop mapreduce tarball 并设置环境变量$HADOOP_MAPRED_HOME到解压的目录中去，同时也设置 $HADOOP_YARN_HOME 环境变量。

注意：下面的操作假定你的hdfs已经开始运行。

 

设置配置文件

启动ResourceManager 和 NodeManager之前你需要更新配置文件。假定$HADOOP_CONF_DIR是你的配置文件所在的目录并且已经为hdfs和 core-site.xml做了配置。那么有2个文件你需要去设置 mapred-site.xml 和 yarn-site.xml

mapred-site.xml配置

在 mapred-site.xml中加入如下配置：

<property>
	<name>mapreduce.cluster.temp.dir</name>
	<value></value>
	<description>No description</description>
	<final>true</final>
</property>

<property>
	<name>mapreduce.cluster.local.dir</name>
	<value></value>
	<description>No description</description>
	<final>true</final>
</property>

 

yarn-site.xml配置

在你的yarn-site.xml中加入如下信息：

  <property>
    <name>yarn.resourcemanager.resource-tracker.address</name>
    <value>host:port</value>
    <description>host is the hostname of the resource manager and 
    port is the port on which the NodeManagers contact the Resource Manager.
    </description>
  </property>

  <property>
    <name>yarn.resourcemanager.scheduler.address</name>
    <value>host:port</value>
    <description>host is the hostname of the resourcemanager and port is the port
    on which the Applications in the cluster talk to the Resource Manager.
    </description>
  </property>

  <property>
    <name>yarn.resourcemanager.scheduler.class</name>
    <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>
    <description>In case you do not want to use the default scheduler</description>
  </property>

  <property>
    <name>yarn.resourcemanager.address</name>
    <value>host:port</value>
    <description>the host is the hostname of the ResourceManager and the port is the port on
    which the clients can talk to the Resource Manager. </description>
  </property>

  <property>
    <name>yarn.nodemanager.local-dirs</name>
    <value></value>
    <description>the local directories used by the nodemanager</description>
  </property>

  <property>
    <name>yarn.nodemanager.address</name>
    <value>0.0.0.0:port</value>
    <description>the nodemanagers bind to this port</description>
  </property>  

  <property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>10240</value>
    <description>the amount of memory on the NodeManager in GB</description>
  </property>
 
  <property>
    <name>yarn.nodemanager.remote-app-log-dir</name>
    <value>/app-logs</value>
    <description>directory on hdfs where the application logs are moved to </description>
  </property>

   <property>
    <name>yarn.nodemanager.log-dirs</name>
    <value></value>
    <description>the directories used by Nodemanagers as log directories</description>
  </property>

  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
    <description>shuffle service that needs to be set for Map Reduce to run </description>
  </property>

 

配置capacity-scheduler.xml文件

确保root queues已经在capacity-scheduler.xml中设置：

  <property>
    <name>yarn.scheduler.capacity.root.queues</name>
    <value>unfunded,default</value>
  </property>
  
  <property>
    <name>yarn.scheduler.capacity.root.capacity</name>
    <value>100</value>
  </property>
  
  <property>
    <name>yarn.scheduler.capacity.root.unfunded.capacity</name>
    <value>50</value>
  </property>
  
  <property>
    <name>yarn.scheduler.capacity.root.default.capacity</name>
    <value>50</value>
  </property>

 

运行守护进程

假定你的环境变量$HADOOP_COMMON_HOME, $HADOOP_HDFS_HOME, $HADOO_MAPRED_HOME, $HADOOP_YARN_HOME,$JAVA_HOME and $HADOOP_CONF_DIR都已经配置好了。跟配置 $HADOOP_CONF_DIR一样配置好$YARN_CONF_DIR。

执行如下命令启动ResourceManager 和NodeManager ：

$ cd $HADOOP_MAPRED_HOME
$ sbin/yarn-daemon.sh start resourcemanager
$ sbin/yarn-daemon.sh start nodemanager

你可以执行randomwriter 来查看是否启动：

$ $HADOOP_COMMON_HOME/bin/hadoop jar hadoop-examples.jar randomwriter out