Hadoop MapReduce2 -单节点集群配置

Mapreduce 压缩包

你可以从release下载一个MapReduce压缩包。如果没有你可以自己创建一个压缩包。

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

注意：你需要安装protoc2.5

忽略本地的MapReduce你可以忽略maven的 —Pnative参数。这个压缩包应该在target/directory下。

设置环境变量

假设你已经安装了hadoop-common/hadoop-hdfs，并设置了 $HADOOP_COMMON_HOME/$HADOOP_HDFS_HOME

解压hadoop-mapreduce压缩包。设置环境变量 $HADOOP_MAPRED_HOME 设置$HADOOP_YARN_HOME的环境变量和 $HADOOP_MAPRED_HOME 一样。

搭建配置文件

要启动ResourceManger和NodeManager，需要设置配置信息。假设$HADOOP_CONF_DIR 是配置HDFS和yarn-site.xml的配置目录。有两个文件你必须进行设置：mapred-site.xml and yarn-site.xml

配置mapred-site.xml：

添加以下配置到你的mapred-site.xml

<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>

设置yarn-site.xml

  添加以下的配置到你的yarn-site.xml中

  <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>

接下来配置capacity-scheduler.xml

   添加以下配置到capacity-scheduler.xml

  <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deamons

   假设环境变量$HADOOP_COMMON_HOME, $HADOOP_HDFS_HOME, $HADOO_MAPRED_HOME, $HADOOP_YARN_HOME,$JAVA_HOME and $HADOOP_CONF_DIR已经正确配置。设置$$YARN_CONF_DIR和$HADOOP_CONF_DIR配置也一样。

运行ResourceManager和NodeManager需要以下命令：

    $ cd $HADOOP_MAPRED_HOME

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

如果ResourceManager和NodeManager能启动运行，你可以运行randomwriter例子测试：

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