Hadoop 2.2 YARN分布式集群搭建配置流程

搭建环境准备：JDK1.6，SSH免密码通信

系统：CentOS 6.3

集群配置：NameNode和ResourceManager在一台服务器上，三个数据节点

搭建用户：YARN

Hadoop2.2下载地址：http://www.apache.org/dyn/closer.cgi/hadoop/common/

步骤一：上传Hadoop 2.2 并解压到/export/yarn/hadoop-2.2.0

外层的启动脚本在sbin目录

内层的被调用脚本在bin目录

Native的so文件都在lib/native目录

配置程序文件都放置在libexec

配置文件都在etc目录，对应以前版本的conf目录

所有的jar包都在share/hadoop目录下面

步骤二：配置环境变量

　　在~/.bashrc文件中添加以下配置：

export JAVA_HOME=/export/servers/jdk1.6.0_25/
export HADOOP_DEV_HOME=/export/yarn/hadoop-2.2.0
export HADOOP_MAPARED_HOME=${HADOOP_DEV_HOME}
export HADOOP_COMMON_HOME=${HADOOP_DEV_HOME}
export HADOOP_HDFS_HOME=${HADOOP_DEV_HOME}
export YARN_HOME=${HADOOP_DEV_HOME}
export HADOOP_CONF_DIR=${HADOOP_DEV_HOME}/etc/hadoop
export HDFS_CONF_DIR=${HADOOP_DEV_HOME}/etc/hadoop
export YARN_CONF_DIR=${HADOOP_DEV_HOME}/etc/hadoop
export PATH=$PATH:$HADOOP_DEV_HOME/bin:$JAVA_HOME/bin:$HADOOP_DEV_HOME/sbin

　　配置完成后，执行source ~/.bashrc命令

步骤三：core-site.xml hdfs-site.xml mapred-site.xml yarn-site.xml配置

• 　　Core-site.xml配置

<configuration >
<property>
  <name>fs.defaultFS</name>
  <value>hdfs://master1:9101</value>
<description></description>
</property>
<property>
  <name>hadoop.tmp.dir</name>
  <value>/export/yarn/hadoop-log/</value>
<description>tmp临时目录</description>
</property>
<property>
  <name>io.compression.codecs</name>
 <value>com.hadoop.compression.lzo.LzoCodec,com.hadoop.compression.lzo.LzopCodec</value>
<description>压缩编码,这里配置了LZO</description>
</property>
<property>
  <name>io.compression.codec.lzo.class</name>
  <value>com.hadoop.compression.lzo.LzoCodec</value>
<description>LZO对应类</description>
</property>
<property>
  <name>io.native.lib.available</name>
  <value>true</value>
  <description>是否启用本地native库</description>
</property>
</configuration>

• Hdfs-site.xml

<configuration>
<property>
  <name>dfs.namenode.name.dir</name>
  <value>/export/yarn/hadoop-log/nd</value>
</property>
<property>
  <name>dfs.datanode.data.dir</name>
  <value>/export/yarn/hadoop-log/dd</value>
</property>
<property>
  <name>dfs.namenode.http-address</name>
  <value>0.0.0.0:60176</value>
  <description>namenode http 地址</description>
</property>              
<property>
  <name>dfs.datanode.address</name>
  <value>0.0.0.0:60116</value>
</property>
<property>
  <name>dfs.datanode.ipc.address</name>
  <value>0.0.0.0:60126</value>
</property>
<property>
  <name>dfs.datanode.http.address</name>
  <value>0.0.0.0:60176</value>
</property>
<property>
  <name>dfs.secondary.http.address</name>
  <value>0.0.0.0:60196</value>
</property>
</configuration>

• Mapred-site.xml

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>        

• yarn-site.xml

<configuration>
<!-- Site specific YARN configuration properties -->
<property>
<name>yarn.resourcemanager.address</name>
<value>master1:8032</value>
</property>
<property>
<name>yarn.resourcemanager.scheduler.address</name>
<value>master1:8030</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.address</name>
<value>master1:8088</value>
</property>
<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>master1:8031</value>
</property>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
 <name>yarn.resourcemanager.scheduler.class</name>
 <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
</property>
<property>
 <name>yarn.scheduler.fair.allocation.file</name>
 <value>fair-scheduler.xml</value>
</property>
</configuration>

　　注：这里配置了Hadoop 2.2 FairScheduler调度器

步骤四：slaves配置

       将三个数据节点配置到slaves中

步骤五：将配置好的Hadoop 2.2 分发同步到各个数据节点

步骤六：格式化NameNode

       执行命令：hdfs namenode –format

              或者 hadoop namenode –format

步骤七：启动hdfs和yarn

       启动Hdfs: start-dfs.sh

       启动yarn: start-yarn.sh

       或者可以执行start-all.sh一起启动hdfs和yarn

步骤八：测试

       Hdfs测试：

              向hdfs中上传文件：hdfs dfs –put abc /input

              查看hdfs文件目录：hdfs dfs –ls /

       Yarn测试：

              运行WordCount测试程序：

　　　　　hadoop jar /export/yarn/hadoop-2.2.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /input /out

 

http://blog.csdn.net/uf_fantn/article/details/16989183