基于hadoop2.7.3搭建多机环境（YARN+HA）

第一：环境说明

parallels desktop CentOS-6.5-x86_64-bin-DVD1.iso jdk-7u79-linux-x64.tar.gz Hadoop-2.7.3.tar.gz 搭建四个节点的集群。他们的hostname分布为hadoopA，hadoopB，hadoopC，hadoopD。其中hadoopA的角色为Activity namnode。hadoopB的角色为standby namenode，datanode，journalnode。hadoopC的角色为datanode，journalnode。hadoopD的角色为datanode，journalnode。

第二：操作系统配置

赋予hadoop用户sudo权限 [[email protected] hadoop]# visudo ## Allow root to run any commands anywhere root ALL=(ALL) ALL hadoop ALL=(ALL) ALL 修改hostname [[email protected] hadoop-2.7.3]$ cat /etc/hosts 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 ::1 localhost localhost.localdomain localhost6 localhost6.localdomain6 192.168.1.201 hadoopA 192.168.1.202 hadoopB 192.168.1.203 hadoopC 192.168.1.204 hadoopD

第三：安装和配置jdk

分别在hadoopA，hadoopB，hadoopC，hadoopD四个节点安装jdk。

[[email protected] ~]$ tar -zxvf jdk-7u79-linux-x64.tar.gz

修改jdk的名称

[[email protected] ~]$ mv jdk1.7.0_79/ jdk1.7

第四：安装和配置hadoop

在hadoopA,hadoopB,hadoopC,hadoopD四个节点上解压hadoop [[email protected] ~]$ tar -zxvf hadoop-2.7.3.tar.gz 在hadoopA上配置hadoop-env.sh # The java implementation to use. export JAVA_HOME=/home/hadoop/jdk1.7 在hadoopA上配置core-site.xml <configuration> <PRoperty> <name>fs.defaultFS</name> <value>hdfs://hadoopA:8020</value> </property> </configuration> 在hadoopA配置hdfs-site.xml <configuration> <property> <name>dfs.nameservices</name> <value>hadoop-test</value> <description> Comma-separated list of nameservices. </description> </property> <property> <name>dfs.ha.namenodes.hadoop-test</name> <value>nn1,nn2</value> <description> The prefix for a given nameservice, contains a comma-separated list of namenodes for a given nameservice (eg EXAMPLENAMESERVICE). </description> </property> <property> <name>dfs.namenode.rpc-address.hadoop-test.nn1</name> <value>hadoopA:8020</value> <description> RPC address for nomenode1 of hadoop-test </description> </property> <property> <name>dfs.namenode.rpc-address.hadoop-test.nn2</name> <value>hadoopB:8020</value> <description> RPC address for nomenode2 of hadoop-test </description> </property> <property> <name>dfs.namenode.http-address.hadoop-test.nn1</name> <value>hadoopA:50070</value> <description> The address and the base port where the dfs namenode1 web ui will listen on. </description> </property> <property> <name>dfs.namenode.http-address.hadoop-test.nn2</name> <value>hadoopB:50070</value> <description> The address and the base port where the dfs namenode2 web ui will listen on. </description> </property> <property> <name>dfs.namenode.name.dir</name> <value>file:///home/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.namenode.shared.edits.dir</name> <value>qjournal://hadoopB:8485;hadoopC:8485;hadoopD:8485/hadoop-test</value> <description>A directory on shared storage between the multiple namenodes in an HA cluster. This directory will be written by the active and read by the standby in order to keep the namespaces synchronized. This directory does not need to be listed in dfs.namenode.edits.dir above. It should be left empty in a non-HA cluster. </description> </property> <property> <name>dfs.datanode.data.dir</name> <value>file:///home/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.ha.automatic-failover.enabled</name> <value>false</value> <description> Whether automatic failover is enabled. See the HDFS High Availability documentation for details on automatic HA configuration. </description> </property> <property> <name>dfs.journalnode.edits.dir</name> <value>/home/hadoop/hdfs/journal/</value> </property> </configuration> 在hadoopA配置mapred-site.xml <configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> <property> <name>mapreduce.jobhistory.address</name> <value>hadoopB:10020</value> </property> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>hadoopB:19888</value> </property> </configuration> 在hadoopA配置yarn-site.xml <configuration> <!-- Resource Manager Configs --> <property> <description>The hostname of the RM.</description> <name>yarn.resourcemanager.hostname</name> <value>hadoopA</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>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>/home/hadoop/hadoop-2.7.3/etc/hadoop/fairscheduler.xml</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>/home/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>/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>8720</value> </property> <property> <description>Number of CPU cores that can be allocated for containers.</description> <name>yarn.nodemanager.resource.cpu-vcores</name> <value>2</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> </configuration> 在hadoopA配置fairscheduler.xml <allocations> <queue name="infrastructure"> <minResources>102400 mb, 50 vcores </minResources> <maxResources>153600 mb, 100 vcores </maxResources> <maxRunningApps>200</maxRunningApps> <minSharePreemptionTimeout>300</minSharePreemptionTimeout> <weight>1.0</weight> <aclSubmitApps>root,yarn,search,hdfs</aclSubmitApps> </queue> <queue name="tool"> <minResources>102400 mb, 30 vcores</minResources> <maxResources>153600 mb, 50 vcores</maxResources> </queue> <queue name="sentiment"> <minResources>102400 mb, 30 vcores</minResources> <maxResources>153600 mb, 50 vcores</maxResources> </queue> </allocations> 在hadoopA配置slaves文件 [[email protected] hadoop]# cat slaves hadoopB hadoopC hadoopD 将hadoopA上hadoop的安装目录复制到其它 [[email protected] hadoop-2.7.3]$ scp etc/hadoop/* hadoopB://home/hadoop/hadoop-2.7.3/etc/hadoop/ [[email protected] hadoop-2.7.3]$ scp etc/hadoop/* hadoopC://home/hadoop/hadoop-2.7.3/etc/hadoop/ [[email protected] hadoop-2.7.3]$ scp etc/hadoop/* hadoopD://home/hadoop/hadoop-2.7.3/etc/hadoop/

第五：启动hadoop

在各个JournalNode节点上，输入以下命令启动journalnode服务 [[email protected] hadoop-2.7.3]$ sbin/hadoop-daemon.sh start journalnode [[email protected] hadoop-2.7.3]$ sbin/hadoop-daemon.sh start journalnode [[email protected] hadoop-2.7.3]$ sbin/hadoop-daemon.sh start journalnode 在[nn1]上，对其进行格式化，并启动： [[email protected] hadoop-2.7.3]# bin/hdfs namenode -format [[email protected] hadoop-2.7.3]# sbin/hadoop-daemon.sh start namenode 在[nn2]上，同步nn1的元数据信息 [[email protected] hadoop-2.7.3]$ bin/hdfs namenode -bootstrapStandby 在[nn2]上，启动NameNode： [[email protected] hadoop-2.7.3]$ sbin/hadoop-daemon.sh start namenode (经过以上四步操作，nn1和nn2均处理standby状态) 在[nn1]上，将NameNode切换为Active [[email protected] hadoop-2.7.3]# bin/hdfs haadmin -transitionToActive nn1 在[nn1]上，启动所有datanode [[email protected] hadoop-2.7.3]# sbin/hadoop-daemons.sh start datanode 启动yarn：在[nn1]上，输入以下命令 [[email protected] hadoop-2.7.3]# sbin/start-yarn.sh 关闭Hadoop集群：在[nn1]上，输入以下命令 [[email protected] hadoop-2.7.3]# sbin/stop-dfs.sh [[email protected] hadoop-2.7.3]# sbin/stop-yarn.sh

第六：验证hadoop

hadoopA输入命令 [[email protected] jdk1.7]# /home/hadoop/jdk1.7/bin/jps 10747 -- process information unavailable 15583 Jps 16576 -- process information unavailable hadoopB输入命令 [[email protected] hadoop-2.7.3]$ /home/hadoop/jdk1.7/bin/jps 15709 NodeManager 2405 JournalNode 11551 NameNode 12862 DataNode 15398 Jps hadoopC输入命令 [[email protected] ~]$ /home/hadoop/jdk1.7/bin/jps 2388 JournalNode 13091 Jps 13553 DataNode 15214 NodeManager hadoopD输入命令 [[email protected] hadoop-2.7.3]$ /home/hadoop/jdk1.7/bin/jps 13506 DataNode 12675 Jps 15334 NodeManager 2570 JournalNode

打开浏览器输入以下地址：

http://192.168.1.201:50070/dfshealth.html#tab-overview http://192.168.1.202:50070/dfshealth.html#tab-overview http://192.168.1.201:8088/cluster/scheduler

第七：关闭hadoop

关闭Hadoop集群：在[nn1]上，输入以下命令 [[email protected] hadoop-2.7.3]# sbin/stop-dfs.sh [[email protected] hadoop-2.7.3]# sbin/stop-yarn.sh

第八：特别说明

说明： 步骤2：在[nn1]上，对其进行格式化，并启动： bin/hdfs namenode -fromal 步骤3：在[nn2]上，同步nn1的元数据信息 bin/hdfs namenode -bootstrapStandby

这两步操作，只是在第一次建立集群的时候才使用 下次重启节点，是不需要操作这两步