搭建hadoop高可用集群（二）
搭建hadoop高可用集群（二）
Triumph-CP
于2023-02-15 18:28:06发布
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hadoop
hdfs
大数据
搭建hadoop高可用集群（一）
配置hadoophadoop-env.shworkerscore-site.xmlhdfs-site.xmlmapred-site.xmlyarn-site.xml/etc/profile拷贝
集群首次启动1、先启动zk集群（自动化脚本）2、在hadoop151,hadoop152,hadoop153启动JournalNode3、在hadoop151格式化4、在hadoop151启动namenode服务5、在hadoop152机器上同步namenode信息6、在hadoop152上启动namenode服务7、关闭所有dfs有关的服务8、格式化zk9、启动dfs10、启动yarn
安装成功
配置hadoop
解压完后，单独配置这6个文件
hadoop-env.sh第54行
export JAVA_HOME=/opt/soft/jdk180 export HDFS_NAMENODE_USER=root export HDFS_DATANODE_USER=root export HDFS_SECONDARYNAMENODE_USER=root export HDFS_JOURNALNODE_USER=root export HDFS_ZKFC_USER=root export YARN_RESOURCEMANAGER_USER=root export YARN_NODEMANAGER_USER=root workers填入ip
hadoop151 hadoop152 hadoop153 hadoop154 core-site.xml <configuration> <property> <name>fs.defaultFS</name> <value>hdfs://gky</value> <description>逻辑名称，必须与hdfs-site.xml中的dfs.nameservice值保持一致</description> </property> <property> <name>hadoop.tmp.dir</name> <value>/opt/soft/hadoop313/tmpdata</value> <description>namenode上本地的hadoop临时文件夹</description> </property> <property> <name>hadoop.http.staticuser.user</name> <value>root</value> <description>默认用户</description> </property> <property> <name>hadoop.proxyuser.root.hosts</name> <value>*</value> <description></description> </property> <property> <name>hadoop.proxyuser.root.groups</name> <value>*</value> <description></description> </property> <property> <name>io.file.buffer.size</name> <value>131072</value> <description>读写文件的buffer大小为：128k</description> </property> <property> <name>ha.zookeeper.quorum</name> <value>hadoop151:2181,hadoop152:2181,hadoop153:2181</value>//改成自己的ip <description>zookeeper队列</description> </property> <property> <name>ha.zookeeper.session-timeout.ms</name> <value>10000</value> <description>hadoop连接zookeeper的超时时长设置为10s</description> </property> </configuration> hdfs-site.xml <configuration> <property> <name>dfs.replication</name> <value>3</value> <description>hadoop中每一个block文件的备份数量</description> </property> <property> <name>dfs.namenode.name.dir</name> <value>/opt/soft/hadoop313/data/dfs/name</value> <description>namenode上存储hdfs名字空间元数据的目录</description> </property> <property> <name>dfs.datanode.data.dir</name> <value>/opt/soft/hadoop313/data/dfs/data</value> <description>datanode上数据块的物理存储位置目录</description> </property> <property> <name>dfs.namenode.secondary.http-address</name> <value>hadoop151:9869</value> <description></description> </property> <property> <name>dfs.nameservices</name> <value>gky</value> <description>指定hdfs的nameservice,需要和core-site.xml中保持一致</description> </property> <property> <name>dfs.ha.namenodes.gky</name> <value>nn1,nn2</value> <description>gky为集群的逻辑名称，映射两个namenode逻辑</description> </property> <property> <name>dfs.namenode.rpc-address.gky.nn1</name> <value>hadoop151:9000</value> <description>namenode1的RPC通信地址</description> </property> <property> <name>dfs.namenode.http-address.gky.nn1</name> <value>hadoop151:9870</value> <description>namenode1的http通信地址</description> </property> <property> <name>dfs.namenode.rpc-address.gky.nn2</name> <value>hadoop152:9000</value> <description>namenode2的RPC通信地址</description> </property> <property> <name>dfs.namenode.http-address.gky.nn2</name> <value>hadoop152:9870</value> <description>namenode2的http通信地址</description> </property> <property> <name>dfs.namenode.shared.edits.dir</name> <value>qjournal://hadoop151:8485;hadoop152:8485;hadoop153:8485/gky</value> <description>指定NameNode的edits元数据的共享存储位置(JournalNode列表)</description> </property> <property> <name>dfs.journalnode.edits.dir</name> <value>/opt/soft/hadoop313/data/journaldata</value> <description>指定JournalNode在本地磁盘存放数据的位置</description> </property> <!-- 容错 --> <property> <name>dfs.ha.automatic-failover.enabled</name> <value>true</value> <description>开启NameNode故障自动切换</description> </property> <property> <name>dfs.client.failover.proxy.provider.gky</name> <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value> <description>失败后自动切换的实现方式</description> </property> <property> <name>dfs.ha.fencing.methods</name> <value>sshfence</value> <description>防止脑裂的处理</description> </property> <property> <name>dfs.ha.fencing.ssh.private-key-files</name> <value>/root/.ssh/id_rsa</value> <description>使用sshfence隔离机制，需要ssh免密登录</description> </property> <property> <name>dfs.permissions.enabled</name> <value>false</value> <description>关闭HDFS操作权限验证</description> </property> <property> <name>dfs.image.transfer.bandwidthPerSec</name> <value>1048576</value> <description></description> </property> <property> <name>dfs.block.scanner.volume.bytes.per.second</name> <value>1048576</value> <description></description> </property> </configuration> mapred-site.xml <configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> <description>job执行框架：local,classic or yarn</description> <final>true</final> </property> <property> <name>mapreduce.application.classpath</name> <value>/opt/soft/hadoop313/etc/hadoop:/opt/soft/hadoop313/share/hadoop/common/lib/*:/opt/soft/hadoop313/share/hadoop/common/*:/opt/soft/hadoop313/share/hadoop/hdfs/*:/opt/soft/hadoop313/share/hadoop/hdfs/lib/*:/opt/soft/hadoop313/share/hadoop/mapreduce/*:/opt/soft/hadoop313/share/hadoop/mapreduce/lib/*:/opt/soft/hadoop313/share/hadoop/yarn/*:/opt/soft/hadoop313/share/hadoop/yarn/lib/*</value> </property> <property> <name>mapreduce.jobhistory.address</name> <value>hadoop151:10020</value> </property> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>hadoop151:19888</value> </property> <property> <name>mapreduce.map.memory.mb</name> <value>1024</value> <description>map阶段的task工作内存</description> </property> <property> <name>mapreduce.reduce.memory.mb</name> <value>2048</value> <description>reduce阶段的task工作内存</description> </property> </configuration> yarn-site.xml <configuration> <property> <name>yarn.resourcemanager.ha.enabled</name> <value>true</value> <description>开启resourcemanager高可用</description> </property> <property> <name>yarn.resourcemanager.cluster-id</name> <value>yrcabc</value> <description>指定yarn的集群中的id</description> </property> <property> <name>yarn.resourcemanager.ha.rm-ids</name> <value>rm1,rm2</value> <description>指定resourcemanager的名字</description> </property> <property> <name>yarn.resourcemanager.hostname.rm1</name> <value>hadoop153</value> <description>设置rm1的名字</description> </property> <property> <name>yarn.resourcemanager.hostname.rm2</name> <value>hadoop154</value> <description>设置rm2的名字</description> </property> <property> <name>yarn.resourcemanager.webapp.address.rm1</name> <value>hadoop153:8088</value> <description></description> </property> <property> <name>yarn.resourcemanager.webapp.address.rm2</name> <value>hadoop154:8088</value> <description></description> </property> <property> <name>yarn.resourcemanager.zk-address</name> <value>hadoop151:2181,hadoop152:2181,hadoop153:2181</value> <description>指定zk集群地址</description> </property> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> <description>运行mapreduce程序必须配置的附属服务</description> </property> <property> <name>yarn.nodemanager.local-dirs</name> <value>/opt/soft/hadoop313/tmpdata/yarn/local</value> <description>nodemanager本地存储目录</description> </property> <property> <name>yarn.nodemanager.log-dirs</name> <value>/opt/soft/hadoop313/tmpdata/yarn/log</value> <description>nodemanager本地日志目录</description> </property> <property> <name>yarn.nodemanager.resource.memory-mb</name> <value>2048</value> <description>resource进程的内存</description> </property> <property> <name>yarn.nodemanager.resource.cpu-vcores</name> <value>2</value> <description>resource工作中所能使用机器的内核数</description> </property> <property> <name>yarn.scheduler.minimum-allocation-mb</name> <value>256</value> <description></description> </property> <property> <name>yarn.log-aggregation-enable</name> <value>true</value> <description>yarn的日志能不能合并</description> </property> <property> <name>yarn.log-aggregation.retain-seconds</name> <value>86400</value> <description>yarn的合并日志保存的时间（多少秒）</description> </property> <property> <name>yarn.nodemanager.vmem-check-enabled</name> <value>false</value> <description></description> </property> <property> <name>yarn.application.classpath</name> <value>/opt/soft/hadoop313/etc/hadoop:/opt/soft/hadoop313/share/hadoop/common/lib/*:/opt/soft/hadoop313/share/hadoop/common/*:/opt/soft/hadoop313/share/hadoop/hdfs/*:/opt/soft/hadoop313/share/hadoop/hdfs/lib/*:/opt/soft/hadoop313/share/hadoop/mapreduce/*:/opt/soft/hadoop313/share/hadoop/mapreduce/lib/*:/opt/soft/hadoop313/share/hadoop/yarn/*:/opt/soft/hadoop313/share/hadoop/yarn/lib/*</value> <description></description> </property> <property> <name>yarn.nodemanager.env-whitelist</name> <value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_MAPRED_HOME</value> <description></description> </property> </configuration> /etc/profile #HADOOP_HOME export HADOOP_HOME=/opt/soft/hadoop313 export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HADOOP_HOME/lib 拷贝将配置好的文件拷贝到另外三台机器中
scp -r ./hadoop313/ root@hadoop151:/opt/soft scp -r ./hadoop313/ root@hadoop152:/opt/soft scp -r ./hadoop313/ root@hadoop153:/opt/soft scp -r ./hadoop313/ root@hadoop154:/opt/soft scp -r /etc/profile root@hadoop151:/etc scp -r /etc/profile root@hadoop152:/etc scp -r /etc/profile root@hadoop153:/etc scp -r /etc/profile root@hadoop154:/etc 集群首次启动 1、先启动zk集群（自动化脚本） 2、在hadoop151,hadoop152,hadoop153启动JournalNode hdfs --daemon start journalnode可以用脚本查看三台机器的启动状态
3、在hadoop151格式化 hdfs namenode -format 4、在hadoop151启动namenode服务 hdfs --daemon start namenode 5、在hadoop152机器上同步namenode信息 hdfs namenode -bootstrapStandby 6、在hadoop152上启动namenode服务 hdfs --daemon start namenode没启动之前的jps
启动之后 查看namenode节点状态
hdfs haadmin -getServiceState nn2 7、关闭所有dfs有关的服务 stop-dfs.sh 8、格式化zk hdfs zkfc -formatZK格式化完可以进工作空间
zkCli.sh 9、启动dfs start-dfs.sh查看namenode节点状态 151挂掉后，152会变成active,如果151又上线，它不会变成active,会变成standby
10、启动yarn start-yarn.sh查看状态 查看resourcemanager节点状态
yarn rmadmin -getServiceState rm1如图153是active 当输入 hadoop153:8088或hadoop154:8088时，页面地址都会转到hadoop153:8088
安装成功上传一个文件，测试wordcount,运行成功，即安装成功 后面hadoop可直接用start-all.sh开启，stop-all.sh关闭；zookeeper可以用脚本一键开启关闭（要注意开启时，要先开启zookeeper）