hadoop高可用，依赖于zookeeper。
用于生产环境, 企业部署必须的模式.
1. 部署环境规划
1.1. 虚拟机及hadoop角色划分
| 主机名称 | namenode | datanode | resourcemanager | nodemanager | zkfc | journalnode | zookeeper |
| master | | | | | | | |
| slave1 | | | | | | | |
| slave2 | | | | | | |
1.2. 软件版本
| java | jdk-1.8 |
| Hadoop | 3.3.0 |
| zookeeper | 3.7.0 |
1.3. 数据目录规划
| 名称 | 目录 |
| namenode目录 | /data/hadoop/dfs/name |
| datanode目录 | /data/hadoop/dfs/data |
| hadoop临时目录 | /data/hadoop/tmp |
| zookeeper数据目录 | /data/zookeeper/data |
2. 免密登录
略
3. 安装jdk
略
4. zookeeper安装
4.1. 解压
解压到目录/usr/local/ 下
tar -zxvf apache-zookeeper-3.7.0-bin.tar.gz -C /usr/local/zookeeper
4.2. 环境配置
cat>>/etc/profile <<EOF
export ZOOKEEPER_HOME=/usr/local/zookeeper/apache-zookeeper-3.7.0-bin
export PATH=\$ZOOKEEPER_HOME/bin:\$PATH
EOF
source /etc/profile
#创建数据/日志目录
mkdir -pv /data/zookeeper/{data,log} 4.3. 修改配置文件
cd /usr/local/zookeeper/apache-zookeeper-3.7.0-bin/conf/
cp zoo_sample.cfg zoo.cfg修改zoo.cfg配置文件
dataDir=/data/zookeeper/data/
dataLogDir=/data/zookeeper/log/
server.1=master:2888:3888
server.2=slave1:2888:3888
server.3=slave2:2888:3888分发到slave1,slave2节点
scp zoo.cfg slave1:/usr/local/zookeeper/apache-zookeeper-3.7.0-bin/conf/
scp zoo.cfg slave2:/usr/local/zookeeper/apache-zookeeper-3.7.0-bin/conf/4.4. 创建myid
根据服务器对应的数字，配置相应的myid，master配置1，slave1配置2，slave2配置3
#各节点配置，根据server.1就是1
echo 1 > /data/zookeeper/data/myid4.5. 启动zookeeper
各个节点启动
zkServer.sh start
zkServer.sh status5. hadoop安装
5.1. 解压
tar -zxvf hadoop-3.3.0.tar.gz -C /usr/local/5.2. 环境配置
环境配置（所有节点都执行），root用户执行
chown -R hadoop:hadoop /usr/local/hadoop-3.3.0
cat>>/etc/profile <<EOF
export HADOOP_HOME=/usr/local/hadoop-3.3.0
export PATH=\$HADOOP_HOME/bin:\$HADOOP_HOME/sbin:\$PATH
EOF
source /etc/profile5.3. 修改配置文件
5.3.1. hadoop-env.sh
cd $HADOOP_HOME/etc/hadoop
vi hadoop-env.shexport JAVA_HOME=/usr/java/jdk1.8.0_3115.3.2. core-site.xml
<configuration><property><name>fs.defaultFS</name><value>hdfs://mycluster/</value><description>自定义的集群名称</description></property><property><name>hadoop.tmp.dir</name><value>/data/hadoop/tmp</value><description>namenode上本地的hadoop临时文件夹</description></property><property><name>ha.zookeeper.quorum</name><value>master:2181,slave1:2181,slave2:2181</value><description>指定zookeeper地址</description></property><property><name>ha.zookeeper.session-timeout.ms</name><value>1000</value><description>hadoop链接zookeeper的超时时长设置ms</description></property>
</configuration>5.3.3. hdfs-site.xml
<configuration><property><name>dfs.replication</name><value>2</value><description>Hadoop的备份系数是指每个block在hadoop集群中有几份，系数越高，冗余性越好，占用存储也越多</description></property><property><name>dfs.namenode.name.dir</name><value>/data/hadoop/dfs/name</value><description>namenode上存储hdfs名字空间元数据 </description></property><property><name>dfs.datanode.data.dir</name><value>/data/hadoop/dfs/data</value><description>datanode上数据块的物理存储位置</description></property><property><name>dfs.webhdfs.enabled</name><value>true</value></property><!--指定hdfs的nameservice为myha01，需要和core-site.xml中的保持一致dfs.ha.namenodes.[nameservice id]为在nameservice中的每一个NameNode设置唯一标示符。配置一个逗号分隔的NameNode ID列表。这将是被DataNode识别为所有的NameNode。例如，如果使用"myha01"作为nameservice ID，并且使用"nn1"和"nn2"作为NameNodes标示符--><property><name>dfs.nameservices</name><value>mycluster</value></property><!-- myha01下面有两个NameNode，分别是nn1，nn2 --><property><name>dfs.ha.namenodes.mycluster</name><value>nn1,nn2</value></property><!-- nn1的RPC通信地址 --><property><name>dfs.namenode.rpc-address.mycluster.nn1</name><value>master:9000</value></property><!-- nn1的http通信地址 --><property><name>dfs.namenode.http-address.mycluster.nn1</name><value>master:50070</value></property><!-- nn2的RPC通信地址 --><property><name>dfs.namenode.rpc-address.mycluster.nn2</name><value>slave1:9000</value></property><!-- nn2的http通信地址 --><property><name>dfs.namenode.http-address.mycluster.nn2</name><value>slave1:50070</value></property><!-- 指定NameNode的edits元数据的共享存储位置。也就是JournalNode列表该url的配置格式：qjournal://host1:port1;host2:port2;host3:port3/journalIdjournalId推荐使用nameservice，默认端口号是：8485 --><property><name>dfs.namenode.shared.edits.dir</name><value>qjournal://master:8485;slave1:8485;slave2:8485/mycluster</value></property><!-- 指定JournalNode在本地磁盘存放数据的位置 --><property><name>dfs.journalnode.edits.dir</name><value>/data/hadoop/data/journaldata</value></property><!-- 开启NameNode失败自动切换 --><property><name>dfs.ha.automatic-failover.enabled</name><value>true</value></property><!-- 配置失败自动切换实现方式 --><property><name>dfs.client.failover.proxy.provider.mycluster</name><value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value></property><!-- 配置隔离机制方法，多个机制用换行分割，即每个机制暂用一行 --><property><name>dfs.ha.fencing.methods</name><value>sshfenceshell(/bin/true)</value></property><!-- 使用sshfence隔离机制时需要ssh免登陆 --><property><name>dfs.ha.fencing.ssh.private-key-files</name><value>/home/hadoop/.ssh/id_rsa</value></property><!-- 配置sshfence隔离机制超时时间 --><property><name>dfs.ha.fencing.ssh.connect-timeout</name><value>30000</value></property><property><name>ha.failover-controller.cli-check.rpc-timeout.ms</name><value>60000</value></property>
</configuration>注意 mycluster 所有地方都要一样
5.3.4. mapred-site.xml
<configuration><property><name>mapreduce.framework.name</name><value>yarn</value><description>The runtime framework for executing MapReduce jobs. Can be one of local, classic or yarn.</description><final>true</final></property><property><name>mapreduce.jobtracker.http.address</name><value>master:50030</value></property><property><name>mapreduce.jobhistory.address</name><value>master:10020</value></property><property><name>mapreduce.jobhistory.webapp.address</name><value>master:19888</value></property><property><name>mapred.job.tracker</name><value>http://master:9001</value></property>
</configuration>5.3.5. yarn-site.xml
<configuration><!-- 开启RM高可用 --><property><name>yarn.resourcemanager.ha.enabled</name><value>true</value></property><!-- 指定RM的cluster id --><property><name>yarn.resourcemanager.cluster-id</name><value>yrc</value></property><!-- 指定RM的名字 --><property><name>yarn.resourcemanager.ha.rm-ids</name><value>rm1,rm2</value></property><!-- 分别指定RM的地址 --><property><name>yarn.resourcemanager.hostname.rm1</name><value>slave1</value></property><property><name>yarn.resourcemanager.hostname.rm2</name><value>slave2</value></property><!-- 指定zk集群地址 --><property><name>yarn.resourcemanager.zk-address</name><value>master:2181,slave1:2181,slave2:2181</value></property><property><name>yarn.nodemanager.aux-services</name><value>mapreduce_shuffle</value></property><property><name>yarn.log-aggregation-enable</name><value>true</value></property><property><name>yarn.log-aggregation.retain-seconds</name><value>86400</value></property><!-- 启用自动恢复 --><property><name>yarn.resourcemanager.recovery.enabled</name><value>true</value></property><!-- 制定resourcemanager的状态信息存储在zookeeper集群上 --><property><name>yarn.resourcemanager.store.class</name><value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value></property><property><name>yarn.application.classpath</name> <value>/usr/local/hadoop-3.3.0/etc/hadoop:/usr/local/hadoop-3.3.0/share/hadoop/common/lib/*:/usr/local/hadoop-3.3.0/share/hadoop/common/*:/usr/local/hadoop-3.3.0/share/hadoop/hdfs:/usr/local/hadoop-3.3.0/share/hadoop/hdfs/lib/*:/usr/local/hadoop-3.3.0/share/hadoop/hdfs/*:/usr/local/hadoop-3.3.0/share/hadoop/mapreduce/*:/usr/local/hadoop-3.3.0/share/hadoop/yarn:/usr/local/hadoop-3.3.0/share/hadoop/yarn/lib/*:/usr/local/hadoop-3.3.0/share/hadoop/yarn/*</value></property>
</configuration>5.3.6. workers
vim workers
master
slave1
slave25.4. 分发到其他服务器
scp -r /usr/local/hadoop-3.3.0/ slave1:/usr/local/
scp -r /usr/local/hadoop-3.3.0/ slave2:/usr/local/6. 启动集群
以下顺序不能错
6.1. 启动journalnode(所有节点)
hadoop-daemon.sh start journalnode6.2. 格式化namenode（master）
hadoop namenode -format6.3. 同步元数据
scp -r /data/hadoop/dfs/name/current/ root@slave1:/data/hadoop/dfs/name/6.4. 格式化zkfc（master）
hdfs zkfc -formatZK6.5. 启动HDFS（master）
start-yarn.sh6.6. 查看各主节点状态hdfs/yarn
hdfs haadmin -getServiceState nn1
hdfs haadmin -getServiceState nn2
yarn rmadmin -getServiceState rm1
yarn rmadmin -getServiceState rm27. 查看页面
hdfs：http://master:9870接付费咨询，调bug， 10元一次。+v：644789108
