1、hadoop三大组件

hdfs(分布式存储)
mapreduce(分布式计算)
yarn(分布式调度)

2. 配置mapreduce

2.1 配置mapred-env.sh文件

# 在hadoop配置文件中
vim mapred-env.sh
# 新增内容
export JAVA_HOME=jdk所在目录
#  设置JobHistorySer\ver进程内存为1G :
 export HADOOP_JOB_HISTORYSERVER_HEAPSIZE=1000
#  设置日志级别为INF0 : 
export HADOOP_MAPRED_ROOT_LOGGER=INF0,RFA

2.2 配置mapred-site.xml文件

vim mapred-site.xml

<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
<description>MapReduce的运行框架设置为YARN</description>
</property>
<property>
<name>mapreduce.jobhistory.address</name>
<value>node1:10020</value>
<description>历史服务器通讯端口为node1:10020</description>
</property>
<property>
<name>mapreduce.webapp.address</name>
<value>node1:19888</value>
<description>历史服务器的web端口为node1的19888</description>
</property>
<property>
<name>mapreduce.jobhistory.intermediate-done-dir</name>
<value>/home/kk/data/mr-history/tmp</value>
<description>历史信息在hdfs的记录临时路径</description>
</property>
<property>
  <name>yarn.app.mapreduce.am.env</name>
  <value>HADOOP_MAPRED_HOME=/home/kk/export/soft/hadoop336</value>
</property>
<property>
  <name>mapreduce.map.env</name>
  <value>HADOOP_MAPRED_HOME=/home/kk/export/soft/hadoop336</value>
</property>
<property>
  <name>mapreduce.reduce.env</name>      
  <value>HADOOP_MAPRED_HOME=/home/kk/export/soft/hadoop336</value>
</property>
</configuration>

3、配置yarn

3.1 配置yarn-env.sh文件

#配置jdk路径
export JAVA_HOME=jdk目录
#配置配置文件的路径
export HADOOP_CONF_DIR=hadoop配置文件hadoop安装文件号下/etc/hadoop目录路劲
#配置日志文件路径
export HADOOP_LOG_DIR=目录路劲

3.2 配置yarn-size.xml文件

vim yarn-size.xml

<configuration>
<property>
<name>yarn.resourcemanager.hostname</name>
<value>node11</value>
<description>ResourceManager设置在node1节点上</description>
</property>
<property>
<name>yarn.nodemanager.local-dirs</name>
<value>/home/kk/data/nodemanager-local</value>
<description>nodeManager中间数据本地存储位置</description>
</property>
<property>
<name>yarn.nodemanager.log-dirs</name>
<value>/home/kk/data/nodemanager-log</value>
<description>nodemanager数据日志本地存储位置</description>
</property>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
<description>为mapreduce程序开放shuffle服务</description>
</property>
<property>
<name>yarn.log.server.url</name>
<value>http://node11:19888/jobhistory/logs</value>
<description>历史服务器url</description>
</property>
<property>
<name>yarn.web-proxy.address</name>
<value>node11:8089</value>
<description>代理服务器主机和端口</description>
</property>
<property>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
<description>开启日志聚合</description>
</property>
<property>
<name>yarn.nodemanager.remote-app-log-dir</name>
<value>/home/kk/export/soft/hadoop336/logs</value>
<description>程序日志HDFS的存储路径</description>
</property>
<property>
<name>yarn.resourcemanager.scheduler.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
<description>选择公平调度器</description>
</property>
<!-- Site specific YARN configuration properties -->
</configuration>

4 服务启动

start-yarn.sh			# 启动yarn
mapred --daemon start | stop historyserver			# 历史服务器的启停
start-all.sh				#启动全部
jps							# 查看进程号

5 web页面访问

浏览器打开: http://resourceManager所在节点ip:8088

6、运行内置的mapreduce程序

对应的jar包在/soft/hadoop336/share/hadoop/mapreduce
文件夹下面

6.1 wordcount(单词计数程序)

hadoop jar hadoop-mapreduce-examples-3.3.6.jar wordcount hdfs://node11:8020/doc hdfs://node11:8020/output/words

6.2 pi(求圆周率)

# 三台机器1000个样本求π
hadoop jar hadoop-mapreduce-examples-3.3.6.jar pi 3 1000

7. yarn高可用(HA)配置

编写yarn-site.xml:

vim yarn-site.xml
# 添加如下内容

 <property>
<!--开启resourcemanager ha-->
<name>yarn.resourcemanager.ha.enabled</name>
<value>true</value>
</property>
<property>
<!--s-->
<name>yarn.resourcemanager.cluster-id</name>
<value>cluster-yarn1</value>
</property>
<property>
<name>yarn.resourcemanager.ha.rm-ids</name>
<value>rm1,rm2</value>
</property>
<!--指定逻辑名称对应的主机名-->
<property>
<name>yarn.resourcemanager.hostname.rm1</name>
<value>node11</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm2</name>
<value>node22</value>
</property>
<!--配置web端王文地址-->
<property>
<name>yarn.resourcemanager.webapp.address.rm1</name>
<value>node11:8088</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.address.rm2</name>
<value>node22:8088</value>
</property>
<!--指定zookeeper节点-->
<property>
<name>hadoop.zk.address</name>
<value>node11:2181,node22:2181,node33:2181</value>
</property>
<property>
<name>yarn.resourcemanager.recovery.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.store.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>

复制到其他节点中:

scp yarn-site.xml node22:`pwd`
scp yarn-site.xml node33:`pwd`

启动yarn进程:

start-yarn.sh 
# 查看状态
yarn rmadmin -getAllServiceState # 查看全部

yarn rmadmin -getServiceState rm1
yarn rmadmin -getServiceState rm2

杀掉node2中的resourceManager进程:

skill 进程号

可见rm1已经变为了active状态: