文档编写目的

在进行CDH集群安装部署的时候，官方提供了三种方式，parcels、packages以及tarball，官方推荐使用parcels的方式进行安装，这也是最常用的安装方式，通常我们使用CM图形化界面的操作方式来安装CDH集群，本文档将介绍的是官方提供的另一种安装方式，使用packages安装，即rpm包的方式进行CDH集群的安装，并且本次安装是使用没有CM的方式进行安装。

环境介绍：

·安装部署使用root用户进行操作

·安装的CDH版本为5.10.0

·服务器的操作系统为RedHat7.2

·安装不使用CM

·CDH集群安装在三个节点

安装前置准备2.1服务器相关设置

安装CDH集群时需要做一些前置的准备，本次安装使用的环境已经做好前置准备，需要做的准备如下：

1.hosts以及hostname配置正确

2.服务器没有启用IPv6且配置了静态IP

3.禁用SELinux

4.关闭防火墙

5.设置swappiness为1

6.关闭透明大页面

7.配置NTP时钟同步

2.2 配置本地Yum源

1.在官网下载好需要的rpm包，地址如下：

将上面所有的rpm包下载到服务器，如下：

在浏览器进行验证

2.执行createrepo命令

createrepo .

3.创建repo文件

[rpmrepo]name = rpm_repobaseurl =  = truegpgcheck = false

4.执行yum命令，查看本地yum源是否配置成功

yum clean allyum repolist

上图可以看到，下载的rpm包制作的本地yum源成功

CDH组件安装

3.1 ZooKeeper

1.在所有节点安装Zookeeper

yum install zookeepe

2.创建数据目录并修改属主

mkdir -p /var/lib/zookeeperchown -R zookeeper /var/lib/zookeeper

3.修改配置文件/etc/zookeeper/conf/zoo.cfg

maxClientCnxns=60tickTime=2000initLimit=10syncLimit=5dataDir=/var/lib/zookeeperclientPort=2181dataLogDir=/var/lib/zookeeperminSessionTimeout=4000maxSessionTimeout=40000server.1=cdh178.macro.com:3181:4181server.2=cdh177.macro.com:3181:4181server.3=cdh176.macro.com:3181:4181

保存修改并同步到所有节点

4.所有节点创建myid文件并修改属主

5.所有节点启动Zookeeper

/usr/lib/zookeeper/bin/zkServer.sh start

查看所有节点启动状态，三个节点均启动成功

/usr/lib/zookeeper/bin/zkServer.sh status

至此Zookeeper安装完成

3.2 HDFS

1.在所有节点安装HDFS必需的包，由于只有三个节点，所以三个节点都安装DataNode

yum -y install hadoop hadoop-hdfs hadoop-client hadoop-doc hadoop-debuginfo hadoop-hdfs-datanode

2.在一个节点安装NameNode以及SecondaryNameNode

yum -y install hadoop-hdfs-namenode hadoop-hdfs-secondarynamenode

3.创建数据目录并修改属主和权限

所有节点创建DataNode的目录

mkdir -p /data0/dfs/dnchown -R hdfs:hadoop /data0/dfs/dnchmod 700 /data0/dfs/dn

NameNode和SecondaryNameNode节点创建数据目录

mkdir -p /data0/dfs/nnchown -R hdfs:hadoop /data0/dfs/nnchmod 700 /data0/dfs/nnmkdir -p /data0/dfs/snnchown -R hdfs:hadoop /data0/dfs/snnchmod 700 /data0/dfs/snn

4.修改配置文件

/etc/hadoop/conf/core-site.xml

<configuration> <property> <name>fs.defaultFS</name> <value>hdfs://cdh178.macro.com:8020</value> </property> <property> <name>fs.trash.interval</name> <value>1</value> </property> <property> <name>io.compression.codecs</name> <value>org.apache.hadoop.io.compress.DefaultCodec,org.apache.hadoop.io.compress.GzipCodec,org.apache.hadoop.io.compress.BZip2Codec,org.apache.hadoop.io.compress.DeflateCodec,org.apache.hadoop.io.compress.SnappyCodec,org.apache.hadoop.io.compress.Lz4Codec</value> </property></configuration>

/etc/hadoop/conf/hdfs-site.xml

<configuration> <property> <name>dfs.namenode.name.dir</name> <value>;/value> </property> <property> <name>dfs.datanode.data.dir</name> <value>;/value> </property> <property> <name>dfs.namenode.servicerpc-address</name> <value>cdh178.macro.com:8022</value> </property> <property> <name>dfs.https.address</name> <value>cdh178.macro.com:9871</value> </property> <property> <name>dfs.secondary.http.address</name> <value>cdh178.macro.com:50090</value> </property> <property> <name>dfs.https.port</name> <value>9871</value> </property> <property> <name>dfs.namenode.http-address</name> <value>cdh178.macro.com:9870</value> </property> <property> <name>dfs.replication</name> <value>3</value> </property> <property> <name>dfs.blocksize</name> <value>134217728</value> </property> <property> <name>dfs.namenode.checkpoint.dir</name> <value>;/value> </property></configuration>

5.将修改的配置文件保存并同步到所有节点

6.格式化NameNode

hdfs namenode -format

7.在所有节点运行命令启动HDFS

systemctl start hadoop-hdfs-namenodesystemctl start hadoop-hdfs-secondarynamenodesystemctl start hadoop-hdfs-datanodesystemctl status hadoop-hdfs-namenodesystemctl status hadoop-hdfs-secondarynamenodesystemctl status hadoop-hdfs-datanode​

8.创建/tmp临时目录，并设置目录权限，然后使用hadoop命令查看创建的目录成功

sudo -u hdfs hadoop fs -mkdir /tmpsudo -u hdfs hadoop fs -chmod -R 1777 /tmp

9.访问NameNode的Web UI

至此HDFS安装完成

3.3 Yarn

1.安装Yarn的包，在一个节点安装ResourceManager和JobHistory Server，另外两个节点安装NodeManager

yum -y install hadoop-yarn hadoop-yarn-resourcemanager hadoop-mapreduce-historyserver hadoop-yarn-proxyserver hadoop-mapreduce

yum -y install hadoop-yarn hadoop-yarn-nodemanager hadoop-mapreduce

2.创建目录并修改属主和权限

在所有节点创建本地目录

mkdir -p /data0/yarn/nmchown yarn:hadoop /data0/yarn/nmmkdir -p /data0/yarn/container-logschown yarn:hadoop /data0/yarn/container-logs

在HDFS上创建logs目录

sudo -u hdfs hdfs dfs -mkdir /tmp/logssudo -u hdfs hdfs dfs -chown mapred:hadoop /tmp/logssudo -u hdfs hdfs dfs -chmod 1777 /tmp/logs​

在HDFS上创建/user/history目录

sudo -u hdfs hdfs dfs -mkdir -p /usersudo -u hdfs hdfs dfs -chmod 777 /usersudo -u hdfs hdfs dfs -mkdir -p /user/historysudo -u hdfs hdfs dfs -chown mapred:hadoop /user/historysudo -u hdfs hdfs dfs -chmod 1777 /user/historysudo -u hdfs hdfs dfs -mkdir -p /user/history/donesudo -u hdfs hdfs dfs -mkdir -p /user/history/done_intermediatesudo -u hdfs hdfs dfs -chown -R mapred:hadoop /user/historysudo -u hdfs hdfs dfs -chmod 771 /user/history/donesudo -u hdfs hdfs dfs -chmod 1777 /user/history/done_intermediate​

3.修改配置文件

/etc/hadoop/conf/yarn-site.xml

<configuration> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name> <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> <property> <name>yarn.log-aggregation-enable</name> <value>true</value> </property> <property> <name>yarn.nodemanager.local-dirs</name> <value>;/value> </property> <property> <name>yarn.nodemanager.log-dirs</name> <value>;/value> </property> <property> <name>yarn.nodemanager.remote-app-log-dir</name> <value>/tmp/logs</value> </property> <property> <name>yarn.application.classpath</name> <value>$HADOOP_CONF_DIR,$HADOOP_COMMON_HOME/*,$HADOOP_COMMON_HOME/lib/*,$HADOOP_HDFS_HOME/*,$HADOOP_HDFS_HOME/lib/*,$HADOOP_MAPRED_HOME/*,$HADOOP_MAPRED_HOME/lib/*,$HADOOP_YARN_HOME/*,$HADOOP_YARN_HOME/lib/*</value> </property> <property> <name>yarn.resourcemanager.address</name> <value>cdh178.macro.com:8032</value> </property> <property> <name>yarn.resourcemanager.admin.address</name> <value>cdh178.macro.com:8033</value> </property> <property> <name>yarn.resourcemanager.scheduler.address</name> <value>cdh178.macro.com:8030</value> </property> <property> <name>yarn.resourcemanager.resource-tracker.address</name> <value>cdh178.macro.com:8031</value> </property> <property> <name>yarn.resourcemanager.webapp.address</name> <value>cdh178.macro.com:8088</value> </property> <property> <name>yarn.resourcemanager.webapp.https.address</name> <value>cdh178.macro.com:8090</value> </property></configuration>​

/etc/hadoop/conf/mapred-site.xml

<configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> <property> <name>mapreduce.jobhistory.address</name> <value>cdh178.macro.com:10020</value> </property> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>cdh178.macro.com:19888</value> </property> <property> <name>mapreduce.jobhistory.webapp.https.address</name> <value>cdh178.macro.com:19890</value> </property> <property> <name>mapreduce.jobhistory.admin.address</name> <value>cdh178.macro.com:10033</value> </property> <property> <name>yarn.app.mapreduce.am.staging-dir</name> <value>/user</value> </property></configuration>​

/etc/hadoop/conf/core-site.xml，下面只贴出修改的部分配置

<property> <name>hadoop.proxyuser.mapred.groups</name> <value>*</value> </property> <property> <name>hadoop.proxyuser.mapred.hosts</name> <value>*</value> </property>​

4.将配置文件保存后同步到所有节点

5.启动Yarn服务

在JobHistoryServer节点上启动mapred-historyserver

/etc/init.d/hadoop-mapreduce-historyserver start

在RM节点启动ResourceManager

systemctl start hadoop-yarn-resourcemanagersystemctl status hadoop-yarn-resourcemanager​

在NM节点启动NodeManager

systemctl start hadoop-yarn-nodemanagersystemctl status hadoop-yarn-nodemanager​

6.访问Yarn服务的Web UI

Yarn的管理页面

JobHistory的管理页面

查看在线的节点

7.运行MR示例程序

使用root用户运行示例程序，所以要先创建root用户的目录

sudo -u hdfs hdfs dfs -mkdir /user/rootsudo -u hdfs hdfs dfs -chown root:root /user/root

运行MR示例程序，运行成功

hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar pi 5 5

至此Yarn服务安装完成

3.4 Spark

1.安装Spark所需的包

yum install spark-core spark-master spark-worker spark-history-server spark-python

2.创建目录并修改属主和权限

sudo -u hdfs hadoop fs -mkdir /user/sparksudo -u hdfs hadoop fs -mkdir /user/spark/applicationHistorysudo -u hdfs hadoop fs -chown -R spark:spark /user/sparksudo -u hdfs hadoop fs -chmod 1777 /user/spark/applicationHistory​

3.修改配置文件/etc/spark/conf/spark-defaults.conf

spark.eventLog.enabled=truespark.eventLog.dir=hdfs://cdh178.macro.com:8020/user/spark/applicationHistoryspark.yarn.historyServer.address=​

4.启动spark-history-server

systemctl start spark-history-serversystemctl status spark-history-server​

访问Web UI

5.修改配置文件并同步到所有节点

6.启动Spark

在Master节点启动spark-master

systemctl start spark-mastersystemctl status spark-master​

在所有节点启动spark-worker

systemctl start spark-workersystemctl status spark-worker​

7.测试Spark使用

至此Spark安装完成