Apache Hadoop集群安装（NameNode HA + SPARK + 机架感知）

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1、主机规划
序号主机名IP地址角色
1nn-1192.168.9.21NameNode、mr-jobhistory、zookeeper、JournalNode
2nn-2192.168.9.22Secondary NameNode、JournalNode
3dn-1192.168.9.23DataNode、JournalNode、zookeeper、ResourceManager、NodeManager
4dn-2192.168.9.24DataNode、zookeeper、NodeManager
5dn-3192.168.9.25DataNode、NodeManager

2、主机环境设置
2.1 配置JDK

卸载OpenJDK：
  1. --查看java版本
  2. [[email protected] ~]# java -version
  3. java version "1.7.0_45"
  4. OpenJDK Runtime Environment (rhel-2.4.3.3.el6-x86_64 u45-b15)
  5. OpenJDK 64-Bit Server VM (build 24.45-b08, mixed mode)
  7. --查看安装源
  8. [[email protected] ~]# rpm -qa | grep java
  9. java-1.7.0-openjdk-1.7.0.45-2.4.3.3.el6.x86_64
  11. -- 卸载
  12. [[email protected] ~]# rpm -e --nodeps java-1.7.0-openjdk-1.7.0.45-2.4.3.3.el6.x86_64
  15. --验证是否卸载成功
  16. [[email protected] ~]# rpm -qa | grep java
  17. [[email protected] ~]# java -version
  18. -bash: /usr/bin/java: 没有那个文件或目录

安装jdk：
  1. -- 下载并解压java源码包
  2. [[email protected] java]# mkdir /usr/local/java
  3. [[email protected] java]# mv jdk-7u79-linux-x64.tar.gz /usr/local/java
  4. [[email protected] java]# cd /usr/local/java
  5. [[email protected] java]# tar xvf jdk-7u79-linux-x64.tar.gz
  6. [[email protected] java]# ls
  7. jdk1.7.0_79  jdk-7u79-linux-x64.tar.gz
  8. [[email protected] java]# 
  10. --- 添加环境变量
  11. [[email protected] java]# vim /etc/profile
  12. [[email protected] java]# tail /etc/profile
  13. export JAVA_HOME=/usr/local/java/jdk1.7.0_79
  14. export JRE_HOME=/usr/local/java/jdk1.7.0_79/jre
  15. export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar:$JRE_HOME/lib:$CLASSPATH
  16. export PATH=$JAVA_HOME/bin:$PATH
  18. -- 生效环境变量
  19. [[email protected] ~]# source /etc/profile
  21. -- 验证
  22. [[email protected] ~]# java -version
  23. java version "1.7.0_79"
  24. Java(TM) SE Runtime Environment (build 1.7.0_79-b15)
  25. Java HotSpot(TM) 64-Bit Server VM (build 24.79-b02, mixed mode)
  26. [[email protected] ~]# javac -version
  27. javac 1.7.0_79

2.2 修改主机名和配置主机名解析
在所有节点按照规划修改主机名， 并将主机名加入/etc/hosts文件。
修改主机名：
  1. [[email protected]-3 ~]# cat /etc/sysconfig/network
  2. NETWORKING=yes
  3. HOSTNAME=dn-3
  4. [[email protected]-3 ~]# hostname dn-3

配置/etc/hosts， 并分发到所有节点：
  1. [[email protected]-3 ~]# cat /etc/hosts
  2. 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
  3. ::1         localhost localhost.localdomain localhost6 localhost6.localdomain6
  4. 192.168.9.21 nn-1
  5. 192.168.9.22 nn-2
  6. 192.168.9.23 dn-1
  7. 192.168.9.24 dn-2
  8. 192.168.9.25 dn-3

2.3 新建hadoop账户
用户和组均为hadoop， 密码为hadoop， home目录为/hadoop。
  1. [[email protected]-3 ~]# useradd -d /hadoop hadoop

2.4 配置ntp时钟同步


2.5 关闭防火墙iptables和selinux
（1）、关闭iptables
  1. [[email protected]-3 ~]# service iptables stop
  2. [[email protected]-3 ~]# chkconfig iptables off
  3. [[email protected]-3 ~]# chkconfig --list | grep iptables
  4. iptables       	0:关闭	1:关闭	2:关闭	3:关闭	4:关闭	5:关闭	6:关闭
  5. [[email protected]-3 ~]#

（2）、关闭selinux
  1. [[email protected]-3 ~]# setenforce 0
  2. setenforce: SELinux is disabled
  3. [[email protected]-3 ~]# vim /etc/sysconfig/selinux 
       SELINUX=disabled

2.6 设置ssh无密码登陆
（1）、在所有节点生成密钥
所有节点， 切换到hadoop用户下，生成密钥，一路回车：
  1. [hadoop@nn-1 ~]$ ssh-keygen -t rsa

（2）、在nn-1上面，将公钥复制到文件authorized_keys中：
命令：$ ssh  主机名   ‘cat ./.ssh/id_rsa.pub‘ >> authorized_keys
将上面的命令的主机名替换成实际的主机名， 在nn-1上面将所有的主机都执行一次，包括自己， 如下示例：
  1. [hadoop@nn-1 ~]$ ssh nn-1 ‘cat ./.ssh/id_rsa.pub‘ >> authorized_keys
  2. hadoop@nn-1‘s password: 
  3. [hadoop@nn-1 ~]$

（3）、设置权限
  1. [[email protected]-1 .ssh]$ chmod 644 authorized_keys

（4）、将authorized_keys分发到所有节点: {hadoop用户home目录}/.ssh/ 。
如下示例：
  1. [[email protected]-1 .ssh]$ scp authorized_keys [email protected]-2:/hadoop/.ssh/

3、安装配置Hadoop

说明： 先在nn-1上面修改配置， 配置完毕批量分发到其他节点。

3.1 上传hadoop、zookeeper安装包
复制安装包到/hadoop目录下。
解压安装包： [[email protected] ~]$ tar -xzvf hadoop2-js-0121.tar.gz

3.2 修改hadoop-env.sh
  1. export JAVA_HOME=/usr/local/java/jdk1.7.0_79
  2. export  HADOOP_HEAPSIZE=2000
  3. export  HADOOP_NAMENODE_INIT_HEAPSIZE=10000
  4. export  HADOOP_OPTS="-server $HADOOP_OPTS -Djava.net.preferIPv4Stack=true"
  5. export HADOOP_NAMENODE_OPTS="-Xmx15000m -Xms15000m -Dhadoop.security.logger=${HADOOP_SECURITY_LOGGER:-INFO,RFAS} -Dhdfs.audit.logger
  6. =${HDFS_AUDIT_LOGGER:-INFO,NullAppender} $HADOOP_NAMENODE_OPTS"

参数说明参考: http://blog.csdn.net/fenglibing/article/details/31051225


3.3 修改core-site.xml
  1. <configuration>
  2.     <property>
  3.         <name>fs.defaultFS</name>
  4.         <value>hdfs://dpi</value>
  5.     </property>
  6.     <property>
  7.         <name>io.file.buffer.size</name>
  8.         <value>131072</value>
  9.     </property>
  10.     <property>
  11.         <name>hadoop.tmp.dir</name>
  12.         <value>file:/hadoop/hdfs/temp</value>
  13.         <description>Abase for other temporary directories.</description>
  14.     </property>
  15.     <property>
  16.         <name>hadoop.proxyuser.hduser.hosts</name>
  17.         <value>*</value>
  18.     </property>
  19.     <property>
  20.         <name>hadoop.proxyuser.hduser.groups</name>
  21.         <value>*</value>
  22.     </property>
  23.         <property>     
  24.         <name>ha.zookeeper.quorum</name>     
  25.         <value>dn-1:2181,dn-2:2181,dn-3:2181</value>    
  26.     </property>
  27. </configuration>

3.4 修改hdfs-site.xml
  1. <configuration>
  2.     <property> 
  3.         <name>dfs.namenode.secondary.http-address</name>
  4.         <value>nn-1:9001</value>
  5.     </property>
  6.     <property>
  7.         <name>dfs.namenode.name.dir</name>
  8.         <value>file:/hadoop/hdfs/name</value>
  9.     </property>
  10.     <property>
  11.         <name>dfs.datanode.data.dir</name>
  12.         <value>file:/hadoop/hdfs/data,file:/hadoopdata/hdfs/data</value>
  13.     </property>
  14.     <property>
  15.         <name>dfs.replication</name>
  16.         <value>3</value>
  17.     </property>
  18.     <property>
  19.         <name>dfs.webhdfs.enabled</name>
  20.         <value>true</value>
  21.     </property>
  22.     <property>
  23.         <name>dfs.nameservices</name>
  24.         <value>dpi</value>
  25.     </property>
  26.     <property>
  27.         <name>dfs.ha.namenodes.dpi</name>
  28.         <value>nn-1,nn-2</value>
  29.     </property>
  30.     <property>
  31.         <name>dfs.namenode.rpc-address.dpi.nn-1</name>
  32.         <value>nn-1:9000</value>
  33.     </property>
  34.     <property>    
  35.         <name>dfs.namenode.http-address.dpi.nn-1</name>    
  36.         <value>nn-1:50070</value>    
  37.     </property>
  38.     <property>    
  39.         <name>dfs.namenode.rpc-address.dpi.nn-2</name>    
  40.         <value>nn-2:9000</value>    
  41.     </property>
  42.     <property>    
  43.         <name>dfs.namenode.http-address.dpi.nn-2</name>    
  44.         <value>nn-2:50070</value>    
  45.     </property>
  46.     <property>
  47.         <name>dfs.namenode.servicerpc-address.dpi.nn-1</name>
  48.         <value>nn-1:53310</value>
  49.     </property>
  50.     <property>
  51.         <name>dfs.namenode.servicerpc-address.dpi.nn-2</name>
  52.         <value>nn-2:53310</value>
  53.     </property>
  54.     <property>  
  55.         <name>dfs.ha.automatic-failover.enabled</name>  
  56.         <value>true</value>  
  57.     </property>  
  58.     <property>
  59.         <name>dfs.namenode.shared.edits.dir</name>
  60.         <value>qjournal://nn-1:8485;nn-2:8485;dn-1:8485/dpi</value>
  61.     </property>
  62.     <property>
  63.         <name>dfs.client.failover.proxy.provider.dpi</name>
  64.         <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value> 
  65.     </property>
  66.     <property>    
  67.         <name>dfs.journalnode.edits.dir</name>    
  68.         <value>/hadoop/hdfs/journal</value>    
  69.     </property>
  70.     <property>    
  71.         <name>dfs.ha.fencing.methods</name>    
  72.         <value>sshfence</value>    
  73.     </property>
  74.     <property>    
  75.         <name>dfs.ha.fencing.ssh.private-key-files</name>    
  76.         <value>/hadoop/.ssh/id_rsa</value>    
  77.     </property>
  78. </configuration>

参数说明参考: http://www.aboutyun.com/thread-10572-1-1.html

新建配置文件中的目录：
  1. mkdir -p /hadoop/hdfs/name
  2. mkdir -p /hadoop/hdfs/data
  3. mkdir -p /hadoop/hdfs/temp
  4. mkdir -p /hadoop/hdfs/journal
  5. 授权：chmod 755 /hadoop/hdfs
  6. mkdir -p /hadoopdata/hdfs/data
  7. chmod 755 /hadoopdata/hdfs

属主和属组修改为：hadoop:hadoop


3.5 修改mapred-site.xml

  1. <configuration>
  2.     <property>
  3.         <name>mapreduce.framework.name</name>
  4.         <value>yarn</value>
  5.     </property>
  6.     <property>
  7.         <name>mapreduce.jobhistory.address</name>
  8.         <value>nn-1:10020</value>
  9.     </property>
  10.     <property>
  11.         <name>mapreduce.jobhistory.webapp.address</name>
  12.         <value>nn-1:19888</value>
  13.     </property>
  14. </configuration>


3.6 修改yarn-site.xml
  1. <configuration>
  2.  <!-- Site specific YARN configuration properties -->
  3.     <property>
  4.         <name>yarn.nodemanager.aux-services</name>
  5.         <value>mapreduce_shuffle</value>
  6.     </property>
  7.     <property>
  8.         <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
  9.         <value>org.apache.hadoop.mapred.ShuffleHandler</value>
  10.     </property>
  11.     <property>
  12.         <name>yarn.resourcemanager.address</name>
  13.         <value>dn-1:8032</value>
  14.     </property>
  15.     <property>
  16.         <name>yarn.resourcemanager.scheduler.address</name>
  17.         <value>dn-1:8030</value>
  18.     </property>
  19.     <property>
  20.         <name>yarn.resourcemanager.resource-tracker.address</name>
  21.         <value>dn-1:8031</value>
  22.     </property>
  23.     <property>
  24.         <name>yarn.resourcemanager.admin.address</name>
  25.         <value>dn-1:8033</value>
  26.     </property>
  27.     <property>
  28.         <name>yarn.resourcemanager.webapp.address</name>
  29.         <value>dn-1:8088</value>
  30.     </property>
  31. </configuration>

3.7 修改slaves
将所有的DataNode节点加入到slaves文件中：
  1. dn-1
  2. dn-2
  3. dn-3


3.8 修改yarn-env.sh
  1. # some Java parameters
  2. # export JAVA_HOME=/home/y/libexec/jdk1.6.0/
  3. if [ "$JAVA_HOME" != "" ]; then
  4.   #echo "run java in $JAVA_HOME"
  5.   JAVA_HOME=/usr/local/java/jdk1.7.0_79
  6. fi
  8. JAVA_HEAP_MAX=-Xmx15000m
  9. YARN_HEAPSIZE=15000
  10. export  YARN_RESOURCEMANAGER_HEAPSIZE=5000
  11. export  YARN_TIMELINESERVER_HEAPSIZE=10000
  12. export  YARN_NODEMANAGER_HEAPSIZE=10000

3.9 分发配置好的hadoop目录到所有节点
  1. [[email protected]-1 ~]$ scp -rp hadoop [email protected]-2:/hadoop
  2. [[email protected]-1 ~]$ scp -rp hadoop [email protected]-1:/hadoop
  3. [[email protected]-1 ~]$ scp -rp hadoop [email protected]-2:/hadoop
  4. [[email protected]-1 ~]$ scp -rp hadoop [email protected]-3:/hadoop

4 安装配置zookeeper
切换到hadoop目录下面， 根据规划， 三台zookeeper节点为：nn-1, dn-1, dn-2。
先在nn-1节点配置zookeeper， 然后分发至三个zookeeper节点：
4.1 在nn-1上传并解压zookeeper

4.2 修改配置文件/hadoop/zookeeper/conf/zoo.cfg
  1. dataDir=/hadoop/zookeeper/data/
  2. dataLogDir=/hadoop/zookeeper/log/
  3. # the port at which the clients will connect
  4. clientPort=2181
  5. server.1=nn-1:2887:3887
  6. server.2=dn-1:2888:3888
  7. server.3=dn-2:2889:3889

4.3 从nn-1分发配置的zookeeper目录到其他节点
  1. [[email protected]-1 ~]$ scp -rp zookeeper  [email protected]-1:/hadoop
  2. [[email protected]-1 ~]$ scp -rp zookeeper  [email protected]-2:/hadoop

4.4 在所有zk节点创建目录
  1. [[email protected]-1 ~]$ mkdir /hadoop/zookeeper/data/
  2. [[email protected]-1 ~]$ mkdir /hadoop/zookeeper/log/

4.5 修改myid
在所有zk节点， 切换到目录/hadoop/zookeeper/data，创建myid文件：
注意：myid文件的内容为zoo.cfg文件中配置的server.后面的数字（即nn-1为1,dn-1为2,dn-2为3）。
在nn-1节点的myid内容为：
  1. [[email protected]-1 data]$ echo 1 > /hadoop/zookeeper/data/myid

其他zk节点也安要求创建myid文件。


4.6 设置环境变量
  1. $ echo "export ZOOKEEPER_HOME=/hadoop/zookeeper" >> $HOME/.bash_profile
  2. $ echo "export PATH=$ZOOKEEPER_HOME/bin:\\$PATH" >> $HOME/.bash_profile
  3. $ source $HOME/.bash_profile


5 集群启动
5.1 启动zookeeper
根据规划， zk的节点为nn-1、dn-1和dn-2， 在这三台节点分别启动zk：

启动命令：
  1. [[email protected]-1 ~]$ /hadoop/zookeeper/bin/zkServer.sh start
  2. JMX enabled by default
  3. Using config: /hadoop/zookeeper/bin/../conf/zoo.cfg
  4. Starting zookeeper ... STARTED

查看进程， 可以看到QuorumPeerMain：
  1. [ha[email protected]-1 ~]$ jps
  2. 9382 QuorumPeerMain
  3. 9407 Jps

查看状态， 可以看到Mode: follower， 说明这是zk的从节点：
  1. [[email protected]-1 ~]$ /hadoop/zookeeper/bin/zkServer.sh status
  2. JMX enabled by default
  3. Using config: /hadoop/zookeeper/bin/../conf/zoo.cfg
  4. Mode: follower

查看状态， 可以看到Mode: leader， 说明这是zk的leader节点：
  1. [[email protected]-1 data]$ /hadoop/zookeeper/bin/zkServer.sh status
  2. JMX enabled by default
  3. Using config: /hadoop/zookeeper/bin/../conf/zoo.cfg
  4. Mode: leader

5.2 格式化zookeeper集群（只做一次）（机器nn-1上执行）

  1. [[email protected]-1 ~]$ /hadoop/hadoop/bin/hdfs zkfc -formatZK
中间有个交互的步骤， 输入Y：
技术分享
 
进入zk， 查看是否创建成功：
  1. [[email protected]-1 bin]$ ./zkCli.sh
技术分享
 

5.3 启动zkfc（机器nn-1,nn-2上执行）
  1. [[email protected]-1 ~]$ /hadoop/hadoop/sbin/hadoop-daemon.sh start zkfc
  2. starting zkfc, logging to /hadoop/hadoop/logs/hadoop-hadoop-zkfc-nn-1.out

使用jps， 可以看到进程DFSZKFailoverController：
  1. [[email protected]-1 ~]$ jps
  2. 9681 Jps
  3. 9638 DFSZKFailoverController
  4. 9382 QuorumPeerMain

 
5.4 启动journalnode
根据规划， 启动journalnode节点为nn-1、nn-2和dn-1， 在这三个节点分别使用如下的命令启动服务：
  1. [[email protected]-1 ~]$ /hadoop/hadoop/sbin/hadoop-daemon.sh  start  journalnode
  2. starting journalnode, logging to /hadoop/hadoop/logs/hadoop-hadoop-journalnode-nn-1.out

使用jps命令可以看到进程JournalNode：
  1. [[email protected]-1 ~]$ jps
  2. 9714 JournalNode
  3. 9638 DFSZKFailoverController
  4. 9382 QuorumPeerMain
  5. 9762 Jps

5.5 格式化namenode（机器nn-1上执行）
  1. [[email protected]-1 ~]$ /hadoop/hadoop/bin/hadoop namenode -format

查看日志信息：
技术分享
 

5.6 启动namenode（机器nn-1上执行）
  1. [[email protected]-1 ~]$ /hadoop/hadoop/sbin/hadoop-daemon.sh  start  namenode
  2. starting namenode, logging to /hadoop/hadoop/logs/hadoop-hadoop-namenode-nn-1.out
使用jps命令可以看到进程NameNode：
  1. [[email protected]-1 ~]$ jps
  2. 9714 JournalNode
  3. 9638 DFSZKFailoverController
  4. 9382 QuorumPeerMain
  5. 10157 NameNode
  6. 10269 Jps

5.7 格式化secondnamnode（机器nn-2上执行）
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs namenode -bootstrapStandby
部分日志如下：
技术分享
 
5.8 启动namenode（机器nn-2上执行）
  1. [[email protected]-2 ~]$ /hadoop/hadoop/sbin/hadoop-daemon.sh  start  namenode
  2. starting namenode, logging to /hadoop/hadoop/logs/hadoop-hadoop-namenode-nn-2.out
使用jps命令可以看到进程NameNode：
  1. [[email protected]-2 ~]$ jps
  2. 53990 NameNode
  3. 54083 Jps
  4. 53824 JournalNode
  5. 53708 DFSZKFailoverController

5.9 启动datanode（机器dn-1到dn-3上执行）
  1. [[email protected]-1 ~]$ /hadoop/hadoop/sbin/hadoop-daemon.sh  start  datanode
使用jps可以看到DataNode进程：
  1. [[email protected]-1 temp]$ jps
  2. 57007 Jps
  3. 56927 DataNode
  4. 56223 QuorumPeerMain


5.10 启动resourcemanager
根据规划，resourcemanager服务在节点dn-1上面， 在dn-1上面启动resourcemanager：
  1. [[email protected]-1 ~]$ /hadoop/hadoop/sbin/yarn-daemon.sh  start  resourcemanager
  2. starting resourcemanager, logging to /hadoop/hadoop/logs/yarn-hadoop-resourcemanager-dn-1.out

使用jps， 可以看到进程ResourceManager：
  1. [[email protected]-1 ~]$ jps
  2. 57173 QuorumPeerMain
  3. 58317 Jps
  4. 57283 JournalNode
  5. 58270 ResourceManager
  6. 58149 DataNode

5.11 启动jobhistory
根据规划， jobhistory服务在nn-1上面， 使用如下命令启动：
  1. [[email protected]-1 ~]$ /hadoop/hadoop/sbin/mr-jobhistory-daemon.sh start historyserver
  2. starting historyserver, logging to /hadoop/hadoop/logs/mapred-hadoop-historyserver-nn-1.out

使用jps， 可以看到进程JobHistoryServer：
  1. [[email protected]-1 ~]$ jps
  2. 11210 JobHistoryServer
  3. 9714 JournalNode
  4. 9638 DFSZKFailoverController
  5. 9382 QuorumPeerMain
  6. 11039 NameNode
  7. 11303 Jps

5.12 启动NodeManager
根据规划， dn-1、dn-2和dn-3是nodemanager， 在这三个节点启动NodeManager：
  1. [[email protected]-1 ~]$ /hadoop/hadoop/sbin/yarn-daemon.sh start nodemanager
  2. starting nodemanager, logging to /hadoop/hadoop/logs/yarn-hadoop-nodemanager-dn-1.out

使用jps可以看到进程NodeManager：
  1. [[email protected]-1 ~]$ jps
  2. 58559 NodeManager
  3. 57173 QuorumPeerMain
  4. 58668 Jps
  5. 57283 JournalNode
  6. 58270 ResourceManager
  7. 58149 DataNode


6、安装后查看和验证

6.1 HDFS相关操作命令
查看NameNode状态的命令
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs haadmin -getServiceState nn-1

手工切换，将active的NameNode从nn-1切换到nn-2 。
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs haadmin -DfSHAadmin -failover nn-1 nn-2
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NameNode健康检查：
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs haadmin -checkHealth nn-1
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 将其中一台NameNode给kill后， 查看健康状态：
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查看所有的DataNode列表：
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs dfsadmin -report | more
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查看正常DataNode列表:
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs dfsadmin -report -live
  2. 17/03/01 22:49:43 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  3. Configured Capacity: 224954695680 (209.51 GB)
  4. Present Capacity: 180557139968 (168.16 GB)
  5. DFS Remaining: 179963428864 (167.60 GB)
  6. DFS Used: 593711104 (566.21 MB)
  7. DFS Used%: 0.33%
  8. Under replicated blocks: 2
  9. Blocks with corrupt replicas: 0
  10. Missing blocks: 0
  12. -------------------------------------------------
  13. Live datanodes (3):
  15. Name: 192.168.9.23:50010 (dn-1)
  16. Hostname: dn-1
  17. Rack: /rack2
  18. Decommission Status : Normal
  19. Configured Capacity: 74984898560 (69.84 GB)
  20. DFS Used: 197902336 (188.73 MB)
  21. Non DFS Used: 14869356544 (13.85 GB)
  22. DFS Remaining: 59917639680 (55.80 GB)
  23. DFS Used%: 0.26%
  24. DFS Remaining%: 79.91%
  25. Configured Cache Capacity: 0 (0 B)
  26. Cache Used: 0 (0 B)
  27. Cache Remaining: 0 (0 B)
  28. Cache Used%: 100.00%
  29. Cache Remaining%: 0.00%
  30. Xceivers: 1
  31. Last contact: Wed Mar 01 22:49:42 CST 2017

查看异常DataNode列表：
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs dfsadmin -report -dead

获取指定DataNode信息(运行时间及版本等):
  1. [hadoop[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs haadmin -checkHealth nn-2
  2. 17/03/01 22:55:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  3. [[email protected]-2 ~]$ /hadoop/hadoop/bin/hdfs haadmin -checkHealth nn-1
  4. 17/03/01 22:55:08 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable


6.2 YARN相关的命令
查看resourceManager状态的命令：
  1. [[email protected]-1 hadoop]$ yarn rmadmin -getServiceState rm1
  2. active
  3. [[email protected]-1 hadoop]$ yarn rmadmin -getServiceState rm2
  4. standby

查看所有的yarn节点：
  1. [[email protected]-1 hadoop]$ /hadoop/hadoop/bin/yarn node -all -list
  2. 17/03/01 23:06:40 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  3. Total Nodes:3
  4.          Node-Id	     Node-State	Node-Http-Address	Number-of-Running-Containers
  5.       dn-2:55506	        RUNNING	        dn-2:8042	                           0
  6.       dn-1:56447	        RUNNING	        dn-1:8042	                           0
  7.       dn-3:37533	        RUNNING	        dn-3:8042	                           0

查看正常的yarn节点：
  1. [[email protected]-1 hadoop]$ /hadoop/hadoop/bin/yarn node -list
  2. 17/03/01 23:07:41 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  3. Total Nodes:3
  4.          Node-Id	     Node-State	Node-Http-Address	Number-of-Running-Containers
  5.       dn-2:55506	        RUNNING	        dn-2:8042	                           0
  6.       dn-1:56447	        RUNNING	        dn-1:8042	                           0
  7.       dn-3:37533	        RUNNING	        dn-3:8042	                           0

查看指定节点的信息：
/hadoop/hadoop/bin/yarn node -status <NodeId>
  1. [[email protected]-1 hadoop]$ /hadoop/hadoop/bin/yarn node -status dn-2:55506
  2. 17/03/01 23:08:16 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  3. Node Report : 
  4. 	Node-Id : dn-2:55506
  5. 	Rack : /default-rack
  6. 	Node-State : RUNNING
  7. 	Node-Http-Address : dn-2:8042
  8. 	Last-Health-Update : 星期三 01/三月/17 11:06:21:373CST
  9. 	Health-Report : 
  10. 	Containers : 0
  11. 	Memory-Used : 0MB
  12. 	Memory-Capacity : 8192MB
  13. 	CPU-Used : 0 vcores
  14. 	CPU-Capacity : 8 vcores
  15. 	Node-Labels :

查看当前运行的MapReduce任务：
  1. [[email protected]-2 ~]$ /hadoop/hadoop/bin/yarn application -list
  2. 17/03/01 23:10:09 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  3. Total number of applications (application-types: [] and states: [SUBMITTED, ACCEPTED, RUNNING]):1
  4.                 Application-Id	    Application-Name	    Application-Type	      User	     Queue	             State	       Final-State	       Progress	                       Tracking-URL
  5. application_1488375590901_0004	     QuasiMonteCarlo	           MAPREDUCE	    hadoop	   default	           RUNNING	         UNDEFINED


6.3 使用自带的例子测试
  1. [[email protected]-1 ~]$ cd hadoop/
  2. [[email protected]-1 hadoop]$ 
  3. [[email protected]-1 hadoop]$ ./bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar pi 2 200

  1. [[email protected]-1 hadoop]$ ./bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar pi 2 200
  2. Number of Maps  = 2
  3. Samples per Map = 200
  4. 17/02/28 01:51:12 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  5. Wrote input for Map #0
  6. Wrote input for Map #1
  7. Starting Job
  8. 17/02/28 01:51:15 INFO input.FileInputFormat: Total input paths to process : 2
  9. 17/02/28 01:51:15 INFO mapreduce.JobSubmitter: number of splits:2
  10. 17/02/28 01:51:15 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1488216892564_0001
  11. 17/02/28 01:51:16 INFO impl.YarnClientImpl: Submitted application application_1488216892564_0001
  12. 17/02/28 01:51:16 INFO mapreduce.Job: The url to track the job: http://dn-1:8088/proxy/application_1488216892564_0001/
  13. 17/02/28 01:51:16 INFO mapreduce.Job: Running job: job_1488216892564_0001
  14. 17/02/28 01:51:24 INFO mapreduce.Job: Job job_1488216892564_0001 running in uber mode : false
  15. 17/02/28 01:51:24 INFO mapreduce.Job:  map 0% reduce 0%
  16. 17/02/28 01:51:38 INFO mapreduce.Job:  map 100% reduce 0%
  17. 17/02/28 01:51:49 INFO mapreduce.Job:  map 100% reduce 100%
  18. 17/02/28 01:51:49 INFO mapreduce.Job: Job job_1488216892564_0001 completed successfully
  19. 17/02/28 01:51:50 INFO mapreduce.Job: Counters: 49
  20. 	File System Counters
  21. 		FILE: Number of bytes read=50
  22. 		FILE: Number of bytes written=326922
  23. 		FILE: Number of read operations=0
  24. 		FILE: Number of large read operations=0
  25. 		FILE: Number of write operations=0
  26. 		HDFS: Number of bytes read=510
  27. 		HDFS: Number of bytes written=215
  28. 		HDFS: Number of read operations=11
  29. 		HDFS: Number of large read operations=0
  30. 		HDFS: Number of write operations=3
  31. 	Job Counters 
  32. 		Launched map tasks=2
  33. 		Launched reduce tasks=1
  34. 		Data-local map tasks=2
  35. 		Total time spent by all maps in occupied slots (ms)=25604
  36. 		Total time spent by all reduces in occupied slots (ms)=7267
  37. 		Total time spent by all map tasks (ms)=25604
  38. 		Total time spent by all reduce tasks (ms)=7267
  39. 		Total vcore-seconds taken by all map tasks=25604
  40. 		Total vcore-seconds taken by all reduce tasks=7267
  41. 		Total megabyte-seconds taken by all map tasks=26218496
  42. 		Total megabyte-seconds taken by all reduce tasks=7441408
  43. 	Map-Reduce Framework
  44. 		Map input records=2
  45. 		Map output records=4
  46. 		Map output bytes=36
  47. 		Map output materialized bytes=56
  48. 		Input split bytes=274
  49. 		Combine input records=0
  50. 		Combine output records=0
  51. 		Reduce input groups=2
  52. 		Reduce shuffle bytes=56
  53. 		Reduce input records=4
  54. 		Reduce output records=0
  55. 		Spilled Records=8
  56. 		Shuffled Maps =2
  57. 		Failed Shuffles=0
  58. 		Merged Map outputs=2
  59. 		GC time elapsed (ms)=419
  60. 		CPU time spent (ms)=6940
  61. 		Physical memory (bytes) snapshot=525877248
  62. 		Virtual memory (bytes) snapshot=2535231488
  63. 		Total committed heap usage (bytes)=260186112
  64. 	Shuffle Errors
  65. 		BAD_ID=0
  66. 		CONNECTION=0
  67. 		IO_ERROR=0
  68. 		WRONG_LENGTH=0
  69. 		WRONG_MAP=0
  70. 		WRONG_REDUCE=0
  71. 	File Input Format Counters 
  72. 		Bytes Read=236
  73. 	File Output Format Counters 
  74. 		Bytes Written=97
  75. Job Finished in 35.466 seconds
  76. Estimated value of Pi is 3.17000000000000000000

6.4 查看NameNode
 链接分别为：

192.168.9.21和192.168.9.22分别为NameNode和Secondary NameNode的地址。
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6.5 查看NameNode 的HA切换是否正常
将nn-1上状态为active的NameNode进程kill， 查看nn-2上的NameNode能否从standby切换为active：
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6.6 查看RM页面

其中192.168.9.23为Resource服务所在的节点。
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7、安装Spark

规划， 在现有的Hadoop集群安装spark集群：
master节点： nn-1
worker节点： nn-2、dn-1、dn-2、dn-3。

7.1 安装配置Scala
上传安装包到nn-1的/hadoop目录下面，解压：
  1. [[email protected]-1 ~]$ tar -xzvf spark-1.6.0-bin-hadoop2.6.tgz
环境变量后面统一配置。

7.2 安装spark

上传安装包spark-1.6.0-bin-hadoop2.6.tgz到nn-1的目录/hadoop下面， 解压
  1. [[email protected]-1 ~]$ tar -xzvf spark-1.6.0-bin-hadoop2.6.tgz

进入目录：/hadoop/spark-1.6.0-bin-hadoop2.6/conf
复制生成文件spark-env.sh和slaves：
  1. [[email protected]-1 conf]$ pwd
  2. /hadoop/spark-1.6.0-bin-hadoop2.6/conf
  3. [[email protected]-1 conf]$ cp spark-env.sh.template spark-env.sh
  4. [[email protected] conf]$ cp slaves.template  slaves
编辑spark-env.sh， 加入如下内容：
  1. export JAVA_HOME=/usr/local/java/jdk1.7.0_79
  2. export SCALA_HOME=/hadoop/scala-2.11.7
  3. export SPARK_HOME=/hadoop/spark-1.6.0-bin-hadoop2.6
  4. export SPARK_MASTER_IP=nn-1
  5. export SPARK_WORKER_MEMORY=2g
  6. export HADOOP_CONF_DIR=/hadoop/hadoop/etc/hadoop
SPARK_WORKER_MEMORY根据实际情况配置。

编辑spark-env.sh， 加入如下内容：slaves
  1. nn-2
  2. dn-1
  3. dn-2
  4. dn-3
slaves指定的是worker节点。

7.3 配置环境变量
  1. [[email protected]-1 ~]$ vim .bash_profile
追加如下内容：
  1. export HADOOP_HOME=/hadoop/hadoop
  2. export SCALA_HOME=/hadoop/scala-2.11.7
  3. export SPARK_HOME=/hadoop/spark-1.6.0-bin-hadoop2.6
  4. export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$SCALA_HOME/bin:$SPARK_HOME/bin:$SPARK_HOME/sbin:$PATH

7.4 分发上面配置好的scala和spark目录到其他节点
  1. [[email protected] bin]$ cd /hadoop
  2. [[email protected] ~]$ scp -rp spark-1.6.0-bin-hadoop2.6 [email protected]:/hadoop
  3. [[email protected] ~]$ scp -rp scala-2.11.7 [email protected]:/hadoop

7.5 启动Spark集群
  1. [[email protected]-1 ~]$ /hadoop/spark-1.6.0-bin-hadoop2.6/sbin/start-all.sh

在nn-1和其他slaves节点查看进程：
在nn-1节点， 可以看到Master进程：
  1. [[email protected]-1 ~]$ jps
  2. 2473 JournalNode
  3. 2541 NameNode
  4. 4401 Jps
  5. 2399 DFSZKFailoverController
  6. 2687 JobHistoryServer
  7. 2775 Master
  8. 2351 QuorumPeerMain

在slaves节点可以看到Worker进程：
  1. [[email protected]-1 ~]$ jps
  2. 2522 NodeManager
  3. 3449 Jps
  4. 2007 QuorumPeerMain
  5. 2141 DataNode
  6. 2688 Worker
  7. 2061 JournalNode
  8. 2258 ResourceManager

查看spark页面：

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7.6 运行测试案例

./bin/spark-submit --class org.apache.spark.examples.SparkPi \\

                   --master yarn --deploy-mode cluster \\

                   --driver-memory 100M \\

                   --executor-memory 200M \\

                   --executor-cores 1 \\

                   --queue default \\

                   lib/spark-examples*.jar 10

或者：

./bin/spark-submit --class org.apache.spark.examples.SparkPi \\

                   --master yarn --deploy-mode cluster \\

                   --executor-cores 1 \\

                   --queue default \\

                   lib/spark-examples*.jar 10


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8、配置机架感知
在nn-1和nn-2节点的配置文件/hadoop/hadoop/etc/hadoop/core-site.xml加入如下配置：
  1. <property>  
  2.     <name>topology.script.file.name</name>  
  3.     <value>/hadoop/hadoop/etc/hadoop/RackAware.py</value>  
  4. </property>
新增文件：/hadoop/hadoop/etc/hadoop/RackAware.py，内容如下：
  1. #!/usr/bin/python
  2. #-*-coding:UTF-8 -*-
  3. import sys
  5. rack = {"dn-1":"rack2",
  6.         "dn-2":"rack1",
  7.         "dn-3":"rack1",
  8.         "192.168.9.23":"rack2",
  9.         "192.168.9.24":"rack1",
  10.         "192.168.9.25":"rack1",
  11. }
  14. if __name__=="__main__":
  15.     print "/" + rack.get(sys.argv[1],"rack0")
设置权限：
  1. [[email protected]-1 hadoop]# chmod +x RackAware.py 
  2. [[email protected]-1 hadoop]# ll RackAware.py
  3. -rwxr-xr-x 1 hadoop hadoop 294 3月   1 21:24 RackAware.py

重启nn-1和nn-2上的NameNode服务：
  1. [[email protected]-1 ~]$ hadoop-daemon.sh stop namenode
  2. stopping namenode
  3. [[email protected]-1 ~]$ hadoop-daemon.sh start namenode
  4. starting namenode, logging to /hadoop/hadoop/logs/hadoop-hadoop-namenode-nn-1.out

查看日志：
  1. [[email protected]-1 logs]# pwd
  2. /hadoop/hadoop/logs
  3. [[email protected]-1 logs]# vim hadoop-hadoop-namenode-nn-1.log

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