centos7防火墙操作介绍 

#centos7启动firewall
systemctl start firewalld.service
#centos7重启firewall
systemctl restart firewalld.service
#centos7停止firewall
systemctl stop firewalld.service 
#centos7禁止firewall开机启动
systemctl disable firewalld.service 
#centos7查看防火墙状态
firewall-cmd --state
#开放防火墙端口
vi /etc/sysconfig/iptables-config
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 6379 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 6380 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 6381 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 16379 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 16380 -j ACCEPT
-A RH-Firewall-1-INPUT -p tcp -m state --state NEW -m tcp --dport 16381 -j ACCEPT

systemctl stop firewalld.service 
systemctl disable firewalld.service

# This file controls the state of SELinux on the system.
# SELINUX= can take one of these three values:
# enforcing - SELinux security policy is enforced.
# permissive - SELinux prints warnings instead of enforcing.
# disabled - No SELinux policy is loaded.
SELINUX=enforcing
# SELINUXTYPE= can take one of these two values:
# targeted - Targeted processes are protected,
# minimum - Modification of targeted policy. Only selected processes are protected. 
# mls - Multi Level Security protection.
SELINUXTYPE=targeted

# This file controls the state of SELinux on the system.
# SELINUX= can take one of these three values:
# enforcing - SELinux security policy is enforced.
# permissive - SELinux prints warnings instead of enforcing.
# disabled - No SELinux policy is loaded.
#SELINUX=enforcing
SELINUX=disabled
# SELINUXTYPE= can take one of these two values:
# targeted - Targeted processes are protected,
# minimum - Modification of targeted policy. Only selected processes are protected. 
# mls - Multi Level Security protection.
#SELINUXTYPE=targeted

setenforce 0

127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.185.31 hadoop31
192.168.185.32 hadoop32
192.168.185.33 hadoop33
192.168.185.34 hadoop34
192.168.185.35 hadoop35

#localdomain
hadoop31

#以root用户创建hadoop用户和组创建hadoop用户和组 
groupadd hadoop 
useradd -g hadoop hadoop 
#修改用户密码
passwd hadoop

#首先切换到上面的hadoop用户,这里我是在hadoop31机器上操作 
ssh hadoop31
su hadoop 
#生成非对称公钥和私钥，这个在集群中所有节点机器都必须执行,一直回车就行 
ssh-keygen -t rsa 
#通过ssh登录远程机器时，本机会默认将当前用户目录下的.ssh/authorized_keys带到远程机器进行验证，这里是/home/hadoop/.ssh/authorized_keys中公钥(来自其他机器上的/home/hadoop/.ssh/id_rsa.pub.pub),以下代码只在主节点执行就可以做到主从节点之间SSH免密码登录 
cd /home/hadoop/.ssh/ 
#首先将Master节点的公钥添加到authorized_keys 
cat id_rsa.pub>>authorized_keys 
#其次将Slaves节点的公钥添加到authorized_keys，这里我是在Hadoop31机器上操作的 
ssh hadoop@192.168.185.32 cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
ssh hadoop@192.168.185.33 cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
ssh hadoop@192.168.185.34 cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
ssh hadoop@192.168.185.35 cat /home/hadoop/.ssh/id_rsa.pub>> authorized_keys 
#必须设置修改/home/hadoop/.ssh/authorized_keys权限 
chmod 600 /home/hadoop/.ssh/authorized_keys 
#这里将Master节点的authorized_keys分发到其他slaves节点 
scp -r /home/hadoop/.ssh/authorized_keys hadoop@192.168.185.32:/home/hadoop/.ssh/ 
scp -r /home/hadoop/.ssh/authorized_keys hadoop@192.168.185.33:/home/hadoop/.ssh/ 
scp -r /home/hadoop/.ssh/authorized_keys hadoop@192.168.185.34:/home/hadoop/.ssh/ 
scp -r /home/hadoop/.ssh/authorized_keys hadoop@192.168.185.35:/home/hadoop/.ssh/

#登录到到到hadoop用户下
su hadoop
#下载jdk-7u65-linux-x64.gz放置于/home/hadoop/java并解压
cd /home/hadoop/java
tar -zxvf jdk-7u65-linux-x64.gz
#编辑vi /home/hadoop/.bashrc,在文件末尾追加如下内容
export JAVA_HOME=/home/hadoop/java/jdk1.7.0_65 
export CLASSPATH=.:$JAVA_HOME/jre/lib/rt.jar:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar 
export PATH=$PATH:$JAVA_HOME/bin 
#使得/home/hadoop/.bashrc配置生效
source /home/hadoop/.bashrc

#1登录hadoop用户并下载并解压zookeeper3.4.6
su hadoop
cd /home/hadoop 
tar -zxvf zookeeper-3.4.6.tar.gz

#2在集群中各个节点中配置/etc/hosts，内容如下：
127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.185.31 hadoop31 
192.168.185.32 hadoop32 
192.168.185.33 hadoop33 
192.168.185.34 hadoop34 
192.168.185.35 hadoop35

#3在集群中各个节点中创建zookeeper数据文件
ssh hadoop31
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop32
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop33
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop34
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper 
ssh hadoop35
cd /home/hadoop 
#zookeeper数据存放位置
mkdir -p /opt/hadoop/zookeeper

#4配置zoo.cfg
ssh hadoop31
cd /home/hadoop/zookeeper-3.4.6/conf
cp zoo_sample.cfg zoo.cfg
vi zoo.cfg
#内容如下
initLimit=10 
syncLimit=5 
dataDir=/opt/hadoop/zookeeper 
clientPort=2181 

#数据文件保存最近的3个快照，默认是都保存，时间长的话会占用很大磁盘空间
autopurge.snapRetainCount=3
#单位为小时，每小时清理一次快照数据
autopurge.purgeInterval=1
server.1=hadoop31:2888:3888 
server.2=hadoop32:2888:3888 
server.3=hadoop33:2888:3888
server.4=hadoop34:2888:3888 
server.5=hadoop35:2888:3888 
#5在hadoop31上远程复制分发安装文件
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop32:/home/hadoop/ 
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop33:/home/hadoop/ 
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop34:/home/hadoop/ 
scp -r /home/hadoop/zookeeper-3.4.6 hadoop@hadoop35:/home/hadoop/

#6在集群中各个节点设置myid必须为数字 
ssh hadoop31 
echo "1" > /opt/hadoop/zookeeper/myid 
ssh hadoop32 
echo "2" > /opt/hadoop/zookeeper/myid 
ssh hadoop33 
echo "3" > /opt/hadoop/zookeeper/myid

#7.各个节点如何启动zookeeper
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start

#8.各个节点如何关闭zookeeper
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh stop

#9.各个节点如何查看zookeeper状态
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh status

#10.各个节点如何通过客户端访问zookeeper上目录数据
ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkCli.sh -server hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181

#下载hadoop-2.7.1.tar.gz放置于/home/hadoop下并解压,这里我在hadoop31操作
ssh hadoop31
su hadoop
cd /home/hadoop
tar –zxvf hadoop-2.7.1.tar.gz

1. <?xml version="1.0" encoding="UTF-8"?>
2. <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
3. <configuration>
4. <!-- 开启垃圾回收站功能,HDFS文件删除后先进入垃圾回收站，垃圾回收站最长保留数据时间为1天，超过一天后就删除 -->
5. <property>
6. <name>fs.trash.interval</name>
7. <value>1440</value>
8. </property>
9. <!-- Hadoop HA部署方式下namenode访问地址，bigdatacluster-ha是名字可自定义，后面hdfs-site.xml会用到 -->
10. <property>
11. <name>fs.defaultFS</name>
12. <value>hdfs:// bigdatacluster-ha</value>
13. </property>
14. <!--hadoop访问文件的IO操作都需要通过代码库。因此，在很多情况下，io.file.buffer.size都被用来设置SequenceFile中用到的读/写缓存大小。不论是对硬盘或者是网络操作来讲，较大的缓存都可以提供更高的数据传输，但这也就意味着更大的内存消耗和延迟。这个参数要设置为系统页面大小的倍数，以byte为单位，默认值是4KB，一般情况下，可以设置为64KB（65536byte）,这里设置128K-->
15. <property>
16. <name>io.file.buffer.size</name>
17. <value>131072</value>
18. </property>
19. <!-- 指定hadoop临时目录 -->
20. <property>
21. <name>hadoop.tmp.dir</name>
22. <value>/opt/hadoop/tmp</value>
23. </property>
24. <!-- 指定zookeeper地址 -->
25. <property>
26. <name>ha.zookeeper.quorum</name>
27. <value>hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181</value>
28. </property>
29. <property>
30. <name>ha.zookeeper.session-timeout.ms</name>
31. <value>300000</value>
32. </property>
33. <!-- 指定Hadoop压缩格式，Apache官网下载的安装包不支持snappy，需要自己编译安装，如何编译安装包我在博客http://aperise.iteye.com/blog/2254487有讲解,不适用snappy的话可以不配置 -->
34. <property>
35. <name>io.compression.codecs</name>
36. <value>org.apache.hadoop.io.compress.SnappyCodec</value>
37. </property>
38. </configuration>

1. <?xml version="1.0" encoding="UTF-8"?>
2. <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
3. <configuration>
4. <!--指定hdfs的nameservice为bigdatacluster-ha，需要和core-site.xml中的保持一致 -->
5. <property>
6. <name>dfs.nameservices</name>
7. <value>bigdatacluster-ha</value>
8. </property>
9. <!—指定磁盘预留多少空间，防止磁盘被撑满用完，单位为bytes -->
10. <property>
11. <name>dfs.datanode.du.reserved</name>
12. <value>107374182400</value>
13. </property>
14. <!-- bigdatacluster-ha下面有两个NameNode，分别是namenode1，namenode2 -->
15. <property>
16. <name>dfs.ha.namenodes.bigdatacluster-ha</name>
17. <value>namenode1,namenode2</value>
18. </property>
19. <!-- namenode1的RPC通信地址,这里端口要和core-site.xml中fs.defaultFS保持一致 -->
20. <property>
21. <name>dfs.namenode.rpc-address.bigdatacluster-ha.namenode1</name>
22. <value>hadoop31:9000</value>
23. </property>
24. <!-- namenode1的http通信地址 -->
25. <property>
26. <name>dfs.namenode.http-address.bigdatacluster-ha.namenode1</name>
27. <value>hadoop31:50070</value>
28. </property>
29. <!-- namenode2的RPC通信地址,这里端口要和core-site.xml中fs.defaultFS保持一致 -->
30. <property>
31. <name>dfs.namenode.rpc-address.bigdatacluster-ha.namenode2</name>
32. <value>hadoop32:9000</value>
33. </property>
34. <!-- namenode2的http通信地址 -->
35. <property>
36. <name>dfs.namenode.http-address.bigdatacluster-ha.namenode2</name>
37. <value>hadoop32:50070</value>
38. </property>
39. <!-- 指定NameNode的元数据在JournalNode上的存放位置 -->
40. <property>
41. <name>dfs.namenode.shared.edits.dir</name>
42. <value>qjournal://hadoop31:8485;hadoop32:8485;hadoop33:8485;hadoop34:8485;hadoop35:8485/bigdatacluster-ha</value>
43. </property>
44. <!-- 配置失败自动切换实现方式 -->
45. <property>
46. <name>dfs.client.failover.proxy.provider.bigdatacluster-ha</name>
47. <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
48. </property>
49. <!-- 配置隔离机制,主要用户远程管理监听其他机器相关服务 -->
50. <property>
51. <name>dfs.ha.fencing.methods</name>
52. <value>sshfence</value>
53. </property>
54. <!-- 使用隔离机制时需要ssh免密码登陆 -->
55. <property>
56. <name>dfs.ha.fencing.ssh.private-key-files</name>
57. <value>/home/hadoop/.ssh/id_rsa</value>
58. </property>
59. <!-- 指定NameNode的元数据在JournalNode上的存放位置 -->
60. <property>
61. <name>dfs.journalnode.edits.dir</name>
62. <value>/opt/hadoop/journal</value>
63. </property>
64. <!--指定支持高可用自动切换机制-->
65. <property>
66. <name>dfs.ha.automatic-failover.enabled</name>
67. <value>true</value>
68. </property>
69. <!--指定namenode名称空间的存储地址-->
70. <property>
71. <name>dfs.namenode.name.dir</name>
72. <value>file:/opt/hadoop/hdfs/name</value>
73. </property>
74. <!--指定datanode数据存储地址-->
75. <property>
76. <name>dfs.datanode.data.dir</name>
77. <value>file:/opt/hadoop/hdfs/data</value>
78. </property>
79. <!--指定数据冗余份数-->
80. <property>
81. <name>dfs.replication</name>
82. <value>3</value>
83. </property>
84. <!--指定可以通过web访问hdfs目录-->
85. <property>
86. <name>dfs.webhdfs.enabled</name>
87. <value>true</value>
88. </property>
89. <property>
90. <name>ha.zookeeper.quorum</name>
91. <value>hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181</value>
92. </property>
93. <property>
94. <name>dfs.namenode.handler.count</name>
95. <value>600</value>
96. <description>The number of server threads for the namenode.</description>
97. </property>
98. <property>
99. <name>dfs.datanode.handler.count</name>
100. <value>600</value>
101. <description>The number of server threads for the datanode.</description>
102. </property>
103. <property>
104. <name>dfs.client.socket-timeout</name>
105. <value>600000</value>
106. </property>
107. <property>
108. <!--这里设置Hadoop允许打开最大文件数，默认4096，不设置的话会提示xcievers exceeded错误-->
109. <name>dfs.datanode.max.transfer.threads</name>
110. <value>409600</value>
111. </property>
112. </configuration>

1. <?xml version="1.0"?>
2. <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
3. <configuration>
4. <!-- 配置MapReduce运行于yarn中 -->
5. <property>
6. <name>mapreduce.framework.name</name>
7. <value>yarn</value>
8. </property>
9. <property>
10. <name>mapreduce.job.maps</name>
11. <value>12</value>
12. </property>
13. <property>
14. <name>mapreduce.job.reduces</name>
15. <value>12</value>
16. </property>
17. <!-- 指定Hadoop压缩格式，Apache官网下载的安装包不支持snappy，需要自己编译安装，如何编译安装包我在博客http://aperise.iteye.com/blog/2254487有讲解,不适用snappy的话可以不配置 -->
18. <property>
19. <name>mapreduce.output.fileoutputformat.compress</name>
20. <value>true</value>
21. <description>Should the job outputs be compressed?
22. </description>
23. </property>
24. <property>
25. <name>mapreduce.output.fileoutputformat.compress.type</name>
26. <value>RECORD</value>
27. <description>If the job outputs are to compressed as SequenceFiles, how should
28. they be compressed? Should be one of NONE, RECORD or BLOCK.
29. </description>
30. </property>
31. <property>
32. <name>mapreduce.output.fileoutputformat.compress.codec</name>
33. <value>org.apache.hadoop.io.compress.SnappyCodec</value>
34. <description>If the job outputs are compressed, how should they be compressed?
35. </description>
36. </property>
37. <property>
38. <name>mapreduce.map.output.compress</name>
39. <value>true</value>
40. <description>Should the outputs of the maps be compressed before being
41. sent across the network. Uses SequenceFile compression.
42. </description>
43. </property>
44. <property>
45. <name>mapreduce.map.output.compress.codec</name>
46. <value>org.apache.hadoop.io.compress.SnappyCodec</value>
47. <description>If the map outputs are compressed, how should they be
48. compressed?
49. </description>
50. </property>
51. </configuration>

1. <?xml version="1.0"?>
2. <configuration>
3. <!--日志聚合功能yarn.log start------------------------------------------------------------------------>
4. <property>
5. <name>yarn.log-aggregation-enable</name>
6. <value>true</value>
7. </property>
8. <!--在HDFS上聚合的日志最长保留多少秒。3天-->
9. <property>
10. <name>yarn.log-aggregation.retain-seconds</name>
11. <value>259200</value>
12. </property>
13. <!--日志聚合功能yarn.log end-------------------------------------------------------------------------->
14. <!--resourcemanager失联后重新链接的时间-->
15. <property>
16. <name>yarn.resourcemanager.connect.retry-interval.ms</name>
17. <value>2000</value>
18. </property>
19. <!--配置resourcemanager start------------------------------------------------------------------------->
20. <property>
21. <name>yarn.resourcemanager.zk-address</name>
22. <value>hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181</value>
23. </property>
24. <property>
25. <name>yarn.resourcemanager.cluster-id</name>
26. <value>besttonecluster-yarn</value>
27. </property>
28. <!--开启resourcemanager HA,默认为false-->
29. <property>
30. <name>yarn.resourcemanager.ha.enabled</name>
31. <value>true</value>
32. </property>
33. <property>
34. <name>yarn.resourcemanager.ha.rm-ids</name>
35. <value>rm1,rm2</value>
36. </property>
37. <property>
38. <name>yarn.resourcemanager.hostname.rm1</name>
39. <value>hadoop31</value>
40. </property>
41. <property>
42. <name>yarn.resourcemanager.hostname.rm2</name>
43. <value>hadoop32</value>
44. </property>
45. <!--配置rm1-->
46. <property>
47. <name>yarn.resourcemanager.webapp.address.rm1</name>
48. <value>hadoop31:8088</value>
49. </property>
50. <!--配置rm2-->
51. <property>
52. <name>yarn.resourcemanager.webapp.address.rm2</name>
53. <value>hadoop32:8088</value>
54. </property>
55. <!--开启故障自动切换-->
56. <property>
57. <name>yarn.resourcemanager.ha.automatic-failover.enabled</name>
58. <value>true</value>
59. </property>
60. <property>
61. <name>yarn.resourcemanager.ha.automatic-failover.embedded</name>
62. <value>true</value>
63. </property>
64. <property>
65. <name>yarn.resourcemanager.ha.automatic-failover.zk-base-path</name>
66. <value>/yarn-leader-election</value>
67. </property>
68. <!--开启自动恢复功能-->
69. <property>
70. <name>yarn.resourcemanager.recovery.enabled</name>
71. <value>true</value>
72. </property>
73. <property>
74. <name>yarn.resourcemanager.store.class</name>
75. <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
76. </property>
77. <!--配置resourcemanager end--------------------------------------------------------------------------->
78. <!--配置nodemanager start----------------------------------------------------------------------------->
79. <property>
80. <name>yarn.nodemanager.aux-services</name>
81. <value>mapreduce_shuffle</value>
82. </property>
83. <property>
84. <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
85. <value>org.apache.hadoop.mapred.ShuffleHandler</value>
86. </property>
87. <!--配置nodemanager end------------------------------------------------------------------------------->
88. </configuration>

Hadoop31
Hadoop32
Hadoop33
Hadoop34
Hadoop35

export JAVA_HOME=/home/hadoop/java/jdk1.7.0_65

export HADOOP_HOME=/home/hadoop/hadoop2.7.1
export PATH=${HADOOP_HOME}/bin:${PATH}

#这里我是在hadoop31上操作
scp -r /home/hadoop/hadoop-2.7.1 hadoop@hadoop32:/home/hadoop/
scp -r /home/hadoop/hadoop-2.7.1 hadoop@ hadoop33:/home/hadoop/
scp -r /home/hadoop/hadoop-2.7.1 hadoop@ hadoop34:/home/hadoop/ 
scp -r /home/hadoop/hadoop-2.7.1 hadoop@ hadoop35:/home/hadoop/

ssh hadoop31
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop32
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop33
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop34
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start
ssh hadoop35
/home/hadoop/zookeeper-3.4.6/bin/zkServer.sh start

/home/hadoop/hadoop-2.7.1/bin/hdfs zkfc –formatZK
#可以通过如下方法检查zookeeper上是否已经有Hadoop HA目录
# /home/hadoop/zookeeper-3.4.6/bin/zkCli.sh -server hadoop31:2181,hadoop32:2181,hadoop33:2181,hadoop34:2181,hadoop35:2181 
#ls /

ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop32
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop33
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop34
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode 
ssh hadoop35
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start journalnode

#这步操作只能在namenode服务节点hadoop31或者hadoop32执行中一台上执行
ssh hadoop31
/home/hadoop/hadoop-2.7.1/bin/hdfs namenode -format

#启动namenode
ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start namenode 
#同步备用namenode、启动备用namenode
ssh hadoop32
/home/hadoop/hadoop-2.7.1/bin/hdfs namenode -bootstrapStandby 
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start namenode

ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start zkfc 
ssh hadoop32
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemon.sh start zkfc

#注意hadoop-daemons.sh datanode是启动所有datanode，而hadoop-daemon.sh datanode是启动单个datanode
ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/hadoop-daemons.sh start datanode

#在hadoop31上启动resouremanager,在hadoop31，hadoop32，hadoop33，hadoop34，hadoop35上启动nodemanager
ssh hadoop31
/home/hadoop/hadoop-2.7.1/sbin/start-yarn.sh 
#在hadoop31上启动备用resouremanager
ssh hadoop32
/home/hadoop/hadoop-2.7.1/sbin/yarn-daemon.sh start resourcemanager