hadoop2.6HA部署
因?yàn)樾枰渴餾park環(huán)境，特意重新安裝了一個(gè)測(cè)試的hadoop集群，現(xiàn)將相關(guān)步驟記錄如下：
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硬件環(huán)境：四臺(tái)虛擬機(jī)，hadoop1~hadoop4，3G內(nèi)存，60G硬盤，2核CPU
軟件環(huán)境：CentOS6.5，hadoop-2.6.0-cdh6.8.2，JDK1.7
部署規(guī)劃：
hadoop1（192.168.0.3）：namenode（active）、resourcemanager
hadoop2（192.168.0.4）：namenode（standby）、journalnode、datanode、nodemanager、historyserver
hadoop3（192.168.0.5）：journalnode、datanode、nodemanager
hadoop4（192.168.0.6）：journalnode、datanode、nodemanager
HDFS的HA采用QJM的方式（journalnode）：
一、系統(tǒng)準(zhǔn)備
1、每臺(tái)機(jī)關(guān)閉selinux
#vi /etc/selinux/config
SELINUX=disabled
2、每臺(tái)機(jī)關(guān)閉防火墻（切記要關(guān)閉，否則格式化hdfs時(shí)會(huì)報(bào)錯(cuò)無法連接journalnode）
#chkconfig iptables off
#service iptables stop
3、每臺(tái)機(jī)安裝jdk1.7
#cd /software
#tar -zxf jdk-7u65-linux-x64.gz -C /opt/
#cd /opt
#ln -s jdk-7u65-linux-x64.gz java
#vi /etc/profile
export JAVA_HOME=/opt/java
export PATH=$PATH:$JAVA_HOME/bin
4、每臺(tái)機(jī)建立hadoop相關(guān)用戶，并建立互信
#useradd grid
#passwd grid
（建立互信步驟略）
5、每臺(tái)機(jī)建立相關(guān)目錄
#mkdir -p /hadoop_data/hdfs/name
#mkdir -p /hadoop_data/hdfs/data
#mkdir -p /hadoop_data/hdfs/journal
#mkdir -p /hadoop_data/yarn/local
#chown -R grid:grid /hadoop_data
二、hadoop部署
HDFS HA主要是指定nameservices（如果不做HDFS ferderation，就只會(huì)有一個(gè)ID），同時(shí)指定該nameserviceID下面的兩個(gè)namenode及其地址。此處的nameservice名設(shè)置為hadoop-spark
1、每臺(tái)機(jī)解壓hadoop包
#cd /software
#tar -zxf hadoop-2.6.0-cdh6.8.2.tar.gz -C /opt/
#cd /opt
#chown -R grid:grid hadoop-2.6.0-cdh6.8.2
#ln -s hadoop-2.6.0-cdh6.8.2 hadoop
2、切換到grid用戶繼續(xù)操作
#su - grid
$cd /opt/hadoop/etc/hadoop
3、配置hadoop-env.sh（其實(shí)只配置JAVA_HOME）
$vi hadoop-env.sh
# The java implementation to use.
export JAVA_HOME=/opt/java
4、設(shè)置hdfs-site.xml
<configuration> <property> <name>dfs.replication</name> <value>1</value> </property> <property> <name>dfs.nameservices</name> <value>hadoop-spark</value> <description> Comma-separated list of nameservices. </description> </property> <property> <name>dfs.ha.namenodes.hadoop-spark</name> <value>nn1,nn2</value> <description> The prefix for a given nameservice, contains a comma-separated list of namenodes for a given nameservice (eg EXAMPLENAMESERVICE). </description> </property> <property> <name>dfs.namenode.rpc-address.hadoop-spark.nn1</name> <value>hadoop1:8020</value> <description> RPC address for nomenode1 of hadoop-spark </description> </property> <property> <name>dfs.namenode.rpc-address.hadoop-spark.nn2</name> <value>hadoop2:8020</value> <description> RPC address for nomenode2 of hadoop-spark </description> </property> <property> <name>dfs.namenode.http-address.hadoop-spark.nn1</name> <value>hadoop1:50070</value> <description> The address and the base port where the dfs namenode1 web ui will listen on. </description> </property> <property> <name>dfs.namenode.http-address.hadoop-spark.nn2</name> <value>hadoop2:50070</value> <description> The address and the base port where the dfs namenode2 web ui will listen on. </description> </property> <property> <name>dfs.namenode.name.dir</name> <value>file:///hadoop_data/hdfs/name</value> <description>Determines where on the local filesystem the DFS name node should store the name table(fsp_w_picpath). If this is a comma-delimited list of directories then the name table is replicated in all of the directories, for redundancy. </description> </property> <property> <name>dfs.namenode.shared.edits.dir</name> <value>qjournal://hadoop2:8485;hadoop3:8485;hadoop4:8485/hadoop-spark</value> <description>A directory on shared storage between the multiple namenodes in an HA cluster. This directory will be written by the active and read by the standby in order to keep the namespaces synchronized. This directory does not need to be listed in dfs.namenode.edits.dir above. It should be left empty in a non-HA cluster. </description> </property> <property> <name>dfs.datanode.data.dir</name> <value>file:///hadoop_data/hdfs/data</value> <description>Determines where on the local filesystem an DFS data node should store its blocks. If this is a comma-delimited list of directories, then data will be stored in all named directories, typically on different devices. Directories that do not exist are ignored. </description> </property> <!-- 這個(gè)如果不設(shè)置，會(huì)造成無法直接通過nameservice名稱來訪問hdfs，只能直接寫active的namenode地址 --> <property> <name>dfs.client.failover.proxy.provider.hadoop-spark</name> <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value> </property> <property> <name>dfs.ha.automatic-failover.enabled</name> <value>false</value> <description> Whether automatic failover is enabled. See the HDFS High Availability documentation for details on automatic HA configuration. </description> </property> <property> <name>dfs.journalnode.edits.dir</name> <value>/hadoop_data/hdfs/journal</value> </property> </configuration>
5、配置core-site.xml（配置fs.defaultFS，使用HA的nameservices名稱）
<property> <name>fs.defaultFS</name> <value>hdfs://hadoop-spark</value> <description>The name of the default file system. A URI whose scheme and authority determine the FileSystem implementation. The uri's scheme determines the config property (fs.SCHEME.impl) naming the FileSystem implementation class. The uri's authority is used to determine the host, port, etc. for a filesystem.</description> </property>
6、配置mapred-site.xml
<configuration> <!-- MR YARN Application properties --> <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> </property> <!-- jobhistory properties --> <property> <name>mapreduce.jobhistory.address</name> <value>hadoop2:10020</value> <description>MapReduce JobHistory Server IPC host:port</description> </property> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>hadoop2:19888</value> <description>MapReduce JobHistory Server Web UI host:port</description> </property> </configuration>
7、配置yarn-site.xml
<configuration>
<!-- Site specific YARN configuration properties -->
<!-- Resource Manager Configs -->
<property>
<description>The hostname of the RM.</description>
<name>yarn.resourcemanager.hostname</name>
<value>hadoop1</value>
</property>
<property>
<description>The address of the applications manager interface in the RM.</description>
<name>yarn.resourcemanager.address</name>
<value>${yarn.resourcemanager.hostname}:8032</value>
</property>
<property>
<description>The address of the scheduler interface.</description>
<name>yarn.resourcemanager.scheduler.address</name>
<value>${yarn.resourcemanager.hostname}:8030</value>
</property>
<property>
<description>The http address of the RM web application.</description>
<name>yarn.resourcemanager.webapp.address</name>
<value>${yarn.resourcemanager.hostname}:8088</value>
</property>
<property>
<description>The https adddress of the RM web application.</description>
<name>yarn.resourcemanager.webapp.https.address</name>
<value>${yarn.resourcemanager.hostname}:8090</value>
</property>
<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>${yarn.resourcemanager.hostname}:8031</value>
</property>
<property>
<description>The address of the RM admin interface.</description>
<name>yarn.resourcemanager.admin.address</name>
<value>${yarn.resourcemanager.hostname}:8033</value>
</property>
<property>
<description>The class to use as the resource scheduler.</description>
<name>yarn.resourcemanager.scheduler.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
</property>
<property>
<description>fair-scheduler conf location</description>
<name>yarn.scheduler.fair.allocation.file</name>
<value>${yarn.home.dir}/etc/hadoop/fairscheduler.xml</value>
</property>
<property>
<description>List of directories to store localized files in. An
application's localized file directory will be found in:
${yarn.nodemanager.local-dirs}/usercache/${user}/appcache/application_${appid}.
Individual containers' work directories, called container_${contid}, will
be subdirectories of this.
</description>
<name>yarn.nodemanager.local-dirs</name>
<value>/hadoop_data/yarn/local</value>
</property>
<property>
<description>Whether to enable log aggregation</description>
<name>yarn.log-aggregation-enable</name>
<value>true</value>
</property>
<property>
<description>Where to aggregate logs to.</description>
<name>yarn.nodemanager.remote-app-log-dir</name>
<value>/tmp/logs</value>
</property>
<property>
<description>Amount of physical memory, in MB, that can be allocated
for containers.</description>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>2048</value>
</property>
<property>
<description>Number of CPU cores that can be allocated
for containers.</description>
<name>yarn.nodemanager.resource.cpu-vcores</name>
<value>2</value>
</property>
<property>
<description>the valid service name should only contain a-zA-Z0-9_ and can not start with numbers</description>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>8、配置slaves
hadoop2
hadoop3
hadoop4
9、配置fairscheduler.xml
<?xml version="1.0"?> <allocations> <queue name="common"> <minResources>0mb, 0 vcores </minResources> <maxResources>6144 mb, 6 vcores </maxResources> <maxRunningApps>50</maxRunningApps> <minSharePreemptionTimeout>300</minSharePreemptionTimeout> <weight>1.0</weight> <aclSubmitApps>grid</aclSubmitApps> </queue> </allocations>
10、同步配置文件到各個(gè)節(jié)點(diǎn)
$cd /opt/hadoop/etc
$scp -r hadoop hadoop2:/opt/hadoop/etc/
$scp -r hadoop hadoop3:/opt/hadoop/etc/
$scp -r hadoop hadoop4:/opt/hadoop/etc/
三、啟動(dòng)集群（格式化文件系統(tǒng)）
1、建立環(huán)境變量
$vi ~/.bash_profile
export HADOOP_HOME=/opt/hadoop
export YARN_HOME_DIR=/opt/hadoop
export HADOOP_CONF_DIR=/opt/hadoop/etc/hadoop
export YARN_CONF_DIR=/opt/hadoop/etc/hadoop
2、啟動(dòng)HDFS
先啟動(dòng)journalnode，在hadoop2~hadoop4上：
$cd /opt/hadoop/
$sbin/hadoop-daemon.sh start journalnode
格式化HDFS，然后啟動(dòng)namenode。在hadoop1上：
$bin/hdfs namenode -format
$sbin/hadoop-daemon.sh start namenode
同步另一個(gè)namenode，并啟動(dòng)。在hadoop2上：
$bin/hdfs namenode -bootstrapStandby
$sbin/hadoop-daemon.sh start namenode
此時(shí)兩個(gè)namenode都是standby狀態(tài)，將hadoop1切換成active（hadoop1在hdfs-site.xml里對(duì)應(yīng)的是nn1）：
$bin/hdfs haadmin -transitionToActive nn1
啟動(dòng)datanode。在hadoop1上（active的namenode）：
$sbin/hadoop-daemons.sh start datanode
注意事項(xiàng)：后續(xù)啟動(dòng)，只需使用sbin/start-dfs.sh即可。但由于沒有配置zookeeper的failover，所以只能HA只能使用手工切換。所以每次啟動(dòng)HDFS，都要執(zhí)行$bin/hdfs haadmin -transitionToActive nn1來使hadoop1的namenode變成active狀態(tài)
2、啟動(dòng)yarn
在hadoop1上（resourcemanager）：
$sbin/start-yarn.sh
————————————————————————————————————————————
以上配置的HDFS HA并不是自動(dòng)故障切換的，如果配置HDFS自動(dòng)故障切換，需要添加以下步驟（先停掉集群）：
1、部署zookeeper，步驟省略。部署在hadoop2、hadoop3、hadoop4，并啟動(dòng)
2、在hdfs-site.xml中添加：
<property> <name>dfs.ha.automatic-failover.enabled</name> <value>true</value> </property>
<property> <name>dfs.ha.fencing.methods</name> <value>sshfence</value> </property> <property> <name>dfs.ha.fencing.ssh.private-key-files</name> <value>/home/exampleuser/.ssh/id_rsa</value> </property>
解釋詳見官方文檔。這樣配置設(shè)定了fencing方法是通過ssh去關(guān)閉前一個(gè)活動(dòng)節(jié)點(diǎn)的端口。前提前兩個(gè)namenode能互相SSH。
還有另外一種配置方法：
<property> <name>dfs.ha.automatic-failover.enabled</name> <value>true</value> </property>
<property> <name>dfs.ha.fencing.methods</name> <value>shell(/path/to/my/script.sh arg1 arg2 ...)</value> </property>
這樣的配置實(shí)際上是使用shell來隔絕端口和程序。如果不想做實(shí)際的動(dòng)作，dfs.ha.fencing.methods可配置成shell(/bin/true)
3、在core-site.xml中添加：
<property> <name>ha.zookeeper.quorum</name> <value>hadoop2:2181,hadoop3:2181,hadoop4:2181</value> </property>
4、初始化zkfc（在namenode上執(zhí)行）
bin/hdfs zkfc -formatZK
5、啟動(dòng)集群
___________________________________________________________________________________________________
zkfc：每個(gè)namenode上都運(yùn)行，是zk的客戶端，負(fù)責(zé)自動(dòng)故障切換
zk：奇數(shù)個(gè)節(jié)點(diǎn)，維護(hù)一致性鎖、負(fù)責(zé)選舉活動(dòng)節(jié)點(diǎn)
joural node：奇數(shù)個(gè)節(jié)點(diǎn)，用于active和standby節(jié)點(diǎn)之間數(shù)據(jù)同步?；顒?dòng)節(jié)點(diǎn)把數(shù)據(jù)寫入這些節(jié)點(diǎn)，standby節(jié)點(diǎn)讀取
————————————————————————————————————————————
更改成resourcemanager HA：
選擇hadoop2作為另一個(gè)rm節(jié)點(diǎn)
1、設(shè)置hadoop2對(duì)其它節(jié)點(diǎn)作互信
2、編譯yarn-site.xml并同步到其它機(jī)器
3、復(fù)制fairSheduler.xml到hadoop2
4、啟動(dòng)rm
5、啟動(dòng)另一個(gè)rm
名稱欄目：hadoop2.6HA部署
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