Hadoop源码解析、配置HDFS-HA自动故障转移

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1.4.3 配置HDFS-HA自动故障转移

1）具体配置 （1）在hdfs-site.xml中增加

<!-- 启用nn故障自动转移 -->
<property>
	<name>dfs.ha.automatic-failover.enabled</name>
	<value>true</value>
</property>

（2）在core-site.xml文件中增加

<!-- 指定zkfc要连接的zkServer地址 -->
<property>
	<name>ha.zookeeper.quorum</name>
	<value>hadoop102:2181,hadoop103:2181,hadoop104:2181</value>
</property>

（3）修改后分发配置文件

[summer@hadoop102 etc]$ pwd
/opt/ha/hadoop-3.1.3/etc
[summer@hadoop102 etc]$ xsync hadoop/

2）启动 （1）关闭所有HDFS服务：

[summer@hadoop102 ~]$ stop-dfs.sh

（2）启动Zookeeper集群：

[summer@hadoop102 ~]$ zkServer.sh start
[summer@hadoop103 ~]$ zkServer.sh start
[summer@hadoop104 ~]$ zkServer.sh start

（3）启动Zookeeper以后，然后再初始化HA在Zookeeper中状态： [summer@hadoop102 ~]hdfszkfc−formatZK（4）启动HDFS服务：[summer@hadoop102 ] hdfs zkfc -formatZK （4）启动HDFS服务： [summer@hadoop102 ~] start-dfs.sh （5）可以去zkCli.sh客户端查看Namenode选举锁节点内容： [zk: localhost:2181(CONNECTED) 7] get -s /hadoop-ha/mycluster/ActiveStandbyElectorLock

myclusternn2	hadoop103 �>(�>

cZxid = 0x10000000b ctime = Tue Jul 14 17:00:13 CST 2020 mZxid = 0x10000000b mtime = Tue Jul 14 17:00:13 CST 2020 pZxid = 0x10000000b cversion = 0 dataVersion = 0 aclVersion = 0 ephemeralOwner = 0x40000da2eb70000 dataLength = 33 numChildren = 0 3）验证 （1）将Active NameNode进程kill，查看网页端三台Namenode的状态变化 [summer@hadoop102 ~]$ kill -9 namenode的进程id

1.4.4 常见问题1--解决NN连接不上JN的问题

自动故障转移配置好以后，然后使用start-dfs.sh群起脚本启动hdfs集群，有可能会遇到NameNode起来一会后，进程自动关闭的问题。查看NameNode日志，报错信息如下：

2020-08-17 10:11:40,658 INFO org.apache.hadoop.ipc.Client: Retrying connect to server: hadoop104/192.168.6.104:8485. Already tried 0 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS) 2020-08-17 10:11:40,659 INFO org.apache.hadoop.ipc.Client: Retrying connect to server: hadoop102/192.168.6.102:8485. Already tried 0 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS) 2020-08-17 10:11:40,659 INFO org.apache.hadoop.ipc.Client: Retrying connect to server: hadoop103/192.168.6.103:8485. Already tried 0 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)

… …

2020-08-17 10:11:49,669 INFO org.apache.hadoop.ipc.Client: Retrying connect to server: hadoop102/192.168.6.102:8485. Already tried 9 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS) 2020-08-17 10:11:49,673 INFO org.apache.hadoop.ipc.Client: Retrying connect to server: hadoop104/192.168.6.104:8485. Already tried 9 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS) 2020-08-17 10:11:49,676 INFO org.apache.hadoop.ipc.Client: Retrying connect to server: hadoop103/192.168.6.103:8485. Already tried 9 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS) 2020-08-17 10:11:49,678 WARN org.apache.hadoop.hdfs.server.namenode.FSEditLog: Unable to determine input streams from QJM to [192.168.6.102:8485, 192.168.6.103:8485, 192.168.6.104:8485]. Skipping. org.apache.hadoop.hdfs.qjournal.client.QuorumException: Got too many exceptions to achieve quorum size 2/3. 3 exceptions thrown: 192.168.6.103:8485: Call From hadoop102/192.168.6.102 to hadoop103:8485 failed on connection exception: java.net.ConnectException: 拒绝连接; For more details see: wiki.apache.org/hadoop/Conn… 192.168.6.102:8485: Call From hadoop102/192.168.6.102 to hadoop102:8485 failed on connection exception: java.net.ConnectException: 拒绝连接; For more details see: wiki.apache.org/hadoop/Conn… 192.168.6.104:8485: Call From hadoop102/192.168.6.102 to hadoop104:8485 failed on connection exception: java.net.ConnectException: 拒绝连接; For more details see: wiki.apache.org/hadoop/Conn…

查看报错日志，可分析出报错原因是因为NameNode连接不上JournalNode，而利用jps命令查看到三台JN都已经正常启动，为什么NN还是无法正常连接到JN呢？这是因为start-dfs.sh群起脚本默认的启动顺序是先启动NN，再启动DN，然后再启动JN，并且默认的rpc连接参数是重试次数为10，每次重试的间隔是1s，也就是说启动完NN以后的10s中内，JN还启动不起来，NN就会报错了。 core-default.xml里面有两个参数如下：

<!-- NN连接JN重试次数，默认是10次 -->
<property>
  <name>ipc.client.connect.max.retries</name>
  <value>10</value>
</property>

<!-- 重试时间间隔，默认1s -->
<property>
  <name>ipc.client.connect.retry.interval</name>
  <value>1000</value>
</property>

解决方案：遇到上述问题后，可以稍等片刻，等JN成功启动后，手动启动下三台NN：

[summer@hadoop102 ~]$ hdfs --daemon start namenode
[summer@hadoop103 ~]$ hdfs --daemon start namenode
[summer@hadoop104 ~]$ hdfs --daemon start namenode

也可以在core-site.xml里面适当调大上面的两个参数：

<!-- NN连接JN重试次数，默认是10次 -->
<property>
  <name>ipc.client.connect.max.retries</name>
  <value>20</value>
</property>

<!-- 重试时间间隔，默认1s -->
<property>
  <name>ipc.client.connect.retry.interval</name>
  <value>5000</value>
</property>

1.5 YARN-HA配置

1.5.1 YARN-HA工作机制

1）官方文档： hadoop.apache.org/docs/r3.1.3… 2）YARN-HA工作机制 在这里插入图片描述

1.5.2 配置YARN-HA集群

1）环境准备 （1）修改IP （2）修改主机名及主机名和IP地址的映射 （3）关闭防火墙 （4）ssh免密登录 （5）安装JDK，配置环境变量等 （6）配置Zookeeper集群 2）规划集群

hadoop102hadoop103hadoop104
ResourceManagerResourceManagerResourceManager
NodeManagerNodeManagerNodeManager
ZookeeperZookeeperZookeeper

3）核心问题 （1）如果当前active rm挂了，其他rm怎么将其他standby rm上位 核心原理跟HDFS一样，利用了zk的临时节点。 （2）当前rm上有很多的计算程序在等待运行，其他的rm怎么将这些程序接手过来接着跑 rm会将当前的所有计算程序的状态存储在zk中，其他rm上位后会去读取，然后接着跑。 4）具体配置 （1）yarn-site.xml

<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>

    <!-- 启用resourcemanager ha -->
    <property>
        <name>yarn.resourcemanager.ha.enabled</name>
        <value>true</value>
    </property>
 
    <!-- 声明两台resourcemanager的地址 -->
    <property>
        <name>yarn.resourcemanager.cluster-id</name>
        <value>cluster-yarn1</value>
    </property>

    <!--指定resourcemanager的逻辑列表-->
    <property>
        <name>yarn.resourcemanager.ha.rm-ids</name>
        <value>rm1,rm2,rm3</value>
    </property>
<!-- ========== rm1的配置 ========== -->
    <!-- 指定rm1的主机名 -->
    <property>
        <name>yarn.resourcemanager.hostname.rm1</name>
        <value>hadoop102</value>
    </property>

    <!-- 指定rm1的web端地址 -->
    <property>
        <name>yarn.resourcemanager.webapp.address.rm1</name>
        <value>hadoop102:8088</value>
    </property>

    <!-- 指定rm1的内部通信地址 -->
    <property>
        <name>yarn.resourcemanager.address.rm1</name>
        <value>hadoop102:8032</value>
    </property>

    <!-- 指定AM向rm1申请资源的地址 -->
    <property>
        <name>yarn.resourcemanager.scheduler.address.rm1</name>  
        <value>hadoop102:8030</value>
    </property>

    <!-- 指定供NM连接的地址 -->  
    <property>
    <name>yarn.resourcemanager.resource-tracker.address.rm1</name>
        <value>hadoop102:8031</value>
    </property>

<!-- ========== rm2的配置 ========== -->
    <!-- 指定rm2的主机名 -->
    <property>
        <name>yarn.resourcemanager.hostname.rm2</name>
        <value>hadoop103</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address.rm2</name>
        <value>hadoop103:8088</value>
    </property>
    <property>
        <name>yarn.resourcemanager.address.rm2</name>
        <value>hadoop103:8032</value>
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address.rm2</name>
        <value>hadoop103:8030</value>
    </property>

    <property>
<name>yarn.resourcemanager.resource-tracker.address.rm2</name>
        <value>hadoop103:8031</value>
    </property>

<!-- ========== rm3的配置 ========== -->
    <!-- 指定rm1的主机名 -->
    <property>
        <name>yarn.resourcemanager.hostname.rm3</name>
        <value>hadoop104</value>
    </property>
    <!-- 指定rm1的web端地址 -->
    <property>
        <name>yarn.resourcemanager.webapp.address.rm3</name>
        <value>hadoop104:8088</value>
    </property>
    <!-- 指定rm1的内部通信地址 -->
    <property>
        <name>yarn.resourcemanager.address.rm3</name>
        <value>hadoop104:8032</value>
    </property>
    <!-- 指定AM向rm1申请资源的地址 -->
    <property>
        <name>yarn.resourcemanager.scheduler.address.rm3</name>  
        <value>hadoop104:8030</value>
    </property>

    <!-- 指定供NM连接的地址 -->  
    <property>
    <name>yarn.resourcemanager.resource-tracker.address.rm3</name>
        <value>hadoop104:8031</value>
    </property>

    <!-- 指定zookeeper集群的地址 --> 
    <property>
        <name>yarn.resourcemanager.zk-address</name>
        <value>hadoop102:2181,hadoop103:2181,hadoop104:2181</value>
    </property>

    <!-- 启用自动恢复 --> 
    <property>
        <name>yarn.resourcemanager.recovery.enabled</name>
        <value>true</value>
    </property>
 
    <!-- 指定resourcemanager的状态信息存储在zookeeper集群 --> 
    <property>
        <name>yarn.resourcemanager.store.class</name>     <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
</property>

    <!-- 环境变量的继承 -->
    <property>
        <name>yarn.nodemanager.env-whitelist</name>
        <value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_MAPRED_HOME</value>
    </property>

</configuration>

（2）同步更新其他节点的配置信息，分发配置文件

[summer@hadoop102 etc]$ xsync hadoop/

4）启动YARN （1）在有ResourceManager的节点启动。 [summer@hadoop102 ~]start−yarn.sh（2）查看服务状态[summer@hadoop102 ] start-yarn.sh （2）查看服务状态 [summer@hadoop102 ~] yarn rmadmin -getServiceState rm1 （3）可以去zkCli.sh客户端查看ResourceManager选举锁节点内容。 [summer@hadoop102 ~]$ zkCli.sh [zk: localhost:2181(CONNECTED) 16] get -s /yarn-leader-election/cluster-yarn1/ActiveStandbyElectorLock

cluster-yarn1rm1 cZxid = 0x100000022 ctime = Tue Jul 14 17:06:44 CST 2020 mZxid = 0x100000022 mtime = Tue Jul 14 17:06:44 CST 2020 pZxid = 0x100000022 cversion = 0 dataVersion = 0 aclVersion = 0 ephemeralOwner = 0x30000da33080005 dataLength = 20 numChildren = 0 （4）web端查看hadoop102:8088和hadoop103:8088的YARN的状态 在这里插入图片描述 将整个ha搭建完成后,集群将形成以下模样

hadoop102hadoop103hadoop104
NameNodeNameNodeNameNode
JournalNodeJournalNodeJournalNode
DataNodeDataNodeDataNode
ZookeeperZookeeperZookeeper
ZKFCZKFCZKFC
ResourceManagerResourceManagerResourceManager
NodeManagerNodeManagerNodeManager
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