Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十）安装hadoop2.9.0搭建HA

如何搭建配置centos虚拟机请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（一）VMW安装四台CentOS，并实现本机与它们能交互，虚拟机内部实现可以上网。》

如何安装hadoop2.9.0请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二）安装hadoop2.9.0》

如何配置zookeeper3.4.12 请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（八）安装zookeeper-3.4.12》

安装hadoop的服务器：

192.168.0.120      master
192.168.0.121      slave1
192.168.0.122      slave2
192.168.0.123      slave3

前言：

配置高可用（HA）集群在实际生产中可保证集群安全稳定高效的运行，下面讲解一下HA中的各个节点的作用。

1，zookeeper的java进程：QuorumPeermain，各种分布式服务（如hdfs，hbase，resourcemanager）注册到zookeeper上去，即可启动2个以上的相同进程（其中一个状态为active，其他为standby），那么，其中active节点挂了，可通过zookeeper进行切换到standby，让他active，保证进程正常运行。

2，namenode：hdfs的主节点，保存hdfs正常运行的各种必要元数据（保存在edits和fsimage文件中），万一挂了，整个集群就挂了，所以要配置多namenode。

3，datanode：hdfs的数据节点，保存真实数据的节点。

4，journalnode：日志节点，将namenode上的edits文件分离出来，弄成一个集群，保证namenode不被挂。

5，zkfc：全称DFSZKFailoverController，监控并管理namenode的状态和切换。

6，Resourcemanager：管理并分配集群的资源，如为nodemanager分配计算资源等。

7，nodemanager：管理datanode，并随datanode的启动而启动。

其实，最好每个节点跑一个进程，奈何机器性能不足，跑不了那么多虚拟机，就将几个节点放在同一个节点上，只将重要的进程（namenode和resourcemanager）放在不同的节点。

搭建集群前的工作：

首先是系统环境：

如何安装hadoop2.9.0请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二）安装hadoop2.9.0》

然后搭建zookeeper3.4.12请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（八）安装zookeeper-3.4.12》

三，修改配置文件

先在master节点上修改配置文件，然后传到其他节点。

1）停掉hadoop服务并清除slaves上hadoop安装目录

master上：关闭hadoop服务，并清除dfs、logs、tmp目录

cd /opt/hadoop-2.9.0
sbin/stop-all.sh
rm -r dfs
rm -r logs
rm -r tmp

slaves上：关闭hadoop服务，清除hadoop安装目录

cd /opt/hadoop-2.9.0
sbin/stop-all.sh
rm -r *

2）etc/hadoop/hadoop-env.sh

cd /opt/hadoop-2.9.0/etc/hadoop
vi hadoop-env.sh
export JAVA_HOME = path_to_jdk  #添加配置

3）core-site.xml修改

cd /opt/hadoop-2.9.0/etc/hadoop
vi core-site.xml
<configuration>
    <!-- 指定hdfs的nameservice为ns1 -->
    <property>
        <name>fs.defaultFS</name>
       <!-- <value>hdfs://master:9000/</value>-->
        <value>hdfs://HA</value>
    </property>
    <!-- 指定hadoop临时目录 -->
    <property>
         <name>hadoop.tmp.dir</name>
         <value>/opt/hadoop-2.9.0/tmp</value>
         <description>这里的路径默认是NameNode、DataNode、JournalNode等存放数据的公共目录</description>
    </property>
    <property>
        <name>io.file.buffer.size</name>
        <value>131702</value>
    </property>
    <!-- 指定zookeeper地址 -->
    <property>
        <name>ha.zookeeper.quorum</name>
        <value>master:2181,slave1:2181,slave2:2181</value>
        <description>这里是ZooKeeper集群的地址和端口。注意，数量一定是奇数，且不少于三个节点</description>
    </property>
    <!-- 下面的配置可解决NameNode连接JournalNode超时异常问题-->
    <property>
        <name>ipc.client.connect.retry.interval</name>
        <value>10000</value>
        <description>Indicates the number of milliseconds a client will wait for before retrying to establish a server connection.
        </description>
    </property>
</configuration>

4）hdfs-site.xml修改

cd /opt/hadoop-2.9.0/etc/hadoop
vi hdfs-site.xml
<configuration>
    <!--指定hdfs的nameservice为HA，需要和core-site.xml中的保持一致 -->
    <property>
        <name>dfs.nameservices</name>
        <value>HA</value>
    </property>
    <!-- HA下面有两个NameNode，分别是nn1，nn2 -->
    <property>
        <name>dfs.ha.namenodes.HA</name>
        <value>nn1,nn2</value>
    </property>
    <!-- nn1的RPC通信地址 -->
    <property>
        <name>dfs.namenode.rpc-address.HA.nn1</name>
        <value>master:9000</value>
        <description>9000为HDFS 客户端接入地址（包括命令行与程序），有的使用8020</description>
    </property>
    <!-- nn1的http通信地址 -->
    <property>
        <name>dfs.namenode.http-address.HA.nn1</name>
        <value>master:50070</value>
        <description> namenode web的接入地址</description>
    </property>
    <!-- nn2的RPC通信地址 -->
    <property>
        <name>dfs.namenode.rpc-address.HA.nn2</name>
        <value>slave1:9000</value>
        <description>9000为HDFS 客户端接入地址（包括命令行与程序），有的使用8020</description>
    </property>
    <!-- nn2的http通信地址 -->
    <property>
        <name>dfs.namenode.http-address.HA.nn2</name>
        <value>slave1:50070</value>
        <description> namenode web的接入地址</description>
    </property>
    <!-- 指定NameNode的edits元数据在JournalNode上的存放位置 -->
    <property>
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://master:8485;slave1:8485;slave2:8485/HA</value>
        <description>指定 nn1 的两个NameNode共享edits文件目录时，使用的JournalNode集群信息。master\slave1主机中使用这个配置</description>
    </property>
    <!-- 指定JournalNode在本地磁盘存放数据的位置 -->
    <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/opt/hadoop-2.9.0/tmp/journal</value>
    </property>
    <!-- 开启NameNode失败自动切换 -->
    <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
    <!-- 配置失败自动切换实现方式 -->
    <property>
        <name>dfs.client.failover.proxy.provider.HA</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property>
    <!-- 配置隔离机制方法，多个机制用换行分割，即每个机制暂用一行-->
    <property>
        <name>dfs.ha.fencing.methods</name>
        <value>
        sshfence
        shell(/bin/true)
        </value>
    </property>
    <!-- 使用sshfence隔离机制时需要ssh免登陆 -->
    <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/home/spark/.ssh/id_rsa</value>
        <description>
        [spark@master hadoop]$ cd /home/spark/.ssh/
        [spark@master .ssh]$ ls
        authorized_keys  id_rsa.pub         id_rsa.pub.slave2  known_hosts
        id_rsa           id_rsa.pub.slave1  id_rsa.pub.slave3
        </description>
    </property>
    <!-- 配置sshfence隔离机制超时时间 -->
    <property>
        <name>dfs.ha.fencing.ssh.connect-timeout</name>
        <value>30000</value>
    </property>
        <!--
    <property>
        <name>dfs.namenode.secondary.http-address</name>
        <value>master:9001</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/opt/hadoop-2.9.0/dfs/name</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>/opt/hadoop-2.9.0/dfs/data</value>
    </property>
    -->
    <property>
        <name>dfs.replication</name>
        <value>3</value>
        <description>指定DataNode存储block的副本数量。默认值是3个，我们现在有4个DataNode，该值不大于4即可</description>
    </property>
    <property>
      <name>dfs.blocksize</name>
      <value>134217728</value>
      <description>
          The default block size for new files, in bytes.
          You can use the following suffix (case insensitive):
          k(kilo), m(mega), g(giga), t(tera), p(peta), e(exa) to specify the size (such as 128k, 512m, 1g, etc.),
          Or provide complete size in bytes (such as 134217728 for 128 MB).
          注：1.X及以前版本默认是64M，而且配置项名为dfs.block.size
      </description>
    </property>
    <property>
         <name>dfs.permissions.enabled</name>
         <value>false</value>
         <description>注：如果还有权限问题，请执行下“/opt/hadoop-2.9.0/bin/hdfs dfs -chmod -R 777 /”命令</description>
    </property>
    <property>
        <name>dfs.webhdfs.enabled</name>
        <value>true</value>
    </property>
</configuration>

注意：这里可能出现问题：The ratio of reported blocks 1.0000 has reached the threshold 0.9990. Safe mode will be turned off automatically in 27 seconds.

原因：

hadoop默认情况下在安全模式运行。可以通过下面hadoop dfsadmin -safemode 参数,查看相关的状态和设置安全模块是否启用。

enter 进入安全模式
leave 强制NameNode离开安全模式
get 返回安全模式是否开启的信息

wait 等待，一直到安全模式结束。

解决办法有二种：

《1》可能运行命令hadoop dfsadmin -safemode leave 离开安全模式，但是每次都需要手动去设置。

《2》通过配置dfs.safemode.threshold.pct的参数。默认情况下是0.9990f。这个配置可以在hdfs-defalut.xml中找到。我们可以把这个参数配置为0，永久关闭安全模式。

在hadoop中的hdfs-site.xml添加如下配置：

        <property>
                <name>dfs.safemode.threshold.pct</name>
                <value>0f</value>
                <description>
                        Specifies the percentage of blocks that should satisfy
                        the minimal replication requirement defined by dfs.replication.min.
                        Values less than or equal to 0 mean not to wait for any particular
                        percentage of blocks before exiting safemode.
                        Values greater than 1 will make safe mode permanent.
                </description>
        </property>

重启NameNode就可以了。

5）mapred-site.xml 修改

cd /opt/hadoop-2.9.0/etc/hadoop
vi mapred-site.xml
<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.jobtracker.http.address</name>
        <value>master:50030</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 slave1:50030 slave2:50030 slave3:50030 ，拷贝过去后请做相应修改</description>
    </property>
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>master:10020</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 slave1:10020、 slave2:10020、 slave3:10020 ，拷贝过去后请做相应修改</description>
    </property>
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>master:19888</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 slave1:19888、 slave2:19888、 slave3:19888 ，拷贝过去后请做相应修改</description>
    </property>
    <property>
        <name>mapred.job.tracker</name>
        <value>http://master:9001</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 http://slave1:9001 、 http://slave2:9001 、 http://slave3:9001 ，拷贝过去后请做相应修改</description>
    </property>
</configuration>

6）yarn-site.xml 修改

cd /opt/hadoop-2.9.0/etc/hadoop
vi yarn-site.xml
<configuration>
    <!--
    <property>
        <name>yarn.resourcemanager.address</name>
        <value>master:8032</value>
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>master:8030</value>
    </property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.address</name>
        <value>master:8035</value>
    </property>
    <property>
        <name>yarn.resourcemanager.admin.address</name>
        <value>master:8033</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address</name>
        <value>master:8088</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>master</value>
    </property>
    <property>
        <name>yarn.nodemanager.resource.memory-mb</name>
        <value>2048</value>
    </property>
    <property>
        <name>yarn.nodemanager.pmem-check-enabled</name>
        <value>false</value>
    </property>
    <property>
        <name>yarn.nodemanager.vmem-check-enabled</name>
        <value>false</value>
        <description>Whether virtual memory limits will be enforced for containers</description>
    </property>
    <property>
        <name>yarn.nodemanager.vmem-pmem-ratio</name>
        <value>4</value>
        <description>Ratio between virtual memory to physical memory when setting memory limits for containers</description>
    </property>
    -->
    <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>
    <!-- 开启RM高可用 -->
    <property>
        <name>yarn.resourcemanager.ha.enabled</name>
        <value>true</value>
    </property>
    <!-- 指定RM的cluster id -->
    <property>
        <name>yarn.resourcemanager.cluster-id</name>
        <value>yarn-cluster</value>
    </property>
    <!-- 分别指定RM的地址 -->
    <property>
        <name>yarn.resourcemanager.ha.rm-ids</name>
        <value>rm1,rm2</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname.rm1</name>
        <value>master</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname.rm2</name>
        <value>slave1</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address.rm1</name>
        <value>master:8088</value>
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address.rm2</name>
        <value>slave1:8088</value>
    </property>
    <!-- 指定zk集群地址 -->
    <property>
        <name>yarn.resourcemanager.zk-address</name>
        <value>master:2181,slave1:2181,slave2:2181</value>
    </property>
    <property>
        <name>yarn.resourcemanager.recovery.enabled</name>
        <value>true</value>
    </property>
    <property>
        <name>yarn.resourcemanager.store.class</name>
        <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
        <description>RM的数据默认存放在ZK上的/rmstore中，可通过yarn.resourcemanager.zk-state-store.parent-path 设定</description>
    </property>
    <property>
        <name>yarn.log-aggregation-enable</name>  
        <value>true</value>
        <description>开启日志收集，这样会将每台执行任务的机上产生的本地日志文件集中拷贝到HDFS的某个地方，这样就可以在任何一台集群中的机器上集中查看作业日志了</description>
    </property>
    <property>
        <name>yarn.log.server.url</name>
        <value>http://master:19888/jobhistory/logs</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如http://slave1:19888/jobhistory/logs、http://slave2:19888/jobhistory/logs、http://slave3:19888/jobhistory/logs，拷贝过去后请做相应修改</description>
    </property>
</configuration>

7） slaves 修改

cd /opt/hadoop-2.9.0/etc/hadoop
vi slaves
[spark@master hadoop]$ more slaves
#localhost
slave1
slave2
slave3

8）配置好后，将配置文件传到其他节点上

scp -r /opt/hadoop-2.9.0 spark@slave1:/opt/
scp -r /opt/hadoop-2.9.0 spark@slave2:/opt/
scp -r /opt/hadoop-2.9.0 spark@slave3:/opt/

分别在slave1,slave2,slave3上修改配置：

修改yarn-site.xml

cd /opt/hadoop-2.9.0/etc/hadoop
vi yarn-site.xml

修改内容

    <property>
        <name>yarn.log.server.url</name>
        <value>http://master:19888/jobhistory/logs</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如http://slave1:19888/jobhistory/logs、http://slave2:19888/jobhistory/logs、http://slave3:19888/jobhistory/logs，拷
贝过去后请做相应修改</description>
    </property>

修改mapred-site.xml

cd /opt/hadoop-2.9.0
vi mapred-site.xml

修改内容

<configuration>
    <property>
        <name>mapreduce.jobtracker.http.address</name>
        <value>master:50030</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 slave1:50030 slave2:50030 slave3:50030 ，拷贝过去后请做相应修改</description>
    </property>
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>master:10020</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 slave1:10020、 slave2:10020、 slave3:10020 ，拷贝过去后请做相应修改</description>
    </property>
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>master:19888</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 slave1:19888、 slave2:19888、 slave3:19888 ，拷贝过去后请做相应修改</description>
    </property>
    <property>
        <name>mapred.job.tracker</name>
        <value>http://master:9001</value>
        <description>注：每台机器上配置都不一样，需要修改成对应的主机名，端口不用修改，比如 http://slave1:9001 、 http://slave2:9001 、 http://slave3:9001 ，拷贝过去后请做相应修改</description>
    </property>
</configuration>

初始化集群，严格按照一下步骤！

1，在3台节点（master，slave1，slave2）启动zookeeper集群，jps查看有没有启动。

cd /opt/zookeeper-3.4.12/bin
./zkServer.sh start
./zkServer.sh status

master

[spark@master hadoop-2.9.0]$ cd /opt/zookeeper-3.4.12/bin
[spark@master bin]$ ./zkServer.sh start
ZooKeeper JMX enabled by default
Using config: /opt/zookeeper-3.4.12/bin/../conf/zoo.cfg
Starting zookeeper ... STARTED
[spark@master bin]$ 
[spark@master bin]$ ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /opt/zookeeper-3.4.12/bin/../conf/zoo.cfg
Mode: follower

slave1

[spark@slave1 hadoop]$ cd /opt/zookeeper-3.4.12/bin
[spark@slave1 bin]$ ./zkServer.sh start
ZooKeeper JMX enabled by default
Using config: /opt/zookeeper-3.4.12/bin/../conf/zoo.cfg
./zkServer.sh status
Starting zookeeper ... STARTED
[spark@slave1 bin]$ 
[spark@slave1 bin]$ ./zkServer.sh status 
ZooKeeper JMX enabled by default
Using config: /opt/zookeeper-3.4.12/bin/../conf/zoo.cfg
Mode: leader

slave2

[spark@slave2 hadoop]$ cd /opt/zookeeper-3.4.12/bin
[spark@slave2 bin]$ ./zkServer.sh start
ZooKeeper JMX enabled by default
Using config: /opt/zookeeper-3.4.12/bin/../conf/zoo.cfg
Starting zookeeper ... ./zkServer.sh status
STARTED
[spark@slave2 bin]$ 
[spark@slave2 bin]$ ./zkServer.sh status
ZooKeeper JMX enabled by default
Using config: /opt/zookeeper-3.4.12/bin/../conf/zoo.cfg
Mode: follower

2，在3台节点（master，slave1，slave2）启动journalnode集群，jps查看有没有启动。

cd /opt/hadoop-2.9.0
sbin/hadoop-daemon.sh start journalnode

master

[spark@master hadoop]$ cd /opt/hadoop-2.9.0
[spark@master hadoop-2.9.0]$ sbin/hadoop-deamon.sh start journalnode
starting journalnode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-journalnode-master.out
[spark@master hadoop-2.9.0]$ jps
1808 Jps
1757 JournalNode
1662 QuorumPeerMain
[spark@master hadoop-2.9.0]$

slave1

[spark@slave1 bin]$ cd /opt/hadoop-2.9.0
[spark@slave1 hadoop-2.9.0]$ sbin/hadoop-daemon.sh start journalnode
starting journalnode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-journalnode-slave1.out
[spark@slave1 hadoop-2.9.0]$ jps
2003 JournalNode
2054 Jps
1931 QuorumPeerMain
[spark@slave1 hadoop-2.9.0]$

slave2

[spark@slave2 bin]$ cd /opt/hadoop-2.9.0
[spark@slave2 hadoop-2.9.0]$ sbin/hadoop-daemon.sh start journalnode
starting journalnode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-journalnode-slave2.out
[spark@slave2 hadoop-2.9.0]$ jps
1978 QuorumPeerMain
2044 JournalNode
2095 Jps
[spark@slave2 hadoop-2.9.0]$

3，在 master 上格式化hdfs

cd /opt/hadoop-2.9.0
bin/hdfs namenode -format   #等同 bin/hadoop namenode -format

[spark@master HA]$ cd /opt/hadoop-2.9.0
[spark@master hadoop-2.9.0]$ bin/hdfs namenode -format 
18/07/01 22:26:11 INFO namenode.NameNode: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG:   host = master/192.168.0.120
STARTUP_MSG:   args = [-format]
STARTUP_MSG:   version = 2.9.0
STARTUP_MSG:   classpath = /opt/hadoop-2.9.0/etc/hadoop:/opt/hadoop-2.9.0/share/hadoop/common/lib/nimbus-jose-jwt-3.9.jar:/opt/hadoop-2.9.0/share/hadoop/common/li....
STARTUP_MSG:   build = https://git-wip-us.apache.org/repos/asf/hadoop.git -r 756ebc8394e473ac25feac05fa493f6d612e6c50; compiled by 'arsuresh' on 2017-11-13T23:15Z
STARTUP_MSG:   java = 1.8.0_171
************************************************************/
18/07/01 22:26:11 INFO namenode.NameNode: registered UNIX signal handlers for [TERM, HUP, INT]
18/07/01 22:26:11 INFO namenode.NameNode: createNameNode [-format]
Formatting using clusterid: CID-01f8ecdf-532b-4415-807e-90b4aa179e29
18/07/01 22:26:12 INFO namenode.FSEditLog: Edit logging is async:true
18/07/01 22:26:12 INFO namenode.FSNamesystem: KeyProvider: null
18/07/01 22:26:12 INFO namenode.FSNamesystem: fsLock is fair: true
18/07/01 22:26:12 INFO namenode.FSNamesystem: Detailed lock hold time metrics enabled: false
18/07/01 22:26:12 INFO namenode.FSNamesystem: fsOwner             = spark (auth:SIMPLE)
18/07/01 22:26:12 INFO namenode.FSNamesystem: supergroup          = supergroup
18/07/01 22:26:12 INFO namenode.FSNamesystem: isPermissionEnabled = false
18/07/01 22:26:12 INFO namenode.FSNamesystem: Determined nameservice ID: HA
18/07/01 22:26:12 INFO namenode.FSNamesystem: HA Enabled: true
18/07/01 22:26:12 INFO common.Util: dfs.datanode.fileio.profiling.sampling.percentage set to 0. Disabling file IO profiling
18/07/01 22:26:12 INFO blockmanagement.DatanodeManager: dfs.block.invalidate.limit: configured=1000, counted=60, effected=1000
18/07/01 22:26:12 INFO blockmanagement.DatanodeManager: dfs.namenode.datanode.registration.ip-hostname-check=true
18/07/01 22:26:12 INFO blockmanagement.BlockManager: dfs.namenode.startup.delay.block.deletion.sec is set to 000:00:00:00.000
18/07/01 22:26:12 INFO blockmanagement.BlockManager: The block deletion will start around 2018 Jul 01 22:26:12
18/07/01 22:26:12 INFO util.GSet: Computing capacity for map BlocksMap
18/07/01 22:26:12 INFO util.GSet: VM type       = 64-bit
18/07/01 22:26:12 INFO util.GSet: 2.0% max memory 966.7 MB = 19.3 MB
18/07/01 22:26:12 INFO util.GSet: capacity      = 2^21 = 2097152 entries
18/07/01 22:26:12 INFO blockmanagement.BlockManager: dfs.block.access.token.enable=false
18/07/01 22:26:12 WARN conf.Configuration: No unit for dfs.namenode.safemode.extension(30000) assuming MILLISECONDS
18/07/01 22:26:12 INFO blockmanagement.BlockManagerSafeMode: dfs.namenode.safemode.threshold-pct = 0.9990000128746033
18/07/01 22:26:12 INFO blockmanagement.BlockManagerSafeMode: dfs.namenode.safemode.min.datanodes = 0
18/07/01 22:26:12 INFO blockmanagement.BlockManagerSafeMode: dfs.namenode.safemode.extension = 30000
18/07/01 22:26:12 INFO blockmanagement.BlockManager: defaultReplication         = 3
18/07/01 22:26:12 INFO blockmanagement.BlockManager: maxReplication             = 512
18/07/01 22:26:12 INFO blockmanagement.BlockManager: minReplication             = 1
18/07/01 22:26:12 INFO blockmanagement.BlockManager: maxReplicationStreams      = 2
18/07/01 22:26:12 INFO blockmanagement.BlockManager: replicationRecheckInterval = 3000
18/07/01 22:26:12 INFO blockmanagement.BlockManager: encryptDataTransfer        = false
18/07/01 22:26:12 INFO blockmanagement.BlockManager: maxNumBlocksToLog          = 1000
18/07/01 22:26:12 INFO namenode.FSNamesystem: Append Enabled: true
18/07/01 22:26:12 INFO util.GSet: Computing capacity for map INodeMap
18/07/01 22:26:12 INFO util.GSet: VM type       = 64-bit
18/07/01 22:26:12 INFO util.GSet: 1.0% max memory 966.7 MB = 9.7 MB
18/07/01 22:26:12 INFO util.GSet: capacity      = 2^20 = 1048576 entries
18/07/01 22:26:12 INFO namenode.FSDirectory: ACLs enabled? false
18/07/01 22:26:12 INFO namenode.FSDirectory: XAttrs enabled? true
18/07/01 22:26:12 INFO namenode.NameNode: Caching file names occurring more than 10 times
18/07/01 22:26:12 INFO snapshot.SnapshotManager: Loaded config captureOpenFiles: falseskipCaptureAccessTimeOnlyChange: false
18/07/01 22:26:12 INFO util.GSet: Computing capacity for map cachedBlocks
18/07/01 22:26:12 INFO util.GSet: VM type       = 64-bit
18/07/01 22:26:12 INFO util.GSet: 0.25% max memory 966.7 MB = 2.4 MB
18/07/01 22:26:12 INFO util.GSet: capacity      = 2^18 = 262144 entries
18/07/01 22:26:12 INFO metrics.TopMetrics: NNTop conf: dfs.namenode.top.window.num.buckets = 10
18/07/01 22:26:12 INFO metrics.TopMetrics: NNTop conf: dfs.namenode.top.num.users = 10
18/07/01 22:26:12 INFO metrics.TopMetrics: NNTop conf: dfs.namenode.top.windows.minutes = 1,5,25
18/07/01 22:26:12 INFO namenode.FSNamesystem: Retry cache on namenode is enabled
18/07/01 22:26:12 INFO namenode.FSNamesystem: Retry cache will use 0.03 of total heap and retry cache entry expiry time is 600000 millis
18/07/01 22:26:12 INFO util.GSet: Computing capacity for map NameNodeRetryCache
18/07/01 22:26:12 INFO util.GSet: VM type       = 64-bit
18/07/01 22:26:12 INFO util.GSet: 0.029999999329447746% max memory 966.7 MB = 297.0 KB
18/07/01 22:26:12 INFO util.GSet: capacity      = 2^15 = 32768 entries
Re-format filesystem in Storage Directory /opt/hadoop-2.9.0/tmp/dfs/name ? (Y or N) y
Re-format filesystem in QJM to [192.168.0.120:8485, 192.168.0.121:8485, 192.168.0.122:8485] ? (Y or N) y
18/07/01 22:26:19 INFO namenode.FSImage: Allocated new BlockPoolId: BP-4950294-192.168.0.120-1530455179314
18/07/01 22:26:19 INFO common.Storage: Will remove files: [/opt/hadoop-2.9.0/tmp/dfs/name/current/VERSION, /opt/hadoop-2.9.0/tmp/dfs/name/current/seen_txid]
18/07/01 22:26:19 INFO common.Storage: Storage directory /opt/hadoop-2.9.0/tmp/dfs/name has been successfully formatted.
18/07/01 22:26:19 INFO namenode.FSImageFormatProtobuf: Saving image file /opt/hadoop-2.9.0/tmp/dfs/name/current/fsimage.ckpt_0000000000000000000 using no compression
18/07/01 22:26:19 INFO namenode.FSImageFormatProtobuf: Image file /opt/hadoop-2.9.0/tmp/dfs/name/current/fsimage.ckpt_0000000000000000000 of size 322 bytes saved in 0 seconds.
18/07/01 22:26:19 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
18/07/01 22:26:19 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at master/192.168.0.120
************************************************************/

4，配置 slave1 上的namenode

先在 master 上启动namenode

cd /opt/hadoop-2.9.0
sbin/hadoop-daemon.sh start namenode

[spark@master hadoop-2.9.0]$ cd /opt/hadoop-2.9.0
[spark@master hadoop-2.9.0]$ sbin/hadoop-daemon.sh start namenode
starting namenode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-namenode-master.out
[spark@master hadoop-2.9.0]$ jps
2016 Jps
1939 NameNode
1757 JournalNode
1662 QuorumPeerMain
[spark@master hadoop-2.9.0]$

再在 slave1 上执行命令：

cd /opt/hadoop-2.9.0
bin/hdfs namenode -bootstrapStandby

[spark@slave1 HA]$ cd /opt/hadoop-2.9.0
[spark@slave1 hadoop-2.9.0]$ bin/hdfs namenode -bootstrapStandby
18/07/01 22:28:58 INFO namenode.NameNode: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG:   host = slave1/192.168.0.121
STARTUP_MSG:   args = [-bootstrapStandby]
STARTUP_MSG:   version = 2.9.0
STARTUP_MSG:   classpath = /opt/hadoop-2.9.0/etc/hadoop:/opt/hadoop-2.9.0/share/hadoop/common/lib...
STARTUP_MSG:   build = https://git-wip-us.apache.org/repos/asf/hadoop.git -r 756ebc8394e473ac25feac05fa493f6d612e6c50; compiled by 'arsuresh' on 2017-11-13T23:15Z
STARTUP_MSG:   java = 1.8.0_171
************************************************************/
18/07/01 22:28:58 INFO namenode.NameNode: registered UNIX signal handlers for [TERM, HUP, INT]
18/07/01 22:28:58 INFO namenode.NameNode: createNameNode [-bootstrapStandby]
=====================================================
About to bootstrap Standby ID nn2 from:
           Nameservice ID: HA
        Other Namenode ID: nn1
  Other NN's HTTP address: http://master:50070
  Other NN's IPC  address: master/192.168.0.120:9000
             Namespace ID: 1875840257
            Block pool ID: BP-4950294-192.168.0.120-1530455179314
               Cluster ID: CID-01f8ecdf-532b-4415-807e-90b4aa179e29
           Layout version: -63
       isUpgradeFinalized: true
=====================================================
18/07/01 22:28:59 INFO common.Storage: Storage directory /opt/hadoop-2.9.0/tmp/dfs/name has been successfully formatted.
18/07/01 22:28:59 INFO namenode.FSEditLog: Edit logging is async:true
18/07/01 22:29:00 INFO namenode.TransferFsImage: Opening connection to http://master:50070/imagetransfer?getimage=1&txid=0&storageInfo=-63:1875840257:1530455179314:CID-01f8ecdf-532b-4415-807e-90b4aa179e29&bootstrapstandby=true
18/07/01 22:29:00 INFO namenode.TransferFsImage: Image Transfer timeout configured to 60000 milliseconds
18/07/01 22:29:00 INFO namenode.TransferFsImage: Combined time for fsimage download and fsync to all disks took 0.02s. The fsimage download took 0.01s at 0.00 KB/s. Synchronous (fsync) write to disk of /opt/hadoop-2.9.0/tmp/dfs/name/current/fsimage.ckpt_0000000000000000000 took 0.00s.
18/07/01 22:29:00 INFO namenode.TransferFsImage: Downloaded file fsimage.ckpt_0000000000000000000 size 322 bytes.
18/07/01 22:29:00 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at slave1/192.168.0.121
************************************************************/

5，格式化ZKFC（在 master 上执行一次即可）

cd /opt/hadoop-2.9.0
bin/hdfs zkfc -formatZK

[spark@master hadoop-2.9.0]$ cd /opt/hadoop-2.9.0
[spark@master hadoop-2.9.0]$ bin/hdfs zkfc -formatZK
18/07/01 22:31:25 INFO tools.DFSZKFailoverController: STARTUP_MSG: 
/************************************************************
STARTUP_MSG: Starting DFSZKFailoverController
STARTUP_MSG:   host = master/192.168.0.120
STARTUP_MSG:   args = [-formatZK]
STARTUP_MSG:   version = 2.9.0
STARTUP_MSG:   classpath = /opt/hadoop-2.9.0/etc/hadoop:/opt/hadoop-2.9.0/share/hadoop/common/lib/nimbus-jose-jwt-3.9.jar:/opt/hadoop-2.9.0/share/hadoop/common/lib/java-xmlbuilder-0.4.jar....
STARTUP_MSG:   build = https://git-wip-us.apache.org/repos/asf/hadoop.git -r 756ebc8394e473ac25feac05fa493f6d612e6c50; compiled by 'arsuresh' on 2017-11-13T23:15Z
STARTUP_MSG:   java = 1.8.0_171
************************************************************/
18/07/01 22:31:25 INFO tools.DFSZKFailoverController: registered UNIX signal handlers for [TERM, HUP, INT]
18/07/01 22:31:25 INFO tools.DFSZKFailoverController: Failover controller configured for NameNode NameNode at master/192.168.0.120:9000
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:zookeeper.version=3.4.6-1569965, built on 02/20/2014 09:09 GMT
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:host.name=master
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:java.version=1.8.0_171
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:java.vendor=Oracle Corporation
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:java.home=/opt/jdk1.8.0_171/jre
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:java.class.path=/opt/hadoop-2.9.0/etc/hadoop:/opt/hadoop-2.9.0/share/hadoop/common/lib/nimbu.....
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:java.library.path=/opt/hadoop-2.9.0/lib/native
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:java.io.tmpdir=/tmp
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:java.compiler=<NA>
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:os.name=Linux
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:os.arch=amd64
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:os.version=3.10.0-862.el7.x86_64
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:user.name=spark
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:user.home=/home/spark
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Client environment:user.dir=/opt/hadoop-2.9.0
18/07/01 22:31:25 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=master:2181,slave1:2181,slave2:2181 sessionTimeout=5000 watcher=org.apache.hadoop.ha.ActiveStandbyElector$WatcherWithClientRef@6ca8564a
18/07/01 22:31:26 INFO zookeeper.ClientCnxn: Opening socket connection to server master/192.168.0.120:2181. Will not attempt to authenticate using SASL (unknown error)
18/07/01 22:31:26 INFO zookeeper.ClientCnxn: Socket connection established to master/192.168.0.120:2181, initiating session
18/07/01 22:31:26 INFO zookeeper.ClientCnxn: Session establishment complete on server master/192.168.0.120:2181, sessionid = 0x100006501250000, negotiated timeout = 5000
18/07/01 22:31:26 INFO ha.ActiveStandbyElector: Successfully created /hadoop-ha/HA in ZK.
18/07/01 22:31:26 INFO zookeeper.ZooKeeper: Session: 0x100006501250000 closed
18/07/01 22:31:26 WARN ha.ActiveStandbyElector: Ignoring stale result from old client with sessionId 0x100006501250000
18/07/01 22:31:26 INFO zookeeper.ClientCnxn: EventThread shut down
18/07/01 22:31:26 INFO tools.DFSZKFailoverController: SHUTDOWN_MSG: 
/************************************************************

现在可以启动集群啦

1，首先启动zookeeper（在master,slave1,slave2）

cd /opt/zookeeper-3.4.12/bin
./zkServer.sh start

2，在master上启动hdfs和yarn

cd /opt/hadoop-2.9.0

sbin/start-dfs.sh
sbin/start-yarn.sh
[spark@master sbin]$ cd /opt/hadoop-2.9.0
[spark@master hadoop-2.9.0]$ sbin/start-dfs.sh
sbin/start-yarn.shStarting namenodes on [master slave1]
slave1: starting namenode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-namenode-slave1.out
master: namenode running as process 1939. Stop it first.
slave3: starting datanode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-datanode-slave3.out
slave2: starting datanode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-datanode-slave2.out
slave1: starting datanode, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-datanode-slave1.out
Starting journal nodes [master slave1 slave2]
slave2: journalnode running as process 2044. Stop it first. # 前边我们已经启动过了，是手动启动的。
master: journalnode running as process 1757. Stop it first.    # 前边我们已经启动过了，是手动启动的。
slave1: journalnode running as process 2003. Stop it first.    # 前边我们已经启动过了，是手动启动的。
Starting ZK Failover Controllers on NN hosts [master slave1]
master: starting zkfc, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-zkfc-master.out
slave1: starting zkfc, logging to /opt/hadoop-2.9.0/logs/hadoop-spark-zkfc-slave1.out
[spark@master hadoop-2.9.0]$ sbin/start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /opt/hadoop-2.9.0/logs/yarn-spark-resourcemanager-master.out
slave1: starting nodemanager, logging to /opt/hadoop-2.9.0/logs/yarn-spark-nodemanager-slave1.out
slave2: starting nodemanager, logging to /opt/hadoop-2.9.0/logs/yarn-spark-nodemanager-slave2.out
slave3: starting nodemanager, logging to /opt/hadoop-2.9.0/logs/yarn-spark-nodemanager-slave3.out
[spark@master hadoop-2.9.0]$ jps
2466 DFSZKFailoverController
1939 NameNode
2567 ResourceManager
2839 Jps
1757 JournalNode
1662 QuorumPeerMain
[spark@master hadoop-2.9.0]$

3，slave1 上的resourcemanager需要手动启动

cd /opt/hadoop-2.9.0
sbin/yarn-deamon.sh start resourcemanager

[spark@slave1 hadoop-2.9.0]$ jps
2003 JournalNode
2292 DataNode
2501 NodeManager
2613 Jps
2424 DFSZKFailoverController
1931 QuorumPeerMain
2191 NameNode
[spark@slave1 sbin]$ cd /opt/hadoop-2.9.0
[spark@slave1 hadoop-2.9.0]$ cd sbin/
[spark@slave1 sbin]$ ls
distribute-exclude.sh hadoop-daemons.sh httpfs.sh refresh-namenodes.sh start-all.sh start-dfs.sh start-yarn.sh stop-balancer.sh stop-secure-dns.sh yarn-daemon.sh
FederationStateStore hdfs-config.cmd kms.sh slaves.sh start-balancer.sh start-secure-dns.sh stop-all.cmd stop-dfs.cmd stop-yarn.cmd yarn-daemons.sh
hadoop-daemon.sh hdfs-config.sh mr-jobhistory-daemon.sh start-all.cmd start-dfs.cmd start-yarn.cmd stop-all.sh stop-dfs.sh stop-yarn.sh
[spark@slave1 sbin]$ ./yarn-daemon.sh start resourcemanager
starting resourcemanager, logging to /opt/hadoop-2.9.0/logs/yarn-spark-resourcemanager-slave1.out
[spark@slave1 sbin]$ jps
2689 ResourceManager
2003 JournalNode
2292 DataNode
2740 Jps
2501 NodeManager
2424 DFSZKFailoverController
1931 QuorumPeerMain
2191 NameNode
[spark@slave1 sbin]$

此时slave2

[spark@slave2 HA]$ jps
2208 DataNode
2470 Jps
2345 NodeManager
1978 QuorumPeerMain
2044 JournalNode
[spark@slave2 HA]$

此时slave3

[spark@slave3 hadoop-2.9.0]$ jps
2247 Jps
2123 NodeManager
2013 DataNode
[spark@slave3 hadoop-2.9.0]$

至此，高可用hadoop配置完毕，可访问浏览器：

http://192.168.0.120:50070 //namenode active
http://192.168.0.121:50070 //namenode standby
http://192.168.0.120:8088 //resourcemanager active
http://192.168.0.121:8088 //resourcemanager standby

此时 http://192.168.0.120:50070/可以访问（显示为： 'master:9000' (active)）,http://192.168.0.121:50070/可以访问（显示为： 'slave1:9000' (standby)）

此时 http://192.168.0.121:8088/ 不可以访问，而http://192.168.0.120:8088/ 可以访问。

关闭的时候按相反的顺序关闭

高可用验证

1，验证hdfs

首先kill掉 master 上active的namenode
kill -9 <pid of NN>

[spark@master hadoop-2.9.0]$ jps
2466 DFSZKFailoverController
1939 NameNode
2567 ResourceManager
1757 JournalNode
1662 QuorumPeerMain
2910 Jps
[spark@master hadoop-2.9.0]$ kill -9 1939
[spark@master hadoop-2.9.0]$ jps
2466 DFSZKFailoverController
2567 ResourceManager
2920 Jps
1757 JournalNode
1662 QuorumPeerMain
[spark@master hadoop-2.9.0]$

然后查看 slave1 上状态为standby的namenode状态变成active。

此时 slave1 jps

[spark@slave1 sbin]$ jps
2689 ResourceManager
2881 Jps
2003 JournalNode
2292 DataNode
2501 NodeManager
2424 DFSZKFailoverController
1931 QuorumPeerMain
2191 NameNode
[spark@slave1 sbin]$

此时 http://192.168.0.120:50070/ 不可以访问,http://192.168.0.121:50070/可以访问（显示为： 'slave1:9000' (active)）
此时 http://192.168.0.121:8088/ 不可以访问，而http://192.168.0.120:8088/ 可以访问。

2，验证yarn

同上，kill掉active状态的rm后，standby状态下的rm即变成active，继续工作。

[spark@master hadoop-2.9.0]$ jps
2466 DFSZKFailoverController
2948 Jps
2567 ResourceManager
1757 JournalNode
1662 QuorumPeerMain
[spark@master hadoop-2.9.0]$ kill -9 2567
[spark@master hadoop-2.9.0]$ jps
2466 DFSZKFailoverController
1757 JournalNode
1662 QuorumPeerMain
2958 Jps

此时 http://192.168.0.121:8088/ 可以访问，而http://192.168.0.120:8088/ 不可以访问。

http://192.168.0.120:50070/dfshealth.html#tab-overview

http://192.168.0.121:50070/dfshealth.html#tab-overview

http://192.168.0.120:8088/cluster/apps/RUNNING

http://192.168.0.121:8088/cluster/apps/ACCEPTED

参考《https://www.cnblogs.com/jiangzhengjun/p/6347916.html》

《https://blog.csdn.net/qq_32166627/article/details/51553394》

posted @ 2018-07-01 17:19 cctext 阅读(1704) 评论(0) 编辑收藏举报

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yy

基础才是编程人员应该深入研究的问题，警告自己问题解决不了时，多从运行原理底层研究后再考虑方案。