大数据技术 YARN HA配置-职坐标

大数据技术 YARN HA配置

沉沙 2018-10-11 来源：阅读 1670 评论 0

摘要：本篇教程探讨了大数据技术 YARN HA配置，希望阅读本篇文章以后大家有所收获，帮助大家对大数据技术的理解更加深入。

本篇教程探讨了大数据技术 YARN HA配置，希望阅读本篇文章以后大家有所收获，帮助大家对大数据技术的理解更加深入。

ResourceManager (RM)负责跟踪集群中的资源，以及调度应用程序(例如，MapReduce作业)。在Hadoop 2.4之前，集群中只有一个ResourceManager，当其中一个宕机时，将影响整个集群。高可用性特性增加了冗余的形式，即一个主动/备用的ResourceManager对，以便可以进行故障转移。
YARN HA的架构如下图所示：

本例中，各节点的角色分配如下表所示：

节点
角色

centos01
ResourceManager NodeManager

centos02
ResourceManager NodeManager

centos03
NodeManager

下面将逐步讲解YARN HA的配置步骤。
7.1 yarn-site.xm文件配置
（1）修改yarn-site.xm文件，加入以下内容：


      yarn.resourcemanager.ha.enabled
      true


      yarn.resourcemanager.cluster-id
      cluster1


      yarn.resourcemanager.ha.rm-ids
      rm1,rm2


      yarn.resourcemanager.hostname.rm1
      centos01


      yarn.resourcemanager.hostname.rm2
      centos02


      yarn.resourcemanager.webapp.address.rm1
      centos01:8088


      yarn.resourcemanager.webapp.address.rm2
      centos02:8088


      yarn.resourcemanager.zk-address
      centos01:2181,centos02:2181,centos03:2181


      yarn.resourcemanager.recovery.enabled
      true


      yarn.resourcemanager.store.class
      org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore

上述配置参数解析：
yarn.resourcemanager.ha.enabled：开启RM HA功能。
yarn.resourcemanager.cluster-id：标识集群中的RM。如果设置该选项，需要确保所有的RMs在配置中都有自己的id。
yarn.resourcemanager.ha.rm-ids：RMs的逻辑id列表。可以自定义，此处设置为“rm1，rm2”。后面的配置将引用该id。
yarn.resourcemanager.hostname.rm1：指定RM对应的主机名。另外，可以设置RM的每个服务地址。
yarn.resourcemanager.webapp.address.rm1：指定RM的Web端访问地址。
yarn.resourcemanager.zk-address：指定集成的ZooKeeper的服务地址。
yarn.resourcemanager.recovery.enabled：启用RM重启的功能，默认为false。
yarn.resourcemanager.store.class：用于状态存储的类，默认为org.apache.hadoop.yarn.server.resourcemanager.recovery.FileSystemRMStateStore，基于Hadoop文件系统的实现。还可以为org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore，该类为基于ZooKeeper的实现。此处指定该类。
（2）yarn-site.xm文件配置好后，需要将其发送到集群中其它节点。
（3）接着上一章启动好的HDFS，继续进行启动YARN。
分别在centos01、centos02节点上执行以下命令，启动ResourceManager：
[hadoop@centos01 hadoop-2.7.1]$ sbin/yarn-daemon.sh start resourcemanager
分别在centos01、centos02、centos03节点上执行以下命令，启动nodemanager：
[hadoop@centos01 hadoop-2.7.1]$ sbin/yarn-daemon.sh start nodemanager
（4）YARN启动后，查看各节点Java进程：
[hadoop@centos01 hadoop-2.7.1]$ jps
3360 QuorumPeerMain
4080 DFSZKFailoverController
4321 NodeManager
4834 Jps
3908 JournalNode
3702 DataNode
4541 ResourceManager
3582 NameNode

[hadoop@centos02 hadoop-2.7.1]$ jps
4486 Jps
3815 DFSZKFailoverController
4071 NodeManager
4359 ResourceManager
3480 NameNode
3353 QuorumPeerMain
3657 JournalNode
3563 DataNode

[hadoop@centos03 hadoop-2.7.1]$ jps
3496 JournalNode
4104 Jps
3836 NodeManager
3293 QuorumPeerMain
3390 DataNode
此时浏览器输入地址//centos01:8088 访问活动状态的ResourceManager，查看YARN的启动状态。如下图所示。

如果访问备份ResourceManager地址：//centos02:8088 发现自动跳转到了地址//centos01:8088。这是因为此时活动状态的ResourceManager在centos01节点上。访问备份节点的ResourceManager会自动跳转到活动节点。
7.2 测试YARN自动故障转移
在centos01节点上执行MapReduce默认的WordCount程序，当正在执行map阶段时，新开一个SSH Shell窗口，杀掉centos01的ResourceManager进程，观察程序执行过程。执行MapReduce默认的WordCount程序的命令如下：
[hadoop@centos01 hadoop-2.7.1]$ bin/yarn jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.1.jar wordcount /input /output
执行结果如下：
[hadoop@centos01 hadoop-2.7.1]$ bin/yarn jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.1.jar wordcount /input /output
18/03/16 10:48:22 INFO input.FileInputFormat: Total input paths to process : 1
18/03/16 10:48:22 INFO mapreduce.JobSubmitter: number of splits:1
18/03/16 10:48:23 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1521168402181_0001
18/03/16 10:48:23 INFO impl.YarnClientImpl: Submitted application application_1521168402181_0001
18/03/16 10:48:23 INFO mapreduce.Job: The url to track the job: //centos01:8088/proxy/application_1521168402181_0001/
18/03/16 10:48:23 INFO mapreduce.Job: Running job: job_1521168402181_0001
18/03/16 10:48:56 INFO mapreduce.Job: Job job_1521168402181_0001 running in uber mode : false
18/03/16 10:48:57 INFO mapreduce.Job:  map 0% reduce 0%
18/03/16 10:50:21 INFO mapreduce.Job:  map 100% reduce 0%
18/03/16 10:50:32 INFO mapreduce.Job:  map 100% reduce 100%
18/03/16 10:50:36 INFO mapreduce.Job: Job job_1521168402181_0001 completed successfully
18/03/16 10:50:37 INFO mapreduce.Job: Counters: 49
        File System Counters
                FILE: Number of bytes read=1321
                FILE: Number of bytes written=239335
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=1094
                HDFS: Number of bytes written=971
                HDFS: Number of read operations=6
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters
                Launched map tasks=1
                Launched reduce tasks=1
                Data-local map tasks=1
                Total time spent by all maps in occupied slots (ms)=14130
                Total time spent by all reduces in occupied slots (ms)=7851
                Total time spent by all map tasks (ms)=14130
                Total time spent by all reduce tasks (ms)=7851
                Total vcore-seconds taken by all map tasks=14130
                Total vcore-seconds taken by all reduce tasks=7851
                Total megabyte-seconds taken by all map tasks=14469120
                Total megabyte-seconds taken by all reduce tasks=8039424
        Map-Reduce Framework
                Map input records=29
                Map output records=109
                Map output bytes=1368
                Map output materialized bytes=1321
                Input split bytes=101
                Combine input records=109
                Combine output records=86
                Reduce input groups=86
                Reduce shuffle bytes=1321
                Reduce input records=86
                Reduce output records=86
                Spilled Records=172
                Shuffled Maps =1
                Failed Shuffles=0
                Merged Map outputs=1
                GC time elapsed (ms)=188
                CPU time spent (ms)=1560
                Physical memory (bytes) snapshot=278478848
                Virtual memory (bytes) snapshot=4195344384
                Total committed heap usage (bytes)=140480512
        Shuffle Errors
                BAD_ID=0
                CONNECTION=0
                IO_ERROR=0
                WRONG_LENGTH=0
                WRONG_MAP=0
                WRONG_REDUCE=0
        File Input Format Counters
                Bytes Read=993
        File Output Format Counters
                Bytes Written=971
从上述结果中可以看出，虽然ResourceManager进程被杀掉了，但是YARN仍然能够流畅的执行，说明自动故障转移功能生效了，ResourceManager遇到故障后，自动切换到了centos02节点上继续执行。此时浏览器访问备用ResourceManager的Web端地址//centos02:8088发现可以成功访问了。显示任务成功执行完毕。

到此，YARN HA集群搭建完毕。