使用Cloudera Manager搭建MapReduce集群及MapReduce HA

　　　　　　　　　　使用Cloudera Manager搭建MapReduce集群及MapReduce HA

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　作者：尹正杰

版权声明：原创作品，谢绝转载！否则将追究法律责任。

 

 

 

一.通过CM部署MapReduce On YARN

1>.进入安装服务向导

2>.选择咱们要安装的服务MR 

3>.为MR分配角色

4>.配置MapReduce存储数据的目录

5>.等待MapReduce部署完成

6>.MapReduce服务成功加入到现有集群 

7>.查看CM管理界面，多出来了一个MapReduce服务

 

 

二.使用Cloudera Manager配置MapReduce HA

1>.点击“启用 High Avarilablity”

2>.选择备用的JobTracker 主机

3>.配置MapReduce的数据存放路径

4>.等待MapReduce HA配置完成 

5>.查明MapReduce的管理界面

6>.查看node101.yinzhengjie.org.cn的JobTracker Web UI（我发现访问node105.yinzhengjie.org.cn会自动给我跳转到node101.yinzhengjie.org.cn的Web UI）

 

 

三.运行一个MapReduce程序

描述：
　　公司一个运维人员尝试优化集群，但反而使得一些以前可以运行的MapReduce作业不能运行了。请你识别问题并予以纠正，并成功运行性能测试，要求为在Linux文件系统上找到hadoop-mapreduce-examples.jar包，并使用它完成三步测试：
　　　　1>.使用teragen 10000000　/user/yinzhengjie/data/day001/test_input 生成10000000行测试记录并输出到指定目录 　　　　
　　　　2>.使用terasort /user/yinzhengjie/data/day001/test_input  /user/yinzhengjie/data/day001/test_output 进行排序并输出到指定目录 　　　　
　　　　3>.使用teravalidate /user/yinzhengjie/data/day001/test_output  /user/yinzhengjie/data/day001/ts_validate检查输出结果 


考点： 　　
　　属于Test类操作，见Benchmark the cluster (I/O, CPU,network)条目。并且包含Troubleshoot类的知识，需要对MapReduce作业的常见错误会排查。

 

1>.生成输入数据

[root@node101.yinzhengjie.org.cn ~]# find / -name hadoop-mapreduce-examples.jar
/opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar
[root@node101.yinzhengjie.org.cn ~]# 
[root@node101.yinzhengjie.org.cn ~]# cd /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/lib/hadoop-mapreduce
[root@node101.yinzhengjie.org.cn /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/lib/hadoop-mapreduce]# 
[root@node101.yinzhengjie.org.cn /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/lib/hadoop-mapreduce]# hadoop jar hadoop-mapreduce-examples.jar teragen 10000000  /user/yinzhengjie/data/day001/test_input

[root@node101.yinzhengjie.org.cn /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/lib/hadoop-mapreduce]# hadoop jar hadoop-mapreduce-examples.jar teragen 10000000  /user/yinzhengjie/data/day001/test_input
19/05/22 19:38:39 INFO terasort.TeraGen: Generating 10000000 using 2
19/05/22 19:38:39 INFO mapreduce.JobSubmitter: number of splits:2
19/05/22 19:38:39 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1558520562958_0001
19/05/22 19:38:39 INFO impl.YarnClientImpl: Submitted application application_1558520562958_0001
19/05/22 19:38:40 INFO mapreduce.Job: The url to track the job: http://node101.yinzhengjie.org.cn:8088/proxy/application_1558520562958_0001/
19/05/22 19:38:40 INFO mapreduce.Job: Running job: job_1558520562958_0001
19/05/22 19:38:47 INFO mapreduce.Job: Job job_1558520562958_0001 running in uber mode : false
19/05/22 19:38:47 INFO mapreduce.Job:  map 0% reduce 0%
19/05/22 19:39:05 INFO mapreduce.Job:  map 72% reduce 0%
19/05/22 19:39:10 INFO mapreduce.Job:  map 100% reduce 0%
19/05/22 19:39:10 INFO mapreduce.Job: Job job_1558520562958_0001 completed successfully
19/05/22 19:39:10 INFO mapreduce.Job: Counters: 31
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=309374
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=167
                HDFS: Number of bytes written=1000000000
                HDFS: Number of read operations=8
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=4
        Job Counters 
                Launched map tasks=2
                Other local map tasks=2
                Total time spent by all maps in occupied slots (ms)=40283
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=40283
                Total vcore-milliseconds taken by all map tasks=40283
                Total megabyte-milliseconds taken by all map tasks=41249792
        Map-Reduce Framework
                Map input records=10000000
                Map output records=10000000
                Input split bytes=167
                Spilled Records=0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=163
                CPU time spent (ms)=29850
                Physical memory (bytes) snapshot=722341888
                Virtual memory (bytes) snapshot=5678460928
                Total committed heap usage (bytes)=552599552
        org.apache.hadoop.examples.terasort.TeraGen$Counters
                CHECKSUM=21472776955442690
        File Input Format Counters 
                Bytes Read=0
        File Output Format Counters 
                Bytes Written=1000000000
[root@node101.yinzhengjie.org.cn /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/lib/hadoop-mapreduce]# 

[root@node101.yinzhengjie.org.cn /opt/cloudera/parcels/CDH-5.15.1-1.cdh5.15.1.p0.4/lib/hadoop-mapreduce]# hadoop jar hadoop-mapreduce-examples.jar teragen 10000000 /user/yinzhengjie/data/day001/test_input