接上文Hadoop完全分布式环境搭建，本文介绍关于 Hadoop 的简单测试及使用
1、HDFS的简单使用测试
- 创建文件夹
在HDFS上创建一个文件夹/test/input
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[hadoop@master ~]$ hadoop fs -mkdir -p /test/input |
- 查看创建的文件夹
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[hadoop@master ~]$ hadoop fs -ls / Found 1 items drwxr-xr-x - hadoop supergroup 0 2018-12-12 17:58 /test |
- 向HDFS上传文件
创建一个文本文件words.txt
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[hadoop@master ~]$ vim words.txt [hadoop@master ~]$ hadoop fs -put words.txt /test/input |
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[hadoop@master ~]$ hadoop fs -ls /test/input Found 1 items -rw-r--r-- 2 hadoop supergroup 35 2018-12-12 18:00 /test/input/words.txt |
- 从HDFS下载文件
将刚刚上传的文件下载到~/data文件夹中
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[hadoop@master ~]$ hadoop fs -get /test/input/words.txt ~/data [hadoop@master ~]$ ls data/ hadoopdata words.txt |
2、运行第一个Map Reduce的例子程序：wordcount
用自带的demo--wordcount来测试hadoop集群能不能正常跑任务：
执行wordcount程序，并将结果放入/test/output/文件夹：
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[hadoop@master ~]$ hadoop jar ~/apps/hadoop-2.9.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar wordcount /test/input /test/output |
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[hadoop@master ~]$ hadoop jar ~/apps/hadoop-2.9.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar wordcount /test/input /test/output 18/12/12 18:02:54 INFO client.RMProxy: Connecting to ResourceManager at slave3/172.20.2.110:8032 18/12/12 18:02:55 INFO input.FileInputFormat: Total input files to process : 1 18/12/12 18:02:56 INFO mapreduce.JobSubmitter: number of splits:1 18/12/12 18:02:56 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled 18/12/12 18:02:57 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1544607847316_0001 18/12/12 18:02:57 INFO impl.YarnClientImpl: Submitted application application_1544607847316_0001 18/12/12 18:02:58 INFO mapreduce.Job: The url to track the job: http://slave3:8088/proxy/application_1544607847316_0001/ 18/12/12 18:02:58 INFO mapreduce.Job: Running job: job_1544607847316_0001 18/12/12 18:03:09 INFO mapreduce.Job: Job job_1544607847316_0001 running in uber mode : false 18/12/12 18:03:09 INFO mapreduce.Job: map 0% reduce 0% 18/12/12 18:03:17 INFO mapreduce.Job: map 100% reduce 0% 18/12/12 18:03:24 INFO mapreduce.Job: map 100% reduce 100% 18/12/12 18:03:25 INFO mapreduce.Job: Job job_1544607847316_0001 completed successfully 18/12/12 18:03:25 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=53 FILE: Number of bytes written=395007 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=139 HDFS: Number of bytes written=31 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)=5738 Total time spent by all reduces in occupied slots (ms)=4348 Total time spent by all map tasks (ms)=5738 Total time spent by all reduce tasks (ms)=4348 Total vcore-milliseconds taken by all map tasks=5738 Total vcore-milliseconds taken by all reduce tasks=4348 Total megabyte-milliseconds taken by all map tasks=5875712 Total megabyte-milliseconds taken by all reduce tasks=4452352 Map-Reduce Framework Map input records=3 Map output records=6 Map output bytes=59 Map output materialized bytes=53 Input split bytes=104 Combine input records=6 Combine output records=4 Reduce input groups=4 Reduce shuffle bytes=53 Reduce input records=4 Reduce output records=4 Spilled Records=8 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=217 CPU time spent (ms)=1580 Physical memory (bytes) snapshot=498122752 Virtual memory (bytes) snapshot=4297453568 Total committed heap usage (bytes)=292028416 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=35 File Output Format Counters Bytes Written=31 |
查看执行结果：
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[hadoop@master ~]$ hadoop fs -ls /test/output Found 2 items -rw-r--r-- 2 hadoop supergroup 0 2018-12-12 18:03 /test/output/_SUCCESS -rw-r--r-- 2 hadoop supergroup 31 2018-12-12 18:03 /test/output/part-r-00000 |
在output/part-r-00000可以看到程序执行结果：
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[hadoop@master ~]$ hadoop fs -cat /test/output/part-r-00000 Ouer 1 hadoop 1 hello 3 root 1 [hadoop@master ~]$ |
3、运行例子程序：求圆周率
用自带的demo--pi来测试hadoop集群能不能正常跑任务：
执行pi程序：
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[hadoop@master ~]$ hadoop jar ~/apps/hadoop-2.9.1/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.1.jar pi 10 10 |
用来求圆周率，pi是类名，第一个10表示Map次数，第二个10表示随机生成点的次数(与计算原理有关)
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博主又更新了哦，支持下
@楚狂人博客 感谢支持
搞个手动安装集群，通过web管理工具管理起来