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运行Hadoop自带的wordcount单词统计程序

时间:2017-09-04  来源:未知  作者:admin666

0.前言

    前面一篇《Hadoop初体验:快速搭建Hadoop伪分布式环境》搭建了一个Hadoop的环境,现在就使用Hadoop自带的wordcount程序来做单词统计的案例。

http://www.linuxidc.com/Linux/2017-09/146694.htm

1.使用示例程序实现单词统计

(1)wordcount程序

wordcount程序在hadoop的share目录下,如下:

[root@linuxidc mapreduce]# pwd
/usr/local/hadoop/share/hadoop/mapreduce
[root@linuxidc mapreduce]# ls
hadoop-mapreduce-client-app-2.6.5.jar        hadoop-mapreduce-client-jobclient-2.6.5-tests.jar
hadoop-mapreduce-client-common-2.6.5.jar      hadoop-mapreduce-client-shuffle-2.6.5.jar
hadoop-mapreduce-client-core-2.6.5.jar        hadoop-mapreduce-examples-2.6.5.jar
hadoop-mapreduce-client-hs-2.6.5.jar          lib
hadoop-mapreduce-client-hs-plugins-2.6.5.jar  lib-examples
hadoop-mapreduce-client-jobclient-2.6.5.jar  sources

就是这个hadoop-mapreduce-examples-2.6.5.jar程序。
 
(2)创建HDFS数据目录
    创建一个目录,用于保存MapReduce任务的输入文件:

[root@linuxidc ~]# hadoop fs -mkdir -p /data/wordcount

    创建一个目录,用于保存MapReduce任务的输出文件:

[root@linuxidc ~]# hadoop fs -mkdir /output

    查看刚刚创建的两个目录:

[root@linuxidc ~]# hadoop fs -ls /
drwxr-xr-x  - root supergroup          0 2017-09-01 20:34 /data
drwxr-xr-x  - root supergroup          0 2017-09-01 20:35 /output

(3)创建一个单词文件,并上传到HDFS
    创建的单词文件如下:

 [root@linuxidc ~]# cat myword.txt 
linuxidc yyh
yyh xplinuxidc
katy ling
yeyonghao linuxidc
xpleaf katy

    上传该文件到HDFS中:

[root@linuxidc ~]# hadoop fs -put myword.txt /data/wordcount

    在HDFS中查看刚刚上传的文件及内容:

[root@linuxidc ~]# hadoop fs -ls /data/wordcount
-rw-r--r--  1 root supergroup        57 2017-09-01 20:40 /data/wordcount/myword.txt
[root@linuxidc ~]# hadoop fs -cat /data/wordcount/myword.txt
linuxidc yyh
yyh xplinuxidc
katy ling
yeyonghao linuxidc
xpleaf katy

(4)运行wordcount程序
    执行如下命令:

[root@linuxidc ~]# hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar wordcount /data/wordcount /output/wordcount
...
17/09/01 20:48:14 INFO mapreduce.Job: Job job_local1719603087_0001 completed successfully
17/09/01 20:48:14 INFO mapreduce.Job: Counters: 38
        File System Counters
                FILE: Number of bytes read=585940
                FILE: Number of bytes written=1099502
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=114
                HDFS: Number of bytes written=48
                HDFS: Number of read operations=15
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=4
        Map-Reduce Framework
                Map input records=5
                Map output records=10
                Map output bytes=97
                Map output materialized bytes=78
                Input split bytes=112
                Combine input records=10
                Combine output records=6
                Reduce input groups=6
                Reduce shuffle bytes=78
                Reduce input records=6
                Reduce output records=6
                Spilled Records=12
                Shuffled Maps =1
                Failed Shuffles=0
                Merged Map outputs=1
                GC time elapsed (ms)=92
                CPU time spent (ms)=0
                Physical memory (bytes) snapshot=0
                Virtual memory (bytes) snapshot=0
                Total committed heap usage (bytes)=241049600
        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=57
        File Output Format Counters 
                Bytes Written=48

(5)查看统计结果
    如下:

[root@linuxidc ~]# hadoop fs -cat /output/wordcount/part-r-00000
katy    2
linuxidc    2
ling    1
xplinuxidc  2
yeyonghao      1
yyh    2

更多Hadoop相关信息见Hadoop 专题页面 http://www.linuxidc.com/topicnews.aspx?tid=13

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