Linux中hadoop运行第一个自带的Wordount程序

首先必须配置SSH免密码登陆

1.启动你的hadoop集群最少三台电脑 启动路径注意你的静态IP对应

四个进程缺一不可执行

2.进入目录文件

[root@master /]# cd home

创建hduser和file文件

[root@master home]# mkdir hduser

[root@master home]# mkdir file

在file文件下创建两个文本

[root@master file]# echo "hello 1" > f1.txt

[root@master file]# echo "hello 2" > f2.txt

进入hadoop目录

[root@master /]# cd bigData
bash: cd: bigData: 没有那个文件或目录
[root@master /]# cd /usr/local
[root@master local]# cd hadoop-2.8.0/
[root@master hadoop-2.8.0]# ll
总用量 136
drwxr-xr-x. 2  502 dialout  4096 3月  17 2017 bin
drwxr-xr-x. 3  502 dialout    19 3月  17 2017 etc
drwxr-xr-x. 3 root root       17 3月   4 22:44 hdfs
drwxr-xr-x. 2  502 dialout   101 3月  17 2017 include
drwxr-xr-x. 3  502 dialout    19 3月  17 2017 lib
drwxr-xr-x. 2  502 dialout  4096 3月  17 2017 libexec
-rw-r--r--. 1  502 dialout 99253 3月  17 2017 LICENSE.txt
drwxr-xr-x. 2 root root     4096 3月  18 11:55 logs
-rw-r--r--. 1  502 dialout 15915 3月  17 2017 NOTICE.txt
-rw-r--r--. 1  502 dialout  1366 3月  17 2017 README.txt
drwxr-xr-x. 2  502 dialout  4096 3月  17 2017 sbin
drwxr-xr-x. 4  502 dialout    29 3月  17 2017 share
drwxr-xr-x. 3 root root       16 3月   4 22:46 tmp
[root@master hadoop-2.8.0]# cd share
[root@master share]# ll
总用量 0
drwxr-xr-x. 3 502 dialout 19 3月  17 2017 doc
drwxr-xr-x. 9 502 dialout 92 3月  17 2017 hadoop
[root@master share]# cd hadoop/
[root@master hadoop]# 

启动Hadoop之后就自动启动了HDFS,创建 HDFS目录/input

[root@master hadoop]#hadoop fs -mkdir /input  创建在根目录下

将f1.txt, f2.txt保存到HDFS中 put上去

[root@master hadoop]# hadoop fs -put home/hduser/file/f*.txt  /input/

查看HDFS上是否存在 f1.txt f2.txt;

[root@master hadoop]# hadoop fs -ls /input

 

通过 “hadoop jar xxx.jar” 来执行WordCount程序 进入安装目录 hadoop

[root@master hadoop]# cd mapreduce/

进入mapreduce的目录执行如下命令

hadoop jar hadoop-mapreduce-examples-2.8.0.jar wordcount /input3 /output

[root@master hadoop]# cd mapreduce/
[root@master mapreduce]# hadoop jar hadoop-mapreduce-examples-2.8.0.jar wordcount /input3 /output
18/03/18 11:56:02 INFO client.RMProxy: Connecting to ResourceManager at master/192.168.10.11:8032
18/03/18 11:56:04 INFO input.FileInputFormat: Total input files to process : 2
18/03/18 11:56:05 INFO mapreduce.JobSubmitter: number of splits:2
18/03/18 11:56:05 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1521345320410_0001
18/03/18 11:56:07 INFO impl.YarnClientImpl: Submitted application application_1521345320410_0001
18/03/18 11:56:07 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1521345320410_0001/
18/03/18 11:56:07 INFO mapreduce.Job: Running job: job_1521345320410_0001
18/03/18 11:56:26 INFO mapreduce.Job: Job job_1521345320410_0001 running in uber mode : false
18/03/18 11:56:26 INFO mapreduce.Job:  map 0% reduce 0%
18/03/18 11:56:49 INFO mapreduce.Job:  map 100% reduce 0%
18/03/18 11:57:01 INFO mapreduce.Job:  map 100% reduce 100%
18/03/18 11:57:03 INFO mapreduce.Job: Job job_1521345320410_0001 completed successfully
18/03/18 11:57:03 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=46
        FILE: Number of bytes written=408373
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=210
        HDFS: Number of bytes written=16
        HDFS: Number of read operations=9
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters 
        Launched map tasks=2
        Launched reduce tasks=1
        Data-local map tasks=2
        Total time spent by all maps in occupied slots (ms)=37460
        Total time spent by all reduces in occupied slots (ms)=10166
        Total time spent by all map tasks (ms)=37460
        Total time spent by all reduce tasks (ms)=10166
        Total vcore-milliseconds taken by all map tasks=37460
        Total vcore-milliseconds taken by all reduce tasks=10166
        Total megabyte-milliseconds taken by all map tasks=38359040
        Total megabyte-milliseconds taken by all reduce tasks=10409984
    Map-Reduce Framework
        Map input records=2
        Map output records=4
        Map output bytes=32
        Map output materialized bytes=52
        Input split bytes=194
        Combine input records=4
        Combine output records=4
        Reduce input groups=3
        Reduce shuffle bytes=52
        Reduce input records=4
        Reduce output records=3
        Spilled Records=8
        Shuffled Maps =2
        Failed Shuffles=0
        Merged Map outputs=2
        GC time elapsed (ms)=458
        CPU time spent (ms)=2110
        Physical memory (bytes) snapshot=464822272
        Virtual memory (bytes) snapshot=6236811264
        Total committed heap usage (bytes)=260870144
    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=16
    File Output Format Counters 
        Bytes Written=16

表示成功

使用如下命令来查看输出目录中所有结果

[root@master hadoop]# hadoop fs -cat /output/*

[root@master hadoop]# hadoop fs -cat /output/*
f    1
hello    2
j    1
[root@master hadoop]# 

至此完毕

配置hadooop环境变量 http://blog.csdn.net/kokjuis/article/details/53537029