hadoop 自带的WordCount实例可以统计一批文本文件中各单词出现的次数。
下面介绍如何执行WordCount实例。
1.启动hadoop
[root@hadoop ~]# start-all.sh #启动hadoop
2.在本地新建目录及2个文件
[root@hadoop ~]# mkdir input
[root@hadoop ~]# cd input/
[root@hadoop input]# echo "hello world">test1.txt #新建2个测试文件
[root@hadoop input]# echo "hello hadoop">test2.txt
3.将本地文件系统上的input目录复制到HDFS根目录下，重命名为in
[root@hadoop ~]# hdfs dfs -put input/ /in
[root@hadoop ~]# hdfs dfs -ls / #查看根目录
Found 1 items
drwxr-xr-x - root supergroup 0 2018-07-20 03:06 /in
[root@hadoop ~]# hdfs dfs -ls /in #查看in根目录
Found 2 items
-rw-r--r-- 1 root supergroup 12 2018-07-20 03:06 /in/test1.txt
-rw-r--r-- 1 root supergroup 13 2018-07-20 03:06 /in/test2.txt
4.执行以下命令
[root@hadoop ~]# cd /usr/local/hadoop/share/hadoop/mapreduce/ #示例jar包在此目录中存放
[root@hadoop mapreduce]# hadoop jar hadoop-mapreduce-examples-2.7.7.jar wordcount /in /out #out为输出目录，执行命令之前必须为空或者不存在否则报错
[root@hadoop ~]# cd /usr/local/hadoop/share/hadoop/mapreduce/ #示例jar包在此目录中存放
[root@hadoop mapreduce]# hadoop jar hadoop-mapreduce-examples-2.7.7.jar wordcount /in /out
18/07/30 14:02:11 INFO client.RMProxy: Connecting to ResourceManager at hadoop/192.168.42.133:8032
18/07/30 14:02:13 INFO input.FileInputFormat: Total input paths to process : 2
18/07/30 14:02:13 INFO mapreduce.JobSubmitter: number of splits:2
18/07/30 14:02:14 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1532913019648_0002
18/07/30 14:02:14 INFO impl.YarnClientImpl: Submitted application application_1532913019648_0002
18/07/30 14:02:14 INFO mapreduce.Job: The url to track the job: http://hadoop:8088/proxy/application_1532913019648_0002/
18/07/30 14:02:14 INFO mapreduce.Job: Running job: job_1532913019648_0002
18/07/30 14:02:36 INFO mapreduce.Job: Job job_1532913019648_0002 running in uber mode : false
18/07/30 14:02:36 INFO mapreduce.Job: map 0% reduce 0%
18/07/30 14:04:37 INFO mapreduce.Job: map 67% reduce 0%
18/07/30 14:04:42 INFO mapreduce.Job: map 100% reduce 0%
18/07/30 14:05:21 INFO mapreduce.Job: map 100% reduce 100%
18/07/30 14:05:23 INFO mapreduce.Job: Job job_1532913019648_0002 completed successfully
18/07/30 14:05:26 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=55
FILE: Number of bytes written=368074
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=217
HDFS: Number of bytes written=25
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)=259093
Total time spent by all reduces in occupied slots (ms)=21736
Total time spent by all map tasks (ms)=259093
Total time spent by all reduce tasks (ms)=21736
Total vcore-milliseconds taken by all map tasks=259093
Total vcore-milliseconds taken by all reduce tasks=21736
Total megabyte-milliseconds taken by all map tasks=265311232
Total megabyte-milliseconds taken by all reduce tasks=22257664
Map-Reduce Framework
Map input records=2
Map output records=4
Map output bytes=41
Map output materialized bytes=61
Input split bytes=192
Combine input records=4
Combine output records=4
Reduce input groups=3
Reduce shuffle bytes=61
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)=847
CPU time spent (ms)=4390
Physical memory (bytes) snapshot=461631488
Virtual memory (bytes) snapshot=6226669568
Total committed heap usage (bytes)=277356544
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=25
File Output Format Counters
Bytes Written=25
执行命令时显示MapReduce过程
5.查看输出结果
1)直接查看HDFS上的输出文件
[root@hadoop mapreduce]# hdfs dfs -ls /out
Found 2 items
-rw-r--r-- 1 root supergroup 0 2018-07-30 14:05 /out/_SUCCESS
-rw-r--r-- 1 root supergroup 25 2018-07-30 14:05 /out/part-r-00000
[root@hadoop mapreduce]# hdfs dfs -cat /out/part-r-00000
hadoop 1
hello 2
world 1
2)也可以输入以下命令查看
[root@hadoop mapreduce]# hdfs dfs -cat /out/*
hadoop 1
hello 2
world 1
3)还可以把文件复制到本地查看
[root@hadoop mapreduce]# hdfs dfs -get /out /root/output
[root@hadoop mapreduce]# cd /root/output/
[root@hadoop output]# ll
总用量 4
-rw-r--r-- 1 root root 25 7月 30 17:18 part-r-00000
-rw-r--r-- 1 root root 0 7月 30 17:18 _SUCCESS
[root@hadoop output]# cat part-r-00000
hadoop 1
hello 2
world 1