hadoop2.2运行wordcount例子
转载请注明出处：
http://kevin12.iteye.com/blog/2028776
hadoop2.2环境搭建好后可以运行wordcount例子来查看一个文件中的单词数量，废话不多说，看下面的步骤：
首先在/usr/local/hadoop/下创建一个目录，是为了存放我们的测试文件，目录名称为myfile,在进入myfile中创建一个名称为wordcount.txt文件里面输入数据如下：
hello hadoop
hello java
hello world
运行命令hadoop fs -mkdir /input在hdfs中创建一个input目录;
运行命令hadoop fs -input /usr/hadoop/myfile/wordcount.txt /input/，将本地系统的wordcount.txt文件上传到hdfs的input目录中；
确保hdfs中的input目录下面没有out目录，否则会报错，将光标定位到/usr/local/hadoop/share/hadoop/mapreduce/目录中，然后运行下面的命令进行统计字母：
hadoop jar hadoop-mapreduce-examples-2.2.0.jar wordcount /input/wordcount.txt /input/out
下面是运行结束打印的结果：
查看结果：
成功！
转载请注明出处： http://kevin12.iteye.com/blog/2028776
hadoop2.2环境搭建好后可以运行wordcount例子来查看一个文件中的单词数量，废话不多说，看下面的步骤：
首先在/usr/local/hadoop/下创建一个目录，是为了存放我们的测试文件，目录名称为myfile,在进入myfile中创建一个名称为wordcount.txt文件里面输入数据如下：
hello hadoop
hello java
hello world
运行命令hadoop fs -mkdir /input在hdfs中创建一个input目录;
运行命令hadoop fs -input /usr/hadoop/myfile/wordcount.txt /input/，将本地系统的wordcount.txt文件上传到hdfs的input目录中；
确保hdfs中的input目录下面没有out目录，否则会报错，将光标定位到/usr/local/hadoop/share/hadoop/mapreduce/目录中，然后运行下面的命令进行统计字母：
hadoop jar hadoop-mapreduce-examples-2.2.0.jar wordcount /input/wordcount.txt /input/out
下面是运行结束打印的结果：
[root@master mapreduce]# hadoop jar hadoop-mapreduce-examples-2.2.0.jar wordcount /input/wordcount.txt /input/out
14/03/09 19:32:19 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
14/03/09 19:32:22 INFO input.FileInputFormat: Total input paths to process : 1
14/03/09 19:32:22 INFO mapreduce.JobSubmitter: number of splits:1
14/03/09 19:32:22 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
14/03/09 19:32:22 INFO Configuration.deprecation: mapreduce.combine.class is deprecated. Instead, use mapreduce.job.combine.class
14/03/09 19:32:22 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name
14/03/09 19:32:22 INFO Configuration.deprecation: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
14/03/09 19:32:22 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
14/03/09 19:32:23 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1394289329220_0002
14/03/09 19:32:25 INFO impl.YarnClientImpl: Submitted application application_1394289329220_0002 to ResourceManager at /0.0.0.0:8032
14/03/09 19:32:25 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1394289329220_0002/
14/03/09 19:32:25 INFO mapreduce.Job: Running job: job_1394289329220_0002
14/03/09 19:32:55 INFO mapreduce.Job: Job job_1394289329220_0002 running in uber mode : false
14/03/09 19:32:55 INFO mapreduce.Job: map 0% reduce 0%
14/03/09 19:33:33 INFO mapreduce.Job: map 100% reduce 0%
14/03/09 19:33:45 INFO mapreduce.Job: map 100% reduce 100%
14/03/09 19:33:46 INFO mapreduce.Job: Job job_1394289329220_0002 completed successfully
14/03/09 19:33:47 INFO mapreduce.Job: Counters: 43
File System Counters
FILE: Number of bytes read=54
FILE: Number of bytes written=158345
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=32
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)=36121
Total time spent by all reduces in occupied slots (ms)=7030
Map-Reduce Framework
Map input records=3
Map output records=6
Map output bytes=60
Map output materialized bytes=54
Input split bytes=103
Combine input records=6
Combine output records=4
Reduce input groups=4
Reduce shuffle bytes=54
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)=588
CPU time spent (ms)=14810
Physical memory (bytes) snapshot=213233664
Virtual memory (bytes) snapshot=720707584
Total committed heap usage (bytes)=136908800
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=36
File Output Format Counters
Bytes Written=32
[root@master mapreduce]#
查看结果：
[root@master mapreduce]# hadoop fs -lsr /input lsr: DEPRECATED: Please use 'ls -R' instead. drwxr-xr-x - root supergroup 0 2014-03-09 19:33 /input/out -rw-r--r-- 3 root supergroup 0 2014-03-09 19:33 /input/out/_SUCCESS -rw-r--r-- 3 root supergroup 32 2014-03-09 19:33 /input/out/part-r-00000 -rw-r--r-- 3 root supergroup 36 2014-03-09 19:26 /input/wordcount.txt [root@master mapreduce]# hadoop fs -cat /input/out/part-r-00000 hadoop 1 hello 3 java 1 world 1 [root@master mapreduce]#
成功！
转载请注明出处： http://kevin12.iteye.com/blog/2028776