hadoop第一个计算任务wordcount的运行

1.上传文件HDFS

[hadoop@master example]$ cat mytest.txt #文件内容
Hello world ,you are My lunky!
Hello you are my friend!
hello ,are you OKay!
hello ,GGG?
no GG上传文件到HDFS
[hadoop@master example]$ hadoop fs -put mytest.txt /example/data/ #上传到目录/example/data/
[hadoop@master example]$ hadoop fs -cat /example/data/mytest.txt #查看上传的内容
Hello world ,you are My lunky!
Hello you are my friend!
hello ,are you OKay!
hello ,GGG?
no GG
[hadoop@master example]$

2.执行计算
[hadoop@master mapreduce]$ pwd
/home/hadoop/hadoop-2.8.1/share/hadoop/mapreduce
[hadoop@master mapreduce]$ hadoop jar hadoop-mapreduce-examples-2.8.1.jar wordcount /example/data/mytest.txt /output
17/09/14 10:51:57 INFO client.RMProxy: Connecting to ResourceManager at master/10.0.1.118:18040
17/09/14 10:51:58 INFO input.FileInputFormat: Total input files to process : 1
17/09/14 10:51:58 INFO mapreduce.JobSubmitter: number of splits:1
17/09/14 10:51:58 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1505390401611_0002
17/09/14 10:51:59 INFO impl.YarnClientImpl: Submitted application application_1505390401611_0002
17/09/14 10:51:59 INFO mapreduce.Job: The url to track the job: http://master:18088/proxy/application_1505390401611_0002/ 17/09/14 10:51:59 INFO mapreduce.Job: Running job: job_1505390401611_0002
17/09/14 10:52:13 INFO mapreduce.Job: Job job_1505390401611_0002 running in uber mode : false
17/09/14 10:52:13 INFO mapreduce.Job: map 0% reduce 0%
17/09/14 10:52:21 INFO mapreduce.Job: map 100% reduce 0%
17/09/14 10:52:28 INFO mapreduce.Job: map 100% reduce 100%
17/09/14 10:52:29 INFO mapreduce.Job: Job job_1505390401611_0002 completed successfully
17/09/14 10:52:30 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=171
FILE: Number of bytes written=272737
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=203
HDFS: Number of bytes written=105
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)=6215
Total time spent by all reduces in occupied slots (ms)=4850
Total time spent by all map tasks (ms)=6215
Total time spent by all reduce tasks (ms)=4850
Total vcore-milliseconds taken by all map tasks=6215
Total vcore-milliseconds taken by all reduce tasks=4850
Total megabyte-milliseconds taken by all map tasks=6364160
Total megabyte-milliseconds taken by all reduce tasks=4966400
Map-Reduce Framework
Map input records=5
Map output records=19
Map output bytes=171
Map output materialized bytes=171
Input split bytes=107
Combine input records=19
Combine output records=15
Reduce input groups=15
Reduce shuffle bytes=171
Reduce input records=15
Reduce output records=15
Spilled Records=30
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=180
CPU time spent (ms)=1620
Physical memory (bytes) snapshot=294346752
Virtual memory (bytes) snapshot=4173053952
Total committed heap usage (bytes)=139882496
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=96
File Output Format Counters
Bytes Written=105

3.查看计算结果

[hadoop@master mapreduce]$ hadoop fs -cat /output/part-r-00000
,GGG? 1
,are 1
,you 1
GG 1
Hello 2
My 1
OKay! 1
are 2
friend! 1
hello 2
lunky! 1
my 1
no 1
world 1
you 2
[hadoop@master mapreduce]$

运行hadoop自带的wordcount 计算，让自己对hadoop 有个初步了解.