1.查看hadoop版本
[hadoop@ltt1 sbin]$ hadoop version
Hadoop 2.6.-cdh5.12.0
Subversion http://github.com/cloudera/hadoop -r dba647c5a8bc5e09b572d76a8d29481c78d1a0dd
Compiled by jenkins on --29T11:33Z
Compiled with protoc 2.5.
From source with checksum 7c45ae7a4592ce5af86bc4598c5b4
This command was run using /home/hadoop/hadoop260/share/hadoop/common/hadoop-common-2.6.-cdh5.12.0.jar
2.通过hadoop自带的jar文件，可以简单测试一些功能。
查看hadoop-mapreduce-examples-2.6.0-cdh5.12.0.jar文件所支持的MapReduce功能列表
[hadoop@ltt1 sbin]$ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.-cdh5.12.0.jar
An example program must be given as the first argument.
Valid program names are:
aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount: An example job that count the pageview counts from a database.
distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
grep: A map/reduce program that counts the matches of a regex in the input.
join: A job that effects a join over sorted, equally partitioned datasets
multifilewc: A job that counts words from several files.
pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.
randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
randomwriter: A map/reduce program that writes 10GB of random data per node.
secondarysort: An example defining a secondary sort to the reduce.
sort: A map/reduce program that sorts the data written by the random writer.
sudoku: A sudoku solver.
teragen: Generate data for the terasort
terasort: Run the terasort
teravalidate: Checking results of terasort
wordcount: A map/reduce program that counts the words in the input files.
wordmean: A map/reduce program that counts the average length of the words in the input files.
wordmedian: A map/reduce program that counts the median length of the words in the input files.
wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files.
3.在hdfs上创建文件夹
hadoop fs -mkdir /input
4.查看hdfs的更目录列表
[hadoop@ltt1 ~]$ hadoop fs -ls /
Found 2 items
drwxr-xr-x - hadoop supergroup 0 2017-09-17 08:11 /input
drwx------ - hadoop supergroup 0 2017-09-17 08:07 /tmp
5.上传本地文件到hdfs
hadoop fs -put $HADOOP_HOME/*.txt /input
6.查看hdfs上input目录下文件
[hadoop@ltt1 ~]$ hadoop fs -ls /input
Found items
-rw-r--r-- hadoop supergroup -- : /input/LICENSE.txt
-rw-r--r-- hadoop supergroup -- : /input/NOTICE.txt
-rw-r--r-- hadoop supergroup -- : /input/README.txt
7.wordcount简单测试。
[hadoop@ltt1 ~]$ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.-cdh5.12.0.jar wordcount /input /output
// :: INFO input.FileInputFormat: Total input paths to process :
// :: INFO mapreduce.JobSubmitter: number of splits:
// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1505605169997_0002
// :: INFO impl.YarnClientImpl: Submitted application application_1505605169997_0002
// :: INFO mapreduce.Job: The url to track the job: http://ltt1.bg.cn:9180/proxy/application_1505605169997_0002/
// :: INFO mapreduce.Job: Running job: job_1505605169997_0002
// :: INFO mapreduce.Job: Job job_1505605169997_0002 running in uber mode : false
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: map % reduce %
// :: INFO mapreduce.Job: Job job_1505605169997_0002 completed successfully
// :: INFO mapreduce.Job: Counters: 50
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File System Counters
FILE: Number of bytes read=
FILE: Number of bytes written=
FILE: Number of read operations=
FILE: Number of large read operations=
FILE: Number of write operations=
HDFS: Number of bytes read=
HDFS: Number of bytes written=
HDFS: Number of read operations=
HDFS: Number of large read operations=
HDFS: Number of write operations=
Job Counters
Launched map tasks=
Launched reduce tasks=
Data-local map tasks=
Rack-local map tasks=
Total time spent by all maps in occupied slots (ms)=
Total time spent by all reduces in occupied slots (ms)=
Total time spent by all map tasks (ms)=
Total time spent by all reduce tasks (ms)=
Total vcore-milliseconds taken by all map tasks=
Total vcore-milliseconds taken by all reduce tasks=
Total megabyte-milliseconds taken by all map tasks=
Total megabyte-milliseconds taken by all reduce tasks=
Map-Reduce Framework
Map input records=
Map output records=
Map output bytes=
Map output materialized bytes=
Input split bytes=
Combine input records=
Combine output records=
Reduce input groups=
Reduce shuffle bytes=
Reduce input records=
Reduce output records=
Spilled Records=
Shuffled Maps =
Failed Shuffles=
Merged Map outputs=
GC time elapsed (ms)=
CPU time spent (ms)=
Physical memory (bytes) snapshot=
Virtual memory (bytes) snapshot=
Total committed heap usage (bytes)=
Shuffle Errors
BAD_ID=
CONNECTION=
IO_ERROR=
WRONG_LENGTH=
WRONG_MAP=
WRONG_REDUCE=
File Input Format Counters
Bytes Read=
File Output Format Counters
Bytes Written=
8.查看wordcount运行结果（由于结果太长，只举出了部分结果）
[hadoop@ltt1 ~]$ hadoop fs -cat /output/*
worldwide, 4
would 1
writing 2
writing, 4
written 19
xmlenc 1
year 1
you 12
your 5
zlib 1
252.227-7014(a)(1)) 1
§ 1
“AS 1
“Contributor 1
“Contributor” 1
“Covered 1
“Executable” 1
“Initial 1
“Larger 1
“Licensable” 1
“License” 1
“Modifications” 1
“Original 1
“Participant”) 1
“Patent 1
“Source 1
“Your”) 1
“You” 2
“commercial 3
“control” 1
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至此，通过一个wordcount的一个小栗子，简介实践了一下hdfs的创建文件夹，上传文件，查看目录，运行wordcount实例。
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