說明：測試hadoop自帶的執行個體 wordcount程式（此程式統計每個單詞在檔案中出現的次數）
2.6.0版本jar程式的路徑是
/usr/local/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar
一、在本地建立目錄和檔案
建立目錄：
mkdir /home/hadoop/input
cd /home/hadoop/input
建立檔案：
touch wordcount1.txt
touch wordcount2.txt
二、添加内容
echo "Hello World" > wordcount1.txt
echo "Hello Hadoop" > wordcount2.txt
三、在hdfs上建立input目錄
hadoop fs -mkdir /input
四、拷貝檔案到/input目錄
hadoop fs -put /home/hadoop/input/* /input
五、執行程式
hadoop jar /usr/local/hadoop-2.6.0/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar wordcount /input /output
說明：wordcount為程式的主類名， /input 輸入目錄 /output 輸出目錄（輸出目錄不能存在）
六、執行過程資訊
15/04/14 15:55:03 INFO client.RMProxy: Connecting to ResourceManager at hdnn140/192.168.152.140:8032
15/04/14 15:55:04 INFO input.FileInputFormat: Total input paths to process : 2
15/04/14 15:55:04 INFO mapreduce.JobSubmitter: number of splits:2
15/04/14 15:55:05 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1428996061278_0002
15/04/14 15:55:05 INFO impl.YarnClientImpl: Submitted application application_1428996061278_0002
15/04/14 15:55:05 INFO mapreduce.Job: The url to track the job: http://hdnn140:8088/proxy/application_1428996061278_0002/
15/04/14 15:55:05 INFO mapreduce.Job: Running job: job_1428996061278_0002
15/04/14 15:55:17 INFO mapreduce.Job: Job job_1428996061278_0002 running in uber mode : false
15/04/14 15:55:17 INFO mapreduce.Job: map 0% reduce 0%
15/04/14 15:56:00 INFO mapreduce.Job: map 100% reduce 0%
15/04/14 15:56:10 INFO mapreduce.Job: map 100% reduce 100%
15/04/14 15:56:11 INFO mapreduce.Job: Job job_1428996061278_0002 completed successfully
15/04/14 15:56:11 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=55
FILE: Number of bytes written=316738
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=235
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)=83088
Total time spent by all reduces in occupied slots (ms)=7098
Total time spent by all map tasks (ms)=83088
Total time spent by all reduce tasks (ms)=7098
Total vcore-seconds taken by all map tasks=83088
Total vcore-seconds taken by all reduce tasks=7098
Total megabyte-seconds taken by all map tasks=85082112
Total megabyte-seconds taken by all reduce tasks=7268352
Map-Reduce Framework
Map input records=2
Map output records=4
Map output bytes=41
Map output materialized bytes=61
Input split bytes=210
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)=1649
CPU time spent (ms)=4260
Physical memory (bytes) snapshot=280866816
Virtual memory (bytes) snapshot=2578739200
Total committed heap usage (bytes)=244625408
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
七、完成後檢視輸出目錄
hadoop fs -ls /output
八、檢視輸出結果
hadoop fs -cat /output/part-r-00000
九、完成
本文轉自 yntmdr 51CTO部落格，原文連結：http://blog.51cto.com/yntmdr/1632323，如需轉載請自行聯系原作者
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