自己写的java程序如何放在YARN环境下运行

1.打包自己的java程序jar包，放在hadoop安装目录下share/test/MyJob.jar

2.执行hadoop命令如下：

[hadoop@Hadoop-Master hadoop]$ bin/hadoop jar share/test/MyJob.jar org.apache.hadoop.examples.WordCount /wanghongen/demo/filein/wordcount_in2.txt /wanghongen/demo/fileout/wordcount_out8

3.查看运动行日志

[hadoop@Hadoop-Master hadoop]$ bin/hadoop jar share/test/MyJob.jar org.apache.hadoop.examples.WordCount /wanghongen/demo/filein/wordcount_in2.txt /wanghongen/demo/fileout/wordcount_out8

15/07/10 18:13:30 INFO client.RMProxy: Connecting to ResourceManager at Hadoop-Master/192.168.70.65:8032

15/07/10 18:13:30 INFO input.FileInputFormat: Total input paths to process : 1

15/07/10 18:13:30 INFO mapreduce.JobSubmitter: number of splits:1

15/07/10 18:13:31 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1430970937754_0003

15/07/10 18:13:31 INFO impl.YarnClientImpl: Submitted application application_1430970937754_0003

15/07/10 18:13:31 INFO mapreduce.Job: The url to track the job: http://Hadoop-Master:8088/proxy/application_1430970937754_0003/

15/07/10 18:13:31 INFO mapreduce.Job: Running job: job_1430970937754_0003

15/07/10 18:13:38 INFO mapreduce.Job: Job job_1430970937754_0003 running in uber mode : false

15/07/10 18:13:38 INFO mapreduce.Job:  map 0% reduce 0%

15/07/10 18:13:45 INFO mapreduce.Job:  map 100% reduce 0%

15/07/10 18:13:50 INFO mapreduce.Job:  map 100% reduce 100%

15/07/10 18:13:51 INFO mapreduce.Job: Job job_1430970937754_0003 completed successfully

15/07/10 18:13:51 INFO mapreduce.Job: Counters: 49

File System Counters

FILE: Number of bytes read=166

FILE: Number of bytes written=194577

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=116

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)=5685

Total time spent by all reduces in occupied slots (ms)=2976

Total time spent by all map tasks (ms)=5685

Total time spent by all reduce tasks (ms)=2976

Total vcore-seconds taken by all map tasks=5685

Total vcore-seconds taken by all reduce tasks=2976

Total megabyte-seconds taken by all map tasks=5821440

Total megabyte-seconds taken by all reduce tasks=3047424

Map-Reduce Framework

Map input records=8

Map output records=15

Map output bytes=175

Map output materialized bytes=166

Input split bytes=131

Combine input records=15

Combine output records=11

Reduce input groups=11

Reduce shuffle bytes=166

Reduce input records=11

Reduce output records=11

Spilled Records=22

Shuffled Maps =1

Failed Shuffles=0

Merged Map outputs=1

GC time elapsed (ms)=37

CPU time spent (ms)=1920

Physical memory (bytes) snapshot=365965312

Virtual memory (bytes) snapshot=1686417408

Total committed heap usage (bytes)=215941120

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=104

File Output Format Counters 

Bytes Written=116

4.查看运行结果

[hadoop@Hadoop-Master hadoop]$ bin/hadoop fs -cat /wanghongen/demo/fileout/wordcount_out8/part-r-00000

c 1

c++ 2

hadoop 2

hbase 1

helloworld 1

java 3

javascript 1

mapreduce 1

python 1