先生成测试数据：while true; do seq 1 100000 >> tmpfile; done; 差不多可以了就Ctrl+c
然后将数据放到hdfs上，hadoop fs -put tmpfile /data/
接着运行MapReduce程序：hadoop jar WordCount.jar mypackage/WordCount1 /data/tmpfile /output2
效果如下：
14/07/15 13:26:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/07/15 13:26:03 INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:8032
14/07/15 13:26:05 INFO input.FileInputFormat: Total input paths to process : 1
14/07/15 13:26:05 INFO mapreduce.JobSubmitter: number of splits:6
14/07/15 13:26:06 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1405397597558_0003
14/07/15 13:26:06 INFO impl.YarnClientImpl: Submitted application application_1405397597558_0003
14/07/15 13:26:06 INFO mapreduce.Job: The url to track the job: :8088/proxy/application_1405397597558_0003/
14/07/15 13:26:06 INFO mapreduce.Job: Running job: job_1405397597558_0003
14/07/15 13:26:20 INFO mapreduce.Job: Job job_1405397597558_0003 running in uber mode : false
14/07/15 13:26:20 INFO mapreduce.Job: map 0% reduce 0%
14/07/15 13:26:34 WARN mapreduce.Counters: Group org.apache.hadoop.mapred.Task$Counter is deprecated. Use org.apache.hadoop.mapreduce.TaskCounter instead
输入行数：0
14/07/15 13:26:48 INFO mapreduce.Job: map 2% reduce 0%
输入行数：3138474
14/07/15 13:26:51 INFO mapreduce.Job: map 5% reduce 0%
14/07/15 13:26:54 INFO mapreduce.Job: map 6% reduce 0%
14/07/15 13:26:55 INFO mapreduce.Job: map 8% reduce 0%
14/07/15 13:26:57 INFO mapreduce.Job: map 9% reduce 0%
14/07/15 13:26:58 INFO mapreduce.Job: map 11% reduce 0%
14/07/15 13:27:00 INFO mapreduce.Job: map 12% reduce 0%
14/07/15 13:27:01 INFO mapreduce.Job: map 13% reduce 0%
输入行数：23383595
14/07/15 13:27:05 INFO mapreduce.Job: map 14% reduce 0%
输入行数：23383595
14/07/15 13:27:23 INFO mapreduce.Job: map 15% reduce 0%
14/07/15 13:27:27 INFO mapreduce.Job: map 16% reduce 0%
14/07/15 13:27:28 INFO mapreduce.Job: map 18% reduce 0%
14/07/15 13:27:30 INFO mapreduce.Job: map 19% reduce 0%
14/07/15 13:27:31 INFO mapreduce.Job: map 21% reduce 0%
14/07/15 13:27:34 INFO mapreduce.Job: map 24% reduce 0%
输入行数：38430301
14/07/15 13:27:37 INFO mapreduce.Job: map 25% reduce 0%
14/07/15 13:27:40 INFO mapreduce.Job: map 26% reduce 0%
输入行数：42826322
14/07/15 13:27:57 INFO mapreduce.Job: map 27% reduce 0%
14/07/15 13:28:00 INFO mapreduce.Job: map 29% reduce 0%
14/07/15 13:28:02 INFO mapreduce.Job: map 30% reduce 0%
14/07/15 13:28:03 INFO mapreduce.Job: map 32% reduce 0%
输入行数：54513531
14/07/15 13:28:05 INFO mapreduce.Job: map 33% reduce 0%
14/07/15 13:28:06 INFO mapreduce.Job: map 34% reduce 0%
14/07/15 13:28:08 INFO mapreduce.Job: map 35% reduce 0%
14/07/15 13:28:09 INFO mapreduce.Job: map 36% reduce 0%
输入行数：60959081
14/07/15 13:28:22 INFO mapreduce.Job: map 42% reduce 0%
14/07/15 13:28:30 INFO mapreduce.Job: map 43% reduce 0%
14/07/15 13:28:31 INFO mapreduce.Job: map 44% reduce 0%
14/07/15 13:28:34 INFO mapreduce.Job: map 45% reduce 0%
14/07/15 13:28:35 INFO mapreduce.Job: map 46% reduce 0%
输入行数：69936159
14/07/15 13:28:37 INFO mapreduce.Job: map 47% reduce 0%
14/07/15 13:28:38 INFO mapreduce.Job: map 48% reduce 0%
14/07/15 13:28:41 INFO mapreduce.Job: map 49% reduce 0%
14/07/15 13:28:44 INFO mapreduce.Job: map 50% reduce 0%
输入行数：77160461
14/07/15 13:29:01 INFO mapreduce.Job: map 51% reduce 0%
14/07/15 13:29:04 INFO mapreduce.Job: map 52% reduce 0%
14/07/15 13:29:05 INFO mapreduce.Job: map 53% reduce 0%
输入行数：83000373
14/07/15 13:29:07 INFO mapreduce.Job: map 54% reduce 0%
14/07/15 13:29:09 INFO mapreduce.Job: map 55% reduce 0%
14/07/15 13:29:10 INFO mapreduce.Job: map 56% reduce 0%
14/07/15 13:29:13 INFO mapreduce.Job: map 57% reduce 0%
14/07/15 13:29:16 INFO mapreduce.Job: map 58% reduce 0%
输入行数：93361766
14/07/15 13:29:32 INFO mapreduce.Job: map 59% reduce 0%
输入行数：98194696
14/07/15 13:29:35 INFO mapreduce.Job: map 60% reduce 0%
14/07/15 13:29:37 INFO mapreduce.Job: map 61% reduce 0%
14/07/15 13:29:38 INFO mapreduce.Job: map 62% reduce 0%
14/07/15 13:29:40 INFO mapreduce.Job: map 63% reduce 0%
14/07/15 13:29:41 INFO mapreduce.Job: map 64% reduce 0%
14/07/15 13:29:44 INFO mapreduce.Job: map 65% reduce 0%
14/07/15 13:29:48 INFO mapreduce.Job: map 66% reduce 0%
输入行数：109562184
14/07/15 13:30:04 INFO mapreduce.Job: map 67% reduce 0%
输入行数：113362818
14/07/15 13:30:06 INFO mapreduce.Job: map 68% reduce 0%
14/07/15 13:30:08 INFO mapreduce.Job: map 69% reduce 0%
14/07/15 13:30:10 INFO mapreduce.Job: map 70% reduce 0%
14/07/15 13:30:12 INFO mapreduce.Job: map 71% reduce 0%
14/07/15 13:30:15 INFO mapreduce.Job: map 72% reduce 0%
输入行数：123074119
14/07/15 13:30:32 INFO mapreduce.Job: map 76% reduce 0%
14/07/15 13:30:33 INFO mapreduce.Job: map 80% reduce 0%
14/07/15 13:30:34 INFO mapreduce.Job: map 83% reduce 0%
14/07/15 13:30:35 INFO mapreduce.Job: map 84% reduce 0%
输入行数：123074119
14/07/15 13:30:37 INFO mapreduce.Job: map 89% reduce 0%
14/07/15 13:30:38 INFO mapreduce.Job: map 92% reduce 0%
14/07/15 13:30:39 INFO mapreduce.Job: map 95% reduce 0%
14/07/15 13:30:40 INFO mapreduce.Job: map 100% reduce 0%
输入行数：123074119
14/07/15 13:30:53 INFO mapreduce.Job: map 100% reduce 100%
14/07/15 13:30:53 INFO mapreduce.Job: Job job_1405397597558_0003 completed successfully
14/07/15 13:30:53 INFO mapreduce.Job: Counters: 50
File System Counters
FILE: Number of bytes read=58256119
FILE: Number of bytes written=66039749
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=724520133
HDFS: Number of bytes written=1088895
HDFS: Number of read operations=21
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Killed map tasks=2
Launched map tasks=8
Launched reduce tasks=1
Data-local map tasks=8
Total time spent by all maps in occupied slots (ms)=1528715
Total time spent by all reduces in occupied slots (ms)=17508
Total time spent by all map tasks (ms)=1528715
Total time spent by all reduce tasks (ms)=17508
Total vcore-seconds taken by all map tasks=1528715
Total vcore-seconds taken by all reduce tasks=17508
Total megabyte-seconds taken by all map tasks=1565404160
Total megabyte-seconds taken by all reduce tasks=17928192
Map-Reduce Framework
Map input records=123074119
Map output records=123074119
Map output bytes=1216795535
Map output materialized bytes=7133406
Input split bytes=594
Combine input records=127374119
Combine output records=4900000
Reduce input groups=100000
Reduce shuffle bytes=7133406
Reduce input records=600000
Reduce output records=100000
Spilled Records=5500000
Shuffled Maps =6
Failed Shuffles=0
Merged Map outputs=6
GC time elapsed (ms)=39761
CPU time spent (ms)=1397060
Physical memory (bytes) snapshot=1797943296
Virtual memory (bytes) snapshot=5082316800
Total committed heap usage (bytes)=1398800384
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=724519539
File Output Format Counters
Bytes Written=1088895