1.命令
[hadoop@namenode mapreduce]$ hadoop jar hadoop-mapreduce-examples-3.3.6.jar wordcount /wordcount/input /wordcount/output
执行命令hadoop jar hadoop-mapreduce-examples-3.3.6.jar wordcount /wordcount/input /wordcount/output
hadoop jar执行jar命令hadoop-mapreduce-examples-3.3.6.jarwordcount程序的所在jar包wordcount程序主类名/wordcount/input输入文件夹/wordcount/output输出文件夹
2.执行信息
2023-10-30 05:20:34,833 INFO client.DefaultNoHARMFailoverProxyProvider: Connecting to ResourceManager at namenode/192.168.42.134:8032
2023-10-30 05:20:35,992 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/hadoop/.staging/job_1698655691785_0002
92023-10-30 05:20:36,400 INFO input.FileInputFormat: Total input files to process : 1 #输入的文件有1个
2023-10-30 05:20:36,614 INFO mapreduce.JobSubmitter: number of splits:1
2023-10-30 05:20:37,219 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_169865561785_0002 #对应的hadoop job的id为job_1698655691785_0002
2023-10-30 05:20:37,219 INFO mapreduce.JobSubmitter: Executing with tokens: []
2023-10-30 05:20:37,478 INFO conf.Configuration: resource-types.xml not found #3.3.6版本，应该是配置没配好，但是不影响此次的运行
2023-10-30 05:20:37,478 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2023-10-30 05:20:37,621 INFO impl.YarnClientImpl: Submitted application application_1698655691785_0002
2023-10-30 05:20:37,690 INFO mapreduce.Job: The url to track the job: http://namenode:8088/proxy/application_1698655691785_0002/
2023-10-30 05:20:37,691 INFO mapreduce.Job: Running job: job_1698655691785_0002
2023-10-30 05:20:55,419 INFO mapreduce.Job: Job job_1698655691785_0002 running in uber mode : false
2023-10-30 05:20:55,429 INFO mapreduce.Job: map 0% reduce 0% #mapreduce分为map和reduce两个阶段进行
2023-10-30 05:21:01,510 INFO mapreduce.Job: map 100% reduce 0%
2023-10-30 05:21:10,630 INFO mapreduce.Job: map 100% reduce 100%
2023-10-30 05:21:11,650 INFO mapreduce.Job: Job job_1698655691785_0002 completed successfully #成功完成，结果将在hdfs的/wordcount/output/下
2023-10-30 05:21:11,831 INFO mapreduce.Job: Counters: 54
File System Counters
FILE: Number of bytes read=77 #job读取本地文件系统的文件字节数
FILE: Number of bytes written=553735 #map task往磁盘写入的字节数
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=159
HDFS: Number of bytes written=47
HDFS: Number of read operations=8
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
HDFS: Number of bytes read erasure-coded=0
Job Counters
Launched map tasks=1 #启动的map task数量(根据输入文件的大小、数量等有关)
Launched reduce tasks=1 #启动的reduce task数量
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=4253
Total time spent by all reduces in occupied slots (ms)=6324
Total time spent by all map tasks (ms)=4253
Total time spent by all reduce tasks (ms)=6324
Total vcore-milliseconds taken by all map tasks=4253
Total vcore-milliseconds taken by all reduce tasks=6324
Total megabyte-milliseconds taken by all map tasks=4355072
Total megabyte-milliseconds taken by all reduce tasks=6475776
Map-Reduce Framework
Map input records=3
Map output records=8
Map output bytes=80
Map output materialized bytes=77
Input split bytes=111
Combine input records=8
Combine output records=6
Reduce input groups=6
Reduce shuffle bytes=77
Reduce input records=6
Reduce output records=6
Spilled Records=12
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=228
CPU time spent (ms)=1190
Physical memory (bytes) snapshot=326664192
Virtual memory (bytes) snapshot=5482606592
Total committed heap usage (bytes)=141778944
Peak Map Physical memory (bytes)=212803584
Peak Map Virtual memory (bytes)=2737623040
Peak Reduce Physical memory (bytes)=113860608
Peak Reduce Virtual memory (bytes)=2744983552
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=48
File Output Format Counters
Bytes Written=47
3.查看结果
方法一：
[hadoop@namenode dfs]$ cat /export/data/hadoop-3.3.6/dfs/data/current/BP-484505762-192.168.42.134-1698145927355/current/finalized/subdir0/subdir0/blk_1073741847
hadoop 2
happy 1
hello 2
new 1
world 1
years 1
参考：https://blog.csdn.net/weixin_43114954/article/details/115571939
方法二：
[hadoop@namenode ~]$ hadoop fs -cat /wordcount/output/part-r-00000
hadoop 2
happy 1
hello 2
new 1
world 1
years 1
4.分析参考