如何运行自带wordcount-Hadoop2

Posted 2023-02-20

参考技术A

1.找到examples例子
我们需要找打这个例子的位置：首先需要找到你的hadoop文件夹，然后依照下面路径：
/hadoop/share/hadoop/mapreduce会看到如下图：

hadoop-mapreduce-examples-2.2.0.jar

 

第二步：
我们需要需要做一下运行需要的工作，比如输入输出路径，上传什么文件等。
1.先在HDFS创建几个数据目录：

hadoop fs -mkdir -p /data/wordcount

hadoop fs -mkdir -p /output/

 

2.目录/data/wordcount用来存放Hadoop自带的WordCount例子的数据文件，运行这个MapReduce任务的结果输出到/output/wordcount目录中。
首先新建文件inputWord：

vi /usr/inputWord

新建完毕，查看内容：

cat /usr/inputWord

 

将本地文件上传到HDFS中：

hadoop fs -put /usr/inputWord /data/wordcount/

可以查看上传后的文件情况，执行如下命令：

hadoop fs -ls /data/wordcount

可以看到上传到HDFS中的文件。

 

通过命令

hadoop fs -text /data/wordcount/inputWord

看到如下内容：

下面，运行WordCount例子，执行如下命令：


hadoop jar /usr/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /data/wordcount /output/wordcount

 
可以看到控制台输出程序运行的信息：

aboutyun@master:~$ hadoop jar /usr/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar wordcount /data/wordcount /output/wordcount
14/05/14 10:33:33 INFO client.RMProxy: Connecting to ResourceManager at master/172.16.77.15:8032
14/05/14 10:33:34 INFO input.FileInputFormat: Total input paths to process : 1
14/05/14 10:33:34 INFO mapreduce.JobSubmitter: number of splits:1
14/05/14 10:33:34 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
14/05/14 10:33:34 INFO Configuration.deprecation: mapreduce.combine.class is deprecated. Instead, use mapreduce.job.combine.class
14/05/14 10:33:34 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name
14/05/14 10:33:34 INFO Configuration.deprecation: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
14/05/14 10:33:34 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
14/05/14 10:33:35 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1400084979891_0004
14/05/14 10:33:36 INFO impl.YarnClientImpl: Submitted application application_1400084979891_0004 to ResourceManager at master/172.16.77.15:8032
14/05/14 10:33:36 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1400084979891_0004/
14/05/14 10:33:36 INFO mapreduce.Job: Running job: job_1400084979891_0004
14/05/14 10:33:45 INFO mapreduce.Job: Job job_1400084979891_0004 running in uber mode : false
14/05/14 10:33:45 INFO mapreduce.Job:  map 0% reduce 0%
14/05/14 10:34:10 INFO mapreduce.Job:  map 100% reduce 0%
14/05/14 10:34:19 INFO mapreduce.Job:  map 100% reduce 100%
14/05/14 10:34:19 INFO mapreduce.Job: Job job_1400084979891_0004 completed successfully
14/05/14 10:34:20 INFO mapreduce.Job: Counters: 43
        File System Counters
                FILE: Number of bytes read=81
                FILE: Number of bytes written=158693
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=175
                HDFS: Number of bytes written=51
                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)=23099
                Total time spent by all reduces in occupied slots (ms)=6768
        Map-Reduce Framework
                Map input records=5
                Map output records=10
                Map output bytes=106
                Map output materialized bytes=81
                Input split bytes=108
                Combine input records=10
                Combine output records=6
                Reduce input groups=6
                Reduce shuffle bytes=81
                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)=377
                CPU time spent (ms)=11190
                Physical memory (bytes) snapshot=284524544
                Virtual memory (bytes) snapshot=2000748544
                Total committed heap usage (bytes)=136450048
        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=67
        File Output Format Counters 
                Bytes Written=51



查看结果，执行如下命令：

hadoop fs -text /output/wordcount/part-r-00000

结果数据示例如下：

aboutyun@master:~$ hadoop fs -text /output/wordcount/part-r-00000

aboutyun        2

first        1

hello        3

master        1

slave        2

what        1

 
登录到Web控制台，访问链接http://master:8088/可以看到任务记录情况。