自定义wordCount程序、

1.MyWordCount代码：

package com.hadoop.mr;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
public class MyWordCount {
         
            Configuration conf =new Configuration(true);
            
            Job job= Job.getInstance(conf);
            
             // Create a new Job
          // Job job = Job.getInstance();
           job.setJarByClass(MyWordCount.class);
           
           // Specify various job-specific parameters     
           job.setJobName("myjob");
           
      //    job.setInputPath(new Path("in"));
      //    job.setOutputPath(new Path("out"));
           
           Path inPath=new Path("/input/LICENSE.txt");
           FileInputFormat.addInputPath(job, inPath); //可以支持多个输入文件处理
           
           Path outPath=new Path("/outpath");
           
           if( outPath.getFileSystem(conf).exists(outPath)){
              outPath.getFileSystem(conf).delete(outPath,true);//如果存在这个目录就递归删除
           }
           
           
           job.setMapperClass(MyMapper.class);
           job.setMapOutputKeyClass(Text.class);
           job.setMapOutputValueClass(IntWritable.class);
           
           job.setReducerClass(MyReducer.class);
           //上面的各种set就是产生一个对应的xml文件
           // Submit the job, then poll for progress until the job is  complete
           job.waitForCompletion(true);

 

       

2.MyMapper代码

package com.hadoop.mr;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class MyMapper extends Mapper<Object, Text, Text, IntWritable>{
      //将这两个对象放在循环体外可以避免多次创建对象造成jvm内存过大，gc处理过于频繁影响程序运行。   
      private final static IntWritable one = new IntWritable(1);
      private Text word = new Text();
         
      public void map(Object key, Text value, Context context) throws  IOException, InterruptedException {
             
            StringTokenizer itr = new StringTokenizer(value.toString());
          while (itr.hasMoreTokens()) {
            word.set(itr.nextToken());
            context.write(word, one);
          }
         }
}

 

3.MyReducer 代码：

package com.hadoop.mr;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
/**
*
* @author Administrator
*    1.shuffer ：reduce从map中copy属于自己的数据过程。
*    2.sort
*    3.reduce
*/
public class MyReducer extends Reducer<Text, IntWritable, Text,  IntWritable>{
      //相同的key为一组,调用一次reduce方法，在方法迭代这一组数据，进行计算：sum  count max min ...
      private IntWritable result = new IntWritable();
       
      public void reduce(Text key, Iterable<IntWritable> values,
                            Context context) throws IOException,  InterruptedException {
            //hello 1
            //hello 1
            //hello 1
            //hello 1
            //hello 1
            
            //key: hello
            //values:(1,1,1,1,1)
           int sum = 0;
           for (IntWritable val : values) {
             sum += val.get();
           }
           result.set(sum);
           context.write(key, result);
         }
       }
       

4.代码导出为jar包

代码写完毕后

右键export 

这个jar包导入到服务器

 

5.运行自定义wordcount程序

使用这个jar包运行 运行自定义wordcount程序

[root@node01 ~]# hadoop jar MyWordCount.jar com.hadoop.mr.MyWordCount
2018-12-27 02:32:35,703 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm2
2018-12-27 02:32:36,301 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2018-12-27 02:32:36,393 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /user/root/.staging/job_1545843601871_0001
2018-12-27 02:32:37,281 INFO input.FileInputFormat: Total input files to process : 1
2018-12-27 02:32:37,531 INFO mapreduce.JobSubmitter: number of splits:1
2018-12-27 02:32:37,853 INFO Configuration.deprecation: yarn.resourcemanager.zk-address is deprecated. Instead, use hadoop.zk.address
2018-12-27 02:32:37,854 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
2018-12-27 02:32:38,548 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1545843601871_0001
2018-12-27 02:32:38,550 INFO mapreduce.JobSubmitter: Executing with tokens: []
2018-12-27 02:32:39,466 INFO conf.Configuration: resource-types.xml not found
2018-12-27 02:32:39,466 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2018-12-27 02:32:39,684 INFO impl.YarnClientImpl: Submitted application application_1545843601871_0001
2018-12-27 02:32:39,788 INFO mapreduce.Job: The url to track the job: http://node04:8088/proxy/application_1545843601871_0001/
2018-12-27 02:32:39,788 INFO mapreduce.Job: Running job: job_1545843601871_0001
2018-12-27 02:32:53,436 INFO mapreduce.Job: Job job_1545843601871_0001 running in uber mode : false
2018-12-27 02:32:53,437 INFO mapreduce.Job:  map 0% reduce 0%
2018-12-27 02:33:14,764 INFO mapreduce.Job:  map 100% reduce 0%
2018-12-27 02:33:21,853 INFO mapreduce.Job:  map 100% reduce 100%
2018-12-27 02:33:22,896 INFO mapreduce.Job: Job job_1545843601871_0001 completed successfully
2018-12-27 02:33:23,093 INFO mapreduce.Job: Counters: 53
    File System Counters
        FILE: Number of bytes read=271802
        FILE: Number of bytes written=977919
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=147243
        HDFS: Number of bytes written=34795
        HDFS: Number of read operations=8
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters
        Launched map tasks=1
        Launched reduce tasks=1
        Rack-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=17818
        Total time spent by all reduces in occupied slots (ms)=4648
        Total time spent by all map tasks (ms)=17818
        Total time spent by all reduce tasks (ms)=4648
        Total vcore-milliseconds taken by all map tasks=17818
        Total vcore-milliseconds taken by all reduce tasks=4648
        Total megabyte-milliseconds taken by all map tasks=18245632
        Total megabyte-milliseconds taken by all reduce tasks=4759552
    Map-Reduce Framework
        Map input records=2746
        Map output records=21463
        Map output bytes=228869
        Map output materialized bytes=271802
        Input split bytes=99
        Combine input records=0
        Combine output records=0
        Reduce input groups=2965
        Reduce shuffle bytes=271802
        Reduce input records=21463
        Reduce output records=2965
        Spilled Records=42926
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=543
        CPU time spent (ms)=7040
        Physical memory (bytes) snapshot=329469952
        Virtual memory (bytes) snapshot=5474177024
        Total committed heap usage (bytes)=143904768
        Peak Map Physical memory (bytes)=206819328
        Peak Map Virtual memory (bytes)=2734362624
        Peak Reduce Physical memory (bytes)=122650624
        Peak Reduce Virtual memory (bytes)=2739814400
    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=147144
    File Output Format Counters
        Bytes Written=34795