Java编程MapReduce实现WordCount

Java编程MapReduce实现WordCount

1.编写Mapper

package net.toocruel.yarn.mapreduce.wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;
import java.util.StringTokenizer;

/**
 * @author : 宋同煜
 * @version : 1.0
 * @createTime : 2017/4/12 14:15
 * @description :
 */
public class WordCountMapper extends Mapper<Object,Text,Text,IntWritable>{

    //对于每个单词赋予出现频数1，因为单词是一个一个取出来的，所以每个数量都为1
    private final static IntWritable one = new IntWritable(1);
    //存储取出来的一行单词
    private Text word = new Text();

    @Override
    protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        //StringTokenizer 对输入单词进行切分
        StringTokenizer itr = new StringTokenizer(value.toString());
        while(itr.hasMoreTokens())
        {
            word.set(itr.nextToken());
            context.write(word, one);
        }
    }
}
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2.编写Reducer

package net.toocruel.yarn.mapreduce.wordcount;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * @author : 宋同煜
 * @version : 1.0
 * @createTime : 2017/4/12 14:16
 * @description :
 */
public class WordCountReducer extends Reducer<Text,IntWritable,Text,IntWritable>{

    //存取对应单词总频数
    private IntWritable result = new IntWritable();

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        //计算频数
        int sum = 0;
        for(IntWritable value:values){
            sum+=value.get();
        }
        result.set(sum);
        //写入输出
        context.write(key, result);
    }
}
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3.编写Job提交器

package net.toocruel.yarn.mapreduce.wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * wordcount 提交器 打包在hadoop集群任意机器执行 hadoop jar  XXX.jar  net.toocruel.yarn.mapreduce.wordcount WordCount
 * @author : 宋同煜
 * @version : 1.0
 * @createTime : 2017/4/12 14:15
 * @description :
 */
public class WordCount {
    public static void main(String[] args)throws Exception {
        //初始化配置
        Configuration conf = new Configuration();
        System.setProperty("HADOOP_USER_NAME","hdfs");
        //创建一个job提交器对象
        Job job = Job.getInstance(conf);
        job.setJobName("WordCount");
        job.setJarByClass(WordCount.class);


        //设置map，reduce处理
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);

        //设置输出格式处理类
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //设置输入输出路径
        FileSystem.get(new Configuration()).delete(new Path("/sty/wordcount/output")); //先清空输出目录
        FileInputFormat.addInputPath(job, new Path("hdfs://cloudera:8020/sty/wordcount/input"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://cloudera:8020/sty/wordcount/output"));

        boolean res =  job.waitForCompletion(true);
        System.out.println("任务名称: "+job.getJobName());
        System.out.println("任务成功: "+(res?"Yes":"No"));
        System.exit(res?0:1);
    }
}
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4.打包

我用的maven打包，也可以Eclipse的直接导出jar包或Idea的build artifacts

hadoopSimple-1.0.jar

5.运行

在Yarn的ResourceManager 或NodeManager节点机器上运行

hadoop jar hadoopSimple-1.0.jar  net.toocruel.yarn.mapreduce.wordcount.WordCount

6.运行结果

[root@cloudera ~]# hadoop jar hadoopSimple-1.0.jar  net.toocruel.yarn.mapreduce.wordcount.WordCount
17/04/13 12:57:13 INFO client.RMProxy: Connecting to ResourceManager at cloudera/192.168.254.203:8032
17/04/13 12:57:14 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
17/04/13 12:57:18 INFO input.FileInputFormat: Total input paths to process : 1
17/04/13 12:57:18 INFO mapreduce.JobSubmitter: number of splits:1
17/04/13 12:57:18 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1491999347093_0012
17/04/13 12:57:19 INFO impl.YarnClientImpl: Submitted application application_1491999347093_0012
17/04/13 12:57:19 INFO mapreduce.Job: The url to track the job: http://cloudera:8088/proxy/application_1491999347093_0012/
17/04/13 12:57:19 INFO mapreduce.Job: Running job: job_1491999347093_0012
17/04/13 12:57:32 INFO mapreduce.Job: Job job_1491999347093_0012 running in uber mode : false
17/04/13 12:57:32 INFO mapreduce.Job:  map 0% reduce 0%
17/04/13 12:57:39 INFO mapreduce.Job:  map 100% reduce 0%
17/04/13 12:57:47 INFO mapreduce.Job:  map 100% reduce 33%
17/04/13 12:57:49 INFO mapreduce.Job:  map 100% reduce 67%
17/04/13 12:57:53 INFO mapreduce.Job:  map 100% reduce 100%
17/04/13 12:57:54 INFO mapreduce.Job: Job job_1491999347093_0012 completed successfully
17/04/13 12:57:54 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=162
FILE: Number of bytes written=497579
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=233
HDFS: Number of bytes written=62
HDFS: Number of read operations=12
HDFS: Number of large read operations=0
HDFS: Number of write operations=6
Job Counters
Launched map tasks=1
Launched reduce tasks=3
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=5167
Total time spent by all reduces in occupied slots (ms)=18520
Total time spent by all map tasks (ms)=5167
Total time spent by all reduce tasks (ms)=18520
Total vcore-seconds taken by all map tasks=5167
Total vcore-seconds taken by all reduce tasks=18520
Total megabyte-seconds taken by all map tasks=5291008
Total megabyte-seconds taken by all reduce tasks=18964480
Map-Reduce Framework
Map input records=19
Map output records=18
Map output bytes=193
Map output materialized bytes=150
Input split bytes=111
Combine input records=0
Combine output records=0
Reduce input groups=7
Reduce shuffle bytes=150
Reduce input records=18
Reduce output records=7
Spilled Records=36
Shuffled Maps =3
Failed Shuffles=0
Merged Map outputs=3
GC time elapsed (ms)=320
CPU time spent (ms)=4280
Physical memory (bytes) snapshot=805298176
Virtual memory (bytes) snapshot=11053834240
Total committed heap usage (bytes)=529731584
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=122
File Output Format Counters
Bytes Written=62
任务名称: WordCount
任务成功: Yes

Java编程MapReduce实现WordCount

原文：https://www.cnblogs.com/suway/p/9606954.html