hadoop mapreduce 基础实例一记词-白红宇

hadoop mapreduce 基础实例一记词

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发布时间：2019-06-25

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mapreduce实现一个简单的单词计数的功能。

一，准备工作：eclipse 安装hadoop 插件:

下载相关版本的hadoop-eclipse-plugin-2.2.0.jar到eclipse/plugins下。

二，实现:

新建mapreduce project

map 用于分词，reduce计数。

package tank.demo;import java.io.IOException;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;/** * @author tank * @date:2015年1月5日 上午10:03:43 * @description:记词器 * @version :0.1 */public class WordCount {    public static class TokenizerMapper extends Mapper
     
       {        private final static IntWritable one = new IntWritable(1);        private Text word = new Text();        public void map(LongWritable 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);            }        }    }    public static class IntSumReducer extends Reducer
      
        {        private IntWritable result = new IntWritable();        public void reduce(Text key, Iterable
       
         values, Context context) throws IOException, InterruptedException {            int sum = 0;            for (IntWritable val : values) {                sum += val.get();            }            result.set(sum);            context.write(key, result);        }    }    public static void main(String[] args) throws Exception {                 Configuration conf = new Configuration();        if (args.length != 2) {            System.err.println("Usage: wordcount  ");            System.exit(2);        }        Job job = new Job(conf, "word count");        //主类        job.setJarByClass(WordCount.class);                job.setMapperClass(TokenizerMapper.class);        job.setReducerClass(IntSumReducer.class);        //map输出格式        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(IntWritable.class);        //输出格式        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);                FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));                System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

打包world-count.jar

三，准备输入数据

hadoop fs -mkdir /user/hadoop/input//建好输入目录

//随便写点数据文件

echo hello my hadoop this is my first application>file1

echo hello world my deer my applicaiton >file2

//拷贝到hdfs中

hadoop fs -put file* /user/hadoop/input

hadoop fs -ls /user/hadoop/input //查看

四，运行

上传到集群环境中:

hadoop jar world-count.jar WordCount input output

截取一段输出如：

15/01/05 11:14:36 INFO mapred.Task: Task:attempt_local1938802295_0001_r_000000_0 is done. And is in the process of committing

15/01/05 11:14:36 INFO mapred.LocalJobRunner:

15/01/05 11:14:36 INFO mapred.Task: Task attempt_local1938802295_0001_r_000000_0 is allowed to commit now

15/01/05 11:14:36 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1938802295_0001_r_000000_0' to hdfs://192.168.183.130:9000/user/hadoop/output/_temporary/0/task_local1938802295_0001_r_000000

15/01/05 11:14:36 INFO mapred.LocalJobRunner: reduce > reduce

15/01/05 11:14:36 INFO mapred.Task: Task 'attempt_local1938802295_0001_r_000000_0' done.

15/01/05 11:14:36 INFO mapreduce.Job: Job job_local1938802295_0001 running in uber mode : false

15/01/05 11:14:36 INFO mapreduce.Job: map 100% reduce 100%

15/01/05 11:14:36 INFO mapreduce.Job: Job job_local1938802295_0001 completed successfully

15/01/05 11:14:36 INFO mapreduce.Job: Counters: 32

File System Counters

FILE: Number of bytes read=17706

FILE: Number of bytes written=597506

FILE: Number of read operations=0

FILE: Number of large read operations=0

FILE: Number of write operations=0