hadoop mapreduce 基础实例一记词

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<LongWritable, Text, Text, IntWritable> {
        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<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> 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
                HDFS: Number of bytes read=205
                HDFS: Number of bytes written=85
                HDFS: Number of read operations=25
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=5
        Map-Reduce Framework
                Map input records=2
                Map output records=14
                Map output bytes=136
                Map output materialized bytes=176
                Input split bytes=232
                Combine input records=0
                Combine output records=0
                Reduce input groups=10
                Reduce shuffle bytes=0
                Reduce input records=14
                Reduce output records=10
                Spilled Records=28
                Shuffled Maps =0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=67
                CPU time spent (ms)=0
                Physical memory (bytes) snapshot=0
                Virtual memory (bytes) snapshot=0
                Total committed heap usage (bytes)=456536064
        File Input Format Counters
                Bytes Read=80
        File Output Format Counters
                Bytes Written=85

查看输出目录下的文件

[[email protected] ~]$ hadoop fs -cat /user/hadoop/output/part-r-00000
applicaiton     1
application     1
deer    1
first   1
hadoop  1
hello   2
is      1
my      4
this    1
world   1

已经正确统计出单词数量！