三.hadoop mapreduce之WordCount例子

目录：

目录见文章1

 

这个案列完成对单词的计数，重写map,与reduce方法，完成对mapreduce的理解。 

Mapreduce初析

　　Mapreduce是一个计算框架，既然是作计算的框架，那么表现形式就是有个输入（input），mapreduce操做这个输入（input），经过自己定义好的计算模型，获得一个输出（output），这个输出就是咱们所须要的结果。

　　咱们要学习的就是这个计算模型的运行规则。在运行一个mapreduce计算任务时候，任务过程被分为两个阶段：map阶段和reduce阶段，每一个阶段都是用键值对（key/value）做为输入（input）和输出（output）。而程序员要作的就是定义好这两个阶段的函数：map函数和reduce函数。

 

 1.准备 w.txt 文件,用于当测试数据

yaojiale hahaha 
yaojiale llllll  

 

2.构建maven项目，将WordCount类打包成mrtest.jar.丢到hadoop所在服务器上

 pom.xml

<!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.3</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-core -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>2.7.3</version>
        </dependency>
        <!-- 加上这个就不报本地某错了 Cannot initialize Cluster 
  https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-mapreduce-client-common -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-common</artifactId>
            <version>2.6.4</version>
        </dependency>

 

WordCount.java 代码：

package org.apache.hadoop.examples; 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.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; import org.apache.hadoop.util.GenericOptionsParser; public class WordCount { //WordCOuntMap方法接收LongWritable，Text的参数，返回<Text， IntWriatable>键值对。
  public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ 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); } } } 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(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } Job job = new Job(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }

 2.将w.txt放到hdfs下（folder下有w.txt文件）

bin/hdfs dfs -put /usr/software/folder input

而后查看

root@ubuntu:/usr/software/hadoop# bin/hdfs dfs -ls
Found 1 items
drwxr-xr-x   - root supergroup          0 2018-07-16 21:50 input //内有w.txt文件

3.运行程序统计WordCount

bin/hadoop jar /usr/software/mrtest2.jar input output

而后查看可得

root@ubuntu:/usr/software/hadoop# bin/hdfs dfs -ls
Found 2 items
drwxr-xr-x - root supergroup 0 2018-07-16 21:50 input
drwxr-xr-x - root supergroup 0 2018-07-16 22:18 output

root@ubuntu:/usr/software/hadoop# bin/hdfs dfs -cat output/*
hahaha 1
llllll 1
yaojiale 2

完毕。

附录：附上一个hadoop自带的例子：

计算圆周率

 bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.6.jar pi 4 1000


result:

　Estimated value of Pi is 3.14000000000000000000

 

 

 

 参考文章： 

Hadoop之MapReduce的HelloWorld（七）

代码详细解释