标签：mat http rup one file ken pom.xml 键值对 文件
目录：
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，既然是做计算的框架，那么表现形式就是有个输入（input），mapreduce操作这个输入（input），通过本身定义好的计算模型，得到一个输出（output），这个输出就是我们所需要的结果。
我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，每个阶段都是用键值对（key/value）作为输入（input）和输出（output）。而程序员要做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>
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
然后查看
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
然后查看可得
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。
参考文章：
Hadoop之MapReduce的HelloWorld（七）
三.hadoop mapreduce之WordCount例子
标签：mat http rup one file ken pom.xml 键值对 文件
原文地址：https://www.cnblogs.com/xiaoliu66007/p/9322992.html
