Hadoop-Eclipse|Hadoop-Eclipse mapreduce测试WordCountMapReduce Hadoop-Eclipsemapreduce测试WordCoun

MapReduce编程重点把握 MapReduce核心概念

中心思想：分而治之，分布式计算模型 Map（映射）分布式的计算模型，处理海量数据一个简单的MR程序需要指定： map()、reduce()、input、output 处理的数据放在input中，处理的结果放在output中![](http://upload-images.jianshu.io/upload_images/2409103-c893fe48912e145a.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240) MR有一定的格式如下：

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以进行流向的
input -> map() -> reduce() -> output 【Hadoop-Eclipse|Hadoop-Eclipse mapreduce测试WordCountMapReduce】
思考几个问题

1. 对处理的文件转化成什么样的？ 2. map()输出结果变成什么样的？ 3. reduce()是怎么处理的，输出的又是什么样的？

词频统计wordcount的具体执行过程

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默认是把每一行当成一个
hadoop hdfs -> <0, hadoop hdfs>
hadoop mapreduce hadoop yarn
hadoop hello
mapreduce hadoop
yarn hadoop
比如我们要统计单词（1）分割单词，按照空格进行分词
hadoop hdfs -> hadoop hdfs
记录出现的次数，每出现一次记录为：
Map()

reduce的输入就是map的输出
reduce() 将相同key的value累加到一起

方法的重写shift+Alt+S
Hadoop-Eclipse|Hadoop-Eclipse mapreduce测试WordCountMapReduce

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MapReduce代码逻辑主干初写 package com.beifeng.bigdata.senior.hadoop.mapreduce;
import java.io.IOException;
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; public class WordCountMapReduce {
//Step 1: Mapper Class
public static class WordCountMapper extends Mapper{
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub } }
//Step 2: Reducer Class public static class WordCountReducer extends
Reducer{
@Override
protected void reduce(Text key, Iterable
values, Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub }
public int run(String[] args) {
// TODO Auto-generated method stub return 0; }
}
//Step 3: Driver public int run(String[] args) throws Exception {
//read configuration file
Configuration configuration = new Configuration();
//create job
Job job = Job.getInstance(configuration, this.getClass().getSimpleName()); job.setJarByClass(this.getClass());
//set job
//input:
Path inpath = new Path(args[0]); FileInputFormat.addInputPath(job, inpath);
//mapper
job.setMapperClass(WordCountMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class);
//reducer
job.setReducerClass(WordCountReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class);
//submit job
boolean isSucess = job.waitForCompletion(true);
return isSucess ? 0:1;
}
public static void main(String[] args) throws Exception { args = new String [] {
"hdfs://bigdata-senior01.beifeng.com:8020/user/beifeng/input/",
//
"hdfs://bigdata-senior01.beifeng.com:8020/user/beifeng/output9"
};
//run job
int status = new WordCountReducer().run(args);
System.exit(status);
}
}
对在map和reduce阶段的处理 package com.beifeng.bigdata.senior.hadoop.mapreduce;
import java.io.IOException;
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; public class WordCountMapReduce {
//Step 1: Mapper Class
public static class WordCountMapper extends
Mapper{
private Text mapOutputKey = new Text();
private final static IntWritable mapOutputValue = https://www.it610.com/article/new IntWritable();
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
//line value
String lineValue = https://www.it610.com/article/value.toString();
//split line words into separated word
String [] strs = lineValue.split(" ");
//record each value by "for" iterator to build
for (String str : strs) {
}
}
}
//Step 2: Reducer Class
public static class WordCountReducer extends
Reducer{
private IntWritable outputValue = https://www.it610.com/article/new IntWritable(); @Override
protected void reduce(Text key, Iterable values,
Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
int sum = 0;
//Iterator
for(IntWritable value : values){
sum += value.get();
}
//set output value
outputValue.set(sum);
//output
context.write(key, outputValue);
}
public int run(String[] args) {
// TODO Auto-generated method stub
return 0;
}
}
//Step 3: Driver
public int run(String[] args) throws Exception {
//read configuration file
Configuration configuration = new Configuration();
//create job
Job job = Job.getInstance(configuration, this.getClass().getSimpleName()); job.setJarByClass(this.getClass());
//set job
//input:
Path inpath = new Path(args[0]); FileInputFormat.addInputPath(job, inpath);
//mapper
job.setMapperClass(WordCountMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); //reducer
job.setReducerClass(WordCountReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class);
//submit job
boolean isSucess = job.waitForCompletion(true);
return isSucess ? 0:1;
}
public static void main(String[] args) throws Exception { args = new String [] {
"hdfs://bigdata-senior01.beifeng.com:8020/user/beifeng/input/",
"hdfs://bigdata-senior01.beifeng.com:8020/user/beifeng/output"
};
//run job
int status = new WordCountMapReduce().run(args); System.exit(status);
}
}
尝试使用mapreduce来处理wordcount package com.beifeng.bigdata.senior.hadoop.mapreduce;
import java.io.IOException;
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; public class WordCountMapReduce {
//Step 1: Mapper Class
public static class WordCountMapper extends Mapper{
private Text mapOutputKey = new Text();
private final static IntWritable mapOutputValue = https://www.it610.com/article/new IntWritable(1);
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub System.out.println("map-in-0-key" + key.get() + " -- " + "map-in-value" + value.toString());
//line value
String lineValue = https://www.it610.com/article/value.toString();
//split line words into separated word
String [] strs = lineValue.split(" ");
//record each value by "for" iterator to build for (String str : strs) {
//set map output key mapOutputKey.set(str);
//output
context.write(mapOutputKey, mapOutputValue); }
}
}
//Step 2: Reducer Class
public static class WordCountReducer extends
Reducer{
private IntWritable outputValue = https://www.it610.com/article/new IntWritable(); @Override
protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
// TODO Auto-generated method stub
int sum = 0;
//Iterator
for(IntWritable value : values){
sum += value.get();
}
//set output value
outputValue.set(sum);
//output
context.write(key, outputValue);
}
}
//Step 3: Driver
public int run(String[] args) throws Exception {
//read configuration file
Configuration configuration = new Configuration();
//create job
Job job = Job.getInstance(configuration, this.getClass().getSimpleName()); job.setJarByClass(this.getClass());
//set job
//input:
Path inpath = new Path(args[0]); FileInputFormat.addInputPath(job, inpath);
//output
Path outPath = new Path(args[1]); FileOutputFormat.setOutputPath(job, outPath); //mapper
job.setMapperClass(WordCountMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); //reducer
job.setReducerClass(WordCountReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class);
//submit job
boolean isSucess = job.waitForCompletion(true);
return isSucess ? 0 : 1;
}
public static void main(String[] args) throws Exception { args = new String [] {
"hdfs://bigdata-senior01.beifeng.com:8020/user/beifeng/input",
"hdfs://bigdata-senior01.beifeng.com:8020/user/beifeng/output13"};
//run job
int status = new WordCountMapReduce().run(args); System.exit(status);
}
}
打成jar包在yarn上运行 Hadoop-Eclipse|Hadoop-Eclipse mapreduce测试WordCountMapReduce