hadoop简单的表白代码 hadoop example

一、倒排索引案例（多job串联）

1、需求

有大量的文本（文档、网页），需要建立搜索索引，如图4-31所示。

（1）数据输入

（2）期望输出数据

atguigu c.txt–>2 b.txt–>2 a.txt–>3

pingping c.txt–>1 b.txt–>3 a.txt–>1

ss c.txt–>1 b.txt–>1 a.txt–>2

2、需求分析

3、第一次处理

（1）第一次处理，编写OneIndexMapper类

package com.atguigu.mapreduce.index;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;

public class OneIndexMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
	
	String name;
	Text k = new Text();
	IntWritable v = new IntWritable();
	
	@Override
	protected void setup(Context context)throws IOException, InterruptedException {

		// 获取文件名称
		FileSplit split = (FileSplit) context.getInputSplit();
		
		name = split.getPath().getName();
	}
	
	@Override
	protected void map(LongWritable key, Text value, Context context)	throws IOException, InterruptedException {

		// 1 获取1行
		String line = value.toString();
		
		// 2 切割
		String[] fields = line.split(" ");
		
		for (String word : fields) {

			// 3 拼接
			k.set(word+"--"+name);
			v.set(1);
			
			// 4 写出
			context.write(k, v);
		}
	}
}

（2）第一次处理，编写OneIndexReducer类

package com.atguigu.mapreduce.index;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class OneIndexReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
	
IntWritable v = new IntWritable();

	@Override
	protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
		
		int sum = 0;

		// 1 累加求和
		for(IntWritable value: values){
			sum +=value.get();
		}
		
       v.set(sum);

		// 2 写出
		context.write(key, v);
	}
}

（3）第一次处理，编写OneIndexDriver类

package com.atguigu.mapreduce.index;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class OneIndexDriver {

	public static void main(String[] args) throws Exception {

       // 输入输出路径需要根据自己电脑上实际的输入输出路径设置
		args = new String[] { "e:/input/inputoneindex", "e:/output5" };

		Configuration conf = new Configuration();

		Job job = Job.getInstance(conf);
		job.setJarByClass(OneIndexDriver.class);

		job.setMapperClass(OneIndexMapper.class);
		job.setReducerClass(OneIndexReducer.class);

		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);

		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		job.waitForCompletion(true);
	}
}

（4）查看第一次输出结果

atguigu--a.txt	3
atguigu--b.txt	2
atguigu--c.txt	2
pingping--a.txt	1
pingping--b.txt	3
pingping--c.txt	1
ss--a.txt	2
ss--b.txt	1
ss--c.txt	1

4、第二次处理

（1）第二次处理，编写TwoIndexMapper类

package com.atguigu.mapreduce.index;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class TwoIndexMapper extends Mapper<LongWritable, Text, Text, Text>{

	Text k = new Text();
	Text v = new Text();
	
	@Override
	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
		
		// 1 获取1行数据
		String line = value.toString();
		
		// 2用“--”切割
		String[] fields = line.split("--");
		
		k.set(fields[0]);
		v.set(fields[1]);
		
		// 3 输出数据
		context.write(k, v);
	}
}

（2）第二次处理，编写TwoIndexReducer类

package com.atguigu.mapreduce.index;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class TwoIndexReducer extends Reducer<Text, Text, Text, Text> {

Text v = new Text();

	@Override
	protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
		// atguigu a.txt 3
		// atguigu b.txt 2
		// atguigu c.txt 2

		// atguigu c.txt-->2 b.txt-->2 a.txt-->3

		StringBuilder sb = new StringBuilder();

        // 1 拼接
		for (Text value : values) {
			sb.append(value.toString().replace("\t", "-->") + "\t");
		}

v.set(sb.toString());

		// 2 写出
		context.write(key, v);
	}
}

（3）第二次处理，编写TwoIndexDriver类

package com.atguigu.mapreduce.index;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class TwoIndexDriver {

	public static void main(String[] args) throws Exception {

       // 输入输出路径需要根据自己电脑上实际的输入输出路径设置
args = new String[] { "e:/input/inputtwoindex", "e:/output6" };

		Configuration config = new Configuration();
		Job job = Job.getInstance(config);

job.setJarByClass(TwoIndexDriver.class);
		job.setMapperClass(TwoIndexMapper.class);
		job.setReducerClass(TwoIndexReducer.class);

		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);

		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		boolean result = job.waitForCompletion(true);
System.exit(result?0:1);
	}
}

（4）第二次查看最终结果

atguigu	c.txt-->2	b.txt-->2	a.txt-->3	
pingping	c.txt-->1	b.txt-->3	a.txt-->1	
ss	c.txt-->1	b.txt-->1	a.txt-->2

二、TopN案例

1、需求

对需求2.3输出结果进行加工，输出流量使用量在前10的用户信息

（1）输入数据 （2）输出数据

2、需求分析

3、实现代码

（1）编写FlowBean类

package com.atguigu.mr.top;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

public class FlowBean implements WritableComparable<FlowBean>{

	private long upFlow;
	private long downFlow;
	private long sumFlow;
	
	
	public FlowBean() {
		super();
	}

	public FlowBean(long upFlow, long downFlow) {
		super();
		this.upFlow = upFlow;
		this.downFlow = downFlow;
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(upFlow);
		out.writeLong(downFlow);
		out.writeLong(sumFlow);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		upFlow = in.readLong();
		downFlow = in.readLong();
		sumFlow = in.readLong();
	}

	public long getUpFlow() {
		return upFlow;
	}

	public void setUpFlow(long upFlow) {
		this.upFlow = upFlow;
	}

	public long getDownFlow() {
		return downFlow;
	}

	public void setDownFlow(long downFlow) {
		this.downFlow = downFlow;
	}

	public long getSumFlow() {
		return sumFlow;
	}

	public void setSumFlow(long sumFlow) {
		this.sumFlow = sumFlow;
	}

	@Override
	public String toString() {
		return upFlow + "\t" + downFlow + "\t" + sumFlow;
	}

	public void set(long downFlow2, long upFlow2) {
		downFlow = downFlow2;
		upFlow = upFlow2;
		sumFlow = downFlow2 + upFlow2;
	}

	@Override
	public int compareTo(FlowBean bean) {
		
		int result;
		
		if (this.sumFlow > bean.getSumFlow()) {
			result = -1;
		}else if (this.sumFlow < bean.getSumFlow()) {
			result = 1;
		}else {
			result = 0;
		}
		
		return result;
	}
}

（2）编写TopNMapper类

package com.atguigu.mr.top;

import java.io.IOException;
import java.util.Iterator;
import java.util.TreeMap;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class TopNMapper extends Mapper<LongWritable, Text, FlowBean, Text>{
	
	// 定义一个TreeMap作为存储数据的容器（天然按key排序）
	private TreeMap<FlowBean, Text> flowMap = new TreeMap<FlowBean, Text>();
	private FlowBean kBean;
	
	@Override
	protected void map(LongWritable key, Text value, Context context)	throws IOException, InterruptedException {
		
		kBean = new FlowBean();
		Text v = new Text();
		
		// 1 获取一行
		String line = value.toString();
		
		// 2 切割
		String[] fields = line.split("\t");
		
		// 3 封装数据
		String phoneNum = fields[0];
		long upFlow = Long.parseLong(fields[1]);
		long downFlow = Long.parseLong(fields[2]);
		long sumFlow = Long.parseLong(fields[3]);
		
		kBean.setDownFlow(downFlow);
		kBean.setUpFlow(upFlow);
		kBean.setSumFlow(sumFlow);
		
		v.set(phoneNum);
		
		// 4 向TreeMap中添加数据
		flowMap.put(kBean, v);
		
		// 5 限制TreeMap的数据量，超过10条就删除掉流量最小的一条数据
		if (flowMap.size() > 10) {
//		flowMap.remove(flowMap.firstKey());
			flowMap.remove(flowMap.lastKey());		
}
	}
	
	@Override
	protected void cleanup(Context context) throws IOException, InterruptedException {
		
		// 6 遍历treeMap集合，输出数据
		Iterator<FlowBean> bean = flowMap.keySet().iterator();

		while (bean.hasNext()) {

			FlowBean k = bean.next();

			context.write(k, flowMap.get(k));
		}
	}
}

（3）编写TopNReducer类

package com.atguigu.mr.top;

import java.io.IOException;
import java.util.Iterator;
import java.util.TreeMap;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class TopNReducer extends Reducer<FlowBean, Text, Text, FlowBean> {

	// 定义一个TreeMap作为存储数据的容器（天然按key排序）
	TreeMap<FlowBean, Text> flowMap = new TreeMap<FlowBean, Text>();

	@Override
	protected void reduce(FlowBean key, Iterable<Text> values, Context context)throws IOException, InterruptedException {

		for (Text value : values) {

			 FlowBean bean = new FlowBean();
			 bean.set(key.getDownFlow(), key.getUpFlow());

			 // 1 向treeMap集合中添加数据
			flowMap.put(bean, new Text(value));

			// 2 限制TreeMap数据量，超过10条就删除掉流量最小的一条数据
			if (flowMap.size() > 10) {
				// flowMap.remove(flowMap.firstKey());
flowMap.remove(flowMap.lastKey());
			}
		}
	}

	@Override
	protected void cleanup(Reducer<FlowBean, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {

		// 3 遍历集合，输出数据
		Iterator<FlowBean> it = flowMap.keySet().iterator();

		while (it.hasNext()) {

			FlowBean v = it.next();

			context.write(new Text(flowMap.get(v)), v);
		}
	}
}

（4）编写TopNDriver类

package com.atguigu.mr.top;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class TopNDriver {

	public static void main(String[] args) throws Exception {
		
		args  = new String[]{"e:/output1","e:/output3"};
		
		// 1 获取配置信息，或者job对象实例
		Configuration configuration = new Configuration();
		Job job = Job.getInstance(configuration);

		// 6 指定本程序的jar包所在的本地路径
		job.setJarByClass(TopNDriver.class);

		// 2 指定本业务job要使用的mapper/Reducer业务类
		job.setMapperClass(TopNMapper.class);
		job.setReducerClass(TopNReducer.class);

		// 3 指定mapper输出数据的kv类型
		job.setMapOutputKeyClass(FlowBean.class);
		job.setMapOutputValueClass(Text.class);

		// 4 指定最终输出的数据的kv类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(FlowBean.class);

		// 5 指定job的输入原始文件所在目录
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
		boolean result = job.waitForCompletion(true);
		System.exit(result ? 0 : 1);
	}
}

三、找博客共同好友案例

1、需求

以下是博客的好友列表数据，冒号前是一个用户，冒号后是该用户的所有好友（数据中的好友关系是单向的）

求出哪些人两两之间有共同好友，及他俩的共同好友都有谁？

（1）数据输入

2、需求分析

先求出A、B、C、….等是谁的好友

第一次输出结果

A	I,K,C,B,G,F,H,O,D,
B	A,F,J,E,
C	A,E,B,H,F,G,K,
D	G,C,K,A,L,F,E,H,
E	G,M,L,H,A,F,B,D,
F	L,M,D,C,G,A,
G	M,
H	O,
I	O,C,
J	O,
K	B,
L	D,E,
M	E,F,
O	A,H,I,J,F,

第二次输出结果

A-B	E C 
A-C	D F 
A-D	E F 
A-E	D B C 
A-F	O B C D E 
A-G	F E C D 
A-H	E C D O 
A-I	O 
A-J	O B 
A-K	D C 
A-L	F E D 
A-M	E F 
B-C	A 
B-D	A E 
B-E	C 
B-F	E A C 
B-G	C E A 
B-H	A E C 
B-I	A 
B-K	C A 
B-L	E 
B-M	E 
B-O	A 
C-D	A F 
C-E	D 
C-F	D A 
C-G	D F A 
C-H	D A 
C-I	A 
C-K	A D 
C-L	D F 
C-M	F 
C-O	I A 
D-E	L 
D-F	A E 
D-G	E A F 
D-H	A E 
D-I	A 
D-K	A 
D-L	E F 
D-M	F E 
D-O	A 
E-F	D M C B 
E-G	C D 
E-H	C D 
E-J	B 
E-K	C D 
E-L	D 
F-G	D C A E 
F-H	A D O E C 
F-I	O A 
F-J	B O 
F-K	D C A 
F-L	E D 
F-M	E 
F-O	A 
G-H	D C E A 
G-I	A 
G-K	D A C 
G-L	D F E 
G-M	E F 
G-O	A 
H-I	O A 
H-J	O 
H-K	A C D 
H-L	D E 
H-M	E 
H-O	A 
I-J	O 
I-K	A 
I-O	A 
K-L	D 
K-O	A 
L-M	E F

3、代码实现

（1）第一次Mapper类

package com.atguigu.mapreduce.friends;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class OneShareFriendsMapper extends Mapper<LongWritable, Text, Text, Text>{
	
	@Override
	protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
			throws IOException, InterruptedException {

		// 1 获取一行 A:B,C,D,F,E,O
		String line = value.toString();
		
		// 2 切割
		String[] fields = line.split(":");
		
		// 3 获取person和好友
		String person = fields[0];
		String[] friends = fields[1].split(",");
		
		// 4写出去
		for(String friend: friends){

			// 输出 <好友，人>
			context.write(new Text(friend), new Text(person));
		}
	}
}

（2）第一次Reducer类

package com.atguigu.mapreduce.friends;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class OneShareFriendsReducer extends Reducer<Text, Text, Text, Text>{
	
	@Override
	protected void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException {
		
		StringBuffer sb = new StringBuffer();

		//1 拼接
		for(Text person: values){
			sb.append(person).append(",");
		}
		
		//2 写出
		context.write(key, new Text(sb.toString()));
	}
}

（3）第一次Driver类

package com.atguigu.mapreduce.friends;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class OneShareFriendsDriver {

	public static void main(String[] args) throws Exception {
		
// 1 获取job对象
		Configuration configuration = new Configuration();
		Job job = Job.getInstance(configuration);
		
		// 2 指定jar包运行的路径
		job.setJarByClass(OneShareFriendsDriver.class);

		// 3 指定map/reduce使用的类
		job.setMapperClass(OneShareFriendsMapper.class);
		job.setReducerClass(OneShareFriendsReducer.class);
		
		// 4 指定map输出的数据类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);
		
		// 5 指定最终输出的数据类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		
		// 6 指定job的输入原始所在目录
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		
		// 7 提交
		boolean result = job.waitForCompletion(true);
		
		System.exit(result?0:1);
	}
}

（4）第二次Mapper类

package com.atguigu.mapreduce.friends;
import java.io.IOException;
import java.util.Arrays;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class TwoShareFriendsMapper extends Mapper<LongWritable, Text, Text, Text>{
	
	@Override
	protected void map(LongWritable key, Text value, Context context)
			throws IOException, InterruptedException {

		// A I,K,C,B,G,F,H,O,D,
		// 友 人，人，人
		String line = value.toString();
		String[] friend_persons = line.split("\t");

		String friend = friend_persons[0];
		String[] persons = friend_persons[1].split(",");

		Arrays.sort(persons);

		for (int i = 0; i < persons.length - 1; i++) {
			
			for (int j = i + 1; j < persons.length; j++) {
				// 发出 <人-人，好友> ，这样，相同的“人-人”对的所有好友就会到同1个reduce中去
				context.write(new Text(persons[i] + "-" + persons[j]), new Text(friend));
			}
		}
	}
}

（5）第二次Reducer类

package com.atguigu.mapreduce.friends;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class TwoShareFriendsReducer extends Reducer<Text, Text, Text, Text>{
	
	@Override
	protected void reduce(Text key, Iterable<Text> values, Context context)	throws IOException, InterruptedException {
		
		StringBuffer sb = new StringBuffer();

		for (Text friend : values) {
			sb.append(friend).append(" ");
		}
		
		context.write(key, new Text(sb.toString()));
	}
}

（6）第二次Driver类

package com.atguigu.mapreduce.friends;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class TwoShareFriendsDriver {

	public static void main(String[] args) throws Exception {
		
		// 1 获取job对象
		Configuration configuration = new Configuration();
		Job job = Job.getInstance(configuration);
		
		// 2 指定jar包运行的路径
		job.setJarByClass(TwoShareFriendsDriver.class);

		// 3 指定map/reduce使用的类
		job.setMapperClass(TwoShareFriendsMapper.class);
		job.setReducerClass(TwoShareFriendsReducer.class);
		
		// 4 指定map输出的数据类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);
		
		// 5 指定最终输出的数据类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		
		// 6 指定job的输入原始所在目录
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		
		// 7 提交
		boolean result = job.waitForCompletion(true);
		System.exit(result?0:1);
	}
}