Hadoop学习：MapReduce实现两张表合并
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一、✌题目要求
record表：
| ID | 城市编号 | 空气指数 |
|---|---|---|
| 001 | 03 | 245 |
| 002 | 02 | 655 |
| 003 | 05 | 743 |
| 004 | 04 | 246 |
| 005 | 02 | 956 |
| 006 | 01 | 637 |
| 007 | 05 | 831 |
| 008 | 03 | 683 |
| 009 | 02 | 349 |
| city表： | ||
| 城市编号 | 城市名称 | |
| -- | -- | |
| 01 | 长沙 | |
| 02 | 株洲 | |
| 03 | 湘潭 | |
| 04 | 怀化 | |
| 05 | 岳阳 |
目标表：
| ID | 城市名称 | 空气指数 |
|---|---|---|
| 001 | 湘潭 | 245 |
| 002 | 株洲 | 655 |
| 003 | 岳阳 | 743 |
| 004 | 怀化 | 246 |
| 005 | 株洲 | 956 |
| 006 | 长沙 | 637 |
| 007 | 岳阳 | 831 |
| 008 | 湘潭 | 683 |
| 009 | 株洲 | 349 |
二、✌实现思想
我们将重复字段作为Map的Key，其他属性封装在Bean中作为Value
经过Map后，文件的格式为：
|城市编号| ID |城市名称|空气指数|文件类型|
|--|--|--|--|--|--|
| 1| 006| | 637 |record|
| 1| |长沙 | |city|
| 2| 002| | 655 |record|
| 2 | 005| | 956 |record|
| 2| 009| | 349 |record|
| 2| | 株洲 | |city|
| 3 |001 | | 245 | record|
| 3| 008| | 683 |record|
| 3| | 湘潭| |city|
| 4| 004| | 246 |record|
| 4| | 怀化| |city|
| 5| 003| | 743 |record|
| 5| 007| | 831 |record|
| 5| | 岳阳| |city|
1.将所有属性封装成一个对象，同时实现序列化
2.Map的输入格式应为LongWritable，Text
3.Map的输出格式为Text，Bean
4.Reduce输出格式为Bean，NullWritable
三、✌代码实现
1.✌Bean类
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class Bean implements Writable {
//定义属性
private String id;
private String pid;
private int amount;
private String pname;
private String type;
//定义空参构造，为后面反射使用
public Bean() {
super();
}
//有参构造
public Bean(String id, String pid, int amount, String pname, String type) {
this.id = id;
this.pid = pid;
this.amount = amount;
this.pname = pname;
this.type = type;
}
//重写toString方法
@Override
public String toString() {
return id + "\t" + pname + "\t\t" + amount;
}
public String getId() {
return id;
}
public void setId(String id) {
this.id = id;
}
public String getPid() {
return pid;
}
public void setPid(String pid) {
this.pid = pid;
}
public int getAmount() {
return amount;
}
public void setAmount(int amount) {
this.amount = amount;
}
public String getPname() {
return pname;
}
public void setPname(String pname) {
this.pname = pname;
}
public String getType() {
return type;
}
public void setType(String type) {
this.type = type;
}
//重写序列化方法
@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(id);
out.writeUTF(pid);
out.writeInt(amount);
out.writeUTF(pname);
out.writeUTF(type);
}
//反序列化方法
@Override
public void readFields(DataInput in) throws IOException {
id = in.readUTF();
pid = in.readUTF();
amount = in.readInt();
pname = in.readUTF();
type = in.readUTF();
}
}
2.✌Map类
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;
import java.io.IOException;
public class Map extends Mapper<LongWritable, Text, Text, Bean> {
String fileName;
//获得文件的名称，因为在同一目录，方便再map阶段对不同表做不同操作
@Override
protected void setup(Context context) throws IOException, InterruptedException {
FileSplit split = (FileSplit) context.getInputSplit();
fileName = split.getPath().getName();
}
//map阶段，将文章内容封装为Bean对象
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
if (fileName.startsWith("record")) {
String[] words = line.split("\t");
context.write(new Text(words[1]), new Bean(words[0], words[1], Integer.parseInt(words[2]), "", "record"));
} else {
String[] words = line.split("\t");
context.write(new Text(words[0]), new Bean("", words[0], 0, words[1], "city"));
}
}
}
3.✌Reduce类
import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
import java.lang.reflect.InvocationTargetException;
import java.util.ArrayList;
public class Reduce extends Reducer<Text, Bean, Bean, NullWritable> {
@Override
protected void reduce(Text key, Iterable<Bean> values, Context context) throws IOException, InterruptedException {
//存取多个Bean对象
ArrayList<Bean> list = new ArrayList<>();
Bean pd = new Bean();
//对不同表做不同操作，设置pname
for (Bean value : values) {
if ("record".equals(value.getType())) {
Bean temp = new Bean();
try {
BeanUtils.copyProperties(temp, value);
} catch (IllegalAccessException e) {
e.printStackTrace();
} catch (InvocationTargetException e) {
e.printStackTrace();
}
list.add(temp);
} else {
try {
BeanUtils.copyProperties(pd, value);
} catch (IllegalAccessException e) {
e.printStackTrace();
} catch (InvocationTargetException e) {
e.printStackTrace();
}
}
}
for (Bean bean : list) {
bean.setPname(pd.getPname());
context.write(bean, NullWritable.get());
}
}
}
4.✌Driver类
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
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;
import org.apache.log4j.BasicConfigurator;
import java.io.IOException;
public class Driver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//设置本地输入输出路径
args = new String[]{"D:/input", "D:/output"};
BasicConfigurator.configure();
//配置信息
Configuration conf = new Configuration();
//获取job对象
Job job = Job.getInstance(conf);
//关联相关类
job.setJarByClass(Driver.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
//设置map输出类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Bean.class);
//设置最终输出类型
job.setOutputKeyClass(Bean.class);
job.setOutputValueClass(NullWritable.class);
//设置文件路径
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//提交任务
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}
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