Reduce Join和Map Join

Reduce Join工作原理

Map端的主要工作：对来自不同表或文件的key/value对，打上标签以区别不同来源的记录。然后用连接字段作为key，其余部分和新加标志作为value，最后进行输出

Reduce端的主要工作：在Reduce端以连接字段作为key的分组已经完成，我们只需要在每一个分组中将哪些来源于不同记录分开，最后进行合并。

编程案例

• 创建商品和合并后的Bean类

package com.atguigu.mapreduce.table;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;

public class TableBean implements Writable {

	private String order_id; // 订单id
	private String p_id;      // 产品id
	private int amount;       // 产品数量
	private String pname;     // 产品名称
	private String flag;      // 表的标记

	public TableBean() {
		super();
	}

	public TableBean(String order_id, String p_id, int amount, String pname, String flag) {

		super();

		this.order_id = order_id;
		this.p_id = p_id;
		this.amount = amount;
		this.pname = pname;
		this.flag = flag;
	}

	public String getFlag() {
		return flag;
	}

	public void setFlag(String flag) {
		this.flag = flag;
	}

	public String getOrder_id() {
		return order_id;
	}

	public void setOrder_id(String order_id) {
		this.order_id = order_id;
	}

	public String getP_id() {
		return p_id;
	}

	public void setP_id(String p_id) {
		this.p_id = p_id;
	}

	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;
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeUTF(order_id);
		out.writeUTF(p_id);
		out.writeInt(amount);
		out.writeUTF(pname);
		out.writeUTF(flag);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		this.order_id = in.readUTF();
		this.p_id = in.readUTF();
		this.amount = in.readInt();
		this.pname = in.readUTF();
		this.flag = in.readUTF();
	}

	@Override
	public String toString() {
		return order_id + "	" + pname + "	" + amount + "	" ;
	}
}

• 编写TableMapper类，获取输入文件名称，键k为连接值，比如两个表的共有属性，输出(k,bean)

package com.atguigu.mapreduce.table;
import java.io.IOException;
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 TableMapper extends Mapper<LongWritable, Text, Text, TableBean>{

String name;
	TableBean bean = new TableBean();
	Text k = new Text();
	
	@Override
	protected void setup(Context context) throws IOException, InterruptedException {

		// 1 获取输入文件切片
		FileSplit split = (FileSplit) context.getInputSplit();

		// 2 获取输入文件名称
		name = split.getPath().getName();
	}

	@Override
	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
		
		// 1 获取输入数据
		String line = value.toString();
		
		// 2 不同文件分别处理
		if (name.startsWith("order")) {// 订单表处理

			// 2.1 切割
			String[] fields = line.split("	");
			
			// 2.2 封装bean对象
			bean.setOrder_id(fields[0]);
			bean.setP_id(fields[1]);
			bean.setAmount(Integer.parseInt(fields[2]));
			bean.setPname("");
			bean.setFlag("order");
			
			k.set(fields[1]);
		}else // 产品表处理

			// 2.3 切割
			String[] fields = line.split("	");
			
			// 2.4 封装bean对象
			bean.setP_id(fields[0]);
			bean.setPname(fields[1]);
			bean.setFlag("pd");
			bean.setAmount(0);
			bean.setOrder_id("");
			
			k.set(fields[0]);
		}

		// 3 写出
		context.write(k, bean);
	}
}

• 编写TableReducer类，合并两个表的内容，输出（bean,nullWritable）

package com.atguigu.mapreduce.table;
import java.io.IOException;
import java.util.ArrayList;
import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class TableReducer extends Reducer<Text, TableBean, TableBean, NullWritable> {

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

		// 1准备存储订单的集合
		ArrayList<TableBean> orderBeans = new ArrayList<>();
		
// 2 准备bean对象
		TableBean pdBean = new TableBean();

		for (TableBean bean : values) {

			if ("order".equals(bean.getFlag())) {// 订单表

				// 拷贝传递过来的每条订单数据到集合中
				TableBean orderBean = new TableBean();

				try {
					BeanUtils.copyProperties(orderBean, bean);
				} catch (Exception e) {
					e.printStackTrace();
				}

				orderBeans.add(orderBean);
			} else {// 产品表

				try {
					// 拷贝传递过来的产品表到内存中
					BeanUtils.copyProperties(pdBean, bean);
				} catch (Exception e) {
					e.printStackTrace();
				}
			}
		}

		// 3 表的拼接
		for(TableBean bean:orderBeans){

			bean.setPname (pdBean.getPname());
			
			// 4 数据写出去
			context.write(bean, NullWritable.get());
		}
	}
}

• 编写TableDriver类

package com.atguigu.mapreduce.table;
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;

public class TableDriver {

	public static void main(String[] args) throws Exception {
		
// 0 根据自己电脑路径重新配置
args = new String[]{"e:/input/inputtable","e:/output1"};

// 1 获取配置信息，或者job对象实例
		Configuration configuration = new Configuration();
		Job job = Job.getInstance(configuration);

		// 2 指定本程序的jar包所在的本地路径
		job.setJarByClass(TableDriver.class);

		// 3 指定本业务job要使用的Mapper/Reducer业务类
		job.setMapperClass(TableMapper.class);
		job.setReducerClass(TableReducer.class);

		// 4 指定Mapper输出数据的kv类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(TableBean.class);

		// 5 指定最终输出的数据的kv类型
		job.setOutputKeyClass(TableBean.class);
		job.setOutputValueClass(NullWritable.class);

		// 6 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);
	}
}

Reduce Join的缺点：合并操作在reduce阶段完成，Reduce端处理压力大，而Map端资源利用率不高，易产生数据倾斜。

解决方案：在Map端实现数据合并

Map Join

Map Join适用于一张表十分小，一张表十分大的场景。

用处：在Map端缓存多张表，提前处理业务逻辑，减少Reduce端的压力，减少数据倾斜。

方法：在Mapper的setup阶段，将小表缓存到集合中。而后在map阶段拼接。

编程案例

不需要Reduce阶段，设置ReduceTask数量为0.

• 在驱动模块中添加缓存文件

package test;
import java.net.URI;
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;

public class DistributedCacheDriver {

	public static void main(String[] args) throws Exception {
		
// 0 根据自己电脑路径重新配置
args = new String[]{"e:/input/inputtable2", "e:/output1"};

// 1 获取job信息
		Configuration configuration = new Configuration();
		Job job = Job.getInstance(configuration);

		// 2 设置加载jar包路径
		job.setJarByClass(DistributedCacheDriver.class);

		// 3 关联map
		job.setMapperClass(DistributedCacheMapper.class);
		
// 4 设置最终输出数据类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(NullWritable.class);

		// 5 设置输入输出路径
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 6 加载缓存数据
		job.addCacheFile(new URI("file:///e:/input/inputcache/pd.txt"));
		
		// 7 Map端Join的逻辑不需要Reduce阶段，设置reduceTask数量为0
		job.setNumReduceTasks(0);

		// 8 提交
		boolean result = job.waitForCompletion(true);
		System.exit(result ? 0 : 1);
	}
}

• 读取缓存的文件数据

package test;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.HashMap;
import java.util.Map;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class DistributedCacheMapper extends Mapper<LongWritable, Text, Text, NullWritable>{

	Map<String, String> pdMap = new HashMap<>();
	
	@Override
	protected void setup(Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {

		// 1 获取缓存的文件
		URI[] cacheFiles = context.getCacheFiles();
		String path = cacheFiles[0].getPath().toString();
		
		BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(path), "UTF-8"));
		
		String line;
		while(StringUtils.isNotEmpty(line = reader.readLine())){

			// 2 切割
			String[] fields = line.split("	");
			
			// 3 缓存数据到集合
			pdMap.put(fields[0], fields[1]);
		}
		
		// 4 关流
		reader.close();
	}
	
	Text k = new Text();
	
	@Override
	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

		// 1 获取一行
		String line = value.toString();
		
		// 2 截取
		String[] fields = line.split("	");
		
		// 3 获取产品id
		String pId = fields[1];
		
		// 4 获取商品名称
		String pdName = pdMap.get(pId);
		
		// 5 拼接
		k.set(line + "	"+ pdName);
		
		// 6 写出
		context.write(k, NullWritable.get());
	}
}