用MR实现Join逻辑的两种方法

date: 2017-09-18 12:59

需求

订单数据表 order.txt

id	date	pid	amount
1001	20150710	P0001	2
1002	20150710	P0001	3
1002	20150710	P0001	3

商品信息表 product.txt

id	pname	category_id	price
P0001	小米5	1001	2
P0002	锤子T1	1000	3
P0003	锤子	1002	3

假如数据量巨大，两表的数据是以文件的形式存储在HDFS中，需要用mapreduce程序来实现一下SQL查询运算：

select  a.id,a.date,b.name,b.category_id,b.price from t_order a join t_product b on a.pid = b.id

reduce端join算法实现

实现机制:

通过将关联的条件作为map输出的key，将两表满足join条件的数据并携带数据所来源的文件信息，发往同一个reduce task，在reduce中进行数据的串联

RJoin.java

public class RJoin {
	
	static class RJoinMapper extends Mapper<LongWritable, Text, Text, InfoBean> {
		InfoBean bean = new InfoBean();
		Text k = new Text();
		
		@Override
		protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
			String line = value.toString();
			String[] fields = line.split("\t");
			String pid = "";
			
			// 通过文件名判断是哪种数据
			FileSplit inputSplit = (FileSplit) context.getInputSplit();
			String name = inputSplit.getPath().getName();
			if (name.startsWith("order")) {
				pid = fields[2];
				bean.set(fields[0], fields[1], pid, Integer.parseInt(fields[3]), "", "", -1, "0");
			} else {
				pid = fields[0];
				bean.set("", "", pid, -1, fields[1], fields[2], Float.parseFloat(fields[3]), "1");
			}
			k.set(pid);
			context.write(k, bean);
		}
	}
	
	
	static class RJoinReducer extends Reducer<Text, InfoBean, InfoBean, NullWritable> {
		@Override
		protected void reduce(Text pid, Iterable<InfoBean> values, Context context) throws IOException, InterruptedException {
			InfoBean pdBean = new InfoBean();
			List<InfoBean> orderBeans = new ArrayList<InfoBean>();
			
			for (InfoBean bean : values) {
				if ("1".equals(bean.getFlag())) { //产品
					try {
						BeanUtils.copyProperties(pdBean, bean);
					} catch (IllegalAccessException | InvocationTargetException e) {
						e.printStackTrace();
					}
				} else {
					InfoBean orderBean = new InfoBean();
					try {
						BeanUtils.copyProperties(orderBean, bean);
						orderBeans.add(orderBean);
					} catch (IllegalAccessException | InvocationTargetException e) {
						e.printStackTrace();
					}
				}
			}
			
			// 拼接两类数据形成最终结果
			for (InfoBean bean : orderBeans) {
				bean.setPname(pdBean.getPname());
				bean.setCategory_id(pdBean.getCategory_id());
				bean.setPrice(pdBean.getPrice());
				
				context.write(bean, NullWritable.get());
			}
		}
	}
	
	public static void main(String[] args) throws IllegalArgumentException, IOException, ClassNotFoundException, InterruptedException {
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf);

		// 指定本程序的jar包所在的本地路径
		job.setJarByClass(RJoin.class);
		
		//System.setProperty("hadoop.home.dir", "D:\\hadoop-2.6.5");

		// 指定本业务job要使用的mapper/Reducer业务类
		job.setMapperClass(RJoinMapper.class);
		job.setReducerClass(RJoinReducer.class);

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

		job.setOutputKeyClass(InfoBean.class);
		job.setOutputValueClass(NullWritable.class);

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

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

缺点

这种方式中，join的操作是在reduce阶段完成，reduce端的处理压力太大，map节点的运算负载则很低，资源利用率不高，且在reduce阶段极易产生数据倾斜

map端join算法实现

原理阐述

适用于关联表中有小表的情形；
可以将小表分发到所有的map节点，这样，map节点就可以在本地对自己所读到的大表数据进行join并输出最终结果，可以大大提高join操作的并发度，加快处理速度

实现示例

--先在mapper类中预先定义好小表，进行join
--引入实际场景中的解决方案：一次加载数据库或者用distributedcache
MapSideJoin.java

public class MapSideJoin {
	
	static class MapSideJoinMapper extends Mapper<LongWritable, Text, InfoBean, NullWritable> {
		Map<String, InfoBean> pdInfoMap = new HashMap<String, InfoBean>();
		
		InfoBean bean = new InfoBean();
		
		/**
		 * 通过阅读父类Mapper的源码，发现 setup方法是在maptask处理数据之前调用一次 可以用来做一些初始化工作
		 */
		@Override
		protected void setup(Context context) throws IOException, InterruptedException {
			BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream("product.txt")));
			String line;
			
			while (StringUtils.isNotEmpty(line = br.readLine())) {
				InfoBean pdBean = new InfoBean();
				String[] fields = line.split("\t");
				pdBean.set("", "", fields[0], -1, fields[1], fields[2], Float.parseFloat(fields[3]), "1");
				pdInfoMap.put(fields[0], pdBean);
			}
			br.close();
		}
		
		// 由于已经持有完整的产品信息表，所以在map方法中就能实现join逻辑了
		@Override
		protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
			String line = value.toString();
			String[] fields = line.split("\t");
			String pid = fields[2];
			//InfoBean productBean = pdInfoMap.get(pid);
			bean.setOrder_id(fields[0]);
			bean.setDate(fields[1]);
			bean.setPid(pid);
			bean.setAmount(Integer.parseInt(fields[3]));
			bean.setPname(pdInfoMap.get(pid).getPname());
			bean.setCategory_id(pdInfoMap.get(pid).getCategory_id());
			bean.setPrice(pdInfoMap.get(pid).getPrice());
			context.write(bean, NullWritable.get());
		}
	}
	
	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException, URISyntaxException {
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf);

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

		//System.setProperty("hadoop.home.dir", "D:\\hadoop-2.6.5");

		// 指定本业务job要使用的mapper/Reducer业务类
		job.setMapperClass(MapSideJoinMapper.class);

		// 指定mapper输出数据的kv类型
		job.setMapOutputKeyClass(InfoBean.class);
		job.setMapOutputValueClass(NullWritable.class);

		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		//FileInputFormat.setInputPaths(job, new Path("hdfs://mini1/mapsidejoin/input"));
		//FileOutputFormat.setOutputPath(job, new Path("hdfs://mini1/mapsidejoin/output"));

		// 指定需要缓存一个文件到所有的maptask运行节点工作目录
		/* job.addArchiveToClassPath(archive); */// 缓存jar包到task运行节点的classpath中
		/* job.addFileToClassPath(file); */// 缓存普通文件到task运行节点的classpath中
		/* job.addCacheArchive(uri); */// 缓存压缩包文件到task运行节点的工作目录
		/* job.addCacheFile(uri) */// 缓存普通文件到task运行节点的工作目录

		// 将产品表文件缓存到task工作节点的工作目录中去
		job.addCacheFile(new URI("hdfs://mini1/mapsidejoin/cache/product.txt"));

		// map端join的逻辑不需要reduce阶段，设置reducetask数量为0
		job.setNumReduceTasks(0);

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

InfoBean.java

public class InfoBean implements Writable {
	private String order_id;
	private String date;
	private String pid;
	private int amount;
	private String pname;
	private String category_id;
	private float price;
	// flag=0表示这个对象是封装订单表记录
	// flag=1表示这个对象是封装产品信息记录
	private String flag;
	
	public void set(String order_id, String date, String pid, int amount, String pname,
			String category_id, float price, String flag) {
		this.order_id = order_id;
		this.date = date;
		this.pid = pid;
		this.amount = amount;
		this.pname = pname;
		this.category_id = category_id;
		this.price = price;
		this.flag = flag;
	}

	public String getOrder_id() {
		return order_id;
	}

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

	public String getDate() {
		return date;
	}

	public void setDate(String date) {
		this.date = date;
	}

	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 getCategory_id() {
		return category_id;
	}

	public void setCategory_id(String category_id) {
		this.category_id = category_id;
	}

	public float getPrice() {
		return price;
	}

	public void setPrice(float price) {
		this.price = price;
	}

	public String getFlag() {
		return flag;
	}

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

	@Override
	public void readFields(DataInput in) throws IOException {
		this.order_id = in.readUTF();
		this.date = in.readUTF();
		this.pid = in.readUTF();
		this.amount = in.readInt();
		this.pname = in.readUTF();
		this.category_id = in.readUTF();
		this.price = in.readFloat();
		this.flag = in.readUTF();
	}

	@Override
	public void write(DataOutput out) throws IOException {		
		out.writeUTF(order_id);
		out.writeUTF(date);
		out.writeUTF(pid);
		out.writeInt(amount);
		out.writeUTF(pname);
		out.writeUTF(category_id);
		out.writeFloat(price);
		out.writeUTF(flag);
	}

	@Override
	public String toString() {
		return "order_id=" + order_id + ", date=" + date + ", pid=" + pid + ", amount=" + amount + ", pname="
				+ pname + ", category_id=" + category_id + ", price=" + price;
	}

	
}

结果