MR实现MapJoin和ReduceJoin

原文链接

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
  
  

  
  

   
   
1
  
  

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);
    }