用MR实现Join逻辑的两种方法

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