Mapjoin和Reducejoin案例

一、Mapjoin案例

　　1.需求：有两个文件，分别是订单表、商品表，

　　订单表有三个属性分别为订单时间、商品id、订单id（表示内容量大的表），

　　商品表有两个属性分别为商品id、商品名称（表示内容量小的表，用于加载到内存），

　　要求结果文件为在订单表中的每一行最后添加商品id对应的商品名称。

　　2.解决思路：

　　将商品表加载到内存中，然后再map方法中将订单表中的商品id对应的商品名称添加到该行的最后，不需要Reducer，并在Driver执行类中设置setCacheFile和numReduceTask。

　　3.代码如下：

public class CacheMapper extends Mapper<LongWritable, Text, Text, NullWritable>{
	
	HashMap<String, String> pdMap = new HashMap<>();
	//1.商品表加载到内存
	protected void setup(Context context) throws IOException {
		
		//加载缓存文件
		BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream("pd.txt"), "Utf-8"));
		
		String line;
		
		while(StringUtils.isNotEmpty(line = br.readLine()) ) {
			
			//切分
			String[] fields = line.split("	");
			
			//缓存
			pdMap.put(fields[0], fields[1]);
			
		}
		
		br.close();
	
	}
		
		
		
	//2.map传输
	@Override
	protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context)
			throws IOException, InterruptedException {
		//获取数据
		String line = value.toString();
		
		//切割
		String[] fields = line.split("	");
		
		//获取订单中商品id
		String pid = fields[1];
		
		//根据订单商品id获取商品名
		String pName = pdMap.get(pid);
		
		//拼接数据
		line = line + "	" + pName;
		
		//输出
		context.write(new Text(line), NullWritable.get());
	}
}

public class CacheDriver {
	public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException, URISyntaxException {
		// 1.获取job信息
		Configuration conf = new Configuration();
		Job job = Job.getInstance(conf);

		// 2.获取jar包
		job.setJarByClass(CacheDriver.class);

		// 3.获取自定义的mapper与reducer类
		job.setMapperClass(CacheMapper.class);

		// 5.设置reduce输出的数据类型（最终的数据类型）
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(NullWritable.class);

		// 6.设置输入存在的路径与处理后的结果路径
		FileInputFormat.setInputPaths(job, new Path("c://table1029//in"));
		FileOutputFormat.setOutputPath(job, new Path("c://table1029//out"));
		
		//加载缓存商品数据
		job.addCacheFile(new URI("file:///c:/inputcache/pd.txt"));
		
		//设置一下reducetask的数量
		job.setNumReduceTasks(0);

		// 7.提交任务
		boolean rs = job.waitForCompletion(true);
		System.out.println(rs ? 0 : 1);
	}
}

　　

二、Reducejoin案例

　　1.需求：同上的两个数据文件，要求将订单表中的商品id替换成对应的商品名称。

　　2.解决思路：封装TableBean类，包含属性：时间、商品id、订单id、商品名称、flag（flag用来判断是哪张表），

　　　　使用Mapper读两张表，通过context对象获取切片对象，然后通过切片获取切片名称和路径的字符串来判断是哪张表，再将切片的数据封装到TableBean对象，最后以产品id为key、TableBean对象为value传输到Reducer端；

　　　　Reducer接收数据后通过flag判断是哪张表，因为一个reduce中的所有数据的key是相同的，将商品表的商品id和商品名称读入到一个TableBean对象中，然后将订单表的中的数据读入到TableBean类型的ArrayList对象中，然后将ArrayList中的每个TableBean的商品id替换为商品名称，然后遍历该数组以TableBean为key输出。

　　3.代码如下：

/**
 * @author: PrincessHug
 * @date: 2019/3/30, 2:37
 * @Blog: https://www.cnblogs.com/HelloBigTable/
 */
public class TableBean implements Writable {
    private String timeStamp;
    private String productId;
    private String orderId;
    private String productName;
    private String flag;

    public TableBean() {
    }

    public String getTimeStamp() {
        return timeStamp;
    }

    public void setTimeStamp(String timeStamp) {
        this.timeStamp = timeStamp;
    }

    public String getProductId() {
        return productId;
    }

    public void setProductId(String productId) {
        this.productId = productId;
    }

    public String getOrderId() {
        return orderId;
    }

    public void setOrderId(String orderId) {
        this.orderId = orderId;
    }

    public String getProductName() {
        return productName;
    }

    public void setProductName(String productName) {
        this.productName = productName;
    }

    public String getFlag() {
        return flag;
    }

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

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(timeStamp);
        out.writeUTF(productId);
        out.writeUTF(orderId);
        out.writeUTF(productName);
        out.writeUTF(flag);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        timeStamp = in.readUTF();
        productId = in.readUTF();
        orderId = in.readUTF();
        productName = in.readUTF();
        flag = in.readUTF();
    }

    @Override
    public String toString() {
        return timeStamp + "	" + productName + "	" + orderId;
    }
}


public class TableMapper extends Mapper<LongWritable, Text,Text,TableBean> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //通过切片获取文件信息
        FileSplit split = (FileSplit) context.getInputSplit();
        String name = split.getPath().getName();

        //获取一行数据、定义TableBean对象
        String line = value.toString();
        TableBean tb = new TableBean();
        Text t = new Text();

        //判断是哪一张表
        if (name.contains("order.txt")){
            String[] fields = line.split("	");
            tb.setTimeStamp(fields[0]);
            tb.setProductId(fields[1]);
            tb.setOrderId(fields[2]);
            tb.setProductName("");
            tb.setFlag("0");
            t.set(fields[1]);
        }else {
            String[] fields = line.split("	");
            tb.setTimeStamp("");
            tb.setProductId(fields[0]);
            tb.setOrderId("");
            tb.setProductName(fields[1]);
            tb.setFlag("1");
            t.set(fields[0]);
        }
        context.write(t,tb);
    }
}

public class TableReducer extends Reducer<Text,TableBean,TableBean, NullWritable> {
    @Override
    protected void reduce(Text key, Iterable<TableBean> values, Context context) throws IOException, InterruptedException {
        //分别创建用来存储订单表和产品表的集合
        ArrayList<TableBean> orderBean = new ArrayList<>();
        TableBean productBean = new TableBean();

        //遍历values，通过flag判断是产品表还是订单表
        for (TableBean v:values){
            if (v.getFlag().equals("0")){
                TableBean tableBean = new TableBean();
                try {
                    BeanUtils.copyProperties(tableBean,v);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
                orderBean.add(tableBean);
            }else {
                try {
                    BeanUtils.copyProperties(productBean,v);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
            }
        }
        //拼接表
        for (TableBean ob:orderBean) {
            ob.setProductName(productBean.getProductName());
            context.write(ob,NullWritable.get());
        }
    }
}

public class TableDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //job信息
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //jar包
        job.setJarByClass(TableDriver.class);

        //Mapper、Reducer
        job.setMapperClass(TableMapper.class);
        job.setReducerClass(TableReducer.class);

        //Mapper输出数据类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(TableBean.class);

        //Reducer输出数据类型
        job.setOutputKeyClass(TableBean.class);
        job.setOutputValueClass(NullWritable.class);

        //输入输出路径
        FileInputFormat.setInputPaths(job,new Path("G:\mapreduce\reducejoin\in"));
        FileOutputFormat.setOutputPath(job,new Path("G:\mapreduce\reducejoin\out"));

        //提交任务
        if (job.waitForCompletion(true)){
            System.out.println("运行完成！");
        }else {
            System.out.println("运行失败！");
        }
    }
}