【MapReduce】经常使用计算模型具体解释

前一阵子參加炼数成金的MapReduce培训，培训中的作业样例比較有代表性，用于解释问题再好只是了。

有一本国外的有关MR的教材，比較有用。点此下载。

一.MapReduce应用场景

MR能解决什么问题？一般来说，用的最多的应该是日志分析，海量数据排序处理。近期一段时间公司用MR来解决大量日志的离线并行分析问题。

二.MapReduce机制

对于不熟悉MR工作原理的同学，推荐大家先去看一篇博文：http://blog.csdn.net/athenaer/article/details/8203990

三.经常使用计算模型

这里举一个样例。数据表在Oracle默认用户Scott下有DEPT表和EMP表。为方便，如今直接写成两个TXT文件例如以下：

1.部门表

DEPTNO,DNAME,LOC    // 部门号。部门名称，所在地

10,ACCOUNTING,NEW YORK
20,RESEARCH,DALLAS
30,SALES,CHICAGO
40,OPERATIONS,BOSTON

2.员工表

EMPNO,ENAME,JOB,HIREDATE,SAL,COMM,DEPTNO,MGR // 员工号，英文名，职位，聘期。工资，奖金，所属部门，管理者

7369,SMITH,CLERK,1980-12-17 00:00:00.0,800,,20,7902
7499,ALLEN,SALESMAN,1981-02-20 00:00:00.0,1600,300,30,7698
7521,WARD,SALESMAN,1981-02-22 00:00:00.0,1250,500,30,7698
7566,JONES,MANAGER,1981-04-02 00:00:00.0,2975,,20,7839
7654,MARTIN,SALESMAN,1981-09-28 00:00:00.0,1250,1400,30,7698
7698,BLAKE,MANAGER,1981-05-01 00:00:00.0,2850,,30,7839
7782,CLARK,MANAGER,1981-06-09 00:00:00.0,2450,    ,10,7839
7839,KING,PRESIDENT,1981-11-17 00:00:00.0,5000,,10,
7844,TURNER,SALESMAN,1981-09-08 00:00:00.0,1500,0,30,7698
7900,JAMES,CLERK,1981-12-03 00:00:00.0,950,,30,7698
7902,FORD,ANALYST,1981-12-03 00:00:00.0,3000,,20,7566
7934,MILLER,CLERK,1982-01-23 00:00:00.0,1300,,10,7782

3.实例化为bean

这两个bean的实际作用都是切割传入的字符串，从字符串内得到所属的属性信息。

emp.java

public Emp(String inStr) {
		String[] split = inStr.split(",");
		this.empno = (split[0].isEmpty()? "" : split[0]);
		this.ename = (split[1].isEmpty() ?
 "" : split[1]);
		this.job = (split[2].isEmpty() ? "" : split[2]);
		this.hiredate = (split[3].isEmpty() ? "" : split[3]);
		this.sal = (split[4].isEmpty() ?
 "0" : split[4]);
		this.comm = (split[5].isEmpty() ? "" : split[5]);
		this.deptno = (split[6].isEmpty() ? "" : split[6]);
		try {
			this.mgr = (split[7].isEmpty() ? "" : split[7]);
		} catch (IndexOutOfBoundsException e) {     //防止最后一位为空的情况
			this.mgr = "";
		}
}

dept.java

public Dept(String string) {
		String[] split = string.split(",");
		this.deptno = split[0];
		this.dname = split[1];
		this.loc = split[2];
	}

4.模型分析

4.1 求和

求各个部门的总工资

public static class Map_1 extends MapReduceBase implements Mapper<Object, Text, Text, IntWritable> {
		public void map(Object key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
			try {
				Emp emp = new Emp(value.toString());
 				output.collect(new Text(emp.getDeptno()), new IntWritable(Integer.parseInt(emp.getSal())));  // { k=部门号，v=员工薪资}
			} catch (Exception e) {
			reporter.getCounter(ErrCount.LINESKIP).increment(1);
			WriteErrLine.write("./input/" + this.getClass().getSimpleName() + "err_lines", reporter.getCounter(ErrCount.LINESKIP).getCounter() + " " + value.toString());
			}
		}	 
	}

	public static class Reduce_1 extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
		public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
			int sum = 0;
			while (values.hasNext()) {
				sum = sum + values.next().get();
			}
			output.collect(key, new IntWritable(sum));
		}

	}

执行结果：

4.3 平均值

求各个部门的人数和平均工资

public static class Map_2 extends MapReduceBase implements Mapper<Object, Text, Text, IntWritable> {
		public void map(Object key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
			try {
				Emp emp = new Emp(value.toString());
				output.collect(new Text(emp.getDeptno()), new IntWritable(Integer.parseInt(emp.getSal())));  //{ k=部门号，v=薪资}
			} catch (Exception e) {
				reporter.getCounter(ErrCount.LINESKIP).increment(1);
				WriteErrLine.write("./input/" + this.getClass().getSimpleName() + "err_lines", reporter.getCounter(ErrCount.LINESKIP).getCounter() + " " + value.toString());
			}

		}
	}

	public static class Reduce_2 extends MapReduceBase implements Reducer<Text, IntWritable, Text, Text> {
		public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			double sum = 0; //部门工资
			int count =0 ; //人数
			while (values.hasNext()) {
				count++;
				sum = sum + values.next().get();
			}
			output.collect(key, new Text( count+" "+sum/count));
		}

	}

执行结果

4.4 分组排序

求每一个部门最早进入公司的员工姓名

	public static class Map_3 extends MapReduceBase implements Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			try {
				Emp emp = new Emp(value.toString());
				output.collect(new Text(emp.getDeptno()), new Text(emp.getHiredate() + "~" + emp.getEname())); // { k=部门号。v=聘期}
			} catch (Exception e) {
				reporter.getCounter(ErrCount.LINESKIP).increment(1);
				WriteErrLine.write("./input/" + this.getClass().getSimpleName() + "err_lines", reporter.getCounter(ErrCount.LINESKIP).getCounter() + " " + value.toString());
			}

		}
	}

	public static class Reduce_3 extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
		public void reduce(Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			DateFormat sdf = DateFormat.getDateInstance();
			Date minDate = new Date(9999, 12, 30);
			Date d;
			String[] strings = null;
			while (values.hasNext()) {
				try {
					strings = values.next().toString().split("~"); // 获取名字和日期
					d = sdf.parse(strings[0].toString().substring(0, 10));
					if (d.before(minDate)) {
						minDate = d;
					}
				} catch (ParseException e) {
					e.printStackTrace();
				}
			}
			output.collect(key, new Text(minDate.toLocaleString() + " " + strings[1]));

		}

	}

执行结果

4.5 多表关联

求各个城市的员工的总工资

public static class Map_4 extends MapReduceBase implements Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			try {
				String fileName = ((FileSplit) reporter.getInputSplit()).getPath().getName();
				if (fileName.equalsIgnoreCase("emp.txt")) {
					Emp emp = new Emp(value.toString());
					output.collect(new Text(emp.getDeptno()), new Text("A#" + emp.getSal()));
				}
				if (fileName.equalsIgnoreCase("dept.txt")) {
					Dept dept = new Dept(value.toString());
					output.collect(new Text(dept.getDeptno()), new Text("B#" + dept.getLoc()));
				}
			} catch (Exception e) {
				reporter.getCounter(ErrCount.LINESKIP).increment(1);
				WriteErrLine.write("./input/" + this.getClass().getSimpleName() + "err_lines", reporter.getCounter(ErrCount.LINESKIP).getCounter() + " " + value.toString());
			}

		}
	}

	public static class Reduce_4 extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
		public void reduce(Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			String deptV;
			Vector<String> empList = new Vector<String>(); // 保存EMP表的工资数据
			Vector<String> deptList = new Vector<String>(); // 保存DEPT表的位置数据
			while (values.hasNext()) {
				deptV = values.next().toString();
				if (deptV.startsWith("A#")) {
					empList.add(deptV.substring(2));
				}
				if (deptV.startsWith("B#")) {
					deptList.add(deptV.substring(2));
				}
			}
			double sumSal = 0;
			for (String location : deptList) {
				for (String salary : empList) {
					//每一个城市员工工资总和
					sumSal = Integer.parseInt(salary) + sumSal;
				}
				output.collect(new Text(location), new Text(Double.toString(sumSal)));
			}
		}

	}

执行结果

4.6 单表关联

工资比上司高的员工姓名及其工资

public static class Map_5 extends MapReduceBase implements Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			try {
				Emp emp = new Emp(value.toString());
				output.collect(new Text(emp.getMgr()), new Text("A#" + emp.getEname() + "~" + emp.getSal()));  // 员工表 { k=上司名。v=员工工资}
				output.collect(new Text(emp.getEmpno()), new Text("B#" + emp.getEname() + "~" + emp.getSal()));// “经理表” { k=员工名，v=员工工资}
			} catch (Exception e) {
				reporter.getCounter(ErrCount.LINESKIP).increment(1);
				WriteErrLine.write("./input/" + this.getClass().getSimpleName() + "err_lines", reporter.getCounter(ErrCount.LINESKIP).getCounter() + " " + value.toString());
			}
		}
	}

	public static class Reduce_5 extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
		public void reduce(Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			String value;
			Vector<String> empList = new Vector<String>(); // 员工表
			Vector<String> mgrList = new Vector<String>(); // 经理表
			while (values.hasNext()) {
				value = values.next().toString();
				if (value.startsWith("A#")) {
					empList.add(value.substring(2));
				}
				if (value.startsWith("B#")) {
					mgrList.add(value.substring(2));
				}
			}
			String empName, empSal, mgrSal;

			for (String emploee : empList) {
				for (String mgr : mgrList) {
					String[] empInfo = emploee.split("~");
					empName = empInfo[0];
					empSal = empInfo[1];
					String[] mgrInfo = mgr.split("~");
					mgrSal = mgrInfo[1];
					if (Integer.parseInt(empSal) > Integer.parseInt(mgrSal)) {
						output.collect(key, new Text(empName + " " + empSal));
					}
				}
			}
		}

	}

执行结果

4.7 TOP N

列出工资最高的头三名员工姓名及其工资

public static class Map_8 extends MapReduceBase implements Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			try {
				Emp emp = new Emp(value.toString());
				output.collect(new Text("1"), new Text(emp.getEname() + "~" + emp.getSal()));    // { k=任意字符串或数字，v=员工名字+薪资}
			} catch (Exception e) {
				reporter.getCounter(ErrCount.LINESKIP).increment(1);
				WriteErrLine.write("./input/" + this.getClass().getSimpleName() + "err_lines", reporter.getCounter(ErrCount.LINESKIP).getCounter() + " " + value.toString());
			}

		}
	}

	public static class Reduce_8 extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
		public void reduce(Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			Map<Integer, String> emp = new TreeMap<Integer, String>();   // TreeMap默认key升序排列，巧妙利用这点能够实现top N
			while (values.hasNext()) {
				String[] valStrings = values.next().toString().split("~");
				emp.put(Integer.parseInt(valStrings[1]), valStrings[0]);
			}
			int count = 0; // 计数器
			for (Iterator<Integer> keySet = emp.keySet().iterator(); keySet.hasNext();) {
				if (count < 3) {  //  N =3
					Integer current_key = keySet.next();
					output.collect(new Text(emp.get(current_key)), new Text(current_key.toString())); // 迭代key，即SAL
					count++;
				} else {
					break;
				}
			}
		}
	}

运算结果

4.8 降序排序

将全体员工依照总收入（工资+提成）从高到低排列。要求列出姓名及其总收入

public static class Map_9 extends MapReduceBase implements Mapper<Object, Text, Text, Text> {
		public void map(Object key, Text value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			try {
				Emp emp = new Emp(value.toString());
				int totalSal = Integer.parseInt(emp.getComm()) + Integer.parseInt(emp.getSal());
				output.collect(new Text("1"), new Text(emp.getEname() + "~" + totalSal));
			} catch (Exception e) {
				reporter.getCounter(ErrCount.LINESKIP).increment(1);
				WriteErrLine.write("./input/" + this.getClass().getSimpleName() + "err_lines", reporter.getCounter(ErrCount.LINESKIP).getCounter() + " " + value.toString());
			}

		}
	}

	public static class Reduce_9 extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
		public void reduce(Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
			Map<Integer, String> emp = new TreeMap<Integer, String>(
			// 重写比較器，使降序排列
					new Comparator<Integer>() {
						public int compare(Integer o1, Integer o2) {
							return o2.compareTo(o1);
						}
					});
			while (values.hasNext()) {
				String[] valStrings = values.next().toString().split("~");
				emp.put(Integer.parseInt(valStrings[1]), valStrings[0]);
			}
			for (Iterator<Integer> keySet = emp.keySet().iterator(); keySet.hasNext();) {
				Integer current_key = keySet.next();
				output.collect(new Text(emp.get(current_key)), new Text(current_key.toString())); // 迭代key，即SAL
			}
		}
	}

执行结果

四.总结

把sql里经常使用的计算模型写成MR是一件比較麻烦的事，由于非常多情况下一行sql预计要十几甚至几十行代码来实现，略显笨拙。可是从数据计算速度来说，MR跟sql不是一个级别的。

但不可否认的一点是。不管是什么技术都有各自的适用范围，MR不是万能的。详细要看使用场景再选择适当的技术。