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MapReduce 常见SQL模型解析
MapReduce应用场景
前一阵子参加炼数成金的MapReduce培训，培训中的作业例子比较有代表性，用于解释问题再好不过了。有一本国外的有关MR的教材，比较实用，点此下载。
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。最近一段时间公司用MR来解决大量日志的离线并行分析问题。
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
常用计算模型
这里举一个例子，数据表在Oracle默认用户Scott下有DEPT表和EMP表。为方便，现在直接写成两个TXT文件如下：
1.部门表
DEPTNO,DNAME,LOC // 部门号，部门名称，所在地
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的实际作用都是分割传入的字符串，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 = ""; } }
dep.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)); } }
运行结果：
4.2 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)); } }
运行结果
4.3 分组排序
求每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])); } }
运行结果
4.4 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) { //每个城市员工工资总和 sumSal = Integer.parseInt(salary) + sumSal; } output.collect(new Text(location), new Text(Double.toString(sumSal))); } } }
运行结果
4.5 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)); } } } } }
运行结果
4.6 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=随意字符串或数字，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，巧妙利用这点可以实现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
4.7 降序排序
将全体员工按照总收入（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>( // 重写比较器，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 } } }
运行结果
总结
把sql里常用的计算模型写成MR是一件比较麻烦的事，因为很多情况下一行sql估计要十几甚至几十行代码来实现，略显笨拙。但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，无论是什么技术都有各自的适用范围，MR不是万能的，具体要看使用场景再选择适当的技术。
MapReduce 常见SQL模型解析
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