MapReduce基本原理

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image.png 基本概念
MapReduce是一种分布式计算模型，由Google提出，主要用于搜索领域，解决海量数据的计算问题。 MapReduce由两个阶段组成：Map和Reduce，用于只需要实现map()和reduce()两个函数，即可实现分布式计算，非常简单。 这两个函数的形参是key、value对，表示函数的输入信息。

#jps #start-all.sh #jps #hdfs dfs -lsr / #hdfs dfs -cat /input/data.txt #cd /root/training/hadoop-2.4.1/share/hadoop/mapreduce #hadoop jar hadoop-mapreduce-examples-2.4.1.jar wordcount /input/data.txt /output #hdfs dfs -lsr / #hdfs dfs -cat /output/part-r-00000 hdfs dfs -cat /input/data.txt

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image.png 第一个MapReduce程序

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package demo; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WordCount {public static void main(String[] args) throws Exception{ //申明一个job Configuration conf = new Configuration(); Job job = new Job(conf); //指明程序的入口 job.setJarByClass(WordCount.class); //指明输入的数据 //FileInputFormat.addInputPath(job,new Path("/input/data.txt")); //第二种 FileInputFormat.addInputPath(job,new Path(args[0])); //组装Mapper和Reducer //设置Mapper job.setMapperClass(WordCountMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); //设置Reducer job.setReducerClass(WordCountReducer.class); job.setOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); //指明数据输出的路径 //FileOutputFormat.setOutputPath(job, new Path("/output1")); FileOutputFormat.setOutputPath(job, new Path(args[1])); //提交任务运行 //job.waitForCompletion(true); job.waitForCompletion(false); }} //k1v1k2v2 //class WordCountMapper extends Mapper{ class WordCountMapper extends Mapper{@Override protected void map(LongWritable key1, Text value1,Context context) throws IOException, InterruptedException { //分词 //key1value1 //1I love Beijing String var = value1.toString(); String[] words = var.split(" "); //统计每个单词的频率，得到k2和v2 for(String word:words) { //k2v2 context.write(new Text(word), new LongWritable(1)); } }} //k3v3k4v4 class WordCountReducer extends Reducer{@Override protected void reduce(Text key, Iterable values,Context context) throws IOException, InterruptedException {//keyvalues // I(1,1) //得到每个单词总的频率 long sum = 0; for(LongWritable value:values) { sum += value.get(); }//将k4和v4输出 context.write(key, new LongWritable(sum)); }}

右击程序，选择Export，Java，JAR file
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image.png 上传到training目录下

#cd ~/training #hadoop jar wc.jar #hdfs dfs -lsr /output1 # hdfs dfs -cat /output1/part-r-00000

#hadoop jar wc.jar /input/data.txt /output2 #hdfs dfs -lsr /output2

MapReduce的序列化
序列化（Serialization）是指把结构化对象转化为字节流。 反序列化（Deserialization）是序列化的逆过程。即把字节流转回结构化对象 Java序列化（java.io.Serializable） Hadoop序列化的特点
序列化格式特点： -紧凑：高效使用存储空间。 -快速：读写数据的额外开销小 -可扩展：可透明地读取老格式的数据 -互操作：支持多语言的交互 Hadoop的序列化格式：Writable Hadoop序列化的作用 序列化在分布式环境的两大作用：进程间通信，永久存储。 Hadoop节点间通信。

#more emp.csv # hdfs dfs -put emp.csv /input/emp.csv

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image.png demo.se-Emp.java

package demo.se; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.Writable; //代表员工 public class Emp implements Writable{ //7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30 private int empno; private String ename; private String job; private int mgr; private String hiredate; private int sal; private int comm; private int deptno; public Emp(){}@Override public String toString() { return "The salary of" + this.ename + "is" + this.sal; }@Override public void readFields(DataInput input) throws IOException { // 反序列化 this.empno = input.readInt(); this.ename = input.readUTF(); this.job = input.readUTF(); this.mgr = input.readInt(); this.hiredate = input.readUTF(); this.sal = input.readInt(); this.comm = input.readInt(); this.deptno = input.readInt(); }@Override public void write(DataOutput output) throws IOException { // 序列化 output.writeInt(empno); output.writeUTF(ename); output.writeUTF(job); output.writeInt(mgr); output.writeUTF(hiredate); output.writeInt(sal); output.writeInt(comm); output.writeInt(deptno); }public int getEmpno() { return empno; }public void setEmpno(int empno) { this.empno = empno; }public String getEname() { return ename; }public void setEname(String ename) { this.ename = ename; }public String getJob() { return job; }public void setJob(String job) { this.job = job; }public int getMgr() { return mgr; }public void setMgr(int mgr) { this.mgr = mgr; }public String getHiredate() { return hiredate; }public void setHiredate(String hiredate) { this.hiredate = hiredate; }public int getSal() { return sal; }public void setSal(int sal) { this.sal = sal; }public int getComm() { return comm; }public void setComm(int comm) { this.comm = comm; }public int getDeptno() { return deptno; }public void setDeptno(int deptno) { this.deptno = deptno; } }

demo.se-EmpMain.java

package demo.se; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class EmpMain {public static void main(String[] args) throws Exception{ //申明一个job Configuration conf = new Configuration(); Job job = new Job(conf); //指明程序的入口 job.setJarByClass(EmpMain.class); //指明输入的数据 FileInputFormat.setInputPaths(job,new Path(args[0])); //组装Mapper和Reducer //设置Mapper job.setMapperClass(EmpMapper.class); job.setMapOutputKeyClass(LongWritable.class); job.setMapOutputValueClass(Emp.class); //指明数据输出的路径 FileOutputFormat.setOutputPath(job, new Path(args[1])); //提交任务运行 job.waitForCompletion(true); }} //7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30 class EmpMapper extends Mapper{@Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { //7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30 String str = value.toString(); String[] words = str.split(","); //创建一个Emp的对象 Emp emp = new Emp(); //设置员工的属性 emp.setEmpno(Integer.parseInt(words[0])); emp.setEname(words[1]); emp.setJob(words[2]); //设置员工的经理 try { emp.setMgr(Integer.parseInt(words[3])); }catch(Exception ex) { emp.setMgr(0); }emp.setHiredate(words[4]); emp.setSal(Integer.parseInt(words[5])); //设置员工的奖金 try { emp.setComm(Integer.parseInt(words[6])); }catch(Exception ex) { emp.setComm(0); } emp.setDeptno(Integer.parseInt(words[7])); //输出key:员工号value：员工hdfs context.write(new LongWritable(emp.getEmpno()), emp); } }

打包。

# hadoop jar se.jar /input/emp.csv /outputemp #hdfs dfs -lsr /outputemp #hdfs dfs -cat /outputemp/part-r-00000

MapReduce的排序
在Map和Reduce阶段进行排序时，比较的是key2 value2是不参与排序比较的。 如果要想让value2也进行排序，需要把key2和value2组装成新的类，作为key2，才能参与比较。 demo.sort.Emp.java

package demo.sort; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.WritableComparable; //代表员工 public class Emp implements WritableComparable{ //7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30 private int empno; private String ename; private String job; private int mgr; private String hiredate; private int sal; private int comm; private int deptno; public Emp(){}@Override public int compareTo(Emp e) { //按照薪水进行排序 if(this.sal >= e.sal) { return 1; }else { return -1; } } @Override public String toString() { return "The salary of" + this.ename + " is" + this.sal; }@Override public void readFields(DataInput input) throws IOException { // 反序列化 this.empno = input.readInt(); this.ename = input.readUTF(); this.job = input.readUTF(); this.mgr = input.readInt(); this.hiredate = input.readUTF(); this.sal = input.readInt(); this.comm = input.readInt(); this.deptno = input.readInt(); }@Override public void write(DataOutput output) throws IOException { // 序列化 output.writeInt(empno); output.writeUTF(ename); output.writeUTF(job); output.writeInt(mgr); output.writeUTF(hiredate); output.writeInt(sal); output.writeInt(comm); output.writeInt(deptno); }public int getEmpno() { return empno; }public void setEmpno(int empno) { this.empno = empno; }public String getEname() { return ename; }public void setEname(String ename) { this.ename = ename; }public String getJob() { return job; }public void setJob(String job) { this.job = job; }public int getMgr() { return mgr; }public void setMgr(int mgr) { this.mgr = mgr; }public String getHiredate() { return hiredate; }public void setHiredate(String hiredate) { this.hiredate = hiredate; }public int getSal() { return sal; }public void setSal(int sal) { this.sal = sal; }public int getComm() { return comm; }public void setComm(int comm) { this.comm = comm; }public int getDeptno() { return deptno; }public void setDeptno(int deptno) { this.deptno = deptno; }}

demo.sort.EmpSortMain.java

package demo.sort; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class EmpSortMain {public static void main(String[] args) throws Exception{ //申明一个job Configuration conf = new Configuration(); Job job = new Job(conf); //指明程序的入口 job.setJarByClass(EmpSortMain.class); //指明输入的数据 FileInputFormat.setInputPaths(job,new Path(args[0])); //组装Mapper和Reducer //设置Mapper job.setMapperClass(EmpMapper.class); job.setMapOutputKeyClass(Emp.class); job.setMapOutputValueClass(NullWritable.class); //指明数据输出的路径 FileOutputFormat.setOutputPath(job, new Path(args[1])); //提交任务运行 job.waitForCompletion(true); }} //7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30 class EmpMapper extends Mapper{@Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { //7499,ALLEN,SALESMAN,7698,1981/2/20,1600,300,30 String str = value.toString(); String[] words = str.split(","); //创建一个Emp的对象 Emp emp = new Emp(); //设置员工的属性 emp.setEmpno(Integer.parseInt(words[0])); emp.setEname(words[1]); emp.setJob(words[2]); //设置员工的经理 try { emp.setMgr(Integer.parseInt(words[3])); }catch(Exception ex) { emp.setMgr(0); }emp.setHiredate(words[4]); emp.setSal(Integer.parseInt(words[5])); //设置员工的奖金 try { emp.setComm(Integer.parseInt(words[6])); }catch(Exception ex) { emp.setComm(0); } emp.setDeptno(Integer.parseInt(words[7])); //输出key:Empvalue：NullWritable context.write(emp,NullWritable.get()); } }

#hadoop jar sort.jar /input/emp.csv /outputsortemp #hdfs dfs -lsr /outputsortemp #hdfs dfs -cat /outputsortemp/part-r-00000

MapReduce的分区

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image.png Partitioner是partitioner的基类，如果需要定制partitioner也需要继承该类 MapReduce有一个默认的分区规则：只会产生一个分区 什么是Combiner？
每一个map可能会产生大量的输出，combiner的作用就是在map端对输出先做一次合并，以减少传输到reducer的数据量 combiner最基本是实现本地key的归并，combiner具有类似本地的reduce功能。 如果不用combiner，那么，所有的结果都是reduce完成，效率会相对低下。使用combiner，先完成的map会在本地聚合，提升速度。 WordCount.java

package demo; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WordCount {public static void main(String[] args) throws Exception{ //申明一个job Configuration conf = new Configuration(); Job job = new Job(conf); //指明程序的入口 job.setJarByClass(WordCount.class); //指明输入的数据 FileInputFormat.addInputPath(job,new Path(args[0])); //组装Mapper和Reducer //设置Mapper job.setMapperClass(WordCountMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); //设置Combiner job.setCombinerClass(WordCountReducer.class); //设置Reducer job.setReducerClass(WordCountReducer.class); job.setOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); //指明数据输出的路径 FileOutputFormat.setOutputPath(job, new Path(args[1])); //提交任务运行 job.waitForCompletion(true); }} //k1v1k2v2 //class WordCountMapper extends Mapper{ class WordCountMapper extends Mapper{@Override protected void map(LongWritable key1, Text value1,Context context) throws IOException, InterruptedException { //分词 //key1value1 //1I love Beijing String var = value1.toString(); String[] words = var.split(" "); //统计每个单词的频率，得到k2和v2 for(String word:words) { //k2v2 context.write(new Text(word), new LongWritable(1)); } }} //k3v3k4v4 class WordCountReducer extends Reducer{@Override protected void reduce(Text key, Iterable values,Context context) throws IOException, InterruptedException {//keyvalues // I(1,1) //得到每个单词总的频率 long sum = 0; for(LongWritable value:values) { sum += value.get(); }//将k4和v4输出 context.write(key, new LongWritable(sum)); }}

#hadoop jar wcd.jar /input/data.txt /dd #hdfs dfs -ls /dd # hdfs dfs -cat /dd/part-r-00000

注意 -Combiner的输出是Reduce的输入，如果Combiner是可插拔的，添加Combiner绝不能改变最终的计算结果。所以Combiner值应该用于那种Reduce的输入key/value与输出key/value类型安全一致，且不影响最终结果的场景。不如累加，最大值等。 什么是Shuffle？
Shuffle的过程
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