Hadoop 的官方文档发行说明 hadoop开发实战

一，WordCount程序

　　案例分析

　　　需求：统计多个文件的每个单词的出现的次数。

　　　分析： 

阶段： 将每一行文本数据变成<单词,1>这样的kv数据

阶段：将相同单词的一组kv数据进行聚合：累加所有的v

　　代码实现

　　　编码实现：Mapper类开发

package cn.edu360.mr.wc;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

/**
 * KEYIN ：是map task读取到的数据的key的类型，是一行的起始偏移量Long
 * VALUEIN:是map task读取到的数据的value的类型，是一行的内容String
 * 
 * KEYOUT：是用户的自定义map方法要返回的结果kv数据的key的类型，在wordcount逻辑中，我们需要返回的是单词String
 * VALUEOUT:是用户的自定义map方法要返回的结果kv数据的value的类型，在wordcount逻辑中，我们需要返回的是整数Integer
 * 但是，在mapreduce中，map产生的数据需要传输给reduce，需要进行序列化和反序列化，而jdk中的原生序列化机制产生的数据量比较冗余，就会导致数据在mapreduce运行过程中传输效率低下
 * 所以，hadoop专门设计了自己的序列化机制，那么，mapreduce中传输的数据类型就必须实现hadoop自己的序列化接口
 * 
 * hadoop为jdk中的常用基本类型Long String Integer Float等数据类型封住了自己的实现了hadoop序列化接口的类型：LongWritable,Text,IntWritable,FloatWritable
 * @author ThinkPad
 *
 */
public class WordcountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        // 切单词
        String line = value.toString();
        String[] words = line.split(" ");
        for(String word:words){
            context.write(new Text(word), new IntWritable(1));
            
        }
    }
}

Mapper

　　　编码实现：Reduce类开发

package cn.edu360.mr.wc;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class WordcountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
    
    
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
    
        
        int count = 0;
        
        Iterator<IntWritable> iterator = values.iterator();
        while(iterator.hasNext()){
            
            IntWritable value = iterator.next();
            count += value.get();
        }
        
        
        context.write(key, new IntWritable(count));
        
    }
    
    

}

Reduce类

      　   编码实现： 提交任务类开发

package cn.edu360.mr.wc;

import java.net.URI;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * 用于提交mapreduce job的客户端程序
 * 功能：
 *   1、封装本次job运行时所需要的必要参数
 *   2、跟yarn进行交互，将mapreduce程序成功的启动、运行
 * @author ThinkPad
 *
 */
public class JobSubmitter {
    
    public static void main(String[] args) throws Exception {
        
        // 在代码中设置JVM系统参数，用于给job对象来获取访问HDFS的用户身份
        System.setProperty("HADOOP_USER_NAME", "root");
        
        
        Configuration conf = new Configuration();
        // 1、设置job运行时要访问的默认文件系统
        conf.set("fs.defaultFS", "hdfs://hdp-01:9000");
        // 2、设置job提交到哪去运行
        conf.set("mapreduce.framework.name", "yarn");
        conf.set("yarn.resourcemanager.hostname", "hdp-01");
        // 3、如果要从windows系统上运行这个job提交客户端程序，则需要加这个跨平台提交的参数
        conf.set("mapreduce.app-submission.cross-platform","true");
        
        Job job = Job.getInstance(conf);
        
        // 1、封装参数：jar包所在的位置
        job.setJar("d:/wc.jar");
        //job.setJarByClass(JobSubmitter.class);
        
        // 2、封装参数： 本次job所要调用的Mapper实现类、Reducer实现类
        job.setMapperClass(WordcountMapper.class);
        job.setReducerClass(WordcountReducer.class);
        
        // 3、封装参数：本次job的Mapper实现类、Reducer实现类产生的结果数据的key、value类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        
        
        
        Path output = new Path("/wordcount/output");
        FileSystem fs = FileSystem.get(new URI("hdfs://hdp-01:9000"),conf,"root");
        if(fs.exists(output)){
            fs.delete(output, true);
        }
        
        // 4、封装参数：本次job要处理的输入数据集所在路径、最终结果的输出路径
        FileInputFormat.setInputPaths(job, new Path("/wordcount/input"));
        FileOutputFormat.setOutputPath(job, output);  // 注意：输出路径必须不存在
        
        
        // 5、封装参数：想要启动的reduce task的数量
        job.setNumReduceTasks(2);
        
        // 6、提交job给yarn
        boolean res = job.waitForCompletion(true);
        
        System.exit(res?0:-1);
        
    }
    
    

}

提交任务类

二，求WordCount的topN程序

　　案例分析

求访问次数最多的topn个网站（只能有1个reduce worker）《全局TOPN》

　　　思路：

阶段：解析数据，将域名作为key，1作为value

　　　　　reduce阶段：

方法中——对一个域名的一组1累加，然后将 <域名,总次数>放入一个成员变量Treemap中

方法中——从treemap中挑出次数最高的n个域名作为结果输出

　　代码实现

　　　编码实现： PageCount类开发

package cn.edu360.mr.page.topn;

public class PageCount implements Comparable<PageCount>{
    
    private String page;
    private int count;
    
    public void set(String page, int count) {
        this.page = page;
        this.count = count;
    }
    
    public String getPage() {
        return page;
    }
    public void setPage(String page) {
        this.page = page;
    }
    public int getCount() {
        return count;
    }
    public void setCount(int count) {
        this.count = count;
    }

    @Override
    public int compareTo(PageCount o) {
        
        return o.getCount()-this.count==0?this.page.compareTo(o.getPage()):o.getCount()-this.count;
    }
    
    

}

PageCount

　　　编码实现：Mapper类开发

package cn.edu360.mr.page.topn;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class PageTopnMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
    
    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        String[] split = line.split(" ");
        context.write(new Text(split[1]), new IntWritable(1));
    }

}

Mapper

　　　编码实现：Reduce类开发

package cn.edu360.mr.page.topn;

import java.io.IOException;
import java.util.Map.Entry;
import java.util.Set;
import java.util.TreeMap;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class PageTopnReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
    
    TreeMap<PageCount, Object> treeMap = new TreeMap<>();
    
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values,
            Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
        int count = 0;
        for (IntWritable value : values) {
            count += value.get();
        }
        PageCount pageCount = new PageCount();
        pageCount.set(key.toString(), count);
        
        treeMap.put(pageCount,null);
        
    }
    
    
    @Override
    protected void cleanup(Context context)
            throws IOException, InterruptedException {
        Configuration conf = context.getConfiguration();
        int topn = conf.getInt("top.n", 5);
        
        
        Set<Entry<PageCount, Object>> entrySet = treeMap.entrySet();
        int i= 0;
        
        for (Entry<PageCount, Object> entry : entrySet) {
            context.write(new Text(entry.getKey().getPage()), new IntWritable(entry.getKey().getCount()));
            i++;
            if(i==topn) return;
        }
        
        
    }
    

}

Reduce

　　　编码实现： 提交任务类开发

package cn.edu360.mr.page.topn;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class JobSubmitter {

    public static void main(String[] args) throws Exception {

        /**
         * 通过加载classpath下的*-site.xml文件解析参数
         */
        Configuration conf = new Configuration();
        conf.addResource("xx-oo.xml");
        
        /**
         * 通过代码设置参数
         */
        //conf.setInt("top.n", 3);
        //conf.setInt("top.n", Integer.parseInt(args[0]));
        
        /**
         * 通过属性配置文件获取参数
         */
        /*Properties props = new Properties();
        props.load(JobSubmitter.class.getClassLoader().getResourceAsStream("topn.properties"));
        conf.setInt("top.n", Integer.parseInt(props.getProperty("top.n")));*/
        
        Job job = Job.getInstance(conf);

        job.setJarByClass(JobSubmitter.class);

        job.setMapperClass(PageTopnMapper.class);
        job.setReducerClass(PageTopnReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\url\\input"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\url\\output"));

        job.waitForCompletion(true);

    }



}

Submit

三，MapReduce中的自定义类型

　　案例分析

　　　需求：统计一下文件中，每一个用户所耗费的总上行流量，总下行流量，总流量

　　　思路：

　　　　　map阶段：将每一行按tab切分成各字段，提取其中的手机号作为输出key，流量信息封装到FlowBean对象中，作为输出的value　　

　　　　　reduce阶段：遍历一组数据的所有value（flowbean），进行累加，然后以手机号作为key输出，以总流量信息bean作为value输出

　　　　　注意：自定义类型如何实现hadoop的序列化接口。

　　　　　FlowBean这种自定义数据类型必须实现hadoop的序列化接口：Writable，实现其中的两个方法：

　　　　　readFields(in)   反序列化方法

　　　　　write(out)   序列化方法

　　代码实现

　　　编码实现： PageCount类开发

package cn.edu360.mr.flow;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.Writable;

/**
 * 本案例的功能：演示自定义数据类型如何实现hadoop的序列化接口
 * 1、该类一定要保留空参构造函数
 * 2、write方法中输出字段二进制数据的顺序  要与  readFields方法读取数据的顺序一致
 * 
 * @author ThinkPad
 *
 */
public class FlowBean implements Writable {

    private int upFlow;
    private int dFlow;
    private String phone;
    private int amountFlow;

    public FlowBean(){}
    
    public FlowBean(String phone, int upFlow, int dFlow) {
        this.phone = phone;
        this.upFlow = upFlow;
        this.dFlow = dFlow;
        this.amountFlow = upFlow + dFlow;
    }

    public String getPhone() {
        return phone;
    }

    public void setPhone(String phone) {
        this.phone = phone;
    }

    public int getUpFlow() {
        return upFlow;
    }

    public void setUpFlow(int upFlow) {
        this.upFlow = upFlow;
    }

    public int getdFlow() {
        return dFlow;
    }

    public void setdFlow(int dFlow) {
        this.dFlow = dFlow;
    }

    public int getAmountFlow() {
        return amountFlow;
    }

    public void setAmountFlow(int amountFlow) {
        this.amountFlow = amountFlow;
    }

    /**
     * hadoop系统在序列化该类的对象时要调用的方法
     */
    @Override
    public void write(DataOutput out) throws IOException {

        out.writeInt(upFlow);
        out.writeUTF(phone);
        out.writeInt(dFlow);
        out.writeInt(amountFlow);

    }

    /**
     * hadoop系统在反序列化该类的对象时要调用的方法
     */
    @Override
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readInt();
        this.phone = in.readUTF();
        this.dFlow = in.readInt();
        this.amountFlow = in.readInt();
    }

    @Override
    public String toString() {
         
        return this.phone + ","+this.upFlow +","+ this.dFlow +"," + this.amountFlow;
    }
    
}

FlowBean

　　　编码实现：Mapper类开发

package cn.edu360.mr.flow;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean>{
    
    
    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        
        String line = value.toString();
        String[] fields = line.split("\t");
        
        String phone = fields[1];
        
        int upFlow = Integer.parseInt(fields[fields.length-3]);
        int dFlow = Integer.parseInt(fields[fields.length-2]);
        
        context.write(new Text(phone), new FlowBean(phone, upFlow, dFlow));
    }
    

}

Mapper

　　　编码实现：Reduce类开发

package cn.edu360.mr.flow;

import java.io.IOException;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
    
    
    
    /**
     *  key：是某个手机号
     *  values：是这个手机号所产生的所有访问记录中的流量数据
     *  
     *  <135,flowBean1><135,flowBean2><135,flowBean3><135,flowBean4>
     */
    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context)
            throws IOException, InterruptedException {

        int upSum = 0;
        int dSum = 0;
        
        for(FlowBean value:values){
            upSum += value.getUpFlow();
            dSum += value.getdFlow();
        }
        
        
        context.write(key, new FlowBean(key.toString(), upSum, dSum));
        
    }
    

}

Reduce

　　　编码实现： 提交任务类开发

package cn.edu360.mr.flow;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class JobSubmitter {

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();

        Job job = Job.getInstance(conf);

        job.setJarByClass(JobSubmitter.class);

        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);
        
        // 设置参数：maptask在做数据分区时，用哪个分区逻辑类  （如果不指定，它会用默认的HashPartitioner）
        job.setPartitionerClass(ProvincePartitioner.class);
        // 由于我们的ProvincePartitioner可能会产生6种分区号，所以，需要有6个reduce task来接收
        job.setNumReduceTasks(6);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);
        
    

        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\flow\\input"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\flow\\province-output"));

        job.waitForCompletion(true);

    }

}

Submit

四，MapReduce中的自定义Partitioner

　　案例分析

　　　需求：统计每一个用户的总流量信息，并且按照其归属地，将统计结果输出在不同的文件中

　　　思路：

想办法让map端worker在将数据分区时，按照我们需要的按归属地划分。

　　　实现方式：自定义一个Partitioner

　　代码实现

　　　编码实现：FlowBean类开发

package cn.edu360.mr.flow;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.Writable;

/**
 * 本案例的功能：演示自定义数据类型如何实现hadoop的序列化接口
 * 1、该类一定要保留空参构造函数
 * 2、write方法中输出字段二进制数据的顺序  要与  readFields方法读取数据的顺序一致
 * 
 * @author ThinkPad
 *
 */
public class FlowBean implements Writable {

    private int upFlow;
    private int dFlow;
    private String phone;
    private int amountFlow;

    public FlowBean(){}
    
    public FlowBean(String phone, int upFlow, int dFlow) {
        this.phone = phone;
        this.upFlow = upFlow;
        this.dFlow = dFlow;
        this.amountFlow = upFlow + dFlow;
    }

    public String getPhone() {
        return phone;
    }

    public void setPhone(String phone) {
        this.phone = phone;
    }

    public int getUpFlow() {
        return upFlow;
    }

    public void setUpFlow(int upFlow) {
        this.upFlow = upFlow;
    }

    public int getdFlow() {
        return dFlow;
    }

    public void setdFlow(int dFlow) {
        this.dFlow = dFlow;
    }

    public int getAmountFlow() {
        return amountFlow;
    }

    public void setAmountFlow(int amountFlow) {
        this.amountFlow = amountFlow;
    }

    /**
     * hadoop系统在序列化该类的对象时要调用的方法
     */
    @Override
    public void write(DataOutput out) throws IOException {

        out.writeInt(upFlow);
        out.writeUTF(phone);
        out.writeInt(dFlow);
        out.writeInt(amountFlow);

    }

    /**
     * hadoop系统在反序列化该类的对象时要调用的方法
     */
    @Override
    public void readFields(DataInput in) throws IOException {
        this.upFlow = in.readInt();
        this.phone = in.readUTF();
        this.dFlow = in.readInt();
        this.amountFlow = in.readInt();
    }

    @Override
    public String toString() {
         
        return this.phone + ","+this.upFlow +","+ this.dFlow +"," + this.amountFlow;
    }
    
}

FlowBean

　　　编码实现：Mapper类开发

package cn.edu360.mr.flow;

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean>{
    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        
        String line = value.toString();
        String[] fields = line.split("\t");
        String phone = fields[1];
        int upFlow = Integer.parseInt(fields[fields.length-3]);
        int dFlow = Integer.parseInt(fields[fields.length-2]);
        context.write(new Text(phone), new FlowBean(phone, upFlow, dFlow));
    }
}

Mapper

　　　编码实现：Reduce类开发

package cn.edu360.mr.flow;

import java.io.IOException;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
    /**
     *  key：是某个手机号
     *  values：是这个手机号所产生的所有访问记录中的流量数据
     *  
     *  <135,flowBean1><135,flowBean2><135,flowBean3><135,flowBean4>
     */
    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context)
            throws IOException, InterruptedException {
        int upSum = 0;
        int dSum = 0;
        for(FlowBean value:values){
            upSum += value.getUpFlow();
            dSum += value.getdFlow();
        }
        context.write(key, new FlowBean(key.toString(), upSum, dSum));
    }
}

Reduce

　　　编码实现：Partitioner类开发

package cn.edu360.mr.flow;

import java.util.HashMap;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
/**
 * 本类是提供给MapTask用的
 * MapTask通过这个类的getPartition方法，来计算它所产生的每一对kv数据该分发给哪一个reduce task
 * @author ThinkPad
 *
 */
public class ProvincePartitioner extends Partitioner<Text, FlowBean>{
    static HashMap<String,Integer> codeMap = new HashMap<>();
    static{
        codeMap.put("135", 0);
        codeMap.put("136", 1);
        codeMap.put("137", 2);
        codeMap.put("138", 3);
        codeMap.put("139", 4);
        
    }
    @Override
    public int getPartition(Text key, FlowBean value, int numPartitions) {
        Integer code = codeMap.get(key.toString().substring(0, 3));
        return code==null?5:code;
    }
}

Partitioner

　　　编码实现：Submiit类开发

package cn.edu360.mr.flow;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class JobSubmitter {

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(JobSubmitter.class);
        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);
        // 设置参数：maptask在做数据分区时，用哪个分区逻辑类  （如果不指定，它会用默认的HashPartitioner）
        job.setPartitionerClass(ProvincePartitioner.class);
        // 由于我们的ProvincePartitioner可能会产生6种分区号，所以，需要有6个reduce task来接收
        job.setNumReduceTasks(6);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);
        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\flow\\input"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\flow\\province-output"));

        job.waitForCompletion(true);

    }
}

Submit

五，MapReduce中的自定义排序

　　案例分析

　　　需求：统计request.dat中每个页面被访问的总次数，同时，要求输出结果文件中的数据按照次数大小倒序排序

　　　思路：

　　　　　关键点：

　　　　　mapreduce程序内置了一个排序机制：

　　　　　map worker 和reduce worker ，都会对数据按照key的大小来排序

　　　　　所以最终的输出结果中，一定是按照key有顺序的结果

　　　　　1、先写一个mr程序，将每个页面的访问总次数统计出来

　　　　　2、再写第二个mr程序：

　　　　　map阶段：读取第一个mr产生的结果文件，将每一条数据解析成一个java对象UrlCountBean（封装着一个url和它的总次数），然后将这个对象作为key，null作为value返回。

　　　　　　　注意：这个java对象要实现WritableComparable接口，以让worker可以调用对象的compareTo方法来进行排序

　　　　　reduce阶段：由于worker已经对收到的数据按照UrlCountBean的compareTo方法排了序，所以，在reduce方法中，只要将数据输出即可，最后的结果自然是按总次数大小的有序结果

　　代码实现

　　　编码实现：PageCount类开发

package cn.edu360.mr.page.count.sort;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

public class PageCount implements WritableComparable<PageCount>{
    
    private String page;
    private int count;
    
    public void set(String page, int count) {
        this.page = page;
        this.count = count;
    }
    
    public String getPage() {
        return page;
    }
    public void setPage(String page) {
        this.page = page;
    }
    public int getCount() {
        return count;
    }
    public void setCount(int count) {
        this.count = count;
    }

    @Override
    public int compareTo(PageCount o) {
        
        return o.getCount()-this.count==0?this.page.compareTo(o.getPage()):o.getCount()-this.count;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(this.page);
        out.writeInt(this.count);
        
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        this.page= in.readUTF();
        this.count = in.readInt();
        
    }
    
    
    @Override
    public String toString() {
        return this.page + "," + this.count;
    }
    

}

PageCount

　　　编码实现：MapRed1类开发

package cn.edu360.mr.page.count.sort;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 PageCountStep1 {
    
    public static class PageCountStep1Mapper extends Mapper<LongWritable, Text, Text, IntWritable>{
        
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            String[] split = line.split(" ");
            context.write(new Text(split[1]), new IntWritable(1));
        }
        
    }
    
    public static class PageCountStep1Reducer extends Reducer<Text, IntWritable, Text, IntWritable>{
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values,
                Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {

            int count = 0;
            for (IntWritable v : values) {
                count += v.get();
            }
            
            context.write(key, new IntWritable(count));
            
        }
    }
    
    public static void main(String[] args) throws Exception {
        /**
         * 通过加载classpath下的*-site.xml文件解析参数
         */
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(PageCountStep1.class);
        job.setMapperClass(PageCountStep1Mapper.class);
        job.setReducerClass(PageCountStep1Reducer.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\url\\input"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\url\\countout"));
        job.setNumReduceTasks(3);
        job.waitForCompletion(true);
        
    }
}

MapReduce1

　　　编码实现：MapRed2类开发

package cn.edu360.mr.page.count.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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class PageCountStep2 {
    public static class PageCountStep2Mapper extends Mapper<LongWritable, Text, PageCount, NullWritable>{
        
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, PageCount, NullWritable>.Context context)
                throws IOException, InterruptedException {
            
            String[] split = value.toString().split("\t");
            PageCount pageCount = new PageCount();
            pageCount.set(split[0], Integer.parseInt(split[1]));
            context.write(pageCount, NullWritable.get());
        }
    }
    
    public static class PageCountStep2Reducer extends Reducer<PageCount, NullWritable, PageCount, NullWritable>{
        @Override
        protected void reduce(PageCount key, Iterable<NullWritable> values,
                Reducer<PageCount, NullWritable, PageCount, NullWritable>.Context context)
                throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }
    
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(PageCountStep2.class);
        job.setMapperClass(PageCountStep2Mapper.class);
        job.setReducerClass(PageCountStep2Reducer.class);
        job.setMapOutputKeyClass(PageCount.class);
        job.setMapOutputValueClass(NullWritable.class);
        job.setOutputKeyClass(PageCount.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\url\\countout"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\url\\sortout"));
        
        job.setNumReduceTasks(1);
        job.waitForCompletion(true);
    }
}

MapReduce2

六，MapReduce中的自定义GroupingComparator

　　案例分析

　　　需求：有如下数据(本案例用了排序控制、分区控制、分组控制)

　　　思路：

读取数据切分字段，封装数据到一个bean中作为key传输，key要按照成交金额比大小

利用自定义GroupingComparator将数据按订单id进行分组，然后在reduce方法中输出每组数据的前N条即可

　　代码实现

　　　　编码实现：OrderBean类开发

package cn.edu360.mr.order.topn.grouping;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.io.Serializable;

import org.apache.hadoop.io.WritableComparable;

public class OrderBean implements WritableComparable<OrderBean>{

    private String orderId;
    private String userId;
    private String pdtName;
    private float price;
    private int number;
    private float amountFee;

    public void set(String orderId, String userId, String pdtName, float price, int number) {
        this.orderId = orderId;
        this.userId = userId;
        this.pdtName = pdtName;
        this.price = price;
        this.number = number;
        this.amountFee = price * number;
    }

    public String getOrderId() {
        return orderId;
    }

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

    public String getUserId() {
        return userId;
    }

    public void setUserId(String userId) {
        this.userId = userId;
    }

    public String getPdtName() {
        return pdtName;
    }

    public void setPdtName(String pdtName) {
        this.pdtName = pdtName;
    }

    public float getPrice() {
        return price;
    }

    public void setPrice(float price) {
        this.price = price;
    }

    public int getNumber() {
        return number;
    }

    public void setNumber(int number) {
        this.number = number;
    }

    public float getAmountFee() {
        return amountFee;
    }

    public void setAmountFee(float amountFee) {
        this.amountFee = amountFee;
    }

    @Override
    public String toString() {
        return this.orderId + "," + this.userId + "," + this.pdtName + "," + this.price + "," + this.number + ","
                + this.amountFee;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(this.orderId);
        out.writeUTF(this.userId);
        out.writeUTF(this.pdtName);
        out.writeFloat(this.price);
        out.writeInt(this.number);

    }

    @Override
    public void readFields(DataInput in) throws IOException {
        this.orderId = in.readUTF();
        this.userId = in.readUTF();
        this.pdtName = in.readUTF();
        this.price = in.readFloat();
        this.number = in.readInt();
        this.amountFee = this.price * this.number;
    }

    // 比较规则：先比总金额，如果相同，再比商品名称
    @Override
    public int compareTo(OrderBean o) {
        return this.orderId.compareTo(o.getOrderId())==0?Float.compare(o.getAmountFee(), this.getAmountFee()):this.orderId.compareTo(o.getOrderId());
        
    }

}

OrderBean

　　　　编码实现：Partitioner类开发

package cn.edu360.mr.order.topn.grouping;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Partitioner;

public class OrderIdPartitioner extends Partitioner<OrderBean, NullWritable>{

    @Override
    public int getPartition(OrderBean key, NullWritable value, int numPartitions) {
        // 按照订单中的orderid来分发数据
        return (key.getOrderId().hashCode() & Integer.MAX_VALUE) % numPartitions;
    }

}

Partitioner

　　　　编码实现：GroupingComparator类开发

package cn.edu360.mr.order.topn.grouping;

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

public class OrderIdGroupingComparator extends WritableComparator{
    
    public OrderIdGroupingComparator() {
        super(OrderBean.class,true);
    }
    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        OrderBean o1 = (OrderBean) a;
        OrderBean o2 = (OrderBean) b;
        return o1.getOrderId().compareTo(o2.getOrderId());
    }
}

View Code

　　　　编码实现：MapperReduce类开发

package cn.edu360.mr.order.topn.grouping;

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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class OrderTopn {

    public static class OrderTopnMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{
        OrderBean orderBean = new OrderBean();
        NullWritable v = NullWritable.get();
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, OrderBean, NullWritable>.Context context)
                throws IOException, InterruptedException {
            
            String[] fields = value.toString().split(",");
            
            orderBean.set(fields[0], fields[1], fields[2], Float.parseFloat(fields[3]), Integer.parseInt(fields[4]));
            
            context.write(orderBean,v);
        }
    }
    
    public static class OrderTopnReducer extends Reducer< OrderBean, NullWritable,  OrderBean, NullWritable>{
        
        /**
         * 虽然reduce方法中的参数key只有一个，但是只要迭代器迭代一次，key中的值就会变
         */
        @Override
        protected void reduce(OrderBean key, Iterable<NullWritable> values,
                Reducer<OrderBean, NullWritable, OrderBean, NullWritable>.Context context)
                throws IOException, InterruptedException {
            int i=0;
            for (NullWritable v : values) {
                context.write(key, v);
                if(++i==3) return;
            }
        }
    }
    
    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration(); // 默认只加载core-default.xml core-site.xml
        conf.setInt("order.top.n", 2);
        Job job = Job.getInstance(conf);

        job.setJarByClass(OrderTopn.class);
        job.setMapperClass(OrderTopnMapper.class);
        job.setReducerClass(OrderTopnReducer.class);
        job.setPartitionerClass(OrderIdPartitioner.class);
        job.setGroupingComparatorClass(OrderIdGroupingComparator.class);
        job.setNumReduceTasks(2);
        job.setMapOutputKeyClass(OrderBean.class);
        job.setMapOutputValueClass(NullWritable.class);
        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\order\\input"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\order\\out-3"));

        job.waitForCompletion(true);
    }
}

MapReduce

七，MapReduce中的输入输出控制

　　案例分析

　　　需求：还是对案例3中的流量数据进行汇总，然后求出汇总结果中的流量最大的TOPN条

　　　步骤1

　　　　　思路：统计逻辑跟之前的流量统计一致：

：以手机号作为key，flowbean作为value

　　　　　注：步骤1输出的结果文件通过指定SequenceFileOutputFormat来产生SequenceFile文件；SequenceFile文件是hadoop定义的一种文件，里面存放的是大量key-value的对象序列化字节（文件头部还存放了key和value所属的类型名）；

　　　步骤2：

　　　　　思路：读取步骤1的SequenceFile结果文件，需要指定inputformatclass为SequenceFileInputFormat组件

　　　　　既然使用了这种输入组件，那么我们的map方法中直接就接收一对KEY-VALUE数据

　　　如何实现topn呢？

　　　通过把所有汇总数据发给同一个reduce端的worker，并且通过定义一个GroupingComparator来让这个worker把所有手机号的flowbean对象看成一组，调用一次reduce方法，我们就只需要在reduce方法中输出前n个即可

　　代码实现

　　　StepOne

package cn.edu360.mr.index.sequence;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;


public class IndexStepOne {

    public static class IndexStepOneMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

        // 产生 <hello-文件名，1> 
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 从输入切片信息中获取当前正在处理的一行数据所属的文件
            FileSplit inputSplit = (FileSplit) context.getInputSplit();
            String fileName = inputSplit.getPath().getName();

            String[] words = value.toString().split(" ");
            for (String w : words) {
                // 将"单词-文件名"作为key，1作为value，输出
                context.write(new Text(w + "-" + fileName), new IntWritable(1));
            }

        }
    }

    public static class IndexStepOneReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

        @Override
        protected void reduce(Text key, Iterable<IntWritable> values,
                Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {

            int count = 0;
            for (IntWritable value : values) {
                count += value.get();
            }

            context.write(key, new IntWritable(count));
        }
    }
    
    
    public static void main(String[] args) throws Exception{
        
        Configuration conf = new Configuration(); 
        
        Job job = Job.getInstance(conf);

        job.setJarByClass(IndexStepOne.class);

        job.setMapperClass(IndexStepOneMapper.class);
        job.setReducerClass(IndexStepOneReducer.class);

        job.setNumReduceTasks(3);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        
        // job.setOutputFormatClass(TextOutputFormat.class);  // 这是默认的输出组件
        job.setOutputFormatClass(SequenceFileOutputFormat.class);
        

        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\index\\input"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\index\\out-seq-1"));

        job.waitForCompletion(true);
        
    }
}

StepOne

　　　StepTwo

package cn.edu360.mr.index.sequence;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class IndexStepTwo {

    public static class IndexStepTwoMapper extends Mapper<Text, IntWritable, Text, Text> {

        @Override
        protected void map(Text key, IntWritable value, Context context) throws IOException, InterruptedException {
            String[] split = key.toString().split("-");
            context.write(new Text(split[0]), new Text(split[1]+"-->"+value));
        }
    }

    public static class IndexStepTwoReducer extends Reducer<Text, Text, Text, Text> {

        // 一组数据：  <hello,a.txt-->4> <hello,b.txt-->4> <hello,c.txt-->4>
        @Override
        protected void reduce(Text key, Iterable<Text> values,Context context)
                throws IOException, InterruptedException {
            // stringbuffer是线程安全的，stringbuilder是非线程安全的，在不涉及线程安全的场景下，stringbuilder更快
            StringBuilder sb = new StringBuilder();
            
            for (Text value : values) {
                sb.append(value.toString()).append("\t");
            }
            
            context.write(key, new Text(sb.toString()));
        }
    }
    
    
    public static void main(String[] args) throws Exception{
        
        Configuration conf = new Configuration(); // 默认只加载core-default.xml core-site.xml
        
        Job job = Job.getInstance(conf);

        job.setJarByClass(IndexStepTwo.class);

        job.setMapperClass(IndexStepTwoMapper.class);
        job.setReducerClass(IndexStepTwoReducer.class);

        job.setNumReduceTasks(1);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);
        
        // job.setInputFormatClass(TextInputFormat.class); 默认的输入组件
        job.setInputFormatClass(SequenceFileInputFormat.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.setInputPaths(job, new Path("F:\\mrdata\\index\\out1"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\mrdata\\index\\out2"));

        job.waitForCompletion(true);
        
    }
}

StepTwo

八，案例所需文档

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