Join方法

需求:处理input1和input2文件,两个文件中的id都一样,也就是key值一样,value值不同,把两者合并。input1存的是id和名字,input2存的是id和各种信息。

处理方法一:

package org.robby.join;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class MyReduceJoin
{
    public static class MapClass extends 
        Mapper<LongWritable, Text, Text, Text>
    {
        //map过程需要用到的中间变量
        private Text key = new Text();
        private Text value = new Text();
        private String[] keyValue = null;
        
        @Override
        protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException
        {
            //用逗号分开后传出
            keyValue = value.toString().split(",", 2);
            this.key.set(keyValue[0]);
            this.value.set(keyValue[1]);
            context.write(this.key, this.value);
        }
        
    }
    
    public static class Reduce extends Reducer<Text, Text, Text, Text>
    {
        private Text value = new Text();
        
        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context)
                throws IOException, InterruptedException
        {
            StringBuilder valueStr = new StringBuilder();
            
            //reduce过程之所以可以用迭代出相同的id,因为shuffle过程进行了分区,排序,在进入reduce之前,有进行排序和分组,
            //相同的key的值默认分在一组
            for(Text val : values)
            {
                valueStr.append(val);
                valueStr.append(",");
            }
            
            this.value.set(valueStr.deleteCharAt(valueStr.length()-1).toString());
            context.write(key, this.value);
        }
        
    }
    
    public static void main(String[] args) throws Exception
    {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        
        job.setJarByClass(MyReduceJoin.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        
        //reduce输出的格式
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        
        Path outputPath = new Path(args[1]);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, outputPath);
        outputPath.getFileSystem(conf).delete(outputPath, true);  

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

方法一缺点:value值无需,可能第一个文件的value在前,也可能第二个文件的value在前;

处理方法二:

引入了一个自定义类型:

package org.robby.join;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;

public class CombineValues implements WritableComparable<CombineValues>{
    //这里的自定义类型,实现WritableComparable接口
    //里面的数据使用的是hadoop自带的类型Text
    private Text joinKey;
    private Text flag;
    private Text secondPart;
    
    public void setJoinKey(Text joinKey) {
        this.joinKey = joinKey;
    }
    public void setFlag(Text flag) {
        this.flag = flag;
    }
    public void setSecondPart(Text secondPart) {
        this.secondPart = secondPart;
    }
    public Text getFlag() {
        return flag;
    }
    public Text getSecondPart() {
        return secondPart;
    }
    public Text getJoinKey() {
        return joinKey;
    }
    public CombineValues() {
        //构造时初始化数据,用set添加
        this.joinKey =  new Text();
        this.flag = new Text();
        this.secondPart = new Text();
    }
    
    //序列与反序列化,其中体现为传入文件流,使用hadoop提供的文件流去传送数据                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     
    @Override
    public void write(DataOutput out) throws IOException {
        //因使用的是hadoop自带的Text,因此序列化时,可以用本身的Text,传入流out即可
        this.joinKey.write(out);
        this.flag.write(out);
        this.secondPart.write(out);
    }
    @Override
    public void readFields(DataInput in) throws IOException {
        this.joinKey.readFields(in);
        this.flag.readFields(in);
        this.secondPart.readFields(in);
    }
    @Override
    public int compareTo(CombineValues o) {
        return this.joinKey.compareTo(o.getJoinKey());
    }
    
    @Override
    public String toString() {
        // TODO Auto-generated method stub
        return "[flag="+this.flag.toString()+",joinKey="+this.joinKey.toString()+",secondPart="+this.secondPart.toString()+"]";
    }

}

处理过程:可以在mapper阶段通过context得到处理的文件是哪一个,因此可以分别处理。

package org.robby.join;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
 
public class MyReduceJoin1
{
    public static class Map extends 
        Mapper<LongWritable, Text, Text, CombineValues>
    {
        private CombineValues combineValues = new CombineValues();
        private Text flag = new Text();
        private Text key = new Text();
        private Text value = new Text();
        private String[] keyValue = null;
        
        @Override
        protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException
        {
            //FileSplit是文件块,通过context,文件处理可以的到处理的文件属于哪一个文件
            String pathName = ((FileSplit) context.getInputSplit()).getPath().toString();
            //通过pathName获得处理文件的名字,然后用flag进行标示
            if(pathName.endsWith("input1.txt"))
                flag.set("0");
            else
                flag.set("1");
            
            combineValues.setFlag(flag);
            keyValue = value.toString().split(",", 2);
            combineValues.setJoinKey(new Text(keyValue[0]));
            combineValues.setSecondPart(new Text(keyValue[1]));

            this.key.set(keyValue[0]);
            //将封装的数据传出,key是id,用于分区排序分组,value是自定义的类,在main函数里需要说明
            context.write(this.key, combineValues);
        }
        
    }
    
    public static class Reduce extends Reducer<Text, CombineValues, Text, Text>
    {
        private Text value = new Text();
        private Text left = new Text();
        private Text right = new Text();
        
        @Override
        protected void reduce(Text key, Iterable<CombineValues> values, Context context)
                throws IOException, InterruptedException
        {
            //因key一样,因此默认分在一组
            for(CombineValues val : values)
            {
                System.out.println("val:" + val.toString());
                Text secondPar = new Text(val.getSecondPart().toString());
                //根据flag,来判断是左边还是右边
                if(val.getFlag().toString().equals("0")){
                    System.out.println("left :" + secondPar);
                    left.set(secondPar);
                }
                else{
                    System.out.println("right :" + secondPar);
                    right.set(secondPar);
                }
            }
            
            //整合value,输出
            Text output = new Text(left.toString() + "," + right.toString());
            
            context.write(key, output);
        }
        
    }
    
    public static void main(String[] args) throws Exception
    {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        
        job.setJarByClass(MyReduceJoin1.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        
        //这里要指明map的输出,因为默认是Text.class
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(CombineValues.class);
        
        //指明reduce的输出
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        
        //job任务的文件输入和输出形式
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        
        //job任务的输出与输入文件路径
        Path outputPath = new Path(args[1]);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, outputPath);
        //通个outputPath,查看hdfs是否已有这个文件,有则删除
        outputPath.getFileSystem(conf).delete(outputPath, true);  

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

缺点:如果两个文件的条数不同,并且还需要把id相同的合并

处理方法三:

package org.robby.join;

import java.io.IOException;
import java.util.ArrayList;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
 
public class MyReduceJoin2
{
    public static class Map extends 
        Mapper<LongWritable, Text, Text, CombineValues>
    {
        private CombineValues combineValues = new CombineValues();
        private Text flag = new Text();
        private Text key = new Text();
        private Text value = new Text();
        private String[] keyValue = null;
        
        @Override
        //map的处理和以前一样,分文件加flag标识,用自定义的类型封装输出
        protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException
        {
            String pathName = ((FileSplit) context.getInputSplit()).getPath().toString();
            if(pathName.endsWith("input1.txt"))
                flag.set("0");
            else
                flag.set("1");
            
            combineValues.setFlag(flag);
            keyValue = value.toString().split(",", 2);
            combineValues.setJoinKey(new Text(keyValue[0]));
            combineValues.setSecondPart(new Text(keyValue[1]));

            this.key.set(keyValue[0]);
            context.write(this.key, combineValues);
        }
        
    }
    
    public static class Reduce extends Reducer<Text, CombineValues, Text, Text>
    {
        private Text value = new Text();
        private Text left = new Text();
        private ArrayList<Text> right = new ArrayList<Text>();
        
        @Override
        protected void reduce(Text key, Iterable<CombineValues> values, Context context)
                throws IOException, InterruptedException
        {
            right.clear();
            for(CombineValues val : values)
            {
                //这里id相同的合并,有多个了
                System.out.println("val:" + val.toString());
                Text secondPar = new Text(val.getSecondPart().toString());
                if(val.getFlag().toString().equals("0")){
                    left.set(secondPar);
                }
                else{
                    //文件一是名字,文件二是各种信息,因此存在一个list集合中
                    right.add(secondPar);
                }
            }
            
            for(Text t : right){
                Text output = new Text(left.toString() + "," + t.toString());
                context.write(key, output);
            }
            
        }
        
    }
    
    public static void main(String[] args) throws Exception
    {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        
        job.setJarByClass(MyReduceJoin2.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(CombineValues.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        
        Path outputPath = new Path(args[1]);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, outputPath);
        outputPath.getFileSystem(conf).delete(outputPath, true);  

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

其他处理方法:

使用distributedCache在mapper环节进行映射;

主要是重写mapper里面的setup方法,通个context去读取job传入的文件,然后存在mapper对象中,从而使得mapper在每次实现map方法时都可以调用这些预先存入的数据;

使用setup预先处理input1,则mapper的map方法处理input2即可。

package org.robby.join;

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.net.URI;
import java.util.HashMap;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class MapJoinWithCache {
    public static class Map extends
            Mapper<LongWritable, Text, Text, Text> {
        private CombineValues combineValues = new CombineValues();
        private Text flag = new Text();
        private Text key = new Text();
        private Text value = new Text();
        private String[] keyValue = null;
        //这个keyMap就是存文件数据供map共享的
        private HashMap<String, String> keyMap = null;

        @Override
        //这个map每行都会调用一次,传入数据
        //每次都会访问keyMap集合
        //因为setup方法处理了input1文件,因此这里只需要处理input2就行
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            keyValue = value.toString().split(",", 2);

            String name = keyMap.get(keyValue[0]);
            
            this.key.set(keyValue[0]);
            
            String output = name + "," + keyValue[1];
            this.value.set(output);
            context.write(this.key, this.value);
        }

        @Override
        //这个setup方法是在mapper类初始化运行的方法
        protected void setup(Context context) throws IOException,
                InterruptedException {
            //context传入文件路径
            URI[] localPaths = context.getCacheFiles();
            
            keyMap = new HashMap<String, String>();
            for(URI url : localPaths){
                 //通过uri打开hdfs文件系统
                 FileSystem fs = FileSystem.get(URI.create("hdfs://hadoop1:9000"), context.getConfiguration());
                 FSDataInputStream in = null;
                 //打开hdfs的对应文件,需要path类创建并传入,获取流对象
                 in = fs.open(new Path(url.getPath()));
                 BufferedReader br=new BufferedReader(new InputStreamReader(in));
                 String s1 = null;
                 while ((s1 = br.readLine()) != null)
                 {
                     keyValue = s1.split(",", 2);
                     
                     keyMap.put(keyValue[0], keyValue[1]);
                     System.out.println(s1);
                 }
                 br.close();
            }
        }
    }

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

        //处理都在mpper中进行,reduce迭代分组后的数据就行
        @Override
        protected void reduce(Text key, Iterable<Text> values,
                Context context) throws IOException, InterruptedException {
            
            for(Text val : values)
                context.write(key, val);
            
        }

    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setJarByClass(MapJoinWithCache.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        Path outputPath = new Path(args[1]);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, outputPath);
        outputPath.getFileSystem(conf).delete(outputPath, true);

        //其他都一样,这里在job中加入了要传入的文件路径,用作cache
        //可以传入多个文件,文件全路径
        job.addCacheFile(new Path(args[2]).toUri());

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}


其他linux指令:

[root@hadoop1 dataFile]# wc test*
 6 14 35 test2.txt
 7 16 41 test.txt
13 30 76 total

可以通过wc查看文件的条数