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MapReduce类型与格式(输入与输出)

一、输入格式

(1)输入分片记录

①JobClient通过指定的输入文件的格式来生成数据分片InputSplit;

②一个分片不是数据本身,而是可分片数据的引用;

③InputFormat接口负责生成分片;

源码位置:org.apache.hadoop.mapreduce.lib.input包(新)

          org.apache.hadoop.mapred.lib 包(旧)

查看其中FileInputFormat类中的getSplits()方法;

computeSplitSize()函数决定分片大小;

各种输入类的结构关系图:

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(2)文件输入

抽象类:FileInputFormat

①FileInputFormat是所有使用文件作为数据源的InputFormat实现的基类;

②FileInputFormat输入数据格式的分配大小由数据块大小决定;

 

 

抽象类:CombineFileInputFormat

①可以使用CombineFileInputFormat来合并小文件;

②因为CombineFileInputFormat是一个抽象类,使用的时候需要创建一个CombineFileInputFormat的实体类,并且实现getRecordReader()的方法;

③避免文件分割的方法:

A.数据块大小尽可能大,这样使文件的大小小于数据块的大小,就不用进行分片;

B.继承FileInputFormat,并且重载isSplitable()方法;

 

(3)文本输入

类名:TextInputFormat

①TextInputFormat是默认的InputFormat,每一行数据就是一条记录;

②TextInputFormat的key是LongWritable类型的,存储该行在整个文件的偏移量,value是每行的数据内容,Text类型;

③输入分片与HDFS数据块关系:TextInputFormat每一条记录就是一行,很有可能某一行跨数据块存放;

 

类名:KeyValueInputFormat类

可以通过key为行号的方式来知道记录的行号,并且可以通过key.value.separator.in.input设置key与value的分割符;

 

类名:NLineInputFormat类

可以设置每个mapper处理的行数,可以通过mapred.line.input.format.lienspermap属性设置;

 

 

(4)二进制输入

类名:SequenceFileInputFormat

SequenceFileAsTextInputFormat

SequenceFileAsBinaryInputFormat

由于SequenceFile能够支持Splittable,所以能够作为mapreduce输入文件的格式,能够很方便的得到已经含有,value>的分片;

 

 

(5)多文件输入

类名:MultipleInputs

①MultipleInputs能够提供多个输入数据类型;

②通过addInputPath()方法来设置多路径;

 

 

(6)数据库格式输入

类名:DBInputFormat

①DBInputFormat是一个使用JDBC并且从关系型数据库中读取数据的一种输入格式;

②避免过多的数据库连接;

③HBase中的TableInputFormat可以让MapReduce程序访问HBase表里的数据;

 

实验部分:

新建项目TestMRInputFormat,新建包com.mr,导入相关依赖包

 

实验①,以SequenceFile作为输入,故预先运行SequenceFileWriter.java产生一个b.seq文件;

新建类:TestInputFormat1.java(基于WordCount.java修改):

package com.mr;

 

import java.io.IOException;

import java.util.StringTokenizer;

 

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.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

 

public class TestInputFormat {

 

  public static class TokenizerMapper

       extends Mapper< IntWritable, Text, Text, IntWritable>{

   

    private final static IntWritable one = new IntWritable(1);

    private Text word = new Text();

     

    public void map(IntWritable key, Text value, Context context

                    ) throws IOException, InterruptedException {

      StringTokenizer itr = new StringTokenizer(value.toString());

      while (itr.hasMoreTokens()) {

        word.set(itr.nextToken());

        context.write(word, one);

      }

    }

  }

 

  public static class IntSumReducer

       extends Reducer {

    private IntWritable result = new IntWritable();

 

    public void reduce(Text key, Iterable values,

                       Context context

                       ) throws IOException, InterruptedException {

      int sum = 0;

      for (IntWritable val : values) {

        sum += val.get();

      }

      result.set(sum);

      context.write(key, result);

    }

  }

 

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

    Configuration conf = new Configuration();

    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

    if (otherArgs.length != 2) {

      System.err.println("Usage: wordcount ");

      System.exit(2);

    }

    Job job = new Job(conf, "word count");

    job.setJarByClass(TestInputFormat.class);

    job.setMapperClass(TokenizerMapper.class);

    job.setCombinerClass(IntSumReducer.class);

    job.setReducerClass(IntSumReducer.class);

    job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

    job.setInputFormatClass(SequenceFileInputFormat.class);//输入格式的设定

    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

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

  }

 

}

Eclipse中运行,参数配置如下图:

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输出统计结果如下:

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实验②,多种来源输入:

TestInputFormat2.java:

package com.mr;

 

import java.io.IOException;

import java.util.StringTokenizer;

 

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.lib.input.TextInputFormat;

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.MultipleInputs;

import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

 

 

 

public class TestInputFormat2 {

 

  public static class Mapper1  //第一个mapper类

       extends Mapper<<font color="#ed1c24">LongWritable, Text, Text, IntWritable>{

    

    private final static IntWritable one = new IntWritable(1);

    private Text word = new Text();

      

    public void map(LongWritable key, Text value, Context context

                    ) throws IOException, InterruptedException {

      StringTokenizer itr = new StringTokenizer(value.toString());

      while (itr.hasMoreTokens()) {

        word.set(itr.nextToken());

        context.write(word, one);

      }

    }

  }

 

public static class Mapper2 extends  //第二个mapper类

Mapper {

 

private final static IntWritable one = new IntWritable(1);

private Text word = new Text();

 

public void map(IntWritable key, Text value, Context context)

throws IOException, InterruptedException {

StringTokenizer itr = new StringTokenizer(value.toString());

while (itr.hasMoreTokens()) {

word.set(itr.nextToken());

context.write(word, one);

}

}

}

  

  

  public static class IntSumReducer 

       extends Reducer {

    private IntWritable result = new IntWritable();

 

    public void reduce(Text key, Iterable values, 

                       Context context

                       ) throws IOException, InterruptedException {

      int sum = 0;

      for (IntWritable val : values) {

        sum += val.get();

      }

      result.set(sum);

      context.write(key, result);

    }

  }

 

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

    Configuration conf = new Configuration();

    Job job = new Job(conf, "word count");

    job.setJarByClass(TestInputFormat2.class);

    job.setReducerClass(IntSumReducer.class);

    job.setOutputKeyClass(Text.class);

    job.setOutputValueClass(IntWritable.class);

    Path path1 = new Path("/a.txt");

    Path path2 = new Path("/b.seq");

   //多输入

    MultipleInputs.addInputPath(job, path1,TextInputFormat.class, Mapper1.class);

    MultipleInputs.addInputPath(job, path2,SequenceFileInputFormat.class, Mapper2.class);

    FileOutputFormat.setOutputPath(job, new Path("/output2"));

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

  }

}

创建输入文本文件a.txt:

aaa bbb

ccc aaa

ddd eee

 

将项目打包为jar(不知道为什么eclipse中不能运行,还没找到原因,用jar命令可以运行):

File->Export->Runnable JAR file,命名jar文件为testMR.jar。

命令行中运行:

$hadoop jar testMR.jar com.mr.TestInputFormat2 

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输出统计结果如下:

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二、输出格式

各种类关系结构图:

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(1)文本输出

类名:TextOutputFormat

①默认的输出方式,key是LongWritable类型的,value是Text类型的;

②以“key \t value”的方式输出行;

 

(2)二进制输出

类名:SequenceFileOutputFormat

SequenceFileAsTextOutputFormat

SequenceFileAsBinaryOutputFormat

MapFileOutputFormat

 

(3)多文件输出

类名:MultipleOutputFormat

      MultipleOutputs

 

区别:MultipleOutputs可以产生不同类型的输出;

 

(4)数据库输出

 

类名:DBOutputFormat

 http://blog.sina.com.cn/s/blog_4438ac090101qfuh.html

MapReduce类型与格式(输入与输出)