尚硅谷大数据技术之Hadoop(MapReduce)(新)第3章 MapReduce框架原理

3.6.3 自定义OutputFormat案例实操

1.需求

过滤输入的log日志,包含atguigu的网站输出到e:/atguigu.log,不包含atguigu的网站输出到e:/other.log。

(1)输入数据

http://www.baidu.comhttp://www.google.comhttp://cn.bing.comhttp://www.atguigu.comhttp://www.sohu.comhttp://www.sina.comhttp://www.sin2a.comhttp://www.sin2desa.comhttp://www.sindsafa.com

(2)期望输出数据

http://www.atguigu.com

 

http://cn.bing.comhttp://www.baidu.comhttp://www.google.comhttp://www.sin2a.comhttp://www.sin2desa.comhttp://www.sina.comhttp://www.sindsafa.comhttp://www.sohu.com

 

2.需求分析

3.案例实操

(1)编写FilterMapper类

package com.atguigu.mapreduce.outputformat;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

 

public class FilterMapper extends Mapper<LongWritable, Text, Text, NullWritable>{

@Override

protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

 

// 写出

context.write(value, NullWritable.get());

}

}

(2)编写FilterReducer类

package com.atguigu.mapreduce.outputformat;

import java.io.IOException;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

 

public class FilterReducer extends Reducer<Text, NullWritable, Text, NullWritable> {

 

Text k = new Text();

 

@Override

protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {

 

       // 1 获取一行

String line = key.toString();

 

       // 2 拼接

line = line + “\r\n”;

 

       // 3 设置key

       k.set(line);

 

       // 4 输出

context.write(k, NullWritable.get());

}

}

(3)自定义一个OutputFormat类

package com.atguigu.mapreduce.outputformat;

import java.io.IOException;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.RecordWriter;

import org.apache.hadoop.mapreduce.TaskAttemptContext;

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

 

public class FilterOutputFormat extends FileOutputFormat<Text, NullWritable>{

 

@Override

public RecordWriter<Text, NullWritable> getRecordWriter(TaskAttemptContext job) throws IOException, InterruptedException {

 

// 创建一个RecordWriter

return new FilterRecordWriter(job);

}

}

(4)编写RecordWriter类

package com.atguigu.mapreduce.outputformat;

import java.io.IOException;

import org.apache.hadoop.fs.FSDataOutputStream;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.RecordWriter;

import org.apache.hadoop.mapreduce.TaskAttemptContext;

 

public class FilterRecordWriter extends RecordWriter<Text, NullWritable> {

 

FSDataOutputStream atguiguOut = null;

FSDataOutputStream otherOut = null;

 

public FilterRecordWriter(TaskAttemptContext job) {

 

// 1 获取文件系统

FileSystem fs;

 

try {

fs = FileSystem.get(job.getConfiguration());

 

// 2 创建输出文件路径

Path atguiguPath = new Path(“e:/atguigu.log”);

Path otherPath = new Path(“e:/other.log”);

 

// 3 创建输出流

atguiguOut = fs.create(atguiguPath);

otherOut = fs.create(otherPath);

} catch (IOException e) {

e.printStackTrace();

}

}

 

@Override

public void write(Text key, NullWritable value) throws IOException, InterruptedException {

 

// 判断是否包含“atguigu”输出到不同文件

if (key.toString().contains(“atguigu”)) {

atguiguOut.write(key.toString().getBytes());

} else {

otherOut.write(key.toString().getBytes());

}

}

 

@Override

public void close(TaskAttemptContext context) throws IOException, InterruptedException {

 

// 关闭资源

IOUtils.closeStream(atguiguOut);

IOUtils.closeStream(otherOut); }

}

(5)编写FilterDriver类

package com.atguigu.mapreduce.outputformat;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.NullWritable;

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 FilterDriver {

 

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

 

// 输入输出路径需要根据自己电脑上实际的输入输出路径设置

args = new String[] { “e:/input/inputoutputformat”, “e:/output2” };

 

Configuration conf = new Configuration();

Job job = Job.getInstance(conf);

 

job.setJarByClass(FilterDriver.class);

job.setMapperClass(FilterMapper.class);

job.setReducerClass(FilterReducer.class);

 

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(NullWritable.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(NullWritable.class);

 

// 要将自定义的输出格式组件设置到job中

job.setOutputFormatClass(FilterOutputFormat.class);

 

FileInputFormat.setInputPaths(job, new Path(args[0]));

 

// 虽然我们自定义了outputformat,但是因为我们的outputformat继承自fileoutputformat

// 而fileoutputformat要输出一个_SUCCESS文件,所以,在这还得指定一个输出目录

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

 

boolean result = job.waitForCompletion(true);

System.exit(result ? 0 : 1);

}

}

 

 


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