MapReduce 框架原理OutputFormat 数据输出

Posted 2021-10-05 ZSYL

OutputFormat 数据输出

• 1. OutputFormat 接口实现类
• 2. 自定义 OutputFormat 案例实操
• • 2.1 需求
  • 2.2 需求分析
  • 2.3 案例实操
  • • LogMapper
    • LogReducer
    • LogOutputFormat
    • LogRecordWriter
    • LogDriver

1. OutputFormat 接口实现类

OutputFormat是MapReduce输出的基类，所有实现MapReduce输出都实现了 OutputFormat 接口。

下面我们介绍几种常见的OutputFormat实现类:

1．OutputFormat实现类

2．TextOutputFormat（默认输出格式）

3．自定义OutputFormat

• 3.1 应用场景：
  例如：输出数据到mysql/HBase/Elasticsearch等存储框架中。
• 3.2 自定义OutputFormat步骤：
  • 自定义一个类继承FileOutputFormat。
  • 改写RecordWriter，具体改写输出数据的方法write()。

2. 自定义 OutputFormat 案例实操

2.1 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.

（1）输入数据

（2）期望输出数据

2.2 需求分析

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

2、输入数据

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

3、输出数据

4、自定义一个OutputFormat类

（1）创建一个类LogRecordWriter继承RecordWriter

• （a）创建两个文件的输出流：atguiguOut、otherOut
• （b）如果输入数据包含atguigu，输出到atguiguOut流

如果不包含atguigu，输出到otherOut流。

5、驱动类Driver

// 要将自定义的输出格式组件设置到job中
job.setOutputFormatClass(LogOutputFormat.class) ;

2.3 案例实操

LogMapper

package com.zs.mapreduce.outputformat;

import org.apache.commons.lang.ObjectUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

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

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // http:www.baidu.com
        // 在Map阶段不做任何处理
        context.write(value, NullWritable.get());
    }
}

LogReducer

package com.zs.mapreduce.outputformat;

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

import java.io.IOException;

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

    @Override
    protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        // 防止有相同数据，丢数据
        for (NullWritable value : values) {
            context.write(key, NullWritable.get());
        }
    }
}

LogOutputFormat

package com.zs.mapreduce.outputformat;

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;

import java.io.IOException;

// reduce输出的key到这里
public class LogOutputFormat extends FileOutputFormat<Text, NullWritable> {

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

        LogRecordWriter lrw = new LogRecordWriter(job);

        return null;
    }
}

LogRecordWriter

package com.zs.mapreduce.outputformat;

import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
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 java.io.IOException;

public class LogRecordWriter extends RecordWriter<Text, NullWritable> {

    private FSDataOutputStream zsOut;
    private FSDataOutputStream otherOut;


    public LogRecordWriter(TaskAttemptContext job) {
        // 创建两条流
        try {
            FileSystem fs = FileSystem.get(job.getConfiguration());

            zsOut = fs.create(new Path("D:\\\\software\\\\hadoop\\\\output\\\\zs.log"));

            otherOut = fs.create(new Path("D:\\\\software\\\\hadoop\\\\output\\\\other.log"));
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    @Override
    public void write(Text key, NullWritable value) throws IOException, InterruptedException {
        String log = key.toString();

        // 具体写
        if (log.contains("atguigu")) {
            zsOut.writeBytes(log);
        } else {
            otherOut.writeBytes(log);
        }
    }

    @Override
    public void close(TaskAttemptContext taskAttemptContext) throws IOException, InterruptedException {
        // 关流
        IOUtils.closeStream(zsOut);
        IOUtils.closeStream(otherOut);
    }
}

LogDriver

package com.zs.mapreduce.outputformat;

import com.zs.mapreduce.combiner.WordCountCombiner;
import com.zs.mapreduce.combiner.WordCountMapper;
import com.zs.mapreduce.combiner.WordCountReducer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;

import java.io.IOException;

public class LogDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        // 1. 获取job
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        // 2. 设置jar包路径
        job.setJarByClass(LogDriver.class);

        // 3. 关联mapper和reducer
        job.setMapperClass(LogMapper.class);
        job.setReducerClass(LogReducer.class);

        // 4. 设置map输出的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        // 5. 设置最终输出kV类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        // 设置自定义的outputformat
        job.setOutputFormatClass(LogOutputFormat.class);

        // 6.设置输入路径和输出路径
        FileInputFormat.setInputPaths(job, new Path("D:\\\\software\\\\hadoop\\\\input\\\\inputoutputformat"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\\\software\\\\hadoop\\\\output\\\\outputFormat"));

        // 7. 提交job
        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);
    }
}

加油!

感谢!

努力!