Hadoop案例（二）压缩解压缩

压缩/解压缩案例

一. 对数据流的压缩和解压缩

CompressionCodec有两个方法可以用于轻松地压缩或解压缩数据。要想对正在被写入一个输出流的数据进行压缩，我们可以使用createOutputStream(OutputStreamout)方法创建一个CompressionOutputStream，将其以压缩格式写入底层的流。相反，要想对从输入流读取而来的数据进行解压缩，则调用createInputStream(InputStreamin)函数，从而获得一个CompressionInputStream，从而从底层的流读取未压缩的数据。

测试一下如下压缩方式：

DEFLATE	org.apache.hadoop.io.compress.DefaultCodec
gzip	org.apache.hadoop.io.compress.GzipCodec
bzip2	org.apache.hadoop.io.compress.BZip2Codec

package com.xyg.mapreduce.compress;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.CompressionCodecFactory;
import org.apache.hadoop.io.compress.CompressionOutputStream;
import org.apache.hadoop.util.ReflectionUtils;

public class TestCompress {

    public static void main(String[] args) throws Exception, IOException {
//        compress("e:/test.txt","org.apache.hadoop.io.compress.BZip2Codec");
        decompres("e:/test.txt.bz2");
    }

    /*
     * 压缩
     * filername：要压缩文件的路径
     * method：欲使用的压缩的方法（org.apache.hadoop.io.compress.BZip2Codec）
     */
    public static void compress(String filername, String method) throws ClassNotFoundException, IOException {

        // 1 创建压缩文件路径的输入流
        File fileIn = new File(filername);
        InputStream in = new FileInputStream(fileIn);

        // 2 获取压缩的方式的类
        Class codecClass = Class.forName(method);

        Configuration conf = new Configuration();
        // 3 通过名称找到对应的编码/解码器
        CompressionCodec codec = (CompressionCodec) ReflectionUtils.newInstance(codecClass, conf);

        // 4 该压缩方法对应的文件扩展名
        File fileOut = new File(filername + codec.getDefaultExtension());

        OutputStream out = new FileOutputStream(fileOut);
        CompressionOutputStream cout = codec.createOutputStream(out);

        // 5 流对接
        IOUtils.copyBytes(in, cout,  *  * , false); // 缓冲区设为5MB

        // 6 关闭资源
        in.close();
        cout.close();
        out.close();
    }

    /*
     * 解压缩
     * filename：希望解压的文件路径
     */
    public static void decompres(String filename) throws FileNotFoundException, IOException {

        Configuration conf = new Configuration();
        CompressionCodecFactory factory = new CompressionCodecFactory(conf);

        // 1 获取文件的压缩方法
        CompressionCodec codec = factory.getCodec(new Path(filename));

        // 2 判断该压缩方法是否存在
        if (null == codec) {
            System.out.println("Cannot find codec for file " + filename);
            return;
        }

        // 3 创建压缩文件的输入流
        InputStream cin = codec.createInputStream(new FileInputStream(filename));

        // 4 创建解压缩文件的输出流
        File fout = new File(filename + ".decoded");
        OutputStream out = new FileOutputStream(fout);

        // 5 流对接
        IOUtils.copyBytes(cin, out,  *  * , false);

        // 6 关闭资源
        cin.close();
        out.close();
    }
}

二. 在Map输出端采用压缩

即使你的MapReduce的输入输出文件都是未压缩的文件，你仍然可以对map任务的中间结果输出做压缩，因为它要写在硬盘并且通过网络传输到reduce节点，对其压缩可以提高很多性能，这些工作只要设置两个属性即可，我们来看下代码怎么设置：

给大家提供的hadoop源码支持的压缩格式有：BZip2Codec 、DefaultCodec

package com.xyg.mapreduce.compress;
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.io.compress.BZip2Codec;
import org.apache.hadoop.io.compress.CompressionCodec;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCountDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        Configuration configuration = new Configuration();

        // 开启map端输出压缩
        configuration.setBoolean("mapreduce.map.output.compress", true);
        // 设置map端输出压缩方式
        configuration.setClass("mapreduce.map.output.compress.codec", BZip2Codec.class, CompressionCodec.class);

        Job job = Job.getInstance(configuration);

        job.setJarByClass(WordCountDriver.class);

        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);

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

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

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

        boolean result = job.waitForCompletion(true);

        System.exit(result ?  : );
    }
}

2）Mapper保持不变

package com.xyg.mapreduce.compress;
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 WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{

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

        String line = value.toString();

        String[] words = line.split(" ");

        for(String word:words){
            context.write(new Text(word), new IntWritable());
        }
    }
}

3）Reducer保持不变

package com.xyg.mapreduce.compress;
import java.io.IOException;
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 = ;

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

        context.write(key, new IntWritable(count));
    }
}

三. 在Reduce输出端采用压缩

基于workcount案例处理

1）修改驱动

package com.xyg.mapreduce.compress;
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.io.compress.BZip2Codec;
import org.apache.hadoop.io.compress.DefaultCodec;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.io.compress.Lz4Codec;
import org.apache.hadoop.io.compress.SnappyCodec;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCountDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        Configuration configuration = new Configuration();

        Job job = Job.getInstance(configuration);

        job.setJarByClass(WordCountDriver.class);

        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);

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

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

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

        // 设置reduce端输出压缩开启
        FileOutputFormat.setCompressOutput(job, true);

        // 设置压缩的方式
        FileOutputFormat.setOutputCompressorClass(job, BZip2Codec.class);
//        FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);
//        FileOutputFormat.setOutputCompressorClass(job, DefaultCodec.class); 

        boolean result = job.waitForCompletion(true);

        System.exit(result?:);
    }
}

2）Mapper和Reducer保持不变