1 概述
压缩策略和原则
2 MR 支持的压缩编码
| 压缩格式 | hadoop自带 | 算法 | 文件扩展名 | 是否可切分 | 换成压缩格式后，原程序是否需要修改 |
|---|---|---|---|---|---|
| DEFLATE | 是，直接使用 | DEFLATE | .deflate | 否 | 和文本处理一样，不需要修改 |
| Gzip | 是，直接使用 | DEFLATE | .gz | 否 | 和文本处理一样，不需要修改 |
| bzip2 | 是，直接使用 | bzip2 | .bz2 | 是 | 和文本处理一样，不需要修改 |
| LZO | 否，需要安装 | LZO | .lzo | 是 | 需要建索引，还需要指定输入格式 |
| Snappy | 否，需要安装 | Snappy | .snappy | 否 | 和文本处理一样，不需要修改 |
为了支持多种压缩/解压缩算法，Hadoop 引入了编码/解码器，如下表所示。
| 压缩格式 | 对应的编码/解码器 |
|---|---|
| DEFLATE | org.apache.hadoop.io.compress.DefaultCodec |
| gzip | org.apache.hadoop.io.compress.GzipCodec |
| bzip2 | org.apache.hadoop.io.compress.BZip2Codec |
| LZO | com.hadoop.compression.lzo.LzopCodec |
| Snappy | org.apache.hadoop.io.compress.SnappyCodec |
压缩性能的比较
| 压缩算法 | 原始文件大小 | 压缩文件大小 | 压缩速度 | 解压速度 |
|---|---|---|---|---|
| gzip | 8.3GB | 1.8GB | 17.5MB/s | 58MB/s |
| bzip2 | 8.3GB | 1.1GB | 2.4MB/s | 9.5MB/s |
| LZO | 8.3GB | 2.9GB | 49.3MB/s | 74.6MB/s |
3 压缩方式选择
3.1 Gzip 压缩
3.2 Bzip2 压缩
3.3 Lzo 压缩
3.4 Snappy 压缩
4 压缩位置选择
5 压缩参数配置
| 参数 | 默认值 | 阶段 |
|---|---|---|
| io.compression.codecs [在core-site.xml] | org.apache.hadoop.io.compress.DefaultCodecorg apache.hadoop.io.compress.GzipCodec org.apache.hadoop.io.compress.BZip2Codec | 输入压缩 |
| mapreduce.map.output.compress [mapred-site.xml] | false | mapper输出 |
| mapreduce.map.output.compress.codec [mapred-site.xml] | org.apache.hadoop.io.compress.DefaultCodec | mapper输出 |
| mapreduce.output.fileoutputformat.compress [mapred-site.xml] | false | reducer输出 |
| mapreduce.output.fileoutputformat.compress.codec [mapred-site.xml] | org.apache.hadoop.io.compress DefaultCodec | reducer输出 |
| mapreduce.output.fileoutputformat.compress.type [mapred-site.xml] | RECORD | reducer输出 |
6 压缩实操案例
6.1 数据流的压缩和解压缩
package com.djm.mapreduce.zip;
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.CompressionInputStream;
import org.apache.hadoop.io.compress.CompressionOutputStream;
import org.apache.hadoop.util.ReflectionUtils;
import java.io.*;
public class CompressUtils {
public static void main(String[] args) throws IOException, ClassNotFoundException {
compress(args[0], args[1]);
decompress(args[0]);
}
private static void decompress(String path) throws IOException {
CompressionCodecFactory factory = new CompressionCodecFactory(new Configuration());
CompressionCodec codec = (CompressionCodec) factory.getCodec(new Path(path));
if (codec == null) {
System.out.println("cannot find codec for file " + path);
return;
}
CompressionInputStream cis = codec.createInputStream(new FileInputStream(new File(path)));
FileOutputStream fos = new FileOutputStream(new File(path + ".decoded"));
IOUtils.copyBytes(cis, fos, 1024);
cis.close();
fos.close();
}
private static void compress(String path, String method) throws IOException, ClassNotFoundException {
FileInputStream fis = new FileInputStream(new File(path));
Class codecClass = Class.forName(method);
CompressionCodec codec = (CompressionCodec) ReflectionUtils.newInstance(codecClass, new Configuration());
FileOutputStream fos = new FileOutputStream(new File(path + codec.getDefaultExtension()));
CompressionOutputStream cos = codec.createOutputStream(fos);
IOUtils.copyBytes(fis, cos, 1024);
cos.close();
fos.close();
fis.close();
}
}6.2 Map 输出端采用压缩
package com.djm.mapreduce.wordcount;
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.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
public class WcDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration configuration = new Configuration();
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(WcDriver.class);
job.setMapperClass(WcMapper.class);
job.setReducerClass(WcReduce.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}6.3 Reduce 输出端采用压缩
package com.djm.mapreduce.wordcount;
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.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.io.IOException;
public class WcDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration);
job.setJarByClass(WcDriver.class);
job.setMapperClass(WcMapper.class);
job.setReducerClass(WcReduce.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// 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);
boolean result = job.waitForCompletion(true);
System.exit(result ? 0 : 1);
}
}