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, 1024 * 1024 * 5, 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, 1024 * 1024 * 5, 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[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); boolean result = job.waitForCompletion(true); System.exit(result ? 1 : 0); } }
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(1)); } } }
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 = 0; 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[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); // FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class); // FileOutputFormat.setOutputCompressorClass(job, DefaultCodec.class); boolean result = job.waitForCompletion(true); System.exit(result?1:0); } }
2）Mapper和Reducer保持不变
