Hadoop Map-Reduce的压缩最终输出文件
Hadoop 可以在作业的Configuration对象中通过设定一系列参数来改变作业的行为，比如，我们需要进行一个map-reduce作业，并且吧最终作业reduce过程的结果输出为压缩的格式，我们可以在一般的map-reduce上进行一些定制。实现
还是以以前做的删选最高气温的例子为参照：
以前的例子可以见这个博文：http://supercharles888.blog.51cto.com/609344/878422
我们现在要求让结果输出为压缩格式，所以保持Map类(MaxTemperatureMapper）和Reduce类（MaxTemperatureReducer）不变，只要在Job类的Configuration作一些压缩的配置即可，见第45-49行所示：
[*]package com.charles.parseweather.compression;
[*]
[*]
[*]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.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.input.TextInputFormat;
[*]import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
[*]import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
[*]
[*]
[*]/**
[*] *
[*] *
[*] * Description: 这个类定义并且运行作业,压缩版
[*] *
[*] * @author charles.wang
[*] * @created May 24, 2012 5:29:12 PM
[*] *
[*] */
[*]
[*]public class MaxTemperatureWithCompression {
[*]
[*] /**
[*] * @param args
[*] */
[*] public static void main(String[] args) throws Exception{
[*] // TODO Auto-generated method stub
[*]
[*]
[*] if (args.length !=2){
[*] System.err.println("Usage: MaxTemperature");
[*] System.exit(-1);
[*] }
[*]
[*] //创建一个Map-Reduce的作业
[*] Configuration conf = new Configuration();
[*] conf.set("hadoop.job.ugi", "hadoop-user,hadoop-user");
[*]
[*] //在这里我们配置一些和压缩有关的参数
[*]
[*] //我们设定reduce输出结果使用gzip压缩的形式
[*] conf.setBoolean("mapred.output.compress", true);
[*] conf.setClass("mapred.output.compression.codec", GzipCodec.class, CompressionCodec.class);
[*]
[*]
[*]
[*] Job job = new Job(conf,"Get Maximum Weather Information with Compression! ^_^");
[*]
[*]
[*]
[*]
[*]
[*] //设定作业的启动类/
[*] job.setJarByClass(MaxTemperatureWithCompression.class);
[*]
[*] //解析输入和输出参数，分别作为作业的输入和输出，都是文件
[*] FileInputFormat.addInputPath(job, new Path(args));
[*] FileOutputFormat.setOutputPath(job, new Path(args));
[*]
[*] //配置作业，设定Mapper类，Reducer类
[*] job.setMapperClass(MaxTemperatureMapper.class);
[*] job.setReducerClass(MaxTemperatureReducer.class);
[*] job.setOutputKeyClass(Text.class);
[*] job.setOutputValueClass(IntWritable.class);
[*]
[*]
[*]
[*]
[*] System.exit(job.waitForCompletion(true)?0:1);
[*]
[*]
[*]
[*]
[*]
[*]
[*] }
[*]
[*]}
要运行这个例子，我们需要给出输入文件，因为Hadoop系统可以根据输入文件的扩展名自动识别基本文件，所以我们创建目录结构，并且上传一个gzip格式的文件作为map-reduce过程的输入：
然后我们运行的main中传入HDFS的输入文件和输出目录：
当执行完成之后，我们就可以在HDFS文件系统中看到最终的输出结果了，正如我们所预期的，这个结果是个gzip格式的文件：
通过日志观察压缩输出文件过程
我们可以观察日志来更细粒度的观察整个过程：
namenode:
[*]2012-05-31 13:11:08,621 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=open src=/user/hadoop-user/compress-input/1901.gz dst=null perm=null
[*]2012-05-31 13:11:08,754 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=open src=/user/hadoop-user/compress-input/1901.gz dst=null perm=null
[*]2012-05-31 13:11:08,758 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=mkdirssrc=/user/hadoop-user/compress-output/_temporary dst=null perm=hadoop-user:supergroup:rwxr-xr-x
[*]2012-05-31 13:11:08,853 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=open src=/user/hadoop-user/compress-input/1901.gz dst=null perm=null
[*]2012-05-31 13:11:09,203 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=createsrc=/user/hadoop-user/compress-output/_temporary/_attempt_local_0001_r_000000_0/part-r-00000.gz dst=null perm=hadoop-user:supergroup:rw-r--r--
[*]2012-05-31 13:11:09,238 INFO org.apache.hadoop.hdfs.StateChange: BLOCK* NameSystem.allocateBlock: /user/hadoop-user/compress-output/_temporary/_attempt_local_0001_r_000000_0/part-r-00000.gz. blk_-3869950436265612646_1016
[*]2012-05-31 13:11:09,292 INFO org.apache.hadoop.hdfs.StateChange: BLOCK* NameSystem.addStoredBlock: blockMap updated: 192.168.129.35:50010 is added to blk_-3869950436265612646_1016 size 29
[*]2012-05-31 13:11:09,686 INFO org.apache.hadoop.hdfs.StateChange: DIR* NameSystem.completeFile: file /user/hadoop-user/compress-output/_temporary/_attempt_local_0001_r_000000_0/part-r-00000.gz is closed by DFSClient_-356100022
[*]2012-05-31 13:11:09,692 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=listStatussrc=/user/hadoop-user/compress-output/_temporary/_attempt_local_0001_r_000000_0 dst=null perm=null
[*]2012-05-31 13:11:09,695 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=mkdirssrc=/user/hadoop-user/compress-output dst=null perm=hadoop-user:supergroup:rwxr-xr-x
[*]2012-05-31 13:11:09,698 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=renamesrc=/user/hadoop-user/compress-output/_temporary/_attempt_local_0001_r_000000_0/part-r-00000.gz dst=/user/hadoop-user/compress-output/part-r-00000.gz perm=hadoop-user:supergroup:rw-r--r--
[*]2012-05-31 13:11:09,699 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=deletesrc=/user/hadoop-user/compress-output/_temporary/_attempt_local_0001_r_000000_0 dst=null perm=null
[*]2012-05-31 13:11:09,703 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.40.16 cmd=deletesrc=/user/hadoop-user/compress-output/_temporary dst=null perm=null
[*]2012-05-31 13:11:51,010 INFO org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit: ugi=hadoop-user,hadoop-user ip=/192.168.129.35cmd=listStatussrc=/user/hadoop-user/compress-output dst=null perm=null
datanode:
[*]2012-05-31 13:11:08,864 INFO org.apache.hadoop.hdfs.server.datanode.DataNode.clienttrace: src: /192.168.129.35:50010, dest: /192.168.40.16:6233, bytes: 74447, op: HDFS_READ, cliID: DFSClient_-356100022, srvID: DS-1002949858-192.168.129.35-50010-1337839176422, blockid: blk_-4455870079864415553_1015
[*]2012-05-31 13:11:09,248 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: Receiving block blk_-3869950436265612646_1016 src: /192.168.40.16:6234 dest: /192.168.129.35:50010
[*]2012-05-31 13:11:09,283 INFO org.apache.hadoop.hdfs.server.datanode.DataNode.clienttrace: src: /192.168.40.16:6234, dest: /192.168.129.35:50010, bytes: 29, op: HDFS_WRITE, cliID: DFSClient_-356100022, srvID: DS-1002949858-192.168.129.35-50010-1337839176422, blockid: blk_-3869950436265612646_1016
[*]2012-05-31 13:11:09,283 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: PacketResponder 0 for block blk_-3869950436265612646_1016 terminating
我们在这里清楚的看到在目标目录下生成gzip格式的输出文件的整个过程,假定namenode第i行日志设为N(i)，datanode第i行日志设为D(i),则执行顺序为：
N1->N2->N3->N4->D1->N5->N6->D2->D3->D4->N7->N8...->N14,
其中N1->N4是namenode做一些准备工作，包括打开输入文件和创建输出目录及其临时子目录。
D1是datanode读取输入文件
N5,N6按照命名规则和配置中压缩文件的设定，创建输出文件到临时目录下（此时这个文件为空），然后用NameSystem吧这个块分配给datanode
D2-D4是datanode写最终reduce结果到被分配的块中。
N7-N14则是namenode吧输出文件的位置复制到命令行第二个参数指定的位置中，作为最终输出结果
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