hadoop2.x之IO：MapReduce压缩

前面我们说到了hadoop的压缩，在Hadoop所运行的数据一般都是很大的，输入的数据很大，输出的数据也很大。因此我们有必要对map和Reduce的数据进行压缩存储。

如果我们想对Reduce进行压缩，有两种方法，一种是配置使用Configuration配置。另一种是还是用FileOutputFormat类对输出进行设置。

1. 对Reduce进行压缩（使用Configuration)

使用Configuration，我们需要将mapred.output.compress设置为true。设置mapred.output.compression.codec为我们想设置的codec的类名。例如：

Job程序：MaxTemperatureWithCompression.java

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.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 MaxTemperatureWithCompression {    public static void main(String[] args) throws Exception {        if (args.length != 2) {            System.err.println("Usage: MaxTemperature <input path> <output path>");            System.exit(-1);        }        Configuration conf = new Configuration();        // 重点是这两句        conf.setBoolean("mapred.output.compress", true);        conf.set("mapred.output.compression.codec", GzipCodec.class.getName());        conf.set("mapred.jar", "MaxTemperature.jar");        Job job = Job.getInstance(conf);        job.setJarByClass(MaxTemperatureWithCompression.class);        job.setJobName("Max temperature");        FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        job.setMapperClass(MaxTemperatureMapper.class);        job.setReducerClass(MaxTemperatureReducer.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

Map程序:

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 MaxTemperatureMapper extends        Mapper<LongWritable, Text, Text, IntWritable> {    private static final int MISSING = 9999;    @Override    public void map(LongWritable key, Text value, Context context)            throws IOException, InterruptedException {        String line = value.toString();        String year = line.substring(15, 23);        int airTemperature;        if (line.charAt(87) == '+') { // parseInt doesn't like leading plus                                        // signs            airTemperature = Integer.parseInt(line.substring(88, 92));        } else {            airTemperature = Integer.parseInt(line.substring(87, 92));        }        String quality = line.substring(92, 93);        if (airTemperature != MISSING && quality.matches("[01459]")) {            context.write(new Text(year), new IntWritable(airTemperature));        }    }}

Reduce程序

import java.io.IOException;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;public class MaxTemperatureReducer extends        Reducer<Text, IntWritable, Text, IntWritable> {    @Override    public void reduce(Text key, Iterable<IntWritable> values, Context context)            throws IOException, InterruptedException {        int maxValue = Integer.MIN_VALUE;        for (IntWritable value : values) {            maxValue = Math.max(maxValue, value.get());        }        context.write(key, new IntWritable(maxValue));    }}

编译打包运行…

[grid@tiny01 myclass]$ hadoop fs -ls /Found 5 items-rw-r--r--   1 grid supergroup      49252 2017-07-29 00:07 /data.txt-rw-r--r--   1 grid supergroup 4848295796 2017-07-01 00:40 /inputdrwx------   - grid supergroup          0 2017-07-01 00:42 /tmpdrwxr-xr-x   - grid supergroup          0 2017-07-01 00:42 /user[grid@tiny01 myclass]$ hadoop jar MaxTemperature.jar MaxTemperatureWithCompression /data.txt /out[grid@tiny01 myclass]$ hadoop fs -cat /out/part-r-00000.gz |gunzip20160622        38020160623        310

有关Reduce结果压缩的属性：

属性名称 类型 默认值 描述 mapred.output.compress boolean false 压缩输出 mapred.output.compression.codec String org.apache.hadoop.io.compress.DefaultCodec reduce输出所用的压缩codec mapred.output.compression String RECORD SqeuenceFile的输出可以使用的压缩类型：NONE,RECORD,BLOCK

2. 对Reduce进行压缩（使用FileOutputFormat)
我们只修改Job类：

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.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 MaxTemperatureWithCompression2 {    public static void main(String[] args) throws Exception {        if (args.length != 2) {            System.err.println("Usage: MaxTemperature <input path> <output path>");            System.exit(-1);        }        Configuration conf = new Configuration();        conf.set("mapred.jar", "MaxTemperature2.jar");        Job job = Job.getInstance(conf);        job.setJarByClass(MaxTemperatureWithCompression2.class);        job.setJobName("Max temperature");        FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        // 添加这两句        FileOutputFormat.setCompressOutput(job, true);        FileOutputFormat.setOutputCompressorClass(job, GzipCodec.class);        job.setMapperClass(MaxTemperatureMapper.class);        job.setReducerClass(MaxTemperatureReducer.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

运行：

 [grid@tiny01 myclass]$ hadoop jar MaxTemperature2.jar MaxTemperatureWithCompression2 /data.txt /out2[grid@tiny01 myclass]$ hadoop fs -cat /out2/part-r-00000.gz |gunzip20160622        38020160623        310

是一样的.

3.对map任务进行压缩

因为map和reduce往往在不同的节点上，因此需要网络传输。如果map任务的输出使用一些能够快速压缩的算法，例如LZO,LZ4等就会使Hadoop的性能提升。map任务的压缩属性：

属性名称 类型 默认值 描述 mapred.compress.map.output boolean false 对map任务输出进行压缩 mapred.map.output.compression.codec String org.apache.hadoop.io.compress.DefaultCodec map输出压缩所用的codec

我们还可以使用另一种方式，使用JobConf(Configuration的子类)对象设置相关9配置：

JobConf conf = new JobConf();conf.setCompressMapOutput(true);conf.setMapOutputCompressorClass(GzipCodec.class)conf.set("mapred.jar", "classname.jar");Job job = Job.getInstance(conf);

4.参考资料
[1] Hadoop:The Definitive Guide,Third Edition, by Tom White. Copyright 2013 Tom White,978-1-449-31152-0