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 /input
drwx------ - grid supergroup 0 2017-07-01 00:42 /tmp
drwxr-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 |gunzip
20160622 380
20160623 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 |gunzip
20160622 380
20160623 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