尚硅谷大数据技术之Hadoop(MapReduce)(新)第4章 Hadoop数据压缩
4.6.2 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。
1.给大家提供的Hadoop源码支持的压缩格式有:BZip2Codec 、DefaultCodec
package com.atguigu.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.atguigu.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>{
Text k = new Text(); IntWritable v = new IntWritable(1);
@Override protected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {
// 1 获取一行 String line = value.toString();
// 2 切割 String[] words = line.split(" ");
// 3 循环写出 for(String word:words){ k.set(word); context.write(k, v); } } } |
3.Reducer保持不变
package com.atguigu.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>{
IntWritable v = new IntWritable();
@Override protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0;
// 1 汇总 for(IntWritable value:values){ sum += value.get(); } v.set(sum);
// 2 输出 context.write(key, v); } } |