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。大数据培训

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);    } }

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