学步园

Reduce代码就是做加和统计，

package org.freebird.reducer;

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer.Context;
import org.apache.hadoop.mapreduce.Reducer;


public class LogReducer<Key> extends Reducer<Key, IntWritable, Key,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Key key, Iterable<IntWritable> values,
                       Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        result.set(sum);
        context.write(key, result);
    }
}

这里框架保证在调用reduce方法之前，相同的key的value已经被放在values中，从而组成一个pair <key, values>，这些pair之间也已经用key做了排序。

参考文档：https://hadoop.apache.org/docs/stable/api/org/apache/hadoop/mapreduce/Reducer.html

迭代遍历values，取出所有的value，都是1, 简单加和。

然后结果写入到context中。 注意，这里的context是Reducer包的Context。

最后，写一个Job类，将初始环境设置好。

package org.freebird;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Job;

public class LogJob {

    public static void main(String[] args) throws Exception {
	Configuration conf = new Configuration();
	Job job = Job.getInstance(conf, "sum_did_from_log_file");
	job.setJarByClass(LogJob.class);

        job.setMapperClass(org.freebird.mapper.LogMapper.class);
        job.setCombinerClass(org.freebird.reducer.LogReducer.class);
        job.setReducerClass(org.freebird.reducer.LogReducer.class);

	job.setOutputKeyClass(Text.class);
	job.setOutputValueClass(IntWritable.class);

	FileInputFormat.addInputPath(job, new Path(args[0]));
	FileOutputFormat.setOutputPath(job, new Path(args[1]));

	System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}