MapReduce的自定义计数器

1. 在map端使用计数器进行统计：

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class MapDemo01 extends Mapper<LongWritable, Text,Text,LongWritable> {
    
    
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    
    
        //map端计数
        context.getCounter("map","map01").increment(1);
       
        String[] split = value.toString().split(",");
        for (String s : split) {
    
    
            context.write(new Text(s),new LongWritable(1));
        }
    }
}

2. 在reduce端使用计数器进行统计

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class ReduceDemo01 extends Reducer<Text, LongWritable,Text,LongWritable> {
    
    
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
    
    

        //reduce端计数
        context.getCounter("reduce","reduce01").increment(1);
        
        int count=0;
        for (LongWritable value : values) {
    
    
            count+=value.get();
        }
        context.write(key,new LongWritable(count));
    }
}