Hadoop的计数器

通过50030 查看Hadoop任务的执行情况，在查看任务详细的时候可以看到有很多计数器，是hadoop框架默认提供的统计MR的详细信息，从计数器中可以看出程序运行的状态，那么我们如何自定义这些信息呢？

我们平时控制台输出的也有计数器

例子:

过滤数据中含有hello的词的数量

package counter;

import java.io.IOException;
import java.net.URI;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
/**
 * 敏感词监控处理   记录敏感单词出现的次数,自定义计数器
 *
 */
public class WordCountApp {

    private static final String inputPaths = "hdfs://hadoop:9000/hello";
    private static final String OUT_PATH = "hdfs://hadoop:9000/out";

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();

        FileSystem fileSystem = FileSystem.get(new URI(OUT_PATH), conf);
        fileSystem.delete(new Path(OUT_PATH), true);

        Job job = new Job(conf, WordCountApp.class.getSimpleName());
        job.setJarByClass(WordCountApp.class);

        FileInputFormat.setInputPaths(job, inputPaths);
        job.setInputFormatClass(TextInputFormat.class);

        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        job.setReducerClass(MyReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
        job.waitForCompletion(true);
    }

    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

        @Override
        protected void map(LongWritable key, Text value,
                org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, LongWritable>.Context context)
                throws IOException, InterruptedException {

            //获得一个计数器
            Counter counter = context.getCounter("Sensitive Words","hello");

            final String line = value.toString();
            //假设hello是敏感词
            if(line.contains("hello")){
                //敏感词出现一次加一
                counter.increment(1L);
            }

            String[] split = line.split("\t");
            for (String word : split) {
                context.write(new Text(word), new LongWritable(1));
            }
        }
    }

    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values,
                org.apache.hadoop.mapreduce.Reducer<Text, LongWritable, Text, LongWritable>.Context context)
                throws IOException, InterruptedException {
            Long count=0L;
            for (LongWritable times : values) {
                count+=times.get();
            }
            context.write(key, new LongWritable(count));
        }
    }

}

运行结果:

控制台