MapReduce的全局计数器

1.1、介绍

计数器是用来记录job的执行进度和状态的。它的作用可以理解为日志。我们可以在程序的某个位置插入计数器，记录数据或者进度的变化情况。

 

MapReduce 计数器（Counter）为我们提供一个窗口，用于观察 MapReduce Job 运行期的各种细节数据。对MapReduce性能调优很有帮助，MapReduce性能优化的评估大部分都是基于这些 Counter 的数值表现出来的。

 

MapReduce 自带了许多默认Counter，现在我们来分析这些默认Counter 的含义，方便大家观察 Job 结果，如输入的字节数、输出的字节数、Map端输入/输出的字节数和条数、Reduce端的输入/输出的字节数和条数等


1.2、需求

在实际生产代码中，常常需要将数据处理过程中遇到的不合规数据行进行全局计数，类似这种需求可以借助MapReduce框架中提供的全局计数器来实现


1.3、场景介绍

现在我们通过全局计数器来实现，统计一个目录下的所有文件或者一个文件中的总行数和总单词数



1.4、代码实现



package com.ghgj.mazh.mapreduce.counter;

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordAndLineCounter {

  /**
   * mapreduce全局计数器,是用枚举类进行计数器的定义
   * COUNT_LINES用来统计总行数， 
   * COUNT_WORDS用来统计总单词数
   */
  enum MyCouterWordCount {
    COUNT_WORDS, COUNT_LINES
  }

  // 驱动程序
  public static void main(String[] args) throws Exception {

    // 指定hdfs相关的参数
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf);
    // 设置jar包所在路径
    job.setJarByClass(WordAndLineCounter.class);

    job.setMapperClass(WordAndLineCounterMapper.class);

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

    // 本地路径
    Path inputPath = new Path("D:\\bigdata\\wordcount\\input");
    FileInputFormat.setInputPaths(job, inputPath);

    Path outputPath = new Path("D:\\bigdata\\wordcount\\output");
    FileSystem fs = FileSystem.get(conf);
    if (fs.exists(outputPath)) {
      fs.delete(outputPath, true);
    }
    FileOutputFormat.setOutputPath(job, outputPath);

    // 最后提交任务
    boolean waitForCompletion = job.waitForCompletion(true);
    System.exit(waitForCompletion ? 0 : 1);
  }

  private static class WordAndLineCounterMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

      // 获取计数器，行数计数器
      Counter counter = context.getCounter(MyCouterWordCount.COUNT_LINES);
      // 计数器计数
      counter.increment(1L);

      String[] words = value.toString().split(" ");
      for (String word : words) {
        if(!StringUtils.isBlank(word)){
          context.getCounter(MyCouterWordCount.COUNT_WORDS).increment(1L);
        }
      }
      
    }
  }
}





1.5、输入数据



hello huangbo
hello xuzheng
hello wangbaoqiang
one two three four five
one two three four
one two three
one two
hello hi



1.6、计算结果

INFO [main] - Job job_local1030848363_0001 completed successfully
 INFO [main] - Counters: 35
  File System Counters
    FILE: Number of bytes read=614
    FILE: Number of bytes written=506970
    FILE: Number of read operations=0
    FILE: Number of large read operations=0
    FILE: Number of write operations=0
  Map-Reduce Framework
    Map input records=8
    Map output records=0
    Map output bytes=0
    Map output materialized bytes=6
    Input split bytes=104
    Combine input records=0
    Combine output records=0
    Reduce input groups=0
    Reduce shuffle bytes=6
    Reduce input records=0
    Reduce output records=0
    Spilled Records=0
    Shuffled Maps =1
    Failed Shuffles=0
    Merged Map outputs=1
    GC time elapsed (ms)=0
    CPU time spent (ms)=0
    Physical memory (bytes) snapshot=0
    Virtual memory (bytes) snapshot=0
    Total committed heap usage (bytes)=514850816
  Shuffle Errors
    BAD_ID=0
    CONNECTION=0
    IO_ERROR=0
    WRONG_LENGTH=0
    WRONG_MAP=0
    WRONG_REDUCE=0
  com.ghgj.mazh.mapreduce.counter.WordAndLineCounter$MyCouterWordCount
    COUNT_LINES=8
    COUNT_WORDS=22
  File Input Format Counters 
    Bytes Read=127
  File Output Format Counters 
    Bytes Written=8



这就是最后的计算结果：在上面结果中的倒数第5-7行


com.ghgj.mazh.mapreduce.counter.WordAndLineCounter$MyCouterWordCount
    COUNT_LINES=8
    COUNT_WORDS=22



各位看官，有没有看懂的，如果有疑问的地方，可以举手提问。