hadoop - 在 MapReduce Word Count Example 中查找在 map 阶段启动的 map 方法的数量

Question

我遇到了一个 MapReduce WordCount 示例应用程序，我想编辑代码，以便它也输出在映射阶段调用 Map 方法的次数。我有两个文本文件，这是我用于应用程序的代码

public class WordCount 
{
public static class TokenizerMapper
        extends Mapper<Object, Text, Text, IntWritable> {

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(Object key, Text value, Context context
    ) throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while (itr.hasMoreTokens()) {
            word.set(itr.nextToken());
            context.write(word, one);
        }
    }
}

public static class IntSumReducer
        extends Reducer<Text, IntWritable, Text, IntWritable> {

    private IntWritable result = new IntWritable();

    public void reduce(Text 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);
    }
}

public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length != 2) {
        System.err.println("Usage: wordcount <in> <out>");
        System.exit(2);
    }
    Job job = new Job(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

这仅用于学习目的，非常感谢您的帮助!

谢谢

Answer 1

Hadoop 已经计算了 map 方法调用的数量。您可以在计数器部分的应用程序 UI 中看到它，或者在它完成后从作业中获取:

int code = job.waitForCompletion(true) ? 0 : 1;

String group = "Map-Reduce Framework";
String counter = "Map input records";

long val = job.getCounters().getGroup(group).findCounter(counter).getValue();

请记住，如果启用推测执行，此数字可能大于输入文件行数。