java - 第一次使用Hadoop，MapReduce Job没有运行Reduce Phase

Question

我编写了一个简单的 map reduce 作业，它会从 DFS 中读取数据并在其上运行一个简单的算法。在尝试调试它时，我决定简单地让映射器输出一组键和值，而缩减器输出一组完全不同的键和值。我在单节点 Hadoop 20.2 集群上运行这个作业。作业完成后，输出仅包含映射器输出的值，让我相信 reducer 没有运行。如果有人对我的代码产生这种输出的原因提供任何见解，我将不胜感激。我已经尝试将 outputKeyClass 和 outputValueClass 设置为不同的东西，以及将 setMapOutputKeyClass 和 setMapOutputValueClass 设置为不同的东西。目前注释我们的代码部分是我正在运行的算法，但我已经更改了 map 和 reduce 方法以简单地输出某些值。同样，作业的输出仅包含映射器输出的值。这是我用来运行作业的类:

import java.io.IOException;
import java.util.*;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
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.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class CalculateHistogram {

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

        private static final int R = 100;
        private int n = 0;

        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            if (n == 0) {
                StringTokenizer tokens = new StringTokenizer(value.toString(), ",");
                int counter = 0;
                while (tokens.hasMoreTokens()) {
                    String token = tokens.nextToken();
                    if (tokens.hasMoreTokens()) {
                        context.write(new LongWritable(-2), new Text("HI"));
                        //context.write(new LongWritable(counter), new Text(token));
                    }
                    counter++;
                    n++;
                }
            } else {
                n++;
                if (n == R) {
                    n = 0;
                }
                
            }
        }
    }

    public static class HistogramReduce extends Reducer<LongWritable, Text, LongWritable, HistogramBucket> {

        private final static int R = 10;

        public void reduce(LongWritable key, Iterator<Text> values, Context context)
                                            throws IOException, InterruptedException {
            if (key.toString().equals("-1")) {
                //context.write(key, new HistogramBucket(key));
            }
            Text t = values.next();
            for (char c : t.toString().toCharArray()) {
                if (!Character.isDigit(c) && c != '.') {
                    //context.write(key, new HistogramBucket(key));//if this isnt a numerical attribute we ignore it
                }
            }
            context.setStatus("Building Histogram");
            HistogramBucket i = new HistogramBucket(key);
            i.add(new DoubleWritable(Double.parseDouble(t.toString())));
            while (values.hasNext()) {
                for (int j = 0; j < R; j++) {
                    t = values.next();
                }
                if (!i.contains(Double.parseDouble(t.toString()))) {
                    context.setStatus("Writing a value to the Histogram");
                    i.add(new DoubleWritable(Double.parseDouble(t.toString())));
                }
            }
            
            context.write(new LongWritable(55555555), new HistogramBucket(new LongWritable(55555555)));
        }
    }

    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, "MRDT - Generate Histogram");
        job.setJarByClass(CalculateHistogram.class);
        job.setMapperClass(HistogramMap.class);
        job.setReducerClass(HistogramReduce.class);

        //job.setOutputValueClass(HistogramBucket.class);
        
        //job.setMapOutputKeyClass(LongWritable.class);
        //job.setMapOutputValueClass(Text.class);

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

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

Answer 1

您的 reduce 方法的签名是错误的。您的方法签名包含 Iterator<Text> .你必须通过 Iterable<Text> .

您的代码不会覆盖 reduce Reducer 的方法基类。因此，Reducer 提供的默认实现使用基类。此实现是身份函数。

使用 @Override注释来预测像这样的错误。