目标:

• 我希望能够指定输入文件中使用的映射器数量
• 同样，我想指定每个映射器将占用的文件行数

简单示例:

对于 10 行的输入文件(长度不等；下面的示例)，我希望有 2 个映射器——因此每个映射器将处理 5 行。

This is
an arbitrary example file
of 10 lines.
Each line does
not have to be
of
the same
length or contain
the same
number of words

这是我的:

(我有它，以便每个映射器生成一个“”键值对......这样它就会在缩减器中求和)

package org.myorg;
import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.NLineInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.InputFormat;


public class Test {

  // prduce one "<map,1>" pair per mapper
  public static class Map extends Mapper<Object, Text, Text, IntWritable>{
    private final static IntWritable one = new IntWritable(1);
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
      context.write(new Text("map"), one);
    }
  }

  // reduce by taking a sum
  public static class Red 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();
    Job job1 = Job.getInstance(conf, "pass01");

    job1.setJarByClass(Test.class);
    job1.setMapperClass(Map.class);
    job1.setCombinerClass(Red.class);
    job1.setReducerClass(Red.class);

    job1.setOutputKeyClass(Text.class);
    job1.setOutputValueClass(IntWritable.class);

    FileInputFormat.addInputPath(job1, new Path(args[0]));
    FileOutputFormat.setOutputPath(job1, new Path(args[1]));

    // // Attempt#1
    // conf.setInt("mapreduce.input.lineinputformat.linespermap", 5);
    // job1.setInputFormatClass(NLineInputFormat.class);

    // // Attempt#2
    // NLineInputFormat.setNumLinesPerSplit(job1, 5);
    // job1.setInputFormatClass(NLineInputFormat.class);

    // // Attempt#3
    // conf.setInt(NLineInputFormat.LINES_PER_MAP, 5);
    // job1.setInputFormatClass(NLineInputFormat.class);

    // // Attempt#4
    // conf.setInt("mapreduce.input.fileinputformat.split.minsize", 234);
    // conf.setInt("mapreduce.input.fileinputformat.split.maxsize", 234);


    System.exit(job1.waitForCompletion(true) ? 0 : 1);
  }
}

上面的代码，使用上面的示例数据，会产生

map 10

我想要的输出是

map 2

第一个映射器将在前 5 行执行操作，第二个映射器将在后 5 行执行操作。

最佳答案

你可以使用 NLineInputFormat .

使用 NLineInputFormat 功能，您可以准确指定应将多少行发送到映射器。 例如。如果您的文件有 500 行，并且您将每个映射器的行数设置为 10，则您有 50 个映射器 (而不是一个 - 假设文件小于 HDFS block 大小)。

编辑:

下面是一个使用 NLineInputFormat 的例子:

映射器类:

import java.io.IOException;

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

public class MapperNLine extends Mapper<LongWritable, Text, LongWritable, Text> {

    @Override
    public void map(LongWritable key, Text value, Context context)
          throws IOException, InterruptedException {

        context.write(key, value);
    }

}

驱动类:

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.NLineInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.LazyOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class Driver extends Configured implements Tool {

    @Override
    public int run(String[] args) throws Exception {

        if (args.length != 2) {
            System.out
                  .printf("Two parameters are required for DriverNLineInputFormat- <input dir> <output dir>\n");
            return -1;
        }

        Job job = new Job(getConf());
        job.setJobName("NLineInputFormat example");
        job.setJarByClass(Driver.class);

        job.setInputFormatClass(NLineInputFormat.class);
        NLineInputFormat.addInputPath(job, new Path(args[0]));
        job.getConfiguration().setInt("mapreduce.input.lineinputformat.linespermap", 5);

        LazyOutputFormat.setOutputFormatClass(job, TextOutputFormat.class);
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.setMapperClass(MapperNLine.class);
        job.setNumReduceTasks(0);

        boolean success = job.waitForCompletion(true);
        return success ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        int exitCode = ToolRunner.run(new Configuration(), new Driver(), args);
        System.exit(exitCode);
    }
}

使用您提供的输入，上述示例映射器的输出将写入两个文件，因为 2 个映射器已初始化:

部分-m-00001

0   This is
8   an arbitrary example file
34  of 10 lines.
47  Each line does
62  not have to be

部分-m-00002

77  of
80  the same
89  length or contain
107 the same
116 number of words

关于java - MapReduce:如何让映射器处理多行？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/26821652/