MapReduce-多个Mapper

MapReduce多输入多mapper处理方法

最新推荐文章于 2021-12-18 15:58:24 发布

转载最新推荐文章于 2021-12-18 15:58:24 发布 · 599 阅读

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原文链接：http://www.cnblogs.com/EnzoDin/p/9534636.html

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#大数据

博客介绍了MapReduce的多输入、多mapper相关内容。一个MapReduce作业输入可能含多个文件，但通常由同一InputFormat和Mapper解释。面对数据格式演变或数据源格式不同问题，可用MultipleInputs类处理，它能为每条输入路径指定InputFormat和Mapper。

MapReduce的多输入、多mapper

虽然一个MapReduce作业的输入可能包含多个输入文件（由文件glob、过滤器和路径组成），但所有文件都由同一个InputFormat和同一个Mapper来解释。然而，数据格式往往会随时间而演变，所以必须写自己的mapper来处理应用中的遗留数据格式问题。或者，有些数据源会提供相同的数据，但是格式不同。
这些问题可以用MultipleInputs类来妥善处理，它允许为每条输入路径指定InputFormat和Mapper。

代码如下

package com.zhen.mapreduce.multipleInput;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.MultipleInputs;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * @author FengZhen
 * @date 2018年8月25日
 * 多输入、多mapper
 */
public class MultipleInputsTest extends Configured implements Tool{

	/**
	 * 根据 ` 分隔字符串
	 * @author FengZhen
	 *
	 */
	static class SplitMapper1 extends Mapper<LongWritable, Text, Text, IntWritable>{
		@Override
		protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
				throws IOException, InterruptedException {
			String[] values = value.toString().split("`");
			for (String string : values) {
				context.write(new Text(string), new IntWritable(1));
			}
		}
	}
	
	/**
	 * 根据 , 分隔字符串
	 * @author FengZhen
	 *
	 */
	static class SplitMapper2 extends Mapper<LongWritable, Text, Text, IntWritable>{
		@Override
		protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
				throws IOException, InterruptedException {
			String[] values = value.toString().split(",");
			for (String string : values) {
				context.write(new Text(string), new IntWritable(1));
			}
		}
	}

	/**
	 * 同一个reduce
	 * @author FengZhen
	 *
	 */
	static class SplitReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
		@Override
		protected void reduce(Text key, Iterable<IntWritable> value,
				Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
			int sum = 0;
			for (IntWritable intWritable : value) {
				sum += intWritable.get();
			}
			context.write(key, new IntWritable(sum));
		}
	}

	public int run(String[] args) throws Exception {
		
		Configuration configuration = new Configuration();
		
		Job job = Job.getInstance(configuration);
		job.setJobName("MultipleInputs");
		job.setJarByClass(MultipleInputsTest.class);
		
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);

		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(IntWritable.class);
		
		job.setReducerClass(SplitReducer.class);
		
		//设置多输入、多mapper
		MultipleInputs.addInputPath(job, new Path(args[0]), TextInputFormat.class, SplitMapper1.class);
		MultipleInputs.addInputPath(job, new Path(args[1]), TextInputFormat.class, SplitMapper2.class);
		
		job.setOutputFormatClass(TextOutputFormat.class);
		TextOutputFormat.setOutputPath(job, new Path(args[2]));
		
		return job.waitForCompletion(true) ? 0 : 1;
	}
	
	public static void main(String[] args) {
		try {
			String[] params = {"hdfs://fz/user/hdfs/MapReduce/data/multipleInputs/test1","hdfs://fz/user/hdfs/MapReduce/data/multipleInputs/test2", "hdfs://fz/user/hdfs/MapReduce/data/multipleInputs/output"};
			int exitCode = ToolRunner.run(new MultipleInputsTest(), params);
			System.exit(exitCode);
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
	
}

转载于:https://www.cnblogs.com/EnzoDin/p/9534636.html