MapReduce-多个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();

		}

	}

}