MapReduce关于类型转换报错记录

@[toc]

0. 写在前面

• 实验环境：Ubuntu Kylin16.04
• Hadoop版本：2.7.2
• IDE：Eclipse3.8

1. 程序代码

Mapper端

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

public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
	@Override
	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
		String[] strs = value.toString().split(" ");
		for (String str : strs) {
			context.write(new Text(str), new IntWritable(1));
		}
	}
}

Reducer端

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public static class WordCountReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
	
	@Override
	protected void reduce(Text key, Iterable<IntWritable> values, Context context)
			throws IOException, InterruptedException {

		int sum = 0;
		for (IntWritable val : values) {
			System.out.println("<" + key + "," + val + ">");
			sum += val.get();
		}
		context.write(key, new IntWritable(sum));
	}
}

Driver端

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
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.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;


public class TxtCntDemo {
	
	public static void main(String[] args) throws Exception {
		args = new String[] { "/input", "/output"};

		Configuration conf = new Configuration();


		Job job = Job.getInstance(conf);/
		job.setJarByClass(TxtCntDemo.class);

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

		job.setMapperClass(WordCountMap.class);
		job.setReducerClass(WordCountReduce.class);
		job.setPartitionerClass(MyPartitioner.class);
		job.setNumReduceTasks(4);
		FileInputFormat.addInputPath(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		job.waitForCompletion(true);

	}
}

「错误描述」

java.lang.Exception: java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be cast to org.apache.hadoop.io.IntWritable

java.lang.Exception: java.lang.ClassCastException: org.apache.hadoop.io.LongWritable cannot be cast to org.apache.hadoop.io.IntWritable

mapper、reducer、driver分开成3个文件，报Text不可转换成IntWritable，还有LongWritable不能转换成IntWritable的错误

关于第二个错误：Mapper端执行时，key的默认输入是LongWritable类型，把LongWritable类型强行转换成Text类型自然就Error了。

java.lang.Exception: java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be cast to org.apache.hadoop.io.IntWritable
	at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)
	at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:522)
Caused by: java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be cast to org.apache.hadoop.io.IntWritable
	at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:715)
	at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
	at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112)
	at cn.mr.WordCountMapper.map(WordCountMapper.java:15)
	at cn.mr.WordCountMapper.map(WordCountMapper.java:1)
	at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)
	at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)
	at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
	at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:243)
	at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

但是同样的代码mapper、reducer、driver直接放在一个文件下就顺利执行MR得出结果

这个属实给我整不会了 😢 😢

2. 参考

https://www.cnblogs.com/1130136248wlxk/p/5010489.html

记录一下