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这篇文章主要介绍了hadoop中如何实现GenericWritable，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。
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package com.test;
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.GenericWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
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.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.input.MultipleInputs;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* 业务场景：
* 含有两个文件，两个文件中单词之间的分隔方式不一样，但是统计出单词在两个文件中公共出现的次数
*
* 文件来源1，逗号分隔text1.txt
* hello,what
* you,haha
* 文件来源2，制表符分隔text2.txt
* girl boy
* father mother
*/
public class WordCountGenericWritable extends Configured implements Tool {
public static class Map1 extends Mapper {
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer st = new StringTokenizer(line, ",");
while(st.hasMoreElements()) {
context.write(new Text(st.nextElement().toString()), new MyGenericWritable(new LongWritable(1)));
}
}
}
public static class Map2 extends Mapper {
public void map(Text key, Text value, Context context) throws IOException, InterruptedException {
context.write(key, new MyGenericWritable(new Text("1")));
context.write(value, new MyGenericWritable(new Text("1")));
}
}
public static class Reduce extends Reducer {
public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
int count = 0;
Iterator it = values.iterator();
while(it.hasNext()) {
MyGenericWritable myGw = it.next();
Writable value = myGw.get();
if(value instanceof LongWritable) {
count = count + Long.valueOf(((LongWritable)value).get()).intValue();
}
if(value instanceof Text) {
count = count + Long.valueOf(((Text)value).toString()).intValue();
}
}
context.write(key, new IntWritable(count));
}
}
public int run(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
Configuration conf = this.getConf();
Job job = new Job(conf);
job.setJobName(WordCountGenericWritable.class.getSimpleName());
job.setJarByClass(WordCountGenericWritable.class);
MultipleInputs.addInputPath(job, new Path("hdfs://grid131:9000/text1.txt"), TextInputFormat.class, Map1.class);
MultipleInputs.addInputPath(job, new Path("hdfs://grid131:9000/text2.txt"), KeyValueTextInputFormat.class, Map2.class);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setReducerClass(Reduce.class);
job.setOutputFormatClass(TextOutputFormat.class);
//当map的输出类型和reduce的输出类型不一致的时候，需要单独设置map输出类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(MyGenericWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.waitForCompletion(true);
return job.isSuccessful()?0:1;
}
public static void main(String[] args) throws Exception {
int exit = ToolRunner.run(new WordCount(), args);
System.exit(exit);
}
}
class MyGenericWritable extends GenericWritable {
public MyGenericWritable() {
}
public MyGenericWritable(LongWritable longWritable) {
super.set(longWritable);
}
public MyGenericWritable(Text text) {
super.set(text);
}
@Override
protected Class[] getTypes() {
return new Class[]{LongWritable.class, Text.class};
}
} 感谢你能够认真阅读完这篇文章，希望小编分享的“hadoop中如何实现GenericWritable”这篇文章对大家有帮助，同时也希望大家多多支持创新互联，关注创新互联行业资讯频道，更多相关知识等着你来学习!