package xlzx.mapreduce;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
import java.io.IOException;
public class Distinct {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException{
if(args.length < 3){
System.out.println("There can be only three input and output parameter");
}
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration, "data Distinct");
job.setJarByClass(Distinct.class);
job.setMapperClass(WordMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setReducerClass(WordReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileInputFormat.setInputPaths(job, new Path(args[1]));
FileOutputFormat.setOutputPath(job, new Path(args[2]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
public static class WordMapper extends Mapper<LongWritable, Text, Text, Text>{
protected void map(LongWritable key, Text value1, Context context) throws IOException,
InterruptedException{
String line = value1.toString();
context.write(new Text(line), new Text(""));
}
}
public static class WordReducer extends Reducer<Text, Text, Text, Text>{
protected void reduce(Text key, Iterable<Text> value2, Context context) throws
IOException, InterruptedException{
context.write(key, new Text(""));
}
}
}
Mapreduce去重~Java
最新推荐文章于 2022-12-19 10:00:00 发布
本文介绍了一种使用Hadoop进行数据去重的方法,通过MapReduce编程模型实现对大规模数据集的处理,有效去除重复记录,提升数据质量。
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