Scala实现Mapreduce程序4-----数据去重

Posted 2020-09-21 流浪在伯纳乌

数据去重，key只输出一次

scala实现：先groupByKey(),然后SortByKey(),然后输出keys

 

object Reduplicate {
  def main(args: Array[String]): Unit = {
    val conf=new SparkConf().setMaster("local").setAppName("remove duplication");
    val sc=new SparkContext(conf);
    val line=sc.textFile("");
    line.filter(_.trim.length>0).map(line=>(line.trim,"")).groupByKey().sortByKey().keys.collect()

  }

}

MapReduce实现：以整个数据作为key，reduce过程中输出key就可以了

package HadoopvsSpark;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 org.apache.hadoop.util.GenericOptionsParser;
import java.io.IOException;

//对数据进行去重，以整个数据作为key，reduce过程中输出key就可以了

/**
 * Created by Administrator on 2017/5/25.
 */
public class Duplicate {
    public static class Map extends Mapper<Object,Text,Text,Text>{

        private static Text text=new Text(  );
        public void map(Object key,Text value,Context context) throws IOException, InterruptedException {
            text=value;
            context.write( new Text(text),new Text(" ") );
        }
    }

    public static class Reduce extends Reducer<Text,Text,Text,Text>{
        public void reduce(Text key,Iterable<Text> values,Context context) throws IOException, InterruptedException {
            context.write( key,new Text( "" ) );
        }
    }

    public static void  main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Job job=new Job();
        Configuration conf=new Configuration();
        conf.set("mapred.job.tracker","192.169.1.101:8200");

        String[] input=new String[]{"a","s"};
        String[] otherArgs=new GenericOptionsParser(conf,input).getRemainingArgs();
        if(otherArgs.length!=2){
            System.err.println("Usage: Data Deduplication <in> <out>");
            System.exit(2);
        }

        job.setJarByClass(Duplicate.class);
        FileInputFormat.addInputPath( job,new Path( otherArgs[0] ) );
        FileOutputFormat.setOutputPath( job,new Path(otherArgs[1]) );

        job.setOutputKeyClass( Text.class);
        job.setOutputValueClass( Text.class );
        job.setMapperClass( Map.class );
        job.setCombinerClass( Reduce.class );
        job.setReducerClass( Reduce.class);
        System.out.println(job.waitForCompletion( true )? 1:0 );
     }

}