java实现spark常用算子之mapPartitions

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.VoidFunction;
import java.util.*;

/**
 * mapPartitions 算子
 * 针对partition的操作,一次会处理一个partition的所有数据
 */
public class MapPartitionsOperator {

    public static void main(String[] args){
        SparkConf conf = new SparkConf().setMaster("local").setAppName("mapPartitions");
        JavaSparkContext sc = new JavaSparkContext(conf);
        List<String> names = Arrays.asList("w1","w2","w3","w4");
        JavaRDD<String> nameRdd = sc.parallelize(names,2);

        final Map<String,Integer> scoreMap = new HashMap<>();
        scoreMap.put("w1",1);
        scoreMap.put("w2",2);
        scoreMap.put("w3",3);
        scoreMap.put("w4",4);

        JavaRDD<Integer> result = nameRdd.mapPartitions(new FlatMapFunction<Iterator<String>, Integer>() {
            private static final long serialVersionUID = 1L;

           @Override
           public Iterator<Integer> call(Iterator<String> iterator) throws Exception{
               List<Integer> list = new ArrayList<>();
               while(iterator.hasNext()){
                   String name = iterator.next();
                   int score = scoreMap.get(name);
                   list.add(score);
               }
               return list.iterator();
           }
        });


        result.foreach(new VoidFunction<Integer>() {
            @Override
            public void call(Integer integer) throws Exception {
                System.err.println("mapPartitions算子:"+integer);
            }
        });

        result.foreachPartition(new VoidFunction<Iterator<Integer>>() {
            @Override
            public void call(Iterator<Integer> integerIterator) throws Exception {
                while (integerIterator.hasNext()){
                    System.err.println("mapPartitions算子遍历:"+integerIterator.next());
                }
            }
        });


    }
}

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