这篇文章主要介绍“如何使用spark Context转成RDD”，在日常操作中，相信很多人在如何使用spark Context转成RDD问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”如何使用spark Context转成RDD”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

一. 背景

在spark rdd转换算子中join和cogroup是有些需要区分的算子转换，这里使用示例来说明一下。

二. 示例

1.构建List示例数据

List<Tuple2<Integer, String>> studentsList = Arrays.asList(
      new Tuple2<Integer,String>(1,"xufengnian"),
      new Tuple2<Integer,String>(2,"xuyao"),
      new Tuple2<Integer,String>(2,"wangchudong"),
      new Tuple2<Integer,String>(3,"laohuang")
      );

List<Tuple2<Integer, Integer>> scoresList = Arrays.asList(
      new Tuple2<Integer,Integer>(1,100),
      new Tuple2<Integer,Integer>(2,90),
      new Tuple2<Integer,Integer>(3,80),
      new Tuple2<Integer,Integer>(1,101),
      new Tuple2<Integer,Integer>(2,91),
      new Tuple2<Integer,Integer>(3,81),
      new Tuple2<Integer,Integer>(3,71)
      );

2.使用sparkContext转成RDD

JavaPairRDD<Integer,String> studentsRDD = sc.parallelizePairs(studentsList);
JavaPairRDD<Integer,Integer> scoresRDD = sc.parallelizePairs(scoresList);

//studentsRDD 为：List<Tuple2<Integer, String>>
//(1,xufengnian)(2,xuyao)(2,wangchudong)(3,laohuang),下面进行打印查看

studentsRDD.foreach(new VoidFunction<Tuple2<Integer,String>>(){
   public void call(Tuple2<Integer,String> tuple){
      System.out.println(tuple._1);//1 2 3
      System.out.println(tuple._2);// xufengnian xuyao laohuang
   }
});

3.进行join

/*
前面数据
(1,xufengnian)(2,xuyao)(2,"wangchudong")(3,laohuang)
(1,100)(2,90)(3,80)(1,101)(2,91)(3,81)(3,71)
join之后：
(1,(xufengnian,100))(1,(xufengnian,101))(3,(laohuang,80))(3,(laohuang,81))(3,(laohuang,71))
(2,(xuyao,90))(2,(xuyao,91))(2,(wangchudong,90))(2,(wangchudong,91))
*/
JavaPairRDD<Integer, Tuple2<String, Integer>> studentScores = studentsRDD.join(scoresRDD);
//join为key相同的join,key不变，value变成(string,integer)
studentScores.foreach(new VoidFunction<Tuple2<Integer,Tuple2<String,Integer>>>() {
   
   private static final long serialVersionUID = 1L;

   @Override
   public void call(Tuple2<Integer, Tuple2<String, Integer>> student)
         throws Exception {
      System.out.println("student id: " + student._1);//1 1 3
      System.out.println("student name: " + student._2._1);//xufengnian xufengnian laohuang
      System.out.println("student score: " + student._2._2);//100 101 80
      System.out.println("===================================");
   }
});

4.进行cogroup

/*
前面的数据
(1,xufengnian)(2,xuyao)(2,"wangchudong")(3,laohuang)
(1,100)(2,90)(3,80)(1,101)(2,91)(3,81)(3,71)
cogroup之后：
(1,([xufengnian],[100,101]))  (3,([laohuang],[80,81,71]))  (2,([xuyao,wangchudong],[90,91]))
*/
JavaPairRDD<Integer,Tuple2<Iterable<String>,Iterable<Integer>>> studentScores2 = studentsRDD.cogroup(scoresRDD);
studentScores2.foreach(new VoidFunction<Tuple2<Integer, Tuple2<Iterable<String>, Iterable<Integer>>>>() {
   @Override
   public void call(Tuple2<Integer, Tuple2<Iterable<String>, Iterable<Integer>>> stu) throws Exception {
      System.out.println("stu id:"+stu._1);//1 3
      System.out.println("stu name:"+stu._2._1);//[xufengnian] [laohuang]
      System.out.println("stu score:"+stu._2._2);//[100,101] [80,81,71]
      Iterable<Integer> integers = stu._2._2;

      for (Iterator iter = integers.iterator(); iter.hasNext();) {
         Integer str = (Integer)iter.next();
         System.out.println(str);//100 101 80 81 71
      }
      System.out.println("===================================");
   }
});

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