大数据Spark（七十）：Transformation转换算子cogroup和zip使用案例

文章目录

Transformation转换算子cogroup和zip使用案例

一、cogroup使用案例

二、zip使用案例

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Transformation转换算子cogroup和zip使用案例

一、cogroup使用案例

作用到K,V格式的两个RDD上，如两个RDD类型为（K,V）和（K，W）格式数据，返回一个数据集RDD(K,(Iterable,Iterable))。

注意：子RDD的分区与父RDD多的一致。

Java代码：

SparkConf conf = new SparkConf().setMaster("local").setAppName("CogroupTest");
JavaSparkContext sc = new JavaSparkContext(conf);

JavaPairRDD<String, Integer> personRDD = sc.<String, Integer>parallelizePairs(Arrays.asList(
        new Tuple2<String, Integer>("zhangsan", 18),
        new Tuple2<String, Integer>("zhangsan", 180),
        new Tuple2<String, Integer>("lisi", 19),
        new Tuple2<String, Integer>("wangwu", 20),
        new Tuple2<String, Integer>("wangwu", 200),
        new Tuple2<String, Integer>("maliu", 21)
),3);

JavaPairRDD<String, Integer> scoreRDD = sc.<String, Integer>parallelizePairs(Arrays.asList(
        new Tuple2<String, Integer>("zhangsan", 90),
        new Tuple2<String, Integer>("zhangsan", 900),
        new Tuple2<String, Integer>("lisi", 80),
        new Tuple2<String, Integer>("lisi", 800),
        new Tuple2<String, Integer>("wangwu", 70),
        new Tuple2<String, Integer>("wangwu", 700),
        new Tuple2<String, Integer>("tianqi", 60),
        new Tuple2<String, Integer>("tianqi", 600)
),4);

JavaPairRDD<String, Tuple2<Iterable<Integer>, Iterable<Integer>>> cogroupRDD = personRDD.cogroup(scoreRDD);
System.out.println("cogroupRDD 分区数：" + cogroupRDD.getNumPartitions());

cogroupRDD.foreach(new VoidFunction<Tuple2<String, Tuple2<Iterable<Integer>, Iterable<Integer>>>>() {
    @Override
    public void call(Tuple2<String, Tuple2<Iterable<Integer>, Iterable<Integer>>> tp) throws Exception {
        String name = tp._1;
        Iterable<Integer> ages = tp._2._1;
        Iterable<Integer> scores = tp._2._2;
        System.out.println("name: " + name+" ages: " + ages + " scores: " + scores);
    }
});

sc.stop();

Scala代码：

val conf: SparkConf = new SparkConf().setMaster("local").setAppName("CogroupTest")
val sc = new SparkContext(conf)

val personRDD: RDD[(String, Int)] = sc.parallelize(List(
  ("zhangsan", 18),
  ("zhangsan", 180),
  ("lisi", 19),
  ("lisi", 190),
  ("wangwu", 20),
  ("wangwu", 200),
  ("maliu", 21)),3)

val scoreRDD: RDD[(String, Int)] = sc.parallelize(List(
  ("zhangsan", 90),
  ("zhangsan", 900),
  ("lisi", 80),
  ("lisi", 800),
  ("wangwu", 70),
  ("wangwu", 700),
  ("tianqi", 60)),2)

//cogroup: 两个RDD中key相同的元素进行连接
val cogroupRDD: RDD[(String, (Iterable[Int], Iterable[Int]))] = personRDD.cogroup(scoreRDD)
println(s"cogroupRDD 分区数:${cogroupRDD.getNumPartitions}")
cogroupRDD.foreach(tp=>{
  val name: String = tp._1
  val ageList: List[Int] = tp._2._1.toList
  val scoreList: List[Int] = tp._2._2.toList
  println(s"name:${name}, ageList:${ageList}, scoreList:${scoreList}")
})

sc.stop()

二、zip使用案例

将两个RDD中的元素（KV格式/非KV格式）变成一个KV格式的RDD,两个RDD的每个分区元素个数必须相同。

Java代码：

SparkConf conf = new SparkConf().setMaster("local").setAppName("zipTest");
JavaSparkContext sc = new JavaSparkContext(conf);

JavaRDD<String> rdd1 = sc.parallelize(Arrays.asList("a", "b", "c", "d"));
JavaRDD<String> rdd2 = sc.parallelize(Arrays.asList("e", "f", "g", "h"));
//zip: 将两个RDD中的元素按照位置一一对应，返回一个新的RDD
JavaPairRDD<String, String> zip = rdd1.zip(rdd2);
zip.foreach(tp-> System.out.println(tp));

sc.stop();

Scala代码：

val conf: SparkConf = new SparkConf().setMaster("local").setAppName("zipTest")
val sc = new SparkContext(conf)

val rdd1: RDD[Int] = sc.parallelize(Array(1, 2, 3, 4, 5))
val rdd2: RDD[String] = sc.parallelize(Array("a", "b", "c", "d", "e"))

rdd1.zip(rdd2).foreach(println)

sc.stop()

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