第17课：RDD案例（join、cogroup等实战)

本节课通过代码实战演示RDD中最重要的两个算子，join和cogroup

join算子代码实战：

//通过代码演示join算子
val conf = new SparkConf().setAppName("RDDDemo").setMaster("local")
val sc = new SparkContext(conf)
val arr1 = Array(Tuple2(1, "Spark"), Tuple2(2, "Hadoop"), Tuple2(3, "Tachyon"))
val arr2 = Array(Tuple2(1, 100), Tuple2(2, 70), Tuple2(3, 90))
val rdd1 = sc.parallelize(arr1)
val rdd2 = sc.parallelize(arr2)

val rdd3 = rdd1.join(rdd2)
rdd3.collect().foreach(println)

运行结果：

(1,(Spark,100))
(3,(Tachyon,90))
(2,(Hadoop,70))

cogroup算子代码实战：
首先通过java的方式编写：
SparkConf conf = new SparkConf().setMaster("local").setAppName("Cogroup");
JavaSparkContext sc = new JavaSparkContext(conf);

List<Tuple2<Integer, String>> nameList = Arrays.asList(new Tuple2<Integer, String>(1, "Spark"),
new Tuple2<Integer, String>(2, "Tachyon"), new Tuple2<Integer, String>(3, "Hadoop"));

List<Tuple2<Integer, Integer>> ScoreList = Arrays.asList(new Tuple2<Integer, Integer>(1, 100),
new Tuple2<Integer, Integer>(2, 95), new Tuple2<Integer, Integer>(3, 80),
new Tuple2<Integer, Integer>(1, 80), new Tuple2<Integer, Integer>(2, 110),
new Tuple2<Integer, Integer>(2, 90));

JavaPairRDD<Integer, String> names = sc.parallelizePairs(nameList);
JavaPairRDD<Integer, Integer> scores = sc.parallelizePairs(ScoreList);

JavaPairRDD<Integer, Tuple2<Iterable<String>, Iterable<Integer>>> nameAndScores = names.cogroup(scores);

nameAndScores.foreach(new VoidFunction<Tuple2<Integer, Tuple2<Iterable<String>, Iterable<Integer>>>>() {

public void call(Tuple2<Integer, Tuple2<Iterable<String>, Iterable<Integer>>> t) throws Exception {

System.out.println("ID:" + t._1);
System.out.println("Name:" + t._2._1);
System.out.println("Score:" + t._2._2);
}
});

sc.close();

运行结果：
ID:1
Name:[Spark]
Score:[100, 80]
ID:3
Name:[Hadoop]
Score:[80]
ID:2
Name:[Tachyon]
Score:[95, 110, 90]

通过Scala的方式：
val conf = new SparkConf().setAppName("RDDDemo").setMaster("local")
val sc = new SparkContext(conf)
val arr1 = Array(Tuple2(1, "Spark"), Tuple2(2, "Hadoop"), Tuple2(3, "Tachyon"))
val arr2 = Array(Tuple2(1, 100), Tuple2(2, 70), Tuple2(3, 90), Tuple2(1, 95), Tuple2(2, 65), Tuple2(1, 110))
val rdd1 = sc.parallelize(arr1)
val rdd2 = sc.parallelize(arr2)

val rdd3 = rdd1.cogroup(rdd2)
rdd3.collect().foreach(println)
sc.stop()

运行结果：

(1,(CompactBuffer(Spark),CompactBuffer(100, 95, 110)))
(3,(CompactBuffer(Tachyon),CompactBuffer(90)))
(2,(CompactBuffer(Hadoop),CompactBuffer(70, 65)))

备注：资料来源于：DT_大数据梦工厂（Spark发行版本定制）更多私密内容，请关注微信公众号：DT_Spark如果您对大数据Spark感兴趣，可以免费听由王家林老师每天晚上20：00开设的Spark永久免费公开课，地址YY房间号：68917580