Question

Given the following definition about the join transformation in Apache Spark: def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] Where join operation is used for joining two datasets. When it is called on datasets of type (K, V) and (K, W), it returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Output the result of joinrdd, when the following code is run. val rdd1 = sc.parallelize(Seq(("m",55),("m",56),("e",57),("e",58),("s",59),("s",54))) val rdd2 = sc.parallelize(Seq(("m",60),("m",65),("s",61),("s",62),("h",63),("h",64))) val joinrdd = rdd1.join(rdd2) joinrdd.collect Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (h,(63,64)), (s,(54,61)), (s,(54,62))) Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (e,(57,58)), (s,(54,61)), (s,(54,62))) Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (s,(54,61)), (s,(54,62))) None of the mentioned

Answer 1

Array[(String, (Int, Int))] = Array((m,(55,60)), (m,(55,65)), (m,(56,60)), (m,(56,65)), (s,(59,61)), (s,(59,62)), (s,(54,61)), (s,(54,62)))