DATABASE FUNDAMENTALS
BASICS OF BIG DATA
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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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
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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)))
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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)))
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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)))
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None of the mentioned
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Explanation:
Detailed explanation-1: -There are Three types of operations on RDDs: Transformations, Actions and Shuffles.
Detailed explanation-2: -Wider Transformation Functions such as groupByKey(), aggregateByKey(), aggregate(), join(), repartition() are some examples of a wider transformations.
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