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This option should be true:
The number of elements in resulting RDD will always be same as original
As shown by:
var evens = nums.fiter(isEven)
1 Upvote ShareHi,
In the questions it is asked which is not True?
After the transformation, the resultant RDD is always different from its parent RDD as datatype.
It can be smaller (e.g. filter, count, distinct, sample), bigger (e.g. flatMap(), union(), Cartesian()) or the same size (e.g. map).
So, The data type of elements in resulting RDD will always be same as original is the False one.
All the best!
Upvote ShareHi,
1. The resulting RDD is created from the same elements as the original RDD. Hence the data type in original and resulting RDD should be same, right ? Hence this statement looks to be true. (last option in above question)
2. The number of elements in resulting RDD can be lesser than the original RDD. So, this statement looks false. (first option in above question).
So, shouldn't the answer to above question be the first option ? Please clarify if I am missing anything.
Upvote Share> The resulting RDD is created from the same elements as the original RDD.
Yes but the result is a function of what are you doing in transformation.
Check this:
var src_rdd = sc.parallelize(1 to 10)
// The src_rdd is of type integers
var result_rdd = src_rdd.map(x => ":"+ x.toString + ":")
// result_rdd is of type string.
1 Upvote ShareI agree, however, since this statement is also `false`:
<i>The number of elements in resulting RDD will always be same as original</i>
.. therefore, shouldnt' it be an accepted answer as well?
Upvote Share> The number of elements in resulting RDD will always be same as original
This is true for map.
Upvote ShareThis comment has been removed.
i think i have not come across anything so far on map transformation, please can you give more examples on map tranformation, it's required for deep learning
Upvote ShareHello Disqus,
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Upvote ShareThe CloudxLab Team
Please provide some example to support the answer.
Upvote ShareCan you please give a example for different datatype of resulting RDD element from map transformation
1 Upvote ShareHello,
As per first option "The number of elements in resulting RDD will always be same as original" here it is different can you please verify ?
scala> val stringRdd = sc.parallelize(Array("one","two","three","four","five"))
stringRdd: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[20] at parallelize at <console>:24
scala> stringRdd.count
res9: Long = 5
scala> val unionRdd = stringRdd.union(stringRdd)
unionRdd: org.apache.spark.rdd.RDD[String] = UnionRDD[21] at union at <console>:25
scala> unionRdd.count
Upvote Shareres11: Long = 10
In question we are talking about map() and in the example you have used union.
Upvote Share