My dataset is a RDD[Array[String]]
with more than 140 columns. How can I select a subset of columns without hard-coding the column numbers (.map(x => (x(0),x(3),x(6)...))
?
This is what I've tried so far (with success):
val peopleTups = people.map(x => x.split(",")).map(i => (i(0),i(1)))
However, I need more than a few columns, and would like to avoid hard-coding them.
This is what I've tried so far (that I think would be better, but has failed):
// Attempt 1
val colIndices = [0,3,6,10,13]
val peopleTups = people.map(x => x.split(",")).map(i => i(colIndices))
// Error output from attempt 1:
<console>:28: error: type mismatch;
found : List[Int]
required: Int
val peopleTups = people.map(x => x.split(",")).map(i => i(colIndices))
// Attempt 2
colIndices map peopleTups.lift
// Attempt 3
colIndices map peopleTups
// Attempt 4
colIndices.map(index => peopleTups.apply(index))
I found this question and tried it, but because I'm looking at an RDD instead of an array, it didn't work: How can I select a non-sequential subset elements from an array using Scala and Spark?
You should map over the RDD
instead of the indices.
val list = List.fill(2)(Array.range(1, 6))
// List(Array(1, 2, 3, 4, 5), Array(1, 2, 3, 4, 5))
val rdd = sc.parallelize(list) // RDD[Array[Int]]
val indices = Array(0, 2, 3)
val selectedColumns = rdd.map(array => indices.map(array)) // RDD[Array[Int]]
selectedColumns.collect()
// Array[Array[Int]] = Array(Array(1, 3, 4), Array(1, 3, 4))
What about this?
val data = sc.parallelize(List("a,b,c,d,e", "f,g,h,i,j"))
val indices = List(0,3,4)
data.map(_.split(",")).map(ss => indices.map(ss(_))).collect
This should give
res1: Array[List[String]] = Array(List(a, d, e), List(f, i, j))
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