使用Scala将RDD映射到Spark中的case（Schema）

Question

I am new to scala and spark. I have a little problem. I have an RDD with following schema.

    RDD[((String, String), (Int, Timestamp, String, Int))]

and I have to map this RDD to transform it like this

   RDD[(Int, String, String, String, Timestamp, Int)]

and I write following code for this

  map { case ((pid, name), (id, date, code, level)) => (id, name, code, pid, date, level) }

this work fine. Now I have another RDD

    RDD[((String, String), List[(Int, Timestamp, String, Int)])]

and I want to transform it like this as above

   RDD[(Int, String, String, String, Timestamp, Int)]

How can I do that I have tried this code but it does not work

  map {
  case ((pid, name), List(id, date, code, level)) => (id, name, code, pid, date, level)
}

How it can be achieved?

Answer 1

Is this the thing you're looking for?

val input: RDD[((String, String), List[(Int, Timestamp, String, Int)])] = ...
val output: RDD[(Int, String, String, String, Timestamp, Int)] = input.flatMap { case ((pid, name), list) =>
  list.map { case (id, date, code, level) =>
    (id, name, code, pid, date, level)
  }
}

or using for comprehension:

val output: RDD[(Int, String, String, String, Timestamp, Int)] = for {
  ((pid, name), list)     <- input
  (id, date, code, level) <- list
} yield (id, name, code, pid, date, level)

Answer 2

try

 map {
  case ((id, name), list) => (id, name, list.flatten)
}