DotProduct matrix in scala and on spark

DotProduct.scala

def dotProduct(vector: Array[Int], matrix: Array[Array[Int]]): Array[Int] = { 
    // ignore dimensionality checks for simplicity of example 
    (0 to (matrix(0).size - 1)).toArray.map( colIdx => { 
        val colVec: Array[Int] = matrix.map( rowVec => rowVec(colIdx) ) 
        val elemWiseProd: Array[Int] = (vector zip colVec).map( entryTuple => entryTuple._1 * entryTuple._2 ) 
        elemWiseProd.sum 
    } )
}
 
 
val A = sc.parallelize(Array(Array(7, 5, 4), Array(0, 3, 2), Array(8, 0, 5), Array(-11, 7, -4), Array(-8, 2, -13), Array(5, 0, -2)))
 
val B = sc.broadcast(Array(Array(100, -80, 75, -105, 30, -50), Array(60, -60, 60, -60, 60, -60), Array(-50, 30, -105, 75, -80, 100)))
 
A.map( row => dotProduct(row, B.value) ).collect
 

Hello Sam! why did you broadcast your B value?

You can also write: (0 until matrix(0).size), which is more readable.

If B is not ginormous, it's actually a good idea to broadcast it, the performance should linearly scale on the number of workers.