I thought of formation #1.
But looks like when there're many fields, formation #2 is cleaner.
Cheers
On Sun, Mar 22, 2015 at 8:14 PM, Cheng Lian <lian.cs....@gmail.com> wrote:
> You need either
>
> .map { row =>
> (row(0).asInstanceOf[Float], row(1).asInstanceOf[Float], ...)
> }
>
> or
>
> .map { case Row(f0: Float, f1: Float, ...) =>
> (f0, f1)
> }
>
> On 3/23/15 9:08 AM, Minnow Noir wrote:
>
> I'm following some online tutorial written in Python and trying to
> convert a Spark SQL table object to an RDD in Scala.
>
> The Spark SQL just loads a simple table from a CSV file. The tutorial
> says to convert the table to an RDD.
>
> The Python is
>
> products_rdd = sqlContext.table("products").map(lambda row:
> (float(row[0]),float(row[1]),float(row[2]),float(row[3]),
> float(row[4]),float(row[5]),float(row[6]),float(row[7]),float(row[8]),float(row[9]),float(row[10]),float(row[11])))
>
> The Scala is *not*
>
> val productsRdd = sqlContext.table("products").map( row => (
> row(0).toFloat,row(1).toFloat,row(2).toFloat,row(3).toFloat,
> row(4).toFloat,row(5).toFloat,row(6).toFloat,row(7).toFloat,row(8).toFloat,
> row(9).toFloat,row(10).toFloat,row(11).toFloat
> ))
>
> I know this, because Spark says that for each of the row(x).toFloat
> calls,
> "error: value toFloat is not a member of Any"
>
> Does anyone know the proper syntax for this?
>
> Thank you
>
>
>
>
