Two simple suggestions:
1. No need to call zipWithIndex twice. Use the earlier RDD dt.
2. Replace zipWithIndex with zipWithUniqueId which does not trigger a spark
job
Below your code with the above changes:
var dataRDD = sc.textFile("/test.csv").map(_.split(","))
val dt = dataRDD.*zipWithUniqueId*.map(_.swap)
val newCol1 = *dt*.map {case (i, x) => (i, x(1)+x(18)) }
val newCol2 = newCol1.join(dt).map(x=> function(.........))
Hope this helps.
Kiran
On Fri, Jun 5, 2015 at 8:15 AM, Carter <gyz...@hotmail.com> wrote:
> Hi, I have a RDD with MANY columns (e.g., hundreds), and most of my
> operation
> is on columns, e.g., I need to create many intermediate variables from
> different columns, what is the most efficient way to do this?
>
> For example, if my dataRDD[Array[String]] is like below:
>
> 123, 523, 534, ..., 893
> 536, 98, 1623, ..., 98472
> 537, 89, 83640, ..., 9265
> 7297, 98364, 9, ..., 735
> ......
> 29, 94, 956, ..., 758
>
> I will need to create a new column or a variable as newCol1 =
> 2ndCol+19thCol, and another new column based on newCol1 and the existing
> columns: newCol2 = function(newCol1, 34thCol), what is the best way of
> doing
> this?
>
> I have been thinking using index for the intermediate variables and the
> dataRDD, and then join them together on the index to do my calculation:
> var dataRDD = sc.textFile("/test.csv").map(_.split(","))
> val dt = dataRDD.zipWithIndex.map(_.swap)
> val newCol1 = dataRDD.map(x => x(1)+x(18)).zipWithIndex.map(_.swap)
> val newCol2 = newCol1.join(dt).map(x=> function(.........))
>
> Is there a better way of doing this?
>
> Thank you very much!
>
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> --
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