Spark RDDs are lazily computed and hence unless an 'action' is applied
which mandates the computation - there won't be any computation. You can
read more on spark docs.
On Mar 9, 2016 7:11 AM, "Hemminger Jeff" <j...@atware.co.jp> wrote:
>
> I'm currently developing a Spark Streaming application.
>
> I have a function that receives an RDD and an object instance as a
> parameter, and returns an RDD:
>
> def doTheThing(a: RDD[A], b: B): RDD[C]
>
>
> Within the function, I do some processing within a map of the RDD.
> Like this:
>
>
> def doTheThing(a: RDD[A], b: B): RDD[C] {
>
> a.combineByKey(...).map(b.function(_))
>
> }
>
>
> I combine the RDD by key, then map the results calling a function of
> instance b, and return the results.
>
> Here is where I ran into trouble.
>
> In a unit test running Spark in memory, I was able to convince myself that
> this worked well.
>
> But in our development environment, the returned RDD results were empty
> and b.function(_) was never executed.
>
> However, when I added an otherwise useless foreach:
>
>
> doTheThing(a: RDD[A], b: B): RDD[C] {
>
> val results = a.combineByKey(...).map(b.function(_))
>
> results.foreach( p => p )
>
> results
>
> }
>
>
> Then it works.
>
> So, basically, adding an extra foreach iteration appears to cause
> b.function(_) to execute and returns results correctly.
>
> I find this confusing. Can anyone shed some light on why this would be?
>
> Thank you,
> Jeff
>
>
>
>
