...and RDD.foreachPartition(_ => {}) is probably marginally faster.
On Thu, Dec 5, 2013 at 2:55 PM, Matei Zaharia <[email protected]>wrote:
> Ah, got it. Then takeSample is going to do what you want, because it needs
> a uniform sample. If you don’t want any result at all, you can also use
> RDD.foreach() with an empty function.
>
> Matei
>
> On Dec 5, 2013, at 12:54 PM, Matt Cheah <[email protected]> wrote:
>
> Actually, we want the opposite – we want as much data to be computed as
> possible.
>
> It's only for benchmarking purposes, of course.
>
> -Matt Cheah
>
> From: Matei Zaharia <[email protected]>
> Reply-To: "[email protected]" <
> [email protected]>
> Date: Thursday, December 5, 2013 10:31 AM
> To: "[email protected]" <[email protected]>
> Cc: Mingyu Kim <[email protected]>
> Subject: Re: takeSample() computation
>
> Hi Matt,
>
> Try using take() instead, which will only begin computing from the start
> of the RDD (first partition) if the number of elements you ask for is small.
>
> Note that if you’re doing any shuffle operations, like groupBy or sort,
> then the stages before that do have to be computed fully.
>
> Matei
>
> On Dec 5, 2013, at 10:13 AM, Matt Cheah <[email protected]> wrote:
>
> Hi everyone,
>
> I have a question about RDD.takeSample(). This is an action, not a
> transformation – but is any optimization made to reduce the amount of
> computation that's done, for example only running the transformations over
> a smaller subset of the data since only a sample will be returned as a
> result?
>
> The context is, I'm trying to measure the amount of time a set of
> transformations takes on our dataset without persisting to disk. So I want
> to stack the operations on the RDD and then invoke an action that doesn't
> save the result to disk but can still give me a good idea of how long
> transforming the whole dataset takes.
>
> Thanks,
>
> -Matt Cheah
>
>
>
>
