Hey Alexander,
On 12 Jan 2015, at 11:42, Alexander Alexandrov
<alexander.s.alexand...@gmail.com> wrote:
> Hi there,
>
> I wished for intermediate datasets, and Santa Ufuk made my wishes come true
> (thank you, Santa)!
>
> Now that FLINK-986 is in the mainline, I want to ask some practical
> questions.
>
> In Spark, there is a way to put a value from the local driver to the
> distributed runtime via
>
> val x = env.parallelize(...) // expose x to the distributed runtime
> val y = dataflow(env, x) // y is produced by a dataflow which reads from x
>
> and also to get a value from the distributed runtime back to the driver
>
> val z = env.collect("y")
>
> As far as I know, in Flink we have an equivalent for parallelize
>
> val x = env.fromCollection(...)
>
> but not for collect. Is this still the case?
Yes, but this will change soon.
> If yes, how hard would it be to add this feature at the moment? Can you
> give me some pointers?
There is a "alpha" version/hack of this using accumulators. See
https://github.com/apache/flink/pull/210. The problem is that each collect call
results in a new program being executed from the sources. I think Stephan is
working on the scheduling to support this kind of programs. From the runtime
perspective, it is not a problem to transfer the produced intermediate results
back to the job manager. The job manager can basically use the same mechanism
that the task managers use. Even the accumulator version should be fine as a
initial version.
