In this case, it is slow to wait for rdd1.saveAsHasoopFile(...) to finish
probably due to writing to hdfs. a walk around for this particular case may be
as follows.
val rdd1 = ......cache()
val rdd2 = rdd1.map().....()
rdd1.count
future { rdd1.saveAsHasoopFile(...) }
future { rdd2.saveAsHadoopFile(…)]
In this way, rdd1 will be calculated once, and two saveAsHadoopFile will happen
concurrently.
Thanks.
Zhan Zhang
On Feb 26, 2015, at 3:28 PM, Corey Nolet
<cjno...@gmail.com<mailto:cjno...@gmail.com>> wrote:
> What confused me is the statement of "The final result is that rdd1 is
> calculated twice.” Is it the expected behavior?
To be perfectly honest, performing an action on a cached RDD in two different
threads and having them (at the partition level) block until the parent are
cached would be the behavior and myself and all my coworkers expected.
On Thu, Feb 26, 2015 at 6:26 PM, Corey Nolet
<cjno...@gmail.com<mailto:cjno...@gmail.com>> wrote:
I should probably mention that my example case is much over simplified- Let's
say I've got a tree, a fairly complex one where I begin a series of jobs at the
root which calculates a bunch of really really complex joins and as I move down
the tree, I'm creating reports from the data that's already been joined (i've
implemented logic to determine when cached items can be cleaned up, e.g. the
last report has been done in a subtree).
My issue is that the 'actions' on the rdds are currently being implemented in a
single thread- even if I'm waiting on a cache to complete fully before I run
the "children" jobs, I'm still in a better placed than I was because I'm able
to run those jobs concurrently- right now this is not the case.
> What you want is for a request for partition X to wait if partition X is
> already being calculated in a persisted RDD.
I totally agree and if I could get it so that it's waiting at the granularity
of the partition, I'd be in a much much better place. I feel like I'm going
down a rabbit hole and working against the Spark API.
On Thu, Feb 26, 2015 at 6:03 PM, Sean Owen
<so...@cloudera.com<mailto:so...@cloudera.com>> wrote:
To distill this a bit further, I don't think you actually want rdd2 to
wait on rdd1 in this case. What you want is for a request for
partition X to wait if partition X is already being calculated in a
persisted RDD. Otherwise the first partition of rdd2 waits on the
final partition of rdd1 even when the rest is ready.
That is probably usually a good idea in almost all cases. That much, I
don't know how hard it is to implement. But I speculate that it's
easier to deal with it at that level than as a function of the
dependency graph.
On Thu, Feb 26, 2015 at 10:49 PM, Corey Nolet
<cjno...@gmail.com<mailto:cjno...@gmail.com>> wrote:
> I'm trying to do the scheduling myself now- to determine that rdd2 depends
> on rdd1 and rdd1 is a persistent RDD (storage level != None) so that I can
> do the no-op on rdd1 before I run rdd2. I would much rather the DAG figure
> this out so I don't need to think about all this.
