Hi, I wonder what version of Spark and different parameter configuration you used. I was able to run CC for 1.8bn edges in about 8 minutes (23 iterations) using 16 nodes with around 80GB RAM each (Spark 1.5, default parameters) John: I suppose your C++ app (algorithm) does not scale if you used only one node. I don’t understand how RDD’s serialization is taking excessive time, compared to the total time or other expected time?
For the different RDD times you have events and UI console and a bunch of papers describing how measure different things, lihu: did you used some incomplete tool or what are you looking for? Best, Ovidiu > On 11 Mar 2016, at 16:02, John Lilley <john.lil...@redpoint.net> wrote: > > A colleague did the experiments and I don’t know exactly how he observed > that. I think it was indirect from the Spark diagnostics indicating the > amount of I/O he deduced that this was RDD serialization. Also when he added > light compression to RDD serialization this improved matters. > > John Lilley > Chief Architect, RedPoint Global Inc. > T: +1 303 541 1516 | M: +1 720 938 5761 | F: +1 781-705-2077 > Skype: jlilley.redpoint | john.lil...@redpoint.net > <mailto:john.lil...@redpoint.net> | www.redpoint.net > <http://www.redpoint.net/> > > From: lihu [mailto:lihu...@gmail.com] > Sent: Friday, March 11, 2016 7:58 AM > To: John Lilley <john.lil...@redpoint.net> > Cc: Andrew A <andrew.a...@gmail.com>; u...@spark.incubator.apache.org > Subject: Re: Graphx > > Hi, John: > I am very intersting in your experiment, How can you get that RDD > serialization cost lots of time, from the log or some other tools? > > On Fri, Mar 11, 2016 at 8:46 PM, John Lilley <john.lil...@redpoint.net > <mailto:john.lil...@redpoint.net>> wrote: > Andrew, > > We conducted some tests for using Graphx to solve the connected-components > problem and were disappointed. On 8 nodes of 16GB each, we could not get > above 100M edges. On 8 nodes of 60GB each, we could not process 1bn edges. > RDD serialization would take excessive time and then we would get failures. > By contrast, we have a C++ algorithm that solves 1bn edges using memory+disk > on a single 16GB node in about an hour. I think that a very large cluster > will do better, but we did not explore that. > > John Lilley > Chief Architect, RedPoint Global Inc. > T: +1 303 541 1516 <tel:%2B1%C2%A0303%C2%A0541%201516> | M: +1 720 938 5761 > <tel:%2B1%20720%20938%205761> | F: +1 781-705-2077 <tel:%2B1%20781-705-2077> > Skype: jlilley.redpoint | john.lil...@redpoint.net > <mailto:john.lil...@redpoint.net> | www.redpoint.net > <http://www.redpoint.net/> > > From: Andrew A [mailto:andrew.a...@gmail.com <mailto:andrew.a...@gmail.com>] > Sent: Thursday, March 10, 2016 2:44 PM > To: u...@spark.incubator.apache.org <mailto:u...@spark.incubator.apache.org> > Subject: Graphx > > Hi, is there anyone who use graphx in production? What maximum size of graphs > did you process by spark and what cluster are you use for it? > > i tried calculate pagerank for 1 Gb edges LJ - dataset for > LiveJournalPageRank from spark examples and i faced with large volume > shuffles produced by spark which fail my spark job. > > Thank you, > Andrew
