It could be possible that you're reaching a hardware limitation. The jump from 8 to 16 total bolt/workers could be more than you hardware can handle efficiently. So it's starting to have to switch out processes and their memory, which can have substantial overhead causing your program to slow down.
On Sat, Jul 25, 2015 at 10:36 AM, Dimitris Sarlis <[email protected]> wrote: > Yes, it listens to its own output. For example, if I have two bolts > (bolt1 and bolt2), I perform the following: > > bolt1.directGrouping("bolt1"); > bolt1.directGrouping("bolt2"); > bolt2.directGrouping("bolt1"); > bolt2.directGrouping("bolt2"); > > I know that this could possibly lead to a cycle, but right now the bolts > I'm trying to run perform the following: > if the inputRecord doesn't contain a "!" { > append a "!" > emit to a random node > } > else { > do nothing with the record > } > > Dimitris > > > On 25/07/2015 06:03 μμ, Enno Shioji wrote: > > > Each bolt is connected with itself as well as with each one of the > other bolts > You mean the bolt listens to its own output? > > > > > > On Sat, Jul 25, 2015 at 1:29 PM, Dimitris Sarlis <[email protected]> > wrote: > >> Hi all, >> >> I'm trying to run a topology in Storm and I am facing some scalability >> issues. Specifically, I have a topology where KafkaSpouts read from a Kafka >> queue and emit messages to bolts which are connected with each other >> through directGrouping. (Each bolt is connected with itself as well as with >> each one of the other bolts). Spouts subscribe to bolts with >> shuffleGrouping. I observe that when I increase the number of spouts and >> bolts proportionally, I don't get the speedup I'm expecting to. In fact, my >> topology seems to run slower and for the same amount of data, it takes more >> time to complete. For example, when I increase spouts from 4->8 and bolts >> from 4->8, it takes longer to process the same amount of kafka messages. >> >> Any ideas why this is happening? Thanks in advance. >> >> Best, >> Dimitris Sarlis >> > > >
