So you want to define only one instance of the spout that reads the file. Number of bolts will only depend on how fast you need to process the data. I have a topology that has a spout with parallelism of 40 - connected to 40 partitions of a kafka topic. It send traffic to the first bolt which has parallelism 320. The whole topology is split up into 4 workers. that makes 10 spout instances in each jvm, feeding 80 bolts. In my case I have grouping so tuples get routed to different physical machines.
Tomas On Wed, Jul 16, 2014 at 8:10 PM, Andrew Xor <[email protected]> wrote: > Michael, > > Thanks for the response but I think another problem arises; as I just > cooked up a small example the increased number of workers only spawns > mirrors of the topology. This poses a problem for me due to the fact that > my spout reads from a very big file and converts each line into a tuple and > feeds that in the topology. What I wanted to do in the first place is to > actually send each tuple produced to a different subscribed bolt each time > (using Round Robing or smth) so that each one of them got 1/n nth (where n > the number of bolts) of the input stream. If I spawn 2 workers both will > read the same file and emit the same tuples so both topology workers will > produce the same results. > > I wanted to avoid to create a spout that takes a file offset as an input > and wire a lot more stuff than I have to; so I was trying to find a way to > perform what I told you in an elegant and scalable fashion...so far I have > found nil. > > > On Thu, Jul 17, 2014 at 2:57 AM, Michael Rose <[email protected]> > wrote: > >> It doesn't say so, but if you have 4 workers, the 4 executors will be >> shared evenly over the 4 workers. Likewise, 16 will partition 4 each. The >> only case where a worker will not get a specific executor is when there are >> less executors than workers (e.g. 8 workers, 4 executors), 4 of the workers >> will receive an executor but the others will not. >> >> It sounds like for your case, shuffle+parallelism is more than sufficient. >> >> Michael Rose (@Xorlev <https://twitter.com/xorlev>) >> Senior Platform Engineer, FullContact <http://www.fullcontact.com/> >> [email protected] >> >> >> On Wed, Jul 16, 2014 at 5:53 PM, Andrew Xor <[email protected]> >> wrote: >> >>> Hey Stephen, Michael, >>> >>> Yea I feared as much... as searching the docs and API did not surface >>> any reliable and elegant way of doing that unless you had a "RouterBolt". >>> If setting the parallelism of a component is enough for load balancing the >>> processes across different machines that are part of the Storm cluster then >>> this would suffice in my use case. Although here >>> <https://storm.incubator.apache.org/documentation/Understanding-the-parallelism-of-a-Storm-topology.html> >>> the documentation says executors are threads and it does not explicitly say >>> anywhere that threads are spawned across different nodes of the cluster... >>> I want to avoid the possibility of these threads only spawning locally and >>> not in a distributed fashion among the cluster nodes.. >>> >>> Andrew. >>> >>> >>> On Thu, Jul 17, 2014 at 2:46 AM, Michael Rose <[email protected]> >>> wrote: >>> >>>> Maybe we can help with your topology design if you let us know what >>>> you're doing that requires you to shuffle half of the whole stream output >>>> to each of the two different types of bolts. >>>> >>>> If bolt b1 and bolt b2 are both instances of ExampleBolt (and not two >>>> different types) as above, there's no point to doing this. Setting the >>>> parallelism will make sure that data is partitioned across machines (by >>>> default, setting parallelism sets tasks = executors = parallelism). >>>> >>>> Unfortunately, I don't know of any way to do this other than shuffling >>>> the output to a new bolt, e.g. bolt "b0" a 'RouterBolt', then having bolt >>>> b0 round-robin the received tuples between two streams, then have b1 and b2 >>>> shuffle over those streams instead. >>>> >>>> >>>> >>>> Michael Rose (@Xorlev <https://twitter.com/xorlev>) >>>> Senior Platform Engineer, FullContact <http://www.fullcontact.com/> >>>> [email protected] >>>> >>>> >>>> On Wed, Jul 16, 2014 at 5:40 PM, Andrew Xor < >>>> [email protected]> wrote: >>>> >>>>> >>>>> Hi Tomas, >>>>> >>>>> As I said in my previous mail the grouping is for a bolt *task* not >>>>> for the actual number of spawned bolts; for example let's say you have two >>>>> bolts that have a parallelism hint of 3 and these two bolts are wired to >>>>> the same spout. If you set the bolts as such: >>>>> >>>>> tb.setBolt("b1", new ExampleBolt(), 2 /* p-hint >>>>> */).shuffleGrouping("spout1"); >>>>> tb.setBolt("b2", new ExampleBolt(), 2 /* p-hint >>>>> */).shuffleGrouping("spout1"); >>>>> >>>>> Then each of the tasks will receive half of the spout tuples but each >>>>> actual spawned bolt will receive all of the tuples emitted from the spout. >>>>> This is more evident if you set up a counter in the bolt counting how many >>>>> tuples if has received and testing this with no parallelism hint as such: >>>>> >>>>> tb.setBolt("b1", new ExampleBolt(),).shuffleGrouping("spout1"); >>>>> tb.setBolt("b2", new ExampleBolt()).shuffleGrouping("spout1"); >>>>> >>>>> Now you will see that both bolts will receive all tuples emitted by >>>>> spout1. >>>>> >>>>> Hope this helps. >>>>> >>>>> >>>>> Andrew. >>>>> >>>>> >>>>> On Thu, Jul 17, 2014 at 2:33 AM, Tomas Mazukna < >>>>> [email protected]> wrote: >>>>> >>>>>> Andrew, >>>>>> >>>>>> when you connect your bolt to your spout you specify the grouping. If >>>>>> you use shuffle grouping then any free bolt gets the tuple - in my >>>>>> experience even in lightly loaded topologies the distribution amongst >>>>>> bolts >>>>>> is pretty even. If you use all grouping then all bolts receive a copy of >>>>>> the tuple. >>>>>> Use shuffle grouping and each of your bolts will get about 1/3 of the >>>>>> workload. >>>>>> >>>>>> Tomas >>>>>> >>>>>> >>>>>> On Wed, Jul 16, 2014 at 7:05 PM, Andrew Xor < >>>>>> [email protected]> wrote: >>>>>> >>>>>>> H >>>>>>> i, >>>>>>> >>>>>>> I am trying to distribute the spout output to it's subscribed bolts >>>>>>> evenly; let's say that I have a spout that emits tuples and three bolts >>>>>>> that are subscribed to it. I want each of the three bolts to receive 1/3 >>>>>>> rth of the output (or emit a tuple to each one of these bolts in turns). >>>>>>> Unfortunately as far as I understand all bolts will receive all of the >>>>>>> emitted tuples of that particular spout regardless of the grouping >>>>>>> defined >>>>>>> (as grouping from my understanding is for bolt *tasks* not actual >>>>>>> bolts). >>>>>>> >>>>>>> I've searched a bit and I can't seem to find a way to accomplish >>>>>>> that... is there a way to do that or I am searching in vain? >>>>>>> >>>>>>> Thanks. >>>>>>> >>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Tomas Mazukna >>>>>> 678-557-3834 >>>>>> >>>>> >>>>> >>>> >>> >> > -- Tomas Mazukna 678-557-3834
