Ok, but upon runtime how to you set in the spout which kafka partition to subscribe at?
Kindly yours, Andrew Grammenos -- PGP PKey -- <https://www.dropbox.com/s/2kcxe59zsi9nrdt/pgpsig.txt> https://www.dropbox.com/s/ei2nqsen641daei/pgpsig.txt On Thu, Jul 17, 2014 at 3:30 AM, Tomas Mazukna <[email protected]> wrote: > 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 >
