Kafka client handles that, it is stored in zookeeper with the offset. I wrote a kafka spout based on kafka groups consumer api. Kafka allows only one consumer per partition per group.
On Wed, Jul 16, 2014 at 8:41 PM, Andrew Xor <[email protected]> wrote: > 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 >> > > -- Tomas Mazukna 678-557-3834
