1) Lets say we have it set at a limit of 100 items. The LBQ currently has 97 items in it. The SQS client runs again, pulls 10 messages, and successfully inputs 3. The other 7 are blocked waiting for the queue to clear out. No new HTTP requests are made to SQS while this LBQ is full (essentially this is just called in a loop). nextTuple() eventually comes around, 6 blocked for insertion. etc. etc. until all 6 blocked messages are inserted into the LBQ. At this point, we call out to the SQS client again to fetch another 10 (and hopefully the LBQ has not completely drained by the time the SQS client returns another 1-10 messages).
This model isn't picky about which SQS client is grabbing messages. SQS doesn't guarantee order (or single delivery) anyways. In one of our topologies, we have 8 spout instances consuming the same SQS queue for throughput purposes. Maybe I've misunderstood your question though. We normally don't have multiple topologies consuming the same queue, but depending on the data there's no reason we couldn't. Also in this model, we don't use a blocking poll method, if the LBQ is empty, we skip emission and let Storm handle backoff if it wants to (see below). 2) By backoff I mean, your spout hasn't emitted in a while, it's going to slow down on calling nextTuple() to not busy-wait your entire CPU. If you're at maxSpoutPending limit, nextTuple is also not called until an ack or fail has been received (which is one of the reasons its so important to not block in the spout as much as possible). Storm will use the https://github.com/apache/incubator-storm/blob/master/storm-core/src/jvm/backtype/storm/spout/SleepSpoutWaitStrategy.java when you don't emit. By default, it waits only 1ms before calling again (not exponential, just fixed), which is enough to prevent 10-100k/s polling behavior but not significantly increase latency -- if you're ever not emitting, you can probably afford to sleep for 1ms. We actually have it set to 10ms, given that 99.9% of the time we'll have a message ready for processing. An alternative to this is the https://github.com/apache/incubator-storm/blob/master/storm-core/src/jvm/backtype/storm/spout/NothingEmptyEmitStrategy.java if you do see an impact on your throughput--but I've never needed this. Michael Rose (@Xorlev <https://twitter.com/xorlev>) Senior Platform Engineer, FullContact <http://www.fullcontact.com/> mich...@fullcontact.com On Fri, Jul 18, 2014 at 2:41 PM, Itai Frenkel <i...@forter.com> wrote: > Thanks for the thorough and quick answer. > > > Two follow up questions. The context is performing low latency stream > manipulation (hopefully process a message the moment it arrives, maybe a > few milliseconds later). > > > 1. What happens if the LBQ contains 10 items, while the Storm topology > does not call nextTuple because of backoffs ? Wouldn't it be better of for > another Amazon SQS client to handle these items? Or are you assuming a > single Storm topology is the sole handler of these items ? > > > 2. If by backoff, you mean storm topology cannot handle any more > messages, or maxSpoutPending is reached, then ignore this question. If by > backoff you mean exponential backoff then I am worried about a message > arriving to the queue and nextTuple is not called for a long time (more > than a few milliseconds). > > > Regards, > > Itai > ------------------------------ > *From:* Michael Rose <mich...@fullcontact.com> > *Sent:* Friday, July 18, 2014 11:27 PM > > *To:* user@storm.incubator.apache.org > *Subject:* Re: Acking is delayed by 5 seconds (in disruptor queue ?) > > I have no experience with multilang spouts, however my impression from > the docs is that you should be handling your own multiplexing if you're > writing a shellspout. Otherwise if you block for 5 seconds emitting a > tuple, you cannot process an ack until that's done. I'd experiment with > that, if you change the sleep.spout.wait time to be 500ms and you don't > block in your spout (instead returning "sync") it should back off just as > it does with a normal spout (see > https://github.com/apache/incubator-storm/blob/master/storm-core/src/jvm/backtype/storm/spout/ShellSpout.java, > "sync" is a no-op). > > The post you linked to was mine, and for a long time that was true > (especially 0.6 and 0.7). Since Storm 0.8, the spout wait strategy will do > automatic backoffs when no tuples are emitted. The only time I've > intentionally blocked in a spout after 0.8.0 is to control throughout (e.g. > only allow 10/s during development). I've never built a multilang spout > before. > > Spouts, like bolts, run in a single-threaded context so blocking at all > prevents acks/fails/emits from being done until the thread is unblocked. > That is why it's best to have another thread dealing with IO and > asynchronously feeding a concurrent data structure the spout can utilize. > For example, in our internal Amazon SQS client our IO thread continuously > fetches up to 10 messages per get and shoves them into a > LinkedBlockingQueue (until full, then it blocks the IO thread only until > the spout emits clear up room). > > Michael Rose (@Xorlev <https://twitter.com/xorlev>) > Senior Platform Engineer, FullContact <http://www.fullcontact.com/> > mich...@fullcontact.com > > > On Fri, Jul 18, 2014 at 1:34 PM, Itai Frenkel <i...@forter.com> wrote: > >> So can you please explain this sentence from the multilang >> documentation? >> >> >> "Also like ISpout, if you have no tuples to emit for a next, you should >> sleep for a small amount of time before syncing. ShellSpout will not >> automatically sleep for you" >> >> https://storm.incubator.apache.org/documentation/Multilang-protocol.html >> >> >> I read it as: "Unless you sleep a small amount of time before syncing, >> the ShellSpout would serialize one "nextTuple" message per 1ms (see >> configuration below) which would require much more CPU cycles" >> >> topology.spout.wait.strategy: "backtype.storm.spout.SleepSpoutWaitStrategy" >> topology.sleep.spout.wait.strategy.time.ms: 1 >> >> You can also refer to the answer here, which refers to regular Spouts >> doing sleep as well: >> >> https://groups.google.com/forum/#!topic/storm-user/OSjaVgTK5m0 >> >> >> Regards, >> >> Itai >> >> >> >> ------------------------------ >> *From:* Michael Rose <mich...@fullcontact.com> >> *Sent:* Friday, July 18, 2014 10:18 PM >> *To:* user@storm.incubator.apache.org >> *Subject:* Re: Acking is delayed by 5 seconds (in disruptor queue ?) >> >> Run your producer code in another thread to fill a LBQ, poll that with >> nextTuple instead. >> >> You should never be blocking yourself inside a spout. >> >> Michael Rose (@Xorlev <https://twitter.com/xorlev>) >> Senior Platform Engineer, FullContact <http://www.fullcontact.com/> >> mich...@fullcontact.com >> >> >> On Fri, Jul 18, 2014 at 1:03 PM, Itai Frenkel <i...@forter.com> wrote: >> >>> Hello again, >>> >>> >>> Attached is a simplified reproduction (without the ShellSpout, but the >>> concepts are the same). >>> >>> >>> It seems that ack() and nextTuple() are always called on the same >>> thread. That means that there is an inherent tradeoff. >>> >>> Either nextTuple sleeps a few ms (and then the ShellSpout would >>> serialize alot of nextTuple messages) >>> >>> or nextTuple can sleep but then the ack is delayed. >>> >>> >>> Is there a way around this limitation? >>> >>> >>> Itai >>> ------------------------------ >>> *From:* Itai Frenkel <i...@forter.com> >>> *Sent:* Thursday, July 17, 2014 9:42 PM >>> *To:* user@storm.incubator.apache.org >>> *Subject:* Acking is delayed by 5 seconds (in disruptor queue ?) >>> >>> Hello, >>> >>> I have noticed that an ack takes 5 seconds to pass from the bolt to >>> the spout (see debug log below). It is a simple topology with 1 spout, 1 >>> bolt and 1 acker all running on the same worker. The spout and the bolt are >>> ShellSpout and ShellBolt respectively. >>> >>> It looks like the message is delayed in the LMAX disruptor​ queue. >>> How can I reduce this delay to ~1ms ? >>> >>> Regards, >>> Itai >>> >>> >>> 2014-07-17 18:30:30 b.s.t.ShellBolt [INFO] Shell msg: Sent process to >>> tuple 2759481868963667531 >>> 2014-07-17 18:30:30 b.s.d.task [INFO] Emitting: bolt __ack_ack >>> [-357211617823660063 -3928495599512172728] >>> 2014-07-17 18:30:30 b.s.t.ShellBolt [INFO] Shell msg: Bolt sent ack to >>> tuple 2759481868963667531 >>> 2014-07-17 18:30:30 b.s.d.executor [INFO] Processing received message >>> source: bolt:2, stream: __ack_ack, id: {}, [-357211617823660063 >>> -3928495599512172728] >>> 2014-07-17 18:30:30 b.s.d.task [INFO] Emitting direct: 3; __acker >>> __ack_ack [-357211617823660063] >>> 2014-07-17 18:30:35 b.s.d.executor [INFO] Processing received message >>> source: __acker:1, stream: __ack_ack, id: {}, [-357211617823660063] >>> 2014-07-17 18:30:35 b.s.d.executor [INFO] Acking message 1138 >>> >>> >>> >>> >> >
