Yeah it is always equal to the fetch size. The fetch size needs to be at least equal to the max message size you have allowed on the server, though.
-Jay On Sun, Aug 25, 2013 at 10:00 PM, Ian Friedman <i...@flurry.com> wrote: > Jay - is there any way to control the size of the interleaved chunks? The > performance hit would likely be negligible for us at the moment. > > -- > Ian Friedman > > > On Sunday, August 25, 2013 at 3:11 PM, Jay Kreps wrote: > > > I'm still a little confused by your description of the problem. It might > be > > easier to understand if you listed out the exact things you have > measured, > > what you saw, and what you expected to see. > > > > Since you mentioned the consumer I can give a little info on how that > > works. The consumer consumes from all the partitions it owns > > simultaneously. The behavior is that we interleve fetched data chunks of > > messages from each partition the consumer is processing. The chunk size > is > > controlled by the fetch size set in the consumer. So the behavior you > would > > expect is that you would get a bunch of messages from one partition > > followed by a bunch from another partition. The reason for doing this > > instead of, say, interleving individual messages is that it is a big > > performance boost--making every message an entry in a blocking queue > gives > > a 5x performance hit in high-throughput cases. Perhaps this interleaving > is > > the problem? > > > > -Jay > > > > > > On Sun, Aug 25, 2013 at 10:22 AM, Ian Friedman <i...@flurry.com (mailto: > i...@flurry.com)> wrote: > > > > > Sorry I reread what I've written so far and found that it doesn't state > > > the actual problem very well. Let me clarify once again: > > > > > > The problem we're trying to solve is that we can't let messages go for > > > unbounded amounts of time without getting processed, and it seems that > > > something about what we're doing (which I suspect is the fact that > > > consumers own several partitions but only consume from one of them at a > > > time until it's caught up) is causing a small number of them to sit > around > > > for hours and hours. This is despite some consumers idling due to being > > > fully caught up on the partitions they own. We've found that > requeueing the > > > oldest messages (consumers ignore messages that have already been > > > processed) is fairly effective in getting them to go away, but I'm > looking > > > for a more stable solution. > > > > > > -- > > > Ian Friedman > > > > > > > > > On Sunday, August 25, 2013 at 1:15 PM, Ian Friedman wrote: > > > > > > > When I said "some messages take longer than others" that may have > been > > > misleading. What I meant there is that the performance of the entire > > > application is inconsistent, mostly due to pressure from other > applications > > > (mapreduce) on our HBase and MySQL backends. On top of that, some > messages > > > just contain more data. Now I suppose what you're suggesting is that I > > > segment my messages by the average or expected time it takes the > payloads > > > to process, but I suspect what will happen if I do that is I will have > > > several consumers doing nothing most of the time, and the rest of them > > > backlogged inconsistently the same way they are now. The problem isn't > so > > > much the size of the payloads but the fact that we're seeing some > messages, > > > which i suspect are in partitions with lots of longer running > processing > > > tasks, sit around for hours without getting consumed. That's what I'm > > > trying to solve. > > > > > > > > Is there any way to "add more consumers" without actually adding more > > > consumer JVM processes? We've hit something of a saturation point for > our > > > MySQL database. Is this maybe where having multiple consumer threads > would > > > help? If so, given that I have a singular shared processing queue in > each > > > consumer, how would I leverage that to solve this problem? > > > > > > > > -- > > > > Ian Friedman > > > > > > > > > > > > On Sunday, August 25, 2013 at 12:13 PM, Mark wrote: > > > > > > > > > I don't think it would matter as long as you separate the types of > > > message in different topics. Then just add more consumers to the ones > that > > > are slow. Am I missing something? > > > > > > > > > > On Aug 25, 2013, at 8:59 AM, Ian Friedman <i...@flurry.com (mailto: > i...@flurry.com) (mailto: > > > i...@flurry.com (mailto:i...@flurry.com))> wrote: > > > > > > > > > > > What if you don't know ahead of time how long a message will > take to > > > consume? > > > > > > > > > > > > -- > > > > > > Ian Friedman > > > > > > > > > > > > > > > > > > On Sunday, August 25, 2013 at 10:45 AM, Neha Narkhede wrote: > > > > > > > > > > > > > Making producer side partitioning depend on consumer behavior > > > might not be > > > > > > > such a good idea. If consumption is a bottleneck, changing > > > > > > > > > > > > > > > > > > > > > producer side > > > > > > > partitioning may not help. To relieve consumption bottleneck, > you > > > > > > > > > > > > > > > > > > > > > may need > > > > > > > to increase the number of partitions for those topics and > increase > > > > > > > > > > > > > > > > > > > > > the > > > > > > > number of consumer instances. > > > > > > > > > > > > > > You mentioned that the consumers take longer to process certain > > > kinds of > > > > > > > messages. What you can do is place the messages that require > slower > > > > > > > processing in separate topics, so that you can scale the > number of > > > > > > > partitions and number of consumer instances, for those messages > > > > > > > independently. > > > > > > > > > > > > > > Thanks, > > > > > > > Neha > > > > > > > > > > > > > > > > > > > > > On Sat, Aug 24, 2013 at 9:57 AM, Ian Friedman > > > > > > > <i...@flurry.com(mailto: > i...@flurry.com)(mailto: > > > i...@flurry.com) (mailto:i...@flurry.com)> wrote: > > > > > > > > > > > > > > > Hey guys! We recently deployed our kafka data pipeline > > > application over > > > > > > > > the weekend and it is working out quite well once we ironed > out > > > > > > > > > > > > > > > > > > > > > > > > > > > > all the > > > > > > > > issues. There is one behavior that we've noticed that is > mildly > > > > > > > > > > > > > > > > > > > > > > > > > > > > troubling, > > > > > > > > though not a deal breaker. We're using a single topic with > many > > > > > > > > > > > > > > > > > > > > > > > > > > > > partitions > > > > > > > > (1200 total) to load balance our 300 consumers, but what > seems > > > > > > > > > > > > > > > > > > > > > > > > > > > > to happen is > > > > > > > > that some partitions end up more backed up than others. This > is > > > > > > > > > > > > > > > > > > > > > > > > > > > > probably > > > > > > > > due more to the specifics of the application since some > messages > > > > > > > > > > > > > > > > > > > > > > > > > > > > take much > > > > > > > > longer than others to process. > > > > > > > > > > > > > > > > I'm thinking that the random partitioning in the producer is > > > unsuited to > > > > > > > > our specific needs. One option I was considering was to > write an > > > > > > > > > > > > > > > > > > > > > > > > > > > > alternate > > > > > > > > partitioner that looks at the consumer offsets from zookeeper > > > > > > > > > > > > > > > > > > > > > > > > > > > > (as in the > > > > > > > > ConsumerOffsetChecker) and probabilistically weights the > > > > > > > > > > > > > > > > > > > > > > > > > > > > partitions by > > > > > > > > their lag. Does this sound like a good idea to anyone else? > Is > > > > > > > > > > > > > > > > > > > > > > > > > > > > there a > > > > > > > > better or preferably already built solution? If anyone has > any > > > > > > > > > > > > > > > > > > > > > > > > > > > > ideas or > > > > > > > > feedback I'd sincerely appreciate it. > > > > > > > > > > > > > > > > Thanks so much in advance. > > > > > > > > > > > > > > > > P.S. thanks especially to everyone who's answered my dumb > > > questions on > > > > > > > > this mailing list over the past few months, we couldn't have > > > > > > > > > > > > > > > > > > > > > > > > > > > > done it > > > > > > > > without you! > > > > > > > > > > > > > > > > -- > > > > > > > > Ian Friedman > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
