Philip, We would not use spooling to local disk on the producer to deal with problems with the connection to the brokers, but rather to absorb temporary spikes in traffic that would overwhelm the brokers. This is assuming that 1) those spikes are relatively short, but when they come they require much higher throughput than normal (otherwise we'd just have a capacity problem and would need more brokers), and 2) the spikes are long enough for just a RAM buffer to be dangerous. If the brokers did go down, spooling to disk would give us more time to react, but that's not the primary reason for wanting the feature.
-Piotr On Fri, Apr 12, 2013 at 8:21 AM, Philip O'Toole <phi...@loggly.com> wrote: > This is just my opinion of course (who else's could it be? :-)) but I think > from an engineering point of view, one must spend one's time making the > Producer-Kafka connection solid, if it is mission-critical. > > Kafka is all about getting messages to disk, and assuming your disks are > solid (and 0.8 has replication) those messages are safe. To then try to > build a system to cope with the Kafka brokers being unavailable seems like > you're setting yourself for infinite regress. And to write code in the > Producer to spool to disk seems even more pointless. If you're that > worried, why not run a dedicated Kafka broker on the same node as the > Producer, and connect over localhost? To turn around and write code to > spool to disk, because the primary system that *spools to disk* is down > seems to be missing the point. > > That said, even by going over local-host, I guess the network connection > could go down. In that case, Producers should buffer in RAM, and start > sending some major alerts to the Operations team. But this should almost > *never happen*. If it is happening regularly *something is fundamentally > wrong with your system design*. Those Producers should also refuse any more > incoming traffic and await intervention. Even bringing up "netcat -l" and > letting it suck in the data and write it to disk would work then. > Alternatives include having Producers connect to a load-balancer with > multiple Kafka brokers behind it, which helps you deal with any one Kafka > broker failing. Or just have your Producers connect directly to multiple > Kafka brokers, and switch over as needed if any one broker goes down. > > I don't know if the standard Kafka producer that ships with Kafka supports > buffering in RAM in an emergency. We wrote our own that does, with a focus > on speed and simplicity, but I expect it will very rarely, if ever, buffer > in RAM. > > Building and using semi-reliable system after semi-reliable system, and > chaining them all together, hoping to be more tolerant of failure is not > necessarily a good approach. Instead, identifying that one system that is > critical, and ensuring that it remains up (redundant installations, > redundant disks, redundant network connections etc) is a better approach > IMHO. > > Philip > > > On Fri, Apr 12, 2013 at 7:54 AM, Jun Rao <jun...@gmail.com> wrote: > > > Another way to handle this is to provision enough client and broker > servers > > so that the peak load can be handled without spooling. > > > > Thanks, > > > > Jun > > > > > > On Thu, Apr 11, 2013 at 5:45 PM, Piotr Kozikowski <pi...@liveramp.com > > >wrote: > > > > > Jun, > > > > > > When talking about "catastrophic consequences" I was actually only > > > referring to the producer side. in our use case (logging requests from > > > webapp servers), a spike in traffic would force us to either tolerate a > > > dramatic increase in the response time, or drop messages, both of which > > are > > > really undesirable. Hence the need to absorb spikes with some system on > > top > > > of Kafka, unless the spooling feature mentioned by Wing ( > > > https://issues.apache.org/jira/browse/KAFKA-156) is implemented. This > is > > > assuming there are a lot more producer machines than broker nodes, so > > each > > > producer would absorb a small part of the extra load from the spike. > > > > > > Piotr > > > > > > On Wed, Apr 10, 2013 at 10:17 PM, Jun Rao <jun...@gmail.com> wrote: > > > > > > > Piotr, > > > > > > > > Actually, could you clarify what "catastrophic consequences" did you > > see > > > on > > > > the broker side? Do clients timeout due to longer serving time or > > > something > > > > else? > > > > > > > > Going forward, we plan to add per client quotas (KAFKA-656) to > prevent > > > the > > > > brokers from being overwhelmed by a runaway client. > > > > > > > > Thanks, > > > > > > > > Jun > > > > > > > > > > > > On Wed, Apr 10, 2013 at 12:04 PM, Otis Gospodnetic < > > > > otis_gospodne...@yahoo.com> wrote: > > > > > > > > > Hi, > > > > > > > > > > Is there anything one can do to "defend" from: > > > > > > > > > > "Trying to push more data than the brokers can handle for any > > sustained > > > > > period of time has catastrophic consequences, regardless of what > > > timeout > > > > > settings are used. In our use case this means that we need to > either > > > > ensure > > > > > we have spare capacity for spikes, or use something on top of Kafka > > to > > > > > absorb spikes." > > > > > > > > > > ? > > > > > Thanks, > > > > > Otis > > > > > ---- > > > > > Performance Monitoring for Solr / ElasticSearch / HBase - > > > > > http://sematext.com/spm > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >________________________________ > > > > > > From: Piotr Kozikowski <pi...@liveramp.com> > > > > > >To: users@kafka.apache.org > > > > > >Sent: Tuesday, April 9, 2013 1:23 PM > > > > > >Subject: Re: Analysis of producer performance > > > > > > > > > > > >Jun, > > > > > > > > > > > >Thank you for your comments. I'll reply point by point for > clarity. > > > > > > > > > > > >1. We were aware of the migration tool but since we haven't used > > Kafka > > > > for > > > > > >production yet we just started using the 0.8 version directly. > > > > > > > > > > > >2. I hadn't seen those particular slides, very interesting. I'm > not > > > sure > > > > > >we're testing the same thing though. In our case we vary the > number > > of > > > > > >physical machines, but each one has 10 threads accessing a pool of > > > Kafka > > > > > >producer objects and in theory a single machine is enough to > > saturate > > > > the > > > > > >brokers (which our test mostly confirms). Also, assuming that the > > > slides > > > > > >are based on the built-in producer performance tool, I know that > we > > > > > started > > > > > >getting very different numbers once we switched to use "real" > > (actual > > > > > >production log) messages. Compression may also be a factor in case > > it > > > > > >wasn't configured the same way in those tests. > > > > > > > > > > > >3. In the latency section, there are two tests, one for average > and > > > > > another > > > > > >for maximum latency. Each one has two graphs presenting the exact > > same > > > > > data > > > > > >but at different levels of zoom. The first one is to observe small > > > > > >variations of latency when target throughput <= actual throughput. > > The > > > > > >second is to observe the overall shape of the graph once latency > > > starts > > > > > >growing when target throughput > actual throughput. I hope that > > makes > > > > > sense. > > > > > > > > > > > >4. That sounds great, looking forward to it. > > > > > > > > > > > >Piotr > > > > > > > > > > > >On Mon, Apr 8, 2013 at 9:48 PM, Jun Rao <jun...@gmail.com> wrote: > > > > > > > > > > > >> Piotr, > > > > > >> > > > > > >> Thanks for sharing this. Very interesting and useful study. A > few > > > > > comments: > > > > > >> > > > > > >> 1. For existing 0.7 users, we have a migration tool that mirrors > > > data > > > > > from > > > > > >> an 0.7 cluster to an 0.8 cluster. Applications can upgrade to > 0.8 > > by > > > > > >> upgrading consumers first, followed by producers. > > > > > >> > > > > > >> 2. Have you looked at the Kafka ApacheCon slides ( > > > > > >> > http://www.slideshare.net/junrao/kafka-replication-apachecon2013 > > )? > > > > > Towards > > > > > >> the end, there are some performance numbers too. The figure for > > > > > throughput > > > > > >> vs #producer is different from what you have. Not sure if this > is > > > > > because > > > > > >> that you have turned on compression. > > > > > >> > > > > > >> 3. Not sure that I understand the difference btw the first 2 > > graphs > > > in > > > > > the > > > > > >> latency section. What's different btw the 2 tests? > > > > > >> > > > > > >> 4. Post 0.8, we plan to improve the producer side throughput by > > > > > >> implementing non-blocking socket on the client side. > > > > > >> > > > > > >> Jun > > > > > >> > > > > > >> > > > > > >> On Mon, Apr 8, 2013 at 4:42 PM, Piotr Kozikowski < > > > pi...@liveramp.com> > > > > > >> wrote: > > > > > >> > > > > > >> > Hi, > > > > > >> > > > > > > >> > At LiveRamp we are considering replacing Scribe with Kafka, > and > > > as a > > > > > >> first > > > > > >> > step we run some tests to evaluate producer performance. You > can > > > > find > > > > > our > > > > > >> > preliminary results here: > > > > > >> > > > > > > > > https://blog.liveramp.com/2013/04/08/kafka-0-8-producer-performance-2/ > > > . > > > > > >> We > > > > > >> > hope this will be useful for some folks, and If anyone has > > > comments > > > > or > > > > > >> > suggestions about what to do differently to obtain better > > results > > > > your > > > > > >> > feedback will be very welcome. > > > > > >> > > > > > > >> > Thanks, > > > > > >> > > > > > > >> > Piotr > > > > > >> > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
