I do not think that Kafa fits here. You should better use another storage for your events, and use kafka to propagate the events to your views/query.
Le sam. 13 juin 2015 à 21:36, Ewen Cheslack-Postava <e...@confluent.io> a écrit : > If you do CAS where you compare the offset of the current record for the > key, then yes. This might work fine for applications that track key, value, > and offset. It is not quite the same as doing a normal CAS. > > On Sat, Jun 13, 2015 at 12:07 PM, Daniel Schierbeck < > daniel.schierb...@gmail.com> wrote: > > > But wouldn't the key->offset table be enough to accept or reject a write? > > I'm not familiar with the exact implementation of Kafka, so I may be > wrong. > > > > On lør. 13. jun. 2015 at 21.05 Ewen Cheslack-Postava <e...@confluent.io> > > wrote: > > > > > Daniel: By random read, I meant not reading the data sequentially as is > > the > > > norm in Kafka, not necessarily a random disk seek. That in-memory data > > > structure is what enables the random read. You're either going to need > > the > > > disk seek if the data isn't in the fs cache or you're trading memory to > > > avoid it. If it's a full index containing keys and values then you're > > > potentially committing to a much larger JVM memory footprint (and all > the > > > GC issues that come with it) since you'd be storing that data in the > JVM > > > heap. If you're only storing the keys + offset info, then you > potentially > > > introduce random disk seeks on any CAS operation (and making page > caching > > > harder for the OS, etc.). > > > > > > > > > On Sat, Jun 13, 2015 at 11:33 AM, Daniel Schierbeck < > > > daniel.schierb...@gmail.com> wrote: > > > > > > > Ewen: would single-key CAS necessitate random reads? My idea was to > > have > > > > the broker maintain an in-memory table that could be rebuilt from the > > log > > > > or a snapshot. > > > > On lør. 13. jun. 2015 at 20.26 Ewen Cheslack-Postava < > > e...@confluent.io> > > > > wrote: > > > > > > > > > Jay - I think you need broker support if you want CAS to work with > > > > > compacted topics. With the approach you described you can't turn on > > > > > compaction since that would make it last-writer-wins, and using any > > > > > non-infinite retention policy would require some external process > to > > > > > monitor keys that might expire and refresh them by rewriting the > > data. > > > > > > > > > > That said, I think any addition like this warrants a lot of > > discussion > > > > > about potential use cases since there are a lot of ways you could > go > > > > adding > > > > > support for something like this. I think this is an obvious next > > > > > incremental step, but someone is bound to have a use case that > would > > > > > require multi-key CAS and would be costly to build atop single key > > CAS. > > > > Or, > > > > > since the compare requires a random read anyway, why not throw in > > > > > read-by-key rather than sequential log reads, which would allow for > > > > > minitransactions a la Sinfonia? > > > > > > > > > > I'm not convinced trying to make Kafka support traditional > key-value > > > > store > > > > > functionality is a good idea. Compacted topics made it possible to > > use > > > > it a > > > > > bit more in that way, but didn't change the public interface, only > > the > > > > way > > > > > storage was implemented, and importantly all the potential > additional > > > > > performance costs & data structures are isolated to background > > threads. > > > > > > > > > > -Ewen > > > > > > > > > > On Sat, Jun 13, 2015 at 9:59 AM, Daniel Schierbeck < > > > > > daniel.schierb...@gmail.com> wrote: > > > > > > > > > > > @Jay: > > > > > > > > > > > > Regarding your first proposal: wouldn't that mean that a producer > > > > > wouldn't > > > > > > know whether a write succeeded? In the case of event sourcing, a > > > failed > > > > > CAS > > > > > > may require re-validating the input with the new state. Simply > > > > discarding > > > > > > the write would be wrong. > > > > > > > > > > > > As for the second idea: how would a client of the writer service > > know > > > > > which > > > > > > writer is the leader? For example, how would a load balancer know > > > which > > > > > web > > > > > > app process to route requests to? Ideally, all processes would be > > > able > > > > to > > > > > > handle requests. > > > > > > > > > > > > Using conditional writes would allow any producer to write and > > > provide > > > > > > synchronous feedback to the producers. > > > > > > On fre. 12. jun. 2015 at 18.41 Jay Kreps <j...@confluent.io> > wrote: > > > > > > > > > > > > > I have been thinking a little about this. I don't think CAS > > > actually > > > > > > > requires any particular broker support. Rather the two writers > > just > > > > > write > > > > > > > messages with some deterministic check-and-set criteria and all > > the > > > > > > > replicas read from the log and check this criteria before > > applying > > > > the > > > > > > > write. This mechanism has the downside that it creates > additional > > > > > writes > > > > > > > when there is a conflict and requires waiting on the full > > roundtrip > > > > > > (write > > > > > > > and then read) but it has the advantage that it is very > flexible > > as > > > > to > > > > > > the > > > > > > > criteria you use. > > > > > > > > > > > > > > An alternative strategy for accomplishing the same thing a bit > > more > > > > > > > efficiently is to elect leaders amongst the writers themselves. > > > This > > > > > > would > > > > > > > require broker support for single writer to avoid the > possibility > > > of > > > > > > split > > > > > > > brain. I like this approach better because the leader for a > > > partition > > > > > can > > > > > > > then do anything they want on their local data to make the > > decision > > > > of > > > > > > what > > > > > > > is committed, however the downside is that the mechanism is > more > > > > > > involved. > > > > > > > > > > > > > > -Jay > > > > > > > > > > > > > > On Fri, Jun 12, 2015 at 6:43 AM, Ben Kirwin <b...@kirw.in> > wrote: > > > > > > > > > > > > > > > Gwen: Right now I'm just looking for feedback -- but yes, if > > > folks > > > > > are > > > > > > > > interested, I do plan to do that implementation work. > > > > > > > > > > > > > > > > Daniel: Yes, that's exactly right. I haven't thought much > about > > > > > > > > per-key... it does sound useful, but the implementation > seems a > > > bit > > > > > > > > more involved. Want to add it to the ticket? > > > > > > > > > > > > > > > > On Fri, Jun 12, 2015 at 7:49 AM, Daniel Schierbeck > > > > > > > > <daniel.schierb...@gmail.com> wrote: > > > > > > > > > Ben: your solutions seems to focus on partition-wide CAS. > > Have > > > > you > > > > > > > > > considered per-key CAS? That would make the feature more > > useful > > > > in > > > > > my > > > > > > > > > opinion, as you'd greatly reduce the contention. > > > > > > > > > > > > > > > > > > On Fri, Jun 12, 2015 at 6:54 AM Gwen Shapira < > > > > > gshap...@cloudera.com> > > > > > > > > wrote: > > > > > > > > > > > > > > > > > >> Hi Ben, > > > > > > > > >> > > > > > > > > >> Thanks for creating the ticket. Having check-and-set > > > capability > > > > > will > > > > > > > be > > > > > > > > >> sweet :) > > > > > > > > >> Are you planning to implement this yourself? Or is it just > > an > > > > idea > > > > > > for > > > > > > > > >> the community? > > > > > > > > >> > > > > > > > > >> Gwen > > > > > > > > >> > > > > > > > > >> On Thu, Jun 11, 2015 at 8:01 PM, Ben Kirwin <b...@kirw.in> > > > > wrote: > > > > > > > > >> > As it happens, I submitted a ticket for this feature a > > > couple > > > > > days > > > > > > > > ago: > > > > > > > > >> > > > > > > > > > >> > https://issues.apache.org/jira/browse/KAFKA-2260 > > > > > > > > >> > > > > > > > > > >> > Couldn't find any existing proposals for similar things, > > but > > > > > it's > > > > > > > > >> > certainly possible they're out there... > > > > > > > > >> > > > > > > > > > >> > On the other hand, I think you can solve your particular > > > issue > > > > > by > > > > > > > > >> > reframing the problem: treating the messages as > 'requests' > > > or > > > > > > > > >> > 'commands' instead of statements of fact. In your > > > > flight-booking > > > > > > > > >> > example, the log would correctly reflect that two > > different > > > > > people > > > > > > > > >> > tried to book the same flight; the stream consumer would > > be > > > > > > > > >> > responsible for finalizing one booking, and notifying > the > > > > other > > > > > > > client > > > > > > > > >> > that their request had failed. (In-browser or by email.) > > > > > > > > >> > > > > > > > > > >> > On Wed, Jun 10, 2015 at 5:04 AM, Daniel Schierbeck > > > > > > > > >> > <daniel.schierb...@gmail.com> wrote: > > > > > > > > >> >> I've been working on an application which uses Event > > > > Sourcing, > > > > > > and > > > > > > > > I'd > > > > > > > > >> like > > > > > > > > >> >> to use Kafka as opposed to, say, a SQL database to > store > > > > > events. > > > > > > > This > > > > > > > > >> would > > > > > > > > >> >> allow me to easily integrate other systems by having > them > > > > read > > > > > > off > > > > > > > > the > > > > > > > > >> >> Kafka topics. > > > > > > > > >> >> > > > > > > > > >> >> I do have one concern, though: the consistency of the > > data > > > > can > > > > > > only > > > > > > > > be > > > > > > > > >> >> guaranteed if a command handler has a complete picture > of > > > all > > > > > > past > > > > > > > > >> events > > > > > > > > >> >> pertaining to some entity. > > > > > > > > >> >> > > > > > > > > >> >> As an example, consider an airline seat reservation > > system. > > > > > Each > > > > > > > > >> >> reservation command issued by a user is rejected if the > > > seat > > > > > has > > > > > > > > already > > > > > > > > >> >> been taken. If the seat is available, a record > describing > > > the > > > > > > event > > > > > > > > is > > > > > > > > >> >> appended to the log. This works great when there's only > > one > > > > > > > producer, > > > > > > > > >> but > > > > > > > > >> >> in order to scale I may need multiple producer > processes. > > > > This > > > > > > > > >> introduces a > > > > > > > > >> >> race condition: two command handlers may simultaneously > > > > > receive a > > > > > > > > >> command > > > > > > > > >> >> to reserver the same seat. The event log indicates that > > the > > > > > seat > > > > > > is > > > > > > > > >> >> available, so each handler will append a reservation > > event > > > – > > > > > thus > > > > > > > > >> >> double-booking that seat! > > > > > > > > >> >> > > > > > > > > >> >> I see three ways around that issue: > > > > > > > > >> >> 1. Don't use Kafka for this. > > > > > > > > >> >> 2. Force a singler producer for a given flight. This > will > > > > > impact > > > > > > > > >> >> availability and make routing more complex. > > > > > > > > >> >> 3. Have a way to do optimistic locking in Kafka. > > > > > > > > >> >> > > > > > > > > >> >> The latter idea would work either on a per-key basis or > > > > > globally > > > > > > > for > > > > > > > > a > > > > > > > > >> >> partition: when appending to a partition, the producer > > > would > > > > > > > > indicate in > > > > > > > > >> >> its request that the request should be rejected unless > > the > > > > > > current > > > > > > > > >> offset > > > > > > > > >> >> of the partition is equal to x. For the per-key setup, > > > Kafka > > > > > > > brokers > > > > > > > > >> would > > > > > > > > >> >> track the offset of the latest message for each unique > > key, > > > > if > > > > > so > > > > > > > > >> >> configured. This would allow the request to specify > that > > it > > > > > > should > > > > > > > be > > > > > > > > >> >> rejected if the offset for key k is not equal to x. > > > > > > > > >> >> > > > > > > > > >> >> This way, only one of the command handlers would > succeed > > in > > > > > > writing > > > > > > > > to > > > > > > > > >> >> Kafka, thus ensuring consistency. > > > > > > > > >> >> > > > > > > > > >> >> There are different levels of complexity associated > with > > > > > > > implementing > > > > > > > > >> this > > > > > > > > >> >> in Kafka depending on whether the feature would work > > > > > > per-partition > > > > > > > or > > > > > > > > >> >> per-key: > > > > > > > > >> >> * For the per-partition optimistic locking, the broker > > > would > > > > > just > > > > > > > > need > > > > > > > > >> to > > > > > > > > >> >> keep track of the high water mark for each partition > and > > > > reject > > > > > > > > >> conditional > > > > > > > > >> >> requests when the offset doesn't match. > > > > > > > > >> >> * For per-key locking, the broker would need to > maintain > > an > > > > > > > in-memory > > > > > > > > >> table > > > > > > > > >> >> mapping keys to the offset of the last message with > that > > > key. > > > > > > This > > > > > > > > >> should > > > > > > > > >> >> be fairly easy to maintain and recreate from the log if > > > > > > necessary. > > > > > > > It > > > > > > > > >> could > > > > > > > > >> >> also be saved to disk as a snapshot from time to time > in > > > > order > > > > > to > > > > > > > cut > > > > > > > > >> down > > > > > > > > >> >> the time needed to recreate the table on restart. > > There's a > > > > > small > > > > > > > > >> >> performance penalty associated with this, but it could > be > > > > > opt-in > > > > > > > for > > > > > > > > a > > > > > > > > >> >> topic. > > > > > > > > >> >> > > > > > > > > >> >> Am I the only one thinking about using Kafka like this? > > > Would > > > > > > this > > > > > > > > be a > > > > > > > > >> >> nice feature to have? > > > > > > > > >> > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > -- > > > > > Thanks, > > > > > Ewen > > > > > > > > > > > > > > > > > > > > > -- > > > Thanks, > > > Ewen > > > > > > > > > -- > Thanks, > Ewen >
