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 >
