if you like SQL dialect, try out products that use streamSQL to do continuous queries. Espers comes to mind. Google to see what other products support streamSQL
On Sat, Jan 3, 2015 at 6:48 PM, Hugo José Pinto <hugo.pi...@inovaworks.com> wrote: > Thanks :) > > Duly noted - this is all uncharted territory for us, hence the value of > seasoned advice. > > > Best > > -- > Hugo José Pinto > > No dia 03/01/2015, às 23:43, Peter Lin <wool...@gmail.com> escreveu: > > > listen to colin's advice, avoid the temptation of anti-patterns. > > On Sat, Jan 3, 2015 at 6:10 PM, Colin <colpcl...@gmail.com> wrote: > >> Use a message bus with a transactional get, get the message, send to >> cassandra, upon write success, submit to esp, commit get on bus. Messaging >> systems like rabbitmq support this semantic. >> >> Using cassandra as a queuing mechanism is an anti-pattern. >> >> -- >> *Colin Clark* >> +1-320-221-9531 >> >> >> On Jan 3, 2015, at 6:07 PM, Hugo José Pinto <hugo.pi...@inovaworks.com> >> wrote: >> >> Thank you all for your answers. >> >> It seems I'll have to go with some event-driven processing before/during >> the Cassandra write path. >> >> My concern would be that I'd love to first guarantee the disk write of >> the Cassandra persistence and then do the event processing (which is mostly >> CRUD intercepts at this point), even if slightly delayed, and doing so via >> triggers would probably bog down the whole processing pipeline. >> >> What I'd probably do is to write, in trigger, a separate key table with >> all the CRUDed elements and to have the ESP process that table. >> >> Thank you for your contribution. Should anyone else have any experiende >> experience in these scenarios I'm obviously all ears as well. >> >> Best, >> >> Hugo >> >> Enviado do meu iPhone >> >> No dia 03/01/2015, às 11:09, DuyHai Doan <doanduy...@gmail.com> escreveu: >> >> Hello Hugo >> >> I was facing the same kind of requirement from some users. Long story >> short, below are the possible strategies with advantages and draw-backs of >> each >> >> 1) Put Spark in front of the back-end, every incoming >> modification/update/insert goes into Spark first, then Spark will forward >> it to Cassandra for persistence. With Spark, you can perform pre or >> post-processing and notify external clients of mutation. >> >> The draw back of this solution is that all the incoming mutations must >> go through Spark. You may set up a Kafka queue as temporary storage to >> distribute the load and consume mutations with Spark but it add ups to the >> architecture complexity with additional components & technologies >> >> 2) For high availability and resilience, you probably want to have all >> mutations saved first into Cassandra then process notifications with Spark. >> In this case the only way to have notifications from Cassandra, as of >> version 2.1, is to rely on manually coded triggers (which is still >> experimental feature). >> >> With the triggers you can notify whatever clients you want, not only >> Spark. >> >> The big draw back of this solution is that playing with triggers is >> dangerous if you are not familiar with Cassandra internals. Indeed the >> trigger is on the write path and may hurt performance if you are doing >> complex and blocking tasks. >> >> That's the 2 solutions I can see, maybe the ML members will propose other >> innovative choices >> >> Regards >> >> On Sat, Jan 3, 2015 at 11:46 AM, Hugo José Pinto < >> hugo.pi...@inovaworks.com> wrote: >> >>> Hello. >>> >>> We're currently using Hazelcast (http://hazelcast.org/) as a >>> distributed in-memory data grid. That's been working sort-of-well for us, >>> but going solely in-memory has exhausted its path in our use case, and >>> we're considering porting our application to a NoSQL persistent store. >>> After the usual comparisons and evaluations, we're borderline close to >>> picking Cassandra, plus eventually Spark for analytics. >>> >>> Nonetheless, there is a gap in our architectural needs that we're still >>> not grasping how to solve in Cassandra (with or without Spark): Hazelcast >>> allows us to create a Continuous Query in that, whenever a row is >>> added/removed/modified from the clause's resultset, Hazelcast calls up back >>> with the corresponding notification. We use this to continuously update the >>> clients via AJAX streaming with the new/changed rows. >>> >>> This is probably a conceptual mismatch we're making, so - how to best >>> address this use case in Cassandra (with or without Spark's help)? Is there >>> something in the API that allows for Continuous Queries on key/clause >>> changes (haven't found it)? Is there some other way to get a stream of >>> key/clause updates? Events of some sort? >>> >>> I'm aware that we could, eventually, periodically poll Cassandra, but in >>> our use case, the client is potentially interested in a large number of >>> table clause notifications (think "all changes to Ship positions on >>> California's coastline"), and iterating out of the store would kill the >>> streamer's scalability. >>> >>> Hence, the magic question: what are we missing? Is Cassandra the wrong >>> tool for the job? Are we not aware of a particular part of the API or >>> external library in/outside the apache realm that would allow for this? >>> >>> Many thanks for any assistance! >>> >>> Hugo >>> >> >> >
