Hi Ewen, Replies inlined.
On Thu, Jul 23, 2015 at 10:25 PM, Ewen Cheslack-Postava <e...@confluent.io> wrote: > Just some notes on the KIP doc itself: > > * It'd be useful to clarify at what point the plain consumer + custom code > + producer breaks down. I think trivial filtering and aggregation on a > single stream usually work fine with this model. Anything where you need > more complex joins, windowing, etc. are where it breaks down. I think most > interesting applications require that functionality, but it's helpful to > make this really clear in the motivation -- right now, Kafka only provides > the lowest level plumbing for stream processing applications, so most > interesting apps require very heavyweight frameworks. > I think for users to efficiently express complex logic like joins windowing, etc, a higher-level programming interface beyond the process() interface would definitely be better, but that does not necessarily require a "heavyweight" frameworks, which usually includes more than just the high-level functional programming model. I would argue that an alternative solution would better be provided for users who want some high-level programming interface but not a heavyweight stream processing framework that include the processor library plus another DSL layer on top of it. > * I think the feature comparison of plain producer/consumer, stream > processing frameworks, and this new library is a good start, but we might > want something more thorough and structured, like a feature matrix. Right > now it's hard to figure out exactly how they relate to each other. > Cool, I can do that. > * I'd personally push the library vs. framework story very strongly -- the > total buy-in and weak integration story of stream processing frameworks is > a big downside and makes a library a really compelling (and currently > unavailable, as far as I am aware) alternative. > Are you suggesting there are still some content missing about the motivations of adding the proposed library in the wiki page? > * Comment about in-memory storage of other frameworks is interesting -- it > is specific to the framework, but is supposed to also give performance > benefits. The high-level functional processing interface would allow for > combining multiple operations when there's no shuffle, but when there is a > shuffle, we'll always be writing to Kafka, right? Spark (and presumably > spark streaming) is supposed to get a big win by handling shuffles such > that the data just stays in cache and never actually hits disk, or at least > hits disk in the background. Will we take a hit because we always write to > Kafka? > I agree with Neha's comments here. One more point I want to make is materializing to Kafka is not necessarily much worse than keeping data in memory if the downstream consumption is caught up such that most of the reads will be hitting file cache. I remember Samza has illustrated that under such scenarios its throughput is actually quite comparable to Spark Streaming / Storm. > * I really struggled with the structure of the KIP template with Copycat > because the flow doesn't work well for proposals like this. They aren't as > concrete changes as the KIP template was designed for. I'd completely > ignore that template in favor of optimizing for clarity if I were you. > > -Ewen > > On Thu, Jul 23, 2015 at 5:59 PM, Guozhang Wang <wangg...@gmail.com> wrote: > > > Hi all, > > > > I just posted KIP-28: Add a transform client for data processing > > < > > > https://cwiki.apache.org/confluence/display/KAFKA/KIP-28+-+Add+a+transform+client+for+data+processing > > > > > . > > > > The wiki page does not yet have the full design / implementation details, > > and this email is to kick-off the conversation on whether we should add > > this new client with the described motivations, and if yes what features > / > > functionalities should be included. > > > > Looking forward to your feedback! > > > > -- Guozhang > > > > > > -- > Thanks, > Ewen > -- -- Guozhang
