"Turning the database inside out with Apache Samza" by Martin Kleppmann

The talk on "Turning the database inside-out" [0][1] by the author, Martin Kleppmann, is a fantastic intro to these dynamics, and it's something I'll always recommend to both experienced and inexperienced data modelers and backend developers. It goes pedagogically through the way things are typically done in a relational database in such a clear way that word-for-word it's one of the best tutorials I've seen... but it also weaves a narrative of "how can this be done better/more scalably/more reliably/more flexibly-to-business-needs" in pointing to a streaming/event-sourcing architecture. You may or not need the latter right away, but it's a fantastic tool to have in your toolbox to be able to say "ah, this new requirement feels like it would benefit hugely from this architecture." Especially for OP who's starting to think about the "why" of messaging queues, this could be a fantastically valuable first step. [0] [this video] [1] [link]

Idk I think caching is a crucial part of many well-designed systems. There’s a lot of very cache-able data out there. If invalidating events are well defined or the data is fine being stale (week/month level dashboards, for example), that’s a fantastic reason to use a cache. I’d much rather just stuff those values in a cache than figure out any other more complicated solution. I also just think it’s a necessary evil of big systems. Sometimes you need derived data. You can even think about databases as a kind of cache: the “real” data is the stream of every event that ever updated data in the database! (Yes this stretching the meaning of cache lol) However I agree that caching is often an easy bandaid for a bad architecture. This talk on Apache Samza completely changed how I think about caching and derived data in general: [this video] And this interview has some interesting insights on the problems that caching faces at super large scale systems (twitter specifically): [link]

Hey OP you may have seen this already, but in case you didn’t see my other comment, you should definitely check out this talk by Martin Kleppman. [this video] It details Apache samza, which I didn’t totally grasp but it seems similar to what you’re talking about here. He talks about how if you could essentially use an event stream as your source of truth instead of a database, and you had a sufficiently powerful stream processor, you could define views on that data by consuming stream events. The end result is kind of like an auto-updating cache with no invalidation issues or race conditions. Need a new view on the data? Just define it and run the entire event stream through it. Once the stream is processed, that source of data is perpetually accurate and up-to-date. I’m not a database guy and most of this stuff is over my head, but I loved this talk and I think you should check it out! It’s the first thing I thought of when I read your post.

Hearing the way Alan Kay talks about "messaging" always reminds me of two technologies: the Flux/Redux architecture and Apache Samza & Kafka. > The key in making great and growable systems is much more to design how its modules communicate rather than what their internal properties and behaviors should be. With React and Flux, this is exactly what's going on! You're defining view components, and when someone interacts with them, there's a very structured, discrete set of actions that can be fired. These actions are then dispatched and handled elsewhere, but somewhere at the core, there's a single source of truth for what happens upon any given message. And for Apache Samza, I think this video[1] does a nice job of talking about stream processing as a programming architecture. There's a connection to be made between processing streams of information and processing messages between modules, and that video goes a good way towards exposing it. [1]: [this video] "Turning the database inside out with Apache Samza"

I second this wholeheartedly because this happened to me. I'm the kind of person who learns by my making my own mistakes (as opposed to learning from others', which is what smart people do)—and boy, did I learn a lot from the mistake of trying to build a general purpose application with event sourcing. Theoretically, event sourcing is the way all applications should be built. I was first taken by this methodology by Martin Kleppmann's talk "Turning the Database Inside Out" (2014)[0]. Looking back, I think I had always subconsciously felt like there was something wrong with mutating state in place. You lose information. You lose accountability. After all, "accountants don't use erasers," and Martin's talk validated many of my obsessive-compulsive insecurities about data. As Charity Majors points out on Twitter [1]: > ...while storing raw events may be "expensive" compared to any single optimization, all optimizations can be derived from raw events. The reverse is impossible. And that's true. The table-stream duality [2][3] means that if you store events, you can later project those events into the exact perfect table for any use-case. But if you transform those events into a projection and store that projection, well, whatever table you store is data you have. You wind up having just an aggregate. Unfortunately, the technical costs you incur when you attempt to store events instead of tabular data are tremendous . The problems and questions that arise are basically endless. To