[slopinthebag]

I wish people weren’t so dogmatic about DOD. It’s applicable mainly when you have extremely large amounts of data which can be processed in parallel, which seems mostly the case with video games (eg. look at most DOD examples) and other niche cases. It’s called “Data-Oriented Design” but it really should be called “parallel-processing design” because the average DOD advocate will never advocate for a different OOP approach if it solves a problem where those techniques are more appropriate. Advocates tend to be quite dogmatic, ask them about RAII or modern C++\Rust for example and you’ll see what I mean.

And I say this as someone who basically sees programming as data and associated algorithms and always approaches problems by considering state or data first.

[crabmusket]

I think this is broadly true, but there's also a subset of DOD design advice (e.g. see Richard Fabian's book) that has a lot to say about relational database design. In our team it's provided us with some very practical motivation for more normalised table designs. Doing 1+n requests to the database due to poor implementation feels similar now to pointer chasing.

The connection is that ORMs convince you to have an object-oriented view of the world, which maps nicely to object classes. But highly normalised designs don't map as cleanly to classes and objects, so you need to approach with a different style of programming on the application side.

Instead of seeing a User instance, you start to see a more complex bundle of login methods, profile events, etc.

[Quothling]

I knew OOP had failed when we started getting programmers who solved memory issues with batching instead of thinking about why they were loading that much data into the memory to begin with. For the previous 20-30 years that didn't really matter. Code was the bottleneck and implicity and abstractions helped developers ship changes faster. It turned out that it didn't give us maintainable or safe code bases, and there is a sweet irony to be found in the world of banking. Where the JAVA systems meant to replace the old parallel paradigms are now being replaced with systems that are better links between the COBOL systems and the customers than JAVA ever was.

AI changes that. Especially because it appears that LLM's can't understand the OOP abstractions any better than your hardware can compute it.

That being said. OOP and DOD both have advantages and disadvantages. If you go back to what I said first it wasn't exactly a failing of the OOP paradigm. The biggest issue I have with OOP is actually that it's too easy to do things wrong with it. Which isn't helped by the multimillion dollar industry which thrives on teaching developers everything except core computer science. People know their DRY, SOLID, CLEAN, TDD, Agile and every design pattern in the world, but they don't know how the interface they've just implemented actually handles their data.