Looking at transactions: The 99% solution is trivial: Every service call is a transaction. AOP can save me a few lines for every method and things look much cleaner.

But then comes the huge excel upload that is performance critical. Batch more service calls to fetch additional information in the background, commit every so-and-so records in a loop depending on the data size, do a custom roll-back if things fail.

And suddenly this whole separation of concerns breaks down and creates a huge mess.

The simple case saves a few minutes, the complicated case causes weeks of depression. Not a good tradeoff from my experience.

An LLM adding to the confusion by only sometimes getting things right and explaining that the separate documents are always valid, except when they are not, well, sounds like a fun experience.

It's possible that what you have here is an idea for what I consider to be eventually very likely, which is a computer languages still built for humans to be able to understand and debug it, but more primarily for LLMs to write it. Write a language designed to be an aspect-oriented language from the beginning. Equip it with the ability to run something like a language server and point it at a system and get all the "aspects" running and you might have something.

But I'm skeptical of bodging this on to an existing language.

One of the reasons I suggest making it a new language is that AOP was hampered by being able to use only what languages already supported. The need for a "weaver" is a smell anyhow. Something where the aspect code is the native representation and the "weaving" simply dissolves into the compilation process would not only make the whole thing more appealing in general, I think it would also allow for some things that even code generation might have found a challenge, like aspects that can maintain guarantees because the whole process is more aspect-aware and not broken by the embedded "payload" code written by a human.

Oh HELL NO.

The LAST thing you want is a non-deterministic process monkey patching your code.

Also one would say monkey patching on Python and Ruby frameworks is another way to do AOP.

But the post entirely lacks the motivation for the AspectJ/AOP join point model: to have principled time/place for concern integration that was statically determined, type-safe, understandable to users -- and suitable for integration.

> I've also always hated the specific mechanism that AOP chose to implement it with – something called the "join point model" which basically amounts to runtime pattern matching on a program's call stack and running some code every time a pattern matches.

AspectJ's join point model is only dynamic where Java as a reference-based language could not support the static analysis. At compile-time, the "static shadow" of the pointcuts was calculated and implemented where staticly determinable; only the dynamic residue is deferred to runtime (e.g., is the caller to this method of type X?).

Many of AspectJ's join points and type extensions - method call or execution, exception throwing, field access - largely have been adopted in many languages (python context managers, swift getter/setters/extensions), and the residue are a bit hard to use.

But nothing really matches the power of pointcuts: to combine these predicates and the type-safe state-management - e.g., "when throwing an exception after a transaction, capture the span id along with the user id into a log message"

AOP was great for the 7% of code that it was intended for, but was largely displaced as too complicated. Now with LLM's it's a decent hypothesis that with proper training LLM's could actually handle the more complicated but ultimately cleaner programming model - cleaner because it avoids the scattering of similar code which makes it hard to change.

The key insight is that dominant concerns establish the basic structure of the application, leaving some important but residual aspects to fit themselves to the structure. That means the dominant structure must be suitable for the AOP integration (i.e., support the right pointcuts and type extensions); solve that and you've solved most integration issues. It's especially helpful for feature architectures, where you offer code in open-source to gain API adoption, paid for by closed-source library integrations with additional features.

Originally, AOP was about separating cross-cutting concerns by centralizing them in one place. It used weaving to separate infrastructure code, and implicitness was inherent in that approach. But the books I read back then said this led to 'ghost code.' It inevitably introduced unpredictability because the behavior wasn't visible in the code. And from a programmer's perspective, that becomes a problem when things break.

On top of that, while the cross-cutting concerns are centralized, they still end up being tied to the framework's syntax, like Spring AOP's Join Point syntax, so they become dependent on the framework itself.

That's why DDD became popular as another way to address OOP's limitations. DDD keeps the business logic pure and framework-agnostic, and that's where things like POJO emerged. At least that's what I read in books, just different approaches to the same problem.

AOP was first presented at ECOOP in 1997, and DDD is usually associated with Evans' book. Both are ways of handling OOP's complexity, but the article here doesn't seem to talk about problems with cross-cutting concerns, which is the core of AOP at all. And this is something AI doesn't handle well. AI makes the most mistakes with implicit knowledge.

Or maybe I'm wrong because I've been studying programming history through older books and have outdated knowledge. Maybe AOP has evolved since then.

What makes it difficult to talk about specific 'oriented' paradigms in programming is that as history moves on and certain problems get solved, if you bring up an older version, you'll get pushback from people who really know their stuff.

'That's a problem that was solved 5 to 10 years ago.' 'That issue has evolved and been covered in other books.'

So it's hard to talk about any 'oriented' approach because you have to specify which era's version you're referring to. For example, even with OOP, which everyone knows, there's a big difference between Smalltalk, C++, and the modern emphasis on composition over inheritance. Someone might say, 'Modern OOP is centered around composition and value objects — you're behind the times.'

AOP might have also evolved and introduced different solutions since then.

So while the perspective on a given 'oriented' paradigm does shift over time, it's really hard to have a conversation about programming because it all depends on which era the programmer is from and how far their knowledge goes.

That's why lately I've been thinking about problems and the approaches used to solve them, rather than focusing on the 'oriented' labels. I wish someone would write a history book about these paradigms. They'd get a lot of criticism, but for programmers like me, it would be much easier to understand.

Sometimes I think it's about time someone wrote a history book on programming

I'm unclear on AOP in general, esp as proposed here. That's a bigger leap...

And one giant problem with it is the reliance on global variables. An AOP wrapper has to modify something, and it typically does not have enough access to enough context to do it.

So it has to rely on ambient data that has to be saved in a global variable. And this is _bad_. It makes data flow opaque and impossible to follow.

And then there are issues with debugging. Where do you put a breakpoint? What happens if you try to step into an instrumented method?

PS: yes, a global variable can technically be a thread-local variable. It doesn't matter, it's still a non-local ambient state.