Having said that, from a practical and experience standpoint, using some of these patterns can really spiral out into an increased complexity and performance issues in Python, specially when you use already opinionated frameworks like Django which already uses the ActiveRecord pattern.
I’ve been in companies big and small using Python, both using and ignoring architectural patterns. Turns out all the big ones with strict architectural (n=3) pattern usage, although “clean”, the code is waaaay to complex and unnecessarily slow in tasks that at first glance should had been simple.
Whereas the big companies that didn’t care for these although the code was REALLY ugly in some places (huge if-else files/functions, huge Django models with all business logic implemented in them), I was most productive because although the code was ugly I could read it, understand it, and modify the 1000 lines of if-else statements.
Maybe this says something about me more than the code but I hate to admit I was more productive in the non clean code companies. And don’t get me started on the huge amount of discussions they avoided on what’s clean or not.
Many files/functions/classes need to be updated to accomplish even simple tasks because somebody made a decision that you aren't allowed to do X or Y thing without creating N other things.
But in those companies that didn't care about architectural patterns its very likely that while there was more ugly code in certain places, it resulted in code with less indirection and more contained to a single area/unit or the task at hand making it easier for people to jump in and understand. I see so many people who create function after function in file after file to abstract away functionality when I'd honestly rather have a 100 line function or method that I can easily jump around and edit/debug vs many tiny functions all in separate areas.
Not to say having some abstractions are bad but the more I work in this field the more I realize the less abstractions there are, the easier it is to reason about singular units/features in code. I've basically landed on just abstract away the really hard stuff, but stop abstracting out things that simple.
The problem is this takes years of on-site experience to attain this level of domain understanding.
Domain modeling should not be about copying the existing model -- it should be about improving on it using all the advantages software has over the physical and social technologies the new software product is meant to replace. People are smart, and in most projects, there are key aspects of the existing domain model that are excellent abstractions that can and should be part of the new model. It's important to understand what stakeholders are trying to achieve with their current system before attempting to replace it.
But the models used in the business and cultural world are often messy, outdated and unoptimized for code. They rely on a human to interpret the edge cases and underspecified parts. We should treat that as inspiration, not the end goal.
Doctor Who fans will note that TARDIS craft seem to follow a different design: they regularly reconfigure themselves to fit their pilot, don't have controls laid out in any sensible fashion, and there's at least one reference to how they're "grown, not built". Then again they were also meant to be piloted by a crew and are most likely sentient, so it's also possible that due to the adaptations, the Doctor's TARDIS is just as eccentric as he is.
It's not like Doctor Who is "hard" sci-fi tho, it's basically Peter Pan in Space.
For example, if I am standing up a straight-forward calendar rest api, I am not going to have a complicated architecture. However, these kinds of patterns, especially an adherence to a ports and adapters architecture, has been critical for me in building trading systems that are easy to switch between simulation and production modes seamlessly. In those cases I am really sure I will need to easily unplug simulators with real trading engines, or historical event feeds with real-time feeds, and its necessary that the business logic have not dual implementations to keep in sync.
The problem with "strict architectural pattern usage" is that people think that a specific implementation, as listed in the reference, is "the pattern".
"The pattern" is the thought process behind what you're doing, and the plan for working with it, and the highest-level design of the API you want to offer to the rest of the code.
A state machine in Python, thanks to functions being objects, can often just be a group of functions that return each other, and an iteration of "f = f(x)". Sometimes people suggest using a Borg pattern in Python rather than a Singleton, but often what you really want is to just use the module. `sys` is making it a singleton for you already. "Dependency injection" is often just a fancy term for passing an argument (possibly another function) to a function. A Flyweight isn't a thing; it's just the technique of interning. The Command pattern described in TFA was half the point of Jack Diederich's famous rant (https://www.youtube.com/watch?v=o9pEzgHorH0); `functools.partial` is your friend.
> Maybe this says something about me more than the code but I hate to admit I was more productive in the non clean code companies.
I think you've come to draw a false dichotomy because you just haven't seen anything better. Short functions don't require complex class hierarchies to exist. They don't require classes to exist at all.
Object-oriented programming is about objects, not classes. If it were about classes, it would be called class-oriented programming.
Finding my way around a soup of ultra abstracted Matryoshka ravioli is my least favourite part of programming. Instead of simplifying things, now I need to consult 12 different objects spread over as many files before I can create a FactoryFactory.
Here's an example of how things can go off the rails very quickly: Rule 1: Functions should be short (no longer than 50 lines). Rule 2: Public functions should be implemented with an interface (so they can be mocked).
Now as a developer who wants to follow the logic of the program, you have to constantly "go to definition" on function calls on interfaces, then "go to implementation" to find the behavior. This breaks your train of thought / flow state very quickly.
Now let's amp it up to another level of suck: replace the interface with a microservice API (gRPC). Now you have to tab between multiple completely different repos to follow the logic of the program. And when opening a new repo, which has its own architectural layers, you have to browse around just to find the implementation of the function you're looking for.
These aren't strawmen either... I've seen these patterns in place at multiple companies, and at this point I yearn for a 1000 line function with all of the behavior in 1 place.
My last job had a Python codebase just like this. Lots of patterns, implemented by people who wanted to do things "right," and it was a big slow mess. You can't get away with nearly as much in Python (pre-JIT, anyway) as you can in a natively compiled language or a JVM language. Every layer of indirection gets executed in the interpreter every single time.
What bothers me about this book and other books that are prescriptive about application architecture is that it pushes people towards baking in all the complexity right at the start, regardless of requirements, instead of adding complexity in response to real demands. You end up implementing both the complexity you need now and the complexity you don't need. You implement the complexity you'll need in two years if the product grows, and you place that complexity on the backs of the small team you have now, at the cost of functionality you need to make the product successful.
To me, that's architectural malpractice. Even worse, it affects how the programmers on your team think. They start thinking that it's always a good idea to make code more abstract. Your code gets bloated with ghosts of dreamed-of future functionality, layers that could hypothetically support future needs if those needs emerged. A culture of "more is better" can really take off with junior programmers who are eager to do good work, and they start implementing general frameworks on top of everything they do, making the codebase progressively more complex and harder to work in. And when a need they anticipated emerges in reality, the code they wrote to prepare for it usually turns out to be a liability.
Looking back on the large codebases I've worked with, they all have had areas where demands were simple and very little complexity was needed. The ones where the developers accepted their good luck and left those parts of the codebase simple were the ones that were relatively trouble-free and could evolve to meet new demands. The ones where the developers did things "right" and made every part of the codebase equally complex were overengineered messes that struggled under their own weight.
My preferred definition of architecture is the subset of design decisions that will be costly to change in the future. It follows that a goal of good design is minimizing architecture, avoiding choices that are costly to walk back. In software, the decision to ignore a problem you don't have is very rarely an expensive decision to undo. When a problem arises, it is almost always cheaper and easier to start from scratch than to adapt a solution that was created when the problem existed only in your head. The rare exceptions to this are extremely important, and from the point of view of optics, it always looks smarter and more responsible to have solved a problem incorrectly than not to have solved it at all, but we shouldn't make the mistake of identifying our worth and responsibility solely with those exceptions.
The trouble is if you strictly wait until it's time then basically everything requires some level of refactoring before you can implement it.
The dream is that new features is just new code, rather than refactoring and modifying existing code. Many people are already used to this idea. If you add a new "view" in a web app, you don't have to touch any other view, nor do you have to touch the URL routing logic. I just think more people are comfortable depending on frameworks for this kind of stuff rather than implementing it themselves.
The trouble is a framework can't know about your business. If you need pluggable validation layers or something you might have to implement it yourself.
The downside, of course, is we're not always great at seeing ahead of time where the application will need to be flexible and grow. So you could build this into everything, leading to unnecessarily complicated code, or nothing, leading to constant refactors which will get worse and worse as the codebase grows.
Your approach can work if developers actually spot what's happening early and actually do what's necessary when it actually is. Unfortunately in my experience people follow by example and the frog can boil for a long time before people start to realise that their time is spent mostly doing large refactors because the code just doesn't support the kind of flexibility and extensibility they need.
Patterns and Abstractions have a HUGE cost in python. They can be zero cost in C++ due to compiler, or very low cost due to JVM JIT, but in Python the cost is very significant, especially once you start adding I/O ops or network calls
That being said, I have a number issues with other parts of it, and I have seen how dangerous it can be when inexperienced developers take it as a gospel and try to implement everything at once (which is a common problem with any collection of design patterns like this.
For example, repository is a helpful pattern in general; but in many cases, including the examples in the book itself, it is a huge overkill that adds complexity with very little benefit. Even more so as they're using SQLAlchemy, which is a "repository" in its own right (or, more precisely, a relational database abstraction layer with an ORM added on top).
Similarly, service layers and unit of work are useful when you have complex applications that cover multiple complex use cases; but in a system consisting of small services with narrow responsibilities they quickly become overly bloated using this pattern. And don't even get me started with dependency injection in Python.
The essential thing about design patterns is that they're tools like any other, and the developers should understand when to use them, and even more importantly when not to use them. This book has some advice in that direction, but in my opinion it should be more prominent and placed upfront rather at the end of each chapter.
In the end, it's just making sure that all database access for a specific entity all goes through one point (the repository for that entity). Inside the repository, you can do whatever you want (run queries yourself, use ORM, etc).
A lot of the stuff written in the article under the section Repository pattern has very little to do with the pattern, and much more to do with all sorts of Python, Django, and SQLAlchemy details.
That's aside from their particular example of SQLAlchemy sessions, which is extra weird because a Session is already a repository, more or less.
I mean, sure, there's a difference between your repository for your things and types you might consider foreign, in theory, but how theoretical are we going to get? For what actual gain? How big of an app are we talking?
You could alias Repository = Session, or define a simple protocol with stubs for some of Session's methods, just for typing, and you'd get the same amount of theoretical decoupling with no extra layer. If you want to test without a database, don't bind your models to a session. If you want to use a session anyway but still not touch the database, replace your Session's scopefunc and your tested code will never know the difference.
It's not a convincing example.
Building your repository layer over theirs, admittedly you stop the Query type from leaking out. But then you implement essentially the Query interface in little bits for use in different layers, just probably worse, and lacking twenty years of testing.
Repository patterns are fine for CRUD but don't really stretch to those endpoints where you really need the query with the two CTEs and the four joins onto a query selecting from another query based on the output of a window function.
I had a former boss who strongly pushed my team to use the repository pattern for a microservice. The team wanted to try it out since it was new to us and, like the other commenters are saying, it worked but we never actually needed it. So it just sat there as another layer of abstraction, more code, more tests, and nothing benefited from it.
Anecdotally, the project was stopped after nine months because it took too long. The decision to use the repository pattern wasn't the straw that broke the camel's back, but I think using patterns that were more complicated than the usecase required was at the heart of it.
Could I get you started? Or could you point me to a place to get myself started? I primarily code in Python and I've found dependency injection, by which I mean giving a function all the inputs it needs to calculate via parameters, is a principle worth designing projects around.
class C:
def __init__(self):
self.foo = ConcreteFoo()
class C:
def __init__(self, foo: SupportsFoo):
self.foo = foo
This book explicitly tells you not to do this.
> Similarly, service layers and unit of work are useful when you have complex applications that cover multiple complex use cases; but in a system consisting of small services with narrow responsibilities they quickly become overly bloated using this pattern. And don't even get me started with dependency injection in Python.
I have found service layers and DI really helpful for writing functional programs. I have some complex image-processing scripts in Python that I can use as plug-ins with a distributed image processing service in Celery. Service layer and DI just takes code from:
```python
dependency.do_thing(params)
```
To:
```python
do_thing(dependency, params)
```
Which ends up being a lot more testable. I can run image processing tasks in a live deployment with all of their I/O mocked, or I can run real image processing tasks on a mocked version of Celery. This lets me test all my different functions end-to-end before I ever do a full deploy. Also using the Result type with service layer has helped me propagate relevant error information back to the web client without crashing the program, since the failure modes are all handled in their specific service layer function.
the two main methods I've seen are to run tasks eagerly, or test the underlying function and avoid test Celery .delay/etc at all
That said, having built a small web app to enable a new business, and learning python along the way to get there, this provided me with some ideas for patterns I could implement to simplify things (but others I think I’ll avoid).
Robert Martin is one of those examples, he did billions in damages by brainwashing inexperienced developers with his gaslighting garbage like "Clean Code".
Software engineering is not a hard science so there is almost never a silver bullet, everything is trade-offs, so people that claim to know the one true way are subcriminal psychopaths or noobs
When people are criticizing it they pick a concept from one or two pages out the hundreds and use it to dismiss the whole book. This is a worse mistake than introducing concepts that may be foot guns in some situations.
Becoming an experienced engineer is learning how, when and where to apply tools from your toolkit.
https://www.obeythetestinggoat.com/pages/book.html
That book is in a similar place in my heart, I barely used Python in my professional life, yet it's a book I often come back to even if I'm using a different language. It's also great that book is available both online and in paper form.
I'll definitely give this book a chance!
You might like this: https://martinfowler.com/bliki/TestDouble.html
I grew tired from the forced OOP mindset, where you have to enforce encapsulation and inheritance on everything, where you only have private fields which are set through methods.
I grew tired of SOLID, clean coding, clean architecture, GoF patterns and Uncle Bob.
I grew tired of the Kingdom of Nouns and of FizzBuzz Enterprise Editions.
I now follow imperative or functional flows with least OOP as possible.
In the rare cases I use Python (not because I don't want to, but because I mainly use .NET at work) I want the experience to be free of objects and patterns.
I am not trying to say that this book doesn't have a value. It does. It's useful to learn some patterns. But don't try to fit everything in real life programming. Don't make everything about patterns, objects and SOLID.
Computers are different than humans.
I think we should be pragmatic and come with the best solution in terms of money/time/complexity. Not trying to mimick human thought using computers.
After all a truck isn't mimicking horse and carriage. A plane isn't mimicking a bird.
- Reimplement SQLAlchemy models (we'll call it a "repository")
- Reimplement SQLAlchemy sessions ("unit of work")
- Add a "service layer" that doesn't even use the models -- we unroll all the model attributes into separate function parameters because that's less coupled somehow
- Scatter everything across a message bus to remove any hope of debugging it
- AND THIS IS JUST FOR WRITES!
- For reads, we have a separate fucking denormalized table that we query using raw SQL. (Seriously, see Chapter 12)
Hey, let's see how much traffic MADE.com serves. 500k total visits from desktop + mobile last month works out to... 12 views per MINUTE.
Gee, I wish my job was cushy enough that I could spend all day writing about "DDD" with my thumb up my ass.
I don't think there's many applications that will require everything in the book but there are certaintly many applications that could apply one or more patterns discussed.
I cannot recommend it enough. Worth every penny.
Just the visual clutter of adding type annotations can make the code flow less immediately clear and then due to broken windows syndrome people naturally care less and less about visual clarity.
It has type hints, such as here: https://www.cosmicpython.com/book/chapter_08_events_and_mess...
Do you mean it's not strict enough? There are some parts of the book without them.
Python does not support static typing. Tooling based on type annotations doesn't affect the compilation process (unless you use metaprogramming, like dataclasses do) and cannot force Python to reject the code; it only offers diagnostics.
I will say that some of the event oriented parts of this book were very interesting, but didn't seem as practical to implement in my current work.
Great book!
I actually do. It’s slow, buggy and not type safe.
Everything good about Python is actually C, namely the good packages. They’re not written in Python, because Python is shit.