An ideal project?

If someone asked about the features of an ideal project, responses would surely mention a few specific things. First of all, an ideal project would have a clean codebase that is simple to read. Secondly, there should be high test coverage to ensure that the project works as expected. What is more, one will instantly know if they broke something thanks to an extensive suite of automated tests. Last, but not least – technical debt should be kept at bay to not pose a threat of lowering a team’s velocity. I bet you already see how enjoyable would be to work on such a project! Unfortunately, software like this is a real rarity. Time to change it.

Building such a masterpiece sounds like a hell of a work. Luckily, countless software engineers have already responded to the challenge. This article describes one of the proposed designs – so-called The Clean Architecture authored by Robert C Martin in his 2012 blog post and a book that was published at the end of 2018. As you can see, the concept has not been conceived yesterday. The Clean Architecture based on even older concepts, such as Onion Architecture or Ports and Adapters (AKA Hexagonal Architecture).

Motivation

I wrote this blog post because I succeeded in applying the Clean Architecture in two Python projects – both of them reached production and are still being used and developed. This article contains python-specific techniques and tools helpful in embracing the Clean Architecture. The article is based on my talk that I gave 13 times so far (including PyCarribean, PyGotham or Python London Meetup). See recording here.

The most interesting observation that leads to searching for better ways of writing software is the fact that all software is created to satisfy business needs. Since the requirements (and code that implements them) are crucial, we want to treat them as the first-class citizen. It means one should not scatter business logic around in random places. It deserves to have its own, designated area which is independent of tools we use. In short, we want our business logic to be:

independent of framework

easily testable in isolation without having to mock out half of the code

independent from UI, API

independent from databases

independent from any third party services

Such a level of decoupling is possible thanks to using abstraction layers and techniques like Dependency Injection.

Example project – auctioning platform

To illustrate the Clean Architecture, we will use an example project of an auctioning system. Among requirements there are these User Stories:

As a bidder, I would like to place a bid to win an auction

As a bidder, I would like to be informed by an e-mail when my bid is the winning one

As an administrator, I would like to withdraw a selected bid from an auction

Let’s use Django + DRF

Django is the most popular web framework in the Python world. It makes it possible to create web applications in no time (at least in the beginning) thanks to a plethora of goodies bundled with the framework. Django Rest Framework (DRF) brings to the party a bunch of classes helpful during creating a REST API. Among these goodies, we get certain building blocks which one can arrange in order to build the project. The building blocks are:

The most important Django + DRF building blocks

At first glance, one can see that there is one central building block, on which all others depend. It is Model – a class that will be mapped onto a table in a database. In other words, in order to do anything meaningful with Django one has to define their Models first. One way is to take a look at User Stories and look for nouns:

As a bidder, I would like to place a bid to win an auction



I would like to place a to win an As a bidder, I would like to be informed by an e-mail when my bid is the winning one



I would like to be informed by an when my is the winning one As an administrator, I would like to withdraw a selected bid from an auction

Every word in bold from above list is a perfect candidate for a Model. Let’s focus on the most important two:

class Auction(models.Model): title = models.CharField(...) initial_price = models.DecimalField(...) current_price = models.DecimalField(...) class Bid(models.Model): amount = models.DecimalField(...) bidder = models.ForeignKey(...) auction = models.ForeignKey() 1 2 3 4 5 6 7 8 9 class Auction ( models . Model ) : title = models . CharField ( . . . ) initial_price = models . DecimalField ( . . . ) current_price = models . DecimalField ( . . . ) class Bid ( models . Model ) : amount = models . DecimalField ( . . . ) bidder = models . ForeignKey ( . . . ) auction = models . ForeignKey ( )

Note: At this stage, before we started to reason about any use cases, we committed to a database structure!

With code above one needs only two commands to generate (makemigrations) and execute migrations (migrate) to prepare a database for saving our Models. Let’s focus on the third User Story:

As an administrator I would like to withdraw a selected bid from an auction

One of the coolest Django’s features is the admin panel. In order to get an edit view of a single auction with a list of all its bids with delete checkboxes one has to write literally six lines of code. If withdrawing a bid amounted to just removing a row from the database we would be completely satisfied with the solution. It is not that easy, though. One still has to recalculate current price of the auction and potentially send an e-mail to a new winner (if an administrator happens to withdraw a currently winning bid). Luckily, there are plenty of places in Django where we can put our custom logic. In this case, we want to override save_related method:

def save_related(self, request, form, formsets, *args, **kwargs): ids_of_deleted_bids = self._get_ids_of_deleted_bids(formsets) # 1 bids_to_withdraw = Bid.objects.filter(pk__in=ids_of_deleted_bids) # 2 auction = form.instance # 3 old_winners = set(auction.winners) auction.withdraw_bids(bids_to_withdraw) # 4 new_winners = set(auction.winners) self._notify_winners(new_winners - old_winners) # 5 super().save_related(request, _form, formsets, *args, **kwargs) 1 2 3 4 5 6 7 8 9 10 11 12 13 def save_related ( self , request , form , formsets , * args , * * kwargs ) : ids_of_deleted_bids = self . _get_ids_of_deleted_bids ( formsets ) # 1 bids_to_withdraw = Bid . objects . filter ( pk__in = ids_of_deleted_bids ) # 2 auction = form . instance # 3 old_winners = set ( auction . winners ) auction . withdraw_bids ( bids_to_withdraw ) # 4 new_winners = set ( auction . winners ) self . _notify_winners ( new_winners - old_winners ) # 5 super ( ) . save_related ( request , _form , formsets , * args , * * kwargs )

ids of Bid models are pulled from form data Bid models are fetched from the DB Auction model is already available thanks to Django We tell auction to run withdrawing bids logic, so it can update its current price and potentially a list of winners We notify new winners (if there are any)

Good job, we fulfilled part of business requirements. At the same time, the code we wrote is tightly coupled with the framework. The implementation is impossible to test without the database. What is worse, it is hidden somewhere in the admin panel. We can do better than that.

The Clean Architecture in action

Use Case – first building block

Using this approach one does not start from business entities (or models in Django), but instead from processes – User Stories. The building block that will implement them is called Use Case or Interactor. Its name is representing the exact business scenario. They are loosely modelled after User Stories.

class WithdrawingBidUseCase: def withdraw_bids(self, auction_id, bids_ids): auction = Auction.objects.get(pk=auction_id) bids_to_withdraw = Bid.objects.filter(pk__in=bids_ids) old_winners = set(auction.winners) auction.withdraw_bids(bids_to_withdraw) new_winners = set(auction.winners) self._notify_winners(new_winners - old_winners) auction.save() 1 2 3 4 5 6 7 8 9 10 11 12 class WithdrawingBidUseCase : def withdraw_bids ( self , auction_id , bids_ids ) : auction = Auction . objects . get ( pk = auction_id ) bids_to_withdraw = Bid . objects . filter ( pk__in = bids_ids ) old_winners = set ( auction . winners ) auction . withdraw_bids ( bids_to_withdraw ) new_winners = set ( auction . winners ) self . _notify_winners ( new_winners - old_winners ) auction . save ( )

Business logic responsible for withdrawing bid has been moved from admin panel to a designated class. Please notice that WithdrawingBidUseCase can be used from any context and it is no longer aware of admin panel existence – it needs just ID of an auction and bids IDs to withdraw.

Use Case / Interactor is to orchestrate a specific scenario.

Use Cases form so-called application layer – set of classes/functions that exposes what our application actually does. Distilling such a layer is a first step towards better code base.

Interface / Port – second build block

That was just the beginning of our way to better model our business logic in code. We did that just by moving code a designated class. Even though we broke chains of the admin panel, we are still tied to Django ORM:

class WithdrawingBidUseCase: def withdraw_bids(self, auction_id, bids_ids): auction = Auction.objects.get(pk=auction_id) # HERE... bids_to_withdraw = Bid.objects.filter(pk__in=bids_ids) # ...HERE... old_winners = set(auction.winners) auction.withdraw_bids(bids_to_withdraw) new_winners = set(auction.winners) self._notify_winners(new_winners - old_winners) auction.save() # ...AND HERE 1 2 3 4 5 6 7 8 9 10 11 12 class WithdrawingBidUseCase : def withdraw_bids ( self , auction_id , bids_ids ) : auction = Auction . objects . get ( pk = auction_id ) # HERE... bids_to_withdraw = Bid . objects . filter ( pk__in = bids_ids ) # ...HERE... old_winners = set ( auction . winners ) auction . withdraw_bids ( bids_to_withdraw ) new_winners = set ( auction . winners ) self . _notify_winners ( new_winners - old_winners ) auction . save ( ) # ...AND HERE

To break free from Django ORM, we are going to use another standard technique – a layer of abstraction. Judging by the way we use Django ORM in the code above, we need something which allows to fetch an auction by its ID and later to persist it. Given these two examples of usage, it is trivial to imagine an abstract class:

class AuctionsRepo(abc.ABC): @abc.abstractmethod def get(self, auction_id): pass @abc.abstractmethod def save(self, auction): pass 1 2 3 4 5 6 7 8 class AuctionsRepo ( abc . ABC ) : @ abc . abstractmethod def get ( self , auction_id ) : pass @ abc . abstractmethod def save ( self , auction ) : pass

Such abstractions which are to separate business logic from the outer world are called Interfaces or Ports. In this case, we are abstracting DB by using a specific pattern called Repository, but in general case, we can use the same technique to decouple from different types of dependencies, e.g. payment provider.

Interface / Port is to abstract away 3rd things from our business logic.

Interface Adapter / Adapter – third building block

Naturally, every abstract class has to have a concrete implementation. After all, we want our code to actually do something 🙂 In the Clean Architecture concrete implementations of Interfaces / Ports are called Interface Adapters or just Adapters. For now, let’s assume a very simple, naive implementation:

class DjangoAuctionsRepo(AuctionsRepo): def get(self, auction_id): return Auction.objects.get(pk=auction_id) def save(self, auction): auction.save() 1 2 3 4 5 6 class DjangoAuctionsRepo ( AuctionsRepo ) : def get ( self , auction_id ) : return Auction . objects . get ( pk = auction_id ) def save ( self , auction ) : auction . save ( )

Ok, so what has really changed? The trick is to make our Use Case know only about AuctionsRepo and nothing about Django:

class WithdrawingBidUseCase: def __init__(self, auctions_repo: AuctionsRepo): self._auctions_repo = auctions_repo def withdraw_bids(self, auction_id, bids_ids): auction = self._auctions_repo.get(auction_id) ... # skipped for readability self._auctions_repo.save(auction_id) 1 2 3 4 5 6 7 8 9 10 class WithdrawingBidUseCase : def __init__ ( self , auctions_repo : AuctionsRepo ) : self . _auctions_repo = auctions_repo def withdraw_bids ( self , auction_id , bids_ids ) : auction = self . _auctions_repo . get ( auction_id ) . . . # skipped for readability self . _auctions_repo . save ( auction_id )

Only now we are truly free from DjangoORM. Now, we could write real unit tests for WithdrawingBidUseCase by either passing in mocked AuctionsRepo or writing a simple implementation that would keep objects in memory.

Some time ago, I have started a pet project to get rid of the necessity of manual writing such repositories. If you are eager to help or can you just give some feedback, feel free to open an issue/contact me: https://github.com/Enforcer/python_entity_framework

Interface Adapter / Adapter is a concrete implementation of Interface / Port. Use Cases MUST NOT know about Interface Adapters / Adapters, but they know Interfaces / Ports.

Dependency Injection

Above example might have suggested that one has to assemble manually classes whenever they want to run a Use Case that depends on AuctionsRepo. Luckily, this is not the case thanks to dependency injection tools, such as python-inject or injector. Let’s see an example of usage the former:

# somewhere in settings import inject def di_configuration(binder): # 1 binder.bind(AuctionsRepo, DjangoAuctionsRepo()) inject.configure(di_configuration) # 2 # Use Case definition leveraging DI class WithdrawingBidUseCase: @inject.autoparams('auctions_repo') # 3 def __init__(self, auctions_repo: AuctionsRepo): self._auctions_repo = auctions_repo ... # Somewhere in code use_case = WithdrawingBidUseCase() # 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # somewhere in settings import inject def di_configuration ( binder ) : # 1 binder . bind ( AuctionsRepo , DjangoAuctionsRepo ( ) ) inject . configure ( di_configuration ) # 2 # Use Case definition leveraging DI class WithdrawingBidUseCase : @ inject . autoparams ( 'auctions_repo' ) # 3 def __init__ ( self , auctions_repo : AuctionsRepo ) : self . _auctions_repo = auctions _ repo . . . # Somewhere in code use_case = WithdrawingBidUseCase ( ) # 4

we write a function that will configure inject’s dependency injection container we run inject.configure to apply the configuration. IMPORTANT: we do this only once when the application starts we decorate __init__ of WithdrawingBidUseCase with inject.autoparams. It will inspect __init__’s signature to determine which argument it should provide from now on, we no longer have to provide an argument for auctions_repo. Of course, we can still do it to override inject.autoparams. This might be helpful in tests when we do not want to perform full-fledged inject.configure

Entity – zeroth building block

We are now ready to get Django’s hands off our business logic completely. It is high time we replaced Django’s Models with Entities. They are three key differences between Models and Entities:

Entities are not aware of the database (in fact, they do not know anything about the outer world)

Entities are not just dummy containers for data. One is discouraged from changing Entities’ data from outside by direct assignment. Rather, one should use methods that encapsulate Entities‘ internal structure

Single Entity can represent an entire graph of objects. Auctions and their bids are a perfect example. It makes little to no sense to reason about bids without taking into consideration auction they were placed on.

class Auction: def __init__(self, id: int, initial_price: Decimal, bids: List[Bid]): self.id = id self.initial_price = initial_price self.bids = bids def withdraw_bids(self, bids_ids: List[int]): ... def place_bid(self, bid: Bid): ... @property def winners(self): ... 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 class Auction : def __init__ ( self , id : int , initial_price : Decimal , bids : List [ Bid ] ) : self . id = id self . initial_price = initial_price self . bids = bids def withdraw_bids ( self , bids_ids : List [ int ] ) : . . . def place_bid ( self , bid : Bid ) : . . . @ property def winners ( self ) : . . .

The most prominent benefit of lack of external dependencies is that we write pure Python here, not being constrained by database features. One can also easily test the class. However, it also means that our Interface Adapter / Adapter – DjangoAuctionsRepo needs to be rewritten to be able to translate from- and to- Models:

class DjangoAuctionsRepo(AuctionsRepo): def get(self, auction_id: int) -> Auction: auction_model = AuctionModel.objects.prefetch_related( 'bids' ).get(pk=auction_id) bids = [ self._bid_from_model(bid_model) for bid_model in auction_model.bids.all() ] return Auction( auction_model.id, auction_model.initial_price, bids ) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 class DjangoAuctionsRepo ( AuctionsRepo ) : def get ( self , auction_id : int ) -> Auction : auction_model = AuctionModel . objects . prefetch_related ( 'bids' ) . get ( pk = auction_id ) bids = [ self . _bid_from_model ( bid_model ) for bid_model in auction_model . bids . all ( ) ] return Auction ( auction_model . id , auction_model . initial_price , bids )

Entities are pure-Python classes that are to free our minds from modelling business rules under the constraints of databases structure.

Why does it work? The Dependency rule

Writing code with the Clean Architecture building block is not everything. It is obligatory to arrange them in a specific way, by using layers (in the simplest version – four):

Basic four layers of The Clean Architecture

The golden rule of maintaining order here is to keep all our dependencies unidirectional. This is illustrated by horizontal arrows in the diagram above. Domain knows nothing about other layers. Application knows about Domain, but it has no clue how it is being invoked (is it API? CLI? Admin panel?) or which database is actually in use (Infrastructure).

Practically, one MUST NOT import anything from upper layer. As a consequence, any changes being made in upper layer does not force us to touch lower layers. On the other hand, if one changes Domain it is expected that Application and all other upper layers will have to adapt to the change.

The greatest value of the project is modelled in Domain and Application. Thanks to careful decoupling from framework and the outer world, one is able to have 100% test coverage in Domain and Application.

Boundaries

The Dependency Rule not only means ban on importing anything from upper layers, but also on passing objects that do not belong to layer which receives then. In other words, we cannot pass to Application or Domain objects which are not declared there or are not part of the standard library. In practice, one has to repack data from the outer world to different structure that will be comfortable to work with in Application layer.

The most vulnerable points are arguments list of Use Cases‘ methods. A very convenient solution is to leverage Data Transfer Objects. In Python, we have an excellent attrs library (or much simpler alternative from the standard library – dataclasses):

@attr.s(auto_attribs=True) class WithdrawingBidRequest: auction_id: int bids_ids: List[int] # Use Case's method is now accepting only one argument: class WithdrawingBidUseCase: def withdraw_bids(self, request: WithdrawingBidRequest): ... 1 2 3 4 5 6 7 8 9 @ attr . s ( auto_attribs = True ) class WithdrawingBidRequest : auction_id : int bids_ids : List [ int ] # Use Case's method is now accepting only one argument: class WithdrawingBidUseCase : def withdraw_bids ( self , request : WithdrawingBidRequest ) : . . .

Type safety thanks to Value Objects

Boundary between Application and outer layer is a special place. We already know we have to pay more attention to what’s passed down. Gaining certainty about types of Request fields would be an enormous help. Python is a dynamically typed language, so in order to achieve certainty we would have to manually check types in this place:

assert isinstance(request.auction_id, int) assert all(isinstance(bid_id, int) for bid_id in request.bids_ids) 1 2 assert isinstance ( request . auction_id , int ) assert all ( isinstance ( bid_id , int ) for bid_id in request . bids_ids )

Statically typed languages are free from this little inconvenience. However, even with static typing, we are not completely from the problem. What difference does it make when we are sure that given argument is indeed a string, but we need it to be a valid IPv4 address or e-mail? Additionally, we would rather be happier without having to check every time if data is valid. Instead of passing often-meaningless strings around, we are going to use Value Objects:

class IpAddress: def __init__(self, raw_ip_address: str): # validation would be here self._value = raw_ip_address def __str__(self): return self._value 1 2 3 4 5 6 7 class IpAddress : def __init__ ( self , raw_ip_address : str ) : # validation would be here self . _value = raw_ip_address def __str__ ( self ) : return self . _value

Value Objects have a few specific features. First of all, they are immutable – once created, cannot be changed. The only way to get Value Object with changed internal value is to create a new instance. Secondly, two Value Objects with identical initial arguments are to be considered indistinguishable. In other words, we consider them to be equal. Thirdly, it is not possible to construct an invalid Value Object. It should take care of its validation.

Example Value Objects from Python’s standard library are Decimal, datetime.timedelta or uuid.UUID.

Is the Clean Architecture a silver bullet?

Of course not!

Non-idiomatic framework usage

When one adopts the Clean Architecture, they will have to accept the fact of no longer following framework’s best practices. This is especially visible with Django which is not an optimal choice for the Clean Architecture. On the other hand, Pyramid or Flask are much more flexible.

Writing more code

Additional abstractions have their price. In extreme cases, the code is few (2-3) times longer. Luckily, there are type annotations which help a lot.

Often copying data between objects

As you could see in DjangoAuctionsRepo above, there is this nasty code responsible for copying data from one object to another. In order to maintain proper layers separation, one has to write such a tedious code from time to time.

Validation

It is not that obvious where validation should be put in the Clean Architecture. The good enough solution is to valdate Request classes that are passed to Use Cases.

Overengineering

Although the Clean Architecture is great for projects with rich domain and dozens of test cases, it might become a burden in simpler cases. Sometimes going with good old Django + DRF is the best option. On the other hand, IT projects tend to become more complicated over time. Even if the Clean Architecture seems to be an overkill in the initial stage, it might prove useful after a few weeks of development.

When should one invest in the Clean Architecture?

Plethora of test cases

When user’s actions have multiple possible implications and we care about checking every scenario we can think of, we would certainly appreciate how easy it is to write tests of Entities and Use Cases without bothering about a database or other stuff. Did I mention such tests are super-fast?

Deferring decisions making

Not all decisions can be made before actual development starts. Sometimes singning contracts with 3rd parties takes months. Sometimes we require more research before we feel confident about commiting to certain solution. Sometimes we just want to switch payment provider, because a new one charges less fees.

Instead of coupling with specific solutions which we suspect may be replaced in the future, one can remain flexible thanks to Ports / Adapters.

Complex domain

The main enemy of software engineering is complexity. According to the famous No Silver Bullet – Essence and Accident in Software Engineering paper, there are two types of complexity – accidental and essential.

The former can be removed with a finite effort by refactoring, following good practices etc. The latter can not. If someone asks you to write a project with 50 features, it will be inherently complex. It does not matter how much effort you will put into making the code simpler. It will still have to implement these 50 features.

This kind of inevitable complexity is exactly what the Clean Architecture tries to fight. It will not eliminate the complexity, but it will let you manage it.

Summary

Business requirements are factors that actually shape projects. Paying them attention they deserve will substantially benefit the quality of our project and will also help us to become better software engineers.

Where to go next?

Congratulations, you have been introduced to the Clean Architecture. However, this is just the tip of the iceberg, when it comes to software engineering.

Please check out example project for the things that were not covered in this article (like returning data from Use Cases). https://github.com/Enforcer/clean-architecture-example-1

<shameless advert>You can also check out my upcoming book on the Clean Architecture. </shameless advert>

For even more advanced example, checkout another repo of mine: https://github.com/Enforcer/clean-architecture

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