Skip to main content

Clean architecture in Python

Project description

clean-python

Tests

clean-python contains abstractions for clean architecture in Python

It is independent of frameworks and has asyncio at its core.

The terminology used is consistently derived from the "Big Blue Book" (Domain Driven Design by E. Evans, 2004). Software consists of one or more modules, each having four layers: presentation, application, domain, and infrastructure. Each layer has its own responsibilities, in short:

  • presentation: show information to the user and interpret the user's commands.
  • application: implement use cases that direct the domain objects.
  • domain: all domain concepts and rules; this layer is the heart of the software.
  • infrastructure: generic capabilities that support the higher layers

A big inspiration for this was the easy typescript framework by S. Hoogendoorn and others (https://github.com/thisisagile/easy).

Motivation

The main goals of using layered architecture is isolating the domain-specific concepts from other functions related only to software technology. In this way:

  • The knowledge embedded in the domain is distilled and can more easily be understood and changed.
  • Developers are able to quickly grasp the code base because it uses a consistent structure and naming system.
  • Depenencies are reduced resulting in a higher maintainability.
  • Unittests can be made more easily (increasing reliability).

Dependencies

Layers are loosly coupled with dependencies in only one direction: presentation > application > infrastructure > domain. In other words: the number of dependencies of the software's core business are as limited as possible.

A module may only depend on another module though its infrastructure layer. See InternalGateway.

This library was initially developed as a web backend using FastAPI. Its core dependency is pydantic, for strict type parsing and validation. Optional dependencies may be added as needed.

Core concepts

Domain Layer

The domain layer is where the model lives. The domain model is a set of concepts; the domain layer is the manifestation of that model. Concepts in the domain model must have a 1:1 representation in the code and vice versa.

THe layer does not depend on all other layers. Interaction with the infrastructure layer may be done using dependency injection from the application layer. It is allowable to have runtime dependencies on the infrastructure layer to set for instance default Gateway implementations.

There are 5 kinds of objects in this layer:

  • Entity: Types that have an identity (all attributes of an instance may change- but the instance is still the same) Entities have an id and default fields associated with state changes ()created_at, updated_at).
  • ValueObject: Types that have no identity (these are just complex values like a datetime).
  • DomainService: Important domain operations that aren't natural to model as objects. A service is stateless.
  • Repository: A repository is responsible for persistence (add / get / filter). This needs a Gateway to interface with e.g. a database; an instance of a Gateway is typically injected into a Repository from the application layer.
  • DomainEvent: A domain event may be emitted to signal a state change.

Associations between objects are hard. Especially many-to-many relations. We approach this by grouping objects into aggregates. An aggregate is a set of objects that change together / have the same lifecycle (e.g. delete together). One entity is the aggregate root; we call this the RootEntity. A ChildEntity occurs only very rarely; mostly a nested object derive its identity from a RootEntity.

All change and access goes through the repository of a RootEntity. The RootEntity can be a complicated nested object; how to map this to an SQL database is the issue of the infrastructure layer.

Infrastructure Layer

An infrastructure layer primarily contains Gateway objects that interface with a single external resource. The Gateway implements persistence methods to support the domain and application layers. Much of the implementation will be in frameworks or other dependencies.

The methods of a Gateway may directly return a domain object, or return a dictionary with built-in types (Json).

Other gateway examples are: email sending and logstash logging.

Application layer

The application layer defines the use cases of the application. Example use cases are create_user or list_user_roles. These methods have nothing to do with a REST API or command-line interface; this is the business of the presentation layer.

In addition to directing the domain objects, an application layer method could trigger other behavior like logging or triggering other applications. At first, it may as well be just a single function call.

This layer is kept thin. It directs domain objects, and possibly interacts with other systems (for instance by sending a message through the infrastructure layer). The application layer should not contain fundamental domain rules.

Presentation Layer

The presentation layer shows information to the user and interprets the user's commands. Its main job is to get the application-layer use cases to be usable for an actual user.

The currently only option in clean-python is a REST API using FastAPI.

Modules

The primary objective of compartimentalizing code into modules is to prevent cognitive overload. The modules divide the domain layer, everything else follows. There should be low coupling between modules and high cohesion whithin a module. Modules are first and foremost a conceptual structure.

In Python, a module should be implemented with a single .py file or a folder of .py files (respectively called modules and packages).

Modules have a public API (presentation layer) and encapsulate their database. Only in this way the internal consistency can be guaranteed by the module's domain layer.

Our current approach is to have 1 aggregate (whose root is implemented as a RootEntity) per module.

Installation

clean-python can be installed with:

$ pip install clean-python

Optional dependencies can be added with:

$ pip install clean-python[sql,fastapi]

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

clean_python-0.18.0.tar.gz (66.4 kB view details)

Uploaded Source

Built Distribution

clean_python-0.18.0-py3-none-any.whl (76.7 kB view details)

Uploaded Python 3

File details

Details for the file clean_python-0.18.0.tar.gz.

File metadata

  • Download URL: clean_python-0.18.0.tar.gz
  • Upload date:
  • Size: 66.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for clean_python-0.18.0.tar.gz
Algorithm Hash digest
SHA256 788b04ffd797f10d4f1ec7ace85e1ce8c18e345e23dbac181ee8d8c68bf2c466
MD5 29987c05f7feb0be4a7011806687f391
BLAKE2b-256 873178e7a113980eb9ca0a34bf963311e46f8714c51dfee6324c9839cd42cbee

See more details on using hashes here.

File details

Details for the file clean_python-0.18.0-py3-none-any.whl.

File metadata

File hashes

Hashes for clean_python-0.18.0-py3-none-any.whl
Algorithm Hash digest
SHA256 e1a40f9f681c8a99c4d12879c223ebce9e73ae10d7127bec83949826e988c7ce
MD5 581e51f317bc1dfcecacba13fba9dcc7
BLAKE2b-256 afd3f898f26567202749c918214c1c36ddb43b5e7995286ea4fd428b3c63c0a7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page