Assimilator - the best Python patterns for the best projects

Install now

pip install py-assimilator
pip install py-assimilator[alchemy] - Optional SQLAlchemy support
pip install py-assimilator[kafka] - Optional Kafka support
pip install py-assimilator[redis] - Optional Redis support
pip install py-assimilator[mongo] - Optional MongoDB support

Simple example

Example usage of the code to create a user using all the DDD patterns:

from assimilator.alchemy.database import AlchemyUnitOfWork, AlchemyRepository
from assimilator.core.database import UnitOfWork

def create_user(username: str, email: str, uow: UnitOfWork):
    with uow:
        repository = uow.repository     # Get Repository pattern
        new_user = repository.save(username=username, email=email, balance=0)
        uow.commit()    # Securely save the data

    return new_user


user_repository = AlchemyRepository(
    session=alchemy_session,    # alchemy db session
    model=User,     # alchemy user model 
)
user_uow = AlchemyUnitOfWork(repository=user_repository)

create_user(
    username="Andrey", 
    email="python.on.papyrus@gmail.com",
    uow=user_uow,
)

Why do I need it?

Patterns are very useful for good code, but only to some extent. Most of them are not suitable for real life applications. DDD(Domain-driven design) is one of the most popular ways of development today, but nobody explains how to write most of DDD patterns in Python. Even if they do, life gives you another issue that cannot be solved with a simple algorithm. That is why Andrey created a library for the patterns that he uses in his projects daily.

Watch our Demo to find out more about pyAssimilator capabilities.

Source

Stars history

Types of patterns

These are different use cases for the patterns implemented:

Database - patterns for database/data layer interactions.
Events(in development) - projects with events or event-driven architecture.
Unidentified - patterns that are useful for different purposes.

Available providers

Providers are different patterns for external modules like SQLAlchemy or FastAPI.

Alchemy(Database, Events) - patterns for SQLAlchemy for both database and events.
Kafka(Events) - patterns in Kafka related to events.
Internal(Database, Events) - internal is the type of provider that saves everything in memory(dict, list and all the tools within your app).
Redis(Database, Events) - redis_ allows us to work with Redis memory database.
MongoDB(Database) - mongo allows us to work with MongoDB database.

Release files for py-assimilator 1.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for py-assimilator 1.1.1
File	Size	Uploaded
py_assimilator-1.1.1.tar.gz	22.8 kB	Mar 5, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for py-assimilator 1.1.1
File	Interpreter	ABI	Platform	Reset
py_assimilator-1.1.1-py3-none-any.whl 43.5 kB Mar 5, 2023	Python 3	none	any	Details

Total release size: 66.3 kB

Release files / py_assimilator-1.1.1.tar.gz

Download URL	py_assimilator-1.1.1.tar.gz
Size	22.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`b430df2d3c7f1c8595e0a5d08d0928a11bc4c0ed60b7c33a3c0ab69f43515d72`
BLAKE2b-256 checksum How to use checksums	`03eabedd6e92935a5ec0ba33f87c0359b339e55d51f92fe1a3de784827e83f22`
Upload date	Mar 5, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.8.10`

Release files / py_assimilator-1.1.1-py3-none-any.whl

Download URL	py_assimilator-1.1.1-py3-none-any.whl
Size	43.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`2eaea1e2711831cf92d13bdc83f36c65acc1669a696c0aa1d2898bd2b32364db`
BLAKE2b-256 checksum How to use checksums	`e137e5b58a8ad0626d9c1df4b056695ea292ac2106946d0905a1df0876c09e38`
Upload date	Mar 5, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.8.10`