We are excited to announce that Beanie is transitioning from solo development to a team-based approach! This move will help us enhance the project with new features and more collaborative development.
At this moment we are establishing a board of members that will decide all the future steps of the project. We are looking for contributors and maintainers to join the board.
If you are interested in contributing or want to stay updated, please join our Discord channel. We're looking forward to your ideas and contributions!
Let’s make Beanie better, together!
Beanie - is an asynchronous Python object-document mapper (ODM) for MongoDB. Data models are based on Pydantic.
When using Beanie each database collection has a corresponding Document that
is used to interact with that collection. In addition to retrieving data,
Beanie allows you to add, update, or delete documents from the collection as
well.
Beanie saves you time by removing boilerplate code, and it helps you focus on the parts of your app that actually matter.
Data and schema migrations are supported by Beanie out of the box.
There is a synchronous version of Beanie ODM - Bunnet
pip install beanie
poetry add beanie
For more installation options (eg: aws, gcp, srv ...) you can look in the getting started
import asyncio
from typing import Optional
from pymongo import AsyncMongoClient
from pydantic import BaseModel
from beanie import Document, Indexed, init_beanie
class Category(BaseModel):
name: str
description: str
class Product(Document):
name: str # You can use normal types just like in pydantic
description: Optional[str] = None
price: Indexed(float) # You can also specify that a field should correspond to an index
category: Category # You can include pydantic models as well
# This is an asynchronous example, so we will access it from an async function
async def example():
# Beanie uses PyMongo async client under the hood
client = AsyncMongoClient("mongodb://user:pass@host:27017")
# Initialize beanie with the Product document class
await init_beanie(database=client.db_name, document_models=[Product])
chocolate = Category(name="Chocolate", description="A preparation of roasted and ground cacao seeds.")
# Beanie documents work just like pydantic models
tonybar = Product(name="Tony's", price=5.95, category=chocolate)
# And can be inserted into the database
await tonybar.insert()
# You can find documents with pythonic syntax
product = await Product.find_one(Product.price < 10)
# And update them
await product.set({Product.name:"Gold bar"})
if __name__ == "__main__":
asyncio.run(example())
Hello!!Supported by JetBrains
FAQs
Asynchronous Python ODM for MongoDB
We found that beanie demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
npm now supports Trusted Publishing with OIDC, enabling secure package publishing directly from CI/CD workflows without relying on long-lived tokens.
Research
/Security News
A RubyGems malware campaign used 60 malicious packages posing as automation tools to steal credentials from social media and marketing tool users.
Security News
The CNA Scorecard ranks CVE issuers by data completeness, revealing major gaps in patch info and software identifiers across thousands of vulnerabilities.