Skip to main content

Asynchronous Python ODM for MongoDB

Project description

Beanie

shields badge pypi

📢 Important Update 📢

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.

Join Us

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!

Join our Discord

Let’s make Beanie better, together!

Overview

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

Installation

PIP

pip install beanie

Poetry

poetry add beanie

For more installation options (eg: aws, gcp, srv ...) you can look in the getting started

Example

import asyncio
from typing import Optional

from motor.motor_asyncio import AsyncIOMotorClient
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 Motor async client under the hood 
    client = AsyncIOMotorClient("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())

Links

Documentation

  • Doc - Tutorial, API documentation, and development guidelines.

Example Projects

Articles

Resources

  • GitHub - GitHub page of the project
  • Changelog - list of all the valuable changes
  • Discord - ask your questions, share ideas or just say Hello!!

Supported by JetBrains

JetBrains

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

beanie-1.27.0.tar.gz (169.4 kB view details)

Uploaded Source

Built Distribution

beanie-1.27.0-py3-none-any.whl (84.1 kB view details)

Uploaded Python 3

File details

Details for the file beanie-1.27.0.tar.gz.

File metadata

  • Download URL: beanie-1.27.0.tar.gz
  • Upload date:
  • Size: 169.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.25.1

File hashes

Hashes for beanie-1.27.0.tar.gz
Algorithm Hash digest
SHA256 a5eee40f1e52214afeb8558c0823d7504856884770c3d56fc3cd5765efb87314
MD5 b9e8b06a8d5c08dbbb446374fb9ee4f2
BLAKE2b-256 cad1ebd474f9e552e32378bad032e2dcce9ba78473b488ef29c9295b1e8d5c23

See more details on using hashes here.

File details

Details for the file beanie-1.27.0-py3-none-any.whl.

File metadata

  • Download URL: beanie-1.27.0-py3-none-any.whl
  • Upload date:
  • Size: 84.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.25.1

File hashes

Hashes for beanie-1.27.0-py3-none-any.whl
Algorithm Hash digest
SHA256 2cc6762bdd59b9040dd004ecbc7d4fd5ddd22e52743915e38d1f0f92f276bcaf
MD5 d0d225858fb3fa59019e5d800b4adf79
BLAKE2b-256 554d9b302c451625e3b570b0dcafd157d92b633f96b4b17eca1c88a081b1a7b9

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