beanie 1.0.1

Asynchronous Python ODM for MongoDB

Getting Started with Beanie

Beanie - is an Asynchronous Python object-document mapper (ODM) for MongoDB, based on Motor and 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 boiler-plate 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.

Installation

PIP

pip install beanie

Poetry

poetry add beanie

Basic Example

Document models

from typing import Optional
from pydantic import BaseModel
from beanie import Document, Indexed


class Category(BaseModel):
    name: str
    description: str


class Product(Document):  # This is the model
    name: str
    description: Optional[str] = None
    price: Indexed(float)
    category: Category

    class Collection:
        name = "products"

Each document by default has id ObjectId field, which reflects _id MongoDB document field. It can be used later as an argument for the get() method.

More details about Documents, collections, and indexes configuration could be found in the tutorial.

Initialization

import motor
from beanie import init_beanie


async def init():
    # Crete Motor client
    client = motor.motor_asyncio.AsyncIOMotorClient(
        "mongodb://user:pass@host:27017"
    )

    # Init beanie with the Note document class
    await init_beanie(database=client.db_name, document_models=[Product])

Create

chocolate = Category(name="Chocolate")

# Single:

bar = Product(name="Tony's", price=5.95, category=chocolate)
await bar.insert()

# Many

milka = Product(name="Milka", price=3.05, category=chocolate)
peanut_bar = Product(name="Peanut Bar", price=4.44, category=chocolate)
await Product.insert_many([milka, peanut_bar])

Other details and examples could be found in the tutorial

Find

# Single

# By id
bar = await Product.get("608da169eb9e17281f0ab2ff")

# By name
bar = await Product.find_one(Product.name == "Peanut Bar")

# Many

# By category

chocolates = await Product.find(
    Product.category.name == "Chocolate"
).to_list()

# And by price

chocolates = await Product.find(
    Product.category.name == "Chocolate",
    Product.price < 5
).to_list()

# OR

chocolates = await Product.find(
    Product.category.name == "Chocolate").find(
    Product.price < 5).to_list()

# Complex example:

class ProductShortView(BaseModel):
    name: str
    price: float


products = await Product.find(
    Product.category.name == "Chocolate",
    Product.price < 3.5
).sort(-Product.price).limit(10).project(ProductShortView)

# All

all_products = await Product.all().to_list()

Information about sorting, skips, limits, and projections could be found in the tutorial

Update

# Single 
await Product.find_one(Product.name == "Milka").set({Product.price: 3.33})

# Or
bar = await Product.find_one(Product.name == "Milka")
await bar.update(Set({Product.price: 3.33}))

# Or
bar.price = 3.33
await bar.replace()

# Many
await Product.find(
    Product.category.name == "Chocolate"
).inc({Product.price: 1})

More details and examples about update queries could be found in the tutorial

Delete

# Single 
await Product.find_one(Product.name == "Milka").delete()

# Or
bar = await Product.find_one(Product.name == "Milka")
await bar.delete()

# Many
await Product.find(
    Product.category.name == "Chocolate"
).delete()

More information could be found in the tutorial

Aggregate

# With preset methods

avg_price = await Product.find(
    Product.category.name == "Chocolate"
).avg(Product.price)

# Or without find query

avg_price = await Product.avg(Product.price)

# Native syntax 

class OutputItem(BaseModel):
    id: str = Field(None, alias="_id")
    total: int
    
result = await Product.find(
    Product.category.name == "Chocolate").aggregate(
    [{"$group": {"_id": "$category.name", "total": {"$avg": "$price"}}}],
    projection_model=OutputItem
).to_list()

Information about aggregation preset aggregation methods and native syntax aggregations could be found in the tutorial

Documentation

• Tutorial - Usage examples with descriptions
• API - Full list of the classes and methods

Example Projects

• FastAPI Demo - Beanie and FastAPI collaboration demonstration. CRUD and Aggregation.
• Indexes Demo - Regular and Geo Indexes usage example wrapped to a microservice.

Articles

• Announcing Beanie - MongoDB ODM
• Build a Cocktail API with Beanie and MongoDB
• MongoDB indexes with Beanie
• Beanie Projections. Reducing network and database load.

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