An asynchronous Object Document Mapper (O.D.M) for MongoDB built on-top of Motor.
Project description
Author: Patrick Prunty.
motormongo
- An Object Document Mapper
for MongoDB built on-top of Motor, the MongoDB recommended asynchronous Python driver for MongoDB Python applications, designed to work with Tornado or
asyncio and enable non-blocking access to MongoDB.
Asynchronous operations in a backend system, built using FastAPI for example, enhances performance and scalability by enabling non-blocking, concurrent handling of multiple requests, leading to more efficient use of server resources.
The interface for instantiating Document classes follows similar logic to mongoengine, enabling ease-of-transition and
migration from mongoengine
to motormongo
.
Installation
To install motormongo, you can use pip
inside your virtual environment:
python -m pip install motormongo
Alternatively, to install motormongo into your poetry
environment:
poetry add motormongo
Quickstart
Step 1. Create a motormongo client:
import asyncio
from motormongo import DataBase
async def init_db():
# This 'connect' method needs to be called inside of an async function
await DataBase.connect(uri="<mongo_uri>", database="<mongo_database>")
if __name__ == "__main__":
asyncio.run(init_db())
or, in a FastAPI application:
from fastapi import FastAPI
from motormongo import DataBase
app = FastAPI()
@app.on_event("startup")
async def startup_db_client():
await DataBase.connect(uri="<mongo_uri>", db="<mongo_database>")
@app.on_event("shutdown")
async def shutdown_db_client():
await DataBase.close()
The mongo_uri
should look something like this:
mongodb+srv://<username>:<password>@<cluster>.mongodb.net
and database
should be the name of an existing MongoDB database in your MongoDB instance.
For details on how to set up a local or cloud MongoDB database instance, see here.
Step 2. Define a motormongo Document:
Define a motormongo User
document:
import re
import bcrypt
from motormongo.abstracts.document import Document
from motormongo.fields.binary_field import BinaryField
from motormongo.fields.string_field import StringField
from motormongo.fields.integer_field import IntegerField
from motormongo.fields.enum_field import EnumField
def hash_password(password) -> bytes:
# Example hashing function
return bcrypt.hashpw(password.encode('utf-8'), salt=bcrypt.gensalt())
class User(Document):
username = StringField(help_text="The username for the user", min_length=3, max_length=50)
email = StringField(help_text="The email for the user", regex=re.compile(r'^\S+@\S+\.\S+$')) # Simple email regex
password = BinaryField(help_text="The hashed password for the user", hash_function=hash_password)
age = IntegerField(help_text="The age of the user")
status = EnumField(enum=Status, help_text="Indicator for whether the user is active or not.")
class Meta:
collection = "users" # < If not provided, will default to class name (ex. User->user, UserDetails->user_details)
created_at_timestamp = True # < Provide a DateTimeField for document creation
updated_at_timestamp = True # < Provide a DateTimeField for document updates
Step 3: Create a MongoDB document using the User class
import bcrypt
await User.insert_one(
{
"username": "johndoe",
"email": "johndoe@portmarnock.ie",
"password": "password123" #< hash_functon will hash the string literal password
}
)
Step 4: Validate user was created in your MongoDB collection
You can do this using MongoDB compass, or alternatively, add a query to find all documents in the user collection after doing the insert in step 3:
users = User.find_many({})
if users:
print("User collection contains the following documents:")
for user in users:
print(user.to_dict())
else:
print("User collection failed to update! Check your MongoDB connection details and try again!")
Step 5: Put all the code above into one file and run it
python main.py
or in a FastAPI application:
uvicorn main:app --reload
Please refer to FastAPI Documentation for more details on how to get setup with FastAPI.
Congratulations 🎉
You've successfully created your first motormongo Object Document Mapper class. 🥳
The subsequent sections detail the datatype fields provided by motormongo, as well as the CRUD operations available on the classmethods and object instance methods of a motormongo document.
If you wish to get straight into how to integrate motormongo with your FastAPI
application, skip ahead to the
FastAPI Integration section.
motormongo Fields
motormongo supports the following datatype fields for your motormongo Document class:
StringField(min_length, max_length, regex)
IntegerField(min_value, max_value)
BooleanField()
EnumField(enum)
DateTimeField(auto_now, auto_now_add)
ListField()
EmbeddedDocumentField(EmbeddedDocument)
ReferenceField(Document)
BinaryField(hash_function: function)
GeoJSONField(return_as_json: bool)
Class methods
Operations
The following class methods are supported by motormongo's Document
class:
All examples below assume User
is a subclass of motormongo provided Document class.
Create
insert_one(document: dict, **kwargs) -> Document
Inserts a single document into the database.
user = await User.insert_one({
"name": "John",
"age": 24,
"alive": True
})
Alternatively, using **kwargs
:
user = await User.insert_one(
name="John",
age=24,
alive=True)
And similarly, with a dictionary:
user_document = {
"name": "John",
"age": 24,
"alive": True
}
user = await User.insert_one(**user_document)
insert_many(List[document]) -> tuple[List['Document'], Any]
users, user_ids = await User.insert_many(
[
{
"name": "John",
"age": 24,
"alive": True
},
{
"name": "Mary",
"age": 2,
"alive": False
}
]
)
or
docs_to_insert = [{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}]
inserted_docs, inserted_ids = await User.insert_many(docs_to_insert)
Read
find_one(query, **kwargs) -> Document
user = await User.find_one(
{
"_id": "655fc281c440f677fa1e117e"
}
)
Alternatively, using **kwargs
:
user = await User.find_one(_id="655fc281c440f677fa1e117e")
Note: The _id
string datatype here is automatically converted to a BSON ObjectID, however, motormongo handles the scenario when a
BSON ObjectId is passed as the _id
datatype:
from bson import ObjectId
user = await User.find_one(
{
"_id": ObjectId("655fc281c440f677fa1e117e")
}
)
find_many(query, limit, **kwargs) -> List[Document]
users = await User.find_many(age={"$gt": 40}, alive=False, limit=20)
or
query = {"age": {"$gt": 40}, "alive": False}
users = await User.find_many(**query, limit=20)
Update
update_one(query, updated_fields) -> Document
updated_user = await User.update_one(
{
"_id": "655fc281c440f677fa1e117e"
},
{
"name": "new_name",
"age": 30
}
)
or
query_criteria = {"name": "old_name"}
update_data = {"name": "updated_name"}
updated_user = await User.update_one(query_criteria, update_data)
update_many(qeury, fields) -> Tuple[List[Any], int]
updated_users, modified_count = await User.update_many({'age': {'$gt': 40}}, {'category': 'senior'})
another example:
updated_users, modified_count = await User.update_many({'name': 'John Doe'}, {'$inc': {'age': 1}})
Destroy
delete_one(query, **kwargs) -> bool
deleted = await User.delete_one({'_id': '507f191e810c19729de860ea'})
Alternatively, using **kwargs
:
deleted = await User.delete_one(name='John Doe')
delete_many(query, **kwargs) -> int
deleted_count = await User.delete_many({'age': {'$gt': 40}})
Another example:
# Delete all users with a specific status
deleted_count = await User.delete_many({'status': 'inactive'})
Alternatively, using **kwargs
:
deleted_count = await User.delete_many(status='inactive')
Mixed
find_one_or_create(query, defaults) -> Tuple['Document', bool]
user, created = await User.find_one_or_create({'username': 'johndoe'}, defaults={'age': 30})
find_one_and_replace(query, replacement) -> Document
replaced_user = await User.find_one_and_replace({'username': 'johndoe'}, {'username': 'johndoe', 'age': 35})
find_one_and_delete(query) -> Document
deleted_user = await User.find_one_and_delete({'username': 'johndoe'})
find_one_and_update_empty_fields(query, update_fields) -> Tuple['Document', bool]
updated_user, updated = await User.find_one_and_update_empty_fields(
{'username': 'johndoe'},
{'email': 'johndoe@example.com', 'age': 30}
)
Instance methods
motormongo also supports the manimulation of fields on the object instance. This allows users to programmatically achieve the same operations listed above through the object instance itself.
Operations
The following are object instance methods are supported by motormongo's Document
class:
CRUD Type | Operation |
---|---|
Create | save() -> None |
Delete | delete() -> None |
NOTE: All update operations can be manipulated on the fields in the Document class object itself.
user.save() -> None
# Find user by MongoDB _id
user = await User.find_one(
{
"_id": "655fc281c440f677fa1e117e"
}
)
# If there age is greater than 80, make them dead
if user.age > 80:
user.alive = False
# Persist update on User instance in MongoDB mongo
user.save()
In this example, User.find_one()
returns an instance of User
. If the age field
is greater than 80, the alive field is set to false. The instance of the document in the MongoDB
database is then updated by calling the .save()
method on the User
object instance.
Destroy
user.delete() -> None
# Find all users where the user is not alive
users = await User.find_many(
{
"alive": False
}
)
# Recursively delete all User instances in the users list who are not alive
for user in users:
user.delete()
FastAPI integration
motormongo can be easily integrated in FastAPI APIs to leverage the asynchronous ability of FastAPI. To leverage motormongo's ease-of-use, Pydantic model's should be created to represent the MongoDB Document as a Pydantic model.
Below are some example APIs detailing how
Creating a document
from models.documents import User
from models.requests import UserModel
@app.post("/users/")
async def create_user(user: UserModel):
new_user = User(**user.model_dump())
await new_user.save()
return new_user.to_dict()
Note:
License
This project is licensed under the MIT License.
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