gdmongolite 0.1.0

gdmongolite: lightweight, auto-maintained all-in-one MongoDB toolkit by Ganeshdatta

gdmongolite

gdmongolite: lightweight, auto-maintained all-in-one MongoDB toolkit by Ganeshdatta

Installation

pip install gdmongolite

Usage

1. Configure Your Environment

Create a .env file at your project root with your MongoDB URI and settings:

MONGO_URI="mongodb://localhost:27017"
MONGO_DB="myapp"
MONGO_MAX_POOL=50

2. Defining Your First Schema

Open src/gdmongolite/schema/__init__.py and define your schema:

from gdmongolite import DB, Schema, Email, Positive

# Bind to default DB from .env
db = DB()

class User(Schema):
    name: str
    email: Email
    age: Positive
    tags: list[str] = []

# Immediately available on DB instance
assert db.User is User

3. Running Migrations

Whenever you add, remove, or change fields in your schema, run:

poetry run gdmongolite migrate

4. Interactive Shell

Start an interactive shell to inspect and test your models:

poetry run gdmongolite shell

Then, in the REPL:

await db.User.insert({"name":"Alice","email":"[e-mail]","age":28})
users = await db.User.find(age__gte=18).to_list()
print(users)

5. Generating Models from Existing Collections

To scaffold a model from a live collection:

poetry run gdmongolite gen-model --collection products --out src/models/product.py

6. Running Tests

Validate core and your extensions:

poetry run pytest --maxfail=1 --disable-warnings -q

7. Adding Telemetry Hooks

In your app top-level (e.g., app.py):

from gdmongolite.core import DBSingleton

@DBSingleton.on("pre_query")
def log_pre(collection, filt, opts):
    print(f"Querying {collection}: {filt}")

@DBSingleton.on("post_query")
def log_post(collection, result):
    print(f"{collection} query completed.")

Cookbook (Advanced Use Cases)

• Complex Queries
• Transactions
• Sharded Clusters
• Change Streams

(To be expanded)