Skip to main content
Model logo

Tests PyPI version

Model

A minimal Python ORM for MariaDB/MySQL and SQLite. Explicit, predictable, and made for humans.

Motivation

Many ORMs try to abstract SQL away entirely, introducing their own query languages and complex concepts that force you to spend time learning “their way” before getting productive.

Model takes a different approach: it embraces native type definitions and SQL instead of hiding them behind unnecessary abstractions.

Features

  • Intuitive and simple to use.
  • Advanced static type checking for fool-proof schema definition. (Fun fact: the Column method has 73 typing @overloads.)
  • Fast and predictable performance, with no hidden bloated queries slowing down your app.
  • One command for automatic schema updates. Safe, rapid and versionless schema evolution.
  • One command to automatically generate models from your existing tables.
  • Primarily written by hand.
Example usage.

Example usage of Model CLI.

Installation

python -m pip install model-py

Note: You import the package as model

Documentation

Read the documentation at https://model.elis.cc

Quickstart

1. Define a model

A model represents a database table. You just need to define the database instance, table name and columns.

Create example file: ./models/user.py

from model import Model
from model.database import SQLiteDatabase

db = SQLiteDatabase("./example.db") # this could be MySQLDatabase

class User(Model):
    table = "user"
    db = db

    id: int = Model.Column(
        type="INT",
        index="PRIMARY",
        auto_increment=True,
    )
    email: str = Model.Column(
        type="VARCHAR",
        length=255,
        index="UNIQUE",
        can_be_null=False,
    )
    age: int | None = Model.Column(type="INT")

2. Configure model discovery

Create model.config.yaml in the project root:

include_dirs:
  - ./models

3. Create the table

The model sync CLI is the easiest way to 'sync' models with the database. It automatically generates SQL diffs based on your model definitions. To keep it safe, there are some restrictions in place for column deletion and renaming.

Run CLI commands from your project root so Model can find the configuration.

Preview the generated schema change:

model sync check

If the SQL looks correct, apply it:

model sync apply

Run model sync check once more. It should report nothing left to apply.

That's it!


Now we can use the model

Model instances always represent persisted database records. Loading, inserting, updating, and querying are explicit operations, so it's easy to understand exactly what your code is doing - no complex object lifecycle, no ambiguous save().

Insert

insert() creates the row and returns a loaded model instance.

from models.user import User

user = User.insert({
    "email": "john.doe@example.com",
    "age": 30
})

print(user.id, user.email)
# prints: 1, john.doe@example.com

Load by the primary key

Constructing a model loads an existing row.

The primary key is automatically inferred for the model class initialization:

Automatic initialization column inferring

from models.user import User

user = User(id=1)

print(user.id, user.email)
# prints: 1, john.doe@example.com

It raises ModelRecordNotFoundError when no row matches.

Query

Find records with SQL conditions. SQLite uses ? for parameter placeholders (while MySQL uses %s):

from models.user import User

user = User.find_one("email = ?", ["john.doe@example.com"])
assert user is not None

print(user.id, user.email) # prints: 1, john.doe@example.com

all_users = User.find_all("age > 24 ORDER BY age")
count = User.count("email LIKE ?", ["%@example.com"])

print(all_users) # prints: [User(...)] 
print(count) # prints: 1

find_one() returns None when there is no match, while find_all() returns an empty list.

Update

Records are updated explicitly; assign new values through update().

from models.user import User

user = User(id=1)

print(user.email)
# prints: john.doe@example.com

user.update({
    "email": "johnny@example.com"
})

print(user.email)
# prints: johnny@example.com

Delete

user.delete()

This deletes the row in the database. After deletion, that instance can no longer be used for record operations.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

model_py-1.0.0.tar.gz (84.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

model_py-1.0.0-py3-none-any.whl (98.6 kB view details)

Uploaded Python 3

File details

Details for the file model_py-1.0.0.tar.gz.

File metadata

  • Download URL: model_py-1.0.0.tar.gz
  • Upload date:
  • Size: 84.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.12.4

File hashes

Hashes for model_py-1.0.0.tar.gz
Algorithm Hash digest
SHA256 d4b394a9a3dd176624707eef205f355aecdba17afaf92b12e5a569266e31260e
MD5 15a4668c48f0856ff1d37d517668adcd
BLAKE2b-256 3aee28b329809e9991a58634fe2dab638ad44be22ac86627eb97ec06af5273d3

See more details on using hashes here.

File details

Details for the file model_py-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: model_py-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 98.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.12.4

File hashes

Hashes for model_py-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 75da5dd2ee1ba9b5dc26bb4b584f590d85c55e08c594870ab846000887f5fd5b
MD5 c37696fc55bbbeae1d25fd1f663894f8
BLAKE2b-256 c22c3a64ab1ccd6a391bf7784b1b76c14babf218d01def77211e6901796bf1b7

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

1.0.0 This release

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page