Ormophine
A fast, Pythonic ORM that gets out of your way.
Write database queries the way you think — in plain Python.
The Problem with Other ORMs
Most Python ORMs are either too verbose, too magical, or too slow. Compare fetching filtered rows with a popular ORM versus Ormophine:
Other ORMs:
# SQLAlchemy (Core)
with engine.connect() as conn:
stmt = select(users.c.phone, users.c.name, users.c.age).where(
and_(
users.c.age > 18,
or_(
users.c.phone.like('+98%'),
func.substr(users.c.phone, 1, 3) == '+98'
)
)
).order_by(users.c.age)
result = conn.execute(stmt).fetchall()
Ormophine:
from Ormophine.Sqlite import Driver
db = Driver('my_db.db')
users = db.users
phone, name, age = users.phone, users.name, users.age
users.get_row(
[phone, name, age],
where = (age > 18) & (phone.startswith('+98') | (phone[:3] == '+98')),
order_by = age
)
Same result. No boilerplate. No imports for every logical operator. No ceremony.
Why Ormophine?
- Intuitive syntax — columns behave like Python variables with full operator overloading (
>,&,|,+,[],.startswith(), etc.) - Fast & Thread-Safe — built on a dedicated writer queue (SQLite) and robust connection pooling (MySQL/PostgreSQL); parallel reads, serialized writes.
- Multi-database — one unified API across SQLite, MySQL, and PostgreSQL. Switch databases by changing your import.
- Dynamic Schema Mapping — tables and columns are discovered automatically and attached to the driver instance.
- WAL mode support (SQLite) — automatic checkpointing for maximum write throughput.
- AI-Ready — includes backend-specific source code reference files to feed to LLMs like ChatGPT or Claude for instant, accurate ORM assistance.
Supported Databases
| Database | Status |
|---|---|
| SQLite | ✅ Available |
| MySQL | ✅ Available |
| PostgreSQL | ✅ Available |
| MariaDB | ✅ Available (via MySQL driver) |
The API is identical across all backends. Switch databases by changing one line.
Video Tutorials
🎥 Coming Soon! We are preparing a comprehensive video series to help you get started with Ormophine, from basic connections to advanced concurrent read/write pooling and schema management.
Stay tuned—links will be posted here soon.
Benchmark Results
📊 Coming Soon! Benchmark results comparing Ormophine against SQLAlchemy, Tortoise ORM, and raw DB-API 2.0 will be published here. We are testing INSERT throughput, SELECT latency, bulk operations, and concurrent read workloads across all three backends.
Quick Examples
Connect and access tables
from Ormophine.Sqlite import Driver
db = Driver('company.db')
# Tables and columns are discovered automatically
users = db.users
orders = db.orders
Insert
users.insert({
users.name: 'Alice',
users.email: 'alice@example.com',
users.age: 30
})
Select with conditions
name, email, age = users.name, users.email, users.age
rows = users.get_row(
[name, email],
where = (age >= 18) & name.startswith('A'),
order_by = age
)
Update
users.update(
update = {users.age: users.age + 1},
where = users.status == 'active'
)
Bulk insert
users.bulk_insert(
columns = [users.name, users.age],
data_list = [['Bob', 25], ['Carol', 32], ['Dave', 28]]
)
Joins
from Ormophine.Sqlite import Join
result = orders.join(
columns = [users.name, orders.amount, orders.date],
joins_list = [Join.Inner(users, users.id == orders.user_id)],
where = orders.amount > 100,
order_by = [orders.date]
)
Schema management
from Ormophine.Sqlite import TableStructure, DataTypes
schema = TableStructure('products', strict=True)
schema.add_column('id', DataTypes.INTEGER(), primary_key=True)
schema.add_column('title', DataTypes.TEXT(max_length=100), not_null=True, unique=True)
schema.add_column('price', DataTypes.REAL(), default_value=0.0)
products = db.create_table(schema)
# Add / rename / drop columns dynamically
products.add_column('stock', DataTypes.INTEGER(), default_value=0, not_null=True)
products.rename_column(products.stock, 'inventory')
products.delete_column(products.inventory, True, True, True)
WAL mode and performance tuning (SQLite)
db.set_WAL_mode(True, wal_timer=60) # automatic checkpoint every 60 s
db.SetPragma.synchronous('NORMAL')
db.SetPragma.cache_size(-4000) # 4 MiB page cache
db.SetPragma.foreign_keys(True)
Operator Reference
Ormophine columns support native Python expressions — all values are automatically parameterized to prevent SQL injection.
| Expression | SQL equivalent |
|---|---|
age > 18 |
age > 18 |
(age >= 18) & (age < 65) |
age >= 18 AND age < 65 |
status == 'active' |
status = 'active' |
name.startswith('A') |
name LIKE 'A%' |
email.contains('@corp.com') |
email LIKE '%@corp.com%' |
code[:3] |
SUBSTR(code, 1, 3) |
name.upper().strip() |
TRIM(UPPER(name)) |
price * qty - discount |
price * qty - discount |
AI-Powered Assistance
To help you write queries and debug your code, Ormophine ships with AI reference files (Sqlite.AI.Reference.txt, MySQL.AI.Reference.txt, PostgreSQL.AI.Reference.txt).
You can attach these files to ChatGPT, Claude, or Gemini, ask your question, and the AI will respond using the exact API and behavior of your Ormophine version.
Installation
pip install Ormophine
Roadmap
- SQLite backend with full ORM
- MySQL backend with connection pooling
- PostgreSQL backend with connection pooling
- Operator overloading for columns
- Read-only connection pool / Non-blocking reads
- WAL mode + automatic checkpointing
- Batch / bulk operations
- AI Reference files for LLM assistance
- Video Tutorials
- Benchmark suite publication
- Async support
Contributing
The codebase is currently in active development. Contributions, bug reports, and feature requests are very welcome! Please feel free to open an issue or submit a pull request.
License
MIT — free to use, modify, and distribute.
1 maintainer
Release files for Ormophine 0.7.12
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ormophine-0.7.12.tar.gz | 569.9 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ormophine-0.7.12-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / ormophine-0.7.12.tar.gz
| Download URL | ormophine-0.7.12.tar.gz |
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| Size | 569.9 kB |
| Tags | Source |
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| Uploaded via |
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