ivbox 0.1.2

Fast SQLite ORM with schema validation

QuantumForge ORM

A next-generation ultra-high-performance Python ORM forged for speed, streaming, and massive data operations.

🚀 Overview

QuantumForge ORM is a high-performance Python ORM designed from the ground up to handle massive datasets, dynamic SQL, and extreme-speed operations using:

• Turbo PRAGMAs
• Intelligent chunking
• Streaming processors
• Dynamic WHERE builders
• Auto-optimizing DELETE, UPDATE and INSERT
• Full SQL flexibility with Python expressiveness

✨ Key Features

• Ultra-fast INSERT engine (5 million rows in ~5s)
• Intelligent chunk size detection
• Dynamic WHERE builder
• Streaming updates & deletes
• Type validation for primary keys
• LIKE, BETWEEN, IN, and all comparison operators
• Auto-VACUUM for massive deletes
• PRAGMA turbo mode
• Modular engines (SQLite, MySQL, Oracle)
• Mini-DSL for flexible filters
• 100% Python

✅ New behavior (INSERT strategy)

QuantumForge now uses standard INSERT INTO instead of INSERT OR IGNORE.

Why this matters:

• No silent skipping of rows (more predictable results).
• You don’t need to validate/guard everything constantly to detect ignored inserts.
• If a constraint fails (e.g., UNIQUE), you get a real error → easier debugging and correct data guarantees.

If you want “ignore duplicates” behavior, you can implement it explicitly per use-case (roadmap / optional mode).

🔥 Benchmarks

Operation	Rows	Time
Massive insert	5,000,000	~5.7 seconds
Massive insert	50,000,000	~50 seconds
Dynamic update	millions	< 0.5 seconds
Streaming delete	millions	instant + progress

🛠 Installation

pip install quantumforge

🧱 Usage Examples

1. Connect

db = SQLiteORM("productos.db")
db.conect_DB()

# 2. Insert

# Simple

db.insert(
    table_name="productos",
    items=[ "producto_x", 10.5, "2023-01-01", 1, 1]
)

# Massive

db.insert_many(
    table_name="productos",
    items=[
        ("producto_x", 10.5, "2023-01-01", 1, 1)
        for _ in range(5_000_000)
    ]
)

# 3. UPDATE

db.update(
    set_values={"nombre": "nuevo", "precio": 50},
    data=["id_producto", "IN", (1,2,3)],
    table_name="productos"
)

"""  Update all records with nombre and precio column"""
db.update(
    set_values={"nombre": "nuevo", "precio": 50},
    table_name="productos"
)

# OTHER UPDATE AND DELETE ALTERNATIVES 
# In case you want delete or update all records without necesity on seeting arguments, take those two functions
# ⚠️ Use delete_all() carefully. This operation removes every row from the table.

db.delete_all(table_name="productos")
db.update_all(set_values={"nombre": "nuevo", "precio": 50}, table_name="productos")

# 4. DELETE with and without conditions

db.delete(
    data=["precio", ">", 100],
    table_name="productos"
)

"""  Delete all records """
db.delete(
    table_name="productos"
)

# 5 

🧠 Architecture

SQLiteORM.py – Main engine

MySQLORM.py – MySQL adapter (in progress)

OracleORM.py – Oracle adapter (in progress)

builders/ – WHERE, SET, placeholders builders

stream/ – streaming operations

optimizers/ – pragma, vacuum, analyze

🧩 Roadmap

Full MySQL engine

Full Oracle engine

QueryBuilder

Model-based ORM layer

Automatic migrations

Batch UPDATE & DELETE

Foreign key inspector

PyPI release


👨‍💻 Author

Iván González Valles
[GitHub(https://github.com/ivanarganda)]