sqlstratum 0.3.0

Lightweight, source-first SQL AST + compiler + runner.

SQLStratum

SQLStratum is a modern, typed, deterministic SQL query builder and compiler for Python with a SQLite runner and a hydration pipeline. It exists to give applications and ORMs a reliable foundation layer with composable SQL, predictable parameter binding, and explicit execution boundaries.

Key Features

• Deterministic compilation: identical AST inputs produce identical SQL + params
• Typed, composable DSL for SELECT/INSERT/UPDATE/DELETE
• Safe parameter binding (no raw interpolation)
• Hydration targets for structured results
• SQLite-first execution via a small Runner API
• Dialect-aware compilation entrypoint (compile(..., dialect="sqlite" | "mysql"))
• Optional MySQL runners for sync (PyMySQL) and async (asyncmy) execution
• Testable compiled output and runtime behavior

Non-Goals

• Not an ORM (no identity map, relationships, lazy loading)
• Not a migrations/DDL system
• Not a full database abstraction layer for every backend yet (SQLite first)
• Not a SQL string templating engine

SQLStratum focuses on queries. DDL statements such as CREATE TABLE or ALTER TABLE are intended to live in a complementary library with similar design goals that is currently in the works.

Quickstart

import sqlite3

from sqlstratum import SELECT, INSERT, Table, col, Runner

users = Table(
    "users",
    col("id", int),
    col("email", str),
    col("active", int),
)

conn = sqlite3.connect(":memory:")
runner = Runner(conn)
runner.exec_ddl("CREATE TABLE users (id INTEGER PRIMARY KEY, email TEXT, active INTEGER)")

runner.execute(INSERT(users).VALUES(email="[e-mail]", active=1))
runner.execute(INSERT(users).VALUES(email="[e-mail]", active=0))

q = (
    SELECT(users.c.id, users.c.email)
    .FROM(users)
    .WHERE(users.c.active.is_true())
    .hydrate(dict)
)

rows = runner.fetch_all(q)
print(rows)

Why Table objects?

SQLStratum’s Table objects are the schema anchor for the typed, deterministic query builder. They provide column metadata and a stable namespace for column access, which enables predictable SQL generation and safe parameter binding. They also support explicit aliasing to avoid ambiguous column names in joins.

Project Structure

• AST: immutable query nodes in sqlstratum/ast.py
• Compiler: SQL + params generation in sqlstratum/compile.py
• Dialects: compiler adapters and registry in sqlstratum/dialects/
• Runner: SQLite execution and transactions in sqlstratum/runner.py
• Hydration: projection rules and targets in sqlstratum/hydrate/

Dialect Compilation

compile(query, dialect=...) now dispatches through a dialect registry.

Supported built-ins:

• sqlite: full support used by Runner
• mysql: compile-only MVP (no MySQL runner yet)

Example:

compiled = compile(
    SELECT(users.c.id, users.c.email).FROM(users).WHERE(users.c.id == 1),
    dialect="mysql",
)
print(compiled.sql)
# SELECT `users`.`id`, `users`.`email` FROM `users` WHERE `users`.`id` = %(p0)s

MySQL Runners (Optional)

Install one or both connectors:

pip install sqlstratum[pymysql]
pip install sqlstratum[asyncmy]
# or both
pip install sqlstratum[mysql]

Synchronous runner (PyMySQL):

from sqlstratum import MySQLRunner

runner = MySQLRunner.connect(
    host="127.0.0.1",
    port=3306,
    user="app",
    password="secret",
    database="appdb",
)
rows = runner.fetch_all(SELECT(users.c.id, users.c.email).FROM(users))

Asynchronous runner (asyncmy):

from sqlstratum import AsyncMySQLRunner

runner = await AsyncMySQLRunner.connect(
    host="127.0.0.1",
    port=3306,
    user="app",
    password="secret",
    database="appdb",
)
rows = await runner.fetch_all(SELECT(users.c.id, users.c.email).FROM(users))

Both runners execute through the same SQL AST + compiler pipeline. Compilation remains deterministic; execution and hydration stay at the runner boundary.

SQL Debugging

SQLStratum can log executed SQL statements (compiled SQL + parameters + duration), but logging is intentionally gated to avoid noisy output in production. Debug output requires two conditions:

• Environment variable gate: SQLSTRATUM_DEBUG must be truthy ("1", "true", "yes", case-insensitive).
• Logger gate: the sqlstratum logger must be DEBUG-enabled.

Why it does not work by default: Python logging defaults to WARNING level, so even if SQLSTRATUM_DEBUG=1 is set, DEBUG logs will not appear unless logging is configured.

To enable debugging in a development app:

Step 1 - set the environment variable:

SQLSTRATUM_DEBUG=1

Step 2 - configure logging early in the app:

import logging

logging.basicConfig(level=logging.DEBUG)
# or
logging.getLogger("sqlstratum").setLevel(logging.DEBUG)

Output looks like:

SQL: <compiled sql> | params={<sorted params>} | duration_ms=<...>

Architectural intent: logging happens at the Runner boundary (after execution). AST building and compilation remain deterministic and side-effect free, preserving separation of concerns.

Pydantic Hydration (Optional)

SQLStratum does not depend on Pydantic, but it provides an optional hydration adapter for Pydantic v2 models.

Install:

pip install sqlstratum[pydantic]

Example:

from pydantic import BaseModel
from sqlstratum.hydrate.pydantic import hydrate_model, using_pydantic

class User(BaseModel):
    id: int
    email: str

row = {"id": "1", "email": "[e-mail]"}
user = hydrate_model(User, row)

q = using_pydantic(
    SELECT(users.c.id, users.c.email).FROM(users).WHERE(users.c.id == 1)
).hydrate(User)
user_row = runner.fetch_one(q)

Logo Inspiration

Vinicunca (Rainbow Mountain) in Peru’s Cusco Region — a high-altitude day hike from Cusco at roughly 5,036 m (16,500 ft). See Vinicunca for background.

Versioning / Roadmap

Current version: 0.3.0. Design notes and current limitations are tracked in NOTES.md. Planned release milestones, including PostgreSQL and MySQL MVP start points, are documented in docs/roadmap.md.

Authorship

Antonio Ognio is the maintainer and author of SQLStratum. ChatGPT is used for brainstorming, architectural thinking, documentation drafting, and project management advisory. Codex (CLI/agentic coding) is used to implement many code changes under Antonio's direction and review. The maintainer reviews and curates changes; AI tools are assistants, not owners, and accountability remains with the maintainer.

License

MIT License.

Contributing

PRs are welcome. Please read CONTRIBUTING.md for the workflow and expectations.

Documentation

Install docs dependencies:

python -m pip install -r docs/requirements.txt

Run the local docs server:

mkdocs serve

Build the static site:

mkdocs build --clean

Read the Docs will build documentation automatically once the repository is imported.

Release Automation

Install dev dependencies:

python -m pip install -e ".[dev]"

Run the full release pipeline:

poe release

This runs, in order:

• python -m unittest
• python -m build --no-isolation
• python -m twine check dist/*
• python -m twine upload dist/*

For a non-publishing verification pass:

poe release-dry-run