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iron_sql generates typed async PostgreSQL clients and runtime helpers from schemas and SQL queries

Project description

iron_sql

License main PyPI - Version

iron_sql is a typed SQL code generator and async runtime for PostgreSQL. Write SQL where you use it, run generate_sql_package, and get a module with typed dataclasses, query helpers, and pooled connections without hand-written boilerplate.

Installation

pip install iron-sql             # runtime only (psycopg + psycopg-pool + pydantic)
pip install iron-sql[codegen]    # + inflection for code generation

You also need sqlc v2 available in your PATH (or pass sqlc_command/sqlc_path to the generator).

Key Features

  • Query discovery. generate_sql_package scans your codebase for calls like <package>_sql("SELECT ..."), runs sqlc for type analysis, and emits a typed module.
  • Strong typing. Generated dataclasses and method signatures flow through your IDE and type checker.
  • Async runtime. Built on psycopg v3 with pooled connections, context-based connection reuse, and transaction helpers.
  • Safe by default. Helper methods enforce expected row counts instead of returning silent None.

Package Layout

  • runtime.py -- async ConnectionPool, row helpers (get_one_row, typed_scalar_row), JSON validation decorators.
  • codegen/generator.py -- query discovery, type resolution, module rendering.
  • codegen/sqlc.py -- wraps the sqlc CLI and models its JSON output.
  • codegen/util.py -- shared codegen utilities (indent_block, write_if_changed).

Getting Started

  1. Add a schema file. A Postgres DDL dump, e.g. db/schema.sql.
  2. Write queries where they live. Import the future helper and use SQL literals inline:
    from myapp.db.mydb import mydb_sql
    
    user = await mydb_sql(
        "SELECT id, username, email, created_at FROM users WHERE id = @user_id"
    ).query_single_row(user_id=uid)
    
    Named parameters use @param (required) or @param? (optional, expands to sqlc.narg). Positional $1 works too.
  3. Generate the client module.
    from pathlib import Path
    
    from iron_sql.codegen import generate_sql_package
    
    generate_sql_package(
        schema_path=Path("schema.sql"),
        package_full_name="myapp.db.mydb",
        dsn_import="myapp.config:DSN",
        src_path=Path("."),
    )
    
    This writes myapp/db/mydb.py containing:
    • a connection pool singleton,
    • *_connection() and *_transaction() context managers,
    • dataclasses for multi-column results (deduplicated by table),
    • StrEnum classes for PostgreSQL enums,
    • a query class per statement with typed methods,
    • overloads for the *_sql() helper so editors infer return types.

Customization

  • Type overrides. type_overrides={"custom_type": "int"} maps database type names to Python type strings.
  • JSON model overrides. json_model_overrides={"users.metadata": "myapp.models:UserMeta"} adds Pydantic validation for JSON/JSONB columns.
  • Naming conventions. Supply to_pascal_fn and to_snake_fn callables to control generated names.
  • DSN configuration. dsn_import is written verbatim into the generated module; point it at a config variable, env var lookup, or function call.
  • Debug artifacts. Pass debug_path to save sqlc inputs and outputs for inspection.

Runtime Highlights

  • ConnectionPool opens lazily and reopens after close(), with ContextVar-based connection reuse for nested contexts.
  • query_single_row() raises NoRowsError; query_optional_row() returns None. Both raise TooManyRowsError on 2+ rows.
  • JSONB params are sent with pgjson.Jsonb; JSON with pgjson.Json. Scalar row factories validate types at runtime.
  • json_validated decorator applies Pydantic model validation to dataclass fields on construction.

Example

The example/ directory contains a complete working setup: a PostgreSQL schema, generation script with testcontainers, and sample query definitions. See example/generate.py for the codegen call and example/myapp/main.py for query usage.

Validation and Troubleshooting

  • Errors identify the file and line where the problematic statement lives.
  • Unknown SQL types map to object and emit UnknownSQLTypeWarning (promotable to error with warnings.filterwarnings).
  • Statements with the same SQL but conflicting row_type values are rejected at generation time.

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