Skip to main content

A Python package that seamlessly integrates PostgreSQL, Jinja templating, and Pydantic for type-safe database queries

Project description

pgjinja

A Python library that combines PostgreSQL with Jinja2 templates to create dynamic SQL queries with a clean, async interface and comprehensive API documentation.

Description

pgjinja simplifies database interactions by allowing you to:

  • Keep SQL queries in separate template files
  • Use Jinja2 templating for dynamic query generation
  • Execute queries asynchronously with connection pooling
  • Automatically map query results to Pydantic models
  • Access comprehensive docstrings and API documentation for all classes and functions

This approach helps separate SQL logic from application code, making your database interactions more maintainable and testable. All classes and functions include detailed docstrings with examples and type annotations for excellent IDE integration.

Installation

pip install pgjinja

API Reference

The pgjinja library provides the following key classes and functions:

Each link will take you to the definition and comprehensive docstring for the respective class or function.

Usage Example

Basic Usage

# Basic imports and setup
from pathlib import Path
from pydantic import BaseModel, SecretStr
from pgjinja import PgJinja, PgJinjaAsync, DBSettings

# Construct DBSettings explicitly
settings = DBSettings(
    user="myuser",
    password=SecretStr("mypass"),
    host="localhost",
    dbname="mydb",
    template_dir=Path("./templates")
)

# Create PgJinja client instance
client = PgJinja(settings)

# Sync query example
result = client.query("users.sql", {"user_id": 1})

# Async query example
async def get_user_async():
    client = PgJinjaAsync(settings)
    return await client.query("users.sql", {"user_id": 1})

# Demonstrating the _model_fields_ template trick
class UserModel(BaseModel):
    user_id: int
    user_name: str

# When used with query, _model_fields_ will automatically provide 'user_id, user_name'
users = client.query("users.sql", model=UserModel)

# Showing connection pooling stats retrieval
pool_stats = client.pool.get_stats()
print("Connection Pool Stats:", pool_stats)

# Async example of pooling stats
async def show_async_pool_stats():
    async_client = PgJinjaAsync(settings)
    stats = async_client.pool.get_stats()
    print("Async Connection Pool Stats:", stats)

Complete Application Example

# src/my_db.py
from functools import cache
from pathlib import Path
from pydantic import BaseModel, SecretStr

from pgjinja import PgJinjaAsync, DBSettings

class Merchant(BaseModel):
    id: int
    name: str

# Create a PostgreSQL connection
@cache
def get_postgres():
    settings = DBSettings(
        user="user",
        password=SecretStr("password"),
        host="dev.postgres",
        template_dir=Path("template"),
        dbname="dbname",
    )
    return PgJinjaAsync(settings)

# Query using a template with parameters
async def select_merchant(limit: int = 3) -> list[Merchant]:
    params = dict(limit=limit)
    template = "select_merchant.sql.jinja"
    return await get_postgres().query(template, params, Merchant)

# Add other database operations here
# ...
# main.py
import asyncio

import src.my_db as db


# Example usage
async def main():
    merchants = await db.select_merchant(limit=5)  # clean and very readable
    # Even with a more complex query, the interface is still the same

    print(merchants)


if __name__ == "__main__":
    asyncio.run(main())

SQL Template Example

Create a file template/select_merchant.sql.jinja:

SELECT id, name
FROM merchants
WHERE active = true
ORDER BY name
LIMIT {{ limit }}

Model-Driven Field Selection with Pydantic[Beta]

pgjinja provides a convenient feature called _model_fields_ that automatically extracts fields from Pydantic models for use in your SQL templates. This helps maintain consistency between your data models and SQL queries.

When you pass a Pydantic model class to the query() method, pgjinja automatically:

  1. Makes all model fields available in templates via the _model_fields_ variable
  2. Creates a comma-separated list of field names that you can use directly in SELECT statements

This feature is compatible with both Pydantic v1 and v2.

Example with Auto Field Selection

Here's how to use the _model_fields_ feature in your SQL templates:

-- template/select_merchant_with_model_fields.sql.jinja
SELECT {{ _model_fields_ }}
FROM merchants
WHERE active = true
ORDER BY name
LIMIT {{ limit }}

With this template, you can use the same Python code:

async def select_merchant(limit: int = 3) -> list[Merchant]:
    params = dict(limit=limit)
    template = "select_merchant_with_model_fields.sql.jinja"
    return await get_postgres().query(template, params, Merchant)

If your Merchant model has fields like id, name, created_at, etc., the SQL query will automatically become:

SELECT id, name, created_at, ...
FROM merchants
WHERE active = true
ORDER BY name
LIMIT 3

This approach ensures your SQL queries always match your model fields, even when you add or remove fields from your Pydantic models.

Advanced Features

Connection Pool Management

Both PgJinja and PgJinjaAsync provide sophisticated connection pool management:

# Access pool statistics
stats = client.pool.get_stats()
print(f"Pool size: {stats.pool_size}")
print(f"Available connections: {stats.pool_available}")
print(f"Active connections: {stats.pool_max_size - stats.pool_available}")

# Configure pool sizing for different workloads
settings = DBSettings(
    user="myuser",
    password=SecretStr("mypass"),
    host="localhost",
    dbname="mydb",
    min_size=5,  # Minimum connections to maintain
    max_size=20  # Maximum connections allowed
)

Using Utility Functions Directly

You can also use the underlying utility functions directly:

from pgjinja import read_template, get_model_fields
from pathlib import Path

# Read template files directly
template_content = read_template(Path("./templates/complex_query.sql"))

# Get model fields for custom template processing
class UserModel(BaseModel):
    id: int
    email: str
    name: str

fields = get_model_fields(UserModel)  # Returns "id, email, name"

Configuration

The PgJinja class accepts the following configuration parameters:

Parameter Description Default
user PostgreSQL user (Required)
password PostgreSQL password (Required)
host Database host localhost
port Database port 5432
dbname Database name public
template_dir Directory containing SQL templates Current directory
template_extension File extension to append to template names Empty string

Asynchronous Execution and Connection Pooling

pgjinja leverages modern Python's async capabilities and PostgreSQL connection pooling for optimal performance:

  • Async/await pattern: All database operations use the async/await pattern for non-blocking execution
  • Connection pooling: Built-in connection pooling via psycopg_pool reduces connection overhead
  • Resource management: Connections are automatically returned to the pool after query execution
  • Concurrent queries: Multiple queries can be executed concurrently without blocking the main thread

This approach is particularly beneficial for web applications and API services where database operations should not block the event loop while waiting for results.

Dependencies

  • asyncio - For asynchronous operations
  • pydantic - For data validation and model mapping (compatible with both Pydantic v1 and v2)
  • jinjasql2 - For SQL templating with Jinja2
  • psycopg - PostgreSQL database adapter for Python
  • psycopg_pool - Connection pooling for psycopg

Development and Testing

Setting Up Development Environment

  1. Clone the repository:

    git clone https://github.com/tungph/pgjinja.git
    cd pgjinja
    
  2. Create and activate a virtual environment:

    uv venv
    . .venv/bin/activate
    
  3. Install development dependencies:

    uv pip install pytest pytest-asyncio pytest-cov
    pip install -e .
    

Running Tests

To run the test suite:

make test

This will:

  • Set up a virtual environment
  • Install necessary test dependencies
  • Run the tests with code coverage reporting

License

MIT License

Project details


Download files

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

Source Distribution

pgjinja-3.0.1.tar.gz (12.8 kB view details)

Uploaded Source

Built Distribution

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

pgjinja-3.0.1-py3-none-any.whl (15.9 kB view details)

Uploaded Python 3

File details

Details for the file pgjinja-3.0.1.tar.gz.

File metadata

  • Download URL: pgjinja-3.0.1.tar.gz
  • Upload date:
  • Size: 12.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.13

File hashes

Hashes for pgjinja-3.0.1.tar.gz
Algorithm Hash digest
SHA256 a49881877b1ebed9c746b5a6b2688cbbff64718bd9e4e9980036591289c351f6
MD5 afb6c8b3b3f62d9bde63a4d624d973c5
BLAKE2b-256 aa750b8db14dc28dfd71a714dad3b9a4d184db833c7f6fd9eff14ffa54f845aa

See more details on using hashes here.

File details

Details for the file pgjinja-3.0.1-py3-none-any.whl.

File metadata

  • Download URL: pgjinja-3.0.1-py3-none-any.whl
  • Upload date:
  • Size: 15.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.13

File hashes

Hashes for pgjinja-3.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 d8f910cdf61080535a2e7606d14d3c588b2bd04c747b569ccb3e63074001feb0
MD5 fc79b050460129d34aff3c6867943a28
BLAKE2b-256 2b37cf1e90e531c1eb077a0fcb34401a93d3b9b6ba763db68943b3f83bd9d189

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page