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:
- Makes all model fields available in templates via the
_model_fields_variable - 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_poolreduces 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 operationspydantic- For data validation and model mapping (compatible with both Pydantic v1 and v2)jinjasql2- For SQL templating with Jinja2psycopg- PostgreSQL database adapter for Pythonpsycopg_pool- Connection pooling for psycopg
Development and Testing
Setting Up Development Environment
-
Clone the repository:
git clone https://github.com/tungph/pgjinja.git cd pgjinja
-
Create and activate a virtual environment:
uv venv . .venv/bin/activate
-
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
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