Two Approaches to Database Interaction with SQLAlchemy in Python

Ever wondered about the best way to work with databases in Python? In this post, I've explored two different approaches using SQLAlchemy: the ORM layer and using raw SQL.

1. Setup: Define Models with SQLAlchemy ORM
First, we define our ORM models in a file (e.g., orm_models.py). These models define the database schema in code using SQLAlchemy’s declarative base. We create a JobDescription model and a CandidateResume model.

2. Bit-ORM Layer: Repository Using the ORM
This approach uses the ORM to automatically convert model objects into SQL queries. Check out the code in repository_orm.py, where we retrieve job descriptions using the ORM.

Advantages of the ORM Approach

• Abstraction & Simplicity: Deal with Python objects and let the ORM handle SQL.
• Maintainability and Safety: Model changes update underlying queries and prevent SQL injection.

3. Bit-Out-ORM Layer: Repository Using Raw SQL
Contrast this with using raw SQL in repository_raw.py. This approach allows for direct query writing with potential optimizations.

Advantages of the Raw SQL Approach

• Fine-Grained Control: Write optimized SQL for complex tasks.
• Transparency & Flexibility: See the exact SQL sent and use database-specific features.

Summary and Comparison
Consider using an ORM for regular tasks and raw SQL for fine-tuned, performance-critical queries. This hybrid approach provides productivity with control when needed.