Today I Learned

Executing Raw SQL in a Flask-SQLAlchemy App

Flask-SQLAlchemy, a popular Flask extension, provides support for SQLAlchemy to your Flask application. It simplifies the tasks of creating complex database queries by offering a high-level ORM (Object Relational Mapping) layer, in addition to providing traditional support for SQL in an easier and more convenient way.

In some situations, however, it can be beneficial to directly execute raw SQL. While the SQLAlchemy ORM provides a great deal of convenience and abstraction, these abstracted functionalities may sometimes make certain tasks, especially analytical tasks, more complicated or less efficient.

When to Use Raw SQL?

The power of raw SQL lies in its direct interaction with the database, rendering it highly efficient in handling complex queries. It’s especially useful for:

  • Analytical queries: Complex calculations or data processing might be faster if written in raw SQL rather than using the ORM. This is largely due to the direct interaction between the SQL and the RDBMS engine, which often have built-in optimizations for such tasks.
  • Complex joins: While SQLAlchemy ORM does a good job handling simple to moderately complex queries, it may get twisted when the join queries become more complex.
  • Database Specific Features: If you’re working with database-specific features (e.g., spatial features in PostGIS for PostgreSQL), SQL will be more straightforward since ORM engines strive to provide a consistent, database-agnostic interface.

How to Execute Raw SQL In Flask-SQLAlchemy?

Let’s use an example to illustrate how to execute raw SQL in a Flask-SQLAlchemy application:

query = 'SELECT id FROM customer;'
result = db.engine.execute(query)
ids = [row[0] for row in result]
print(ids)

In the code snippet above,

  • query: This is a string containing the raw SQL query.
  • db.engine.execute(query): The execute() method runs raw SQL query directly on the engine provided by SQLAlchemy.
  • ids = [row[0] for row in result] and print(ids): All the returned rows from the query are processed. Here we’re just extracting the first column from each row (the ID) and printing out the list of IDs.

In conclusion, while using an ORM like Flask-SQLAlchemy can help you prototype faster and write database-agnostic code, raw SQL still has its place when you need to perform complex analytics, access unique database features, or optimize for performance. It’s important to balance the need for speed and simplicity in development with the need for more powerful, direct database operations.