Demystifying Subqueries in SQLAlchemy: From Simple to Complex

Building the Subquery:

• Core vs. ORM: SQLAlchemy supports both the Object Relational Mapper (ORM) and core SQL expression approaches.

• Placement: Subqueries can be used in various parts of the main query, including the SELECT, FROM, or WHERE clause. The placement depends on your specific goal.

  • Common Use Cases:
    • Filtering: A subquery in the WHERE clause allows you to filter based on results from another query.
    • Aggregation: You can use subqueries with aggregate functions (e.g., count, avg) to perform calculations on data retrieved by the subquery.

from sqlalchemy import or_

from your_models import Order, Item

# Subquery to find expensive items (price > 100)
expensive_items_query = session.query(Item.id).filter(Item.price > 100)

# Main query to find orders containing at least one expensive item
orders = session.query(Order).join(Order.items).filter(
    Order.items.any(expensive_items_query.subquery())
)

Explanation:

• We define a subquery using session.query on the Item model to find items with a price greater than 100. We use .subquery() to mark it for use in the main query.
• The main query uses .join to link Order and Item tables. Then, it filters orders where there's at least one item (Order.items.any()) matching the criteria defined in the subquery.

Core SQL - Finding Users with Most Orders:

from sqlalchemy import select, func

# Subquery to find the highest order count for a user
highest_order_count_subquery = (
    select(func.max(Order.id)).group_by(Order.user_id).subquery()
)

# Main query to find users with the highest order count
users_with_max_orders = (
    select(User)
    .join(Order)
    .where(Order.user_id == highest_order_count_subquery.c.id)
    .group_by(User.id)
)

# Execute the core query and fetch results
results = session.execute(users_with_max_orders).scalars().all()

• We build a subquery using core functions like select, func.max, and group_by to find the maximum order count for each user. We use .subquery() to prepare it for the main query.
• The main query joins User and Order tables and uses a comparison with the subquery alias (highest_order_count_subquery.c.id) to find users whose order count matches the maximum obtained in the subquery.
• We execute the core query using session.execute and fetch the results.

• Standard subqueries execute entirely before the main query. Correlated subqueries, however, are nested within the WHERE clause and can reference columns from the main query.
• This approach can be more efficient for certain scenarios, but it can also lead to less readable code.

Example (ORM):

from sqlalchemy import func

orders = session.query(Order).filter(
    Order.total_amount > (
        session.query(func.avg(Order.total_amount)).filter(Order.user_id == Order.user_id).subquery()
    )
)

Here, the subquery calculates the average order amount per user. The main query then filters orders where the individual order's total amount is greater than the user's average (obtained from the subquery).

EXISTS Clause:

• The EXISTS clause allows you to check if rows exist in another table based on a specific condition. It can be an alternative to filtering with subqueries in some cases.

from sqlalchemy import exists

orders = session.query(Order).filter(
    exists([Order.items]).filter(Item.price > 100)
)

This query checks if an order has any associated items (exists([Order.items])) where the item price is greater than 100. It achieves a similar outcome to the first ORM example using the any method with a subquery.

Window Functions:

• Window functions like ROW_NUMBER or RANK can be used for ranking or partitioning data within the main query itself, potentially eliminating the need for subqueries.

Choosing the Right Method:

The best approach depends on your specific needs and the complexity of the query. Standard subqueries offer clarity and flexibility, while correlated subqueries might be more efficient for specific cases. EXISTS and window functions can be alternatives for certain filtering or ranking scenarios.