Optimizing Subquery Performance in SQL

Many SQL queries rely on subqueries to retrieve or filter data. However, inefficiently written subqueries can lead to significant performance degradation, especially with large datasets. This challenge focuses on identifying and refactoring a common subquery pattern to improve execution speed. You will be tasked with transforming a query that uses a correlated subquery into a more efficient alternative, demonstrating your understanding of SQL optimization techniques.

Problem Description

You are given a dataset representing customers and their orders. The goal is to find all customers who have placed at least one order with a total amount greater than a certain threshold.

Your task is to take an existing SQL query that uses a correlated subquery to achieve this and rewrite it to use a more performant approach, such as a JOIN or a Common Table Expression (CTE) with appropriate aggregation.

Key Requirements:

• Identify the performance bottleneck caused by the correlated subquery.
• Rewrite the query to achieve the same result set without a correlated subquery.
• The rewritten query should be demonstrably more efficient.

Expected Behavior:

The output should be a list of customer IDs who meet the specified criteria.

Edge Cases:

• Customers with no orders.
• Customers with multiple orders, some above and some below the threshold.
• Orders with zero total amount.

Examples

Example 1:

Assume the following simplified table structures:

Customers table:

Orders table:

Input Query (to be optimized):

SELECT DISTINCT c.customer_id
FROM Customers c
WHERE EXISTS (
    SELECT 1
    FROM Orders o
    WHERE o.customer_id = c.customer_id
      AND o.order_amount > 100.00
);

Output:

Explanation:

The subquery (SELECT 1 FROM Orders o WHERE o.customer_id = c.customer_id AND o.order_amount > 100.00) is correlated with the outer query. For each customer in the Customers table, this subquery executes, checking if there's any order for that specific customer with an amount greater than 100.00. Customers 101 (order 3) and 102 (order 5) meet this condition.

Example 2:

Using the same tables as Example 1.

Input Query (to be optimized):

SELECT c.customer_id
FROM Customers c
WHERE c.customer_id IN (
    SELECT o.customer_id
    FROM Orders o
    WHERE o.order_amount > 100.00
);

Output:

Explanation:

This IN subquery also presents a similar performance challenge. For each customer, it might perform a lookup against the result of the subquery, which in this case, would be (101, 102). While often optimized by modern SQL engines, it can still be less efficient than a join for very large datasets.

Example 3: Edge Case - No Matching Orders

Using the same tables as Example 1, but with a higher threshold.

Input Query (to be optimized):

SELECT DISTINCT c.customer_id
FROM Customers c
WHERE EXISTS (
    SELECT 1
    FROM Orders o
    WHERE o.customer_id = c.customer_id
      AND o.order_amount > 300.00
);

Output:

(Empty result set)

Explanation:

No customer has an order with an amount greater than 300.00, so the EXISTS clause evaluates to false for all customers, resulting in an empty output. An optimized query should also handle this correctly.

Constraints

• The Customers table can contain up to 1,000,000 rows.
• The Orders table can contain up to 10,000,000 rows.
• order_amount is a floating-point number.
• The threshold for order_amount will be a positive floating-point number.
• The optimized query should aim for a query execution time that is significantly faster than the original correlated subquery version, especially under load. Aim for a solution that scales well with data volume.

Notes

• Consider using JOIN operations (e.g., INNER JOIN, LEFT JOIN) combined with GROUP BY and HAVING clauses, or Common Table Expressions (CTEs) to achieve the desired result.
• Think about how the database engine might execute the correlated subquery and why it can be slow.
• The goal is not just to get the correct answer, but to get it efficiently.