2.3 Query Refactoring and CTEs

Core Skills Guide for AI Interviews (Math, Code, SQL) 2025

5 min read 1025 words

🪄 Step 1: Intuition & Real-World Analogy

🎯 Core Idea

Common Table Expressions (CTEs) help us break down complex SQL queries into readable, logical building blocks. Instead of writing a monster query full of nested subqueries, you can name each step — like chapters in a story.

Think of a CTE as a temporary named result set that lives only for the duration of your query.

🌍 Real-Life Analogy

Imagine cooking a big meal — say, a lasagna. You don’t make it in one giant step. You:

Prepare the sauce.
Boil the pasta.
Assemble and bake.

Each step depends on the previous one — and each has a name. CTEs do exactly that in SQL: they let you prepare intermediate results (like the “sauce”) before assembling the final output (“the lasagna”). 🍝

💬 Conversational Style

You might ask,

“But can’t I just use subqueries for that?”

Sure — but CTEs bring clarity and maintainability. They make debugging and reasoning about complex joins or aggregates much easier. However, here’s the catch: not all databases treat CTEs the same way — some optimize them away, others materialize them (store results temporarily), which affects performance.

So, CTEs improve readability, but not always speed.

🌱 Step 2: The Core Concept Explained

What It Really Does

A CTE (Common Table Expression) defines a temporary result set that can be referenced multiple times within a query. It’s introduced using the WITH keyword, followed by a name and a query block.

How It Works Internally

When you write:

WITH regional_sales AS (
    SELECT region, SUM(amount) AS total
    FROM sales
    GROUP BY region
)
SELECT * FROM regional_sales;

the DB first executes the inner query (the CTE block) and treats the result as a virtual table named regional_sales.

You can even reference CTEs multiple times or chain them (one CTE uses another).
In PostgreSQL, by default, CTEs are materialized (the result is stored temporarily). In SQL Server or MySQL 8+, they are often inlined — meaning the optimizer merges them into the main query for potential speed gains.

Common Use Cases

Simplifying multi-step aggregations.
Making long queries easier to maintain.
Debugging and refactoring deeply nested SQL.
Recursive queries (e.g., organizational hierarchies, graph traversals).

💻 Step 3: Code Demonstration & Explanation

🧩 Example 1: Simple Aggregation with a CTE

WITH regional_sales AS (
  SELECT region, SUM(amount) AS total
  FROM sales
  GROUP BY region
)
SELECT region, total
FROM regional_sales
WHERE total > 10000;

Explanation:

The WITH clause defines a virtual table regional_sales.
The outer query filters regions where total sales exceed 10,000.
This is equivalent to nesting the aggregation inside the WHERE — but now it’s readable and modular.

🧩 Example 2: Multiple CTEs (Chained Steps)

WITH
regional_sales AS (
  SELECT region, SUM(amount) AS total
  FROM sales
  GROUP BY region
),
top_regions AS (
  SELECT region
  FROM regional_sales
  WHERE total > 10000
)
SELECT o.order_id, o.region, o.amount
FROM orders o
JOIN top_regions t ON o.region = t.region;

Explanation:

regional_sales computes totals.
top_regions filters regions above 10,000.
The final query joins back to orders to show matching orders.

This is step-wise SQL thinking — turning spaghetti code into a logical flowchart.

🧩 Example 3: Recursive CTE (For Hierarchies)

WITH RECURSIVE employee_hierarchy AS (
  SELECT id, name, manager_id
  FROM employees
  WHERE manager_id IS NULL  -- top-level managers

  UNION ALL

  SELECT e.id, e.name, e.manager_id
  FROM employees e
  INNER JOIN employee_hierarchy eh ON e.manager_id = eh.id
)
SELECT * FROM employee_hierarchy;

Explanation: This query traverses an employee hierarchy, starting from top-level managers, and recursively pulls in subordinates — perfect for org charts, file trees, or dependency graphs.

⚙️ Step 4: Performance Insights & Optimization

ℹ️

CTE vs Subquery:
- Some databases (like PostgreSQL ≤12) materialize CTEs:
  - Stored temporarily → re-used efficiently but incurs extra memory/disk overhead.
- Others (like SQL Server, MySQL 8, PostgreSQL ≥12) inline CTEs into the main query:
  - No extra materialization; often faster.
Performance Tip:
- Avoid using CTEs in tight loops or huge data transformations unless reused multiple times.
- If the CTE is only referenced once, consider inlining it as a subquery — might be faster.
- If referenced multiple times, CTE saves redundant computation.

Execution Check: Always use EXPLAIN ANALYZE:

EXPLAIN ANALYZE
WITH regional_sales AS (
    SELECT region, SUM(amount)
    FROM sales
    GROUP BY region
)
SELECT * FROM regional_sales WHERE sum > 10000;

Compare with the non-CTE version to see if the optimizer inlined it.

🎯 Step 5: Interview Perspective & Reasoning

Common Interview Questions

“What is a Common Table Expression (CTE) and why use it?”
“How do CTEs differ from subqueries or temporary tables?”
“Do CTEs always improve performance?”
“What’s the difference between materialized and inlined CTEs?”
“Explain how a recursive CTE works.”

Reasoning Expectation

Interviewers look for:

Clear understanding that CTEs improve readability, not guaranteed speed.
Awareness of engine-specific behavior (materialization vs inlining).
Ability to reason about execution flow — understanding when to refactor for clarity vs optimization.

Bonus Challenge

Rewrite this nested query using a CTE for clarity:
SELECT region, COUNT(*) 
FROM orders 
WHERE customer_id IN (
  SELECT id FROM customers WHERE city = 'New York'
)
GROUP BY region;
Then explain whether your CTE will be materialized or inlined in PostgreSQL 15 vs MySQL 8.

🧩 Step 6: Common Mistakes & Debugging Insights

🚨 Common Mistakes (Click to Expand)

Assuming CTEs are always faster. In PostgreSQL, a CTE might be fully materialized — even if the optimizer could have inlined it.
Reusing expensive CTEs unnecessarily. Each reference triggers a new scan unless explicitly cached (depending on engine).
Not analyzing execution plans. Always check EXPLAIN (ANALYZE, BUFFERS) to confirm whether your CTE was optimized away.
Recursive CTE infinite loops. Always include a clear termination condition in recursive CTEs to avoid runaway queries.

🧠 Step 7: Key Takeaways

🧠 Main Idea: CTEs improve readability and modularity — not necessarily performance. They are your best friend for breaking down complex queries into logical stages.

💻 How It Works: CTEs act as temporary result sets, materialized or inlined depending on the engine. They can even be recursive for hierarchical data.

🚀 Why It Matters: Great SQL developers write readable, maintainable queries that scale — knowing when to refactor and when to optimize is the hallmark of mastery.

2.4 Avoiding Common Pitfalls 2.2 Indexing for Speed