1.1. Learn the SQL Query Lifecycle

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

5 min read 954 words

🪄 Step 1: Intuition & Real-World Analogy

🎯 Core Idea: A SQL query is not executed top-to-bottom as you read it — the database engine interprets and executes it in a logical order that ensures correctness and efficiency.

🌍 Real-Life Analogy: Imagine SQL as a restaurant kitchen:

You (the analyst) give an order: “SELECT dessert FROM menu WHERE calories < 300 ORDER BY price LIMIT 3.”
The chef (the database engine) doesn’t start plating desserts immediately.
Instead, they first look at the menu (FROM), then filter the dishes (WHERE), then group ingredients (GROUP BY) if needed, and only at the end serve the plated dishes (SELECT).

So while you say “SELECT dessert…”, the database internally starts with FROM — not SELECT!

💬 Conversational Curiosity: This means when your WHERE clause complains about a missing alias, or your aggregate doesn’t work inside it — it’s not SQL being weird. It’s just following its own assembly-line order. Let’s decode that next.

🌱 Step 2: The Core Concept Explained

Let’s uncover what actually happens when a SQL query runs.

What It Really Does

SQL doesn’t read queries in the written order. It follows a logical processing order — a defined sequence that transforms raw data into the final result set. The order ensures each step has the data context it needs.

SQL’s Logical Execution Order

Step	Clause	Purpose
1️⃣	FROM / JOIN	Identify and combine the source tables
2️⃣	WHERE	Filter out rows that don’t meet conditions
3️⃣	GROUP BY	Group remaining rows based on column values
4️⃣	HAVING	Filter entire groups (post-aggregation)
5️⃣	SELECT	Pick and compute output columns
6️⃣	ORDER BY	Sort the final result set
7️⃣	LIMIT / OFFSET	Return the final slice of data

Key Insight: By the time SELECT executes, the data is already grouped, filtered, and ready — which is why you can’t use column aliases in WHERE, but you can use them in ORDER BY.

Why WHERE Cannot Contain Aggregates

Because the WHERE phase happens before grouping and aggregation. At that stage, SQL is still working with individual rows, not summary groups.

Aggregates like SUM(), AVG(), or COUNT() only exist after grouping — meaning only the HAVING clause (which runs after GROUP BY) can use them.

So this will fail:

SELECT region, SUM(amount)
FROM sales
WHERE SUM(amount) > 10000  -- ❌ Invalid
GROUP BY region;

But this will work:

SELECT region, SUM(amount)
FROM sales
GROUP BY region
HAVING SUM(amount) > 10000;  -- ✅ Valid

💻 Step 3: Code Demonstration & Explanation

Let’s see SQL’s execution flow in action.

Example 1: Understanding Query Order

SELECT region, COUNT(*) AS num_sales
FROM sales
WHERE amount > 500
GROUP BY region
ORDER BY num_sales DESC;

Internal Flow:

FROM: Load sales table.
WHERE: Keep rows with amount > 500.
GROUP BY: Aggregate remaining rows by region.
SELECT: Display region and count.
ORDER BY: Sort by num_sales.

Even though SELECT appears first, it runs after grouping and filtering.

Example 2: Adding HAVING After Aggregation

SELECT region, SUM(amount) AS total_sales
FROM sales
WHERE amount > 500
GROUP BY region
HAVING SUM(amount) > 10000
ORDER BY total_sales DESC;

Here:

WHERE filters out small orders first (pre-group).
GROUP BY forms groups by region.
HAVING removes low-performing regions (post-group).
SELECT finally returns columns.

This is the classic “WHERE vs HAVING” trap interviewers love.

⚙️ Step 4: Performance Insights & Optimization

ℹ️

Execution Order ≠ Written Order: SQL engines often rewrite queries internally for efficiency (using query planners). But the logical order always stays consistent — FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT.
Index Impact: Filters in WHERE can leverage indexes, but HAVING cannot (because aggregates are computed in memory after grouping).

Optimization Tip: Move filters from HAVING to WHERE whenever possible to reduce data early.

-- ❌ Inefficient
HAVING SUM(amount) > 5000;

-- ✅ Better
WHERE amount > 0
HAVING SUM(amount) > 5000;

Trade-off: Adding ORDER BY or DISTINCT forces sorting — a costly step on large datasets.

🎯 Step 5: Interview Perspective & Reasoning

Common Interview Questions

“Explain SQL’s logical query processing order.”
“Why can’t you use an alias in WHERE?”
“Difference between WHERE and HAVING?”
“What happens internally when you run a query?”

Reasoning Expectation

Strong candidates describe the data flow between clauses — not just memorize order.

✅ Example of a great answer:

“SQL starts with FROM to fetch data, applies WHERE to remove irrelevant rows, then groups data with GROUP BY, filters those groups with HAVING, and finally selects what to show.”

Bonus Challenge

Find all regions with total sales above 10,000, but only counting orders above 500.

SELECT region, SUM(amount) AS total_sales
FROM sales
WHERE amount > 500
GROUP BY region
HAVING SUM(amount) > 10000;

Explain:

Why WHERE filters before grouping
Why HAVING filters after
And which step can use indexes

🧩 Step 6: Common Mistakes & Debugging Insights

🚨 Common Mistakes (Click to Expand)

❌ Using aggregate functions in WHERE → Aggregates don’t exist yet! Use HAVING instead.
❌ Relying on column aliases in WHERE → Aliases appear only after SELECT executes.
❌ Assuming written order = execution order → SQL rewrites queries internally, trust logical order instead.
💡 Debug Tip: When something “makes no sense,” mentally walk through the steps: FROM → WHERE → GROUP → HAVING → SELECT → ORDER → LIMIT

🧠 Step 7: Key Takeaways

🧠 Main Idea: SQL queries are evaluated logically, not in the order you write them.

💻 How It Works: The DB engine first identifies data sources (FROM), filters rows (WHERE), groups them (GROUP BY), applies group filters (HAVING), and finally outputs results (SELECT).

🚀 Why It Matters: Understanding execution order lets you:
Write correct queries (no WHERE aggregates)
Optimize filters early
Explain SQL reasoning confidently in interviews

1.2 Filter and Aggregate Like an Analyst 📘 SQL Theoretical Interview Questions