How to write Complex SQL Queries Easily

 Complex SQL queries appear long and intimidating at times, but with careful planning we can simplify them. SQL (Structured Query Language) is an extremely important and one of the most widely used tool for working with databases. From writing simple queries like “SELECT * from table_name”, to writing really complex queries for advanced operations, we should always try to keep it simple. With the right approach, we can simplify complex queries by breaking them into small, logical steps.

Let’s learn together how to write complex SQL queries easily, even if you are at beginner level right now in your coding journey.

In this article, we will explore how to write complex SQL queries easily, even if you are a beginner.

1. Understand the Requirement Clearly of Complex SQL Queries

Before start writing the query, take some time for strategic thinking. Visualize what you want to fetch from the database by asking these questions –

1. Which tables have the data that I need to retrieve?
2. Are these tables related and how?
3. Should I apply any conditions or filters?
4. What is the final output I am looking for?

These simple questions can go a long way in preparing ourselves in thinking clearly which will enable us for writing complex SQL queries easily.

2. Break Down the Complex SQL Queries into Smaller Parts

After having the mental clarity via asking yourself above questions, break the task into smaller queries and also cross check each part individually.

For example, if we need the total amount spent by customers recently, we can break it into 3 small steps –

1. Retrieve customer details first.
2. Check their recent purchases.
3. Now calculate the total amount they have spent.

Now write a simple query for each step. Once we are sure each query is working fine, we can combine them step by step.

3. Use Common Table Expressions (CTEs) and Subqueries

Using CTEs (WITH statements) and subqueries can help in organizing the logic that we need to write complex SQL queries. Let’s look at below example –

Example:

Consider the purchases table and customers table for input. Below query will fetch a list of customers along with the total amount they have spent on purchases made after January 1, 2024 –

"WITH recent_purchases AS (
    SELECT customer_id, SUM(amount) AS total_spent
    FROM purchases
    WHERE purchase_date > '2024-01-01'
    GROUP BY customer_id
)
SELECT c.customer_name, r.total_spent
FROM customers c
JOIN recent_purchases r ON c.customer_id = r.customer_id;"

How is this query helping? 

1. It helps in analyzing recent spending habits of customers.
2. It simplified complex calculations by using a CTE, making the query easier to read.
3. It did not modify the original data while allowing temporary calculations.

4. Use Joins Effectively in Complex SQL Queries –

JOINs help in retrieving data from related tables. JOINs are of following 4 types –

1. INNER JOIN – Retrieves only the matching rows from both tables.
2. LEFT JOIN – Returns all records from the left table and matched rows from the right table.
3. RIGHT JOIN – Returns all records from the right table and matched rows from the left table.
4. FULL JOIN – Combines all records from both tables, filling in NULLs where there is no match.

Example: Consider these two input tables –

Let’s run INNER JOIN on these two in following manner –

“Output Table (Result of INNER JOIN)
SELECT customers.name, orders.order_id
FROM customers
INNER JOIN orders ON customers.customer_id = orders.customer_id;”

Here is the out put –

5. Use Indexes to Optimize Performance of Complex SQL Queries

If we are scanning large tables in the database, it may lead to queries running slowly. That’s why we index to help queries in directly going to the desired section, just like an index in a book helps us going to the right page.

Example: If you frequently search by email in a users table, create an index:

"CREATE INDEX idx_email ON users(email);"

We should take care of not creating too many indexes as it can have the opposite effect. Create indexes only for sections we scan frequently.

6. Use GROUP BY and HAVING for Aggregations

When dealing with summary data, use GROUP BY to group results and HAVING to filter them.

Example:

Find customers who have spent more than ₹10,000:

"SELECT customer_id, SUM(amount) AS total_spent
FROM purchases
GROUP BY customer_id
HAVING SUM(amount) > 10000;"

7. Use CASE Statements for Conditional Logic

The CASE statement helps in applying conditional logic within queries.

Example:

Below query can classify customers based on their spending:

"SELECT customer_name,
    CASE
        WHEN total_spent > 50000 THEN 'VIP'
        WHEN total_spent BETWEEN 20000 AND 50000 THEN 'Regular'
        ELSE 'New'
    END AS customer_type
FROM (
    SELECT customer_id, customer_name, SUM(amount) AS total_spent
    FROM purchases
    JOIN customers ON purchases.customer_id = customers.customer_id
    GROUP BY customer_id, customer_name
) AS customer_data;"

Here, The inner query first joins purchases and customers, then it groups by customer_id to calculate total_spent. Thereafter, the outer query used a CASE statement to classify customers as ‘VIP’ (> ₹50,000), ‘Regular’ (₹20,000 – ₹50,000), or ‘New’ (< ₹20,000). That’s how it eventually segmented customers based on their spending behavior.

8. Use EXPLAIN PLAN to Debug and Optimize

We can take SQL’s help to understand why a query is running surprisingly slow. We have EXPLAIN (MySQL/PostgreSQL) or EXPLAIN PLAN (Oracle) at our disposal for this. Hence, let’s look at below example –

"EXPLAIN SELECT * FROM purchases WHERE amount > 5000;"

This significantly helps to identify performance bottlenecks and areas for improvement.

9. Write Clean and Readable SQL Code

Follow these practices to make your SQL queries comparatively easier for others to understand:

1. Firstly, use proper indentation and line breaks.
2. Use meaningful aliases (AS keyword).
3. Also add comments for clarity.
4. Finally, avoid unnecessary columns in SELECT *.

Example:

"-- Get top 5 highest spending customers
SELECT c.customer_name, SUM(p.amount) AS total_spent
FROM customers c
JOIN purchases p ON c.customer_id = p.customer_id
GROUP BY c.customer_name
ORDER BY total_spent DESC
LIMIT 5;"

10. Practice Regularly to Master Complex SQL Queries

Like any skill, mastering SQL certainly requires practice. Use:

• LeetCode SQL challenges
• SQL JOINS Explained With Examples
• Mode Analytics SQL tutorials
• W3Schools SQL exercises

Additionally, building real-world queries will boost your confidence in handling complex problems.

Final Thoughts

Basically, writing complex SQL queries is not at all a difficult task. By breaking queries into small parts, using CTEs, joins, and optimization techniques, we can write efficient and readable SQL code. Keep practicing and soon, writing complex SQL queries will feel easy!

Have questions? Drop them in the comments!