🗄️ The Ultimate SQL Cheat Sheet (With Examples)

Structured Query Language (SQL) is the backbone of working with relational databases. Whether you’re a beginner or an advanced developer, having a cheat sheet helps you quickly recall commands, patterns, and functions when writing queries.

In this article, we’ll walk through a categorized SQL Cheat Sheet with definitions and examples to strengthen your understanding.

🔹 1. Basic SQL Commands

These are the building blocks of SQL.

• SELECT → fetches data

SELECT * FROM employees;

• INSERT INTO → inserts data

INSERT INTO employees (name, role) VALUES ('John Doe', 'Developer');

• UPDATE → modifies existing data

UPDATE employees SET role = 'Manager' WHERE id = 1;

• DELETE → removes data

DELETE FROM employees WHERE id = 1;

🔹 2. Filtering Data

Use conditions to narrow results.

SELECT * FROM employees WHERE role = 'Developer';

🔹 3. Sorting and Limiting Data

• ORDER BY → sort results
• LIMIT → restrict row count
• DISTINCT → remove duplicates

SELECT DISTINCT role FROM employees ORDER BY role LIMIT 5;

🔹 4. Aggregating Data

Perform calculations on sets of rows.

SELECT COUNT(*) AS total, AVG(salary) AS avg_salary
FROM employees
GROUP BY role
HAVING AVG(salary) > 50000;

🔹 5. Joins

Combine data across tables.

SELECT e.name, d.department_name
FROM employees e
INNER JOIN departments d ON e.dept_id = d.id;

Types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, CROSS JOIN, SELF JOIN

🔹 6. Subqueries

Queries inside other queries.

SELECT name FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);

🔹 7. Set Operations

Combine multiple queries.

SELECT name FROM employees
UNION
SELECT name FROM contractors;

🔹 8. Indexing

Speed up queries.

CREATE INDEX idx_name ON employees(name);
DROP INDEX idx_name;

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🔹 9. Transactions

Ensure data consistency.

BEGIN TRANSACTION;
UPDATE employees SET salary = salary * 1.1 WHERE dept_id = 2;
COMMIT;

Transaction with Rollback :

START TRANSACTION;

-- Step 1: Insert a new product
INSERT INTO products (product_id, product_name, price)
VALUES (101, 'Smartphone', 599.99);

-- Step 2: Update the price of an existing product
UPDATE products
SET price = 15.00
WHERE product_name = 'T-Shirt';

-- At this point, the changes are not yet permanent.

-- You can now decide to either COMMIT or ROLLBACK.
-- Example of a successful transaction:
-- COMMIT; -- This would make the insert and update permanent.

-- Example of rolling back:
ROLLBACK; -- This discards all changes made since START TRANSACTION.

🔹 10. Views

Save reusable queries as virtual tables.

CREATE VIEW high_paid AS
SELECT name, salary FROM employees WHERE salary > 60000;

🔹 11. Data Modification (Conditions)

Filter with logic.

SELECT * FROM employees
WHERE role LIKE 'Dev%' AND salary BETWEEN 40000 AND 80000;

🔹 12. Common Table Expressions (CTE)

Reusable query blocks.

WITH DeptAvg AS (
  SELECT dept_id, AVG(salary) AS avg_salary
  FROM employees
  GROUP BY dept_id
)
SELECT * FROM employees e
JOIN DeptAvg d ON e.dept_id = d.dept_id
WHERE e.salary > d.avg_salary;

🔹 13. Window Functions

Powerful analytics.

SELECT name, salary,
RANK() OVER (ORDER BY salary DESC) AS rank
FROM employees;

🔹 14. Date and Time Functions

Work with timestamps.

SELECT GETDATE();            -- Current date
SELECT DATEDIFF(year, hire_date, GETDATE()) AS years_worked FROM employees;

🔹 15. Triggers

Automate actions.

CREATE TRIGGER trg_salary_update
AFTER UPDATE ON employees
FOR EACH ROW
INSERT INTO audit_log (emp_id, action, updated_at)
VALUES (NEW.id, 'Salary updated', NOW());

🔹 16. Conditional Logic

Handle conditional cases.

SELECT name,
CASE
  WHEN salary > 70000 THEN 'High'
  WHEN salary BETWEEN 40000 AND 70000 THEN 'Medium'
  ELSE 'Low'
END AS salary_band
FROM employees;

🎯 Key Takeaways

• SQL is versatile: from simple SELECT to advanced CTEs and Window Functions.
• Always use indexes wisely for performance.
• Transactions and Triggers help in maintaining data integrity.
• Conditional logic + aggregation gives deeper insights into your data.

✅ This SQL Cheat Sheet is not just for memorization but also for hands-on practice. The more queries you write, the better you’ll get at spotting patterns and using SQL effectively.

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