SQL Query Examples: A Complete Cheat Sheet for Developers

SQL Query Examples - Complete Cheat Sheet for Developers

SQL (Structured Query Language) is the universal language of databases. Whether you are building web apps, performing data analysis, or working on backend microservices, SQL is an absolutely essential skill.

This cheat sheet provides the most common and useful SQL query examples that you will use in real-world projects.

The Core SQL Operations (CRUD)

SQL databases store data in tables (similar to spreadsheets with rows and columns). The core operations you perform on these tables are known as CRUD:

Create (INSERT)
Read (SELECT)
Update (UPDATE)
Delete (DELETE)

1. SELECT: Retrieving Data

The SELECT statement is the most frequent command you will use. It is how you ask the database a question.

sql

-- Grab absolutely everything (Bad practice for production)
SELECT * FROM users;

-- Select specific columns (Best practice)
SELECT id, first_name, email FROM users;

-- Rename columns in the output using AS (Alias)
SELECT 
  id,
  first_name AS name,
  email AS contact_info
FROM users;

-- Select with a calculated column
SELECT 
  product_name,
  price,
  (price * 0.9) AS discounted_price
FROM products;

-- Return only unique values (Remove duplicates)
SELECT DISTINCT country FROM users;

-- Limit results for pagination (Show rows 21-30)
SELECT * FROM products
ORDER BY created_at DESC
LIMIT 10 OFFSET 20; 

2. WHERE: Filtering Data

Without a WHERE clause, you are querying every single row in the table, which is extremely slow. WHERE lets you filter the data based on conditions.

sql

-- Basic Math Comparisons
SELECT * FROM users WHERE age >= 18;
SELECT * FROM orders WHERE total_amount <= 100;

-- Multiple Conditions (AND / OR)
SELECT * FROM users 
WHERE status = 'active' AND created_at > '2026-01-01';

SELECT * FROM products
WHERE category = 'electronics' OR price < 20;

-- NULL Checks (You cannot use "= NULL")
SELECT * FROM users WHERE phone_number IS NULL;
SELECT * FROM users WHERE email IS NOT NULL;

-- Pattern Matching with LIKE (% is a wildcard)
SELECT * FROM users WHERE first_name LIKE 'John%';      -- Starts with "John"
SELECT * FROM users WHERE email LIKE '%@gmail.com';     -- Ends with "@gmail.com"

-- IN clause (Matches any value in the list)
SELECT * FROM users WHERE country IN ('US', 'UK', 'CA');

3. JOIN: Combining Tables

In a well-designed relational database, data is split across multiple tables. JOINs act as the glue to bring that data back together.

sql

-- INNER JOIN (Only returns rows that have matches in BOTH tables)
SELECT 
  users.first_name,
  orders.total_amount
FROM orders
INNER JOIN users ON orders.user_id = users.id;

-- LEFT JOIN (Returns ALL users, even if they haven't made an order)
SELECT 
  users.first_name,
  orders.id AS order_id
FROM users
LEFT JOIN orders ON users.id = orders.user_id;

-- Joining 3 Tables Together
SELECT 
  u.first_name,
  o.id AS order_id,
  p.product_name
FROM orders o
JOIN users u ON o.user_id = u.id
JOIN order_items oi ON oi.order_id = o.id
JOIN products p ON oi.product_id = p.id;

4. GROUP BY & Aggregates

Sometimes you don't need individual rows; you need a summary. Aggregate functions (COUNT, SUM, AVG) calculate totals, and GROUP BY organizes them into categories.

sql

-- Basic Aggregates
SELECT 
  COUNT(*) AS total_users,
  AVG(age) AS average_age,
  MAX(age) AS oldest_user
FROM users;

-- Grouping data by a category
SELECT 
  country,
  COUNT(*) AS user_count
FROM users
GROUP BY country
ORDER BY user_count DESC;

-- HAVING clause (Filters POST-aggregation. WHERE filters PRE-aggregation)
SELECT 
  user_id,
  SUM(total_amount) AS total_spent
FROM orders
GROUP BY user_id
HAVING SUM(total_amount) > 500;

5. Modifying Data (INSERT, UPDATE, DELETE)

These commands physically alter the data stored in the database.

Danger Zone!

Always use a WHERE clause with UPDATE and DELETE. If you run 'DELETE FROM users;' without a WHERE clause, you will delete every user in your database.

sql

-- INSERT a single row
INSERT INTO users (first_name, email, country)
VALUES ('John', 'john@example.com', 'US');

-- INSERT multiple rows at once
INSERT INTO products (name, price) VALUES
  ('Laptop', 999.99),
  ('Mouse', 24.99),
  ('Keyboard', 49.99);

-- UPDATE existing records
UPDATE users 
SET status = 'active', 
    updated_at = NOW()
WHERE email = 'john@example.com';

-- DELETE records based on a condition
DELETE FROM sessions 
WHERE expires_at < NOW();

Conclusion

Mastering SQL is a superpower. While Object-Relational Mappers (ORMs) like Prisma or TypeORM are great, understanding the underlying SQL queries they generate is what separates junior developers from senior engineers.

Bookmark this cheat sheet and refer to it the next time you need to write a complex database query!