Applications of MySQL

WordPress · Facebook · YouTube · Booking — anywhere rows live.

Visual explanation diagram · click steps to walk through it

Definition

MySQL powers a huge chunk of the public web: WordPress (40% of all websites), Facebook (over a million MySQL instances), YouTube, Booking.com, Shopify, GitHub, Twitter (before MySQL+Manhattan), Uber's early years, and most LAMP-stack apps.

How It Works

It's the default relational database for the LAMP (Linux + Apache + MySQL + PHP) and LEMP (NGINX) stacks; every PHP CMS targets it as the primary database.

Example

sql
SELECT 'Hello, MySQL!' AS greeting;

Expected Output

(Run in MySQL Workbench or `mysql` CLI — values depend on your data.)

Why It Matters

When you ask 'is MySQL good enough for X?', the answer is almost always 'yes' — these companies prove it scales to billions of rows.

Key Takeaways

WordPress runs on MySQL — 40% of all websites globally.
Facebook runs millions of MySQL instances behind their TAO graph layer.
Booking.com, Shopify, GitHub — all heavy MySQL users.
LAMP / LEMP — MySQL is the M.
Banking, ecommerce, ticketing, CMSes — all common.

Interview Questions

Practice Questions

Inspect a WordPress database — note the wp_posts, wp_users, wp_options tables.
Read the Facebook engineering blog post on their MySQL sharding strategy.
Set up a tiny CMS (WordPress, Ghost) and watch the SQL it generates via the slow-query log.

Pro Tips

Inspect the schemas of open-source apps you use — best free curriculum on real-world MySQL design.
Read MySQL post-mortems from large companies — they teach you what fails at scale.
Pair MySQL with a search engine (Elastic, Meili) for full-text — MySQL's built-in FT is slow at scale.

// Try It Yourself · MySQL Playground

in-browser SQLite · no network

Run any SELECT, INSERT, JOIN or GROUP BY against the seeded users · products · orders tables — your changes are sandboxed and reset whenever you click Reset DB.

// editor · Ctrl + Enter to run

// result

Click Run to see results here.

// see seed schema (3 tables · 8 users · 8 products · 12 orders)

users (id, name, email, country, age, created_at)
products (id, name, price, stock, category)
orders (id, user_id → users.id, product_id → products.id, qty, total, created_at)

// Challenges0 / 6 solved

Each challenge is auto-graded — write the SQL, click Submit answer, and we'll compare your result-set to the canonical one.