🔗 The hidden cost of MySQL defaults in Laravel apps

Most Laravel applications run on MySQL with default configurations designed for minimal hardware, not production workloads. Laravel's ecosystem creates specific database patterns - Eloquent queries across related tables, queue workers spawning connections, scheduled tasks during peak traffic - that these defaults can't handle efficiently.

Optimizing your Eloquent queries is important (check out Jonathan Reinink's course on Laravel Eloquent performance patterns), but don't forget the database server itself. MySQL has dozens of settings that can dramatically impact your Laravel app's performance and stability.

Common Performance Issues Laravel Developers Face

Pages load painfully slowly: Your homepage loaded in 300ms during development but now takes 4-5 seconds in production. Users abandon pages showing Eloquent relationships because the database reads from disk repeatedly instead of keeping data in memory.

API response times degrade over time: Mobile app endpoints that used to respond in 100ms now take 2-3 seconds as your user base grows, even though your code hasn't changed.

Search becomes unusable with growth: Product search worked perfectly with 1,000 items but times out with 100,000 items. Users abandon searches because results take too long to load.

Laravel jobs fail during peak traffic: Queue workers get stuck in "pending" status when MySQL runs out of connections. Background email jobs that worked fine in staging timeout when real users are browsing your site simultaneously.

Admin panels freeze during business hours: Your Laravel Nova interface loads quickly at 3 AM but becomes unusable when users are active. Simple user listings with joins across orders and payments take 15+ seconds with real data.

Checkout pages timeout during sales: Flash sales bring payment processing to a crawl. MySQL can't handle the burst of writes (inventory updates, new orders) while serving product pages to other customers.

Background jobs kill site performance: Your nightly analytics report makes the entire app slow. Large sorting operations interfere with regular queries because the database spills temporary tables to disk.

So how do you tackle these bottlenecks?