Laravel Performance Optimization: Caching Strategies That Actually Work

Caching is the most impactful performance optimization you can make, but it's also the most misunderstood. Poorly implemented caching creates stale data bugs that are harder to fix than the performance problems they solved.

The Cache Hierarchy

Think of caching in layers. Application config and route caching (php artisan optimize) should always run in production — it's free performance. Query result caching stores database results in memory. Full response caching stores entire HTTP responses. Each layer has different tradeoffs between speed and freshness.

Smart Cache Invalidation

The hardest problem in caching is knowing when to clear it. Use Laravel model events to automatically invalidate related caches: when a blog post is updated, clear its individual cache, the blog listing cache, and the RSS feed cache. Use cache tags for group invalidation: Cache::tags(['blogs'])->flush().

Cache-Aside Pattern

The most common pattern: check the cache first, and if it's a miss, fetch from the database and store in cache. Laravel's Cache::remember() does this elegantly: Cache::remember('key', 3600, fn() => DB::table('posts')->get()). Set TTL based on how stale the data can be — 5 minutes for a blog listing, 1 hour for static pages.

When NOT to Cache

Don't cache personalized data (user dashboards, cart contents) — the cache hit rate will be terrible. Don't cache data that changes every request (CSRF tokens, real-time notifications). Don't cache in development — it makes debugging miserable. Use Cache::store('array') in tests to keep them fast and isolated.

Measuring Cache Effectiveness

Track cache hit rates, not just response times. A cache with a 10% hit rate is wasting memory and adding complexity. Use Cache::getStore() metrics or add simple counters to measure hits vs. misses. Aim for 80%+ hit rates on your cached endpoints.

Start by caching your most expensive queries and most-visited pages. Profile first with Laravel Debugbar, then cache strategically based on actual bottlenecks — not assumptions.