Performance Fixes That Actually Move the Needle
Stop optimizing the wrong things. Learn how to identify real performance bottlenecks and make improvements that users actually notice.
Measure first, optimize second. Most performance issues are in 20% of the code—find that 20%.
The Optimization Trap
Developers love optimizing. It’s satisfying to shave milliseconds off a function or reduce bundle sizes by a few kilobytes.
But here’s the thing: most optimizations don’t matter.
If you’re optimizing code that runs once on page load and takes 50ms, cutting it to 25ms won’t change user experience. But if you’re ignoring a database query that takes 2 seconds on every request… that’s the real problem.
How to Find What Actually Matters
Profile Before You Optimize
Never guess where the bottleneck is. Use profiling tools:
- Frontend: Chrome DevTools, Lighthouse, WebPageTest
- Backend: Application Performance Monitoring (APM) tools, database query analyzers
- General: Flame graphs, trace analysis
The 80/20 Rule Applies Here
In most codebases, 80% of performance problems come from 20% of the code. Find that 20%.
Common culprits:
- N+1 database queries
- Unindexed database columns
- Blocking the main thread with heavy computations
- Unnecessary re-renders in frontend frameworks
- Missing caching for expensive operations
Measure the User Experience
Technical metrics are helpful, but user-perceived performance is what matters:
- Time to First Byte (TTFB): How long until the server responds?
- First Contentful Paint (FCP): How long until users see something?
- Time to Interactive (TTI): How long until they can use it?
- Core Web Vitals: LCP, FID, CLS
High-Impact Fixes We Commonly Make
Database Optimization
- Adding missing indexes
- Rewriting inefficient queries
- Implementing connection pooling
- Adding query result caching
Frontend Performance
- Code splitting and lazy loading
- Image optimization and lazy loading
- Reducing bundle sizes
- Caching static assets properly
Architecture Changes
- Adding CDN for static assets
- Implementing proper caching layers
- Moving heavy operations to background jobs
- Database read replicas for read-heavy applications
When Not to Optimize
Sometimes the answer is “don’t.” Consider:
- Is this actually causing problems for users?
- Will the optimization introduce complexity?
- Is the current performance “good enough”?
Premature optimization really is the root of a lot of evil.
Is your app slow? Let’s talk about what’s actually slowing it down.