Performance Optimization

Overview

Optimize performance with a measure-first approach. Never optimize without data. Establish baselines, identify bottlenecks, apply targeted fixes, and verify improvements.

When to Use

• Performance requirements are stated or implied
• You suspect a regression after recent changes
• Page load or interaction speed is noticeably slow
• Bundle size has grown beyond acceptable thresholds
• Database queries are slow under load

Core Web Vitals Targets

Metric	Good	Needs Improvement	Poor
LCP (Largest Contentful Paint)	< 2.5s	2.5s - 4s	> 4s
FID (First Input Delay)	< 100ms	100ms - 300ms	> 300ms
CLS (Cumulative Layout Shift)	< 0.1	0.1 - 0.25	> 0.25
INP (Interaction to Next Paint)	< 200ms	200ms - 500ms	> 500ms

Process

Step 1: Establish Baseline

Measure current performance before making any changes:

• Run Lighthouse audit
• Record Core Web Vitals
• Profile the specific operation in question
• Document the baseline numbers

Step 2: Identify Bottlenecks

Analyze measurements to find the actual bottleneck:

• Is it network (slow API calls, large assets)?
• Is it rendering (layout thrash, excessive repaints)?
• Is it JavaScript (long tasks, excessive computation)?
• Is it data (N+1 queries, missing indexes, large payloads)?

Step 3: Apply Targeted Fix

Fix the identified bottleneck:

• One change at a time
• Prefer the simplest fix that addresses the root cause
• Document what was changed and why

Step 4: Verify Improvement

Measure again after the fix:

• Compare against baseline
• Confirm the bottleneck is resolved
• Check for regressions in other areas

Common Anti-Patterns

Frontend

• Unnecessary re-renders in component frameworks
• Large unoptimized images or assets
• Render-blocking JavaScript or CSS
• Missing lazy loading for below-fold content
• Excessive DOM manipulation

Backend

• N+1 database queries
• Missing database indexes for common query patterns
• Synchronous processing of independent operations
• Unbounded result sets without pagination
• Missing caching for frequently accessed data

Bundle

• Importing entire libraries when only parts are needed
• Duplicate dependencies in the bundle
• Missing code splitting for route-based chunks
• Unminified or uncompressed assets in production

Common Rationalizations

Rationalization	Reality
"Premature optimization is the root of all evil"	That quote is about guessing, not about measuring and fixing known bottlenecks.
"It is fast enough on my machine"	Your machine is not your users' machines.
"We can optimize later"	Performance debt compounds like technical debt.

Verification

• Baseline measurements recorded before changes
• Bottleneck identified with evidence
• Targeted fix applied
• Post-fix measurements show improvement
• No regressions in other performance metrics

Anti-Rationalization Table

Excuse	Counter
"Premature optimization is the root of all evil"	That quote is about guessing, not about measuring and fixing known bottlenecks.
"It's fast enough on my machine"	Your machine is not your users' machines.
"We can optimize later"	Performance debt compounds like technical debt.
"The Lighthouse score is good enough"	Lighthouse is a lab metric. Real user metrics tell the actual story.
"One optimization should fix everything"	Performance issues are often multi-factor. Measure after each fix to find remaining bottlenecks.