TL;DR: Large frontend apps accumulate performance debt through three mechanisms: unused JavaScript (dependencies you don’t need), inefficient rendering (re-rendering too often), and poor state management (cascading updates). Measure with real tools, fix systematically, and automate budgets into CI.

It Was Fast at the Beginning

Six months ago, your app loaded in 1.2 seconds. Interaction was instant. The lighthouse score was 94.

Now it takes 3.5 seconds. Scroll jank is visible when you apply a filter. The score is 62. You didn’t add features that seem that heavy, yet the app feels sluggish.

This isn’t unusual. It’s what happens when a small application scales without intentional performance work. Each decision seemed reasonable at the time: add a date picker library, use a UI framework, load a charting library for one page. None of these individually breaks performance. Together, they degrade the user experience significantly.

The problem: performance debt compounds faster than code complexity debt.

Why Performance Degrades Over Time

It Starts Invisibly

Early on, a 50 KB library matters. You notice it. You consider alternatives.

At 2 MB (typical for a mid-size app), a 50 KB addition barely registers. It’s 2.5%. The bundle is already large. One more dependency seems harmless.

By 4 MB, you stop thinking about it. You just add what you need and assume it will be fine.

This is how apps become bloated. Not through bad decisions, but through the normalization of bloat.

JavaScript Execution Time Becomes Dominant

At small scale, JavaScript parsing and execution is negligible. A 200 KB bundle parses in 50 ms on a decent device.

At 2.5 MB, parsing takes 250—400 ms (depends on device speed). Execution takes another 200—500 ms. These aren’t quick times.

On slower devices (which represent 40%+ of real users), JavaScript parsing and execution can exceed 1.5 seconds. Your actual feature code might run in 50 ms, but you’re paying 1.5+ seconds before the app is interactive.

Rendering Becomes Inefficient

Small apps can often re-render aggressively without consequence. A component unnecessary re-renders every 100 ms? Fine—the user won’t notice 16 ms of jank.

Larger apps have hundreds of components. If 50 of them re-render every time state changes, that’s 50+ DOM reconciliation cycles. Multiply that by events triggered throughout a session, and you’re looking at significant main-thread blocking.

JavaScript Bloat and Dependency Creep

The Cost of Convenience

Most bloat comes from dependencies you use, but not efficiently.

moment.js          67 KB   (for date formatting)
lodash             70 KB   (for a few utility functions)
chart.js         140 KB   (used by one admin page)
date-fns           40 KB   (alternative to moment, but coexists)
bootstrap         200 KB   (CSS included in JS bundle)
uuid               10 KB   (for ID generation)

None of these are “bad.” Each serves a purpose. But the cumulative weight is 537 KB—often before gzip, and much of it never executed on typical user journeys.

Transitive Dependencies Multiply the Problem

You install a UI library for a modal component. It depends on react-dom. React-dom depends on utilities. Those utilities depend on other polyfills. Suddenly, installing “one thing” adds 5 transitive dependencies and 300 KB.

You can’t see this easily unless you inspect. Most engineers don’t. They assume installation size ≈ actual impact.

Tree-Shaking Doesn’t Always Work

Modern bundlers promise to remove unused code. In practice, tree-shaking is fragile:

  • CommonJS modules (not ES6) don’t tree-shake well
  • Dynamic imports confuse bundlers (they can’t know what might be used)
  • Some libraries explicitly prevent tree-shaking (side effects)

You might think you’re shipping 70 KB of lodash, but the actual bundled code is 67 KB because tree-shaking removed the unused parts. You might also be shipping 67 KB when only 15 KB is actually imported. It depends on how well the library is written.

Rendering Inefficiencies

Prop Drilling and Re-Render Cascades

A component receives props and passes them through 5 layers to a grandchild. When those props change, all 5 layers re-render, even the intermediate ones that don’t use the data.

Multiply this across your app (data flows through layout → container → page → section → card → component), and re-renders become cascade events. Users see jank when scrolling, typing, or navigating.

Virtual DOM Cost

Frameworks like Angular, React, and Vue use virtual reconciliation. This is efficient for app code. But the reconciliation itself is work. For large trees with many components, reconciliation can take 50—200 ms even if DOM changes are small.

On a 60 FPS target (16.7 ms per frame), 50 ms blocks 3+ frames. Users see visible lag.

Unoptimized Change Detection

In Angular, default change detection runs on every async event (click, timer, HTTP response). Without OnPush strategy, even unaffected components get checked.

Imagine 500 components, each with a change detection cycle that’s 0.1 ms. It’s only 50 ms, but if this fires for every keystroke and every timer tick, you’re blocking frames.

State-Driven Re-Render Problems

Global State Updates Trigger Everything

A centralised state store makes sense for data consistency. But it creates a problem: updating anything notifies everything listening.

Change a filter flag? The entire product list re-renders, even the components that don’t care about filters. Done 50 times as a user types, that’s 50 unnecessary re-renders.

Well-designed stores (with fine-grained selectors) help, but many projects don’t invest in this.

Implicit Dependencies

A component listens to user state. User state listens to a side effect. The side effect is triggered by a route change. The route is triggered by a UI interaction.

When something slow happens during user state update, tracing it back to the original cause is hard. You don’t know if it’s the component re-render, the state selector, the side effect, or all three.

How to Diagnose Performance Issues

Establish a Baseline

Use Lighthouse (lab), WebPageTest (lab + real conditions), and real-user monitoring (RUM).

In Angular, check the browser’s DevTools Performance tab:

  • Record a user interaction (type, click, scroll)
  • Look for long tasks (>50 ms)
  • Check main-thread blocking
  • Look for unexpected layout thrashing

Bundle Analysis

Run a bundle analyzer:

# Angular
ng build --configuration production && source-map-explorer dist/**/*.js

# Generic
npm install --save-dev webpack-bundle-analyzer

Look for:

  • Libraries taking > 50 KB (gzipped)
  • Unexpected dependencies in the bundle
  • Multiple versions of the same library

Check What Actually Runs

Just because code is in the bundle doesn’t mean it executes on your critical user journey.

Add performance marks at key points:

performance.mark('app-start');
// ... your code ...
performance.mark('app-end');
performance.measure('app', 'app-start', 'app-end');
console.log(performance.getEntriesByName('app')[0].duration);

Time the expensive parts. You might find that 80% of time is spent in code you could defer or optimize.

Performance Audit Checklist

  • Measure Time to Interactive (TTI) on real devices (not just localhost)
  • Check if unused dependencies are in bundle (run analyzer)
  • Identify which components re-render most frequently (DevTools Profiler)
  • Check if state updates cascade (log state changes and re-renders)
  • Measure JavaScript parse/execute time on a slow device
  • Verify images are optimized and use modern formats (AVIF/WebP)
  • Check if third-party scripts (analytics, ads) block rendering
  • Verify lazy loading is working (routes, heavy components)

How to Recover Performance

1. Fix Bundle Size First

Remove or replace heavy dependencies:

Audit aggressively. If a library is > 50 KB, question it. Can you use a lighter alternative? Can you defer loading it?

Example: Moment.js is 67 KB. Date-fns is 13 KB for the same features. Replacing it saves 54 KB.

Lazy load heavy features. If a charting library is only used in admin pages, don’t bundle it globally. Load it dynamically.

// Angular example: lazy load a component with its dependencies
const AdminDashboard = () => import('./admin/dashboard.component');

// In routing
routes: [
  {
    path: 'admin',
    loadComponent: () => import('./admin/admin.component'),
    loadChildren: () => import('./admin/admin.routes'),
  }
];

2. Fix Rendering Efficiency

Use OnPush change detection in Angular (or equivalents in other frameworks):

@Component({
  selector: 'app-product-card',
  templateUrl: './product-card.component.html',
  changeDetection: ChangeDetectionStrategy.OnPush,
})
export class ProductCardComponent {
  @Input() product: Product;
}

This tells Angular: only re-render this component if its inputs change, not on every global change event.

Memoize selectors:

// Instead of:
products$ = this.store.select(state => state.products.items.filter(...));

// Use a memoized selector:
products$ = this.store.select(selectFilteredProducts);

Memoized selectors prevent re-computation if inputs haven’t changed.

3. Fix State Shape

Normalize state. Instead of:

{
  products: [
    { id: 1, name: 'Shirt', category: { id: 72, name: 'Clothing' } },
    { id: 2, name: 'Pants', category: { id: 72, name: 'Clothing' } },
  ]
}

Use:

{
  products: [1, 2],
  byId: {
    1: { id: 1, name: 'Shirt', categoryId: 72 },
    2: { id: 2, name: 'Pants', categoryId: 72 },
  },
  categories: {
    72: { id: 72, name: 'Clothing' }
  }
}

Normalized state is smaller and updates don’t cascade through nested objects.

4. Automate Performance Budgets

Add CI checks to prevent regressions:

// package.json
{
  "bundlesize": [
    {
      "path": "./dist/**/*.{js,css}",
      "maxSize": "200 kb"
    }
  ]
}

npm install --save-dev bundlesize
npm run build && npx bundlesize

This is critical. Without automation, performance slowly degrades again.

Conclusion

Large frontend apps feel slow because performance debt accumulates invisibly. A 50 KB library here, unnecessary re-renders there, a few deeply nested components—each is small. Together, they add seconds of latency.

Recovery is systematic:

  1. Measure with real tools (not guesses)
  2. Identify the biggest problems (usually bundle size or rendering)
  3. Fix in priority order (biggest wins first)
  4. Automate budgets to prevent regression

Start with bundle analysis. Remove unused dependencies. Then fix rendering inefficiencies. Most performance problems aren’t mysterious—they’re the visible result of accumulated, unchecked growth.

The good news: you already wrote the code. You’re not rewriting the app. You’re just being intentional about what you ship and how it runs. A week of performance work now saves months of user frustration later.