How to Design a Frontend Error Monitoring System

Attach a global error handler using window.onerror or window.addEventListener with the error event type. This captures all unhandled exceptions thrown anywhere in the application including third-party scripts. Separately attach an unhandledrejection event listener to capture Promise rejections that are never caught. Without these global handlers, the majority of production errors are invisible to the engineering team.

JavaScript errors inside React component render functions cause the entire component tree to unmount, showing a blank page. Wrap major sections of the application in React Error Boundary components that implement componentDidCatch. When a rendering error occurs, the boundary catches it, logs it to your monitoring service, and renders a fallback UI for just that section. The rest of the application continues functioning normally.

A raw error message and stack trace is rarely sufficient to reproduce a bug. Before sending error reports, enrich them with the current user ID, their subscription tier, the current route, recent user actions as a breadcrumb trail, browser and operating system details, screen resolution, network connection type, and the application version. This context reduces the time to reproduce and fix errors from hours to minutes.

Production JavaScript is minified and bundled, making stack traces unreadable. Upload source maps to your error monitoring service during every deployment. Source maps translate minified file and line references back to the original source code file and line number. Never expose source maps publicly as they reveal your original source code. Upload them to the monitoring service directly and configure the server to serve them only to authenticated monitoring requests.

A single bug triggered by millions of users can flood your monitoring service with millions of identical error reports, making the important signal invisible in noise and exploding your monitoring costs. Configure error sampling to capture a percentage of identical errors after the first occurrence. Implement client-side deduplication to avoid sending the same error multiple times within a single session. Alert on error rate changes rather than raw error volume.

Associate every error with the exact application version that produced it. Tag each error report with the current deployment version or git commit hash. This allows you to immediately identify which deployment introduced a new error, verify that a fix actually eliminated the error after deployment, and track error trends across releases. Configure the monitoring service to automatically resolve errors that have not recurred after a new version deploys.

Session replay records a video-like reconstruction of exactly what a user did before an error occurred. The replay captures DOM mutations, network requests, console logs, and user interactions without recording actual screen video. When an error is reported, reviewing the session replay immediately reveals what sequence of actions triggered the bug. This eliminates the largest time sink in frontend debugging, which is reproducing the exact conditions of the error.

Not every error requires immediate attention. Define severity tiers based on impact. Critical errors affecting core user flows like checkout or login require immediate on-call alerts. Errors affecting secondary features are triaged in the next sprint. Configure alert thresholds based on error rate rather than absolute count so a spike in traffic does not trigger false alarms. Route alerts to the right team based on which part of the codebase produced the error.