Error Tracking Setup

Error tracking captures exceptions and runtime failures in your application so you can fix issues before they impact users at scale. This goes beyond logging errors locally; it centralizes, groups, and enriches errors with context such as stack traces, environment tags, and frequency. Effective error tracking accelerates debugging and helps teams prioritize fixes based on impact.

Set up a new application in your dashboard and copy the API key for use in your code.

Use the SDK tailored to your language or framework (e.g., JavaScript, Python, Java, Ruby). Many frameworks provide native plugins or wrappers for seamless integration.

Add the initialization snippet in your application entry point with your API key and environment name.

Configure user context (e.g., user ID or session) to link errors to user sessions.

Define ignore lists for benign or expected errors (like custom validation failures) to reduce noise in your dashboard.

Configure alert thresholds (error rate spikes, new error types) and choose notification channels (Slack, email, webhook) for actionable alerts.

Catch and report uncaught exceptions at the top level of your process or application framework.

Wrap API handlers or controllers to capture errors with request context (URL, payload, headers).

Ensure that exceptions in scheduled jobs or workers are reported just like web requests.

Integrate with error handling in ORM libraries, messaging clients, or SDKs if available.

For web applications, enable error listeners that capture unhandled exceptions, resource loading errors, and promise rejections.

Every integration point ensures that no meaningful error goes unnoticed and each error carries enough context for debugging.

Introduce known errors (e.g., undefined variable or forced throw) in a staging environment to verify capture.

Inspect captured errors to confirm stack traces, environment tags, and request/user context are present and accurate.

Send repeated errors of the same type to ensure they group correctly and don't generate duplicate entries.

Force a high error rate to trigger configured alerts and confirm notifications arrive where expected.

Simulate high‑volume error bursts to verify sampling or rate limiting settings behave as intended and don't drop critical errors.

Testing confirms that errors are captured with context, grouped sensibly, and alerts fire without overwhelming teams.