error-tracking

Error tracking requires initializing a monitoring provider like Sentry, Datadog, or New Relic at application startup, then wrapping critical code paths with try/catch blocks to capture exceptions. This ensures all errors are logged centrally rather than lost to console output, giving you visibility into production issues.

Console.error logs to stdout and is lost unless actively monitored. Proper error tracking sends exceptions to a centralized service with context: stack traces, request metadata, user info, and performance data. This enables alerting, error grouping, and root cause analysis across your entire service.

Yes. Error tracking works with both FastAPI (Python) and Express (TypeScript) through standardized patterns: initialize the monitoring library in your app startup, wrap route handlers and background jobs with exception capture, and use structured logging to add context to each error event.

Monitoring solutions like Sentry, Datadog, and New Relic support performance instrumentation through spans and tracing. Beyond capturing errors, you instrument database queries, API calls, and background jobs to measure latency and identify bottlenecks, giving a complete view of service health.

Start with critical paths—route handlers, external API calls, and database operations—to establish baseline coverage. The core principle is that all errors must be captured and sent to your monitoring provider, not silently logged or dropped. Expand instrumentation based on performance insights.

You need a monitoring provider account (Sentry, Datadog, New Relic, or equivalent), an API key or DSN from that provider, and a dependency installed in your project. Then initialize the library in your app's startup sequence before your service begins handling requests.