sentry-setup-metrics

To set up Sentry custom metrics, you initialize the Sentry SDK and use Sentry.metrics to emit counters, gauges, and distributions directly in your JavaScript or Python code. This instruments your application to track performance and system health.

Sentry metrics use counters to track cumulative values, gauges to record instantaneous snapshots, and distributions to capture statistical data like latency. You emit these metrics with attributes to observe application behavior and monitor endpoint performance.

Yes, you can filter sensitive data when instrumenting app performance with Sentry metrics. The setup includes filtering options that suppress sensitive data before emitting metrics with attributes from your JavaScript or Python service.

Yes, Sentry metrics instrumentation works for both JavaScript and Python projects. It demonstrates initialization and usage of Sentry.metrics in code for both environments to monitor API usage, latency, and error rates across a service.

The best way to monitor API latency is by instrumenting your application with Sentry custom metrics. By emitting distributions for latency and counters for error rates with attributes, you can observe endpoint performance and system health across your service.