Enhancing GBIM Observability with Correlation IDs and k6 Dashboards
This technical update details significant improvements to the observability of the GBIM application, focusing on both backend and frontend monitoring capabilities. The primary objective was to ensure that operational data relevant to application workflows is effectively captured in Prometheus, Grafana, Sentry, Google Analytics 4 (GA4), and k6 dashboards. Key implementations include the introduction of custom Prometheus metrics for critical business flows such as user registration, account activation, and admin verification. These metrics provide detailed outcomes like success rates and specific error types, moving beyond generic HTTP status codes. Additionally, end-to-end correlation IDs were integrated via the X-Correlation-ID header, enabling seamless tracing of requests from the frontend to backend logs for faster debugging. Frontend analytics were standardized using a GA4 helper that strictly validates environment conditions to prevent data leakage from local development setups. Finally, the configuration of k6 jobs to utilize Prometheus remote write resolved previous issues where performance test dashboards remained empty. These changes collectively strengthen the monitoring stack, ensuring comprehensive visibility into system performance and user activity.
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Enhancing GBIM Observability with Correlation IDs and k6 Dashboards
This technical update details significant improvements to the observability of the GBIM application, focusing on both backend and frontend monitoring capabilities. The primary objective was to ensure that operational data relevant to application workflows is effectively captured in Prometheus, Grafana, Sentry, Google Analytics 4 (GA4), and k6 dashboards. Key implementations include the introduction of custom Prometheus metrics for critical business flows such as user registration, account activation, and admin verification. These metrics provide detailed outcomes like success rates and specific error types, moving beyond generic HTTP status codes. Additionally, end-to-end correlation IDs were integrated via the X-Correlation-ID header, enabling seamless tracing of requests from the frontend to backend logs for faster debugging. Frontend analytics were standardized using a GA4 helper that strictly validates environment conditions to prevent data leakage from local development setups. Finally, the configuration of k6 jobs to utilize Prometheus remote write resolved previous issues where performance test dashboards remained empty. These changes collectively strengthen the monitoring stack, ensuring comprehensive visibility into system performance and user activity.
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