A Guide to Building Pricing Uptime Dashboards from Metrics and Logs

To create an internal uptime dashboard for a pricing-rule rollout, gather recent health metrics and structured logs, then categorize them as green, yellow, or red based on service performance. Access these data points directly without relying on non-existent log streams. The dashboard should reflect signal quality over noise. A red indicator should signal that the marketplace's pricing path deviates from an objective, not that a single request experienced latency.

For accurate rate measurements, use a five-minute window and show the failure count alongside the percentage. A small error rate in a few requests warrants different attention compared to the same rate across a large number of requests. Consolidate the dashboard's components—flag, metrics, logs, and error groups—into a single REST API, key, and bill to reduce credential sprawl and simplify reconciliation.

While this simplifies the setup, you must still implement polling, aggregation, and alert delivery. When evaluating a bounded failure scenario, such as a new pricing rule rollout, base the dashboard on service status metrics and logged health information rather than relying solely on process uptime or exception rates. The key is to ensure that the rollout state and user-visible health align within the decision window.

Establish a minimum sample count and absolute failure guard to adjust for capacity planning changes. Shorter windows enable quicker rollbacks, while longer windows minimize flapping. The optimal window depends on the rollout's error-budget policy. Silence also serves as a signal; Infrai lacks synthetic probes or heartbeat monitors, so pair the dashboard with health checks or a dead-man's switch to handle cases where a job fails to start.

Implement a polling mechanism to query metrics and logs through dedicated endpoints. Avoid relying on speculative query fields; instead, retrieve the current request schemas from public discovery parameters. The provided Go code example demonstrates how to authenticate a metrics query, handle rate limits, and validate JSON responses.

Continuously poll and aggregate data to generate snapshots, ensuring the UI adheres to a deterministic rule that reviewers can audit.