Design a Monitoring and Diagnosis Visualization for a Video-Streaming Metric

Context

You are building an observability dashboard for a global consumer video product. Choose one critical streaming quality metric and design how you would visualize it to monitor health and diagnose issues.

Assume you have standard playback events (play_attempt, start, first_frame, rebuffer, stop), client metadata (device, OS, app version, network), and infrastructure metadata (CDN/POP, ISP, region). The dashboard should support both real-time monitoring and retrospective diagnosis.

Task

Pick one critical metric (e.g., start-failure rate, rebuffering ratio, time-to-first-frame, hours watched per user) and specify:

1. Primary time-series view
   • Time granularity (near real-time vs. daily); zoom behavior
   • Smoothing and seasonality handling
   • Anomaly detection and bands (baseline and confidence/prediction intervals)
2. Key breakdowns for diagnosis
   • Device/OS/app version
   • Network type/ISP
   • Geography (region/country/city)
   • Cohorts (new vs. returning, app release, experiment group)
3. Diagnostic visuals
   • Percentile bands where applicable (e.g., p50/p90/p99)
   • Geographic heatmap
   • Funnel from play attempt → start → watch ≥ X minutes
4. Interaction patterns
   • How users drill down, pivot dimensions, and compare baselines
5. Guardrail metrics
   • Which additional metrics you would co-display to prevent blind spots