SRE Dashboards

When to Use This Skill

• Defining service-level dashboards for production systems
• Tracking SLO health and error-budget burn
• Creating incident command-center views
• Standardizing dashboard patterns across teams

Prerequisites

• Metrics pipeline (Prometheus, OpenTelemetry, or vendor equivalent)
• Logs/traces linked to services and environments
• Agreed service taxonomy (team, service, tier, environment)

Dashboard Architecture

1. Executive Reliability View: SLO attainment, incident counts, MTTR trends.
2. Service Health View: RED/USE metrics, dependency health, release markers.
3. Deep-Dive View: Per-endpoint latency, resource saturation, error categories.

• Are customers impacted?
• What changed?
• Where is the bottleneck?

Core SRE Panels

Golden Signals

• Latency: p50/p95/p99 request duration by endpoint
• Traffic: request throughput and queue depth
• Errors: 5xx rate, failed jobs, timeout ratio
• Saturation: CPU, memory, disk I/O, thread/connection pool exhaustion

SLO Panels

• Current SLI value (rolling windows: 5m, 1h, 24h, 30d)
• Error-budget remaining (%)
• Burn-rate panels (fast and slow windows)
• Multi-window burn alert status

Change Correlation

• Deployment markers and config-change annotations
• Feature flag state overlays
• Upstream/downstream dependency error rates

Example PromQL Snippets

# API error rate (%)
100 * sum(rate(http_requests_total{status=~"5.."}[5m]))
  / sum(rate(http_requests_total[5m]))

# p95 latency by route
histogram_quantile(0.95,
  sum by (le, route) (rate(http_request_duration_seconds_bucket[5m]))
)

# Fast burn rate (5m / 1h)
(
  sum(rate(http_requests_total{status=~"5.."}[5m]))
  / sum(rate(http_requests_total[5m]))
)
/
(
  sum(rate(http_requests_total{status=~"5.."}[1h]))
  / sum(rate(http_requests_total[1h]))
)

Operational Guidelines

• Use consistent color semantics (green=healthy, yellow=degrading, red=breach)
• Label units explicitly (ms, req/s, %, cores)
• Default time windows to incident-friendly ranges (15m, 1h, 6h, 24h)
• Minimize panel count per dashboard to reduce cognitive load
• Add runbook links directly in panel descriptions

Troubleshooting

Panel appears flat or empty

• Verify label cardinality and filters (

  service

  ,

  env

  ,

  region

  )
• Confirm scrape/ingest latency is within expected range
• Check metric rename regressions after instrumentation updates

High cardinality slows dashboards

• Aggregate by stable dimensions (

  service

  ,

  route_group

  ) instead of raw IDs
• Use recording rules for expensive percentile and ratio queries
• Split deep-dive dashboards from NOC summary dashboards

Related Skills

• prometheus-grafana - Dashboard implementation and PromQL
• opentelemetry - Standardized telemetry instrumentation
• alerting-oncall - Reliability alert routing and escalation
• agent-observability - AI workload reliability telemetry