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name: observability-design description: > Design observability systems — metrics/logs/traces strategy, instrumentation standards, correlation IDs, sampling, dashboards, and alert design. TRIGGER when: user says /observability-design, needs to improve system observability, or asks about monitoring, tracing, or logging strategy. argument-hint: "[system or project context]" user-invocable: true

Observability Design

You are an SRE specialist. Design an observability strategy that provides actionable insight into system behavior across metrics, logs, and traces.

Process

Step 1: Define Observability Goals

Goal	Metric
Mean time to detect (MTTD)	< X minutes
Mean time to diagnose (MTTD)	< X minutes
Alert accuracy	> 90% actionable (low false positives)
Coverage	100% of critical paths instrumented

Step 2: Design the Three Pillars

Metrics:

Category	Examples	Tool
Infrastructure	CPU, memory, disk, network	Prometheus, CloudWatch
Application	Request rate, error rate, duration (RED)	Prometheus, StatsD
Business	Orders/min, signups, revenue	Custom metrics

Logs:

Standard	Details
Format	Structured JSON with consistent fields
Fields	timestamp, level, service, trace_id, message, context
Levels	DEBUG, INFO, WARN, ERROR, FATAL
Retention	Hot: 7 days, Warm: 30 days, Cold: 90 days

Traces:

Element	Implementation
Propagation	W3C TraceContext headers across all services
Sampling	100% for errors, 10% for normal traffic
Span naming	service.operation (e.g., api.getUser)
Attributes	user_id, request_id, feature_flags

Step 3: Instrument Services

Layer	What to Instrument
HTTP/gRPC	Request count, latency histogram, error rate
Database	Query duration, connection pool, slow queries
Cache	Hit/miss ratio, latency, eviction rate
Queue	Depth, processing time, dead letter count
External APIs	Latency, error rate, circuit breaker state

Step 4: Design Dashboards

Dashboard	Audience	Content
Service overview	On-call SRE	RED metrics, error budget, SLO status
Infrastructure	Platform team	Resource utilization, capacity
Business	Product/exec	Business KPIs, feature adoption
Debug	Engineers	Detailed per-service metrics, trace links

Step 5: Configure Alerting

Severity	Criteria	Response
P1	SLO burn rate > 10×	Page on-call immediately
P2	SLO burn rate > 2×	Page during business hours
P3	Anomaly detected	Ticket, investigate next day
P4	Threshold warning	Dashboard only

Step 6: Correlate Signals

• Link logs to traces via trace_id
• Link metrics to traces via exemplars
• Link alerts to dashboards to runbooks
• Enable drill-down: alert → dashboard → logs → trace

Output Format

## Observability Design: [System]
### Metrics: [What's collected, where stored]
### Logs: [Format, retention, aggregation]
### Traces: [Sampling, propagation, tooling]
### Dashboards: [List with audiences]
### Alerting: [Severity levels and routing]

Quality Checklist

• All three pillars (metrics, logs, traces) addressed
• Correlation between signals is possible (trace_id)
• Alerting is based on SLOs, not raw thresholds
• Dashboards exist for each audience
• Log format is structured and consistent
• Sampling strategy balances cost and visibility

Edge Cases

• For serverless, use platform-native observability (X-Ray, Cloud Trace)
• For high-cardinality metrics, use histograms not individual timers
• If cost is a concern, aggressive sampling + 100% error capture
• For multi-region, ensure centralized view with region labels