Observability

Decision Framework: What to Instrument

Golden Signals (prefer for services): Latency, Traffic, Errors, Saturation

• •RED (request-scoped): Rate, Errors, Duration
• •USE (resource-scoped): Utilization, Saturation, Errors

Pick RED for microservices, USE for infrastructure. Don't mix.

Metric Design Opinions

• •Always use histograms over summaries for latency -- histograms are aggregatable, summaries are not
• •Bucket defaults [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2, 5] cover most HTTP services
• •Exclude /health and /metrics endpoints from SLI calculations
• •Use recording rules for any query used in alerts or dashboards -- never put raw PromQL in alerts
• •Label cardinality kills Prometheus: never use user IDs, request IDs, or unbounded values as labels

Service Tier Classification

Assign tiers before writing SLOs. Tier drives alert routing and error budget policy.

SLO Framework

Error Budget Policy (non-negotiable escalation)

Release Decision Matrix

Burn Rate Alert Thresholds

Multi-window burn rate is the only correct SLO alerting pattern. Single-window alerts produce false positives or miss slow degradation.

Progressive SLO Rollout

Start at 99.0% for 1 month baseline, then tighten: 99.5% (2 months) -> 99.9% (3 months) -> 99.95% (ongoing). Never set SLO tighter than current measured reliability.

SLO Templates

API service: availability (99.9% over 30d) + latency (95% of requests < 500ms over 30d) Data pipeline: freshness (99% batches within 30 min over 7d) + completeness (99.95% records processed over 7d)

Distributed Tracing Strategy

Sampling Decisions

• •Dev/staging: 100% sampling
• •Production low-traffic (<1k rps): 10-50% probabilistic
• •Production high-traffic (>10k rps): 1% probabilistic or rate-limit to ~100 traces/sec
• •Always use ParentBased sampler so child spans follow parent's decision
• •Force-sample all errors and high-latency requests regardless of probabilistic rate

Context Propagation

• •Use W3C traceparent header (not B3 or Jaeger-native) for new systems
• •Always inject trace_id into structured logs for correlation
• •Propagate context through async boundaries (queues, event buses) explicitly

Backend Choice

• •Tempo (Grafana stack): prefer when already using Grafana; object-storage backed, cheap at scale
• •Jaeger: prefer when you need standalone deployment or Elasticsearch integration
• •Both support OTLP -- always send via OpenTelemetry Collector, never direct from app to backend

Alerting Opinions

• •Alert on symptoms, not causes -- alert on error rate, not "pod restarted"
• •Severity levels: critical (pages), warning (tickets), info (dashboard only)
• •Every alert must have a runbook link in annotations
• •for: duration: critical >= 2m, warning >= 5m, info >= 15m -- prevents flapping
• •Route critical to PagerDuty, warning to Slack channel, info to dashboard only

Stack Preferences

Gotchas

• •Prometheus rate() requires at least 2 data points in the window -- use [5m] minimum with 15s scrape interval
• •histogram_quantile is an estimate; accuracy degrades with poor bucket choices
• •OTel Collector batch processor default timeout is 200ms -- increase to 5-10s for production to reduce export overhead
• •Grafana dashboards without variables become unmaintainable past 3 services
• •Never scrape intervals faster than 10s in production -- it causes storage and CPU issues
• •Alertmanager grouping: group by alertname, namespace, service -- too broad silences everything, too narrow floods on-call