observability-distributed-tracing-and-ebpf-strategy

Observability Distributed Tracing And eBPF Strategy

Use When

• Root cause analysis is slow in distributed systems
• Service mesh or network-layer behavior is hard to debug

Workflow

1. Define trace propagation and correlation requirements.
2. Instrument critical paths with spans and semantic attributes.
3. Add RED/USE metrics and high-cardinality-safe labels.
4. Use eBPF-based telemetry for network and syscall visibility where supported.
5. Build SLO-linked dashboards and alerting runbooks.

Required Checks

• Trace context survives all async and sync hops
• Alert noise is controlled with actionable thresholds
• Telemetry costs and retention policies are defined

Decision Framework

• Propagate W3C trace context through every sync hop, async queue, and browser-initiated request.
• Instrument RED metrics per service and USE metrics per resource; cap label cardinality before production.
• Adopt eBPF for kernel/network visibility only where root cause spans logs and metrics cannot explain latency.
• Tie alerts to SLO burn rates and user journeys, not raw infrastructure thresholds.

Common Rationalizations And Rebuttals

• "Logs are enough for debugging." -> Cross-service failures need span correlation; logs alone multiply MTTR.
• "Trace everything at max detail." -> Unsampled full fidelity blows cost and storage; sample with tail-based retention for errors.
• "eBPF replaces application instrumentation." -> eBPF sees network/kernel; business spans still need app-level attributes.

Evidence Pack

• Trace propagation test across UI, API gateway, and downstream services
• Dashboard links for SLO-aligned RED/USE panels with on-call runbook mapping
• Sampling and retention policy with cost estimate
• eBPF deployment scope note (if used): what visibility gap it closes

Exit Criteria

• Teams can isolate failure domains within minutes
• Observability supports both incident response and capacity planning