Week 0 (Immediate, first 48–72 hrs)
- Triage & mitigation: enable rate limiting, circuit breakers, graceful degradation (return cached or reduced payloads), increase request queue/timeouts conservatively, and throttle noisy clients. Roll these as config flags to avoid full deploys.
- Communication: notify execs, SRE, product and affected customers with known impact, ETA for investigation, and mitigation actions.
Week 1–2 (Observability & quick wins)
- Instrumentation: add fine-grained tracing (distributed trace IDs), capture percentiles (p50,p90,p95,p99,p999) for latency, record RPCs, DB queries, GC pauses, thread pool metrics, queue lengths, and resource metrics (CPU, mem, NIC, I/O).
- Profiling: lightweight async CPU and heap profiles in staging and selectively in prod during low-risk windows.
- Short-term fixes: tune thread pools, database connection pools, and increase capacity for hotspots discovered.
Week 3 (Reproduce & root-cause)
- Load testing: build scenario tests that mirror production traffic shape (bursts, long tail) using recorded traffic; ramp to peak and beyond. Instrument synthetic tests to measure tail.
- RCA: correlate traces with resource spikes, lock contention, GC, slow downstreams, or retry storms. Identify top offending code paths or services.
Week 4 (Design remediation)
- Architecture options (trade-offs):
- Caching layer (edge or service-level) to cut downstream calls — low risk, quick ROI.
- Backpressure and async patterns (message queues, eventing) to decouple spikes — higher effort, improves resilience.
- Sharding/partitioning or read-replicas for DB hot partitions.
- Autoscaling with predictive policies and warm pools for JVM cold-starts.
- Select solution(s) with cost/complexity/latency benefit analysis and implementation plan.
Week 5 (Implement & test)
- Implement chosen fixes in staging: caching, circuit breaker libs, async queues, query optimizations, code fixes from profiling.
- Run load tests with canary traffic patterns and monitor tail percentiles and error budgets.
- Prepare rollout playbook: canary → gradual ramp (5%,25%,50%,100%), health checks, automated rollback triggers (error rate, latency SLO breach).
Week 6 (Rollout, monitoring, and handoff)
- Production rollout following playbook; run real-time observability dashboards and on-call presence.
- Post-rollout validation: compare baseline vs new tail percentiles, SLA attainment, customer impact.
- Rollback: immediate config switch to previous behavior and traffic cutover; detailed postmortem if rollback required.
- Stakeholder communication: provide daily updates during rollout, final report with root cause, mitigations, residual risks, timelines for remaining architectural work, and recommended SLO/alert tightening.
- Knowledge transfer: docs, runbooks, and monitoring dashboards for SRE and customer-facing teams.
Metrics to track throughout:
- p50/p95/p99/p999 latency, error rate, queue lengths, tail CPU/GC pauses, retry counts, SLA/SLO compliance.
This plan balances immediate relief, data-driven RCA, reproducible testing, and durable architectural fixes with safe rollout and clear stakeholder visibility.
