gh-lyndonkl-claude/skills/chain-spec-risk-metrics/resources/examples/microservices-migration.md

Microservices Migration: Spec, Risks, Metrics

Complete worked example showing how to chain specification, risk analysis, and success metrics for a complex migration initiative.

1. Specification

1.1 Executive Summary

Goal: Migrate our monolithic e-commerce platform to microservices architecture to enable independent team velocity, improve reliability through service isolation, and reduce deployment risk.

Timeline: 18-month program (Q1 2024 - Q2 2025)

• Phase 1 (Q1-Q2 2024): Foundation + Auth service
• Phase 2 (Q3-Q4 2024): User, Order, Inventory services
• Phase 3 (Q1-Q2 2025): Payment, Notification services + monolith sunset

Stakeholders:

• Exec Sponsor: CTO (accountable for initiative success)
• Engineering: 3 teams (15 engineers total)
• Product: Head of Product (feature velocity impact)
• Operations: SRE team (operational complexity)
• Finance: CFO (infrastructure cost impact)

Success Criteria: Independent service deployments with <5min MTTR, 99.95% uptime, no customer-facing regressions.

1.2 Current State (Baseline)

Architecture: Ruby on Rails monolith (250K LOC) serving all e-commerce functions.

Current Metrics:

• Deployments: 2 per week (all-or-nothing deploys)
• Deployment time: 45 minutes (build + test + deploy)
• Mean time to recovery (MTTR): 2 hours (requires full rollback)
• P99 API latency: 450ms (slower than target)
• Uptime: 99.8% (below SLA of 99.9%)
• Developer velocity: 3 weeks average for feature to production

Pain Points:

• Any code change requires full system deployment (high risk)
• One team's bug can take down entire platform
• Database hot spots limit scaling (users table has 50M rows)
• Hard to onboard new engineers (entire codebase is context)
• A/B testing is difficult (can't target specific services)

Previous Attempts: Tried service-oriented architecture in 2021 but stopped due to data consistency issues and operational complexity.

1.3 Proposed Approach

Target Architecture:

                    [API Gateway / Envoy]
                            |
        ┌───────────────────┼───────────────────┐
        ↓                   ↓                   ↓
   [Auth Service]    [User Service]      [Order Service]
        ↓                   ↓                   ↓
   [Auth DB]           [User DB]           [Order DB]

        ↓                   ↓                   ↓
 [Inventory Service]  [Payment Service]  [Notification Service]
        ↓                   ↓                   ↓
 [Inventory DB]       [Payment DB]       [Notification Queue]

Service Boundaries (based on DDD):

1. Auth Service: Authentication, authorization, session management
2. User Service: User profiles, preferences, account management
3. Order Service: Order creation, status, history
4. Inventory Service: Product catalog, stock levels, reservations
5. Payment Service: Payment processing, refunds, wallet
6. Notification Service: Email, SMS, push notifications

Technology Stack:

• Language: Node.js (team expertise, async I/O for APIs)
• API Gateway: Envoy (service mesh, observability)
• Databases: PostgreSQL per service (team expertise, ACID guarantees)
• Messaging: RabbitMQ (reliable delivery, team familiar)
• Observability: OpenTelemetry → DataDog (centralized logging, metrics, tracing)

Data Migration Strategy:

• Phase 1: Read from monolith DB, write to both (dual-write pattern)
• Phase 2: Read from service DB, write to both (validate consistency)
• Phase 3: Cut over fully to service DB, decommission monolith tables

Deployment Strategy:

• Canary releases: 1% → 10% → 50% → 100% traffic per service
• Feature flags for gradual rollout within traffic tiers
• Automated rollback on error rate spike (>0.5%)

1.4 Scope

In Scope (18 months):

• Extract 6 services from monolith with independent deployability
• Implement API gateway for routing and observability
• Set up per-service CI/CD pipelines
• Migrate data to per-service databases
• Establish SLOs for each service (99.95% uptime, <200ms p99)
• Train engineers on microservices patterns and operational practices

Out of Scope:

• Rewrite service internals (lift-and-shift code from monolith)
• Multi-region deployment (deferred to 2026)
• GraphQL federation (REST APIs sufficient for now)
• Service mesh full rollout (Envoy at gateway only, not sidecar)
• Real-time event streaming (async via RabbitMQ sufficient)

Future Considerations (post-Q2 2025):

• Replatform services to Go or Rust for performance
• Implement CQRS/Event Sourcing for Order/Inventory
• Multi-region active-active deployment
• GraphQL federation layer for frontend

1.5 Requirements

Functional Requirements:

• FR-001: Each service must be independently deployable without affecting others
• FR-002: API Gateway must route requests to appropriate service based on URL path
• FR-003: Services must communicate asynchronously for non-blocking operations (e.g., send notification after order)
• FR-004: All services must implement health checks (liveness, readiness)
• FR-005: Data consistency must be maintained across service boundaries (eventual consistency acceptable for non-critical paths)

Non-Functional Requirements:

• Performance:
  • P50 API latency: <100ms (target: 50ms improvement from baseline)
  • P99 API latency: <200ms (target: 250ms improvement)
  • API Gateway overhead: <10ms added latency
• Reliability:
  • Per-service uptime: 99.95% (vs. 99.8% current)
  • MTTR: <30 minutes (vs. 2 hours current)
  • Zero-downtime deployments (vs. maintenance windows)
• Scalability:
  • Each service must handle 10K req/s independently
  • Database connections pooled (max 100 per service)
  • Horizontal scaling via Kubernetes (3-10 replicas per service)
• Security:
  • Service-to-service auth via mTLS
  • API Gateway enforces rate limiting (100 req/min per user)
  • Secrets managed via Vault (no hardcoded credentials)
• Operability:
  • Centralized logging (all services → DataDog)
  • Distributed tracing (OpenTelemetry)
  • Automated alerts on SLO violations

1.6 Dependencies & Assumptions

Dependencies:

• Kubernetes cluster provisioned by Infrastructure team (ready by Jan 2024)
• DataDog enterprise license approved (ready by Feb 2024)
• RabbitMQ cluster available (SRE team owns, ready by Mar 2024)
• Engineers complete microservices training (2-week program, Jan 2024)

Assumptions:

• No major product launches during migration (to reduce risk overlap)
• Database schema changes can be coordinated across monolith and services
• Existing test coverage is sufficient (80% for critical paths)
• Customer traffic grows <20% during migration period

Constraints:

• Budget: $500K (infrastructure + tooling + training)
• Team: 15 engineers (no additional headcount)
• Timeline: 18 months firm (customer commitment for improved reliability)
• Compliance: Must maintain PCI-DSS for payment service

1.7 Timeline & Milestones

Milestone	Date	Deliverable	Success Criteria
M0: Foundation	Mar 2024	API Gateway deployed, observability in place	Gateway routes traffic, 100% tracing coverage
M1: Auth Service	Jun 2024	Auth extracted, deployed independently	Zero auth regressions, <200ms p99 latency
M2: User + Order	Sep 2024	User and Order services live	Independent deploys, data consistency validated
M3: Inventory	Dec 2024	Inventory service live	Stock reservations work, no overselling
M4: Payment	Mar 2025	Payment service live (PCI-DSS compliant)	Payment success rate >99.9%, audit passed
M5: Notification	May 2025	Notification service live	Email/SMS delivery >99%, queue processed <1min
M6: Monolith Sunset	Jun 2025	Monolith decommissioned	All traffic via services, monolith DB read-only

2. Risk Analysis

2.1 Premortem Summary

Exercise Prompt: "It's June 2025. We launched microservices migration and it failed catastrophically. Engineering morale is low, customers are experiencing outages, and the CTO is considering rolling back to the monolith. What went wrong?"

Top Failure Modes Identified (from cross-functional team premortem):

1. Data Consistency Nightmare (Engineering): Dual-write bugs caused order/inventory mismatches, overselling inventory, customer complaints.

2. Distributed System Cascading Failures (SRE): One slow service (Payment) caused timeouts in all upstream services, bringing down entire platform.

3. Operational Complexity Overwhelmed Team (SRE): Too many services to monitor, alert fatigue, SRE team couldn't keep up with incidents.

4. Performance Degradation (Engineering): Network hops between services added latency, checkout flow is now slower than monolith.

5. Data Migration Errors (Engineering): Production migration script had bugs, lost 50K user records, required emergency restore.

6. Team Skill Gaps (Management): Engineers lacked distributed systems expertise, made common mistakes (distributed transactions, thundering herd).

7. Cost Overruns (Finance): Per-service databases and infrastructure cost 3× more than budgeted, CFO halted project.

8. Feature Velocity Dropped (Product): Cross-service changes require coordinating 3 teams, slowing down product velocity.

9. Security Vulnerabilities (Security): Service-to-service auth misconfigured, unauthorized service access, data breach.

10. Incomplete Migration (Management): Ran out of time, stuck with half-migrated state (some services live, monolith still critical).

2.2 Risk Register

Risk ID	Risk Description	Category	Likelihood	Impact	Score	Mitigation Strategy	Owner	Status
R1	Data inconsistency between monolith DB and service DBs during dual-write phase, leading to customer-facing bugs (order not found, wrong inventory)	Technical	High (60%)	High	9	Mitigation: (1) Implement reconciliation job to detect mismatches, (2) Extensive integration tests for dual-write paths, (3) Shadow mode: write to both but read from monolith initially to validate, (4) Automated rollback if mismatch rate >0.1%	Tech Lead - Data	Open
R2	Cascading failures: slow/failing service causes timeouts in all dependent services, total outage	Technical	Medium (40%)	High	6	Mitigation: (1) Circuit breakers on all service clients (fail fast), (2) Bulkhead pattern (isolate thread pools per service), (3) Timeout tuning (aggressive timeouts <1sec), (4) Graceful degradation (fallback to cached data)	SRE Lead	Open
R3	Operational complexity overwhelms SRE team: too many alerts, incidents, runbooks to manage	Operational	High (70%)	Medium	6	Mitigation: (1) Standardize observability across services (common dashboards), (2) SLO-based alerting only (eliminate noise), (3) Automated remediation for common issues, (4) Hire 2 additional SREs (approved)	SRE Manager	Open
R4	Performance degradation: added network latency from service calls makes checkout slower than monolith	Technical	Medium (50%)	Medium	4	Mitigation: (1) Baseline perf tests on monolith, (2) Set p99 latency budget per service (<50ms), (3) Async where possible (notification, inventory reservation), (4) Load tests at 2× expected traffic	Perf Engineer	Open
R5	Data migration script errors cause data loss in production	Technical	Low (20%)	High	3	Mitigation: (1) Dry-run migration on prod snapshot, (2) Automated validation (row counts, checksums), (3) Incremental migration (batch of 10K users at a time), (4) Keep monolith DB as source of truth during migration, (5) Point-in-time recovery tested	DB Admin	Open
R6	Team lacks microservices expertise, makes preventable mistakes (no circuit breakers, distributed transactions, etc.)	Organizational	Medium (50%)	Medium	4	Mitigation: (1) Mandatory 2-week microservices training (Jan 2024), (2) Architecture review board for service designs, (3) Pair programming with experienced engineers, (4) Reference implementation as template	Engineering Manager	Open
R7	Infrastructure costs exceed budget: per-service DBs, K8s overhead, observability tooling cost 3× estimate	Organizational	Medium (40%)	Medium	4	Mitigation: (1) Detailed cost model before each phase, (2) Right-size instances (start small, scale up), (3) Use managed services (RDS) to reduce ops cost, (4) Monthly cost reviews with Finance	Finance Partner	Open
R8	Feature velocity drops: cross-service changes require coordination, slowing product development	Organizational	High (60%)	Low	3	Mitigation: (1) Design services with clear boundaries (minimize cross-service changes), (2) Establish API contracts early, (3) Service teams own full stack (no handoffs), (4) Track deployment frequency as leading indicator	Product Manager	Accepted
R9	Service-to-service auth misconfigured, allowing unauthorized access or data leaks	External	Low (15%)	High	3	Mitigation: (1) mTLS enforced at gateway and between services, (2) Security audit before each service goes live, (3) Penetration test after full migration, (4) Principle of least privilege (services can only access what they need)	Security Lead	Open
R10	Migration incomplete by deadline: stuck in half-migrated state, technical debt accumulates	Organizational	Medium (40%)	High	6	Mitigation: (1) Phased rollout with hard cutover dates, (2) Executive sponsorship to protect time, (3) No feature work during final 2 months (migration focus), (4) Rollback plan if timeline slips	Program Manager	Open

2.3 Risk Mitigation Timeline

Pre-Launch (Jan-Feb 2024):

• R6: Complete microservices training for all engineers
• R7: Finalize cost model and get CFO sign-off
• R3: Hire additional SREs, onboard them

Phase 1 (Mar-Jun 2024 - Auth Service):

• R1: Validate dual-write reconciliation with Auth DB
• R2: Implement circuit breakers in Auth service client
• R4: Baseline latency tests, set p99 budgets
• R5: Dry-run migration on prod snapshot

Phase 2 (Jul-Dec 2024 - User, Order, Inventory):

• R1: Reconciliation jobs running for all 3 services
• R2: Bulkhead pattern implemented across services
• R4: Load tests at 2× traffic before each service launch

Phase 3 (Jan-Jun 2025 - Payment, Notification, Sunset):

• R9: Security audit and pen test before Payment goes live
• R10: Hard cutover date for monolith sunset, no slippage

Continuous (Throughout):

• R3: Monthly SRE team health check (alert fatigue, runbook gaps)
• R7: Monthly cost reviews vs. budget
• R8: Track feature velocity every sprint

3. Success Metrics

3.1 Leading Indicators (Early Signals)

Metric	Baseline	Target	Measurement Method	Tracking Cadence	Owner
Deployment Frequency (per service)	2/week (monolith)	10+/week (per service)	Git tag count in CI/CD	Weekly	Tech Lead
Build + Test Time	25 min (monolith)	<10 min (per service)	CI/CD pipeline duration	Per build	DevOps
Code Review Cycle Time	2 days	<1 day	GitHub PR metrics	Weekly	Engineering Manager
Test Coverage (per service)	80% (monolith)	>85% (per service)	Jest coverage report	Per commit	QA Lead
Incident Detection Time (MTTD)	15 min	<5 min	DataDog alert → Slack	Per incident	SRE

Rationale: These predict future success. If deployment frequency increases early, it validates independent deployability. If incident detection improves, observability is working.

3.2 Lagging Indicators (Outcome Measures)

Metric	Baseline	Target	Measurement Method	Tracking Cadence	Owner
System Uptime (SLO)	99.8%	99.95%	DataDog uptime monitor	Daily	SRE Lead
API p99 Latency	450ms	<200ms	OpenTelemetry traces	Real-time dashboard	Perf Engineer
Mean Time to Recovery (MTTR)	2 hours	<30 min	Incident timeline analysis	Per incident	SRE Lead
Customer-Impacting Incidents	8/month	<3/month	PagerDuty severity 1 & 2	Monthly	Engineering Manager
Feature Velocity (stories/sprint)	12 stories	>15 stories	Jira velocity report	Per sprint	Product Manager
Infrastructure Cost	$50K/month	<$70K/month	AWS billing dashboard	Monthly	Finance

Rationale: These measure actual outcomes. Uptime and MTTR validate reliability improvements. Latency and velocity validate performance and productivity gains.

3.3 Counter-Metrics (What We Won't Sacrifice)

Metric	Threshold	Monitoring Method	Escalation Trigger
Code Quality (bug rate)	<5 bugs/sprint	Jira bug tickets	>10 bugs/sprint → halt features, fix bugs
Team Morale (happiness score)	>7/10	Quarterly eng survey	<6/10 → leadership 1:1s, workload adjustment
Security Posture (critical vulns)	0 critical	Snyk security scans	Any critical vuln → immediate fix before ship
Data Integrity (order accuracy)	>99.99%	Reconciliation jobs	<99.9% → halt migrations, investigate
Customer Satisfaction (NPS)	>40	Quarterly NPS survey	<30 → customer interviews, rollback if needed

Rationale: Prevent gaming the system. Don't ship faster at the expense of quality. Don't improve latency by cutting security. Don't optimize costs by burning out the team.

3.4 Success Criteria Summary

Must-Haves (hard requirements):

• All 6 services independently deployable by Jun 2025
• 99.95% uptime maintained throughout migration
• Zero data loss during migrations
• PCI-DSS compliance for Payment service
• Infrastructure cost <$70K/month

Should-Haves (target achievements):

• P99 latency <200ms (250ms improvement from baseline)
• MTTR <30 min (90-minute improvement)
• Deployment frequency >10/week per service (5× improvement)
• Feature velocity >15 stories/sprint (25% improvement)
• Customer-impacting incidents <3/month (63% reduction)

Nice-to-Haves (stretch goals):

• Multi-region deployment capability (deferred to 2026)
• GraphQL federation layer (deferred)
• Event streaming for real-time analytics (deferred)
• Team self-rates microservices maturity >8/10 (vs. 4/10 today)

4. Self-Assessment

Evaluated using rubric_chain_spec_risk_metrics.json:

Specification Quality

• Clarity (4.5/5): Goal, scope, timeline, approach clearly stated with diagrams
• Completeness (4.0/5): All sections covered; could add more detail on monolith sunset plan
• Feasibility (4.0/5): 18-month timeline is aggressive but achievable with current team; phased approach mitigates risk

Risk Analysis Quality

• Comprehensiveness (4.5/5): 10 risks across technical, operational, organizational; premortem surfaced non-obvious risks (team skill gaps, cost overruns)
• Quantification (3.5/5): Likelihood/impact scored; could add $ impact estimates for high-risk items
• Mitigation Depth (4.5/5): Each high-risk item has detailed mitigation with specific actions (circuit breakers, reconciliation jobs, etc.)

Metrics Quality

• Measurability (5.0/5): All metrics have clear baseline, target, measurement method, cadence
• Leading/Lagging Balance (4.5/5): Good mix of early signals (deployment frequency, MTTD) and outcomes (uptime, MTTR)
• Counter-Metrics (4.0/5): Explicit guardrails (quality, morale, security); prevents optimization myopia

Integration

• Spec→Risk Mapping (4.5/5): Major spec decisions (dual-write, service boundaries) have corresponding risks (R1, R8)
• Risk→Metrics Mapping (4.5/5): High-risk items tracked by metrics (R1 → data integrity counter-metric, R2 → MTTR)
• Coherence (4.5/5): All three components tell consistent story of complex migration with proactive risk management

Actionability

• Stakeholder Clarity (4.5/5): Clear owners for each risk, metric; stakeholders can act on this document
• Timeline Realism (4.0/5): 18-month timeline is aggressive; includes buffer (80% work, 20% buffer in each phase)

Overall Average: 4.3/5 ✓ (Exceeds 3.5 minimum standard)

Strengths:

• Comprehensive risk analysis with specific, actionable mitigations
• Clear metrics with baselines, targets, and measurement methods
• Phased rollout reduces big-bang risk

Areas for Improvement:

• Add quantitative cost impact for high-risk items (e.g., R1 data inconsistency could cost $X in customer refunds)
• More detail on monolith sunset plan (how to decommission safely)
• Consider adding "reverse premortem" (what would make this wildly successful?) to identify opportunities

Recommendation: Proceed with migration. Risk mitigation strategies are sound. Metrics will provide early warning if initiative is off track. Schedule quarterly reviews to update risks/metrics as we learn.