Architecture Documentation and Communication Questions

Title & Status

• What is the decision and its current status (proposed/accepted/deprecated)?
• Who authored and who approved it? Date and review cadence?

Context / Background

• Why are we considering decomposition now? (pain points: scalability, release velocity, org boundaries)
• Current monolith architecture, size, tech stack, and traffic patterns.

Decision

• What decomposition approach are we choosing? (bounded contexts, service-per-domain, vertical slices)
• Scope: which modules or capabilities move first?

Motivation / Goals

• What problems are we solving? (scalability, independent deploys, team autonomy, fault isolation)
• Success criteria and measurable KPIs.

Constraints & Assumptions

• Team skills, timelines, regulatory or data residency constraints, legacy dependencies.

Scalability Considerations

• How will services scale (horizontal, autoscaling policies)?
• Which services are expected to be high-throughput/have state?
• How will data partitioning and sharding be handled?

Integration & Interfaces

• What API/contract patterns (REST/gRPC/events) and versioning strategy?
• How will synchronous vs asynchronous communication be decided?
• Backwards compatibility and consumer migration plan?

Transactions & Data Consistency

• How will cross-service transactions be handled? (sagas, compensating actions, eventual consistency)
• Which operations require strong consistency and how will they be implemented?
• Data ownership and replication strategy.

Testing Strategy

• How will unit, integration, contract, and end-to-end tests be organized?
• Consumer-driven contract testing and test environments/CI pipeline changes.
• Strategies to test failure modes and distributed tracing for observability.

Operational Costs & Runbook

• Estimated infra and operational cost impacts (more containers, network, DBs).
• Monitoring, logging, alerting, and SLA expectations per service.
• Deployment pipeline changes, rollout/rollback plan, and incident playbooks.

Migration Plan & Rollout

• Phased cutover steps, strangler pattern details, data migration approach.
• Rollback criteria and feature-flagging approach.

Alternatives Considered

• Keep monolith with modularization, hybrid modular-monolith, or different decomposition schemes — pros/cons.

Risks & Mitigations

• Key technical, organizational, and security risks and planned mitigations.

Consequences

• Long-term maintenance, operational overhead, team structure changes.

Appendices

• Diagrams, sequence flows, cost estimates, benchmark results, linked RFCs.

Single ADR with multiple alternatives — Pros:

• Consolidated context: all options, criteria, and comparisons live in one place, making side‑by‑side evaluation easy.
• Easier to maintain decision metadata (drivers, constraints) once.
• Useful when alternatives are tightly related or the decision is atomic.

Single ADR — Cons:

• Can become long and hard to scan; poor discoverability for a specific alternative.
• Review overhead can spike: reviewers must read a larger doc to evaluate one option.
• Historical clarity suffers over time: edits may blur which alternative was ultimately rejected and why unless careful versioning is kept.

Separate ADRs per alternative — Pros:

• High discoverability: each alternative has its own title/tags; easy to link from project issues.
• Smaller, focused reviews; reviewers can evaluate one approach at a time.
• Clear historical trail: each ADR records reasoning and outcome for that alternative independently.

Separate ADRs — Cons:

• Context duplication (requirements, constraints) across ADRs unless you reference a shared root ADR.
• Harder to compare alternatives side‑by‑side without an index ADR.
• More artifacts to maintain and navigate.

Effects summary:

• Discoverability: separate ADRs win (clear titles/links); single ADR needs a strong TOC and anchors.
• Review overhead: single ADR concentrates effort but can overwhelm reviewers; separate ADRs distribute, faster per‑doc reviews.
• Historical clarity: separate ADRs provide cleaner audit trails; single ADR can work if you preserve versions and explicit “decision” section.

Recommendation: For closely related alternatives evaluated together, use one ADR with a clear TOC and an explicit resolution section. For distinct approaches or when you expect prolonged debate, create separate ADRs plus an index ADR that compares them.

Start by clarifying goals and constraints (business domains, SLAs, team size, timeline). I use a mix of domain-driven heuristics, technical metrics, organizational factors, and NFRs to decide boundaries — and record everything in an architecture decision record (ADR) + service catalog and boundary diagram.

Heuristics:

• Bounded Contexts (DDD): identify core domains, ubiquitous language, and aggregate roots; prefer one service per bounded context to minimize semantic leaks.
• Single Responsibility & Cohesion: group functionality that changes for the same business reason.
• Low Coupling: avoid splitting flows that require synchronous chatty calls; prefer async integration for cross-cutting concerns.
• Data Ownership: each service owns its schema; avoid shared DB tables.
• Transactional Scope: if strong ACID across functions is required, keep them together or use sagas/compensations.
• Latency/Throughput: place high-traffic, low-latency paths together; separate heavy batch jobs.
• Team-per-Service: align services to team boundaries (Conway’s Law) for clearer ownership.

Metrics/analysis:

• Call graph / dependency heatmap to spot high coupling
• Change history/co-change frequency to find cohesive modules
• Size & complexity (LOC, cyclomatic) to balance service granularity

Documentation artifacts to create:

• ADRs: decision, alternatives considered, rationale, risks, rollback plan
• Service catalog: responsibilities, APIs, data owned, SLA, team owner
• Sequence diagrams for critical flows (sync/async)
• Deployment topology and data flow diagrams
• Migration plan: cutover steps, throttling, compatibility strategies, feature flags
• Operational runbook and SLO/SLA definitions

How to capture & justify in architecture docs:

• Start each ADR with problem statement, constraints, and options evaluated (e.g., split by feature vs. by domain). Include evidence: dependency graphs, co-change stats, expected traffic, and team availability.
• State chosen boundary and why it best meets business/technical goals (trade-offs: complexity vs. deployability, latency vs. consistency).
• Provide migration steps, testing strategy (canary, contract tests), and rollback criteria.
• Revisit decisions iteratively; record outcomes and follow-up ADRs.

This approach balances DDD principles, measurable coupling/cohesion, team organization, and non-functional needs — and makes choices defensible via documented evidence and clear rollback/migration paths.

Requirements (clarify): run across 3 AZs in region; active-active front end; autoscaling groups; RDS primary + read replicas; global CDN; show failure isolation, cross-AZ flow, RPO/RTO.

High-level ASCII topology:

Internet
|
Global CDN (edge POPs)
|
Global LB (DNS geo + Anycast) -> Regional ALB (single DNS)
|
+-------------------------------+-------------------------------+-------------------------------+

                                              |
                                        RDS Primary (in AZ-A)  <-- async/sync replication -->
                                        Read Replicas (AZ-B, AZ-C)
                                              |
                                       Shared Cache (multi-AZ Redis) 
                                              |
                                       Cross-AZ replication & backups to S3 (snapshots)

Annotations to show:

• Failure isolation boundaries: draw boxes around each AZ; annotate that compute ASGs are AZ-failure isolated (if AZ down, traffic redistributed to other AZs via ALB and autoscaling). Mark separate VPC subnets per AZ and separate NAT/gateway per AZ for networking isolation.
• Cross-AZ traffic flow: arrows from ALB to ASGs in each AZ (intra-region). Show replication arrows from RDS primary to replicas across AZs (label sync vs async). Show cache replication cross-AZ with failover.
• Single points & mitigation: RDS primary marked as single-writer; show automated Multi-AZ failover option (standby in another AZ) vs read-replica promotion; indicate backups to S3 and point-in-time recovery.

Narrative to include (RPO / RTO assumptions):

• RPO: with synchronous Multi-AZ standby RDS, RPO ~= 0 for committed transactions; with async read replicas, RPO > 0 (seconds to minutes) if relying on replica promotion. State your choice: prefer Multi-AZ for primary durability; read replicas for read-scaling and non-zero RPO.
• RTO: with Multi-AZ failover, RTO typically ~30–60s (DNS and DB failover time); if promoting async replica, RTO may be minutes depending on promotion and cache warm-up. For compute layer, ALB + ASGs recover capacity within 1–3 minutes depending on scaling policies and health checks.
• Backup/restore: snapshots to S3 allow RPO of last snapshot frequency; restore RTO measured in minutes–hours depending on dataset size—include SLA targets and runbooks.
• Operational controls: health checks, automated failover tests, chaos experiments, cross-AZ latency monitoring, and runbooks for manual promotion and DNS TTL tuning to reduce cutover time.

Trade-offs:

• Multi-AZ synchronous gives better RPO at cost of write latency; async replicas better for scale but increase RPO risk.
• Reduce RTO by pre-creating standby/promoted replicas and low DNS TTLs; increase cost for faster recovery.

This diagram and narrative communicate where failures are contained, how traffic and replication traverse AZs, and the expected recovery and data-loss characteristics for on-call/runbook decisions.

Executive summary: We will execute a staged, team-by-team migration from monolith to microservices over six quarters using a phased approach that minimizes customer impact and preserves backward compatibility. The plan defines discovery, strangler-pattern extraction, consumer migration windows, and decommissioning milestones; enforces API versioning and contract-first development with clear consumer guarantees (dual-write/dual-read, feature flags, and compatibility SLAs); includes concrete rollback criteria and short iteration timelines with automated tests and canary deployments; mandates a stakeholder communication cadence (weekly engineering syncs, biweekly product reviews, monthly customer updates) and updates to monitoring, alerting and runbooks to ensure operability during and after migration.

Technical appendix — key implementation points:

• Phases & milestones:
  1. Discovery (4 weeks): map domains, dependencies, critical paths, data ownership; milestone: dependency inventory + migration backlog.
  2. Pilot extraction (8–12 weeks): extract one low-risk bounded context, implement API gateway, CI/CD, monitoring; milestone: successful canary serving 10% traffic.
  3. Iterative migrations (rolling, 12–18 weeks per major domain): strangler facade, traffic split 0→100%, consumer migration windows; milestone per domain: 95% traffic routed to service, fallbacks removed.
  4. Hardening & decommission (6–8 weeks): remove legacy code, finalize SLAs, update docs; milestone: monolith components retired.
• API versioning strategy:
  • Contract-first OpenAPI; use major.minor semantic versions; keep v1 live while v2 introduced; non-breaking changes use minor; breaking changes require new major, deprecation timeline (6 months) and automated compatibility tests.
  • Gateway supports route-by-header/version and transformation adapters for temporary compatibility.
• Consumer migration guarantees:
  • Dual-read for state verification, dual-write where needed, feature flags to toggle behavior; define consumer SLA during migration (e.g., 99.5% success for 30-day window) and a “compatibility window” per API version.
  • Provide SDKs and migration guides, and a sandbox environment for testing.
• Timeline & rollback criteria:
  • Short iterations (2–4 weeks) per service with integrated smoke, contract, load tests. Rollback triggers: error rate > 3x baseline, latency > 2x P90, data divergence detected, or failed critical end-to-end tests—automated rollback to previous version + alerting.
• Stakeholder communication cadence:
  • Daily standups within migrating squads; weekly cross-team engineering sync (migration board & blockers); biweekly product steering meeting; monthly executive report; customer-facing release notes and migration advisories before breaking-major-version launches (30/14/3-day cadence).
• Monitoring & runbooks:
  • Extend observability: distributed tracing propagated across gateway, metrics for traffic split, consumer error counts, data drift detectors. Update runbooks with rollback steps, runbook playbooks for common failures, contact matrix (on-call, owners), and postmortem checklist. Automate runbook tests in staging.
• Governance:
  • Migration steering committee for prioritization, API review board for approve major changes, and cadence of contract compliance audits.
• Deliverables:
  • Dependency map, OpenAPI specs, SDKs, migration checklist per consumer, updated runbooks, dashboards, and a deprecation calendar published centrally.