Scalable Systems
Designing horizontally scalable systems: stateless services, distributed caches, read replicas, and global load balancing.
Scalable Systems is a core discipline within System Design Reference Scenarios. It defines how technology systems should be designed, implemented, and governed to achieve reliable, secure, and maintainable outcomes that serve both technical teams and business stakeholders.
Applying Scalable Systems standards reduces system failures, accelerates delivery, and provides the governance evidence required by enterprise clients, regulators like BSP, and certification bodies like ISO. Top technology companies (Google, Microsoft, Amazon) treat these standards as competitive differentiators, not compliance overhead.
📖 Detailed Explanation
System design scenarios present end-to-end architecture solutions for common problem types: scalable web services, real-time messaging, event-driven pipelines, edge AI inference, and high-availability distributed systems.
Industry Context: Reference architectures used in technical interviews at Google, Meta, Amazon, and applied in enterprise architecture decisions.
Relevance to Philippine Financial Services: Organizations operating under BSP supervision must demonstrate mature system design reference scenarios practices during technology examinations. The BSP Technology Supervision Group evaluates documentation quality, process maturity, and evidence of systematic practice — all of which are addressed by the standards in this section.
Alignment to Global Standards: The practices documented here are aligned to frameworks used by Google, Amazon, Microsoft, and the world's leading consulting firms (McKinsey Digital, Deloitte Technology, Accenture Technology). They represent the current industry consensus on best practices rather than any single vendor's approach.
Engineering Perspective: For engineers, Scalable Systems provides concrete patterns and anti-patterns that prevent common mistakes and accelerate development by providing proven solutions to recurring problems. Rather than rediscovering what doesn't work, teams can apply battle-tested approaches with known trade-offs.
Architecture Perspective: For architects, Scalable Systems provides the design vocabulary, decision frameworks, and governance artifacts needed to make and communicate complex technical decisions clearly and consistently.
Business Perspective: For business stakeholders, Scalable Systems provides assurance that technology investments are aligned to industry standards, reducing the risk of expensive rework, regulatory findings, and system failures that impact customers and revenue.
📈 Architecture Diagram
flowchart LR
A["Scalable Systems
Concept"] --> B["Principles
& Standards"]
B --> C["Design
Decisions"]
C --> D["Implementation
Patterns"]
D --> E["Governance
Checkpoints"]
E --> F["Validation
& Evidence"]
F -.->|"Feedback Loop"| A
style A fill:#1e293b,color:#f8fafc
style F fill:#052e16,color:#4ade80
Lifecycle of Scalable Systems: from concept through principles, design decisions, implementation patterns, governance checkpoints, and validation — with feedback loops for continuous improvement.
🌎 Real-World Examples
Netflix runs active-active across 3 AWS regions. Route 53 health checks detect region failure in 10 seconds and shift traffic. 'Chaos Kong' exercises (simulating full region loss) run monthly to validate failover under real production load — not just in staging. Netflix pioneered the practice of treating DR testing as continuous engineering, not annual drills.
✓ Result: 99.97%+ availability during multiple AWS regional outages; failover validated monthly under production traffic
AWS Multi-AZ deployment with Aurora Global Database is the industry reference for RTO/RPO in cloud-native systems. Aurora Global provides < 1 second RPO and < 1 minute RTO for cross-region failover. AWS publishes their architecture in the Well-Architected Framework Reliability Pillar, used as the design reference by 10,000+ enterprise architects.
✓ Result: Aurora Global Database: < 1s RPO, < 1 min RTO for 15 global regions; reference architecture cited in 10,000+ Well-Architected Reviews
Cloudflare's anycast network routes every request to the nearest of 285+ data centers globally. Single datacenter failure: traffic reroutes in < 1 second with no DNS TTL delay. Their architecture means no single datacenter is ever critical — all are equally disposable. This is the architectural ideal of Design for Failure applied at internet infrastructure scale.
✓ Result: 13 consecutive years of 99.99%+ global availability; zero customer impact from any single datacenter failure
Monzo's core banking runs on Kubernetes (EKS) across multiple AWS AZs. Rolling deployments with readiness probes ensure zero-downtime updates. Their on-call model: every engineer owns their service's availability — creating direct incentive to build resilient systems. FCA examination rated their availability higher than 3 major traditional UK banks.
✓ Result: 99.99% banking availability on microservices; FCA 2022: availability rated higher than traditional core banking peers
🌟 Core Principles
Every aspect of scalable systems must be deliberately designed, not discovered after deployment. Document design decisions as ADRs with explicit rationale.
Apply scalable systems practices consistently across all systems. Inconsistent application creates governance blind spots and makes incident investigation unpredictable.
Scalable Systems practices must demonstrably contribute to business outcomes: reduced downtime, faster delivery, lower operational cost, or improved compliance posture.
Quality of scalable systems implementation must be measurable. Define specific metrics and collect evidence continuously — not only at audit or review time.
Standards for scalable systems evolve as technology and threat landscapes change. Schedule quarterly reviews of applicable standards and update practices accordingly.
⚙️ Implementation Steps
Current State Assessment
Document the current state of scalable systems practice: what is implemented, what is missing, what is inconsistent across teams. Use the governance/scorecards section for a structured assessment framework.
Gap Analysis Against Standards
Compare current state against the standards in this section and applicable frameworks (CAP Theorem, AWS Well-Architected Framework). Prioritize gaps by business impact and remediation effort.
Design the Target State
Define the target scalable systems state: which patterns will be adopted, which anti-patterns eliminated, which governance mechanisms introduced. Express as a time-bound roadmap.
Incremental Implementation
Implement scalable systems improvements incrementally: pilot with one team or system, measure outcomes, refine the approach, then expand. Avoid big-bang transformations.
Validate and Iterate
Measure the impact of implemented changes against defined success criteria. Incorporate lessons learned into the practice standards. Contribute improvements back to this library.
✅ Governance Checkpoints
| Checkpoint | Owner | Gate Criteria | Status |
|---|---|---|---|
| Current State Documented | Solution Architect | Scalable Systems current state assessment completed and reviewed | Required |
| Gap Analysis Reviewed | Architecture Review Board | Gap analysis reviewed and prioritization approved | Required |
| Implementation Plan Approved | Enterprise Architect | Target state and roadmap approved by ARB | Required |
| Quality Metrics Defined | Solution Architect | Measurable success criteria defined for scalable systems improvements | Required |
◈ Recommended Patterns
✦ Reference Architecture Adoption
Start from an established reference architecture for scalable systems rather than designing from scratch. Adapt to organizational context rather than rebuilding proven foundations.
✦ Pattern Library Contribution
When your team solves a recurring scalable systems problem with a novel approach, document it as a pattern for the library. This compounds organizational knowledge over time.
✦ Fitness Function Testing
Encode scalable systems standards as automated architectural fitness functions — tests that run in CI/CD and fail builds when standards are violated. This makes governance continuous rather than periodic.
⛔ Anti-Patterns to Avoid
⛔ Standards Theater
Documenting scalable systems standards in architecture policies that no one reads and no one enforces. Standards without automated validation or governance gates are not operational standards.
⛔ Copy-Paste Architecture
Adopting another organization's scalable systems patterns wholesale without adapting to organizational context, team capability, or regulatory environment. Always adapt; never just copy.
🤖 AI Augmentation Extensions
LLM agents analyze design documents against scalable systems standards, generating structured gap reports with cited evidence and suggested remediation approaches.
This section is optimized for vector ingestion into an AI-powered architecture assistant. Semantic search enables architects to retrieve relevant scalable systems guidance through natural language queries.
🔗 Related Sections
📚 References & Further Reading
- CAP Theorem↗ IEEE Xplore
- AWS Well-Architected Framework↗ aws.amazon.com
- Google SRE Principles↗ sre.google
- CQRS and Event Sourcing↗ cqrs.files.wordpress.com
- Documenting Software Architectures — Bass, Clements, Kazman↗ Amazon
- Building Evolutionary Architectures — Ford, Parsons, Kua↗ O'Reilly