Architecture

System Design & Architecture

This section explores distributed systems architecture, design patterns, and engineering principles that power scalable, reliable, and high-performance applications.

Core Architecture Topics

Distributed Systems Fundamentals

Consistency Models

  • Strong consistency vs. eventual consistency
  • CAP theorem in practice
  • Consensus algorithms (Raft, Paxos)
  • Vector clocks and conflict resolution
  • Distributed transactions and 2PC/3PC

Scalability Patterns

  • Horizontal vs. vertical scaling
  • Database sharding and partitioning strategies
  • Read replicas and write scaling
  • Caching layers (CDN, Application, Database)
  • Load balancing algorithms

Reliability Engineering

  • Fault tolerance and redundancy
  • Circuit breakers and bulkheads
  • Graceful degradation
  • Health checks and self-healing systems
  • Disaster recovery and backup strategies

Microservices Architecture

Service Design

  • Domain-driven design principles
  • Service boundaries and decomposition
  • API design and versioning
  • Backward compatibility strategies
  • Contract testing

Inter-Service Communication

  • Synchronous (REST, gRPC) vs. Asynchronous (Message Queues)
  • Service mesh and sidecar patterns
  • API gateway patterns
  • Event-driven architecture
  • Saga pattern for distributed transactions

Operational Concerns

  • Service discovery and registration
  • Distributed tracing
  • Centralized logging
  • Metrics and monitoring
  • Configuration management

Data Architecture

Storage Patterns

  • CQRS (Command Query Responsibility Segregation)
  • Event sourcing
  • Database per service pattern
  • Shared database anti-pattern
  • Polyglot persistence

Data Pipeline Design

  • Batch vs. stream processing
  • Lambda and Kappa architectures
  • Change Data Capture (CDC)
  • Data lake vs. data warehouse
  • Real-time analytics pipelines

Cloud-Native Architecture

Container Orchestration

  • Kubernetes deployment strategies
  • Pod design patterns (sidecar, adapter, ambassador)
  • StatefulSets for stateful applications
  • Auto-scaling (HPA, VPA, Cluster Autoscaler)
  • Service mesh (Istio, Linkerd)

Serverless Patterns

  • Function as a Service (FaaS)
  • Event-driven serverless architectures
  • Cold start optimization
  • State management in serverless
  • Cost optimization strategies

Design Principles

System Design Philosophy

  1. Design for Failure: Assume everything can and will fail
  2. Loose Coupling: Minimize dependencies between components
  3. High Cohesion: Keep related functionality together
  4. Idempotency: Operations should be safely retryable
  5. Observability: Build in monitoring and debugging from the start
  6. Scalability: Design for growth from day one
  7. Security: Defense in depth at every layer

Performance Considerations

Latency Optimization

  • Minimize network round trips
  • Efficient serialization formats (Protocol Buffers, FlatBuffers)
  • Connection pooling and keep-alive
  • Asynchronous processing
  • Batch operations where appropriate

Throughput Enhancement

  • Parallel processing
  • Efficient resource utilization
  • Rate limiting and backpressure
  • Streaming vs. batch processing trade-offs
  • Hardware acceleration (GPU, FPGA)

Resources & Tools

Design Tools

  • C4 Model: Software architecture diagrams
  • PlantUML: Text-based diagram creation
  • Draw.io: Visual architecture diagrams
  • Mermaid: Markdown-based diagrams

Reference Architectures

  • AWS Well-Architected Framework
  • Google Cloud Architecture Framework
  • Azure Architecture Center
  • CNCF Cloud Native Landscape

Books & Papers

  • “Designing Data-Intensive Applications” by Martin Kleppmann
  • “Building Microservices” by Sam Newman
  • “Site Reliability Engineering” by Google
  • “The Phoenix Project” by Gene Kim

Stay Updated

New architecture patterns, case studies, and design principles are regularly added to this section. Check back often or subscribe to the blog for updates.

← Back to Home
Read Blog Posts →