Notes for Architects in Distributed System Design

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1. Scalability Strategy

  • Definition: Ensure the system can handle growth in data, users, and traffic.
  • Approaches: Horizontal vs. vertical scaling, sharding, partitioning.

2. System Reliability and Fault Tolerance

  • Definition: Maintain uptime despite component failures.
  • Techniques: Redundancy, failover mechanisms, replication.

3. CAP Theorem Considerations

  • Definition: Balancing Consistency, Availability, and Partition Tolerance.
  • Architect Decisions: Which trade-off (e.g., eventual consistency vs. strict consistency) best fits business needs.

4. Data Consistency Models

  • Definition: Understanding how data consistency affects user experience and system design.
  • Types: Strong, eventual, causal consistency.

5. Microservices vs. Monolith

  • Definition: Choosing an architecture model based on the system’s complexity and scalability requirements.
  • Considerations: Service boundaries, data ownership, communication overhead.

6. Inter-Service Communication

  • Definition: Design efficient communication between services.
  • Options: Synchronous (HTTP/gRPC) vs. asynchronous (message queues, event streaming).

7. Data Replication and Partitioning

  • Definition: How and where data is replicated and partitioned.
  • Impacts: Performance optimization, data locality, and fault tolerance.

8. Security and Compliance

  • Definition: Protect data and communications across distributed components.
  • Focus Areas: Encryption, authentication, authorization, secure APIs.

9. Performance Optimization

  • Definition: Reducing latency and improving throughput.
  • Strategies: Caching, CDNs, asynchronous processing, database indexing.

10. Resilience Patterns

  • Definition: Designing systems to recover from partial failures.
  • Examples: Circuit breakers, retries, bulkheads, timeouts.

11. Load Balancing and Traffic Distribution

  • Definition: Efficiently distributing requests across system components.
  • Techniques: DNS-based balancing, reverse proxies, software load balancers.

12. Service Discovery and Orchestration

  • Definition: Managing dynamic service endpoints and dependencies.
  • Tools: Service registries (e.g., Consul, Zookeeper), Kubernetes.

13. Observability and Monitoring

  • Definition: Ensure real-time visibility into the system’s performance and health.
  • Components: Logs, metrics, tracing (e.g., Prometheus, Grafana, OpenTelemetry).

14. Consistency vs. Availability Decisions

  • Definition: Balancing data consistency and high availability under failure scenarios.
  • Key Consideration: Which business use cases can tolerate eventual consistency.

15. Disaster Recovery and Business Continuity

  • Definition: Ensure the system can recover from catastrophic failures.
  • Strategies: Backup and restore plans, multi-region deployments.

16. Concurrency and Resource Management

  • Definition: Managing concurrent operations to avoid resource contention.
  • Techniques: Distributed locking, optimistic concurrency control.

17. Leader Election and Consensus

  • Definition: Coordinating distributed components to agree on shared state.
  • Algorithms: Paxos, Raft, Zookeeper.

18. Design for Failure

  • Definition: Assume and design for inevitable component failures.
  • Principle: Fail fast, degrade gracefully.

19. Governance and Standards

  • Definition: Establish system-wide guidelines to ensure maintainability and scalability.
  • Areas: API design standards, versioning policies, schema evolution.

20. Data Serialization and Format

  • Definition: Ensure efficient and compatible data exchange.
  • Common Formats: JSON, Protobuf, Avro.

21. Latency and Bandwidth Management

  • Definition: Optimize communication across nodes with network constraints.
  • Techniques: Compression, batching, edge computing.

22. Service Ownership and Domain-Driven Design (DDD)

  • Definition: Define clear boundaries and responsibilities for each service.
  • Goal: Reduce coupling and improve system evolution.

23. Vendor Lock-In and Portability

  • Definition: Avoid dependence on a single vendor’s infrastructure or services.
  • Approach: Use open standards and abstractions.

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