Notes for Architects in Distributed System Design
Thu Feb 06 2025
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.
- 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.
- 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.