Systems Architecture & Distributed Systems Topics
Large-scale distributed system design, service architecture, microservices patterns, global distribution strategies, scalability, and fault tolerance at the service/application layer. Covers microservices decomposition, caching strategies, API design, eventual consistency, multi-region systems, and architectural resilience patterns. Excludes storage and database optimization (see Database Engineering & Data Systems), data pipeline infrastructure (see Data Engineering & Analytics Infrastructure), and infrastructure platform design (see Cloud & Infrastructure).
Stateful Service Design and State Management
Handling state in otherwise-distributed systems: stateful versus stateless service design, session management, sticky routing, in-memory state with durable backing, and state replication. Covers where state should live, how to recover it after a crash, and the scaling constraints stateful services impose. Complements the stateless-first default with when and how to hold state.
Caching Strategies and Distributed Caching
Using caches to reduce latency and load: cache-aside, read-through, write-through, and write-behind patterns, TTLs, eviction policies, and distributed caches such as Redis or Memcached. Covers cache invalidation, stampede and thundering-herd protection, and the consistency tradeoffs of caching. Focuses on where and how to cache across tiers.
Observability and Monitoring for Distributed Systems
Understanding system behavior in production: metrics, logs, and distributed tracing, SLIs/SLOs/SLAs, alerting, dashboards, and service mesh observability. Covers correlating signals across services, defining meaningful telemetry, and reducing mean-time-to-detect. The visibility layer that makes distributed systems operable at scale.
Fault Tolerance, High Availability, and Disaster Recovery
Keeping a system serving despite failure, from code-level resilience to infrastructure-level recovery: circuit breakers, retries with backoff and jitter, timeouts, bulkheads, graceful degradation, and preventing cascading failures, alongside redundancy, failover (active-active versus active-passive), RPO and RTO objectives, backup and restore, and multi-region failover. Covers dependency-failure isolation, chaos engineering to validate resilience, failure-mode analysis, designing to nines of availability, cost-versus-availability tradeoffs, and recovery runbooks. Spans both the patterns that isolate partial failure and the disaster-recovery planning that restores a business-critical system after a major outage.
Service Discovery and Configuration Management
Letting services find and configure each other at runtime: service registries, client-side versus server-side discovery, DNS-based discovery, dynamic configuration, feature flags, and secrets distribution. Covers how services stay wired together as instances come and go, and how config changes propagate safely. The connective plumbing of a microservices deployment.
Load Balancing and Traffic Management
Distributing requests across capacity: load-balancing algorithms (round-robin, least-connections, consistent hashing), L4 versus L7 balancing, health checks, and traffic shaping. Covers sticky sessions, canary and blue-green routing, rate limiting, and graceful draining. The traffic-distribution layer that keeps a scaled system balanced and available.
Distributed Systems Security and Trust
Securing communication and trust across a distributed system: service-to-service authentication and authorization, mutual TLS, zero-trust boundaries, secrets management, and multi-party protocols and distributed trust models. Covers securing data in transit and at rest across service boundaries and isolating tenant and network zones. The security-architecture concerns specific to distributed designs.
Content Delivery and Edge Networking
Serving content and computation close to users: CDN architecture, edge caching, cache-control and purge strategies, and edge computing versus centralized cloud processing. Covers origin shielding, geo-routing, and the tradeoffs of pushing logic to the edge for latency-sensitive workloads. Application-layer content distribution rather than raw network engineering.
Scalability Patterns and Techniques
Scaling a system to handle growth in traffic and data: horizontal versus vertical scaling, statelessness, sharding and partitioning strategies, read replicas, and connection pooling. Covers capacity estimation, identifying bottlenecks, and the tradeoffs each scaling axis introduces. The general toolkit for taking a design from thousands to millions of users.