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).
Real-Time and Streaming System Design
Designing client-facing, always-on live delivery systems: real-time communication transports (WebSockets, server-sent events, long-polling, WebRTC, MQTT), connection lifecycle and scaling for millions of persistent connections, presence, pub/sub fan-out to online users, chat and notifications, live feeds and tickers, real-time collaboration (CRDT vs OT, offline sync and reconciliation), and live and on-demand video delivery (ingest, transcoding, adaptive bitrate, CDN delivery, low-latency protocols, playback entitlement and content protection). Covers latency budgets, per-client ordering, delivery guarantees across disconnect and reconnect, per-client backpressure, capacity estimation for connection fleets, authentication, authorization, and revocation for real-time channels and premium content, and operational readiness (SLOs and error budgets, observability and incident response, rate limiting, safe rollout, and load and chaos testing) for live-delivery platforms. Data-pipeline stream processing (Kafka or Flink jobs, windowed aggregation, exactly-once pipelines, real-time analytics ingestion) is out of scope.
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.
End-to-End Feature Design and Development
The integrated "build a complete feature" interview format: taking one feature from user-facing flow through API design, backend logic, data model, and storage in a single coherent walkthrough. Tests the ability to connect the layers and make consistent trade-offs across them, rather than depth in any single layer.
Multi-Tenancy and Isolation
Serving many tenants from shared infrastructure: tenancy models (silo, pool, bridge), data isolation, per-tenant data residency, noisy-neighbor mitigation, per-tenant limits, and security boundaries between tenants. Covers the cost, isolation, and blast-radius tradeoffs of shared versus dedicated resources, and business continuity: per-tenant backup, disaster recovery, and compliant tenant offboarding and deletion. The architecture layer specific to SaaS and platform products.
Architectural Patterns and Anti-Patterns
Architecture-level patterns and the anti-patterns that signal a wrong turn. Patterns: layered and n-tier architecture, including where cross-cutting concerns like authentication, rate-limiting and tracing belong, dependency injection trade-offs, and thin-versus-fat controller design; hexagonal (ports and adapters) and clean architecture; CQRS and event sourcing; backend-for-frontend; plugin (microkernel) extension models; and the coupling, cohesion, encapsulation and separation-of-concerns principles behind them, including when each applies and what it costs. Anti-patterns: distributed monolith, chatty services, shared-database coupling, cyclic service dependencies, leaky abstractions that expose internal schemas, and golden-hammer pattern adoption. Covers the detection signals (deploy coupling, call-graph fan-out, change amplification, trace evidence), incremental remediation, and architecture governance that keeps smells from recurring. This is about diagnosing and fixing the smell in an existing design, not the monolith-versus-microservices decision itself.
API and Interface Design for Distributed Services
Designing the contracts between services and clients: REST, gRPC, and GraphQL tradeoffs, versioning and backward compatibility, pagination, rate limiting, and idempotent endpoints. Covers request/response modeling, error contracts, and API gateway responsibilities. Focuses on the interface layer that ties distributed components together, not internal data schemas.
Consensus and Coordination Algorithms
How independent nodes agree on shared state: Paxos and Raft, leader election, quorum reads and writes, distributed locks, coordination services such as ZooKeeper or etcd, and Byzantine fault tolerance for replicas that cannot be trusted. Covers split-brain avoidance, fencing tokens, and the cost of coordination on throughput and latency. Frames when consensus is required versus when it can be designed away.
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.