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.
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.
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.
Payment and Transaction Processing Systems
Designing systems that move money correctly: idempotent payment flows, exactly-once semantics, reconciliation, ledgers, double-entry accounting, and fraud-detection architecture. Covers handling retries and partial failures without double-charging, and the consistency guarantees payments demand. Also covers protecting cardholder data through tokenization and PCI scope reduction, reconciling processor webhooks, and merchant and partner payouts. A high-stakes specialization of distributed transactions.
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, how config changes propagate safely, and how to monitor and diagnose the outages that stale endpoints or bad config pushes cause. The connective plumbing of a microservices deployment.
System Design Methodology and Trade-off Analysis
The end-to-end approach to an open-ended design problem and the judgment that resolves it: clarifying scope and constraints, gathering functional and non-functional requirements, capacity and back-of-envelope estimation, and mapping requirements to a high-level architecture, then reasoning explicitly about competing options on cost, complexity, latency, and reliability to defend a choice. Covers driving a design interview from ambiguity to a proposal, trade-off frameworks, decision-making under uncertainty and incomplete information, reversible-versus-irreversible decisions, and defending choices under scrutiny. The process-and-judgment skill underneath every system-design case study.