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 low-latency, always-on systems: real-time communication (WebSockets, long-polling, server-sent events), presence and pub/sub fan-out, live streaming and video delivery, and real-time collaboration. Covers latency budgets, connection scaling, and delivering ordered updates to many concurrent clients. The design surface for chat, feeds, streaming, and collaborative apps.
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.
Mobile System Architecture and Offline-First Design
Architecting the mobile side of a distributed system: client-server sync, offline-first storage and conflict resolution, background processing, push notifications, and modular mobile architecture at scale. Covers syncing state across intermittent connectivity and designing backends for millions of mobile clients. The mobile-specific slice of distributed design.
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.
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.
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.
Data Consistency and Distributed Transactions
Maintaining correctness of state across services and replicas: eventual consistency, conflict resolution (last-write-wins, CRDTs, vector clocks), the saga pattern, two-phase commit, and idempotency keys for exactly-once effects. Covers when to trade strict consistency for availability and how to reason about read-your-writes and monotonic guarantees. Focuses on the application/service layer rather than storage-engine internals.