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).
End-to-End Feature Design and Development
The integrated 'build a complete feature' walkthrough: taking one concrete feature (a follow button, a like button, a comments thread, a create-post form, a profile editor, a large file upload) from the user-facing flow through the API contract, backend logic, data model and storage in a single coherent answer. Includes optimistic UI and the backend idempotency it needs, concurrent and repeated requests, denormalized counts that must stay correct, client and server validation with errors mapped back to the right field, a contract shared by web and mobile clients, and how the feature is tested, rolled out behind a flag and observed end to end. Tests whether the candidate connects the layers and makes consistent trade-offs across them rather than depth in any one. Deep dives in a single layer (UI state, API style, schema, caching, pipelines, observability, security and authentication, rate limiting), payments and notification delivery, feature-flag platforms, migrations and deployment pipelines, and standalone large-scale system design are covered elsewhere.
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.
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.