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).
Server Side and Business Logic Implementation
Focuses on designing and writing backend code that implements business requirements and domain rules. Topics include separation of concerns between presentation application and domain layers, where to place business logic versus orchestration and infrastructure code, data validation and transformation, conditional and transactional logic, error handling and graceful degradation, input sanitization and security considerations, idempotency and concurrency control, database interaction patterns and transaction management, caching and performance trade offs, testing strategies including unit and integration tests for business rules, logging and observability, and maintainability practices such as modularization and clear interfaces.
Full Stack Architecture and Feature Design
Ability to design complete features considering frontend UX, backend API design, database schema, and data flow. Understanding of separation of concerns and communication between layers. API-first design thinking. Discussing trade-offs (e.g., complex queries on backend vs post-processing on frontend). Designing for extensibility and maintainability.
Scalability and Performance Optimization
Evaluates how a candidate measures, diagnoses and resolves performance and scalability challenges across the stack. Candidates should explain instrumentation and profiling approaches, define performance metrics such as latency and throughput, and present concrete before and after numbers for optimizations. Topics include caching strategies at different layers, database indexing and query optimization, denormalization trade offs, batching and asynchronous processing, efficient network usage and serialization, connection management and pooling, load balancing and horizontal scaling, partitioning and sharding strategies, client side optimizations such as asset bundling and lazy loading, and techniques for load testing and capacity planning. Interviewers expect discussion of trade offs, cost implications and how changes were validated in production.
Backend Layered Architecture and API Design
Covers server side layering patterns and API design best practices. Topics include controller or handler layers, service and business logic layers, data access layers, repository patterns, dependency injection, API design (RESTful, gRPC), versioning, authentication and authorization patterns, pagination and rate limiting, and when to apply specific patterns to improve maintainability and testability.
System Design and Architecture
Design large scale reliable systems that meet requirements for scale latency cost and durability. Cover distributed patterns such as publisher subscriber models caching sharding load balancing replication strategies and fault tolerance, trade off analysis among consistency availability and partition tolerance, and selection of storage technologies including relational and nonrelational databases with reasoning about replication and consistency guarantees.
Caching Strategies and Patterns
Comprehensive knowledge of caching principles, architectures, patterns, and operational practices used to improve latency, throughput, and scalability. Covers multi level caching across browser or client, edge content delivery networks, application in memory caches, dedicated distributed caches such as Redis and Memcached, and database or query caches. Includes cache design and selection of technologies, defining cache boundaries to match access patterns, and deciding when caching is appropriate such as read heavy workloads or expensive computations versus when it is harmful such as highly write heavy or rapidly changing data. Candidates should understand and compare cache patterns including cache aside, read through, write through, write behind, lazy loading, proactive refresh, and prepopulation. Invalidation and freshness strategies include time to live based expiration, explicit eviction and purge, versioned keys, event driven or messaging based invalidation, background refresh, and cache warming. Discuss consistency and correctness trade offs such as stale reads, race conditions, eventual consistency versus strong consistency, and tactics to maintain correctness including invalidate on write, versioning, conditional updates, and careful ordering of writes. Operational concerns include eviction policies such as least recently used and least frequently used, hot key mitigation, partitioning and sharding of cache data, replication, cache stampede prevention techniques such as request coalescing and locking, fallback to origin and graceful degradation, monitoring and metrics such as hit ratio, eviction rates, and tail latency, alerting and instrumentation, and failure and recovery strategies. At senior levels interviewers may probe distributed cache design, cross layer consistency trade offs, global versus regional content delivery choices, measuring end to end impact on user facing latency and backend load, incident handling, rollbacks and migrations, and operational runbooks.
Full Stack Architecture and Integration
Designing end to end application architecture across presentation, application programming interface layer, business logic, persistence, and infrastructure. Candidates should be able to reason about trade offs across frontend state management, client side processing versus server side work, API contract design, data modeling and database selection, caching strategies, offline and real time update mechanisms, observability and operational concerns, and how infrastructure choices constrain or enable patterns at each layer. Emphasis is on separation of concerns while recognizing when tighter integration is needed for performance, consistency, or reduced latency.
Data Consistency and Distributed Transactions
In depth focus on data consistency models and practical approaches to maintaining correctness across distributed components. Covers strong consistency models including linearizability and serializability, causal consistency, eventual consistency, and the implications of each for replication, latency, and user experience. Discusses CAP theorem implications for consistency choices, idempotency, exactly once and at least once semantics, concurrency control and isolation levels, handling race conditions and conflict resolution, and concrete patterns for coordinating updates across services such as two phase commit, three phase commit, and the saga pattern with compensating transactions. Also includes operational challenges like retries, timeouts, ordering, clocks and monotonic timestamps, trade offs between throughput and consistency, and when eventual consistency is acceptable versus when strong consistency is required for correctness (for example financial systems versus social feeds).
Caching and Asynchronous Processing
Design and operational patterns for reducing latency and decoupling components using caching layers and asynchronous communication. For caching, understand when to introduce caches, cache placement, eviction policies, cache coherence, cache invalidation strategies, read through and write through and write behind patterns, cache warming, and trade offs between consistency and freshness. For asynchronous processing and message driven systems, understand producer consumer and publish subscribe patterns, event streaming architectures, common brokers and systems such as Kafka, RabbitMQ, and Amazon Simple Queue Service, and the difference between queues and streams. Be able to reason about delivery semantics including at most once, at least once, and exactly once delivery, and mitigation techniques such as idempotency, deduplication, acknowledgements, retries, and dead letter queues. Know how to handle ordering, partitioning, consumer groups, batching, and throughput tuning. Cover reliability and operational concerns such as backpressure and flow control, rate limiting, monitoring and alerting, failure modes and retry strategies, eventual consistency and how to design for it, and when to choose synchronous versus asynchronous approaches to meet performance, scalability, and correctness goals.