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).
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.
Offline First Architecture and Data Synchronization
Designing systems and applications that work seamlessly without network connectivity and reliably synchronize state when connectivity returns. Core areas include local first data models and client side storage strategies, efficient synchronization protocols and delta encoding, approaches for conflict detection and resolution, and trade offs between strong and eventual consistency. Candidates should understand algorithms and patterns such as operational transformation and conflict free replicated data types, optimistic versus pessimistic concurrency, reconciliation and merge strategies, and techniques for preserving ordering and causality such as vector clocks and logical clocks. Practical concerns include batching and incremental sync, retry and backoff strategies, partial and resumable synchronization, idempotent operations, schema migration and versioning, encryption and access control for local data and transport, handling network transitions and intermittent connectivity, background synchronization and push update coordination, and testing and observability for sync correctness and performance. Typical application domains include mobile apps, offline maps, note taking, messaging, and financial or transactional flows where correctness, durability, and user experience during offline periods are critical.
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).
Trade Off Analysis and Decision Frameworks
Covers the practice of structured trade off evaluation and repeatable decision processes across product and technical domains. Topics include enumerating alternatives, defining evaluation criteria such as cost risk time to market and user impact, building scoring matrices and weighted models, running sensitivity or scenario analysis, documenting assumptions, surfacing constraints, and communicating clear recommendations with mitigation plans. Interviewers will assess the candidate's ability to justify choices logically, quantify impacts when possible, and explain governance or escalation mechanisms used to make consistent decisions.
Scalability and Performance
Focuses on capacity planning, performance tradeoffs, and strategies for handling growth. Topics include the relationship between latency, throughput, consistency, and availability, when to accept eventual consistency, vertical versus horizontal scaling, caching, sharding, load distribution, back pressure and throttling patterns, performance testing and benchmarking, capacity forecasting, and triggers for scaling decisions. Candidates should be able to identify bottlenecks, justify tradeoffs between cost and performance, and recommend mitigation approaches for common performance problems.
Scalability and Code Organization
Focuses on designing software and codebases that remain maintainable and performant as features and user load grow. Areas include modularity and separation of concerns, component and API boundaries, when and how to refactor, trade offs between monolith and service oriented architectures, data partitioning and caching strategies, performance optimization, testing strategies, dependency management, code review practices, and patterns for maintainability and evolvability. Interview questions may ask candidates to reason about design choices, identify coupling and cohesion issues, and propose practical steps to evolve an existing codebase safely.
State Management and Data Flow Architecture
Design and reasoning about where and how data is stored, moved, synchronized, and represented across the full application stack and in distributed systems. Topics include data persistence strategies in databases and services, application programming interface shape and schema design to minimize client complexity, validation and security at each layer, pagination and lazy loading patterns, caching strategies and cache invalidation, approaches to asynchronous fetching and loading states, real time updates and synchronization techniques, offline support and conflict resolution, optimistic updates and reconciliation, eventual consistency models, and deciding what data lives on the client versus the server. Coverage also includes separation between user interface state and persistent data state, local component state versus global state stores including lifted state and context patterns, frontend caching strategies, data flow and event propagation patterns, normalization and denormalization trade offs, unidirectional versus bidirectional flow, and operational concerns such as scalability, failure modes, monitoring, testing, and observability. Candidates should be able to reason about trade offs between latency, consistency, complexity, and developer ergonomics and propose monitoring and testing strategies for these systems.
Scaling Systems and Platforms Through Growth
Describe experiences scaling systems, platforms, or services through significant growth phases. Examples: scaling from 1 million to 100 million users, migrating from monolith to microservices as organization grew, or building infrastructure to support 10x team growth. For each example: What was working before that stopped working at scale? What bottlenecks did you encounter? How did you identify and address them? What architectural changes were necessary? How did you sequence the work to minimize disruption? What did you learn? Discuss both technical and organizational scaling—they're intertwined.
Real Time Data and Communication
Covers designing and implementing real time client server communication and data flows. Topics include transport mechanisms such as WebSockets, Server Sent Events, long polling and regular polling with appropriate intervals; connection lifecycle management including reconnection strategies, exponential backoff, and error recovery; data synchronization patterns including optimistic updates, conflict resolution, reconciliation strategies, and eventual consistency; offline first approaches, caching strategies, and state reconciliation when network connectivity is restored. Also includes streaming concerns such as memory management, backpressure, batching and windowing, message ordering and idempotency, sequencing and versioning, and techniques for broadcasting and multicasting data to multiple clients using pub sub or message broker architectures. Considerations for authentication and authorization over realtime channels, scalability and load balancing for persistent connections, monitoring and observability, and trade offs between latency, consistency and throughput are also assessed. Candidates may be asked to design end to end solutions, justify technology choices, and explain implementation details and failure modes.