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).
Mobile Architecture and Modularity
High level architectural principles and practical patterns for building maintainable, scalable mobile applications and codebases. Topics include modular design and decomposition into feature modules, shared libraries, and core infrastructure components; defining clear module boundaries and application programming interfaces between modules; dependency management and versioning; strategies to avoid circular dependencies; patterns for inter module communication such as event driven messaging, callbacks, and dependency injection; separation of concerns between presentation, business logic, and data layers; build and continuous integration considerations for modular projects; packaging and deployment strategies; approaches to evolve architecture as teams and codebases grow, including code ownership, incremental refactoring, and migration plans; and ensuring modularity supports testability, observability, and performance at scale.
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.
Advanced Real World Problem Solving
Evaluate the candidates ability to solve complex multi layered technical and design problems by making reasonable assumptions, articulating trade offs, and handling edge cases. Candidates should show how to decompose problems that span networking caching persistence and performance optimization, select architectures and algorithms with explicit trade off analysis such as speed versus simplicity and functionality versus performance, and consider failure modes including network failures device limitations and concurrent access patterns. Strong responses include clear assumption statements, alternative approaches, complexity and cost considerations, testing and validation strategies, and plans to monitor and mitigate operational risks.
Meta Mobile Challenges and Solutions
Assess knowledge of company specific issues at scale and practical proposals to address them. Candidates should show awareness of the technical challenges that large consumer mobile platforms face such as cross platform consistency, modularization and code sharing, offline synchronization at scale, push and real time delivery infrastructure, performance and energy constraints, observability, privacy compliance, and rapid safe rollouts. Expected responses include concrete architectures, operational practices, telemetry strategies, and trade off analysis appropriate for high scale environments.
Long Term Sustainability and Scalability of Solutions
Designing infrastructure that will remain maintainable and effective over 3-5 years. Considering technical debt, documentation, knowledge transfer, and how solutions will evolve. Discussion of reducing operational burden and building systems that scale gracefully as demands grow.
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.
System Design and Scalability
Covers architectural thinking and design tradeoffs for building reliable, high performance systems. Topics include design decision reasoning given constraints such as cost, latency and availability; scaling strategies including horizontal and vertical scaling, load balancing, caching patterns, database partitioning and sharding, read replicas, and asynchronous processing; capacity planning and observability; spotting and explaining bottlenecks such as hot partitions, single points of failure, database locks and network limits; and communicating technical impact in business terms. Candidates should be able to justify choices, compare alternatives, and articulate metrics and monitoring approaches to validate design decisions.
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.