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).
Requirements to Architecture Mapping
Bridges business and customer requirements to concrete architectural or non functional specifications. Candidates should extract throughput, concurrency, availability, latency, durability, security, compliance and budget constraints from scenarios and translate them into measurable goals such as requests per second targets, latency SLOs, durability levels, retention and encryption requirements. The topic includes creating a requirements matrix that directly informs component choices, capacity planning, and trade off justification.
High Availability and Disaster Recovery
Designing systems to remain available and recoverable in the face of infrastructure failures, outages, and disasters. Candidates should be able to define and reason about Recovery Time Objective and Recovery Point Objective targets and translate service level agreement goals such as 99.9 percent to 99.999 percent into architecture choices. Core topics include redundancy strategies such as N plus one and N plus two, active active and active passive deployment patterns, multi availability zone and multi region topologies, and the trade offs between same region high availability and cross region disaster recovery. Discuss load balancing and traffic shaping, redundant load balancer design, and algorithms such as round robin, least connections, and consistent hashing. Explain failover detection, health checks, automated versus manual failover, convergence and recovery timing, and orchestration of failover and reroute. Cover backup, snapshot, and restore strategies, replication and consistency trade offs for stateful components, leader election and split brain mitigation, runbooks and recovery playbooks, disaster recovery testing and drills, and cost and operational trade offs. Include capacity planning, autoscaling, network redundancy, and considerations for security and infrastructure hardening so that identity, key management, and logging remain available and recoverable. Emphasize monitoring, observability, alerting for availability signals, and validation through chaos engineering and regular failover exercises.
Scalability and Future Extension
Design systems that scale: handle 10 items, 1000 items, 10,000 items efficiently. Design for future feature additions without major refactoring. Use abstraction and interfaces to allow flexibility. Discuss how your solution would adapt if requirements changed. This shows you think beyond the immediate requirement.
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.
Surge Pricing and Dynamic Pricing System Design
Design considerations for building a scalable, low-latency surge pricing engine and dynamic pricing system within a distributed architecture. Covers data modeling for pricing rules, real-time computation, demand/supply signal integration, multi-region consistency, latency and throughput requirements, caching and cache invalidation strategies, event-driven and microservices approaches, fault tolerance, data synchronization with inventory and orders, feature flags and A/B testing, deployment strategies, monitoring, and reliability concerns.
Architecture Patterns and Tradeoffs
Covers decision making between monolithic and microservice architectures and the broader architecture pattern trade offs that drive those choices. Candidates should explain when a monolith is appropriate versus when to adopt or migrate to microservices, describing technical and organizational criteria such as team size and structure, release cadence, performance and scaling requirements, and business domain boundaries. Expect discussion of trade offs including operational complexity, service independence, deployment velocity, testing and debugging difficulty, data consistency and transactions, latency, cost of infrastructure and monitoring, and security and governance. Evaluate knowledge of Conway law and how team boundaries shape architecture decisions. Be able to articulate migration strategies and concrete approaches such as the strangler pattern, bounded context decomposition, incremental service extraction, API versioning, database migration techniques, deployment and testing strategies to minimize disruption, rollout and rollback plans, and metrics for measuring migration success.
Technical Risk Assessment and Mitigation
Technical risk assessment and mitigation covers systematically identifying, prioritizing, and addressing potential failure modes and implementation pitfalls across architecture, integration, data migration, scalability, performance, security, third party dependencies, and team skill gaps. Candidates should demonstrate methods for analyzing and categorizing risks, such as fault tree analysis and failure mode and effects analysis, and describe practical mitigations including staged rollouts, canary deployments, redundancy and failover, rollback and contingency plans, increased testing, capacity planning, security hardening, monitoring and observability, runbooks, and training or vendor support. Interviewers expect discussion of validation strategies for mitigations, including dry runs, experiments, load and performance testing, chaos engineering, staged deployments, and monitoring driven verification before full production release. Strong answers will show how to prioritize by likelihood and impact, trade off cost and schedule, define measurable success criteria, and iterate on mitigations based on operational feedback.
Multi Region and Geo Distributed Systems
Designing and operating systems and infrastructure that span multiple geographic regions and cloud or on premise environments. Candidates should cover data placement and replication strategies and trade offs such as synchronous versus asynchronous replication, single primary versus multi master topologies, read replica placement, quorum selection, conflict detection and resolution, and techniques for minimizing replication lag. Discuss consistency models across regions including strong, causal, and eventual consistency, cross region transactions and the trade offs of two phase commit versus compensation patterns or eventual reconciliation. Explain latency optimization and traffic routing strategies including read and write locality, routing users to the nearest region, domain name system based routing, anycast, global load balancers, traffic steering, edge caching and content delivery networks, and deployment techniques such as blue green and canary rollouts across regions. Cover network and interconnect considerations such as direct private links, virtual private network tunnels, internet based links, peering strategies and internet exchange points, bandwidth and latency implications, and how they influence failover and replication choices. Describe availability zones and their role in fault isolation, how to design for high availability within a region using multiple availability zones, and when to use multi region active active or active passive topologies for resilience. Plan for disaster recovery and resilience including failover detection and automation, backup and restore, recovery time objectives and recovery point objectives, cross region failover testing, run books, and operational playbooks. Include security, identity, and compliance concerns such as data residency and sovereignty, regulatory constraints, cross border encryption and key management, identity federation and authorization across regions, and cost and legal implications of region selection. Discuss operational practices including monitoring and alerting for region health and replication metrics, capacity planning, deployment automation, observability, run book procedures, and testing strategies for simulated region failures. Finally reason about workload partitioning and state localization, replication frequency, read and write locality, cost and complexity trade offs, and provide concrete patterns or examples that justify chosen architectures for global user bases.
Load Balancing and Traffic Distribution
Covers why load balancers are used and how traffic is distributed across backend servers to avoid single server bottlenecks, enable horizontal scaling, and provide fault tolerance. Candidates should know common distribution algorithms such as round robin, least connections, weighted balancing, and consistent hashing, and understand trade offs among them. Explain the difference between layer four and layer seven load balancing and the implications for routing, request inspection, and protocol awareness. Discuss stateless design versus stateful services, the impact of session affinity and sticky sessions, and alternatives such as external session stores or token based sessions to preserve scalability. Describe high availability and resilience patterns to mitigate a single point of failure, including active active and active passive configurations, health checks, connection draining, and global routing options such as DNS based and geo aware routing. At senior and staff levels, cover advanced capabilities like request routing based on metadata or headers, weighted traffic shifting for canary and blue green deployments, traffic mirroring, rate limiting and throttling, integration with autoscaling, and strategies for graceful degradation and backpressure. Also include operational concerns such as secure termination of transport layer security, connection pooling, caching and consistent hashing for caches, monitoring and observability, capacity planning, and common debugging and failure modes.