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).
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.
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.
System Architecture Principles
Core principles and patterns for designing and evaluating high level and system architectures for distributed and cloud based systems. Candidates should understand high availability and redundancy, fault tolerance and graceful degradation, and how to design stateless and stateful components. They should be able to explain scalability and capacity planning strategies including horizontal and vertical scaling, partitioning and sharding, load balancing, caching and replication, and the trade offs involved. The topic covers consistency models and the trade offs between consistency, availability and partition tolerance, performance and latency optimization, reliability and durability, security for data and access control, and cost efficiency. Candidates should be able to discuss fault domains and why critical components are replicated across availability zones and regions, as well as backup, recovery and disaster recovery approaches. Common architectural patterns such as monolithic and microservice architectures, layered design, event driven and message based systems, and command query responsibility segregation are relevant. Monitoring and observability practices including metrics, logging, distributed tracing and alerting are part of assessments, together with the ability to justify architecture decisions based on functional and nonfunctional requirements, constraints, expected load and operational complexity.
Architectural Decision Making
Assess how a candidate thinks through major system and technical decisions, including selecting architectures, technologies, and technical strategies. Expect discussion of evaluation criteria such as performance, reliability, scalability, complexity, cost, development velocity, team capability, maintenance burden, and long term evolution. Candidates should explain specific past decisions with clear articulation of the options considered, trade offs accepted, risk mitigation, observed consequences over time, what they would change with current knowledge, and evidence of nuanced judgment when balancing competing priorities. For senior and staff levels, this includes demonstrating influence across teams when making architecture calls, recognizing organization level costs of choices, and surfacing hidden operational or people costs.
Technical Leadership and Architectural Influence
Demonstrating leadership in technical decisions at the architecture or system level. Candidates should prepare concrete examples where they identified architectural problems, evaluated alternative solutions and trade offs, proposed a preferred design, gained buy in from engineers and stakeholders, and drove implementation. Discuss systems thinking and long term impact on team velocity, code quality, reliability, and product features. Include examples of championing new tools or frameworks, leading migrations or refactors, negotiating trade offs between time to market and technical debt, and occasions when you reversed a decision based on new data. Emphasize communication of complex technical ideas, consensus building with peers, and measurable outcomes.
Lyft ETA & Routing System Architecture
Design and architecture of large-scale ride-hailing ETA and routing systems, covering distributed system design, real-time data processing, routing algorithms, fault tolerance, geo-distributed services, data consistency considerations, and integration with external mapping and traffic data sources.