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.
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.
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.
Fault Tolerance and Failure Scenarios
Designing systems resilient to component failures: timeouts, retries with exponential backoff, circuit breakers, bulkheads. Discuss cascading failure prevention and graceful degradation. At Staff level, demonstrate thinking about multi-layer failures (service failures, database failures, network partitions) and how to detect and recover from them.
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.
Architecture and Technical Trade Offs
Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.
System Design in Coding
Assess the ability to apply system design thinking while solving coding problems. Candidates should demonstrate how implementation level choices relate to overall architecture and production concerns. This includes designing lightweight data pipelines or data models as part of a coding solution, reasoning about algorithmic complexity, throughput, and memory use at scale, and explaining trade offs between different algorithms and data structures. Candidates should discuss bottlenecks and pragmatic mitigations such as caching strategies, database selection and schema design, indexing, partitioning, and asynchronous processing, and explain how components integrate into larger systems. They should be able to describe how they would implement parts of a design, justify code level trade offs, and consider deployment, monitoring, and reliability implications. Demonstrating this mindset shows the candidate is thinking beyond a single function and can balance correctness, performance, maintainability, and operational considerations.
Deep Dive into Complex System or Project
Being prepared to discuss any significant system or project from your background in detail. Be ready for followup questions testing depth of understanding. Interviewers will probe: What were the constraints? How did you make key decisions? What would you do differently? What surprised you? This validates that your understanding is genuine, not just surface-level.
Basic Fault Tolerance Patterns
Understanding common patterns that make systems fault-tolerant: replication (data redundancy across multiple servers), failover (switching to backup when primary fails), circuit breakers (stopping requests to failing services to prevent cascades), retry with exponential backoff (intelligent retrying with delays), timeouts (preventing hanging requests), and graceful degradation (providing partial functionality when components fail). Know when each pattern is appropriate and its trade-offs. Understand that fault tolerance usually involves trade-offs: more replicas cost more but tolerate more failures.