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).
Infrastructure Design for Scale and Reliability
Covers the principles and practical patterns for designing infrastructure that supports organizational growth while maintaining availability and predictable performance. Topics include redundancy and failover strategies, multi site and geographic distribution, capacity planning and growth forecasting, load balancing and traffic distribution, data locality and storage scaling, caching and consistency trade offs, fault isolation and degradation modes, disaster recovery and backup planning, observability and monitoring design, capacity testing and performance tuning, dependency mapping and minimization, and balancing cost, operational complexity, and reliability requirements. Candidates should be able to reason about trade offs, design for incremental growth, and describe tooling and testing approaches used to validate designs.
System Design and Architecture
Design large scale reliable systems that meet requirements for scale latency cost and durability. Cover distributed patterns such as publisher subscriber models caching sharding load balancing replication strategies and fault tolerance, trade off analysis among consistency availability and partition tolerance, and selection of storage technologies including relational and nonrelational databases with reasoning about replication and consistency guarantees.
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.
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.
Fault Tolerance and System Resilience
Designing systems to anticipate, tolerate, contain, and recover from component and network failures while minimizing customer impact and preserving correctness. Topics include identifying common failure modes and single points of failure, redundancy and isolation patterns at hardware, service, and geographic levels, and failover strategies including active active and active passive. Cover retry policies with exponential backoff, timeouts, circuit breaker and bulkhead patterns, graceful degradation, rate limiting, and backpressure techniques to protect systems during overload. Discuss orchestration of node rejoin and state rebuild, replication strategies and consistency trade offs, leader election and consensus implications, and techniques to avoid and mitigate split brain. Explain monitoring, health checks, alerting, and metrics such as mean time to recovery and mean time between failures to guide operational improvements. Include testing for resilience through chaos engineering and fault injection, handling flaky components in test environments, analysis of past failures and refactoring for resiliency, and operational practices that reduce blast radius and speed recovery.
System Design and Architecture Fundamentals
Comprehensive coverage of designing scalable, reliable, and maintainable software systems, combining foundational concepts, common architectural patterns, decomposition techniques, infrastructure design, and operational considerations. Candidates should understand core principles such as horizontal and vertical scaling, caching strategies and placement, data storage trade offs between relational structured query language databases and non relational databases, application programming interface design, load distribution and fault tolerance. They should be familiar with architectural styles and patterns including client server and layered architectures, monolithic and microservices decomposition, service oriented and event driven designs, gateway and proxy patterns, and resilience patterns such as circuit breakers and asynchronous processing. Assessment includes the ability to decompose a problem into logical components and layers, define component responsibilities, map data flows between ingestion processing storage and serving layers, and select appropriate infrastructure elements such as application servers caches message queues and database replication models. Interviewers evaluate estimation of scale and load and reasoning about trade offs such as consistency versus availability and partition tolerance latency versus throughput coupling versus cohesion and cost versus complexity, and the ability to justify architecture decisions. Candidates should be able to sketch high level designs, communicate architecture to technical and non technical stakeholders, propose migration paths such as when to combine or transition between patterns, and describe operational runbooks including failure mode mitigation monitoring observability and incident recovery. Practical topics include caching eviction policies such as least recently used and least frequently used load balancing approaches such as round robin and least connections rate limiting techniques replication and sharding strategies and design choices for synchronous request response versus asynchronous queue based messaging. Emphasis is on clarifying requirements estimating constraints proposing reasonable architectures and articulating trade offs and evolution paths rather than only low level implementation details.
Complex System Design for Mid Scale Operations
Design systems handling millions of concurrent users, multi-region operations, and complex operational requirements. Practice going deep on one aspect (e.g., designing a resilient database cluster) while covering architecture broadly. Show understanding of trade-offs between reliability, latency, and operational complexity.
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.
Scale and Complexity Experience
Experience supporting or building large scale systems and complex enterprise environments including high traffic applications, distributed systems, global operations, incident patterns, and operational trade offs. Candidates should be able to discuss scaling bottlenecks, observability strategies, capacity planning, and examples demonstrating handling complexity at product and infrastructure levels.