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).
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.
Trade-Off Analysis and Justification
Ability to identify key nonfunctional requirements and constraints and to compare alternative designs with clear, quantitative reasoning. Expect discussion of consistency versus availability, latency versus throughput, cost versus performance, operational complexity, and implementation risk. Candidates should demonstrate how to quantify trade offs using metrics such as latency percentiles, throughput, cost per request, and availability targets, how to choose appropriate consistency models and failure modes, and how to document and justify the selected architecture given product and business priorities.
Stateful Service Design
Design services that maintain state across requests and nodes and reason about their correctness and reliability. Topics include consistency models and trade offs, transactions and isolation, replication and leader election, sharding and partitioning strategies, cache design and eviction policies, durable queues and ordering guarantees, idempotency and concurrency control, failure modes and recovery patterns, and operational concerns such as backups, migrations, and testing for stateful components.
Concurrency and Distributed Algorithms
Covers fundamental concurrency primitives and distributed algorithm concepts that are commonly tested in system and backend interviews. Candidates should understand thread safety, race conditions, locks and lock free techniques, mutual exclusion, atomic operations, and memory visibility. In the distributed domain candidates should be able to explain consistency models such as strong consistency and eventual consistency, trade offs between consistency and availability under network partitions, partitioning and replication strategies, and common patterns for consensus and leader election. Practical problems include designing thread safe queues, concurrent counters, or distributed caches and describing how to test and debug concurrency and partial failure modes.
System Design and Reliability
Design principles and trade offs for building highly scalable and reliable distributed systems. Expect discussion of capacity planning, partitioning and sharding, caching and load balancing strategies, replication and consistency models, latency and throughput trade offs, fault tolerance, graceful degradation, redundancy, disaster recovery, monitoring and alerting, and postmortem culture. Candidates should reason about non functional requirements and propose architectures meeting targets for scale, performance, and operational resilience.
Dependency Failures and Graceful Degradation
Handling failures in external services or dependencies: rate limiting (HTTP 429), timeouts, quota exhaustion. Understanding circuit breakers, intelligent retries, and how to design services that behave well when dependencies fail. Knowing when to disable features vs. when to queue/cache.
System Architecture and Tradeoffs
Ability to decompose complex systems into components and define clear responsibilities, interfaces, and interactions. Evaluate architectural alternatives and articulate core trade offs such as consistency versus availability, latency versus throughput, simplicity versus extensibility, and cost versus performance. Explain how design choices affect scalability, resilience, failure modes, and operational burden, and justify architecture decisions based on expected load patterns and business requirements.
Stateful Services and Distributed Systems Patterns
This topic covers design and implementation patterns for building stateful distributed systems in production. Candidates should be able to discuss data partitioning and sharding, replication and leader election strategies, consistency models and their trade offs, consensus algorithms, idempotent processing and at least once versus exactly once semantics, event sourcing and materialized views, stream processing with state and checkpointing, caching and eviction policies, distributed transactions or saga patterns, and techniques for scaling state under high concurrency. Strong answers also cover monitoring, observability, testing strategies for correctness under failure, and operational practices for safe rollouts and recovery.
Algorithm Design & Real-Time System Optimization
Algorithm design techniques and real-time optimization strategies applicable to distributed systems and latency-sensitive architectures. Covers scheduling, resource management, concurrency, distributed algorithms, load balancing, and performance optimization under strict latency requirements.