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).
CDN Caching and Content Delivery
Strategies and operational trade offs for caching and delivering content at edge locations. Coverage includes HTTP caching semantics and headers, cache keys and cache partitioning, edge caching versus origin, cache invalidation and purge workflows, origin shielding, serving dynamic content through caches, cost and latency trade offs, handling origin outages with cache fallbacks, signed and private content delivery, and metrics and testing approaches for content correctness and cache efficiency.
Payments and Idempotency Systems
Design, reliability, and troubleshooting of payment flows and idempotency mechanisms. Coverage includes integration with payment gateways, the transaction lifecycle, duplicate charges and reconciliation, idempotency key design and enforcement, deduplication windows, compensating transactions and the saga pattern for long running operations, error classification and retry policies, observability and reconciliation tooling, dispute and chargeback handling, testing of partial success scenarios, and regulatory and payment card industry considerations.
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.
Data Consistency and Distributed Transactions
In depth focus on data consistency models and practical approaches to maintaining correctness across distributed components. Covers strong consistency models including linearizability and serializability, causal consistency, eventual consistency, and the implications of each for replication, latency, and user experience. Discusses CAP theorem implications for consistency choices, idempotency, exactly once and at least once semantics, concurrency control and isolation levels, handling race conditions and conflict resolution, and concrete patterns for coordinating updates across services such as two phase commit, three phase commit, and the saga pattern with compensating transactions. Also includes operational challenges like retries, timeouts, ordering, clocks and monotonic timestamps, trade offs between throughput and consistency, and when eventual consistency is acceptable versus when strong consistency is required for correctness (for example financial systems versus social feeds).
System Architecture and Component Interactions
Evaluates understanding of how components interact in a system architecture, including databases, caches, message queues, microservices, load balancers, and external services. Interviewers assess ability to diagram data flows, reason about failure domains, propagation of faults, dependency surfaces, scaling and bottleneck tradeoffs, and strategies for isolation and resilience.
Message Queues and Asynchronous Processing
Fundamental concepts of asynchronous systems that use message brokers and background job processing. Candidates should explain how messages are enqueued and consumed, acknowledgement semantics and visibility timeouts, retry strategies and dead letter queues, ordering and partitioning trade offs, idempotency and deduplication strategies, and common failure modes such as consumer crashes or poisoned messages. The topic also covers monitoring queue depth and latency and debugging delayed or failed jobs.
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.
Data Modeling and API Design
Evaluate a candidate ability to design clean, extensible data models and service interfaces for complex domains. Topics include modeling entities and relationships, normalization and denormalization trade offs, indexing and query patterns, schema evolution and migrations, backwards compatibility, API contract design for cross team collaboration, pagination and filtering strategies, error handling and idempotency, authentication considerations, and documenting contracts. Candidates should be able to balance performance, maintainability, and operational complexity while enabling future evolution.