Started with the basics of a doubly linked list plus hashmap, but they pushed hard on concurrency almost immediately.
Start by clarifying requirements (expected read/write ratio, latency targets, consistency needs) and then design the API and data structures. Walk through concurrency control, eviction, TTL, and observability, explaining trade-offs at each step. Conclude with how you would test and monitor the service in production.
Pro tip: Emphasize that thread-safety and eviction correctness must be considered together; a common pitfall is to use a lock per operation but forget that eviction and TTL expiration also mutate shared state. Propose a design that minimizes lock contention, such as sharding or read-write locks, and discuss how you would verify correctness under concurrency with stress tests.
Ask about expected throughput, read/write ratio, latency SLAs, consistency requirements, and whether the cache is in-process or distributed. Define the API surface (e.g., get, put, delete) and error handling.
Choose a hash map for O(1) access and a doubly linked list for LRU ordering. Explain how to handle TTL (e.g., timestamps per entry, lazy vs. active expiration) and size limits (max entries or memory-based).
Select synchronization primitives (mutex, read-write lock, sharded locks) and justify based on contention. Ensure atomicity of operations that combine map and list updates, and handle eviction and TTL expiration safely.
Instrument hit/miss ratio, eviction count, latency, and error rates. Expose via a metrics endpoint (e.g., Prometheus) and integrate with logging and tracing for debugging.
Outline unit tests for eviction and TTL, concurrency stress tests, and integration tests. Mention deployment considerations like warm-up, graceful shutdown, and monitoring alerts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then propose a distributed caching architecture that addresses each concern (hot keys, replication, failover, backpressure, consistency) with specific techniques and trade-offs. Emphasize that the solution should be tailored to Adyen's high-throughput, low-latency payments environment, and discuss how you would measure and iterate.
Pro tip: Frame your answer around the CAP theorem and explicitly state which trade-offs you prioritize (e.g., availability over strong consistency for cache) and why that aligns with Adyen's business needs. Also, mention that you would start with a simple solution and only add complexity when metrics prove it necessary.
Ask about scale (QPS, data size), latency SLAs, consistency requirements, and failure tolerance. Confirm that the cache is for read-heavy workloads and that eventual consistency is acceptable for most use cases.
Propose a partitioned, replicated cache using consistent hashing (e.g., Redis Cluster) to distribute keys. Discuss replication strategies (async vs sync) and how to handle hot keys via local caching or key splitting.
Explain how to detect failures (heartbeats, health checks) and automatically promote replicas. For backpressure, suggest techniques like request throttling, circuit breakers, and bounded queues to prevent overload.
Describe cache invalidation strategies (TTL, write-through, write-behind) and how to handle consistency across replicas (e.g., read-your-writes with sticky sessions or versioning). Discuss trade-offs between consistency and latency.
Outline key metrics (hit rate, latency, error rates) and how to use them to tune the system. Mention the importance of load testing and gradual rollout to validate the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Second big question and I was already a bit drained.
Start by clarifying the problem scope and requirements, then propose a high-level architecture that separates stateless API gateways from stateful workers. Dive into each concern (memory, caching, deduplication, rate limiting, timeouts, idempotency) with concrete strategies and trade-offs, emphasizing scalability and reliability.
Pro tip: Emphasize idempotency and deduplication as key to handling spikes without duplicating work, and discuss how precomputation and caching can reduce compute load. Show awareness of cost and latency trade-offs in your design choices.
Ask about expected request volume, spike patterns, latency SLAs, data size, and consistency needs. Define what 'transfer combinations' means and the compute complexity.
Propose a layered design: stateless API gateways for request handling, a queue for buffering, and stateful workers for heavy computation. Discuss horizontal scaling and load balancing.
Detail strategies for per-request memory management (e.g., streaming, bounded data structures), caching and precomputation (e.g., memoization, precomputed tables), request deduplication (e.g., idempotency keys, bloom filters), rate limiting (e.g., token bucket per user/IP), timeouts (e.g., deadline propagation, cancellation), and idempotency (e.g., idempotent operations, exactly-once semantics).
Discuss trade-offs between stateless vs stateful workers, caching vs freshness, and rate limiting strictness. Suggest optimizations like adaptive rate limiting, circuit breakers, and autoscaling.
Recap the design, highlighting how it handles spikes and ensures reliability. Ask if the interviewer wants to dive deeper into any area.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Capacity estimation I handled okay, rough numbers for memory per request times concurrency, then worked backward from a target RPS.
Start by clarifying the API's purpose, expected traffic patterns, and spike characteristics. Then walk through a structured capacity estimation using a mix of theoretical modeling and empirical data, followed by a testing plan that includes load, stress, and spike tests with clear success criteria. Emphasize iterative refinement and monitoring.
Pro tip: Tie your capacity estimates to business metrics (e.g., transactions per second during peak sales) and propose a phased testing approach that starts with small-scale experiments before full-scale spike tests to avoid production incidents.
Ask about expected peak TPS, payload sizes, latency SLOs, and spike duration. State assumptions explicitly to ground your estimates.
Use a combination of top-down (business volume) and bottom-up (resource-based) calculations to derive required instances, CPU, memory, and network. Include headroom for spikes.
Outline load tests to validate baseline, stress tests to find breaking points, and spike tests to simulate sudden traffic surges. Define metrics like error rate, latency, and throughput.
Run tests in a staging environment that mirrors production, analyze results, and adjust capacity estimates and infrastructure accordingly. Automate tests for regression.
Set up real-time monitoring and alerting for key metrics, and define auto-scaling policies to handle spikes dynamically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.