Start by clarifying requirements (capacity, thread-safety, eviction policy) and then describe the data structures: a hash map for O(1) key lookup and a doubly linked list to track recency. Explain how get and put operations manipulate the list and map to maintain O(1) time, and how eviction occurs when capacity is exceeded.
Pro tip: Mention that you would use a sentinel head and tail node to simplify edge cases in list manipulation, and discuss how you would make the cache thread-safe if needed (e.g., using locks or concurrent data structures).
Ask about expected capacity, whether the cache needs to be thread-safe, and if there are any constraints on key/value types. Confirm that the eviction policy is LRU.
Propose a hash map (dictionary) that maps keys to nodes in a doubly linked list. The list maintains the order of usage, with the most recently used at the head and least recently used at the tail.
If the key exists, retrieve the node from the map, move it to the head of the list (mark as most recently used), and return its value. If not, return null or -1.
If the key exists, update its value and move the node to the head. If not, create a new node, add it to the head, and add to the map. If capacity is exceeded, remove the tail node (least recently used) from both the list and the map.
Explain that both get and put are O(1) because hash map operations are O(1) and list insertions/deletions are O(1) with direct node references. Discuss edge cases like updating an existing key, evicting when capacity is 1, and handling null values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the concurrency requirements and the expected read/write ratio, then systematically compare locking strategies (global, segmented, readers-writer) and lock-free approaches, highlighting trade-offs in throughput, latency, and complexity. Conclude with a recommendation tailored to the scenario, emphasizing race condition prevention and memory safety.
Pro tip: Mention that you would measure contention and choose the simplest correct solution first, then optimize based on profiling—this shows pragmatism and avoids over-engineering.
Ask about expected read/write ratio, latency SLAs, and whether the cache is in-memory or distributed. This determines the appropriate concurrency strategy.
Discuss global locking (simple but low concurrency), segmented/per-bucket locking (higher concurrency with more complexity), and readers-writer locks (good for read-heavy workloads but writer starvation risk).
Explain lock-free techniques like atomic operations, CAS loops, and concurrent data structures (e.g., ConcurrentHashMap in Java). Highlight challenges like ABA problem and memory reclamation.
Detail how to prevent races via atomicity, ordering, and visibility (e.g., using volatile, memory barriers). Discuss safe memory reclamation (e.g., hazard pointers, epoch-based reclamation) to avoid use-after-free.
Choose a strategy based on trade-offs, e.g., segmented locking with read-write locks for read-heavy workloads, and mention fallback to global lock for simplicity if contention is low.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly stating the time and space complexity of your design's core operations, then discuss the tradeoffs between different approaches you considered. Finally, highlight production pitfalls such as scalability bottlenecks, data consistency issues, and operational overhead, and explain how you would mitigate them.
Pro tip: Tie the tradeoffs back to DoorDash's specific scale and latency requirements—showing you understand their business context makes your answer stand out. Also, mention monitoring and observability as key to catching production issues early.
Clearly articulate the time and space complexity of your design's key operations (e.g., read/write, search) and explain how you arrived at those bounds.
Compare alternative designs (e.g., indexing vs. no indexing, caching vs. no caching) and explain why you chose your approach, focusing on the balance between time and space.
List major pitfalls such as hot partitions, thundering herd, data skew, network latency, and failure modes, and describe how they manifest in production.
For each pitfall, suggest concrete mitigation strategies (e.g., sharding, rate limiting, circuit breakers, backpressure) and explain their impact on complexity.
Relate your choices to DoorDash's scale, latency requirements, and cost constraints, showing that you understand the practical implications for their system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.