Started with a hash map, explained O(1) average for all three ops, talked through collision handling briefly.
Start by clarifying requirements (e.g., expected operations, concurrency, persistence) and then propose a hash table as the core data structure. Walk through the implementation of get, set, and delete, and analyze time and space complexity for each operation. Optionally, discuss extensions like thread safety or eviction policies if relevant.
Pro tip: Demonstrate awareness of real-world constraints by mentioning concurrency and memory management, and be prepared to discuss trade-offs between different data structures (e.g., hash table vs. balanced tree).
Ask about expected operations, concurrency needs, persistence, and performance requirements to tailor your design.
Select a hash table for average O(1) operations, and justify why it's suitable over alternatives like balanced trees.
Describe how get, set, and delete work using the chosen data structure, including handling of collisions and resizing.
Provide time and space complexity for each operation, noting average vs. worst-case scenarios.
Mention potential enhancements like thread safety, persistence, or eviction policies to show depth.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Lazy expiration felt natural to explain: check the timestamp on get and return null if expired.
Start by clarifying requirements (TTL granularity, memory constraints, consistency needs) and then propose a design that combines lazy expiration on read with an optional background sweeper. Explain the trade-offs between the two approaches and justify your recommendation based on the use case.
Pro tip: Mention that lazy expiration alone can lead to memory bloat if keys are never accessed, so a background sweeper is often necessary for production systems. Also, discuss how to avoid race conditions between the sweeper and read/write operations.
Ask about TTL precision, expected read/write patterns, memory constraints, and whether expired keys must be removed immediately or can linger.
On each read, check if the key's expiration timestamp has passed; if so, treat it as missing and optionally delete it. This is simple and avoids background overhead.
Consider that lazy expiration only cleans keys that are accessed. A background sweeper periodically scans and removes expired keys to reclaim memory, but adds complexity and potential contention.
Ensure that expiration checks and deletions are atomic or properly synchronized to avoid race conditions with concurrent reads/writes. Use locks or atomic operations as needed.
Compare lazy vs. active expiration: lazy is simpler but may waste memory; active is more complex but keeps memory bounded. Suggest hybrid approaches (e.g., probabilistic sweeps) or using existing libraries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the key-value store's expected workload (read/write ratio, contention, latency requirements) and then systematically compare each locking strategy against those requirements. For each approach, discuss correctness, performance, scalability, and implementation complexity, and conclude with a recommendation based on the workload.
Pro tip: Demonstrate that you understand the trade-offs by mentioning real-world examples (e.g., Java's ConcurrentHashMap uses lock striping, Redis is single-threaded) and by acknowledging that the best choice depends on the specific access patterns and consistency guarantees.
Ask about the expected read/write ratio, number of concurrent clients, latency and throughput targets, and consistency requirements. State any assumptions you make.
Briefly explain how each method works: single global lock, reader-writer locks, sharded locks, and lock-free (e.g., CAS-based) approaches.
Compare the approaches on correctness, performance (throughput, latency), scalability (contention, core count), complexity, and memory overhead.
Choose the most suitable approach for the given scenario, justifying your choice with the earlier analysis. Mention possible hybrid solutions.
Outline how you would implement and test the chosen solution, including stress testing, race detection, and monitoring for contention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Picked LRU and justified it with the doubly-linked list plus hash map combo giving O(1) moves.
Start by clarifying the workload characteristics (access patterns, data size, cost of miss) and then compare LRU, LFU, and TTL on those dimensions. Justify your choice by tying it to the specific requirements and mention hybrid approaches if appropriate.
Pro tip: Acknowledge that eviction is a trade-off between hit rate and implementation complexity; showing awareness of real-world systems (e.g., Redis uses approximated LRU with sampling) demonstrates practical maturity.
Ask about the workload: Is it read-heavy or write-heavy? Are there temporal or frequency patterns? What is the cost of a cache miss? Are there TTL constraints?
Briefly define LRU (evict least recently used), LFU (evict least frequently used), and TTL (evict expired items). Mention their ideal use cases and limitations.
Discuss pros and cons: LRU handles recency well but can be polluted by scans; LFU handles frequency but can be slow to adapt; TTL ensures freshness but may evict useful items.
Select a policy based on the clarified requirements, or propose a hybrid (e.g., LRU with TTL, or LFU with aging). Explain why it best balances hit rate, complexity, and cost.
Briefly note how you would implement it efficiently (e.g., LRU with hash map + doubly linked list, LFU with frequency buckets, TTL with min-heap) and any approximations for scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.