Start by clarifying the requirements and constraints, then propose a design using a hash map and a doubly linked list to achieve O(1) operations. Walk through the implementation details, including edge cases and potential optimizations, and discuss how you would test the solution.
Pro tip: Mention that you would use a doubly linked list to maintain the access order and a hash map for O(1) lookups, and highlight that this combination is the standard for LRU caches. Also, discuss thread-safety if the cache might be used in a concurrent environment, as Oracle often deals with high-performance systems.
Ask about the expected operations, capacity, and whether thread-safety is required. Confirm that get and put must be O(1) on average.
Explain that a hash map provides O(1) access to cache entries, and a doubly linked list maintains the order of usage, allowing O(1) removal and insertion.
Describe how get moves the accessed item to the front (most recently used), and put adds or updates an item, evicting the least recently used item when capacity is exceeded.
Discuss handling of capacity 0 or 1, updating existing keys, and ensuring the eviction policy works correctly when the cache is full.
Write clean code with helper functions for adding/removing nodes, and outline test cases including basic operations, eviction, and concurrency if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: TTL semantics, eviction policy, and concurrency needs. Then present a baseline approach (e.g., lazy expiration with a min-heap) and compare it to alternatives like timing wheels or hierarchical caches, discussing trade-offs in time/space complexity, accuracy, and operational overhead. Conclude with a recommendation based on the use case.
Pro tip: Mention that TTL and LRU can conflict: an expired entry might still be the most recently used, so you need to decide whether expiration should trigger immediate eviction or be handled lazily. Also, highlight that in distributed systems, TTL often requires a background sweeper to avoid memory leaks.
Ask about TTL semantics (per-entry, global, sliding vs. absolute), expected load, memory constraints, and whether strict expiration is required. This ensures you design for the right problem.
Extend the LRU cache with a min-heap keyed by expiration time. On access, check if the entry is expired; if so, evict it. Periodically or on eviction, remove expired entries from the heap. Discuss complexity: O(log n) for heap operations, but lazy checks add overhead.
Use a timing wheel (hierarchical or simple) to efficiently track expiration times. This provides O(1) insertion and deletion, and periodic advancement triggers expiration. Trade-off: more complex to implement and may have coarser granularity.
Run a background thread that periodically scans and evicts expired entries. This decouples expiration from access, but can cause memory spikes and requires synchronization. Trade-off: simplicity vs. potential latency and memory overhead.
Summarize trade-offs: min-heap is simple but adds O(log n) overhead; timing wheel is efficient but complex; background sweeper is easy but may delay eviction. Recommend based on requirements (e.g., for high-throughput, timing wheel; for simplicity, lazy with heap).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.