Pretty standard stuff if you've seen it before.
Clarify the requirements (fixed capacity, get/put, eviction of least recently used) and then propose a solution using a hash map and a doubly linked list to achieve O(1) time for both operations. Walk through the design, explaining how the map provides fast access to nodes and the list maintains usage order, then discuss edge cases and potential optimizations.
Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases and avoid null checks, and note that this design is thread-safe if needed with additional synchronization.
Ask about expected capacity, whether operations need to be thread-safe, and if there are any constraints on time/space complexity. Confirm that get should return -1 if key not found and put should update value if key exists.
Select a hash map for O(1) key lookup and a doubly linked list to maintain access order. Explain that the map stores key to node references, and the list orders nodes from most recently used (head) to least recently used (tail).
For get: if key exists, move node to head and return value; else return -1. For put: if key exists, update value and move to head; else create new node, add to head, and if capacity exceeded, remove tail node and delete its key from map.
Discuss handling capacity 0 or 1, updating existing keys, and using sentinel nodes to simplify list operations. Mention that all operations are O(1) time and O(capacity) space.
Walk through a small example to demonstrate correctness, and consider writing unit tests for scenarios like eviction, updating existing keys, and accessing keys to change recency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easier than I expected after the main cache part.
Clarify that the cache is implemented as a hash map plus a doubly linked list to achieve O(1) operations, and that the snapshot should return keys in MRU-to-LRU order. Then describe how to traverse the linked list from head to tail, collecting keys, and discuss trade-offs like time complexity, memory, and thread safety.
Pro tip: Mention that if the snapshot is called frequently, maintaining a separate ordered structure or using a read-write lock can balance performance and consistency, showing you consider real-world usage.
Confirm the cache data structure (e.g., hash map + doubly linked list), the definition of 'in order' (MRU to LRU), and whether the snapshot should be a deep copy or a view.
Traverse the doubly linked list from the head (most recently used) to the tail (least recently used), collecting each key into a list. Ensure the traversal is O(n) and does not modify the cache.
Discuss thread safety: use a lock (e.g., read-write lock) to prevent modifications during traversal, or document that the snapshot is a best-effort view if lock-free.
Compare time and space complexity: O(n) time and O(n) space for the snapshot. Mention alternatives like maintaining a separate ordered list (O(1) snapshot but higher update cost) or returning an iterator (lazy, but risk of concurrent modification).
Write clean, efficient code with comments, handling edge cases like empty cache. If time permits, mention unit tests for order and concurrency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what 'snapshot' means in this context, the expected output format, and how ordering should be defined. Then propose a solution that groups keys by value using a hash map, while preserving the original order of keys within each group and the order of groups based on first occurrence. Discuss trade-offs between compression ratio, time/space complexity, and stability of ordering.
Pro tip: Mention that preserving ordering across groups can be achieved by tracking the first occurrence index of each value, and that this approach maintains O(n) time complexity. Also, highlight that if the snapshot is large, you might consider streaming or external sorting, but for typical interview scenarios, an in-memory solution is acceptable.
Ask questions to understand what the snapshot represents, the expected output format, and any constraints on ordering (e.g., stable order, sorted order). Confirm whether compression should be lossless and how groups should be ordered.
Propose using a hash map to group keys by their values. Iterate through the snapshot in order, appending each key to the list for its value. This preserves the original order of keys within each group.
Decide how to order the groups themselves. A natural choice is by the first occurrence of each value in the original snapshot. Track the first occurrence index for each value to sort groups accordingly.
Discuss time and space complexity: O(n) time and O(n) space for the hash map and output. Mention trade-offs: higher compression may require more complex ordering, and if order doesn't matter, sorting groups by value could be simpler.
Address edge cases like empty snapshot, duplicate values, and non-hashable values. If the snapshot is very large, discuss external sorting or streaming approaches. Also, consider if the output should be serialized in a specific format.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.