Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Anthropic SWE interview with a classic LRU cache design question, but they pushed way further than the usual 'just use a hashmap and doubly linked list' answer. Spent a good chunk of time on the extensions and edge cases.

Questions Asked (1)

Design an in-memory key-value store with fixed capacity N that evicts the least recently used entry when full. Support get and put in amortized O(1) time and O(N) space. Walk through your data structures, pseudocode for both operations, and how you'd handle updates to existing keys, N=0, thread safety, and extensions for a peek operation and delete.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The core LRU part I knew cold.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the data structures, pseudocode for get and put, and explicitly address updates, N=0, thread safety, and extensions like peek and delete.

Pro tip: Emphasize the importance of handling edge cases like N=0 and updating existing keys without increasing size, and discuss trade-offs between different thread-safety approaches (e.g., coarse vs. fine-grained locking) to demonstrate depth.

1. Clarify Requirements and Edge Cases

Ask clarifying questions about expected operations, concurrency requirements, and edge cases like N=0. Confirm that get and put should be O(1) amortized and space O(N).

2. Propose Data Structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order. The hash map maps keys to nodes in the list, with the most recently used at the head and least recently used at the tail.

3. Detail Operations with Pseudocode

Write pseudocode for get (check map, move node to head, return value) and put (if key exists, update value and move to head; else create node, add to head and map, if size > N evict tail). Mention handling N=0 by rejecting puts or always evicting.

4. Address Thread Safety and Extensions

Discuss thread safety using locks (e.g., a mutex around operations) or concurrent data structures, noting trade-offs. For peek, return value without updating recency; for delete, remove node from list and map.

5. Analyze Complexity and Trade-offs

Summarize that both operations are O(1) amortized due to hash map and linked list operations. Mention space O(N) and potential overhead of locks or alternative eviction policies.

Key Points to Mention

Hash map for O(1) key lookup and doubly linked list for O(1) recency updates.
Handling updates to existing keys: update value and move node to head without changing size.
Edge case N=0: either reject all puts or immediately evict, ensuring no storage.
Thread safety: use locks (e.g., synchronized methods or ReentrantLock) or consider concurrent hash map with careful synchronization; discuss trade-offs.
Extensions: peek returns value without updating recency; delete removes key from both map and list.
Complexity: O(1) amortized for get and put, O(N) space; mention potential overhead of locks.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.