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LinkedIn·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

LinkedIn SWE interview that came down to implementing an LFU cache from scratch. Pretty classic hard problem but the O(1) constraint is what makes it actually interesting.

Questions Asked (1)

Q1

Design and implement an LFU cache that supports get and put operations in O(1) average time, with eviction of the least frequently used key and LRU tiebreaking.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The naive solution comes to mind fast, a simple frequency map, but then they ask about O(1) and you realize a sorted structure won't cut it.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a design using a combination of a hash map and a doubly linked list of frequency nodes, each containing a doubly linked list of keys. Explain how this achieves O(1) average time for get and put, and handle edge cases like updating frequency and eviction with LRU tiebreaking.

Pro tip: Mention that you would use a sentinel head and tail in the frequency list to simplify edge cases, and discuss how to handle capacity zero or one. Also, note that the design can be extended to support concurrency with fine-grained locking if needed.

1. Clarify Requirements

Ask about cache capacity, expected operations, and whether thread safety is required. Confirm that O(1) average time is needed for both get and put.

2. Design Data Structures

Propose a hash map mapping keys to nodes, and a doubly linked list of frequency nodes. Each frequency node contains a set of keys implemented as a doubly linked list to maintain LRU order.

3. Explain Operations

Detail how get and put work: for get, move the key to the next frequency node; for put, insert or update the key and handle eviction when capacity is exceeded.

4. Handle Eviction

Describe eviction: remove the least frequently used key from the lowest frequency node, and if that node becomes empty, remove it. Use LRU tiebreaking by removing the least recently used key from that node.

5. Analyze Complexity and Edge Cases

Discuss time and space complexity, and cover edge cases like capacity 0, updating existing keys, and frequency updates. Mention potential optimizations or concurrency considerations.

Key Points to Mention

  • Use of hash map for O(1) key lookup
  • Doubly linked list of frequency nodes to maintain frequency order
  • Each frequency node contains a doubly linked list of keys for LRU order
  • Eviction: remove from lowest frequency node, and within that, least recently used key
  • Handling frequency updates: move key to next frequency node, creating if necessary
  • Edge cases: capacity 0, updating existing key, and thread safety if required

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