Topics: Hash Table, Heap, Cache, Data Structures, Algorithms
Location: Mountain View, CA
Interview date: 2026-01-22
Summary
I had a phone screen with an engineer at LinkedIn. The question involved implementing a cache with a rankable object as the value. The eviction policy was based on the value's rank. The interviewer followed up with questions about using last-seen timestamps as a tiebreaker and how to handle changes in rank.
Details
The coding question I got was to implement a cache where the value is a rankable object, and the eviction policy depends on the value's rank().
I implemented this using a hashmap and a min-heap. The interviewer then asked follow-up questions:
Using last-seen timestamp as a tiebreaker: I suggested using a double-linked list.
Rank changes: I challenged the interviewer on why the eviction policy would be based on a variable. The interviewer acknowledged that I was the first person to point that out and that I made a valid point.
Key insights:
Understanding different eviction policies for caches (e.g., rank-based, LRU) is crucial.
Being able to discuss the trade-offs of different data structures (hashmap, heap, linked list) in the context of cache implementation is important.
Don't be afraid to challenge assumptions or point out potential issues with the interviewer's proposed design.
Preparation Tips:
Review the implementation details of common cache eviction policies like LRU, LFU, and rank-based eviction.
Practice implementing these policies using different data structures (hashmaps, heaps, linked lists).
Consider edge cases and potential issues with different designs, and be prepared to discuss them with the interviewer.