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LRU Cache at Google — Interview Guide

How to approach LRU Cache in a Google interview: the optimal approach, what Google interviewers look for, and company-specific tips.

Updated 2026-09MediumDesign

The problem at Google

Design a data structure that supports get(key) and put(key, value) in O(1) average time, with a fixed capacity that evicts the least-recently-used item when full.

Google uses this problem to assess algorithmic rigor and generalist problem-solving. Expect Google-specific follow-ups that tie the solution back to production reasoning and the company's engineering bar.

Key concepts

A hash map gives O(1) lookup by key.
A doubly linked list ordered by recency gives O(1) eviction and move-to-front.
Store list-node references in the map so you can detach a node without scanning.

Approach

On get: move the accessed node to the head (most recent); return its value.
On put: update if present (and move to head); otherwise insert at head and evict the tail if over capacity.
Use sentinel head/tail nodes to avoid null checks on detach.

Complexity

Operation	Time	Space
get	O(1)	O(capacity)
put	O(1)	O(capacity)

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