Implement an Efficient LRU Cache
Last updated: February 21, 2025
Quick Overview
Design and implement a Least Recently Used cache with O(1) time complexity for both get and put operations.
Rivian
February 21, 20256
12
2,296 solved
Design and implement a Least Recently Used cache with O(1) time complexity for both get and put operations.
Caching is fundamental in vehicle software for telemetry buffering, map tile caching, and configuration lookups. This classic data structure problem appears frequently in Rivian coding rounds to evaluate your ability to combine hash maps and linked lists cleanly.
What the Interviewer Expects
- Implement using a hash map plus doubly linked list for O(1) operations
- Handle edge cases including empty cache, single-element cache, and capacity of zero
- Write clean, well-structured code with clear variable naming
- Discuss the time and space complexity of your solution
- Explain why this data structure combination achieves O(1) for both operations
Key Topics to Cover
How to Approach This
- Clarify input constraints and edge cases before writing code.
- Walk through your approach verbally and confirm with the interviewer before coding.
- Start with a brute force solution, then optimize. Mention time and space complexity.
- Test your solution with examples, including edge cases like empty input or duplicates.
- Consider common patterns: sliding window, two pointers, hash map, BFS/DFS, dynamic programming.
Possible Follow-up Questions
- How would you make this thread-safe for concurrent access?
- How would you modify this for an LFU (Least Frequently Used) cache?
- What if you needed to support TTL-based expiration in addition to capacity eviction?
- How would you implement this cache in a distributed system?
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Problem Analysis
To implement an LRU (Least Recently Used) Cache, we need to efficiently track the order of access to items while also being able to retrieve and update these items quickly. The optimal approach combin...
Approach
- Data Structures: Use a hash map to store the key-value pairs and a doubly linked list to maintain the order of usage. Each node in the doubly linked list will contain the key and the value.
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