Oracle·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Oracle software engineer interview that came down to a classic LRU cache problem. Pretty standard for this kind of role but there's more to it than people think once you get into the implementation details.

Questions Asked (1)

Design and implement an LRU cache with get and put operations that both run in O(1) time. When capacity is hit, evict the least recently used entry. Walk through your data structure choices and any edge cases you'd need to handle.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew it was a hashmap plus doubly linked list but fumbled explaining why the doubly linked list specifically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the design, explain how get and put work, and discuss edge cases and potential optimizations.

Pro tip: Mention that you would use a doubly linked list with sentinel nodes to simplify edge cases, and discuss thread-safety if the cache might be accessed concurrently, showing awareness of real-world usage.

1. Clarify Requirements

Ask about expected capacity, concurrency needs, and whether keys/values are generic. Confirm that both get and put must be O(1) and that eviction is based on least recent use.

2. Choose Data Structures

Propose a hash map for O(1) key lookup and a doubly linked list to track usage order. Explain that the map stores key to node references, and the list maintains most-recently used at one end and least-recently used at the other.

3. Detail Operations

Describe get: if key exists, move node to front and return value; else return -1. Describe put: if key exists, update value and move to front; else create new node, add to front, and if capacity exceeded, remove tail node and delete from map.

4. Handle Edge Cases

Discuss capacity 0 or 1, updating existing keys, and eviction when full. Mention using sentinel head/tail nodes to avoid null checks and simplify list manipulation.

5. Discuss Trade-offs and Optimizations

Mention alternative implementations (e.g., OrderedDict in Python) and trade-offs like memory overhead. If relevant, discuss thread-safety using locks or concurrent data structures.

Key Points to Mention

Hash map provides O(1) access to cache entries.
Doubly linked list maintains usage order with O(1) node moves and removals.
Sentinel nodes (dummy head and tail) simplify insertion and deletion logic.
Eviction removes the least recently used node (tail) and its map entry.
Edge cases: capacity 0, updating existing key, and cache full.
Potential thread-safety considerations for concurrent access.

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