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Oracle·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Oracle SWE interview, got a classic LRU cache problem. Pretty standard for this kind of role but the O(1) constraint is where things get interesting if you haven't thought about it before.

Questions Asked (1)

Q1

Design and implement an LRU cache with a fixed capacity, supporting get and put operations in O(1) average time.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

The get/put interface sounds simple enough but the O(1) requirement is what actually matters here.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the design, implement the core methods, and discuss trade-offs and edge cases.

Pro tip: Mention that you would use a sentinel head and tail to simplify edge cases in the linked list, and discuss how you would handle concurrency if needed, showing awareness of real-world scenarios.

1. Clarify Requirements

Confirm the cache capacity, whether it's thread-safe, and the expected behavior for get and put operations. Ask about eviction policy (LRU) and any constraints.

2. Choose Data Structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains the order of usage. The combination allows O(1) get and put.

3. Design the Algorithm

Detail how get moves the accessed node to the front (most recently used), and put adds or updates a node, evicting the least recently used (tail) if capacity is exceeded.

4. Implement Core Operations

Write pseudocode or actual code for get and put, using helper functions for adding to front and removing nodes. Handle edge cases like updating an existing key.

5. Analyze and Discuss Trade-offs

State the time and space complexity (O(1) time, O(capacity) space). Discuss alternatives like using an ordered dictionary or a different eviction policy, and mention concurrency considerations.

Key Points to Mention

  • Hash map for O(1) key lookup, mapping keys to nodes in the linked list.
  • Doubly linked list to maintain access order, with most recently used at the head and least recently used at the tail.
  • Sentinel nodes (dummy head and tail) to simplify insertion and deletion logic.
  • Eviction of the least recently used item when capacity is exceeded.
  • Handling of edge cases: updating existing keys, cache size 1, and null values.
  • Thread-safety considerations and potential use of locks or concurrent data structures.

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