← Ambience Healthcare Interview Insights

Ambience Healthcare·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Interviewed for a Software Engineer role at Ambience Healthcare and got hit with a classic LRU cache design question. Pretty standard for a coding round but there were enough follow-up angles that it kept me on my toes.

Questions Asked (1)

Design and implement an LRU cache with a fixed capacity that supports get and put operations in average O(1) time. Walk through your data structure choices, how you track recency on reads and writes, how you handle updates to existing keys, missing keys, and edge cases like a capacity of zero. Then analyze time and space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went with a hashmap plus a doubly linked list, which is the right call, but I fumbled explaining why the doubly linked list specifically.

Create a free account to read the full note

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 implementation details, covering get, put, updates, and edge cases, and finally analyze time and space complexity.

Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases and avoid null checks, and discuss how this design can be extended for thread safety if needed.

1. Clarify Requirements

Confirm the expected operations (get, put), capacity constraints, and any assumptions about key/value types. Ask about thread safety if relevant.

2. Choose Data Structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order. Together they enable O(1) get and put.

3. Detail Operations

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

4. Handle Edge Cases

Discuss handling of capacity zero (no storage), updating existing keys (update value and move to front), and missing keys (return -1 or equivalent).

5. Analyze Complexity

State that both get and put are O(1) average time due to hash map and linked list operations, and space is O(capacity) for storing up to capacity entries.

Key Points to Mention

Hash map for O(1) key lookup, mapping keys to nodes in the doubly linked list.
Doubly linked list to maintain recency 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 removal operations.
On get: if key exists, move the corresponding node to the head; else return -1.
On put: if key exists, update value and move to head; else create new node, add to head, and if size exceeds capacity, remove tail node and delete its key from the map.
Edge case: capacity 0 means put does nothing and get always returns -1.
Time complexity: O(1) average for both get and put; space complexity: O(capacity).

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