Implement Deque with with follow-up
Last updated: February 18, 2026
Quick Overview
Implement a LRU Cache data structure that supports get and put operations in O(log n) time.
Atlassian
February 18, 202623
17
4,616 solved
Implement a LRU Cache data structure that supports get and put operations in O(log n) time.
Coding interviews at Atlassian focus on problem-solving approach as much as the final solution. The interviewer wants to see you break down the problem, consider edge cases, and optimize iteratively. Communication throughout the process is key.
What the Interviewer Expects
- Identify the correct data structure and algorithm for the problem
- Write clean, bug-free code with proper variable naming
- Analyze time and space complexity correctly
- Handle basic edge cases (empty input, single element)
- Communicate your thought process while coding
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
- Can you optimize the space complexity of your solution?
- Can you solve this in a single pass?
- What if the input doesn't fit in memory?
- How would you modify your solution to handle streaming input?
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Problem Analysis
To implement an LRU (Least Recently Used) Cache, we need to efficiently track the order of access to keys. The main operations, 'get' and 'put', should operate in O(log n) time. A Deque is suitable fo...
Approach
- Initialize a hash map (for O(1) access of key-value pairs) and a self-balancing tree (like AVL or Red-Black tree) to maintain the order of keys.
- For the 'put' operation:
- If the key alre...
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