Implement Hash Map with in-place
Last updated: August 5, 2025
Quick Overview
Implement a hash map data structure that supports insert, delete, and lookup operations in O(1) average time complexity. Your implementation should handle collisions using chaining or open addressing, and it should allow for dynamic resizing when the load factor exceeds a certain threshold. The input will consist of a series of key-value pairs for insertion, keys for deletion and lookup, and the output should be the result of the lookup operations.
Figma
August 5, 20253
8
2,076 solved
Implement a hash map data structure that supports insert, delete, and lookup operations in O(1) average time complexity. Your implementation should handle collisions using chaining or open addressing, and it should allow for dynamic resizing when the load factor exceeds a certain threshold. The input will consist of a series of key-value pairs for insertion, keys for deletion and lookup, and the output should be the result of the lookup operations.
Coding interviews at Figma 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
- Recognize the underlying problem pattern (sliding window, two pointers, BFS/DFS, etc.)
- Discuss multiple approaches and trade-offs before coding
- Implement an optimal solution with clean, production-quality code
- Handle all edge cases including boundary conditions and invalid input
- Optimize both time and space complexity with clear justification
- Test your solution systematically with well-chosen examples
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?
- What happens if the input contains duplicates?
- How would you parallelize this solution?
- What if the input doesn't fit in memory?
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Problem Analysis
The problem requires implementing a hash map that supports insert, delete, and lookup operations in O(1) average time complexity. The key pattern here is the use of hashing along with chaining...
Approach
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Initialize the Hash Map: Start with a fixed-size array. For example, let’s say we start with a size of 10. Each index will contain a linked list for chaining.
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Hash Function: Implement a...
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