Maintain the Median of an Integer Stream
Last updated: August 10, 2026
Quick Overview
The question asks the candidate to design a solution for maintaining the median of a stream of integers as new numbers are added.
StackAdapt
August 10, 20261
0
33 solved
The question asks the candidate to design a solution for maintaining the median of a stream of integers as new numbers are added.
This question is typically asked in the technical screen round for a Machine Learning Engineer position at StackAdapt. The interviewer is evaluating the candidate's coding skills, understanding of data structures, and ability to optimize for performance. The ability to maintain the median efficiently in a dynamic data set is a common problem that tests both algorithmic thinking and practical coding skills.
What the Interviewer Expects
- Explain the problem clearly and outline the requirements.
- Discuss the choice of data structures for efficient median calculation.
- Provide a clear algorithm with time and space complexity analysis.
- Write clean, functional code that implements the solution.
- Consider edge cases and discuss how to handle them.
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
- How would you modify your solution to find the k-th percentile instead of the median?
- What are the trade-offs of using different data structures for this problem?
- Can you explain how your solution would handle a large volume of data?
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Practice DSA ProblemsSample Answer
Requirements
The task is to maintain the median of a continuously growing stream of integers. As new integers are added, we need to efficiently update the median without having to sort the entire list each time. T...
High-Level Design
To maintain the median efficiently, we can use two heaps: a max-heap to store the lower half of the numbers and a min-heap for the upper half. This allows us to keep track of the median value, which c...
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