This is a premium problem. We're working on making it available for free soon.
Explore Free ProblemsSolutions for this premium problem will be available for free soon.
Browse Free ProblemsWatch expert explanations and walkthroughs
Practice problems asked by these companies to ace your technical interviews.
Explore More ProblemsJot down your thoughts, approach, and key learnings
Yes, this problem is a common design and data stream question asked in technical interviews. It tests understanding of queues, sliding window techniques, and efficient state maintenance in streaming scenarios.
A running sum avoids recalculating the sum of the last k elements every time a new value arrives. Instead, you add the new value and subtract the removed value, which keeps the computation efficient and reduces time complexity to O(1).
A queue is the most suitable data structure because it naturally supports first-in-first-out behavior. It allows efficient insertion of new stream values and removal of the oldest values when the window size is exceeded.
The optimal approach uses a sliding window with a queue and a running sum. Each new value is added to the queue and the oldest value is removed when the window exceeds the given size. This allows the moving average to be computed in constant time for every incoming element.