Compute a moving average on a stream
Company: Atlassian
Role: Machine Learning Engineer
Category: Coding & Algorithms
Difficulty: hard
Interview Round: Onsite
Design a data structure to compute the moving average over the last **N** values from a stream.
## Input/Operations
- Initialize with an integer window size `N`.
- Repeatedly receive values `x` (integers or floats).
- After each new value arrives, return the average of the most recent `min(N, count_so_far)` values.
## Constraints/Expectations
- Each update should be **O(1)** time.
- Use **O(N)** memory.
## Example
If `N = 3` and the stream is `1, 10, 3, 5`:
- after 1 → 1.0
- after 10 → (1+10)/2 = 5.5
- after 3 → (1+10+3)/3 = 4.666...
- after 5 → (10+3+5)/3 = 6.0
Quick Answer: This question evaluates understanding of streaming data structures, sliding-window aggregation, and the time/space trade-offs involved in maintaining running statistics over recent inputs, testing algorithmic efficiency and numerical handling skills.
Return the moving average after each input value over the latest N values.
Constraints
- Inputs are Python literals matching the function signature.
- Return a deterministic exact-match value.
Examples
Input: (3, [1,10,3,5])
Expected Output: [1.0, 5.5, 4.666667, 6.0]
Explanation: Prompt example.
Input: (1, [2,4,6])
Expected Output: [2.0, 4.0, 6.0]
Explanation: Window of one.
Input: (5, [])
Expected Output: []
Explanation: Empty stream.
Hints
- Model object-style prompts as operation streams when needed.
- Handle empty and boundary cases before the main logic.