Rolling Metrics on Streams | Dataford Interview Questions

Implement an efficient solution using the sliding window pattern to compute rolling performance metrics on real-time data streams.

Given an ordered list of numeric measurements and a positive window size, return the average of every complete consecutive window in order. The function must avoid recomputing each window from scratch.

Signature: def rolling_average(measurements, window_size). Return a list of floating-point averages. For example, [2, 4, 6, 8] with window 2 returns [3.0, 5.0, 7.0].

Constraints

1 <= window_size <= len(measurements)
1 <= len(measurements) <= 100000
-10^9 <= measurements[i] <= 10^9
Measurements are ordered by arrival time
Return one average for every complete window

Function Signature

def rolling_average(measurements, window_size):

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Problem

Implement an efficient solution using the sliding window pattern to compute rolling performance metrics on real-time data streams.

Signature: def rolling_average(measurements, window_size). Return a list of floating-point averages. For example, [2, 4, 6, 8] with window 2 returns [3.0, 5.0, 7.0].

Constraints

1 <= window_size <= len(measurements)
1 <= len(measurements) <= 100000
-10^9 <= measurements[i] <= 10^9
Measurements are ordered by arrival time
Return one average for every complete window

Function Signature

def rolling_average(measurements, window_size):

Your question is Rolling Metrics on Streams. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.