Large-Scale Data Streams Algorithm | Dataford Interview Questions

Design and implement an algorithm to process large-scale data streams.

Implement process_stream(stream, window_size) to consume a numeric iterable once and return the average of every complete sliding window of window_size consecutive values. The function must use bounded memory relative to the window size, preserve stream order, and return an empty list when the stream is shorter than the window.

Input: an iterable of numbers and a positive integer window size. Output: a list of floating-point averages, one for each complete window.

Constraints

stream is an iterable of integers or floating-point numbers
1 <= window_size
The stream may be substantially larger than available memory
Return one average for each complete consecutive window
Values may be positive, zero, or negative

Function Signature

def process_stream(stream, window_size):

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Problem

Design and implement an algorithm to process large-scale data streams.

Input: an iterable of numbers and a positive integer window size. Output: a list of floating-point averages, one for each complete window.

Constraints

stream is an iterable of integers or floating-point numbers
1 <= window_size
The stream may be substantially larger than available memory
Return one average for each complete consecutive window
Values may be positive, zero, or negative

Function Signature

def process_stream(stream, window_size):

Your question is Large-Scale Data Streams Algorithm. 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.