Feature #6: Incoming Tweets Predictor
Explore how to build a system that predicts incoming Twitter traffic by calculating a moving average over 15-minute intervals using a sliding window approach. Learn to implement an efficient solution with constant time complexity using a deque in Kotlin, optimizing server deployment based on user activity.
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Description
Twitter wants to adjust the number of servers deployed in a cluster, according to the user traffic, in 15-minute intervals. A metering service collects user traffic statistics over five-minute intervals. These user statistics are stored in a list, for example, [5,7,15,8,10]. We subscribe to the stream from this service. However, the five-minute interval is too short a time window to help the server deployment adapt. We want to aggregate this data to determine the average moving traffic in the last 15-minute interval.
The first two data points are an exception. When the first data point is received, it is used as the average itself. When the second data point is received, the average of the first two data points is used.
Solution
To solve this problem, we can start by initializing an empty deque (double-ended queue) to keep track of the incoming values. For simplicity, we will call this a queue. The ...