Given a stream of API request sizes arriving continuously, how can the sliding window algorithm help in monitoring the average request size over the last 5 requests efficiently?

hard📝 Application Q9 of 15

Rest API - Rate Limiting and Throttling

ABy storing all requests indefinitely and averaging them all

BBy recalculating the sum of all 5 requests every time a new request arrives

CBy maintaining a running sum and updating it as the window slides forward

DBy ignoring the oldest requests and only averaging new ones

Step-by-Step Solution

Solution:

Step 1: Understand efficient sliding window monitoring
Maintaining a running sum allows updating the average by subtracting the oldest and adding the newest request size.
Step 2: Avoid recalculating sum from scratch each time
Recalculating sum every time is inefficient; running sum is optimal.
Final Answer:
By maintaining a running sum and updating it as the window slides forward -> Option C
Quick Check:
Running sum updates = efficient sliding window [OK]

Quick Trick: Use running sum to update averages efficiently [OK]

Common Mistakes:

Recalculating sums fully each time
Ignoring oldest data in window

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More Rest API Quizzes

Given a stream of API request sizes arriving continuously, how can the sliding window algorithm help in monitoring the average request size over the last 5 requests efficiently?

Step 1: Understand efficient sliding window monitoring

Step 2: Avoid recalculating sum from scratch each time

Final Answer:

Quick Check:

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