
Explanation:
In Azure Stream Analytics, window functions are used to group events within specific time intervals for aggregation operations like counting. The key requirement here is that each tweet must be counted exactly once within the 10-second windows.
Hopping Window: Moves forward in time by a fixed hop size and aggregates events within a fixed window size. By default, hopping windows can overlap if the hop size is smaller than the window size, potentially causing events to be counted multiple times.
Tumbling Window: A special case of hopping window where the hop size equals the window size. This creates non-overlapping, contiguous time windows, ensuring each event falls into exactly one window.
The solution specifies:
This configuration makes the hopping window behave identically to a tumbling window. When hop size equals window size:
While a tumbling window would be the more straightforward choice for this scenario, the hopping window with equal hop and window sizes is functionally equivalent and meets all requirements. The key insight is that when hop size equals window size, the hopping window loses its overlapping characteristic and becomes non-overlapping.
Yes, this solution meets the goal. The hopping window configuration with both hop size and window size set to 10 seconds ensures non-overlapping windows where each tweet is counted exactly once in the appropriate 10-second interval.
Ultimate access to all questions.
You are designing an Azure Stream Analytics solution to analyze Twitter data. The requirement is to count tweets in each 10-second window, ensuring each tweet is counted only once.
Proposed Solution: Use a hopping window with a hop size of 10 seconds and a window size of 10 seconds.
Does this solution meet the goal?
A
Yes
B
No
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