I'm reading a long article about Data Stream Management, and I'm a bit confused by the difference between Sliding and Tumbling Windows. So far I've understood that tumbling windows can be time-based and has fixed (start,end)-points which "tumbles" when that window expires. E.g. A time-based window can be 1 minute long. So for every minute the window tumbles to process aggregations for a data set.
It is sliding windows that gets confused me. Is sliding windows like count-based such that a window tumbles when x-number of tuples have entered the window. Or is it that the x-recent tuples that entered the window will be part of the window, and that the older tuples will be evicted from that window. I.e. a window that is continuously updated as new tuples arrives?
The main difference between these windows is that, Tumbling windows are non-overlapping whereas Sliding windows can be overlapping.
When a windowed query processes each window in a non-overlapping manner, the window is referred to as a tumbling window. In this case, each record on an in-application stream belongs to a specific window. It is processed only once (when the query processes the window to which the record belongs).
The hopping window is similar to the tumbling window, except that it is not contiguous. It aggregate events with a fixed time sized window, but you can choose to update that information in another time frame. Another example: “Every 20 minutes, give me the number of pizza orders I got in the last 10 minutes”.
They optimize natural light and outdoor views: Sliding windows typically have oversized glass panels that let in plenty of natural light and offer homeowners wide, unobstructed views. They provide plenty of ventilation: Because of their streamlined design, most sliders open fully to let in fresh air.
Below is a graphical representation showing different types of Data Stream Management System (DSMS) window - tumbling, hopping, timing policy sliding, and eviction policy(count) sliding. I used the above example to create the image (making assumptions).
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