Understanding Sliding and Tumbling Windows
This subsection describes how sliding and tumbling windows work. Both types of windows move across continuous streaming data, splitting the data into finite sets. Finite windows are helpful for operations such as aggregations, joins, and pattern matching.
In a sliding window, tuples are grouped within a window that slides across the data stream according to a specified interval. A time-based sliding window with a length of ten seconds and a sliding interval of five seconds contains tuples that arrive within a ten-second window. The set of tuples within the window are evaluated every five seconds. Sliding windows can contain overlapping data; an event can belong to more than one sliding window.
In the following image, the first window (w1, in the box with dashed lines) contains events that arrived between the zeroth and tenth seconds. The second window (w2, in the box with solid lines) contains events that arrived between the fifth and fifteenth seconds. Note that events e3 through e6 are in both windows. When window w2 is evaluated at time t = 15 seconds, events e1 and e2 are dropped from the event queue.
An example would be to compute the moving average of a stock price across the last five minutes, triggered every second.
In a tumbling window, tuples are grouped in a single window based on time or count. A tuple belongs to only one window.
For example, consider a time-based tumbling window with a length of five seconds. The first window (w1) contains events that arrived between the zeroth and fifth seconds. The second window (w2) contains events that arrived between the fifth and tenth seconds, and the third window (w3) contains events that arrived between tenth and fifteenth seconds. The tumbling window is evaluated every five seconds, and none of the windows overlap; each segment represents a distinct time segment.
An example would be to compute the average price of a stock over the last five minutes, computed every five minutes.