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Sliding and Tumbling Window

The sliding window has a simple definition: it covers a fixed-size interval of event time which slides along as the event time advances. In order to provide better performance, in Jet we don't literally implement it this way: instead of smoothly sliding, the window advances in discrete steps. The user defines both the window and the step size, with the restriction that the window must contain an integer number of steps. With this kind of definition, the tumbling window is just a special case of a sliding window with step size equal to window size.

The reason for the above definition of the sliding window lies in the way we implement the computation. We divide the timestamp axis into frames of length equal to the step size and assign each event to its frame. To compute a sliding window, we just take all the frames covered by it and combine them. This way we provide a configurable tradeoff between the smoothness of sliding and the cost of storage/computation.

Furthermore, we provide support to optimize a very important class of aggregate operations: those that can be expressed in terms of a pair of functions where one is commutative/associative and maintains the running state of computation, and the other one is unrestricted and transforms the running state into the final result. We call the former one combine and the latter one finish. Examples include minimum, maximum, average, variance, standard deviation, linear regression, etc. The point of the commutative/associative property is that you can apply the function to the running state of two frames and get the same result as if you applied it to each item in them. This means we don't have to hold on to each individual item: we just maintain one instance of running state per frame.

A slightly narrower class of aggregate operations also supports the inverse of combine, which we call deduct: it undoes a previous combine (i.e., deduct(combine(x, y), x) = y). When this is provided, we can compute the entire sliding window in just two fixed steps: deduct the trailing frame and combine the leading frame into the constantly maintained sliding window result.

Note that these techniques are non-restrictive: you can always provide a trivial combining function that just merges sets of individual items, and provide an arbitrary finishing function that acts upon them.

Example: 30-second Window Sliding by 10 Seconds

The diagram below summarizes the process of constructing a 30-second window which slides by 10 seconds, and computing the number of events that occurred within it. For brevity we label the events as minutes:seconds. This is the outline of the process:

  1. Throw each event into its "bucket" (the frame whose time interval it belongs to).
  2. Instead of keeping the items in the frame, just keep the item count.
  3. Combine the frames into three different positions of the sliding window, yielding the final result: the number of events that occurred within the window's timespan.

Grouping disordered events by frame and then to sliding window

This would be a useful interpretation of the results: "At the time 1:30, the 30-second running average was 8/30 = 0.27 events per second. Over the next 20 seconds it increased to 10/30 = 0.33 events per second."

Keep in mind that the whole diagram represents what happens on just one cluster member and for just one grouping key. The same process is going on simultaneously for all the keys on all the members.

Two-stage aggregation

The concept of frame combining helps us implement two-stage aggregation as well. In the first stage the individual members come up with their partial results by frame and send them over a distributed edge to the second stage, which combines the frames with the same timestamp. After having combined all the partial frames from members, it combines the results along the event time axis into the sliding window.

Combining partial frames in two-stage aggregation

Session Window

In the abstract sense, the session window is a quite intuitive concept: it simply captures a burst of events. As soon as the gap between two events exceeds the configured session timeout, the window closes. However, because the Jet processor encounters events out of their original order, this kind of window becomes quite tricky to compute.

The way Jet computes the session windows is easiest to explain in terms of the event interval: the range [eventTimestamp, eventTimestamp + sessionTimeout]. Initially an event causes a new session window to be created, covering exactly the event interval.

Session window: single event

A following event under the same key belongs to this window iff its interval overlaps it. The window is extended to cover the entire interval of the new event.

Session window: extend with another event

If the event interval don't overlap, a new session window is created for the new event.

Session window: create a new window after session timeout

The event may happen to belong to two existing windows if its interval bridges the gap between them; in that case they are combined into one.

Session window: an event may merge two existing windows