learning/tour-of-beam/learning-content/windowing/sliding-time-window/description.md

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### Sliding time windows

A sliding time window also represents time intervals in the data stream; however, sliding time windows can overlap. For example, each window might capture 60 seconds' worth of data, but a new window starts every 30 seconds. The frequency with which sliding windows begin is called the period. Therefore, our example would have a window duration of 60 seconds and a period of 30 seconds.

Because multiple windows overlap, most elements in a data set will belong to more than one window. This kind of windowing is helpful for taking running data averages; using sliding time windows, you can compute a running average of the past 60 seconds’ worth of data, updated every 30 seconds.

The following example code shows how to apply `Window` to divide a `PCollection` into sliding time windows. Each window is 30 seconds in length, and a new window begins every five seconds:

{{if (eq .Sdk "go")}}
```
slidingWindowedItems := beam.WindowInto(s,
	window.NewSlidingWindows(5*time.Second, 30*time.Second),
	input)
```
{{end}}

{{if (eq .Sdk "java")}}
```
PCollection<String> input = ...;
    PCollection<String> slidingWindowedItems = input.apply(
        Window.<String>into(SlidingWindows.of(Duration.standardSeconds(30)).every(Duration.standardSeconds(5))));
```
{{end}}
{{if (eq .Sdk "python")}}
```
from apache_beam import window

sliding_windowed_items = (
    input | 'window' >> beam.WindowInto(window.SlidingWindows(30, 5)))
```
{{end}}

### Playground exercise

Because multiple windows overlap, most elements in a data set will belong to more than one window. This kind of windowing is useful for taking running averages of data; using sliding time windows, you can compute a running average of the past 60 seconds’ worth of data, updated every 30 seconds, in our example.

{{if (eq .Sdk "go")}}
```
max := stats.Max(s, windowedData)
mean := stats.Mean(s, windowedData)
min := stats.Min(s, windowedData)
```
{{end}}
{{if (eq .Sdk "java")}}
```
Combine.globally(Max.ofIntegers())
Combine.globally(Mean.ofIntegers())
Combine.globally(Min.ofIntegers())
```
{{end}}
{{if (eq .Sdk "python")}}
```
beam.CombineGlobally.globally(beam.combiners.MaxCombineFn())
beam.CombineGlobally.globally(beam.combiners.MeanCombineFn())
beam.CombineGlobally.globally(beam.combiners.MinCombineFn())
```
{{end}}