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

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### Fixed time windows

The simplest form of windowing is using fixed time windows: given a timestamped `PCollection` which might be continuously updating, each window might capture (for example) all elements with timestamps that fall into a 30-second interval.

A fixed time window represents a consistent duration, non overlapping time interval in the data stream. Consider windows with a 30-second duration: all the elements in your unbounded PCollection with timestamp values from 0:00:00 up to (but not including) 0:00:30 belong to the first window, elements with timestamp values from 0:00:30 up to (but not including) 0:01:00 belong to the second window, and so on.

{{if (eq .Sdk "go")}}
```
fixedWindowedItems := beam.WindowInto(s,
	window.NewFixedWindows(30*time.Second),
	items)
```
{{end}}

{{if (eq .Sdk "java")}}
```
PCollection<String> input = ...;
    PCollection<String> fixedWindowedItems = input.apply(
        Window.<String>into(FixedWindows.of(Duration.standardSeconds(30))));
```
{{end}}

{{if (eq .Sdk "python")}}
```
from apache_beam import window

fixed_windowed_items = (
    input | 'window' >> beam.WindowInto(window.FixedWindows(30)))
```
{{end}}

### Playground exercise

You can start displaying elements from the beginning but also from the end:

{{if (eq .Sdk "go")}}
You can write your logic inside `MyCombineFn`:
`accumulated := beam.CombinePerKey(s, &MyCombineFn{}, windowed)`
{{end}}

{{if (eq .Sdk "java")}}
```
input.apply(...)
 .apply(Window.<Type>into(FixedWindows.of(Duration.standardMinutes(10))
    .withTimestampCombiner(TimestampCombiner.END_OF_WINDOW))
```
{{end}}

{{if (eq .Sdk "python")}}
```
from apache_beam import window

fixed_windowed_items = (
    input | 'window' >> beam.WindowInto(window.FixedWindows(30),timestamp_combiner=TimestampCombiner.OUTPUT_AT_END)))
```
{{end}}