learning/katas/java/Windowing/Fixed Time Window/Fixed Time Window/task.html - beam - Git at Google

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 <html>
 <h2>Fixed Time Window</h2>
 <p>
   Windowing subdivides a PCollection according to the timestamps of its individual elements.
   Transforms that aggregate multiple elements, such as GroupByKey and Combine, work implicitly on
   a per-window basis — they process each PCollection as a succession of multiple, finite windows,
   though the entire collection itself may be of unbounded size.
 </p>
 <p>
   In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into
   logical windows. Each element in a PCollection is assigned to one or more windows according to
   the PCollection’s windowing function, and each individual window contains a finite number of
   elements. Grouping transforms then consider each PCollection’s elements on a per-window basis.
   GroupByKey, for example, implicitly groups the elements of a PCollection by key and window.
 </p>
 <div>
   Beam provides several windowing functions, including:
   <ul>
     <li>Fixed Time Windows</li>
     <li>Sliding Time Windows</li>
     <li>Per-Session Windows</li>
     <li>Single Global Window</li>
   </ul>
 </div>
 <p>
   The simplest form of windowing is using fixed time windows. A fixed time window represents a
   consistent duration, non overlapping time interval in the data stream.
 </p>
 <p>
   <b>Kata:</b> Please count the number of events that happened based on fixed window with
   1-day duration.
 </p>
 <br>
 <div class="hint">
   Use <a href="https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/windowing/FixedWindows.html">
   FixedWindows</a> with 1-day duration.
 </div>
 <div class="hint">
   Refer to the Beam Programming Guide
   <a href="https://beam.apache.org/documentation/programming-guide/#fixed-time-windows">
     "Fixed time windows"</a> section for more information.
 </div>
 </html>
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	<html>
	<h2>Fixed Time Window</h2>
	<p>
	Windowing subdivides a PCollection according to the timestamps of its individual elements.
	Transforms that aggregate multiple elements, such as GroupByKey and Combine, work implicitly on
	a per-window basis — they process each PCollection as a succession of multiple, finite windows,
	though the entire collection itself may be of unbounded size.
	</p>
	<p>
	In the Beam model, any PCollection (including unbounded PCollections) can be subdivided into
	logical windows. Each element in a PCollection is assigned to one or more windows according to
	the PCollection’s windowing function, and each individual window contains a finite number of
	elements. Grouping transforms then consider each PCollection’s elements on a per-window basis.
	GroupByKey, for example, implicitly groups the elements of a PCollection by key and window.
	</p>
	<div>
	Beam provides several windowing functions, including:
	<ul>
	<li>Fixed Time Windows</li>
	<li>Sliding Time Windows</li>
	<li>Per-Session Windows</li>
	<li>Single Global Window</li>
	</ul>
	</div>
	<p>
	The simplest form of windowing is using fixed time windows. A fixed time window represents a
	consistent duration, non overlapping time interval in the data stream.
	</p>
	<p>
	<b>Kata:</b> Please count the number of events that happened based on fixed window with
	1-day duration.
	</p>
	<br>
	<div class="hint">
	Use <a href="https://beam.apache.org/releases/javadoc/current/org/apache/beam/sdk/transforms/windowing/FixedWindows.html">
	FixedWindows</a> with 1-day duration.
	</div>
	<div class="hint">
	Refer to the Beam Programming Guide
	<a href="https://beam.apache.org/documentation/programming-guide/#fixed-time-windows">
	"Fixed time windows"</a> section for more information.
	</div>
	</html>