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new 39a6ff0fc09 Publishing website 2022/11/14 16:17:10 at commit 5a72696
39a6ff0fc09 is described below
commit 39a6ff0fc09f527ed828c3ebf8ab8cd69e6881cb
Author: jenkins <[email protected]>
AuthorDate: Mon Nov 14 16:17:11 2022 +0000
Publishing website 2022/11/14 16:17:10 at commit 5a72696
---
.../documentation/basics/index.html | 30 +++++++++++-----------
website/generated-content/documentation/index.xml | 26 +++++++++----------
website/generated-content/get-started/index.html | 2 +-
website/generated-content/sitemap.xml | 2 +-
4 files changed, 30 insertions(+), 30 deletions(-)
diff --git a/website/generated-content/documentation/basics/index.html
b/website/generated-content/documentation/basics/index.html
index 91da6624b41..9830a2db2ff 100644
--- a/website/generated-content/documentation/basics/index.html
+++ b/website/generated-content/documentation/basics/index.html
@@ -19,7 +19,7 @@
function addPlaceholder(){$('input:text').attr('placeholder',"What are you
looking for?");}
function endSearch(){var
search=document.querySelector(".searchBar");search.classList.add("disappear");var
icons=document.querySelector("#iconsBar");icons.classList.remove("disappear");}
function blockScroll(){$("body").toggleClass("fixedPosition");}
-function openMenu(){addPlaceholder();blockScroll();}</script><div
class="clearfix container-main-content"><div class="section-nav closed"
data-offset-top=90 data-offset-bottom=500><span class="section-nav-back
glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list
data-section-nav><li><span
class=section-nav-list-main-title>Documentation</span></li><li><a
href=/documentation>Using the Documentation</a></li><li
class=section-nav-item--collapsible><span class=section-nav-lis [...]
+function openMenu(){addPlaceholder();blockScroll();}</script><div
class="clearfix container-main-content"><div class="section-nav closed"
data-offset-top=90 data-offset-bottom=500><span class="section-nav-back
glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list
data-section-nav><li><span
class=section-nav-list-main-title>Documentation</span></li><li><a
href=/documentation>Using the Documentation</a></li><li
class=section-nav-item--collapsible><span class=section-nav-lis [...]
data-parallel processing pipelines. To get started with Beam, you’ll
need to
understand an important set of core concepts:</p><ul><li><a
href=#pipeline><em>Pipeline</em></a> - A pipeline is a user-constructed graph of
transformations that defines the desired data processing
operations.</li><li><a href=#pcollection><em>PCollection</em></a> - A
<code>PCollection</code> is a data set or data
@@ -47,7 +47,7 @@ collections that grow over time.</li><li><a
href=#splittable-dofn><em>Splittable
elements in a non-monolithic way. You can checkpoint the processing of an
element, and the runner can split the remaining work to yield additional
parallelism.</li></ul><p>The following sections cover these concepts in more
detail and provide links to
-additional documentation.</p><h3 id=pipeline>Pipeline</h3><p>A Beam pipeline
is a graph (specifically, a
+additional documentation.</p><h2 id=pipeline>Pipeline</h2><p>A Beam pipeline
is a graph (specifically, a
<a href=https://en.wikipedia.org/wiki/Directed_acyclic_graph>directed acyclic
graph</a>)
of all the data and computations in your data processing task. This includes
reading input data, transforming that data, and writing output data. A pipeline
@@ -61,7 +61,7 @@ be unbounded streams of data. In Beam, most transforms apply
equally to bounded
and unbounded data.</p><p>You can express almost any computation that you can
think of as a graph as a
Beam pipeline. A Beam driver program typically starts by creating a
<code>Pipeline</code>
object, and then uses that object as the basis for creating the pipeline’s data
-sets and its transforms.</p><p>For more information about pipelines, see the
following pages:</p><ul><li><a
href=/documentation/programming-guide/#overview>Beam Programming Guide:
Overview</a></li><li><a
href=/documentation/programming-guide/#creating-a-pipeline>Beam Programming
Guide: Creating a pipeline</a></li><li><a
href=/documentation/pipelines/design-your-pipeline>Design your
pipeline</a></li><li><a
href=/documentation/pipelines/create-your-pipeline>Create your
pipeline</a></li></u [...]
+sets and its transforms.</p><p>For more information about pipelines, see the
following pages:</p><ul><li><a
href=/documentation/programming-guide/#overview>Beam Programming Guide:
Overview</a></li><li><a
href=/documentation/programming-guide/#creating-a-pipeline>Beam Programming
Guide: Creating a pipeline</a></li><li><a
href=/documentation/pipelines/design-your-pipeline>Design your
pipeline</a></li><li><a
href=/documentation/pipelines/create-your-pipeline>Create your
pipeline</a></li></u [...]
potentially distributed, homogeneous data set or data stream, and is owned by
the specific <code>Pipeline</code> object for which it is created. Multiple
pipelines
cannot share a <code>PCollection</code>. Beam pipelines process PCollections,
and the
@@ -108,7 +108,7 @@ frequently used, but there are a few common key formats
(such as key-value pairs
and timestamps) so the runner can understand them.</p><p><strong>Windowing
strategy</strong>:</p><p>Every <code>PCollection</code> has a windowing
strategy, which is a specification of
essential information for grouping and triggering operations. The
<code>Window</code>
transform sets up the windowing strategy, and the <code>GroupByKey</code>
transform has
-behavior that is governed by the windowing strategy.</p><br><p>For more
information about PCollections, see the following page:</p><ul><li><a
href=/documentation/programming-guide/#pcollections>Beam Programming Guide:
PCollections</a></li></ul><h3 id=ptransform>PTransform</h3><p>A
<code>PTransform</code> (or transform) represents a data processing operation,
or a step,
+behavior that is governed by the windowing strategy.</p><br><p>For more
information about PCollections, see the following page:</p><ul><li><a
href=/documentation/programming-guide/#pcollections>Beam Programming Guide:
PCollections</a></li></ul><h2 id=ptransform>PTransform</h2><p>A
<code>PTransform</code> (or transform) represents a data processing operation,
or a step,
in your pipeline. A transform is usually applied to one or more input
<code>PCollection</code> objects. Transforms that read input are an exception;
these
transforms might not have an input <code>PCollection</code>.</p><p>You provide
transform processing logic in the form of a function object
@@ -126,7 +126,7 @@ collection. You can also define your own more complex
composite transforms to
fit your pipeline’s exact use case.</p><p>The following list has some common
transform types:</p><ul><li>Source transforms such as <code>TextIO.Read</code>
and <code>Create</code>. A source transform
conceptually has no input.</li><li>Processing and conversion operations such
as <code>ParDo</code>, <code>GroupByKey</code>,
<code>CoGroupByKey</code>, <code>Combine</code>, and
<code>Count</code>.</li><li>Outputting transforms such as
<code>TextIO.Write</code>.</li><li>User-defined, application-specific composite
transforms.</li></ul><p>For more information about transforms, see the
following pages:</p><ul><li><a
href=/documentation/programming-guide/#overview>Beam Programming Guide:
Overview</a></li><li><a href=/documentation/programming-guide/#transforms>Beam
Programming Guide: Transforms</a></li><li>Beam t [...]
-<a href=/documentation/transforms/python/overview/>Python</a>)</li></ul><h3
id=aggregation>Aggregation</h3><p>Aggregation is computing a value from
multiple (1 or more) input elements. In
+<a href=/documentation/transforms/python/overview/>Python</a>)</li></ul><h2
id=aggregation>Aggregation</h2><p>Aggregation is computing a value from
multiple (1 or more) input elements. In
Beam, the primary computational pattern for aggregation is to group all
elements
with a common key and window then combine each group of elements using an
associative and commutative operation. This is similar to the
“Reduce” operation
@@ -151,7 +151,7 @@ results of aggregation involves three
concepts:</p><ul><li><a href=#window>Windo
can be complete.</li><li><a href=#watermark>Watermarks</a>, which estimate the
completeness of your input.</li><li><a href=#trigger>Triggers</a>, which govern
when and how to emit aggregated results.</li></ul><p>For more information about
available aggregation transforms, see the following
pages:</p><ul><li><a
href=/documentation/programming-guide/#core-beam-transforms>Beam Programming
Guide: Core Beam transforms</a></li><li>Beam Transform catalog
(<a href=/documentation/transforms/java/overview/#aggregation>Java</a>,
-<a
href=/documentation/transforms/python/overview/#aggregation>Python</a>)</li></ul><h3
id=user-defined-function-udf>User-defined function (UDF)</h3><p>Some Beam
operations allow you to run user-defined code as a way to configure
+<a
href=/documentation/transforms/python/overview/#aggregation>Python</a>)</li></ul><h2
id=user-defined-function-udf>User-defined function (UDF)</h2><p>Some Beam
operations allow you to run user-defined code as a way to configure
the transform. For example, when using <code>ParDo</code>, user-defined code
specifies what
operation to apply to every element. For <code>Combine</code>, it specifies
how values
should be combined. By using <a
href=/documentation/patterns/cross-language/>cross-language transforms</a>,
@@ -173,7 +173,7 @@ without communicating or sharing state with any of the
other copies. Each copy
of your user code function might be retried or run multiple times, depending on
the pipeline runner and the processing backend that you choose for your
pipeline. Beam also supports stateful processing through the
-<a href=/blog/stateful-processing/>stateful processing API</a>.</p><p>For more
information about user-defined functions, see the following
pages:</p><ul><li><a
href=/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms>Requirements
for writing user code for Beam transforms</a></li><li><a
href=/documentation/programming-guide/#pardo>Beam Programming Guide:
ParDo</a></li><li><a
href=/documentation/programming-guide/#setting-your-pcollections-windowing-fun
[...]
+<a href=/blog/stateful-processing/>stateful processing API</a>.</p><p>For more
information about user-defined functions, see the following
pages:</p><ul><li><a
href=/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms>Requirements
for writing user code for Beam transforms</a></li><li><a
href=/documentation/programming-guide/#pardo>Beam Programming Guide:
ParDo</a></li><li><a
href=/documentation/programming-guide/#setting-your-pcollections-windowing-fun
[...]
schema for a <code>PCollection</code> defines elements of that
<code>PCollection</code> as an ordered
list of named fields. Each field has a name, a type, and possibly a set of user
options.</p><p>In many cases, the element type in a <code>PCollection</code>
has a structure that can be
@@ -187,14 +187,14 @@ example, <a href=/documentation/dsls/sql/overview/>Beam
SQL</a> is a common tran
that operates on schemas. These transforms allow selections and aggregations in
terms of named schema fields. Another advantage of schemas is that they allow
referencing of element fields by name. Beam provides a selection syntax for
-referencing fields, including nested and repeated fields.</p><p>For more
information about schemas, see the following pages:</p><ul><li><a
href=/documentation/programming-guide/#schemas>Beam Programming Guide:
Schemas</a></li><li><a href=/documentation/patterns/schema/>Schema
Patterns</a></li></ul><h3 id=runner>Runner</h3><p>A Beam runner runs a Beam
pipeline on a specific platform. Most runners are
+referencing fields, including nested and repeated fields.</p><p>For more
information about schemas, see the following pages:</p><ul><li><a
href=/documentation/programming-guide/#schemas>Beam Programming Guide:
Schemas</a></li><li><a href=/documentation/patterns/schema/>Schema
Patterns</a></li></ul><h2 id=runner>Runner</h2><p>A Beam runner runs a Beam
pipeline on a specific platform. Most runners are
translators or adapters to massively parallel big data processing systems, such
as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. For example,
the
Flink runner translates a Beam pipeline into a Flink job. The Direct Runner
runs
pipelines locally so you can test, debug, and validate that your pipeline
adheres to the Apache Beam model as closely as possible.</p><p>For an
up-to-date list of Beam runners and which features of the Apache Beam
model they support, see the runner
-<a href=/documentation/runners/capability-matrix/>capability
matrix</a>.</p><p>For more information about runners, see the following
pages:</p><ul><li><a href=/documentation/#choosing-a-runner>Choosing a
Runner</a></li><li><a href=/documentation/runners/capability-matrix/>Beam
Capability Matrix</a></li></ul><h3 id=window>Window</h3><p>Windowing subdivides
a <code>PCollection</code> into <em>windows</em> according to the timestamps
+<a href=/documentation/runners/capability-matrix/>capability
matrix</a>.</p><p>For more information about runners, see the following
pages:</p><ul><li><a href=/documentation/#choosing-a-runner>Choosing a
Runner</a></li><li><a href=/documentation/runners/capability-matrix/>Beam
Capability Matrix</a></li></ul><h2 id=window>Window</h2><p>Windowing subdivides
a <code>PCollection</code> into <em>windows</em> according to the timestamps
of its individual elements. Windows enable grouping operations over unbounded
collections by dividing the collection into windows of finite
collections.</p><p>A <em>windowing function</em> tells the runner how to assign
elements to one or more
initial windows, and how to merge windows of grouped elements. Each element in
a
@@ -213,7 +213,7 @@ with windows that are five minutes long. For each window,
Beam must collect all
the data with an event time timestamp in the given window range (between 0:00
and 4:59 in the first window, for instance). Data with timestamps outside that
range (data from 5:00 or later) belongs to a different window.</p><p>Two
concepts are closely related to windowing and covered in the following
-sections: <a href=#watermark>watermarks</a> and <a
href=#trigger>triggers</a>.</p><p>For more information about windows, see the
following page:</p><ul><li><a
href=/documentation/programming-guide/#windowing>Beam Programming Guide:
Windowing</a></li><li><a
href=/documentation/programming-guide/#setting-your-pcollections-windowing-function>Beam
Programming Guide: WindowFn</a></li></ul><h3 id=watermark>Watermark</h3><p>In
any data processing system, there is a certain amount of lag between [...]
+sections: <a href=#watermark>watermarks</a> and <a
href=#trigger>triggers</a>.</p><p>For more information about windows, see the
following page:</p><ul><li><a
href=/documentation/programming-guide/#windowing>Beam Programming Guide:
Windowing</a></li><li><a
href=/documentation/programming-guide/#setting-your-pcollections-windowing-function>Beam
Programming Guide: WindowFn</a></li></ul><h2 id=watermark>Watermark</h2><p>In
any data processing system, there is a certain amount of lag between [...]
a data event occurs (the “event time”, determined by the timestamp on the data
element itself) and the time the actual data element gets processed at any
stage
in your pipeline (the “processing time”, determined by the clock on the system
@@ -232,7 +232,7 @@ is finite. After the watermark progresses past the end of a
window, any further
element that arrives with a timestamp in that window is considered <em>late
data</em>.</p><p><a href=#trigger>Triggers</a> are a related concept that allow
you to modify and refine
the windowing strategy for a <code>PCollection</code>. You can use triggers to
decide when
each individual window aggregates and reports its results, including how the
-window emits late elements.</p><p>For more information about watermarks, see
the following page:</p><ul><li><a
href=/documentation/programming-guide/#watermarks-and-late-data>Beam
Programming Guide: Watermarks and late data</a></li></ul><h3
id=trigger>Trigger</h3><p>When collecting and grouping data into windows, Beam
uses <em>triggers</em> to
+window emits late elements.</p><p>For more information about watermarks, see
the following page:</p><ul><li><a
href=/documentation/programming-guide/#watermarks-and-late-data>Beam
Programming Guide: Watermarks and late data</a></li></ul><h2
id=trigger>Trigger</h2><p>When collecting and grouping data into windows, Beam
uses <em>triggers</em> to
determine when to emit the aggregated results of each window (referred to as a
<em>pane</em>). If you use Beam’s default windowing configuration and default
trigger,
Beam outputs the aggregated result when it estimates all data has arrived, and
@@ -250,7 +250,7 @@ arrives in each window, and firing when that data meets a
certain property.
Currently, data-driven triggers only support firing after a certain number of
data elements.</li><li><strong>Composite triggers</strong>: These triggers
combine multiple triggers in various
ways. For example, you might want one trigger for early data and a different
-trigger for late data.</li></ul><p>For more information about triggers, see
the following page:</p><ul><li><a
href=/documentation/programming-guide/#triggers>Beam Programming Guide:
Triggers</a></li></ul><h3 id=state-and-timers>State and timers</h3><p>Beam’s
windowing and triggers provide an abstraction for grouping and
+trigger for late data.</li></ul><p>For more information about triggers, see
the following page:</p><ul><li><a
href=/documentation/programming-guide/#triggers>Beam Programming Guide:
Triggers</a></li></ul><h2 id=state-and-timers>State and timers</h2><p>Beam’s
windowing and triggers provide an abstraction for grouping and
aggregating unbounded input data based on timestamps. However, there are
aggregation use cases that might require an even higher degree of control.
State
and timers are two important concepts that help with these uses cases. Like
@@ -286,7 +286,7 @@ time passes. This is often used to create larger batches of
data before
processing. It can also be used to schedule events that should occur at a
specific time.</li><li><strong>Dynamic timer tags</strong>: Beam also supports
dynamically setting a timer tag. This
allows you to set multiple different timers in a <code>DoFn</code> and
dynamically
-choose timer tags (for example, based on data in the input
elements).</li></ul><p>For more information about state and timers, see the
following pages:</p><ul><li><a
href=/documentation/programming-guide/#state-and-timers>Beam Programming Guide:
State and Timers</a></li><li><a href=/blog/stateful-processing/>Stateful
processing with Apache Beam</a></li><li><a href=/blog/timely-processing/>Timely
(and Stateful) Processing with Apache Beam</a></li></ul><h3
id=splittable-dofn>Splittable DoF [...]
+choose timer tags (for example, based on data in the input
elements).</li></ul><p>For more information about state and timers, see the
following pages:</p><ul><li><a
href=/documentation/programming-guide/#state-and-timers>Beam Programming Guide:
State and Timers</a></li><li><a href=/blog/stateful-processing/>Stateful
processing with Apache Beam</a></li><li><a href=/blog/timely-processing/>Timely
(and Stateful) Processing with Apache Beam</a></li></ul><h2
id=splittable-dofn>Splittable DoF [...]
elements in a non-monolithic way. Splittable <code>DoFn</code> makes it easier
to create
complex, modular I/O connectors in Beam.</p><p>A regular <code>ParDo</code>
processes an entire element at a time, applying your regular
<code>DoFn</code> and waiting for the call to terminate. When you instead
apply a
@@ -303,9 +303,9 @@ checkpoint the sub-element and the runner repeats step
2.</li></ol><p>You can al
processing. For example, if you write a splittable <code>DoFn</code> to watch
a set of
directories and output filenames as they arrive, you can split to subdivide the
work of different directories. This allows the runner to split off a hot
-directory and give it additional resources.</p><p>For more information about
Splittable <code>DoFn</code>, see the following pages:</p><ul><li><a
href=/documentation/programming-guide/#splittable-dofns>Splittable
DoFns</a></li><li><a href=/blog/splittable-do-fn-is-available/>Splittable DoFn
in Apache Beam is Ready to Use</a></li></ul><h3 id=whats-next>What’s
next</h3><p>Take a look at our <a href=/documentation/>other documentation</a>
such as the Beam
+directory and give it additional resources.</p><p>For more information about
Splittable <code>DoFn</code>, see the following pages:</p><ul><li><a
href=/documentation/programming-guide/#splittable-dofns>Splittable
DoFns</a></li><li><a href=/blog/splittable-do-fn-is-available/>Splittable DoFn
in Apache Beam is Ready to Use</a></li></ul><h2 id=whats-next>What’s
next</h2><p>Take a look at our <a href=/documentation/>other documentation</a>
such as the Beam
programming guide, pipeline execution information, and transform reference
-catalogs.</p><div class=feedback><p class=update>Last updated on
2022/10/31</p><h3>Have you found everything you were looking for?</h3><p
class=description>Was it all useful and clear? Is there anything that you would
like to change? Let us know!</p><button class=load-button><a
href="mailto:[email protected]?subject=Beam Website Feedback">SEND
FEEDBACK</a></button></div></div></div><footer class=footer><div
class=footer__contained><div class=footer__cols><div class="footer__cols__col f
[...]
+catalogs.</p><div class=feedback><p class=update>Last updated on
2022/11/07</p><h3>Have you found everything you were looking for?</h3><p
class=description>Was it all useful and clear? Is there anything that you would
like to change? Let us know!</p><button class=load-button><a
href="mailto:[email protected]?subject=Beam Website Feedback">SEND
FEEDBACK</a></button></div></div></div><footer class=footer><div
class=footer__contained><div class=footer__cols><div class="footer__cols__col f
[...]
<a href=https://www.apache.org>The Apache Software Foundation</a>
| <a href=/privacy_policy>Privacy Policy</a>
| <a href=/feed.xml>RSS Feed</a><br><br>Apache Beam, Apache, Beam, the Beam
logo, and the Apache feather logo are either registered trademarks or
trademarks of The Apache Software Foundation. All other products or name brands
are trademarks of their respective holders, including The Apache Software
Foundation.</div></div><div class="footer__cols__col
footer__cols__col__logos"><div class=footer__cols__col--group><div
class=footer__cols__col__logo><a href=https://github.com/apache/beam><im [...]
\ No newline at end of file
diff --git a/website/generated-content/documentation/index.xml
b/website/generated-content/documentation/index.xml
index 49a6f93c313..847fe402a3a 100644
--- a/website/generated-content/documentation/index.xml
+++ b/website/generated-content/documentation/index.xml
@@ -3665,7 +3665,7 @@ parallelism.</li>
</ul>
<p>The following sections cover these concepts in more detail and provide
links to
additional documentation.</p>
-<h3 id="pipeline">Pipeline</h3>
+<h2 id="pipeline">Pipeline</h2>
<p>A Beam pipeline is a graph (specifically, a
<a href="https://en.wikipedia.org/wiki/Directed_acyclic_graph";>directed
acyclic graph</a>)
of all the data and computations in your data processing task. This includes
@@ -3691,7 +3691,7 @@ sets and its transforms.</p>
<li><a href="/documentation/pipelines/design-your-pipeline">Design your
pipeline</a></li>
<li><a href="/documentation/pipelines/create-your-pipeline">Create your
pipeline</a></li>
</ul>
-<h3 id="pcollection">PCollection</h3>
+<h2 id="pcollection">PCollection</h2>
<p>A <code>PCollection</code> is an unordered bag of elements. Each
<code>PCollection</code> is a
potentially distributed, homogeneous data set or data stream, and is owned by
the specific <code>Pipeline</code> object for which it is created.
Multiple pipelines
@@ -3770,7 +3770,7 @@ behavior that is governed by the windowing
strategy.</p>
<ul>
<li><a href="/documentation/programming-guide/#pcollections">Beam
Programming Guide: PCollections</a></li>
</ul>
-<h3 id="ptransform">PTransform</h3>
+<h2 id="ptransform">PTransform</h2>
<p>A <code>PTransform</code> (or transform) represents a data
processing operation, or a step,
in your pipeline. A transform is usually applied to one or more input
<code>PCollection</code> objects. Transforms that read input are an
exception; these
@@ -3805,7 +3805,7 @@ conceptually has no input.</li>
<li>Beam transform catalog (<a
href="/documentation/transforms/java/overview/">Java</a>,
<a href="/documentation/transforms/python/overview/">Python</a>)</li>
</ul>
-<h3 id="aggregation">Aggregation</h3>
+<h2 id="aggregation">Aggregation</h2>
<p>Aggregation is computing a value from multiple (1 or more) input
elements. In
Beam, the primary computational pattern for aggregation is to group all
elements
with a common key and window then combine each group of elements using an
@@ -3848,7 +3848,7 @@ pages:</p>
(<a href="/documentation/transforms/java/overview/#aggregation">Java</a>,
<a
href="/documentation/transforms/python/overview/#aggregation">Python</a>)</li>
</ul>
-<h3 id="user-defined-function-udf">User-defined function (UDF)</h3>
+<h2 id="user-defined-function-udf">User-defined function (UDF)</h2>
<p>Some Beam operations allow you to run user-defined code as a way to
configure
the transform. For example, when using <code>ParDo</code>, user-defined
code specifies what
operation to apply to every element. For <code>Combine</code>, it
specifies how values
@@ -3891,7 +3891,7 @@ pipeline. Beam also supports stateful processing through
the
<li><a
href="/documentation/programming-guide/#data-encoding-and-type-safety">Beam
Programming Guide: Coder</a></li>
<li><a href="/documentation/programming-guide/#side-inputs">Beam
Programming Guide: Side inputs</a></li>
</ul>
-<h3 id="schema">Schema</h3>
+<h2 id="schema">Schema</h2>
<p>A schema is a language-independent type definition for a
<code>PCollection</code>. The
schema for a <code>PCollection</code> defines elements of that
<code>PCollection</code> as an ordered
list of named fields. Each field has a name, a type, and possibly a set of user
@@ -3914,7 +3914,7 @@ referencing fields, including nested and repeated
fields.</p>
<li><a href="/documentation/programming-guide/#schemas">Beam Programming
Guide: Schemas</a></li>
<li><a href="/documentation/patterns/schema/">Schema
Patterns</a></li>
</ul>
-<h3 id="runner">Runner</h3>
+<h2 id="runner">Runner</h2>
<p>A Beam runner runs a Beam pipeline on a specific platform. Most runners
are
translators or adapters to massively parallel big data processing systems, such
as Apache Flink, Apache Spark, Google Cloud Dataflow, and more. For example,
the
@@ -3929,7 +3929,7 @@ model they support, see the runner
<li><a href="/documentation/#choosing-a-runner">Choosing a
Runner</a></li>
<li><a href="/documentation/runners/capability-matrix/">Beam Capability
Matrix</a></li>
</ul>
-<h3 id="window">Window</h3>
+<h2 id="window">Window</h2>
<p>Windowing subdivides a <code>PCollection</code> into
<em>windows</em> according to the timestamps
of its individual elements. Windows enable grouping operations over unbounded
collections by dividing the collection into windows of finite
collections.</p>
@@ -3968,7 +3968,7 @@ sections: <a href="#watermark">watermarks</a> and
<a href="#trigger">tr
<li><a href="/documentation/programming-guide/#windowing">Beam
Programming Guide: Windowing</a></li>
<li><a
href="/documentation/programming-guide/#setting-your-pcollections-windowing-function">Beam
Programming Guide: WindowFn</a></li>
</ul>
-<h3 id="watermark">Watermark</h3>
+<h2 id="watermark">Watermark</h2>
<p>In any data processing system, there is a certain amount of lag between
the time
a data event occurs (the “event time”, determined by the timestamp on the data
element itself) and the time the actual data element gets processed at any
stage
@@ -3996,7 +3996,7 @@ window emits late elements.</p>
<ul>
<li><a
href="/documentation/programming-guide/#watermarks-and-late-data">Beam
Programming Guide: Watermarks and late data</a></li>
</ul>
-<h3 id="trigger">Trigger</h3>
+<h2 id="trigger">Trigger</h2>
<p>When collecting and grouping data into windows, Beam uses
<em>triggers</em> to
determine when to emit the aggregated results of each window (referred to as a
<em>pane</em>). If you use Beam’s default windowing configuration and
default trigger,
@@ -4033,7 +4033,7 @@ trigger for late data.</li>
<ul>
<li><a href="/documentation/programming-guide/#triggers">Beam
Programming Guide: Triggers</a></li>
</ul>
-<h3 id="state-and-timers">State and timers</h3>
+<h2 id="state-and-timers">State and timers</h2>
<p>Beam’s windowing and triggers provide an abstraction for grouping and
aggregating unbounded input data based on timestamps. However, there are
aggregation use cases that might require an even higher degree of control.
State
@@ -4102,7 +4102,7 @@ choose timer tags (for example, based on data in the
input elements).</li>
<li><a href="/blog/stateful-processing/">Stateful processing with Apache
Beam</a></li>
<li><a href="/blog/timely-processing/">Timely (and Stateful) Processing
with Apache Beam</a></li>
</ul>
-<h3 id="splittable-dofn">Splittable DoFn</h3>
+<h2 id="splittable-dofn">Splittable DoFn</h2>
<p>Splittable <code>DoFn</code> (SDF) is a generalization of
<code>DoFn</code> that lets you process
elements in a non-monolithic way. Splittable <code>DoFn</code> makes it
easier to create
complex, modular I/O connectors in Beam.</p>
@@ -4136,7 +4136,7 @@ directory and give it additional resources.</p>
<li><a
href="/documentation/programming-guide/#splittable-dofns">Splittable
DoFns</a></li>
<li><a href="/blog/splittable-do-fn-is-available/">Splittable DoFn in
Apache Beam is Ready to Use</a></li>
</ul>
-<h3 id="whats-next">What&rsquo;s next</h3>
+<h2 id="whats-next">What&rsquo;s next</h2>
<p>Take a look at our <a href="/documentation/">other
documentation</a> such as the Beam
programming guide, pipeline execution information, and transform reference
catalogs.</p></description></item><item><title>Documentation: Beam
glossary</title><link>/documentation/glossary/</link><pubDate>Mon, 01 Jan 0001
00:00:00 +0000</pubDate><guid>/documentation/glossary/</guid><description>
diff --git a/website/generated-content/get-started/index.html
b/website/generated-content/get-started/index.html
index dfb0fb21ceb..b2b9b0fc082 100644
--- a/website/generated-content/get-started/index.html
+++ b/website/generated-content/get-started/index.html
@@ -20,7 +20,7 @@ function
addPlaceholder(){$('input:text').attr('placeholder',"What are you looki
function endSearch(){var
search=document.querySelector(".searchBar");search.classList.add("disappear");var
icons=document.querySelector("#iconsBar");icons.classList.remove("disappear");}
function blockScroll(){$("body").toggleClass("fixedPosition");}
function openMenu(){addPlaceholder();blockScroll();}</script><div
class="clearfix container-main-content"><div class="section-nav closed"
data-offset-top=90 data-offset-bottom=500><span class="section-nav-back
glyphicon glyphicon-menu-left"></span><nav><ul class=section-nav-list
data-section-nav><li><span class=section-nav-list-main-title>Get
started</span></li><li><a href=/get-started/beam-overview/>Beam
Overview</a></li><li><a href=/get-started/tour-of-beam/>Tour of
Beam</a></li><li><s [...]
-Java</a></li></ul></li><li><a href=/get-started/downloads>Install the
SDK</a></li><li><span class=section-nav-list-title>Tutorials</span><ul
class=section-nav-list><li><a
href=/get-started/wordcount-example/>WordCount</a></li><li><a
href=/get-started/mobile-gaming-example/>Mobile Gaming</a></li></ul></li><li
class=section-nav-item--collapsible><span class=section-nav-list-title>Learning
resources</span><ul class=section-nav-list><li><a
href=/get-started/resources/learning-resources/#gett [...]
+Java</a></li></ul></li><li><a href=/get-started/downloads>Install the
SDK</a></li><li><span class=section-nav-list-title>Tutorials</span><ul
class=section-nav-list><li><a
href=/get-started/wordcount-example/>WordCount</a></li><li><a
href=/get-started/mobile-gaming-example/>Mobile Gaming</a></li></ul></li><li
class=section-nav-item--collapsible><span class=section-nav-list-title>Learning
resources</span><ul class=section-nav-list><li><a
href=/get-started/resources/learning-resources/#gett [...]
<a href=https://www.apache.org>The Apache Software Foundation</a>
| <a href=/privacy_policy>Privacy Policy</a>
| <a href=/feed.xml>RSS Feed</a><br><br>Apache Beam, Apache, Beam, the Beam
logo, and the Apache feather logo are either registered trademarks or
trademarks of The Apache Software Foundation. All other products or name brands
are trademarks of their respective holders, including The Apache Software
Foundation.</div></div><div class="footer__cols__col
footer__cols__col__logos"><div class=footer__cols__col--group><div
class=footer__cols__col__logo><a href=https://github.com/apache/beam><im [...]
\ No newline at end of file
diff --git a/website/generated-content/sitemap.xml
b/website/generated-content/sitemap.xml
index c59a7134c8b..0c7c6b8ecf7 100644
--- a/website/generated-content/sitemap.xml
+++ b/website/generated-content/sitemap.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";
xmlns:xhtml="http://www.w3.org/1999/xhtml";><url><loc>/categories/blog/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/blog/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/categories/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/blog/ml-resources/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/categ
[...]
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8" standalone="yes"?><urlset
xmlns="http://www.sitemaps.org/schemas/sitemap/0.9";
xmlns:xhtml="http://www.w3.org/1999/xhtml";><url><loc>/categories/blog/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/blog/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/categories/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/blog/ml-resources/</loc><lastmod>2022-11-10T17:26:15-05:00</lastmod></url><url><loc>/categ
[...]
\ No newline at end of file
