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The following commit(s) were added to refs/heads/asf-site by this push:
new d5904c3 Publishing website 2021/01/08 00:03:03 at commit 74ec609
d5904c3 is described below
commit d5904c342a74285e79461ffeb8fd7b4b4ca59302
Author: jenkins <bui...@apache.org>
AuthorDate: Fri Jan 8 00:03:03 2021 +0000
Publishing website 2021/01/08 00:03:03 at commit 74ec609
---
.../documentation/runners/direct/index.html | 64 +----
.../documentation/runners/flink/index.html | 2 +-
.../get-started/beam-overview/index.html | 3 +-
.../get-started/downloads/index.html | 2 +-
.../get-started/from-spark/index.html | 90 +++++++
website/generated-content/get-started/index.html | 2 +-
website/generated-content/get-started/index.xml | 293 +++++++++++++++++++++
.../get-started/mobile-gaming-example/index.html | 2 +-
.../get-started/quickstart-go/index.html | 2 +-
.../get-started/quickstart-java/index.html | 2 +-
.../get-started/quickstart-py/index.html | 2 +-
.../get-started/try-apache-beam/index.html | 2 +-
.../get-started/wordcount-example/index.html | 2 +-
.../security/cve-2020-1929/index.html | 2 +-
website/generated-content/security/index.html | 2 +-
website/generated-content/sitemap.xml | 2 +-
16 files changed, 403 insertions(+), 71 deletions(-)
diff --git a/website/generated-content/documentation/runners/direct/index.html
b/website/generated-content/documentation/runners/direct/index.html
index 2d38163..61ecb97 100644
--- a/website/generated-content/documentation/runners/direct/index.html
+++ b/website/generated-content/documentation/runners/direct/index.html
@@ -1,70 +1,18 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Direct Runner</title><meta
name=description content="Apache Beam is an open source, unified model and set
of language-specific SDKs for defining and executing data processing workflows,
and also data ingestion and integration flows, supporting Enterprise
Integration Patterns (EIPs) and Domain Specific [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
<span class=o><</span><span class=n>groupId</span><span
class=o>></span><span class=n>org</span><span class=o>.</span><span
class=na>apache</span><span class=o>.</span><span class=na>beam</span><span
class=o></</span><span class=n>groupId</span><span class=o>></span>
<span class=o><</span><span class=n>artifactId</span><span
class=o>></span><span class=n>beam</span><span class=o>-</span><span
class=n>runners</span><span class=o>-</span><span class=n>direct</span><span
class=o>-</span><span class=n>java</span><span class=o></</span><span
class=n>artifactId</span><span class=o>></span>
<span class=o><</span><span class=n>version</span><span
class=o>></span><span class=n>2</span><span class=o>.</span><span
class=na>26</span><span class=o>.</span><span class=na>0</span><span
class=o></</span><span class=n>version</span><span class=o>></span>
<span class=o><</span><span class=n>scope</span><span
class=o>></span><span class=n>runtime</span><span class=o></</span><span
class=n>scope</span><span class=o>></span>
-<span class=o></</span><span class=n>dependency</span><span
class=o>></span></code></pre></div></div></p><p><span class=language-py>This
section is not applicable to the Beam SDK for Python.</span></p><h2
id=pipeline-options-for-the-direct-runner>Pipeline options for the Direct
Runner</h2><p>When executing your pipeline from the command-line, set
<code>runner</code> to <code>direct</code> or <code>DirectRunner</code>. The
default values for the other pipeline options are generally [...]
+<span class=o></</span><span class=n>dependency</span><span
class=o>></span></code></pre></div></div></p><p><span class=language-py>This
section is not applicable to the Beam SDK for Python.</span></p><h2
id=pipeline-options-for-the-direct-runner>Pipeline options for the Direct
Runner</h2><p>For general instructions on how to set pipeline options, see the
<a
href=/documentation/programming-guide/#configuring-pipeline-options>programming
guide</a>.</p><p>When executing your pipeline [...]
<span class=language-java><a
href=https://beam.apache.org/releases/javadoc/2.26.0/index.html?org/apache/beam/runners/direct/DirectOptions.html><code>DirectOptions</code></a></span>
<span class=language-py><a
href=https://beam.apache.org/releases/pydoc/2.26.0/apache_beam.options.pipeline_options.html#apache_beam.options.pipeline_options.DirectOptions><code>DirectOptions</code></a></span>
-interface for defaults and additional pipeline configuration options.</p><h2
id=additional-information-and-caveats>Additional information and
caveats</h2><h3 id=memory-considerations>Memory considerations</h3><p>Local
execution is limited by the memory available in your local environment. It is
highly recommended that you run your pipeline with data sets small enough to
fit in local memory. You can create a small in-memory data set using a <span
class=language-java><a href=https://beam.a [...]
-Python <a
href=https://beam.apache.org/contribute/runner-guide/#the-fn-api>FnApiRunner</a>
supports multi-threading and multi-processing mode.</p><p>{:.language-py}
-<strong>Setting parallelism</strong></p><p>{:.language-py}
-Number of threads or subprocesses is defined by setting the
<code>direct_num_workers</code> option.
-From 2.22.0, <code>direct_num_workers = 0</code> is supported. When
<code>direct_num_workers</code> is set to 0, it will set the number of
threads/subprocess to the number of cores of the machine where the pipeline is
running.</p><p>{:.language-py}</p><ul><li>There are several ways to set this
option.</li></ul><div class=highlight><pre class=chroma><code class=language-py
data-lang=py><span class=n>python</span> <span class=n>wordcount</span><span
class=o>.</span><span class=n>py</span> [...]
-</code></pre></div><p>{:.language-py}</p><ul><li>Setting with
<code>PipelineOptions</code>.</li></ul><div class=highlight><pre
class=chroma><code class=language-py data-lang=py><span class=kn>from</span>
<span class=nn>apache_beam.options.pipeline_options</span> <span
class=kn>import</span> <span class=n>PipelineOptions</span>
-<span class=n>pipeline_options</span> <span class=o>=</span> <span
class=n>PipelineOptions</span><span class=p>([</span><span
class=s1>'--direct_num_workers'</span><span class=p>,</span> <span
class=s1>'2'</span><span class=p>])</span>
-</code></pre></div><p>{:.language-py}</p><ul><li>Adding to existing
<code>PipelineOptions</code>.</li></ul><div class=highlight><pre
class=chroma><code class=language-py data-lang=py><span class=kn>from</span>
<span class=nn>apache_beam.options.pipeline_options</span> <span
class=kn>import</span> <span class=n>DirectOptions</span>
-<span class=n>pipeline_options</span> <span class=o>=</span> <span
class=n>PipelineOptions</span><span class=p>(</span><span
class=n>xxx</span><span class=p>)</span>
-<span class=n>pipeline_options</span><span class=o>.</span><span
class=n>view_as</span><span class=p>(</span><span
class=n>DirectOptions</span><span class=p>)</span><span class=o>.</span><span
class=n>direct_num_workers</span> <span class=o>=</span> <span class=mi>2</span>
-</code></pre></div><p>{:.language-py}
-<strong>Setting running mode</strong></p><p>{:.language-py}
-From 2.19, a new option was added to set running mode. We can use
<code>direct_running_mode</code> option to set the running mode.
-<code>direct_running_mode</code> can be one of [<code>'in_memory'</code>,
<code>'multi_threading'</code>,
<code>'multi_processing'</code>].</p><p>{:.language-py}
-<b>in_memory</b>: Runner and workers’ communication happens in memory
(not through gRPC). This is a default mode.</p><p>{:.language-py}
-<b>multi_threading</b>: Runner and workers communicate through gRPC and each
worker runs in a thread.</p><p>{:.language-py}
-<b>multi_processing</b>: Runner and workers communicate through gRPC and each
worker runs in a subprocess.</p><p>{:.language-py}
-Same as other options, <code>direct_running_mode</code> can be passed through
CLI or set with <code>PipelineOptions</code>.</p><p>{:.language-py}
-For the versions before 2.19.0, the running mode should be set with
<code>FnApiRunner()</code>. Please refer following
examples.</p><p>{:.language-py}</p><h4
id=running-with-multi-threading-mode>Running with multi-threading mode</h4><div
class=highlight><pre class=chroma><code class=language-py data-lang=py><span
class=kn>import</span> <span class=nn>argparse</span>
-
-<span class=kn>import</span> <span class=nn>apache_beam</span> <span
class=kn>as</span> <span class=nn>beam</span>
-<span class=kn>from</span> <span
class=nn>apache_beam.options.pipeline_options</span> <span
class=kn>import</span> <span class=n>PipelineOptions</span>
-<span class=kn>from</span> <span
class=nn>apache_beam.runners.portability</span> <span class=kn>import</span>
<span class=n>fn_api_runner</span>
-<span class=kn>from</span> <span class=nn>apache_beam.portability.api</span>
<span class=kn>import</span> <span class=n>beam_runner_api_pb2</span>
-<span class=kn>from</span> <span class=nn>apache_beam.portability</span> <span
class=kn>import</span> <span class=n>python_urns</span>
-
-<span class=n>parser</span> <span class=o>=</span> <span
class=n>argparse</span><span class=o>.</span><span
class=n>ArgumentParser</span><span class=p>()</span>
-<span class=n>parser</span><span class=o>.</span><span
class=n>add_argument</span><span class=p>(</span><span class=o>...</span><span
class=p>)</span>
-<span class=n>known_args</span><span class=p>,</span> <span
class=n>pipeline_args</span> <span class=o>=</span> <span
class=n>parser</span><span class=o>.</span><span
class=n>parse_known_args</span><span class=p>(</span><span
class=n>argv</span><span class=p>)</span>
-<span class=n>pipeline_options</span> <span class=o>=</span> <span
class=n>PipelineOptions</span><span class=p>(</span><span
class=n>pipeline_args</span><span class=p>)</span>
-
-<span class=n>p</span> <span class=o>=</span> <span class=n>beam</span><span
class=o>.</span><span class=n>Pipeline</span><span class=p>(</span><span
class=n>options</span><span class=o>=</span><span
class=n>pipeline_options</span><span class=p>,</span>
- <span class=n>runner</span><span class=o>=</span><span
class=n>fn_api_runner</span><span class=o>.</span><span
class=n>FnApiRunner</span><span class=p>(</span>
- <span class=n>default_environment</span><span class=o>=</span><span
class=n>beam_runner_api_pb2</span><span class=o>.</span><span
class=n>Environment</span><span class=p>(</span>
- <span class=n>urn</span><span class=o>=</span><span
class=n>python_urns</span><span class=o>.</span><span
class=n>EMBEDDED_PYTHON_GRPC</span><span class=p>)))</span>
-</code></pre></div><p>{:.language-py}</p><h4
id=running-with-multi-processing-mode>Running with multi-processing
mode</h4><div class=highlight><pre class=chroma><code class=language-py
data-lang=py><span class=kn>import</span> <span class=nn>argparse</span>
-<span class=kn>import</span> <span class=nn>sys</span>
-
-<span class=kn>import</span> <span class=nn>apache_beam</span> <span
class=kn>as</span> <span class=nn>beam</span>
-<span class=kn>from</span> <span
class=nn>apache_beam.options.pipeline_options</span> <span
class=kn>import</span> <span class=n>PipelineOptions</span>
-<span class=kn>from</span> <span
class=nn>apache_beam.runners.portability</span> <span class=kn>import</span>
<span class=n>fn_api_runner</span>
-<span class=kn>from</span> <span class=nn>apache_beam.portability.api</span>
<span class=kn>import</span> <span class=n>beam_runner_api_pb2</span>
-<span class=kn>from</span> <span class=nn>apache_beam.portability</span> <span
class=kn>import</span> <span class=n>python_urns</span>
-
-<span class=n>parser</span> <span class=o>=</span> <span
class=n>argparse</span><span class=o>.</span><span
class=n>ArgumentParser</span><span class=p>()</span>
-<span class=n>parser</span><span class=o>.</span><span
class=n>add_argument</span><span class=p>(</span><span class=o>...</span><span
class=p>)</span>
-<span class=n>known_args</span><span class=p>,</span> <span
class=n>pipeline_args</span> <span class=o>=</span> <span
class=n>parser</span><span class=o>.</span><span
class=n>parse_known_args</span><span class=p>(</span><span
class=n>argv</span><span class=p>)</span>
-<span class=n>pipeline_options</span> <span class=o>=</span> <span
class=n>PipelineOptions</span><span class=p>(</span><span
class=n>pipeline_args</span><span class=p>)</span>
-
-<span class=n>p</span> <span class=o>=</span> <span class=n>beam</span><span
class=o>.</span><span class=n>Pipeline</span><span class=p>(</span><span
class=n>options</span><span class=o>=</span><span
class=n>pipeline_options</span><span class=p>,</span>
- <span class=n>runner</span><span class=o>=</span><span
class=n>fn_api_runner</span><span class=o>.</span><span
class=n>FnApiRunner</span><span class=p>(</span>
- <span class=n>default_environment</span><span class=o>=</span><span
class=n>beam_runner_api_pb2</span><span class=o>.</span><span
class=n>Environment</span><span class=p>(</span>
- <span class=n>urn</span><span class=o>=</span><span
class=n>python_urns</span><span class=o>.</span><span
class=n>SUBPROCESS_SDK</span><span class=p>,</span>
- <span class=n>payload</span><span class=o>=</span><span
class=sa>b</span><span class=s1>'</span><span class=si>%s</span><span
class=s1> -m apache_beam.runners.worker.sdk_worker_main'</span>
- <span class=o>%</span> <span class=n>sys</span><span
class=o>.</span><span class=n>executable</span><span class=o>.</span><span
class=n>encode</span><span class=p>(</span><span
class=s1>'ascii'</span><span class=p>))))</span>
-</code></pre></div></div></div><footer class=footer><div
class=footer__contained><div class=footer__cols><div
class=footer__cols__col><div class=footer__cols__col__logo><img
src=/images/beam_logo_circle.svg class=footer__logo alt="Beam logo"></div><div
class=footer__cols__col__logo><img src=/images/apache_logo_circle.svg
class=footer__logo alt="Apache logo"></div></div><div class="footer__cols__col
footer__cols__col--md"><div class=footer__cols__col__title>Start</div><div
class=footer__c [...]
+interface for defaults and additional pipeline configuration options.</p><h2
id=additional-information-and-caveats>Additional information and
caveats</h2><h3 id=memory-considerations>Memory considerations</h3><p>Local
execution is limited by the memory available in your local environment. It is
highly recommended that you run your pipeline with data sets small enough to
fit in local memory. You can create a small in-memory data set using a <span
class=language-java><a href=https://beam.a [...]
+By default, <code>targetParallelism</code> is the greater of the number of
available processors and 3.</p><p class=language-py>Number of threads or
subprocesses is defined by setting the <code>direct_num_workers</code> pipeline
option.
+From 2.22.0, <code>direct_num_workers = 0</code> is supported. When
<code>direct_num_workers</code> is set to 0, it will set the number of
threads/subprocess to the number of cores of the machine where the pipeline is
running.</p><p class=language-py><strong>Setting running mode</strong></p><p
class=language-py>In Beam 2.19.0 and newer, you can use the
<code>direct_running_mode</code> pipeline option to set the running mode.
+<code>direct_running_mode</code> can be one of [<code>'in_memory'</code>,
<code>'multi_threading'</code>, <code>'multi_processing'</code>].</p><p
class=language-py><b>in_memory</b>: Runner and workers’ communication
happens in memory (not through gRPC). This is a default mode.</p><p
class=language-py><b>multi_threading</b>: Runner and workers communicate
through gRPC and each worker runs in a thread.</p><p
class=language-py><b>multi_processing</b>: Runner and workers communicate th
[...]
<a href=http://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></footer></body></html>
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diff --git a/website/generated-content/documentation/runners/flink/index.html
b/website/generated-content/documentation/runners/flink/index.html
index 4657c23..a478c40 100644
--- a/website/generated-content/documentation/runners/flink/index.html
+++ b/website/generated-content/documentation/runners/flink/index.html
@@ -92,7 +92,7 @@ The minor version is the first two numbers in the version
string, e.g. in <code>
minor version is <code>1.8</code>.</p><p>We try to track the latest version of
Apache Flink at the time of the Beam release.
A Flink version is supported by Beam for the time it is supported by the Flink
community.
The Flink community supports the last two minor versions. When support for a
Flink version is dropped, it may be deprecated and removed also from Beam.
-To find out which version of Flink is compatible with Beam please see the
table below:</p><table class="table table-bordered"><tr><th>Beam
Version</th><th>Flink Version</th><th>Artifact Id</th></tr><tr><td
rowspan=3>≥
2.21.0</td><td>1.10.x</td><td>beam-runners-flink-1.10</td></tr><tr><td>1.9.x</td><td>beam-runners-flink-1.9</td></tr><tr><td>1.8.x</td><td>beam-runners-flink-1.8</td></tr><tr><td
rowspan=3>2.17.0 -
2.20.0</td><td>1.9.x</td><td>beam-runners-flink-1.9</td></tr><tr><td>1.8. [...]
+To find out which version of Flink is compatible with Beam please see the
table below:</p><table class="table table-bordered"><tr><th>Beam
Version</th><th>Flink Version</th><th>Artifact Id</th></tr><tr><td
rowspan=5>≥ 2.27.0</td><td>1.12.x
<sup>*</sup></td><td>beam-runners-flink-1.12</td></tr><tr><td>1.11.x
<sup>*</sup></td><td>beam-runners-flink-1.11</td></tr><tr><td>1.10.x</td><td>beam-runners-flink-1.10</td></tr><tr><td>1.9.x</td><td>beam-runners-flink-1.9</td></tr><tr><td>1.8.x</t
[...]
capabilities of the classic Flink Runner.</p><p>The <a
href=https://s.apache.org/apache-beam-portability-support-table>Portable
Capability
Matrix</a> documents
the capabilities of the portable Flink Runner.</p></div></div><footer
class=footer><div class=footer__contained><div class=footer__cols><div
class=footer__cols__col><div class=footer__cols__col__logo><img
src=/images/beam_logo_circle.svg class=footer__logo alt="Beam logo"></div><div
class=footer__cols__col__logo><img src=/images/apache_logo_circle.svg
class=footer__logo alt="Apache logo"></div></div><div class="footer__cols__col
footer__cols__col--md"><div class=footer__cols__col__title> [...]
diff --git a/website/generated-content/get-started/beam-overview/index.html
b/website/generated-content/get-started/beam-overview/index.html
index 3581aad..3807d88 100644
--- a/website/generated-content/get-started/beam-overview/index.html
+++ b/website/generated-content/get-started/beam-overview/index.html
@@ -1,7 +1,8 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Beam Overview</title><meta
name=description content="Apache Beam is an open source, unified model and set
of language-specific SDKs for defining and executing data processing workflows,
and also data ingestion and integration flows, supporting Enterprise
Integration Patterns (EIPs) and Domain Specific [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+check our <a href=/get-started/from-spark>Getting started from Apache
Spark</a> page.</p></blockquote><ol><li><p><a
href=/get-started/try-apache-beam>Try Apache Beam</a> in an online interactive
environment.</p></li><li><p>Follow the Quickstart for the <a
href=/get-started/quickstart-java>Java SDK</a>, the <a
href=/get-started/quickstart-py>Python SDK</a>, or the <a
href=/get-started/quickstart-go>Go SDK</a>.</p></li><li><p>See the <a
href=/get-started/wordcount-example>WordCount Example [...]
<a href=http://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></footer></body></html>
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diff --git a/website/generated-content/get-started/downloads/index.html
b/website/generated-content/get-started/downloads/index.html
index d9381e6..fe77ecc 100644
--- a/website/generated-content/get-started/downloads/index.html
+++ b/website/generated-content/get-started/downloads/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Beam Releases</title><meta
name=description content="Apache Beam is an open source, unified model and set
of language-specific SDKs for defining and executing data processing workflows,
and also data ingestion and integration flows, supporting Enterprise
Integration Patterns (EIPs) and Domain Specific [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
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href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
central repository. The Java SDK is available on <a
href=https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.beam%22>Maven
Central Repository</a>,
and the Python SDK is available on <a
href=https://pypi.python.org/pypi/apache-beam>PyPI</a>.</p><p>For example, if
you are developing using Maven and want to use the SDK for Java
with the <code>DirectRunner</code>, add the following dependencies to your
<code>pom.xml</code> file:</p><pre><code><dependency>
diff --git a/website/generated-content/get-started/from-spark/index.html
b/website/generated-content/get-started/from-spark/index.html
new file mode 100644
index 0000000..f52382d
--- /dev/null
+++ b/website/generated-content/get-started/from-spark/index.html
@@ -0,0 +1,90 @@
+<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Getting started from Apache
Spark</title><meta name=description content="Apache Beam is an open source,
unified model and set of language-specific SDKs for defining and executing data
processing workflows, and also data ingestion and integration flows, supporting
Enterprise Integration Patterns (EIPs) [...]
+<span class=sr-only>Toggle navigation</span>
+<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+learning <em>Apache Beam</em> is familiar.
+The Beam and Spark APIs are similar, so you already know the basic
concepts.</p><p>Spark stores data <em>Spark DataFrames</em> for structured data,
+and in <em>Resilient Distributed Datasets</em> (RDD) for unstructured data.
+We are using RDDs for this guide.</p><p>A Spark RDD represents a collection of
elements,
+while in Beam it’s called a <em>Parallel Collection</em> (PCollection).
+A PCollection in Beam does <em>not</em> have any ordering
guarantees.</p><p>Likewise, a transform in Beam is called a <em>Parallel
Transform</em> (PTransform).</p><p>Here are some examples of common operations
and their equivalent between PySpark and Beam.</p><h2
id=overview>Overview</h2><p>Here’s a simple example of a PySpark pipeline
that takes the numbers from one to four,
+multiplies them by two, adds all the values together, and prints the
result.</p><div class=language-py><div class=highlight><pre class=chroma><code
class=language-py data-lang=py><span class=kn>import</span> <span
class=nn>pyspark</span>
+
+<span class=n>sc</span> <span class=o>=</span> <span
class=n>pyspark</span><span class=o>.</span><span
class=n>SparkContext</span><span class=p>()</span>
+<span class=n>result</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>sc</span><span class=o>.</span><span
class=n>parallelize</span><span class=p>([</span><span class=mi>1</span><span
class=p>,</span> <span class=mi>2</span><span class=p>,</span> <span
class=mi>3</span><span class=p>,</span> <span class=mi>4</span><span
class=p>])</span>
+ <span class=o>.</span><span class=n>map</span><span class=p>(</span><span
class=k>lambda</span> <span class=n>x</span><span class=p>:</span> <span
class=n>x</span> <span class=o>*</span> <span class=mi>2</span><span
class=p>)</span>
+ <span class=o>.</span><span class=n>reduce</span><span
class=p>(</span><span class=k>lambda</span> <span class=n>x</span><span
class=p>,</span> <span class=n>y</span><span class=p>:</span> <span
class=n>x</span> <span class=o>+</span> <span class=n>y</span><span
class=p>)</span>
+<span class=p>)</span>
+<span class=k>print</span><span class=p>(</span><span
class=n>result</span><span class=p>)</span></code></pre></div></div><p>In Beam
you pipe your data through the pipeline using the
+<em>pipe operator</em> <code>|</code> like <code>data | beam.Map(...)</code>
instead of chaining
+methods like <code>data.map(...)</code>, but they’re doing the same
thing.</p><p>Here’s what an equivalent pipeline looks like in
Beam.</p><div class=language-py><div class=highlight><pre class=chroma><code
class=language-py data-lang=py><span class=kn>import</span> <span
class=nn>apache_beam</span> <span class=kn>as</span> <span class=nn>beam</span>
+
+<span class=k>with</span> <span class=n>beam</span><span class=o>.</span><span
class=n>Pipeline</span><span class=p>()</span> <span class=k>as</span> <span
class=n>pipeline</span><span class=p>:</span>
+ <span class=n>result</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>pipeline</span>
+ <span class=o>|</span> <span class=n>beam</span><span
class=o>.</span><span class=n>Create</span><span class=p>([</span><span
class=mi>1</span><span class=p>,</span> <span class=mi>2</span><span
class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span
class=mi>4</span><span class=p>])</span>
+ <span class=o>|</span> <span class=n>beam</span><span
class=o>.</span><span class=n>Map</span><span class=p>(</span><span
class=k>lambda</span> <span class=n>x</span><span class=p>:</span> <span
class=n>x</span> <span class=o>*</span> <span class=mi>2</span><span
class=p>)</span>
+ <span class=o>|</span> <span class=n>beam</span><span
class=o>.</span><span class=n>CombineGlobally</span><span class=p>(</span><span
class=nb>sum</span><span class=p>)</span>
+ <span class=o>|</span> <span class=n>beam</span><span
class=o>.</span><span class=n>Map</span><span class=p>(</span><span
class=k>print</span><span class=p>)</span>
+ <span class=p>)</span></code></pre></div></div><blockquote><p>ℹ️ Note that
we called <code>print</code> inside a <code>Map</code> transform.
+That’s because we can only access the elements of a PCollection
+from within a PTransform.</p></blockquote><p>Another thing to note is that
Beam pipelines are constructed lazily.
+This means that when you pipe <code>|</code> data you’re only declaring
the
+transformations and the order you want them to happen,
+but the actual computation doesn’t happen.
+The pipeline is run after the <code>with beam.Pipeline() as pipeline</code>
context has
+closed.</p><blockquote><p>ℹ️ When the <code>with beam.Pipeline() as
pipeline</code> context closes,
+it implicitly calls <code>pipeline.run()</code> which triggers the computation
to happen.</p></blockquote><p>The pipeline is then sent to your
+<a
href=https://beam.apache.org/documentation/runners/capability-matrix/>runner of
choice</a>
+and it processes the data.</p><blockquote><p>ℹ️ The pipeline can run locally
with the <em>DirectRunner</em>,
+or in a distributed runner such as Flink, Spark, or Dataflow.
+The Spark runner is not related to PySpark.</p></blockquote><p>A label can
optionally be added to a transform using the
+<em>right shift operator</em> <code>>></code> like <code>data | 'My
description' >> beam.Map(...)</code>.
+This serves both as comments and makes your pipeline easier to
debug.</p><p>This is how the pipeline looks after adding labels.</p><div
class=language-py><div class=highlight><pre class=chroma><code
class=language-py data-lang=py><span class=kn>import</span> <span
class=nn>apache_beam</span> <span class=kn>as</span> <span class=nn>beam</span>
+
+<span class=k>with</span> <span class=n>beam</span><span class=o>.</span><span
class=n>Pipeline</span><span class=p>()</span> <span class=k>as</span> <span
class=n>pipeline</span><span class=p>:</span>
+ <span class=n>result</span> <span class=o>=</span> <span class=p>(</span>
+ <span class=n>pipeline</span>
+ <span class=o>|</span> <span class=s1>'Create numbers'</span>
<span class=o>>></span> <span class=n>beam</span><span
class=o>.</span><span class=n>Create</span><span class=p>([</span><span
class=mi>1</span><span class=p>,</span> <span class=mi>2</span><span
class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span
class=mi>4</span><span class=p>])</span>
+ <span class=o>|</span> <span class=s1>'Multiply by two'</span>
<span class=o>>></span> <span class=n>beam</span><span
class=o>.</span><span class=n>Map</span><span class=p>(</span><span
class=k>lambda</span> <span class=n>x</span><span class=p>:</span> <span
class=n>x</span> <span class=o>*</span> <span class=mi>2</span><span
class=p>)</span>
+ <span class=o>|</span> <span class=s1>'Sum everything'</span>
<span class=o>>></span> <span class=n>beam</span><span
class=o>.</span><span class=n>CombineGlobally</span><span class=p>(</span><span
class=nb>sum</span><span class=p>)</span>
+ <span class=o>|</span> <span class=s1>'Print results'</span>
<span class=o>>></span> <span class=n>beam</span><span
class=o>.</span><span class=n>Map</span><span class=p>(</span><span
class=k>print</span><span class=p>)</span>
+ <span class=p>)</span></code></pre></div></div><h2
id=setup>Setup</h2><p>Here’s a comparison on how to get started both in
PySpark and Beam.</p><div
class=table-wrapper><table><tr><th></th><th>PySpark</th><th>Beam</th></tr><tr><td><b>Install</b></td><td><code>$
pip install pyspark</code></td><td><code>$ pip install
apache-beam</code></td></tr><tr><td><b>Imports</b></td><td><code>import
pyspark</code></td><td><code>import apache_beam as
beam</code></td></tr><tr><td><b>Creating a [...]
+<a href=/documentation/transforms/python/overview>Python transform
gallery</a>.</p></blockquote><h2 id=using-calculated-values>Using calculated
values</h2><p>Since we are working in potentially distributed environments,
+we can’t guarantee that the results we’ve calculated are available
at any given machine.</p><p>In PySpark, we can get a result from a collection
of elements (RDD) by using
+<code>data.collect()</code>, or other aggregations such as
<code>reduce()</code>, <code>count()</code>, and more.</p><p>Here’s an
example to scale numbers into a range between zero and one.</p><div
class=language-py><div class=highlight><pre class=chroma><code
class=language-py data-lang=py><span class=kn>import</span> <span
class=nn>pyspark</span>
+
+<span class=n>sc</span> <span class=o>=</span> <span
class=n>pyspark</span><span class=o>.</span><span
class=n>SparkContext</span><span class=p>()</span>
+<span class=n>values</span> <span class=o>=</span> <span
class=n>sc</span><span class=o>.</span><span class=n>parallelize</span><span
class=p>([</span><span class=mi>1</span><span class=p>,</span> <span
class=mi>2</span><span class=p>,</span> <span class=mi>3</span><span
class=p>,</span> <span class=mi>4</span><span class=p>])</span>
+<span class=n>total</span> <span class=o>=</span> <span
class=n>values</span><span class=o>.</span><span class=n>reduce</span><span
class=p>(</span><span class=k>lambda</span> <span class=n>x</span><span
class=p>,</span> <span class=n>y</span><span class=p>:</span> <span
class=n>x</span> <span class=o>+</span> <span class=n>y</span><span
class=p>)</span>
+
+<span class=c1># We can simply use `total` since it's already a Python
`int` value from `reduce`.</span>
+<span class=n>scaled_values</span> <span class=o>=</span> <span
class=n>values</span><span class=o>.</span><span class=n>map</span><span
class=p>(</span><span class=k>lambda</span> <span class=n>x</span><span
class=p>:</span> <span class=n>x</span> <span class=o>/</span> <span
class=n>total</span><span class=p>)</span>
+
+<span class=c1># But to access `scaled_values`, we need to call
`collect`.</span>
+<span class=k>print</span><span class=p>(</span><span
class=n>scaled_values</span><span class=o>.</span><span
class=n>collect</span><span class=p>())</span></code></pre></div></div><p>In
Beam the results from all transforms result in a PCollection.
+We use <a href=/documentation/programming-guide/#side-inputs><em>side
inputs</em></a>
+to feed a PCollection into a transform and access its values.</p><p>Any
transform that accepts a function, like
+<a href=/documentation/transforms/python/elementwise/map><code>Map</code></a>,
+can take side inputs.
+If we only need a single value, we can use
+<a
href=https://beam.apache.org/releases/pydoc/current/apache_beam.pvalue.html#apache_beam.pvalue.AsSingleton><code>beam.pvalue.AsSingleton</code></a>
and access them as a Python value.
+If we need multiple values, we can use
+<a
href=https://beam.apache.org/releases/pydoc/current/apache_beam.pvalue.html#apache_beam.pvalue.AsIter><code>beam.pvalue.AsIter</code></a>
+and access them as an <a
href=https://docs.python.org/3/glossary.html#term-iterable><code>iterable</code></a>.</p><div
class=language-py><div class=highlight><pre class=chroma><code
class=language-py data-lang=py><span class=kn>import</span> <span
class=nn>apache_beam</span> <span class=kn>as</span> <span class=nn>beam</span>
+
+<span class=k>with</span> <span class=n>beam</span><span class=o>.</span><span
class=n>Pipeline</span><span class=p>()</span> <span class=k>as</span> <span
class=n>pipeline</span><span class=p>:</span>
+ <span class=n>values</span> <span class=o>=</span> <span
class=n>pipeline</span> <span class=o>|</span> <span class=n>beam</span><span
class=o>.</span><span class=n>Create</span><span class=p>([</span><span
class=mi>1</span><span class=p>,</span> <span class=mi>2</span><span
class=p>,</span> <span class=mi>3</span><span class=p>,</span> <span
class=mi>4</span><span class=p>])</span>
+ <span class=n>total</span> <span class=o>=</span> <span
class=n>values</span> <span class=o>|</span> <span class=n>beam</span><span
class=o>.</span><span class=n>CombineGlobally</span><span class=p>(</span><span
class=nb>sum</span><span class=p>)</span>
+
+ <span class=c1># To access `total`, we need to pass it as a side
input.</span>
+ <span class=n>scaled_values</span> <span class=o>=</span> <span
class=n>values</span> <span class=o>|</span> <span class=n>beam</span><span
class=o>.</span><span class=n>Map</span><span class=p>(</span>
+ <span class=k>lambda</span> <span class=n>x</span><span
class=p>,</span> <span class=n>total</span><span class=p>:</span> <span
class=n>x</span> <span class=o>/</span> <span class=n>total</span><span
class=p>,</span>
+ <span class=n>total</span><span class=o>=</span><span
class=n>beam</span><span class=o>.</span><span class=n>pvalue</span><span
class=o>.</span><span class=n>AsSingleton</span><span class=p>(</span><span
class=n>total</span><span class=p>))</span>
+
+ <span class=n>scaled_values</span> <span class=o>|</span> <span
class=n>beam</span><span class=o>.</span><span class=n>Map</span><span
class=p>(</span><span class=k>print</span><span
class=p>)</span></code></pre></div></div><blockquote><p>ℹ️ In Beam we need to
pass a side input explicitly, but we get the
+benefit that a reduction or aggregation does <em>not</em> have to fit into
memory.</p></blockquote><h2 id=next-steps>Next Steps</h2><ul><li>Take a look at
all the available transforms in the <a
href=/documentation/transforms/python/overview>Python transform
gallery</a>.</li><li>Learn how to read from and write to files in the <a
href=/documentation/programming-guide/#pipeline-io><em>Pipeline I/O</em>
section of the <em>Programming guide</em></a></li><li>Walk through additional
WordCount [...]
+<a href=http://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></footer></body></html>
\ No newline at end of file
diff --git a/website/generated-content/get-started/index.html
b/website/generated-content/get-started/index.html
index a8bbed9..73f33a0 100644
--- a/website/generated-content/get-started/index.html
+++ b/website/generated-content/get-started/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Use Beam</title><meta
name=description content="Apache Beam is an open source, unified model and set
of language-specific SDKs for defining and executing data processing workflows,
and also data ingestion and integration flows, supporting Enterprise
Integration Patterns (EIPs) and Domain Specific Lang [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
<a href=http://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></footer></body></html>
\ No newline at end of file
diff --git a/website/generated-content/get-started/index.xml
b/website/generated-content/get-started/index.xml
index c8195e9..738a2b4 100644
--- a/website/generated-content/get-started/index.xml
+++ b/website/generated-content/get-started/index.xml
@@ -876,6 +876,10 @@ limitations under the License.
<p><strong>Note:</strong> You can always execute your pipeline
locally for testing and debugging purposes.</p>
<h2 id="get-started">Get Started</h2>
<p>Get started using Beam for your data processing tasks.</p>
+<blockquote>
+<p>If you already know <a href="http://spark.apache.org/";>Apache
Spark</a>,
+check our <a href="/get-started/from-spark">Getting started from Apache
Spark</a> page.</p>
+</blockquote>
<ol>
<li>
<p><a href="/get-started/try-apache-beam">Try Apache Beam</a> in an
online interactive environment.</p>
@@ -2980,6 +2984,295 @@ using <a
href="https://beam.apache.org/releases/pydoc/2.26.0/apache_beam.io.g
<li>Dive in to some of our favorite <a
href="/documentation/resources/videos-and-podcasts">Videos and
Podcasts</a>.</li>
<li>Join the Beam <a href="/community/contact-us">users@</a> mailing
list.</li>
</ul>
+<p>Please don&rsquo;t hesitate to <a
href="/community/contact-us">reach out</a> if you encounter any
issues!</p></description></item><item><title>Get-Started: Getting started
from Apache Spark</title><link>/get-started/from-spark/</link><pubDate>Mon, 01
Jan 0001 00:00:00
+0000</pubDate><guid>/get-started/from-spark/</guid><description>
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+<h1 id="getting-started-from-apache-spark">Getting started from Apache
Spark</h1>
+<script type="text/javascript">
+localStorage.setItem("language", "language-py")
+</script>
+<p>If you already know <a href="http://spark.apache.org/";><em>Apache
Spark</em></a>,
+learning <em>Apache Beam</em> is familiar.
+The Beam and Spark APIs are similar, so you already know the basic
concepts.</p>
+<p>Spark stores data <em>Spark DataFrames</em> for structured data,
+and in <em>Resilient Distributed Datasets</em> (RDD) for unstructured
data.
+We are using RDDs for this guide.</p>
+<p>A Spark RDD represents a collection of elements,
+while in Beam it&rsquo;s called a <em>Parallel Collection</em>
(PCollection).
+A PCollection in Beam does <em>not</em> have any ordering
guarantees.</p>
+<p>Likewise, a transform in Beam is called a <em>Parallel
Transform</em> (PTransform).</p>
+<p>Here are some examples of common operations and their equivalent between
PySpark and Beam.</p>
+<h2 id="overview">Overview</h2>
+<p>Here&rsquo;s a simple example of a PySpark pipeline that takes the
numbers from one to four,
+multiplies them by two, adds all the values together, and prints the
result.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py"
data-lang="py"><span class="kn">import</span> <span
class="nn">pyspark</span>
+<span class="n">sc</span> <span class="o">=</span> <span
class="n">pyspark</span><span class="o">.</span><span
class="n">SparkContext</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span
class="p">(</span>
+<span class="n">sc</span><span class="o">.</span><span
class="n">parallelize</span><span class="p">([</span><span
class="mi">1</span><span class="p">,</span> <span
class="mi">2</span><span class="p">,</span> <span
class="mi">3</span><span class="p">,</span> <span
class="mi">4</span><span class="p">])</span>
+<span class="o">.</span><span class="n">map</span><span
class="p">(</span><span class="k">lambda</span> <span
class="n">x</span><span class="p">:</span> <span
class="n">x</span> <span class="o">*</span> <span
class="mi">2</span><span class="p">)</span>
+<span class="o">.</span><span class="n">reduce</span><span
class="p">(</span><span class="k">lambda</span> <span
class="n">x</span><span class="p">,</span> <span
class="n">y</span><span class="p">:</span> <span
class="n">x</span> <span class="o">+</span> <span
class="n">y</span><span class="p">)</span>
+<span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span
class="n">result</span><span
class="p">)</span></code></pre></div>
+</div>
+<p>In Beam you pipe your data through the pipeline using the
+<em>pipe operator</em> <code>|</code> like <code>data |
beam.Map(...)</code> instead of chaining
+methods like <code>data.map(...)</code>, but they&rsquo;re doing the
same thing.</p>
+<p>Here&rsquo;s what an equivalent pipeline looks like in Beam.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py"
data-lang="py"><span class="kn">import</span> <span
class="nn">apache_beam</span> <span class="kn">as</span> <span
class="nn">beam</span>
+<span class="k">with</span> <span class="n">beam</span><span
class="o">.</span><span class="n">Pipeline</span><span
class="p">()</span> <span class="k">as</span> <span
class="n">pipeline</span><span class="p">:</span>
+<span class="n">result</span> <span class="o">=</span> <span
class="p">(</span>
+<span class="n">pipeline</span>
+<span class="o">|</span> <span class="n">beam</span><span
class="o">.</span><span class="n">Create</span><span
class="p">([</span><span class="mi">1</span><span
class="p">,</span> <span class="mi">2</span><span
class="p">,</span> <span class="mi">3</span><span
class="p">,</span> <span class="mi">4</span><span
class="p">])</span>
+<span class="o">|</span> <span class="n">beam</span><span
class="o">.</span><span class="n">Map</span><span
class="p">(</span><span class="k">lambda</span> <span
class="n">x</span><span class="p">:</span> <span
class="n">x</span> <span class="o">*</span> <span
class="mi">2</span><span class="p">)</span>
+<span class="o">|</span> <span class="n">beam</span><span
class="o">.</span><span class="n">CombineGlobally</span><span
class="p">(</span><span class="nb">sum</span><span
class="p">)</span>
+<span class="o">|</span> <span class="n">beam</span><span
class="o">.</span><span class="n">Map</span><span
class="p">(</span><span class="k">print</span><span
class="p">)</span>
+<span class="p">)</span></code></pre></div>
+</div>
+<blockquote>
+<p>ℹ️ Note that we called <code>print</code> inside a
<code>Map</code> transform.
+That&rsquo;s because we can only access the elements of a PCollection
+from within a PTransform.</p>
+</blockquote>
+<p>Another thing to note is that Beam pipelines are constructed lazily.
+This means that when you pipe <code>|</code> data you&rsquo;re only
declaring the
+transformations and the order you want them to happen,
+but the actual computation doesn&rsquo;t happen.
+The pipeline is run after the <code>with beam.Pipeline() as
pipeline</code> context has
+closed.</p>
+<blockquote>
+<p>ℹ️ When the <code>with beam.Pipeline() as pipeline</code> context
closes,
+it implicitly calls <code>pipeline.run()</code> which triggers the
computation to happen.</p>
+</blockquote>
+<p>The pipeline is then sent to your
+<a
href="https://beam.apache.org/documentation/runners/capability-matrix/";>runner
of choice</a>
+and it processes the data.</p>
+<blockquote>
+<p>ℹ️ The pipeline can run locally with the <em>DirectRunner</em>,
+or in a distributed runner such as Flink, Spark, or Dataflow.
+The Spark runner is not related to PySpark.</p>
+</blockquote>
+<p>A label can optionally be added to a transform using the
+<em>right shift operator</em> <code>&gt;&gt;</code> like
<code>data | 'My description' &gt;&gt; beam.Map(...)</code>.
+This serves both as comments and makes your pipeline easier to debug.</p>
+<p>This is how the pipeline looks after adding labels.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py"
data-lang="py"><span class="kn">import</span> <span
class="nn">apache_beam</span> <span class="kn">as</span> <span
class="nn">beam</span>
+<span class="k">with</span> <span class="n">beam</span><span
class="o">.</span><span class="n">Pipeline</span><span
class="p">()</span> <span class="k">as</span> <span
class="n">pipeline</span><span class="p">:</span>
+<span class="n">result</span> <span class="o">=</span> <span
class="p">(</span>
+<span class="n">pipeline</span>
+<span class="o">|</span> <span class="s1">&#39;Create
numbers&#39;</span> <span class="o">&gt;&gt;</span>
<span class="n">beam</span><span class="o">.</span><span
class="n">Create</span><span class="p">([</span><span
class="mi">1</span><span class="p">,</span> <span
class="mi">2</span><span class="p">,</span> <span
class="mi">3</span><span class="p">,</span> <span
class="mi">4</span><sp [...]
+<span class="o">|</span> <span class="s1">&#39;Multiply by
two&#39;</span> <span class="o">&gt;&gt;</span> <span
class="n">beam</span><span class="o">.</span><span
class="n">Map</span><span class="p">(</span><span
class="k">lambda</span> <span class="n">x</span><span
class="p">:</span> <span class="n">x</span> <span
class="o">*</span> <span class="mi">2</span><span
class="p">)</span>
+<span class="o">|</span> <span class="s1">&#39;Sum
everything&#39;</span> <span class="o">&gt;&gt;</span>
<span class="n">beam</span><span class="o">.</span><span
class="n">CombineGlobally</span><span class="p">(</span><span
class="nb">sum</span><span class="p">)</span>
+<span class="o">|</span> <span class="s1">&#39;Print
results&#39;</span> <span class="o">&gt;&gt;</span>
<span class="n">beam</span><span class="o">.</span><span
class="n">Map</span><span class="p">(</span><span
class="k">print</span><span class="p">)</span>
+<span class="p">)</span></code></pre></div>
+</div>
+<h2 id="setup">Setup</h2>
+<p>Here&rsquo;s a comparison on how to get started both in PySpark and
Beam.</p>
+<div class="table-wrapper"><table>
+<tr>
+<th></th>
+<th>PySpark</th>
+<th>Beam</th>
+</tr>
+<tr>
+<td><b>Install</b></td>
+<td><code>$ pip install pyspark</code></td>
+<td><code>$ pip install apache-beam</code></td>
+</tr>
+<tr>
+<td><b>Imports</b></td>
+<td><code>import pyspark</code></td>
+<td><code>import apache_beam as beam</code></td>
+</tr>
+<tr>
+<td><b>Creating a<br>local pipeline</b></td>
+<td>
+<code>sc = pyspark.SparkContext() as sc:</code><br>
+<code># Your pipeline code here.</code>
+</td>
+<td>
+<code>with beam.Pipeline() as pipeline:</code><br>
+<code>&nbsp;&nbsp;&nbsp;&nbsp;# Your pipeline code
here.</code>
+</td>
+</tr>
+<tr>
+<td><b>Creating values</b></td>
+<td><code>values = sc.parallelize([1, 2, 3, 4])</code></td>
+<td><code>values = pipeline | beam.Create([1, 2, 3, 4])</code></td>
+</tr>
+<tr>
+<td><b>Creating<br>key-value pairs</b></td>
+<td>
+<code>pairs = sc.parallelize([</code><br>
+<code>&nbsp;&nbsp;&nbsp;&nbsp;('key1',
'value1'),</code><br>
+<code>&nbsp;&nbsp;&nbsp;&nbsp;('key2',
'value2'),</code><br>
+<code>&nbsp;&nbsp;&nbsp;&nbsp;('key3',
'value3'),</code><br>
+<code>])</code>
+</td>
+<td>
+<code>pairs = pipeline | beam.Create([</code><br>
+<code>&nbsp;&nbsp;&nbsp;&nbsp;('key1',
'value1'),</code><br>
+<code>&nbsp;&nbsp;&nbsp;&nbsp;('key2',
'value2'),</code><br>
+<code>&nbsp;&nbsp;&nbsp;&nbsp;('key3',
'value3'),</code><br>
+<code>])</code>
+</td>
+</tr>
+<tr>
+<td><b>Running a<br>local pipeline</b></td>
+<td><code>$ spark-submit spark_pipeline.py</code></td>
+<td><code>$ python beam_pipeline.py</code></td>
+</tr>
+</table></div>
+<h2 id="transforms">Transforms</h2>
+<p>Here are the equivalents of some common transforms in both PySpark and
Beam.</p>
+<div class="table-wrapper"><table>
+<thead>
+<tr>
+<th></th>
+<th>PySpark</th>
+<th>Beam</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><a
href="/documentation/transforms/python/elementwise/map/"><strong>Map</strong></a></td>
+<td><code>values.map(lambda x: x * 2)</code></td>
+<td><code>values | beam.Map(lambda x: x * 2)</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/elementwise/filter/"><strong>Filter</strong></a></td>
+<td><code>values.filter(lambda x: x % 2 == 0)</code></td>
+<td><code>values | beam.Filter(lambda x: x % 2 == 0)</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/elementwise/flatmap/"><strong>FlatMap</strong></a></td>
+<td><code>values.flatMap(lambda x: range(x))</code></td>
+<td><code>values | beam.FlatMap(lambda x: range(x))</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/groupbykey/"><strong>Group
by key</strong></a></td>
+<td><code>pairs.groupByKey()</code></td>
+<td><code>pairs | beam.GroupByKey()</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/combineglobally/"><strong>Reduce</strong></a></td>
+<td><code>values.reduce(lambda x, y: x+y)</code></td>
+<td><code>values | beam.CombineGlobally(sum)</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/combineperkey/"><strong>Reduce
by key</strong></a></td>
+<td><code>pairs.reduceByKey(lambda x, y: x+y)</code></td>
+<td><code>pairs | beam.CombinePerKey(sum)</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/distinct/"><strong>Distinct</strong></a></td>
+<td><code>values.distinct()</code></td>
+<td><code>values | beam.Distinct()</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/count/"><strong>Count</strong></a></td>
+<td><code>values.count()</code></td>
+<td><code>values | beam.combiners.Count.Globally()</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/count/"><strong>Count by
key</strong></a></td>
+<td><code>pairs.countByKey()</code></td>
+<td><code>pairs | beam.combiners.Count.PerKey()</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/top/"><strong>Take
smallest</strong></a></td>
+<td><code>values.takeOrdered(3)</code></td>
+<td><code>values | beam.combiners.Top.Smallest(3)</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/top/"><strong>Take
largest</strong></a></td>
+<td><code>values.takeOrdered(3, lambda x: -x)</code></td>
+<td><code>values | beam.combiners.Top.Largest(3)</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/sample/"><strong>Random
sample</strong></a></td>
+<td><code>values.takeSample(False, 3)</code></td>
+<td><code>values |
beam.combiners.Sample.FixedSizeGlobally(3)</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/other/flatten/"><strong>Union</strong></a></td>
+<td><code>values.union(otherValues)</code></td>
+<td><code>(values, otherValues) | beam.Flatten()</code></td>
+</tr>
+<tr>
+<td><a
href="/documentation/transforms/python/aggregation/cogroupbykey/"><strong>Co-group</strong></a></td>
+<td><code>pairs.cogroup(otherPairs)</code></td>
+<td><code>{'Xs': pairs, 'Ys': otherPairs} |
beam.CoGroupByKey()</code></td>
+</tr>
+</tbody>
+</table></div>
+<blockquote>
+<p>ℹ️ To learn more about the transforms available in Beam, check the
+<a href="/documentation/transforms/python/overview">Python transform
gallery</a>.</p>
+</blockquote>
+<h2 id="using-calculated-values">Using calculated values</h2>
+<p>Since we are working in potentially distributed environments,
+we can&rsquo;t guarantee that the results we&rsquo;ve calculated are
available at any given machine.</p>
+<p>In PySpark, we can get a result from a collection of elements (RDD) by
using
+<code>data.collect()</code>, or other aggregations such as
<code>reduce()</code>, <code>count()</code>, and more.</p>
+<p>Here&rsquo;s an example to scale numbers into a range between zero
and one.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py"
data-lang="py"><span class="kn">import</span> <span
class="nn">pyspark</span>
+<span class="n">sc</span> <span class="o">=</span> <span
class="n">pyspark</span><span class="o">.</span><span
class="n">SparkContext</span><span class="p">()</span>
+<span class="n">values</span> <span class="o">=</span> <span
class="n">sc</span><span class="o">.</span><span
class="n">parallelize</span><span class="p">([</span><span
class="mi">1</span><span class="p">,</span> <span
class="mi">2</span><span class="p">,</span> <span
class="mi">3</span><span class="p">,</span> <span
class="mi">4</span><span class="p">])</span>
+<span class="n">total</span> <span class="o">=</span> <span
class="n">values</span><span class="o">.</span><span
class="n">reduce</span><span class="p">(</span><span
class="k">lambda</span> <span class="n">x</span><span
class="p">,</span> <span class="n">y</span><span
class="p">:</span> <span class="n">x</span> <span
class="o">+</span> <span class="n">y</span><span
class="p">)</span>
+<span class="c1"># We can simply use `total` since it&#39;s already a
Python `int` value from `reduce`.</span>
+<span class="n">scaled_values</span> <span class="o">=</span>
<span class="n">values</span><span class="o">.</span><span
class="n">map</span><span class="p">(</span><span
class="k">lambda</span> <span class="n">x</span><span
class="p">:</span> <span class="n">x</span> <span
class="o">/</span> <span class="n">total</span><span
class="p">)</span>
+<span class="c1"># But to access `scaled_values`, we need to call
`collect`.</span>
+<span class="k">print</span><span class="p">(</span><span
class="n">scaled_values</span><span class="o">.</span><span
class="n">collect</span><span
class="p">())</span></code></pre></div>
+</div>
+<p>In Beam the results from all transforms result in a PCollection.
+We use <a href="/documentation/programming-guide/#side-inputs"><em>side
inputs</em></a>
+to feed a PCollection into a transform and access its values.</p>
+<p>Any transform that accepts a function, like
+<a
href="/documentation/transforms/python/elementwise/map"><code>Map</code></a>,
+can take side inputs.
+If we only need a single value, we can use
+<a
href="https://beam.apache.org/releases/pydoc/current/apache_beam.pvalue.html#apache_beam.pvalue.AsSingleton";><code>beam.pvalue.AsSingleton</code></a>
and access them as a Python value.
+If we need multiple values, we can use
+<a
href="https://beam.apache.org/releases/pydoc/current/apache_beam.pvalue.html#apache_beam.pvalue.AsIter";><code>beam.pvalue.AsIter</code></a>
+and access them as an <a
href="https://docs.python.org/3/glossary.html#term-iterable";><code>iterable</code></a>.</p>
+<div class=language-py>
+<div class="highlight"><pre class="chroma"><code class="language-py"
data-lang="py"><span class="kn">import</span> <span
class="nn">apache_beam</span> <span class="kn">as</span> <span
class="nn">beam</span>
+<span class="k">with</span> <span class="n">beam</span><span
class="o">.</span><span class="n">Pipeline</span><span
class="p">()</span> <span class="k">as</span> <span
class="n">pipeline</span><span class="p">:</span>
+<span class="n">values</span> <span class="o">=</span> <span
class="n">pipeline</span> <span class="o">|</span> <span
class="n">beam</span><span class="o">.</span><span
class="n">Create</span><span class="p">([</span><span
class="mi">1</span><span class="p">,</span> <span
class="mi">2</span><span class="p">,</span> <span
class="mi">3</span><span class="p">,</span> <span
class="mi">4</span><span c [...]
+<span class="n">total</span> <span class="o">=</span> <span
class="n">values</span> <span class="o">|</span> <span
class="n">beam</span><span class="o">.</span><span
class="n">CombineGlobally</span><span class="p">(</span><span
class="nb">sum</span><span class="p">)</span>
+<span class="c1"># To access `total`, we need to pass it as a side
input.</span>
+<span class="n">scaled_values</span> <span class="o">=</span>
<span class="n">values</span> <span class="o">|</span> <span
class="n">beam</span><span class="o">.</span><span
class="n">Map</span><span class="p">(</span>
+<span class="k">lambda</span> <span class="n">x</span><span
class="p">,</span> <span class="n">total</span><span
class="p">:</span> <span class="n">x</span> <span
class="o">/</span> <span class="n">total</span><span
class="p">,</span>
+<span class="n">total</span><span class="o">=</span><span
class="n">beam</span><span class="o">.</span><span
class="n">pvalue</span><span class="o">.</span><span
class="n">AsSingleton</span><span class="p">(</span><span
class="n">total</span><span class="p">))</span>
+<span class="n">scaled_values</span> <span class="o">|</span>
<span class="n">beam</span><span class="o">.</span><span
class="n">Map</span><span class="p">(</span><span
class="k">print</span><span
class="p">)</span></code></pre></div>
+</div>
+<blockquote>
+<p>ℹ️ In Beam we need to pass a side input explicitly, but we get the
+benefit that a reduction or aggregation does <em>not</em> have to fit
into memory.</p>
+</blockquote>
+<h2 id="next-steps">Next Steps</h2>
+<ul>
+<li>Take a look at all the available transforms in the <a
href="/documentation/transforms/python/overview">Python transform
gallery</a>.</li>
+<li>Learn how to read from and write to files in the <a
href="/documentation/programming-guide/#pipeline-io"><em>Pipeline
I/O</em> section of the <em>Programming guide</em></a></li>
+<li>Walk through additional WordCount examples in the <a
href="/get-started/wordcount-example">WordCount Example
Walkthrough</a>.</li>
+<li>Take a self-paced tour through our <a
href="/documentation/resources/learning-resources">Learning
Resources</a>.</li>
+<li>Dive in to some of our favorite <a
href="/documentation/resources/videos-and-podcasts">Videos and
Podcasts</a>.</li>
+<li>Join the Beam <a href="/community/contact-us">users@</a> mailing
list.</li>
+<li>If you&rsquo;re interested in contributing to the Apache Beam
codebase, see the <a href="/contribute">Contribution Guide</a>.</li>
+</ul>
<p>Please don&rsquo;t hesitate to <a
href="/community/contact-us">reach out</a> if you encounter any
issues!</p></description></item><item><title>Get-Started: Try Apache
Beam</title><link>/get-started/try-apache-beam/</link><pubDate>Mon, 01 Jan 0001
00:00:00 +0000</pubDate><guid>/get-started/try-apache-beam/</guid><description>
<!--
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navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
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href=/roadmap/>Roadmap</a></li>< [...]
(<a
href=https://issues.apache.org/jira/browse/BEAM-4293>BEAM-4293</a>).</p></blockquote><p>Every
time a user plays an instance of our hypothetical mobile game, they generate a
data event. Each data event consists of the following
information:</p><ul><li>The unique ID of the user playing the game.</li><li>The
team ID for the team to which the user belongs.</li><li>A score value for that
particular instance of play.</li><li>A timestamp that records when the
particular instance of play hap [...]
occurred. The Y-axis represents processing time: the time at which a game event
was processed. Ideally, events should be processed as they occur, depicted by
diff --git a/website/generated-content/get-started/quickstart-go/index.html
b/website/generated-content/get-started/quickstart-go/index.html
index 24c4e46..9e191c8 100644
--- a/website/generated-content/get-started/quickstart-go/index.html
+++ b/website/generated-content/get-started/quickstart-go/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Beam Quickstart for
Go</title><meta name=description content="Apache Beam is an open source,
unified model and set of language-specific SDKs for defining and executing data
processing workflows, and also data ingestion and integration flows, supporting
Enterprise Integration Patterns (EIPs) and Domain [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
<a
href=https://github.com/apache/beam/tree/master/sdks/go/examples>examples</a>
directory has many examples. All examples can be run by passing the
required arguments described in the examples.</p><p>For example, to run
<code>wordcount</code>, run:</p><div class=runner-direct><pre><code>$ go
install github.com/apache/beam/sdks/go/examples/wordcount
diff --git a/website/generated-content/get-started/quickstart-java/index.html
b/website/generated-content/get-started/quickstart-java/index.html
index 637ebec..1324f64 100644
--- a/website/generated-content/get-started/quickstart-java/index.html
+++ b/website/generated-content/get-started/quickstart-java/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Beam Quickstart for
Java</title><meta name=description content="Apache Beam is an open source,
unified model and set of language-specific SDKs for defining and executing data
processing workflows, and also data ingestion and integration flows, supporting
Enterprise Integration Patterns (EIPs) and Doma [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
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closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
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href=/documentation/sdks/java/>Languages</a></li><li><a
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href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
-DarchetypeGroupId=org.apache.beam \
-DarchetypeArtifactId=beam-sdks-java-maven-archetypes-examples \
-DarchetypeVersion=2.26.0 \
diff --git a/website/generated-content/get-started/quickstart-py/index.html
b/website/generated-content/get-started/quickstart-py/index.html
index 202011e..940e015 100644
--- a/website/generated-content/get-started/quickstart-py/index.html
+++ b/website/generated-content/get-started/quickstart-py/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Beam Quickstart for
Python</title><meta name=description content="Apache Beam is an open source,
unified model and set of language-specific SDKs for defining and executing data
processing workflows, and also data ingestion and integration flows, supporting
Enterprise Integration Patterns (EIPs) and Do [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
install it. This command might require administrative privileges.</p><div
class=shell-unix><pre><code>pip install --upgrade pip</code></pre></div><div
class=shell-PowerShell><pre><code>PS> python -m pip install --upgrade
pip</code></pre></div><h3 id=install-python-virtual-environment>Install Python
virtual environment</h3><p>It is recommended that you install a <a
href=https://docs.python-guide.org/en/latest/dev/virtualenvs/>Python virtual
environment</a>
for initial experiments. If you do not have <code>virtualenv</code> version
13.1.0 or
newer, run the following command to install it. This command might require
diff --git a/website/generated-content/get-started/try-apache-beam/index.html
b/website/generated-content/get-started/try-apache-beam/index.html
index e43e84a..f5f6b05 100644
--- a/website/generated-content/get-started/try-apache-beam/index.html
+++ b/website/generated-content/get-started/try-apache-beam/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Try Apache
Beam</title><meta name=description content="Apache Beam is an open source,
unified model and set of language-specific SDKs for defining and executing data
processing workflows, and also data ingestion and integration flows, supporting
Enterprise Integration Patterns (EIPs) and Domain Specif [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
<span class=kn>import</span> <span
class=nn>org.apache.beam.sdk.Pipeline</span><span class=o>;</span>
<span class=kn>import</span> <span
class=nn>org.apache.beam.sdk.io.TextIO</span><span class=o>;</span>
diff --git a/website/generated-content/get-started/wordcount-example/index.html
b/website/generated-content/get-started/wordcount-example/index.html
index 73abcc8..a92833a 100644
--- a/website/generated-content/get-started/wordcount-example/index.html
+++ b/website/generated-content/get-started/wordcount-example/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Beam WordCount
Examples</title><meta name=description content="Apache Beam is an open source,
unified model and set of language-specific SDKs for defining and executing data
processing workflows, and also data ingestion and integration flows, supporting
Enterprise Integration Patterns (EIPs) and Domai [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
read text, tokenize the text lines into individual words, and perform a
frequency count on each of those words. The Beam SDKs contain a series of these
four successively more detailed WordCount examples that build on each other.
The
diff --git a/website/generated-content/security/cve-2020-1929/index.html
b/website/generated-content/security/cve-2020-1929/index.html
index 9946985..f469e20 100644
--- a/website/generated-content/security/cve-2020-1929/index.html
+++ b/website/generated-content/security/cve-2020-1929/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>CVE-2020-1929</title><meta
name=description content="Apache Beam is an open source, unified model and set
of language-specific SDKs for defining and executing data processing workflows,
and also data ingestion and integration flows, supporting Enterprise
Integration Patterns (EIPs) and Domain Specific [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
<a href=http://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></footer></body></html>
\ No newline at end of file
diff --git a/website/generated-content/security/index.html
b/website/generated-content/security/index.html
index 45326e9..b34e8ec 100644
--- a/website/generated-content/security/index.html
+++ b/website/generated-content/security/index.html
@@ -1,7 +1,7 @@
<!doctype html><html lang=en class=no-js><head><meta charset=utf-8><meta
http-equiv=x-ua-compatible content="IE=edge"><meta name=viewport
content="width=device-width,initial-scale=1"><title>Beam Security</title><meta
name=description content="Apache Beam is an open source, unified model and set
of language-specific SDKs for defining and executing data processing workflows,
and also data ingestion and integration flows, supporting Enterprise
Integration Patterns (EIPs) and Domain Specific [...]
<span class=sr-only>Toggle navigation</span>
<span class=icon-bar></span><span class=icon-bar></span><span
class=icon-bar></span></button>
-<a href=/ class=navbar-brand><img alt=Brand style=height:25px
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closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
+<a href=/ class=navbar-brand><img alt=Brand style=height:25px
src=/images/beam_logo_navbar.png></a></div><div class="navbar-mask
closed"></div><div id=navbar class="navbar-container closed"><ul class="nav
navbar-nav"><li><a href=/get-started/beam-overview/>Get Started</a></li><li><a
href=/documentation/>Documentation</a></li><li><a
href=/documentation/sdks/java/>Languages</a></li><li><a
href=/documentation/runners/capability-matrix/>RUNNERS</a></li><li><a
href=/roadmap/>Roadmap</a></li>< [...]
Team</a> for reporting vulnerabilities. Note
that vulnerabilities should not be publicly disclosed until the project has
responded.</p><p>To report a possible security vulnerability, please email
diff --git a/website/generated-content/sitemap.xml
b/website/generated-content/sitemap.xml
index 32b57c8..2c94b99 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>2020-12-23T09:07:16-08:00</lastmod></url><url><loc>/blog/</loc><lastmod>2020-12-23T09:07:16-08:00</lastmod></url><url><loc>/categories/</loc><lastmod>2020-12-23T09:07:16-08:00</lastmod></url><url><loc>/blog/dataframe-api-preview-available/</loc><lastmod>2020-12-17T16:58:23-08:00</lastmod></u
[...]
\ 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>2020-12-23T09:07:16-08:00</lastmod></url><url><loc>/blog/</loc><lastmod>2020-12-23T09:07:16-08:00</lastmod></url><url><loc>/categories/</loc><lastmod>2020-12-23T09:07:16-08:00</lastmod></url><url><loc>/blog/dataframe-api-preview-available/</loc><lastmod>2020-12-17T16:58:23-08:00</lastmod></u
[...]
\ No newline at end of file
