beam-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From git-site-r...@apache.org
Subject [beam] branch asf-site updated: Publishing website 2019/01/03 17:02:59 at commit a2986cc
Date Thu, 03 Jan 2019 17:03:04 GMT
This is an automated email from the ASF dual-hosted git repository.

git-site-role pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/beam.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new 56f1fdd  Publishing website 2019/01/03 17:02:59 at commit a2986cc
56f1fdd is described below

commit 56f1fddc64a5b6041d9e4153fe27fcf03510727d
Author: jenkins <builds@apache.org>
AuthorDate: Thu Jan 3 17:02:59 2019 +0000

    Publishing website 2019/01/03 17:02:59 at commit a2986cc
---
 website/generated-content/blog/2017/01/09/added-apex-runner.html      | 4 ++--
 .../documentation/io/built-in/google-bigquery/index.html              | 2 +-
 website/generated-content/documentation/runners/apex/index.html       | 2 +-
 website/generated-content/documentation/sdks/java/euphoria/index.html | 4 ++--
 4 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/website/generated-content/blog/2017/01/09/added-apex-runner.html b/website/generated-content/blog/2017/01/09/added-apex-runner.html
index a21eee1..b6e6d5c 100644
--- a/website/generated-content/blog/2017/01/09/added-apex-runner.html
+++ b/website/generated-content/blog/2017/01/09/added-apex-runner.html
@@ -184,11 +184,11 @@ limitations under the License.
 
 <h2 id="stateful-stream-processor">Stateful Stream Processor</h2>
 
-<p>Apex was built as stateful stream processor from the ground up. Operators <a
href="https://www.datatorrent.com/blog/blog-introduction-to-checkpoint/">checkpoint</a>
state in a distributed and asynchronous manner that produces a consistent snapshot for the
entire processing graph, which can be used for recovery. Apex also supports such recovery
in an incremental, or fine grained, manner. This means only the portion of the DAG that is
actually affected by a failure will be recovered whi [...]
+<p>Apex was built as stateful stream processor from the ground up. Operators checkpoint
state in a distributed and asynchronous manner that produces a consistent snapshot for the
entire processing graph, which can be used for recovery. Apex also supports such recovery
in an incremental, or fine grained, manner. This means only the portion of the DAG that is
actually affected by a failure will be recovered while the remaining pipeline continues processing
(this can be leveraged to impleme [...]
 
 <h2 id="translation-to-apex-dag">Translation to Apex DAG</h2>
 
-<p>A Beam runner needs to implement the translation from the Beam model to the underlying
frameworks execution model. In the case of Apex, the runner will translate the pipeline into
the <a href="https://www.datatorrent.com/blog/tracing-dags-from-specification-to-execution/">native
(compositional, low level) DAG API</a> (which is also the base for a number of other
API that are available to specify applications that run on Apex). The DAG consists of operators
(functional building blocks  [...]
+<p>A Beam runner needs to implement the translation from the Beam model to the underlying
frameworks execution model. In the case of Apex, the runner will translate the pipeline into
the native (compositional, low level) DAG API (which is also the base for a number of other
API that are available to specify applications that run on Apex). The DAG consists of operators
(functional building blocks that are connected with streams. The runner provides the execution
layer. In the case of Apex [...]
 
 <h2 id="execution-and-testing">Execution and Testing</h2>
 
diff --git a/website/generated-content/documentation/io/built-in/google-bigquery/index.html
b/website/generated-content/documentation/io/built-in/google-bigquery/index.html
index 96ad228..96d1fbf 100644
--- a/website/generated-content/documentation/io/built-in/google-bigquery/index.html
+++ b/website/generated-content/documentation/io/built-in/google-bigquery/index.html
@@ -418,7 +418,7 @@ you omit the project ID, Beam uses the default project ID from your
   <a href="https://beam.apache.org/releases/javadoc/2.9.0/org/apache/beam/sdk/extensions/gcp/options/GcpOptions.html">pipeline
options</a>.
 </span>
 <span class="language-py">
-  <a href="https://beam.apache.org/releases/javadoc/2.9.0/apache_beam.options.pipeline_options.html#apache_beam.options.pipeline_options.GoogleCloudOptions">pipeline
options</a>.
+  <a href="https://beam.apache.org/releases/pydoc/2.9.0/apache_beam.options.pipeline_options.html#apache_beam.options.pipeline_options.GoogleCloudOptions">pipeline
options</a>.
 </span></p>
 
 <div class="language-java highlighter-rouge"><pre class="highlight"><code><span
class="n">String</span> <span class="n">tableSpec</span> <span class="o">=</span>
<span class="s">"samples.weather_stations"</span><span class="o">;</span>
diff --git a/website/generated-content/documentation/runners/apex/index.html b/website/generated-content/documentation/runners/apex/index.html
index 2aa679f..38f42b0 100644
--- a/website/generated-content/documentation/runners/apex/index.html
+++ b/website/generated-content/documentation/runners/apex/index.html
@@ -216,7 +216,7 @@ They are not required for Apex in embedded mode (see <a href="/get-started/quick
 <p>You may set up your own Hadoop cluster. Beam does not require anything extra to
launch the pipelines on YARN.
 An optional Apex installation may be useful for monitoring and troubleshooting.
 The Apex CLI can be <a href="http://apex.apache.org/docs/apex/apex_development_setup/">built</a>
or
-obtained as <a href="http://www.atrato.io/blog/2017/04/08/apache-apex-cli/">binary
build</a>.
+obtained as binary build.
 For more download options see <a href="http://apex.apache.org/downloads.html">distribution
information on the Apache Apex website</a>.</p>
 
 <h2 id="running-wordcount-with-apex">Running wordcount with Apex</h2>
diff --git a/website/generated-content/documentation/sdks/java/euphoria/index.html b/website/generated-content/documentation/sdks/java/euphoria/index.html
index 24e47ff..67ade47 100644
--- a/website/generated-content/documentation/sdks/java/euphoria/index.html
+++ b/website/generated-content/documentation/sdks/java/euphoria/index.html
@@ -583,7 +583,7 @@ the API as a high level DSL over Beam Java SDK and share our effort with
the com
 <span class="c1">// KV(3, "3+rat"), KV(1, "1+X")]</span>
 </code></pre>
 </div>
-<p>Euphoria support performance optimization called ‘BroadcastHashJoin’ for the
<code class="highlighter-rouge">LeftJoin</code>. Broadcast join can be very efficient
when joining two datasets where one fits in memory (in <code class="highlighter-rouge">LeftJoin</code>
right dataset has to fit in memory). How to use ‘Broadcast Hash Join’ is described in
<a href="#Translation">Translation</a> section.</p>
+<p>Euphoria support performance optimization called ‘BroadcastHashJoin’ for the
<code class="highlighter-rouge">LeftJoin</code>. Broadcast join can be very efficient
when joining two datasets where one fits in memory (in <code class="highlighter-rouge">LeftJoin</code>
right dataset has to fit in memory). How to use ‘Broadcast Hash Join’ is described in
<a href="#translation">Translation</a> section.</p>
 
 <h3 id="rightjoin"><code class="highlighter-rouge">RightJoin</code></h3>
 <p>Represents right join of two (left and right) datasets on given key producing single
new dataset. Key is extracted from both datasets by separate extractors so elements in left
and right can have different types denoted as <code class="highlighter-rouge">LeftT</code>
and <code class="highlighter-rouge">RightT</code>. The join itself is performed
by user-supplied <code class="highlighter-rouge">BinaryFunctor</code> which consumes
one element from both dataset, where left is present opt [...]
@@ -602,7 +602,7 @@ the API as a high level DSL over Beam Java SDK and share our effort with
the com
     <span class="c1">// KV(8, "null+elephant"), KV(5, "null+mouse")]</span>
 </code></pre>
 </div>
-<p>Euphoria support performance optimization called ‘BroadcastHashJoin’ for the
<code class="highlighter-rouge">RightJoin</code>. Broadcast join can be very efficient
when joining two datasets where one fits in memory (in <code class="highlighter-rouge">RightJoin</code>
left dataset has to fit in memory). How to use ‘Broadcast Hash Join’ is described in <a
href="#Translation">Translation</a> section.</p>
+<p>Euphoria support performance optimization called ‘BroadcastHashJoin’ for the
<code class="highlighter-rouge">RightJoin</code>. Broadcast join can be very efficient
when joining two datasets where one fits in memory (in <code class="highlighter-rouge">RightJoin</code>
left dataset has to fit in memory). How to use ‘Broadcast Hash Join’ is described in <a
href="#translation">Translation</a> section.</p>
 
 <h3 id="fulljoin"><code class="highlighter-rouge">FullJoin</code></h3>
 <p>Represents full outer join of two (left and right) datasets on given key producing
single new dataset. Key is extracted from both datasets by separate extractors so elements
in left and right can have different types denoted as <code class="highlighter-rouge">LeftT</code>
and <code class="highlighter-rouge">RightT</code>. The join itself is performed
by user-supplied <code class="highlighter-rouge">BinaryFunctor</code> which consumes
one element from both dataset, where both are prese [...]


Mime
View raw message