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new 720cd45 Publishing website 2019/03/21 15:32:43 at commit 133c56d
720cd45 is described below
commit 720cd458beb8619a1daca90260354929712c82b1
Author: jenkins <bui...@apache.org>
AuthorDate: Thu Mar 21 15:32:43 2019 +0000
Publishing website 2019/03/21 15:32:43 at commit 133c56d
---
.../documentation/runners/spark/index.html | 16 ++++++++--------
1 file changed, 8 insertions(+), 8 deletions(-)
diff --git a/website/generated-content/documentation/runners/spark/index.html
b/website/generated-content/documentation/runners/spark/index.html
index 650aaf2..e8b947f 100644
--- a/website/generated-content/documentation/runners/spark/index.html
+++ b/website/generated-content/documentation/runners/spark/index.html
@@ -224,8 +224,8 @@ The Spark Runner can execute Spark pipelines just like a
native Spark applicatio
<ul>
<li>Batch and streaming (and combined) pipelines.</li>
- <li>The same fault-tolerance <a
href="http://spark.apache.org/docs/1.6.3/streaming-programming-guide.html#fault-tolerance-semantics";>guarantees</a>
as provided by RDDs and DStreams.</li>
- <li>The same <a
href="http://spark.apache.org/docs/1.6.3/security.html";>security</a> features
Spark provides.</li>
+ <li>The same fault-tolerance <a
href="http://spark.apache.org/docs/latest/streaming-programming-guide.html#fault-tolerance-semantics";>guarantees</a>
as provided by RDDs and DStreams.</li>
+ <li>The same <a
href="http://spark.apache.org/docs/latest/security.html";>security</a> features
Spark provides.</li>
<li>Built-in metrics reporting using Spark’s metrics system, which reports
Beam Aggregators as well.</li>
<li>Native support for Beam side-inputs via spark’s Broadcast variables.</li>
</ul>
@@ -236,7 +236,7 @@ The Spark Runner can execute Spark pipelines just like a
native Spark applicatio
<h2 id="spark-runner-prerequisites-and-setup">Spark Runner prerequisites and
setup</h2>
-<p>The Spark runner currently supports Spark’s 1.6 branch, and more
specifically any version greater than 1.6.0.</p>
+<p>The Spark runner currently supports Spark’s 2.x branch, and more
specifically any version greater than 2.2.0.</p>
<p>You can add a dependency on the latest version of the Spark runner by
adding to your pom.xml the following:</p>
<div class="language-java highlighter-rouge"><pre
class="highlight"><code><span class="o"><</span><span
class="n">dependency</span><span class="o">></span>
@@ -365,14 +365,14 @@ For more details on the different deployment modes see:
<a href="http://spark.ap
<p>When submitting a Spark application to cluster, it is common (and
recommended) to use the <code>spark-submit</code> script that is provided with
the spark installation.
The <code>PipelineOptions</code> described above are not to replace
<code>spark-submit</code>, but to complement it.
Passing any of the above mentioned options could be done as one of the
<code>application-arguments</code>, and setting <code>--master</code> takes
precedence.
-For more on how to generally use <code>spark-submit</code> checkout Spark <a
href="http://spark.apache.org/docs/1.6.3/submitting-applications.html#launching-applications-with-spark-submit";>documentation</a>.</p>
+For more on how to generally use <code>spark-submit</code> checkout Spark <a
href="http://spark.apache.org/docs/latest/submitting-applications.html#launching-applications-with-spark-submit";>documentation</a>.</p>
<h3 id="monitoring-your-job">Monitoring your job</h3>
-<p>You can monitor a running Spark job using the Spark <a
href="http://spark.apache.org/docs/1.6.3/monitoring.html#web-interfaces";>Web
Interfaces</a>. By default, this is available at port <code
class="highlighter-rouge">4040</code> on the driver node. If you run Spark on
your local machine that would be <code
class="highlighter-rouge">http://localhost:4040</code>.
-Spark also has a history server to <a
href="http://spark.apache.org/docs/1.6.3/monitoring.html#viewing-after-the-fact";>view
after the fact</a>.
-Metrics are also available via <a
href="http://spark.apache.org/docs/1.6.3/monitoring.html#rest-api";>REST API</a>.
-Spark provides a <a
href="http://spark.apache.org/docs/1.6.3/monitoring.html#metrics";>metrics
system</a> that allows reporting Spark metrics to a variety of Sinks. The Spark
runner reports user-defined Beam Aggregators using this same metrics system and
currently supports <code>GraphiteSink</code> and <code>CSVSink</code>, and
providing support for additional Sinks supported by Spark is easy and
straight-forward.</p>
+<p>You can monitor a running Spark job using the Spark <a
href="http://spark.apache.org/docs/latest/monitoring.html#web-interfaces";>Web
Interfaces</a>. By default, this is available at port <code
class="highlighter-rouge">4040</code> on the driver node. If you run Spark on
your local machine that would be <code
class="highlighter-rouge">http://localhost:4040</code>.
+Spark also has a history server to <a
href="http://spark.apache.org/docs/latest/monitoring.html#viewing-after-the-fact";>view
after the fact</a>.
+Metrics are also available via <a
href="http://spark.apache.org/docs/latest/monitoring.html#rest-api";>REST
API</a>.
+Spark provides a <a
href="http://spark.apache.org/docs/latest/monitoring.html#metrics";>metrics
system</a> that allows reporting Spark metrics to a variety of Sinks. The Spark
runner reports user-defined Beam Aggregators using this same metrics system and
currently supports <code>GraphiteSink</code> and <code>CSVSink</code>, and
providing support for additional Sinks supported by Spark is easy and
straight-forward.</p>
<h3 id="streaming-execution">Streaming Execution</h3>
