[beam] branch asf-site updated: Publishing website 2019/04/04 20:45:51 at commit aa5f604

[gi...@apache.org]

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 8fd022e  Publishing website 2019/04/04 20:45:51 at commit aa5f604
8fd022e is described below

commit 8fd022e79f41fda11bd9fe888ae75f1b0d244536
Author: jenkins <bu...@apache.org>
AuthorDate: Thu Apr 4 20:45:52 2019 +0000

    Publishing website 2019/04/04 20:45:51 at commit aa5f604
---
 website/generated-content/get-started/mobile-gaming-example/index.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/generated-content/get-started/mobile-gaming-example/index.html b/website/generated-content/get-started/mobile-gaming-example/index.html
index 2c32e50..f574fce 100644
--- a/website/generated-content/get-started/mobile-gaming-example/index.html
+++ b/website/generated-content/get-started/mobile-gaming-example/index.html
@@ -487,7 +487,7 @@ looks more like what is depicted by the red squiggly line above the ideal line.<
 
 <p>The <code class="highlighter-rouge">HourlyTeamScore</code> pipeline expands on the basic batch analysis principles used in the <code class="highlighter-rouge">UserScore</code> pipeline and improves upon some of its limitations. <code class="highlighter-rouge">HourlyTeamScore</code> performs finer-grained analysis, both by using additional features in the Beam SDKs, and taking into account more aspects of the game data. For example, <code class="highlighter-rouge">HourlyTeamScore</code [...]
 
-<p>Like <code class="highlighter-rouge">UserScore</code>, <code class="highlighter-rouge">HourlyTeamScore</code> is best thought of as a job to be run periodically after all the relevant data has been gathered (such as once per day). The pipeline reads a fixed data set from a file, and writes the results to a Google Cloud BigQuery table.</p>
+<p>Like <code class="highlighter-rouge">UserScore</code>, <code class="highlighter-rouge">HourlyTeamScore</code> is best thought of as a job to be run periodically after all the relevant data has been gathered (such as once per day). The pipeline reads a fixed data set from a file, and writes the results <span class="language-java">back to a text file</span><span class="language-py">to a Google Cloud BigQuery table</span>.</p>
 
 <blockquote class="language-java">
   <p><strong>Note:</strong> See <a href="https://github.com/apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java">HourlyTeamScore on GitHub</a> for the complete example pipeline program.</p>