This is an automated email from the ASF dual-hosted git repository.
melap pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/beam.git
The following commit(s) were added to refs/heads/master by this push:
new aa5f604 Update HourlyTeamScore write destinations
aa5f604 is described below
commit aa5f6049f69eb2b5ddcd953ba31961f8ae33a97a
Author: Sylwester Kardziejonek <sylwester.kardziejo...@gmail.com>
AuthorDate: Thu Apr 4 22:44:50 2019 +0200
Update HourlyTeamScore write destinations
* HourlyTeamScore writes to a file
The docs say the data is written to a BigQuery database. It's not what's in
the code. The data is written to a text file.
* Change docs statement about HourlyTeamScore based on language toggle
HourlyTeamScore writes back to a different sink in code examples for Java
vs Python.
This changes the statement based on the language toggle.
---
website/src/get-started/mobile-gaming-example.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/website/src/get-started/mobile-gaming-example.md
b/website/src/get-started/mobile-gaming-example.md
index 39d1480..6d1c8f1 100644
--- a/website/src/get-started/mobile-gaming-example.md
+++ b/website/src/get-started/mobile-gaming-example.md
@@ -144,7 +144,7 @@ Starting with the next pipeline example, we'll discuss how
you can use Beam's fe
The `HourlyTeamScore` pipeline expands on the basic batch analysis principles
used in the `UserScore` pipeline and improves upon some of its limitations.
`HourlyTeamScore` 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, `HourlyTeamScore` can filter out data that isn't part of the
relevant analysis period.
-Like `UserScore`, `HourlyTeamScore` 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.
+Like `UserScore`, `HourlyTeamScore` 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>.
{:.language-java}
> **Note:** See [HourlyTeamScore on
> GitHub](https://github.com/apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java)
> for the complete example pipeline program.
@@ -407,4 +407,4 @@ We can use the resulting information to find, for example,
what times of day our
* Dive in to some of our favorite [Videos and Podcasts]({{ site.baseurl
}}/documentation/resources/videos-and-podcasts).
* Join the Beam [users@]({{ site.baseurl }}/community/contact-us) mailing list.
-Please don't hesitate to [reach out]({{ site.baseurl }}/community/contact-us)
if you encounter any issues!
\ No newline at end of file
+Please don't hesitate to [reach out]({{ site.baseurl }}/community/contact-us)
if you encounter any issues!
