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From mergebot-r...@apache.org
Subject [beam-site] 01/02: Fix broken links due to code directory moves
Date Tue, 30 Jan 2018 17:54:52 GMT
This is an automated email from the ASF dual-hosted git repository.

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

commit 985fefd87d21bfed0d66c832acff46ff66c2067c
Author: melissa <melissapa@google.com>
AuthorDate: Mon Jan 29 10:28:50 2018 -0800

    Fix broken links due to code directory moves
---
 .../2016-10-12-strata-hadoop-world-and-beam.md     |  2 +-
 src/_posts/2016-10-20-test-stream.md               |  4 +--
 src/documentation/io/testing.md                    |  2 +-
 src/documentation/programming-guide.md             |  8 +++---
 src/get-started/mobile-gaming-example.md           | 30 +++++++++++-----------
 5 files changed, 23 insertions(+), 23 deletions(-)

diff --git a/src/_posts/2016-10-12-strata-hadoop-world-and-beam.md b/src/_posts/2016-10-12-strata-hadoop-world-and-beam.md
index 1642edd..3b8eb37 100644
--- a/src/_posts/2016-10-12-strata-hadoop-world-and-beam.md
+++ b/src/_posts/2016-10-12-strata-hadoop-world-and-beam.md
@@ -12,7 +12,7 @@ Tyler Akidau and I gave a [three-hour tutorial](http://conferences.oreilly.com/s
 
 <img src="{{ "/images/blog/IMG_20160927_170956.jpg" | prepend: site.baseurl }}" alt="Exercise
time">
 
-If you want to take a look at the tutorial materials, we’ve put them up [on GitHub](https://github.com/eljefe6a/beamexample).
This includes the [actual slides](https://github.com/eljefe6a/beamexample/blob/master/BeamTutorial/slides.pdf)
as well as the [exercises](https://github.com/eljefe6a/beamexample/tree/master/BeamTutorial/src/main/java/org/apache/beam/examples/tutorial/game)
that we covered. If you’re looking to learn a little about Beam, this is a good way to start.
The exercises a [...]
+If you want to take a look at the tutorial materials, we’ve put them up [on GitHub](https://github.com/eljefe6a/beamexample).
This includes the [actual slides](https://github.com/eljefe6a/beamexample/blob/master/BeamTutorial/slides.pdf)
as well as the [exercises](https://github.com/eljefe6a/beamexample/tree/master/BeamTutorial/src/main/java/org/apache/beam/examples/tutorial/game)
that we covered. If you’re looking to learn a little about Beam, this is a good way to start.
The exercises a [...]
 
 I want to share some of takeaways I had about Beam during the conference.
 
diff --git a/src/_posts/2016-10-20-test-stream.md b/src/_posts/2016-10-20-test-stream.md
index 500680a..a65a8b7 100644
--- a/src/_posts/2016-10-20-test-stream.md
+++ b/src/_posts/2016-10-20-test-stream.md
@@ -45,8 +45,8 @@ from the Mobile Gaming example series.
 
 ## LeaderBoard and the Mobile Gaming Example
 
-[LeaderBoard](https://github.com/apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java#L177)
-is part of the [Beam mobile gaming examples](https://github.com/apache/beam/tree/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game)
+[LeaderBoard](https://github.com/apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java#L177)
+is part of the [Beam mobile gaming examples](https://github.com/apache/beam/tree/master/examples/java/src/main/java/org/apache/beam/examples/complete/game)
 (and [walkthroughs]({{ site.baseurl }}/get-started/mobile-gaming-example/))
 which produces a continuous accounting of user and team scores. User scores are
 calculated over the lifetime of the program, while team scores are calculated
diff --git a/src/documentation/io/testing.md b/src/documentation/io/testing.md
index 0c3f439..cac7b8a 100644
--- a/src/documentation/io/testing.md
+++ b/src/documentation/io/testing.md
@@ -561,7 +561,7 @@ You can do this by:
 1.  Creating two Kubernetes scripts: one for a small instance of the data store, and one
for a large instance.
 1.  Having your test take a pipeline option that decides whether to generate a small or large
amount of test data (where small and large are sizes appropriate to your data store)
 
-An example of this is [HadoopInputFormatIO](https://github.com/apache/beam/tree/master/sdks/java/io/hadoop/input-format)'s
tests.
+An example of this is [HadoopInputFormatIO](https://github.com/apache/beam/tree/master/sdks/java/io/hadoop-input-format)'s
tests.
 
 <!--
 # Next steps
diff --git a/src/documentation/programming-guide.md b/src/documentation/programming-guide.md
index 796f664..99b0a9f 100644
--- a/src/documentation/programming-guide.md
+++ b/src/documentation/programming-guide.md
@@ -876,7 +876,7 @@ data contains names and phone numbers.
 </span>
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/test/java/org/apache/beam/examples/website_snippets/SnippetsTest.java
tag:CoGroupByKeyTupleInputs
+{% github_sample /apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/snippets/SnippetsTest.java
tag:CoGroupByKeyTupleInputs
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py
tag:model_group_by_key_cogroupbykey_tuple_inputs
@@ -886,7 +886,7 @@ After `CoGroupByKey`, the resulting data contains all data associated
with each
 unique key from any of the input collections.
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/test/java/org/apache/beam/examples/website_snippets/SnippetsTest.java
tag:CoGroupByKeyTupleOutputs
+{% github_sample /apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/snippets/SnippetsTest.java
tag:CoGroupByKeyTupleOutputs
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py
tag:model_group_by_key_cogroupbykey_tuple_outputs
@@ -897,7 +897,7 @@ followed by a `ParDo` to consume the result. Then, the code uses tags
to look up
 and format data from each collection.
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/website_snippets/Snippets.java
tag:CoGroupByKeyTuple
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/snippets/Snippets.java
tag:CoGroupByKeyTuple
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets.py
tag:model_group_by_key_cogroupbykey_tuple
@@ -906,7 +906,7 @@ and format data from each collection.
 The formatted data looks like this:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/test/java/org/apache/beam/examples/website_snippets/SnippetsTest.java
tag:CoGroupByKeyTupleFormattedOutputs
+{% github_sample /apache/beam/blob/master/examples/java/src/test/java/org/apache/beam/examples/snippets/SnippetsTest.java
tag:CoGroupByKeyTupleFormattedOutputs
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/snippets_test.py
tag:model_group_by_key_cogroupbykey_tuple_formatted_outputs
diff --git a/src/get-started/mobile-gaming-example.md b/src/get-started/mobile-gaming-example.md
index 9a734c0..49d1530 100644
--- a/src/get-started/mobile-gaming-example.md
+++ b/src/get-started/mobile-gaming-example.md
@@ -60,7 +60,7 @@ The Mobile Gaming example pipelines vary in complexity, from simple batch
analys
 The `UserScore` pipeline is the simplest example for processing mobile game data. `UserScore`
determines the total score per user over a finite data set (for example, one day's worth of
scores stored on the game server). Pipelines like `UserScore` are best run periodically after
all relevant data has been gathered. For example, `UserScore` could run as a nightly job over
data gathered during that day.
 
 {:.language-java}
-> **Note:** See [UserScore on GitHub](https://github.com/apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/UserScore.java)
for the complete example pipeline program.
+> **Note:** See [UserScore on GitHub](https://github.com/apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/UserScore.java)
for the complete example pipeline program.
 
 {:.language-py}
 > **Note:** See [UserScore on GitHub](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/user_score.py)
for the complete example pipeline program.
@@ -93,7 +93,7 @@ This example uses batch processing, and the diagram's Y axis represents
processi
 After reading the score events from the input file, the pipeline groups all of those user/score
pairs together and sums the score values into one total value per unique user. `UserScore`
encapsulates the core logic for that step as the [user-defined composite transform]({{ site.baseurl
}}/documentation/programming-guide/#composite-transforms) `ExtractAndSumScore`:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/UserScore.java
tag:DocInclude_USExtractXform
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/UserScore.java
tag:DocInclude_USExtractXform
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/user_score.py
tag:extract_and_sum_score
@@ -104,7 +104,7 @@ After reading the score events from the input file, the pipeline groups
all of t
 Here's the main method of `UserScore`, showing how we apply all three steps of the pipeline:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/UserScore.java
tag:DocInclude_USMain
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/UserScore.java
tag:DocInclude_USMain
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/user_score.py
tag:main
@@ -131,7 +131,7 @@ The `HourlyTeamScore` pipeline expands on the basic batch analysis principles
us
 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.
 
 {:.language-java}
-> **Note:** See [HourlyTeamScore on GitHub](https://github.com/apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java)
for the complete example pipeline program.
+> **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.
 
 {:.language-py}
 > **Note:** See [HourlyTeamScore on GitHub](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/hourly_team_score.py)
for the complete example pipeline program.
@@ -173,7 +173,7 @@ Beam's windowing feature uses the [intrinsic timestamp information]({{
site.base
 The following code shows this:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java
tag:DocInclude_HTSAddTsAndWindow
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java
tag:DocInclude_HTSAddTsAndWindow
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/hourly_team_score.py
tag:add_timestamp_and_window
@@ -192,7 +192,7 @@ It also lets the pipeline include relevant **late data**—data events
with vali
 The following code shows how `HourlyTeamScore` uses the `Filter` transform to filter events
that occur either before or after the relevant analysis period:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java
tag:DocInclude_HTSFilters
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java
tag:DocInclude_HTSFilters
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/hourly_team_score.py
tag:filter_by_time_range
@@ -203,7 +203,7 @@ The following code shows how `HourlyTeamScore` uses the `Filter` transform
to fi
 `HourlyTeamScore` uses the same `ExtractAndSumScores` transform as the `UserScore` pipeline,
but passes a different key (team, as opposed to user). Also, because the pipeline applies
`ExtractAndSumScores` _after_ applying fixed-time 1-hour windowing to the input data, the
data gets grouped by both team _and_ window. You can see the full sequence of transforms in
`HourlyTeamScore`'s main method:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java
tag:DocInclude_HTSMain
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/HourlyTeamScore.java
tag:DocInclude_HTSMain
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/hourly_team_score.py
tag:main
@@ -224,7 +224,7 @@ The `LeaderBoard` pipeline also demonstrates how to process game score
data with
 Because the `LeaderBoard` pipeline reads the game data from an unbounded source as that data
is generated, you can think of the pipeline as an ongoing job running concurrently with the
game process. `LeaderBoard` can thus provide low-latency insights into how users are playing
the game at any given moment — useful if, for example, we want to provide a live web-based
scoreboard so that users can track their progress against other users as they play.
 
 {:.language-java}
-> **Note:** See [LeaderBoard on GitHub](https://github.com/apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java)
for the complete example pipeline program.
+> **Note:** See [LeaderBoard on GitHub](https://github.com/apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java)
for the complete example pipeline program.
 
 {:.language-py}
 > **Note:** See [LeaderBoard on GitHub](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/leader_board.py)
for the complete example pipeline program.
@@ -261,7 +261,7 @@ As processing time advances and more scores are processed, the trigger
outputs t
 The following code example shows how `LeaderBoard` sets the processing time trigger to output
the data for user scores:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java
tag:DocInclude_ProcTimeTrigger
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java
tag:DocInclude_ProcTimeTrigger
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/leader_board.py
tag:processing_time_trigger
@@ -295,7 +295,7 @@ Data arriving above the solid watermark line is _late data_ — this is
a score
 The following code example shows how `LeaderBoard` applies fixed-time windowing with the
appropriate triggers to have our pipeline perform the calculations we want:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java
tag:DocInclude_WindowAndTrigger
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/LeaderBoard.java
tag:DocInclude_WindowAndTrigger
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/leader_board.py
tag:window_and_trigger
@@ -310,7 +310,7 @@ While `LeaderBoard` demonstrates how to use basic windowing and triggers
to perf
 Like `LeaderBoard`, `GameStats` reads data from an unbounded source. It is best thought of
as an ongoing job that provides insight into the game as users play.
 
 {:.language-java}
-> **Note:** See [GameStats on GitHub](https://github.com/apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/GameStats.java)
for the complete example pipeline program.
+> **Note:** See [GameStats on GitHub](https://github.com/apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/GameStats.java)
for the complete example pipeline program.
 
 {:.language-py}
 > **Note:** See [GameStats on GitHub](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/game_stats.py)
for the complete example pipeline program.
@@ -335,7 +335,7 @@ Since the average depends on the pipeline data, we need to calculate it,
and the
 The following code example shows the composite transform that handles abuse detection. The
transform uses the `Sum.integersPerKey` transform to sum all scores per user, and then the
`Mean.globally` transform to determine the average score for all users. Once that's been calculated
(as a `PCollectionView` singleton), we can pass it to the filtering `ParDo` using `.withSideInputs`:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_AbuseDetect
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_AbuseDetect
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/game_stats.py
tag:abuse_detect
@@ -344,7 +344,7 @@ The following code example shows the composite transform that handles
abuse dete
 The abuse-detection transform generates a view of users supected to be spambots. Later in
the pipeline, we use that view to filter out any such users when we calculate the team score
per hour, again by using the side input mechanism. The following code example shows where
we insert the spam filter, between windowing the scores into fixed windows and extracting
the team scores:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_FilterAndCalc
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_FilterAndCalc
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/game_stats.py
tag:filter_and_calc
@@ -368,7 +368,7 @@ between instances are.*
 We can use the session-windowed data to determine the average length of uninterrupted play
time for all of our users, as well as the total score they achieve during each session. We
can do this in the code by first applying session windows, summing the score per user and
session, and then using a transform to calculate the length of each individual session:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_SessionCalc
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_SessionCalc
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/game_stats.py
tag:session_calc
@@ -377,7 +377,7 @@ We can use the session-windowed data to determine the average length of
uninterr
 This gives us a set of user sessions, each with an attached duration. We can then calculate
the _average_ session length by re-windowing the data into fixed time windows, and then calculating
the average for all sessions that end in each hour:
 
 ```java
-{% github_sample /apache/beam/blob/master/examples/java8/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_Rewindow
+{% github_sample /apache/beam/blob/master/examples/java/src/main/java/org/apache/beam/examples/complete/game/GameStats.java
tag:DocInclude_Rewindow
 %}```
 ```py
 {% github_sample /apache/beam/blob/master/sdks/python/apache_beam/examples/complete/game/game_stats.py
tag:rewindow

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