[beam-site] branch asf-site updated (84d3ac9 -> b25b152)

[lc...@apache.org]

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


    from 84d3ac9  [BEAM-3940] Remove reference to updating container version in pom.xml
     new 931c697  Fix typos in programming guide
     new b25b152  Fix typos in mobile gaming example

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Summary of changes:
 src/documentation/programming-guide.md   | 8 ++++----
 src/get-started/mobile-gaming-example.md | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

[beam-site] 01/02: Fix typos in programming guide

[lc...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

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

commit 931c69741547b91e0dd21a2c6abb8cd1b52b1b29
Author: Yueyang Qiu <ro...@gmail.com>
AuthorDate: Tue Jul 10 12:59:03 2018 -0700

    Fix typos in programming guide
---
 src/documentation/programming-guide.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/documentation/programming-guide.md b/src/documentation/programming-guide.md
index 091c130..4b55fcb 100644
--- a/src/documentation/programming-guide.md
+++ b/src/documentation/programming-guide.md
@@ -79,14 +79,14 @@ include:
 
 A typical Beam driver program works as follows:
 
-* Create a `Pipeline` object and set the pipeline execution options, including
+* **Create** a `Pipeline` object and set the pipeline execution options, including
   the Pipeline Runner.
 * Create an initial `PCollection` for pipeline data, either using the IOs
   to read data from an external storage system, or using a `Create` transform to
   build a `PCollection` from in-memory data.
-* Apply **PTransforms** to each `PCollection`. Transforms can change, filter,
+* **Apply** `PTransforms` to each `PCollection`. Transforms can change, filter,
   group, analyze, or otherwise process the elements in a `PCollection`. A
-  transform creates a new output `PCollection` *without consuming the input
+  transform creates a new output `PCollection` *without modifying the input
   collection*. A typical pipeline applies subsequent transforms to the each new
   output `PCollection` in turn until processing is complete. However, note that
   a pipeline does not have to be a single straight line of transforms applied
@@ -1072,7 +1072,7 @@ If your input `PCollection` uses the default global windowing, the default
 behavior is to return a `PCollection` containing one item. That item's value
 comes from the accumulator in the combine function that you specified when
 applying `Combine`. For example, the Beam provided sum combine function returns
-a zero value (the sum of an empty input), while the max combine function returns
+a zero value (the sum of an empty input), while the min combine function returns
 a maximal or infinite value.
 
 To have `Combine` instead return an empty `PCollection` if the input is empty,

[beam-site] 02/02: Fix typos in mobile gaming example

[lc...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

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

commit b25b1522e5214e31aaac1b88b3fe2c90e1b41c79
Author: Yueyang Qiu <ro...@gmail.com>
AuthorDate: Tue Jul 10 13:01:28 2018 -0700

    Fix typos in mobile gaming example
---
 src/get-started/mobile-gaming-example.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/get-started/mobile-gaming-example.md b/src/get-started/mobile-gaming-example.md
index f6c6c46..4a289b4 100644
--- a/src/get-started/mobile-gaming-example.md
+++ b/src/get-started/mobile-gaming-example.md
@@ -344,7 +344,7 @@ Below, we'll look at these features in more detail.
 
 Let's suppose scoring in our game depends on the speed at which a user can "click" on their phone. `GameStats`'s abuse detection analyzes each user's score data to detect if a user has an abnormally high "click rate" and thus an abnormally high score. This might indicate that the game is being played by a bot that operates significantly faster than a human could play.
 
-To determine whether or not a score is "abnormally" high, `GameStats` calculates the average of every score in that fixed-time window, and then checks each score individual score against the average score multiplied by an arbitrary weight factor (in our case, 2.5). Thus, any score more than 2.5 times the average is deemed to be the product of spam. The `GameStats` pipeline tracks a list of "spam" users and filters those users out of the team score calculations for the team leader board.
+To determine whether or not a score is "abnormally" high, `GameStats` calculates the average of every score in that fixed-time window, and then checks each individual score against the average score multiplied by an arbitrary weight factor (in our case, 2.5). Thus, any score more than 2.5 times the average is deemed to be the product of spam. The `GameStats` pipeline tracks a list of "spam" users and filters those users out of the team score calculations for the team leader board.
 
 Since the average depends on the pipeline data, we need to calculate it, and then use that calculated data in a subsequent `ParDo` transform that filters scores that exceed the weighted value. To do this, we can pass the calculated average to as a [side input]({{ site.baseurl }}/documentation/programming-guide/#side-inputs) to the filtering `ParDo`.