incubator-quarks-website git commit: [QUARKS-173] add load balanced parallel recipe

[dl...@apache.org]

Repository: incubator-quarks-website
Updated Branches:
  refs/heads/master 93f231df8 -> d6bf44821


[QUARKS-173] add load balanced parallel recipe


Project: http://git-wip-us.apache.org/repos/asf/incubator-quarks-website/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-quarks-website/commit/d6bf4482
Tree: http://git-wip-us.apache.org/repos/asf/incubator-quarks-website/tree/d6bf4482
Diff: http://git-wip-us.apache.org/repos/asf/incubator-quarks-website/diff/d6bf4482

Branch: refs/heads/master
Commit: d6bf448218acb1170c389acf694a138eb049f141
Parents: 93f231d
Author: Dale LaBossiere <dl...@us.ibm.com>
Authored: Mon May 9 15:07:59 2016 -0400
Committer: Dale LaBossiere <dl...@us.ibm.com>
Committed: Mon May 9 15:07:59 2016 -0400

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 site/recipes/recipe_parallel_analytics.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)
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http://git-wip-us.apache.org/repos/asf/incubator-quarks-website/blob/d6bf4482/site/recipes/recipe_parallel_analytics.md
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diff --git a/site/recipes/recipe_parallel_analytics.md b/site/recipes/recipe_parallel_analytics.md
index 965f455..4a3b13f 100644
--- a/site/recipes/recipe_parallel_analytics.md
+++ b/site/recipes/recipe_parallel_analytics.md
@@ -52,6 +52,8 @@ int width = 5;  // number of parallel channels
 ToIntFunction<Double> splitter = PlumbingStreams.roundRobinSplitter(width);
 ```
 
+Another possibility is to use a "load balanced splitter" configuration.  That is covered below.
+
 ## Define the pipeline to run in parallel
 
 Define a `BiFunction<TStream<T>, Integer, TStream<R>>` that builds the pipeline. That is, define a function that receives a `TStream<T>` and an integer `channel` and creates a pipeline for that channel that returns a `TStream<R>`.
@@ -85,6 +87,16 @@ Given a width, splitter and pipeline function it just takes a single call:
 TStream<String> results = PlumbingStreams.parallel(readings, width, splitter, pipeline());
 ```
 
+## Load balanced parallel flow
+
+A load balanced parallel flow allocates an incoming tuple to the first available parallel channel. When tuple processing times are variable, using a load balanced parallel flow can result in greater overall throughput.
+
+To create a load balanced parallel flow simply use the `parallelBalanced()` method instead of `parallel()`. Everything is the same except you don't supply a splitter: 
+
+```java
+TStream<String> results = PlumbingStreams.parallelBalanced(readings, width, pipeline());
+```
+
 ## The final application
 
 When the application is run it prints out 5 (width) tuples every second. Without the parallel channels, it would only print one tuple each second.