SPARK-1307 [DOCS] Don't use term 'standalone' to refer to a Spark Application

HT to Diana, just proposing an implementation of her suggestion, which I rather agreed with. Is there a second/third for the motion?

Refer to "self-contained" rather than "standalone" apps to avoid confusion with standalone deployment mode. And fix placement of reference to this in MLlib docs.

Author: Sean Owen <sowen@cloudera.com>

Closes #2787 from srowen/SPARK-1307 and squashes the following commits:

b5b82e2 [Sean Owen] Refer to "self-contained" rather than "standalone" apps to avoid confusion with standalone deployment mode. And fix placement of reference to this in MLlib docs.

1 files changed, 10 insertions, 10 deletions

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index d31bec3e1b..bc914a1899 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -247,7 +247,7 @@ val modelL1 = svmAlg.run(training)
 All of MLlib's methods use Java-friendly types, so you can import and call them there the same
 way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
 Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
-calling `.rdd()` on your `JavaRDD` object. A standalone application example
+calling `.rdd()` on your `JavaRDD` object. A self-contained application example
 that is equivalent to the provided example in Scala is given bellow:
 
 {% highlight java %}
@@ -323,9 +323,9 @@ svmAlg.optimizer()
 final SVMModel modelL1 = svmAlg.run(training.rdd());
 {% endhighlight %}
 
-In order to run the above standalone application, follow the instructions
-provided in the [Standalone
-Applications](quick-start.html#standalone-applications) section of the Spark
+In order to run the above application, follow the instructions
+provided in the [Self-Contained
+Applications](quick-start.html#self-contained-applications) section of the Spark
 quick-start guide. Be sure to also include *spark-mllib* to your build file as
 a dependency.
 </div>
@@ -482,12 +482,6 @@ public class LinearRegression {
   }
 }
 {% endhighlight %}
-
-In order to run the above standalone application, follow the instructions
-provided in the [Standalone
-Applications](quick-start.html#standalone-applications) section of the Spark
-quick-start guide. Be sure to also include *spark-mllib* to your build file as
-a dependency.
 </div>
 
 <div data-lang="python" markdown="1">
@@ -519,6 +513,12 @@ print("Mean Squared Error = " + str(MSE))
 </div>
 </div>
 
+In order to run the above application, follow the instructions
+provided in the [Self-Contained Applications](quick-start.html#self-contained-applications)
+section of the Spark
+quick-start guide. Be sure to also include *spark-mllib* to your build file as
+a dependency.
+
 ## Streaming linear regression
 
 When data arrive in a streaming fashion, it is useful to fit regression models online,