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: srowen

docs/mllib-clustering.md

@@ -69,7 +69,7 @@ println("Within Set Sum of Squared Errors = " + WSSSE)
 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 below:
 
 {% highlight java %}
@@ -113,12 +113,6 @@ public class KMeansExample {
   }
 }
 {% 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">
@@ -153,3 +147,9 @@ print("Within Set Sum of Squared Error = " + str(WSSSE))
 </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.

docs/mllib-collaborative-filtering.md

@@ -110,7 +110,7 @@ val model = ALS.trainImplicit(ratings, rank, numIterations, alpha)
 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 %}
@@ -184,12 +184,6 @@ public class CollaborativeFiltering {
   }
 }
 {% 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">
@@ -229,6 +223,12 @@ model = ALS.trainImplicit(ratings, rank, numIterations, alpha = 0.01)
 
 </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.
+
 ## Tutorial
 
 The [training exercises](https://databricks-training.s3.amazonaws.com/index.html) from the Spark Summit 2014 include a hands-on tutorial for

docs/mllib-dimensionality-reduction.md

@@ -121,9 +121,9 @@ public class SVD {
 The same code applies to `IndexedRowMatrix` if `U` is defined as an
 `IndexedRowMatrix`.
 
-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.
 
@@ -200,10 +200,11 @@ public class PCA {
 }
 {% 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>
+
+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.

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, 

docs/quick-start.md

@@ -8,7 +8,7 @@ title: Quick Start
 
 This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's
 interactive shell (in Python or Scala),
-then show how to write standalone applications in Java, Scala, and Python.
+then show how to write applications in Java, Scala, and Python.
 See the [programming guide](programming-guide.html) for a more complete reference.
 
 To follow along with this guide, first download a packaged release of Spark from the
@@ -215,8 +215,8 @@ a cluster, as described in the [programming guide](programming-guide.html#initia
 </div>
 </div>
 
-# Standalone Applications
-Now say we wanted to write a standalone application using the Spark API. We will walk through a
+# Self-Contained Applications
+Now say we wanted to write a self-contained application using the Spark API. We will walk through a
 simple application in both Scala (with SBT), Java (with Maven), and Python.
 
 <div class="codetabs">
@@ -387,7 +387,7 @@ Lines with a: 46, Lines with b: 23
 </div>
 <div data-lang="python" markdown="1">
 
-Now we will show how to write a standalone application using the Python API (PySpark).
+Now we will show how to write an application using the Python API (PySpark).
 
 As an example, we'll create a simple Spark application, `SimpleApp.py`: