diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index d10bd63746629..7978e934fb36b 100644
--- a/docs/mllib-clustering.md
+++ b/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.
@@ -153,3 +147,9 @@ print("Within Set Sum of Squared Error = " + str(WSSSE))
+
+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.
diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index d5c539db791be..2094963392295 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/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.
@@ -229,6 +223,12 @@ model = ALS.trainImplicit(ratings, rank, numIterations, alpha = 0.01)
+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
diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md
index 21cb35b4270ca..870fed6cc5024 100644
--- a/docs/mllib-dimensionality-reduction.md
+++ b/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.
+
+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.
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index d31bec3e1bd01..bc914a1899801 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.
@@ -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.
@@ -519,6 +513,12 @@ print("Mean Squared Error = " + str(MSE))
+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,
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 23313d8aa6152..6236de0e1f2c4 100644
--- a/docs/quick-start.md
+++ b/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
-# 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.
@@ -387,7 +387,7 @@ Lines with a: 46, Lines with b: 23
-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`: