Repository: spark Updated Branches: refs/heads/master 66af8e250 -> 18ab6bd70
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 <[email protected]> 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. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/18ab6bd7 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/18ab6bd7 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/18ab6bd7 Branch: refs/heads/master Commit: 18ab6bd709bb9fcae290ffc43294d13f06670d55 Parents: 66af8e2 Author: Sean Owen <[email protected]> Authored: Tue Oct 14 21:37:51 2014 -0700 Committer: Xiangrui Meng <[email protected]> Committed: Tue Oct 14 21:37:51 2014 -0700 ---------------------------------------------------------------------- docs/mllib-clustering.md | 14 +++++++------- docs/mllib-collaborative-filtering.md | 14 +++++++------- docs/mllib-dimensionality-reduction.md | 17 +++++++++-------- docs/mllib-linear-methods.md | 20 ++++++++++---------- docs/quick-start.md | 8 ++++---- 5 files changed, 37 insertions(+), 36 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/18ab6bd7/docs/mllib-clustering.md ---------------------------------------------------------------------- diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index d10bd63..7978e93 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. </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. http://git-wip-us.apache.org/repos/asf/spark/blob/18ab6bd7/docs/mllib-collaborative-filtering.md ---------------------------------------------------------------------- diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index d5c539d..2094963 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. </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 http://git-wip-us.apache.org/repos/asf/spark/blob/18ab6bd7/docs/mllib-dimensionality-reduction.md ---------------------------------------------------------------------- diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md index 21cb35b..870fed6 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. </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. http://git-wip-us.apache.org/repos/asf/spark/blob/18ab6bd7/docs/mllib-linear-methods.md ---------------------------------------------------------------------- diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index d31bec3..bc914a1 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, http://git-wip-us.apache.org/repos/asf/spark/blob/18ab6bd7/docs/quick-start.md ---------------------------------------------------------------------- diff --git a/docs/quick-start.md b/docs/quick-start.md index 23313d8..6236de0 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 </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`: --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
