Merge remote-tracking branch 'upstream/master' into sparsesvd

Conflicts:
	docs/mllib-guide.md

15 files changed, 433 insertions, 149 deletions

diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index de001e6c52..c4f1f6d6ad 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -157,8 +157,8 @@ trait Message[K] {
 
 # Where to Go from Here
 
-Two example jobs, PageRank and shortest path, are included in `examples/src/main/scala/org/apache/spark/examples/bagel`. You can run them by passing the class name to the `run-example` script included in Spark; e.g.:
+Two example jobs, PageRank and shortest path, are included in `examples/src/main/scala/org/apache/spark/examples/bagel`. You can run them by passing the class name to the `bin/run-example` script included in Spark; e.g.:
 
-    ./run-example org.apache.spark.examples.bagel.WikipediaPageRank
+    ./bin/run-example org.apache.spark.examples.bagel.WikipediaPageRank
 
 Each example program prints usage help when run without any arguments.
diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md
index c709001632..b9ff0af76f 100644
--- a/docs/building-with-maven.md
+++ b/docs/building-with-maven.md
@@ -37,20 +37,16 @@ For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions wit
     # Cloudera CDH 4.2.0 with MapReduce v1
     $ mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
 
-For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you should enable the "hadoop2-yarn" profile and set the "yarn.version" property:
+For Apache Hadoop 2.x, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, you should enable the "yarn-alpha" or "yarn" profile and set the "hadoop.version", "yarn.version" property:
 
     # Apache Hadoop 2.0.5-alpha
-    $ mvn -Phadoop2-yarn -Dhadoop.version=2.0.5-alpha -Dyarn.version=2.0.5-alpha -DskipTests clean package
+    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.5-alpha -Dyarn.version=2.0.5-alpha -DskipTests clean package
 
     # Cloudera CDH 4.2.0 with MapReduce v2
-    $ mvn -Phadoop2-yarn -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-chd4.2.0 -DskipTests clean package
+    $ mvn -Pyarn-alpha -Dhadoop.version=2.0.0-cdh4.2.0 -Dyarn.version=2.0.0-chd4.2.0 -DskipTests clean package
 
-Hadoop versions 2.2.x and newer can be built by setting the ```new-yarn``` and the ```yarn.version``` as follows:
- 
-    # Apache Hadoop 2.2.X and newer
-    $ mvn -Dyarn.version=2.2.0 -Dhadoop.version=2.2.0 -Pnew-yarn
-
-The build process handles Hadoop 2.2.x as a special case that uses the directory ```new-yarn```, which supports the new YARN API. Furthermore, for this version, the build depends on artifacts published by the spark-project to enable Akka 2.0.5 to work with protobuf 2.5. 
+    # Apache Hadoop 2.2.X ( e.g. 2.2.0 as below ) and newer
+    $ mvn -Pyarn -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -DskipTests clean package
 
 ## Spark Tests in Maven ##
 
diff --git a/docs/configuration.md b/docs/configuration.md
index 567aba07f0..6717757781 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -77,12 +77,14 @@ there are at least five properties that you will commonly want to control:
 </tr>
 <tr>
   <td>spark.cores.max</td>
-  <td>(infinite)</td>
+  <td>(not set)</td>
   <td>
     When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a
     <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained"
-    sharing mode</a>, how many CPU cores to request at most. The default will use all available cores
-    offered by the cluster manager.
+    sharing mode</a>, the maximum amount of CPU cores to request for the application from
+    across the cluster (not from each machine). If not set, the default will be
+    <code>spark.deploy.defaultCores</code> on Spark's standalone cluster manager, or
+    infinite (all available cores) on Mesos.
   </td>
 </tr>
 </table>
@@ -128,7 +130,7 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
-  <td>spark.ui.retained_stages</td>
+  <td>spark.ui.retainedStages</td>
   <td>1000</td>
   <td>
     How many stages the Spark UI remembers before garbage collecting.
@@ -360,6 +362,14 @@ Apart from these, the following properties are also available, and may be useful
   </td>
 </tr>
 <tr>
+  <td>akka.x.y....</td>
+  <td>value</td>
+  <td>
+    An arbitrary akka configuration can be set directly on spark conf and it is applied for all the ActorSystems created spark wide for that SparkContext and its assigned executors as well.
+  </td>
+</tr>
+
+<tr>
   <td>spark.shuffle.consolidateFiles</td>
   <td>false</td>
   <td>
@@ -394,6 +404,37 @@ Apart from these, the following properties are also available, and may be useful
     How many times slower a task is than the median to be considered for speculation.
   </td>
 </tr>
+<tr>
+  <td>spark.logConf</td>
+  <td>false</td>
+  <td>
+    Log the supplied SparkConf as INFO at start of spark context.
+  </td>
+</tr>
+<tr>
+  <td>spark.deploy.spreadOut</td>
+  <td>true</td>
+  <td>
+    Whether the standalone cluster manager should spread applications out across nodes or try
+    to consolidate them onto as few nodes as possible. Spreading out is usually better for
+    data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/>
+    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
+    applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>.
+  </td>
+</tr>
+<tr>
+  <td>spark.deploy.defaultCores</td>
+  <td>(infinite)</td>
+  <td>
+    Default number of cores to give to applications in Spark's standalone mode if they don't
+    set <code>spark.cores.max</code>. If not set, applications always get all available
+    cores unless they configure <code>spark.cores.max</code> themselves.
+    Set this lower on a shared cluster to prevent users from grabbing
+    the whole cluster by default. <br/>
+    <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual
+    applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>.
+  </td>
+</tr>
 </table>
 
 ## Viewing Spark Properties
diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css
index 119bb5dba7..30ea440c00 100644
--- a/docs/css/bootstrap.min.css
+++ b/docs/css/bootstrap.min.css
@@ -6,4 +6,4 @@
  * http://www.apache.org/licenses/LICENSE-2.0
  *
  * Designed and built with all the love in the world @twitter by @mdo and @fat.
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diff --git a/docs/index.md b/docs/index.md
index d3ac696d1e..86d574daaa 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -24,9 +24,9 @@ For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VE
 # Running the Examples and Shell
 
 Spark comes with several sample programs in the `examples` directory.
-To run one of the samples, use `./run-example <class> <params>` in the top-level Spark directory
-(the `run-example` script sets up the appropriate paths and launches that program).
-For example, try `./run-example org.apache.spark.examples.SparkPi local`.
+To run one of the samples, use `./bin/run-example <class> <params>` in the top-level Spark directory
+(the `bin/run-example` script sets up the appropriate paths and launches that program).
+For example, try `./bin/run-example org.apache.spark.examples.SparkPi local`.
 Each example prints usage help when run with no parameters.
 
 Note that all of the sample programs take a `<master>` parameter specifying the cluster URL
@@ -34,8 +34,8 @@ to connect to. This can be a [URL for a distributed cluster](scala-programming-g
 or `local` to run locally with one thread, or `local[N]` to run locally with N threads. You should start by using
 `local` for testing.
 
-Finally, you can run Spark interactively through modified versions of the Scala shell (`./spark-shell`) or
-Python interpreter (`./pyspark`). These are a great way to learn the framework.
+Finally, you can run Spark interactively through modified versions of the Scala shell (`./bin/spark-shell`) or
+Python interpreter (`./bin/pyspark`). These are a great way to learn the framework.
 
 # Launching on a Cluster
 
diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md
index 53085cc671..07732fa122 100644
--- a/docs/java-programming-guide.md
+++ b/docs/java-programming-guide.md
@@ -190,9 +190,9 @@ We hope to generate documentation with Java-style syntax in the future.
 
 Spark includes several sample programs using the Java API in
 [`examples/src/main/java`](https://github.com/apache/incubator-spark/tree/master/examples/src/main/java/org/apache/spark/examples).  You can run them by passing the class name to the
-`run-example` script included in Spark; for example:
+`bin/run-example` script included in Spark; for example:
 
-    ./run-example org.apache.spark.examples.JavaWordCount
+    ./bin/run-example org.apache.spark.examples.JavaWordCount
 
 Each example program prints usage help when run
 without any arguments.
diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md
index 5951155fe3..df2faa5e41 100644
--- a/docs/job-scheduling.md
+++ b/docs/job-scheduling.md
@@ -32,9 +32,8 @@ Resource allocation can be configured as follows, based on the cluster type:
 
 * **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in
   FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit
-  the number of nodes an application uses by setting the `spark.cores.max` configuration property in it. This
-  will allow multiple users/applications to run concurrently. For example, you might launch a long-running
-  server that uses 10 cores, and allow users to launch shells that use 20 cores each.
+  the number of nodes an application uses by setting the `spark.cores.max` configuration property in it,
+  or change the default for applications that don't set this setting through `spark.deploy.defaultCores`. 
   Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls
   its memory use.
 * **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`,
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 44e6c8f58b..21d0464852 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -3,6 +3,9 @@ layout: global
 title: Machine Learning Library (MLlib)
 ---
 
+* Table of contents
+{:toc}
+
 MLlib is a Spark implementation of some common machine learning (ML)
 functionality, as well associated tests and data generators.  MLlib
 currently supports four common types of machine learning problem settings,
@@ -39,57 +42,6 @@ underlying gradient descent primitive (described
 parameter (*regParam*) along with various parameters associated with gradient
 descent (*stepSize*, *numIterations*, *miniBatchFraction*). 
 
-The following code snippet illustrates how to load a sample dataset, execute a
-training algorithm on this training data using a static method in the algorithm
-object, and make predictions with the resulting model to compute the training
-error.
-
-{% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.mllib.classification.SVMWithSGD
-import org.apache.spark.mllib.regression.LabeledPoint
-
-// Load and parse the data file
-val data = sc.textFile("mllib/data/sample_svm_data.txt")
-val parsedData = data.map { line =>
-  val parts = line.split(' ')
-  LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray)
-}
-
-// Run training algorithm
-val numIterations = 20
-val model = SVMWithSGD.train(parsedData, numIterations)
- 
-// Evaluate model on training examples and compute training error
-val labelAndPreds = parsedData.map { point =>
-  val prediction = model.predict(point.features)
-  (point.label, prediction)
-}
-val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count
-println("trainError = " + trainErr)
-{% endhighlight %}
-
-The `SVMWithSGD.train()` method by default performs L2 regularization with the
-regularization parameter set to 1.0. If we want to configure this algorithm, we
-can customize `SVMWithSGD` further by creating a new object directly and
-calling setter methods. All other MLlib algorithms support customization in
-this way as well. For example, the following code produces an L1 regularized
-variant of SVMs with regularization parameter set to 0.1, and runs the training
-algorithm for 200 iterations. 
-
-{% highlight scala %}
-import org.apache.spark.mllib.optimization.L1Updater
-
-val svmAlg = new SVMWithSGD()
-svmAlg.optimizer.setNumIterations(200)
-  .setRegParam(0.1)
-  .setUpdater(new L1Updater)
-val modelL1 = svmAlg.run(parsedData)
-{% endhighlight %}
-
-Both of the code snippets above can be executed in `spark-shell` to generate a
-classifier for the provided dataset.
-
 Available algorithms for binary classification:
 
 * [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD)
@@ -121,14 +73,14 @@ of entities with one another based on some notion of similarity.  Clustering is
 often used for exploratory analysis and/or as a component of a hierarchical
 supervised learning pipeline (in which distinct classifiers or regression
 models are trained for each cluster). MLlib supports
-[k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, arguably
-the most commonly used clustering approach that clusters the data points into
-*k* clusters. The MLlib implementation includes a parallelized 
+[k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, one of
+the most commonly used clustering algorithms that clusters the data points into
+predfined number of clusters. The MLlib implementation includes a parallelized
 variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method
 called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf).
 The implementation in MLlib has the following parameters:  
 
-* *k* is the number of clusters.
+* *k* is the number of desired clusters.
 * *maxIterations* is the maximum number of iterations to run.
 * *initializationMode* specifies either random initialization or
 initialization via k-means\|\|.
@@ -169,7 +121,7 @@ the entries in the user-item matrix as *explicit* preferences given by the user
 It is common in many real-world use cases to only have access to *implicit feedback* 
 (e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with 
 such data is taken from 
-[Collaborative Filtering for Implicit Feedback Datasets](http://research.yahoo.com/pub/2433).
+[Collaborative Filtering for Implicit Feedback Datasets](http://www2.research.att.com/~yifanhu/PUB/cf.pdf).
 Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as 
 a combination of binary preferences and *confidence values*. The ratings are then related 
 to the level of confidence in observed user preferences, rather than explicit ratings given to items. 
@@ -211,6 +163,271 @@ Available algorithms for gradient descent:
 
 * [GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent)
 
+# Using MLLib in Scala
+
+Following code snippets can be executed in `spark-shell`.
+
+## Binary Classification
+
+The following code snippet illustrates how to load a sample dataset, execute a
+training algorithm on this training data using a static method in the algorithm
+object, and make predictions with the resulting model to compute the training
+error.
+
+{% highlight scala %}
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.classification.SVMWithSGD
+import org.apache.spark.mllib.regression.LabeledPoint
+
+// Load and parse the data file
+val data = sc.textFile("mllib/data/sample_svm_data.txt")
+val parsedData = data.map { line =>
+  val parts = line.split(' ')
+  LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray)
+}
+
+// Run training algorithm to build the model
+val numIterations = 20
+val model = SVMWithSGD.train(parsedData, numIterations)
+
+// Evaluate model on training examples and compute training error
+val labelAndPreds = parsedData.map { point =>
+  val prediction = model.predict(point.features)
+  (point.label, prediction)
+}
+val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count
+println("Training Error = " + trainErr)
+{% endhighlight %}
+
+
+The `SVMWithSGD.train()` method by default performs L2 regularization with the
+regularization parameter set to 1.0. If we want to configure this algorithm, we
+can customize `SVMWithSGD` further by creating a new object directly and
+calling setter methods. All other MLlib algorithms support customization in
+this way as well. For example, the following code produces an L1 regularized
+variant of SVMs with regularization parameter set to 0.1, and runs the training
+algorithm for 200 iterations.
+
+{% highlight scala %}
+import org.apache.spark.mllib.optimization.L1Updater
+
+val svmAlg = new SVMWithSGD()
+svmAlg.optimizer.setNumIterations(200)
+  .setRegParam(0.1)
+  .setUpdater(new L1Updater)
+val modelL1 = svmAlg.run(parsedData)
+{% endhighlight %}
+
+## Linear Regression
+The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The
+example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We
+compute the Mean Squared Error at the end to evaluate
+[goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit)
+
+{% highlight scala %}
+import org.apache.spark.mllib.regression.LinearRegressionWithSGD
+import org.apache.spark.mllib.regression.LabeledPoint
+
+// Load and parse the data
+val data = sc.textFile("mllib/data/ridge-data/lpsa.data")
+val parsedData = data.map { line =>
+  val parts = line.split(',')
+  LabeledPoint(parts(0).toDouble, parts(1).split(' ').map(x => x.toDouble).toArray)
+}
+
+// Building the model
+val numIterations = 20
+val model = LinearRegressionWithSGD.train(parsedData, numIterations)
+
+// Evaluate model on training examples and compute training error
+val valuesAndPreds = parsedData.map { point =>
+  val prediction = model.predict(point.features)
+  (point.label, prediction)
+}
+val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.reduce(_ + _)/valuesAndPreds.count
+println("training Mean Squared Error = " + MSE)
+{% endhighlight %}
+
+
+Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training
+[Mean Squared Errors](http://en.wikipedia.org/wiki/Mean_squared_error).
+
+## Clustering
+In the following example after loading and parsing data, we use the KMeans object to cluster the data
+into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
+Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
+optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
+
+{% highlight scala %}
+import org.apache.spark.mllib.clustering.KMeans
+
+// Load and parse the data
+val data = sc.textFile("kmeans_data.txt")
+val parsedData = data.map( _.split(' ').map(_.toDouble))
+
+// Cluster the data into two classes using KMeans
+val numIterations = 20
+val numClusters = 2
+val clusters = KMeans.train(parsedData, numClusters, numIterations)
+
+// Evaluate clustering by computing Within Set Sum of Squared Errors
+val WSSSE = clusters.computeCost(parsedData)
+println("Within Set Sum of Squared Errors = " + WSSSE)
+{% endhighlight %}
+
+
+## Collaborative Filtering
+In the following example we load rating data. Each row consists of a user, a product and a rating.
+We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation
+model by measuring the Mean Squared Error of rating prediction.
+
+{% highlight scala %}
+import org.apache.spark.mllib.recommendation.ALS
+import org.apache.spark.mllib.recommendation.Rating
+
+// Load and parse the data
+val data = sc.textFile("mllib/data/als/test.data")
+val ratings = data.map(_.split(',') match {
+    case Array(user, item, rate) =>  Rating(user.toInt, item.toInt, rate.toDouble)
+})
+
+// Build the recommendation model using ALS
+val numIterations = 20
+val model = ALS.train(ratings, 1, 20, 0.01)
+
+// Evaluate the model on rating data
+val usersProducts = ratings.map{ case Rating(user, product, rate)  => (user, product)}
+val predictions = model.predict(usersProducts).map{
+    case Rating(user, product, rate) => ((user, product), rate)
+}
+val ratesAndPreds = ratings.map{
+    case Rating(user, product, rate) => ((user, product), rate)
+}.join(predictions)
+val MSE = ratesAndPreds.map{
+    case ((user, product), (r1, r2)) =>  math.pow((r1- r2), 2)
+}.reduce(_ + _)/ratesAndPreds.count
+println("Mean Squared Error = " + MSE)
+{% endhighlight %}
+
+If the rating matrix is derived from other source of information (i.e., it is inferred from
+other signals), you can use the trainImplicit method to get better results.
+
+{% highlight scala %}
+val model = ALS.trainImplicit(ratings, 1, 20, 0.01)
+{% endhighlight %}
+
+# Using MLLib in Python
+Following examples can be tested in the PySpark shell.
+
+## Binary Classification
+The following example shows how to load a sample dataset, build Logistic Regression model,
+and make predictions with the resulting model to compute the training error.
+
+{% highlight python %}
+from pyspark.mllib.classification import LogisticRegressionWithSGD
+from numpy import array
+
+# Load and parse the data
+data = sc.textFile("mllib/data/sample_svm_data.txt")
+parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
+model = LogisticRegressionWithSGD.train(sc, parsedData)
+
+# Build the model
+labelsAndPreds = parsedData.map(lambda point: (int(point.item(0)),
+        model.predict(point.take(range(1, point.size)))))
+
+# Evaluating the model on training data
+trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())
+print("Training Error = " + str(trainErr))
+{% endhighlight %}
+
+## Linear Regression
+The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The
+example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We
+compute the Mean Squared Error at the end to evaluate
+[goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit)
+
+{% highlight python %}
+from pyspark.mllib.regression import LinearRegressionWithSGD
+from numpy import array
+
+# Load and parse the data
+data = sc.textFile("mllib/data/ridge-data/lpsa.data")
+parsedData = data.map(lambda line: array([float(x) for x in line.replace(',', ' ').split(' ')]))
+
+# Build the model
+model = LinearRegressionWithSGD.train(sc, parsedData)
+
+# Evaluate the model on training data
+valuesAndPreds = parsedData.map(lambda point: (point.item(0),
+        model.predict(point.take(range(1, point.size)))))
+MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y)/valuesAndPreds.count()
+print("Mean Squared Error = " + str(MSE))
+{% endhighlight %}
+
+
+## Clustering
+In the following example after loading and parsing data, we use the KMeans object to cluster the data
+into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within
+Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the
+optimal *k* is usually one where there is an "elbow" in the WSSSE graph.
+
+{% highlight python %}
+from pyspark.mllib.clustering import KMeans
+from numpy import array
+from math import sqrt
+
+# Load and parse the data
+data = sc.textFile("kmeans_data.txt")
+parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))
+
+# Build the model (cluster the data)
+clusters = KMeans.train(sc, parsedData, 2, maxIterations=10,
+        runs=30, initialization_mode="random")
+
+# Evaluate clustering by computing Within Set Sum of Squared Errors
+def error(point):
+    center = clusters.centers[clusters.predict(point)]
+    return sqrt(sum([x**2 for x in (point - center)]))
+
+WSSSE = parsedData.map(lambda point: error(point)).reduce(lambda x, y: x + y)
+print("Within Set Sum of Squared Error = " + str(WSSSE))
+{% endhighlight %}
+
+Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training Mean Squared
+Errors.
+
+## Collaborative Filtering
+In the following example we load rating data. Each row consists of a user, a product and a rating.
+We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
+recommendation by measuring the Mean Squared Error of rating prediction.
+
+{% highlight python %}
+from pyspark.mllib.recommendation import ALS
+from numpy import array
+
+# Load and parse the data
+data = sc.textFile("mllib/data/als/test.data")
+ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))
+
+# Build the recommendation model using Alternating Least Squares
+model = ALS.train(sc, ratings, 1, 20)
+
+# Evaluate the model on training data
+testdata = ratings.map(lambda p: (int(p[0]), int(p[1])))
+predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
+ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
+MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y)/ratesAndPreds.count()
+print("Mean Squared Error = " + str(MSE))
+{% endhighlight %}
+
+If the rating matrix is derived from other source of information (i.e., it is inferred from other
+signals), you can use the trainImplicit method to get better results.
+
+{% highlight python %}
+# Build the recommendation model using Alternating Least Squares based on implicit ratings
+model = ALS.trainImplicit(sc, ratings, 1, 20)
+{% endhighlight %}
 
 
 # Singular Value Decomposition
@@ -221,13 +438,13 @@ Given an *m x n* matrix *A*, we can compute matrices *U, S, V* such that
 
 There is no restriction on m, but we require n^2 doubles to fit in memory.
 Further, n should be less than m.
- 
+
 The decomposition is computed by first computing *A^TA = V S^2 V^T*,
 computing svd locally on that (since n x n is small),
-from which we recover S and V. 
+from which we recover S and V.
 Then we compute U via easy matrix multiplication
 as *U =  A * V * S^-1*
- 
+
 Only singular vectors associated with largest k singular values
 are recovered. If there are k
 such values, then the dimensions of the return will be:
@@ -237,7 +454,7 @@ such values, then the dimensions of the return will be:
 * *V* is *n x k* and satisfies V^TV = eye(k).
 
 All input and output is expected in sparse matrix format, 1-indexed
-as tuples of the form ((i,j),value) all in 
+as tuples of the form ((i,j),value) all in
 SparseMatrix RDDs. Below is example usage.
 
 {% highlight scala %}
@@ -261,10 +478,3 @@ val decomposed = SVD.sparseSVD(SparseMatrix(data, m, n), k)
 val = decomposed.S.data
 
 println("singular values = " + s.toArray.mkString)
-
-{% endhighlight %}
-
-
-
-
-
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 96f93e24fe..c4236f8312 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -47,7 +47,7 @@ PySpark will automatically ship these functions to workers, along with any objec
 Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers.
 The [Standalone Use](#standalone-use) section describes how to ship code dependencies to workers.
 
-In addition, PySpark fully supports interactive use---simply run `./pyspark` to launch an interactive shell.
+In addition, PySpark fully supports interactive use---simply run `./bin/pyspark` to launch an interactive shell.
 
 
 # Installing and Configuring PySpark
@@ -60,17 +60,17 @@ By default, PySpark requires `python` to be available on the system `PATH` and u
 
 All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
 
-Standalone PySpark applications should be run using the `pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh` or `.cmd`.
-The script automatically adds the `pyspark` package to the `PYTHONPATH`.
+Standalone PySpark applications should be run using the `bin/pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh` or `.cmd`.
+The script automatically adds the `bin/pyspark` package to the `PYTHONPATH`.
 
 
 # Interactive Use
 
-The `pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
+The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
 
 {% highlight bash %}
 $ sbt/sbt assembly
-$ ./pyspark
+$ ./bin/pyspark
 {% endhighlight %}
 
 The Python shell can be used explore data interactively and is a simple way to learn the API:
@@ -82,35 +82,36 @@ The Python shell can be used explore data interactively and is a simple way to l
 >>> help(pyspark) # Show all pyspark functions
 {% endhighlight %}
 
-By default, the `pyspark` shell creates SparkContext that runs applications locally on a single core.
+By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on a single core.
 To connect to a non-local cluster, or use multiple cores, set the `MASTER` environment variable.
-For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
+For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
 
 {% highlight bash %}
-$ MASTER=spark://IP:PORT ./pyspark
+$ MASTER=spark://IP:PORT ./bin/pyspark
 {% endhighlight %}
 
 Or, to use four cores on the local machine:
 
 {% highlight bash %}
-$ MASTER=local[4] ./pyspark
+$ MASTER=local[4] ./bin/pyspark
 {% endhighlight %}
 
 
 ## IPython
 
-It is also possible to launch PySpark in [IPython](http://ipython.org), the enhanced Python interpreter.
-To do this, set the `IPYTHON` variable to `1` when running `pyspark`:
+It is also possible to launch PySpark in [IPython](http://ipython.org), the 
+enhanced Python interpreter. PySpark works with IPython 1.0.0 and later. To 
+use IPython, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
 
 {% highlight bash %}
-$ IPYTHON=1 ./pyspark
+$ IPYTHON=1 ./bin/pyspark
 {% endhighlight %}
 
 Alternatively, you can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch
 the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing support:
 
 {% highlight bash %}
-$ IPYTHON_OPTS="notebook --pylab inline" ./pyspark
+$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
 {% endhighlight %}
 
 IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable.
@@ -118,7 +119,7 @@ IPython also works on a cluster or on multiple cores if you set the `MASTER` env
 
 # Standalone Programs
 
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `pyspark`.
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/pyspark`.
 The Quick Start guide includes a [complete example](quick-start.html#a-standalone-app-in-python) of a standalone Python application.
 
 Code dependencies can be deployed by listing them in the `pyFiles` option in the SparkContext constructor:
@@ -153,6 +154,6 @@ Many of the methods also contain [doctests](http://docs.python.org/2/library/doc
 PySpark also includes several sample programs in the [`python/examples` folder](https://github.com/apache/incubator-spark/tree/master/python/examples).
 You can run them by passing the files to `pyspark`; e.g.:
 
-    ./pyspark python/examples/wordcount.py
+    ./bin/pyspark python/examples/wordcount.py
 
 Each program prints usage help when run without arguments.
diff --git a/docs/quick-start.md b/docs/quick-start.md
index bdf127d404..153081bdaa 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -20,7 +20,7 @@ $ sbt/sbt assembly
 ## Basics
 
 Spark's interactive shell provides a simple way to learn the API, as well as a powerful tool to analyze datasets interactively.
-Start the shell by running `./spark-shell` in the Spark directory.
+Start the shell by running `./bin/spark-shell` in the Spark directory.
 
 Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Let's make a new RDD from the text of the README file in the Spark source directory:
 
@@ -99,7 +99,7 @@ scala> linesWithSpark.count()
 res9: Long = 15
 {% endhighlight %}
 
-It may seem silly to use Spark to explore and cache a 30-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes. You can also do this interactively by connecting `spark-shell` to a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
+It may seem silly to use Spark to explore and cache a 30-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes. You can also do this interactively by connecting `bin/spark-shell` to a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark).
 
 # A Standalone App in Scala
 Now say we wanted to write a standalone application using the Spark API. We will walk through a simple application in both Scala (with SBT), Java (with Maven), and Python. If you are using other build systems, consider using the Spark assembly JAR described in the developer guide.
@@ -146,7 +146,7 @@ If you also wish to read data from Hadoop's HDFS, you will also need to add a de
 libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "<your-hdfs-version>"
 {% endhighlight %}
 
-Finally, for sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the application's code, then use `sbt run` to execute our program.
+Finally, for sbt to work correctly, we'll need to layout `SimpleApp.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the application's code, then use `sbt/sbt run` to execute our program.
 
 {% highlight bash %}
 $ find .
@@ -157,8 +157,8 @@ $ find .
 ./src/main/scala
 ./src/main/scala/SimpleApp.scala
 
-$ sbt package
-$ sbt run
+$ sbt/sbt package
+$ sbt/sbt run
 ...
 Lines with a: 46, Lines with b: 23
 {% endhighlight %}
@@ -277,11 +277,11 @@ We can pass Python functions to Spark, which are automatically serialized along
 For applications that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide.html).
 `SimpleApp` is simple enough that we do not need to specify any code dependencies.
 
-We can run this application using the `pyspark` script:
+We can run this application using the `bin/pyspark` script:
 
 {% highlight python %}
 $ cd $SPARK_HOME
-$ ./pyspark SimpleApp.py
+$ ./bin/pyspark SimpleApp.py
 ...
 Lines with a: 46, Lines with b: 23
 {% endhighlight python %}
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index aa75ca4324..b206270107 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -12,7 +12,7 @@ was added to Spark in version 0.6.0, and improved in 0.7.0 and 0.8.0.
 We need a consolidated Spark JAR (which bundles all the required dependencies) to run Spark jobs on a YARN cluster.
 This can be built by setting the Hadoop version and `SPARK_YARN` environment variable, as follows:
 
-    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly
+    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
 The assembled JAR will be something like this:
 `./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly_{{site.SPARK_VERSION}}-hadoop2.0.5.jar`.
@@ -54,7 +54,7 @@ There are two scheduler mode that can be used to launch spark application on YAR
 
 The command to launch the YARN Client is as follows:
 
-    SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./spark-class org.apache.spark.deploy.yarn.Client \
+    SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./bin/spark-class org.apache.spark.deploy.yarn.Client \
       --jar <YOUR_APP_JAR_FILE> \
       --class <APP_MAIN_CLASS> \
       --args <APP_MAIN_ARGUMENTS> \
@@ -72,14 +72,14 @@ The command to launch the YARN Client is as follows:
 For example:
 
     # Build the Spark assembly JAR and the Spark examples JAR
-    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true ./sbt/sbt assembly
+    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
     # Configure logging
     $ cp conf/log4j.properties.template conf/log4j.properties
 
     # Submit Spark's ApplicationMaster to YARN's ResourceManager, and instruct Spark to run the SparkPi example
     $ SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
-        ./spark-class org.apache.spark.deploy.yarn.Client \
+        ./bin/spark-class org.apache.spark.deploy.yarn.Client \
           --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
           --class org.apache.spark.examples.SparkPi \
           --args yarn-standalone \
@@ -107,16 +107,16 @@ For example:
 
     SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
     SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
-    ./run-example org.apache.spark.examples.SparkPi yarn-client
+    ./bin/run-example org.apache.spark.examples.SparkPi yarn-client
 
 
     SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \
     SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \
-    MASTER=yarn-client ./spark-shell
+    MASTER=yarn-client ./bin/spark-shell
 
-# Building Spark for Hadoop/YARN 2.2.x
+You can also send extra files to yarn cluster for worker to use by exporting SPARK_YARN_DIST_FILES=file1,file2... etc.
 
-Hadoop 2.2.x users must build Spark and publish it locally. The SBT build process handles Hadoop 2.2.x as a special case. This version of Hadoop has new YARN API changes and depends on a Protobuf version (2.5) that is not compatible with the Akka version (2.0.5) that Spark uses. Therefore, if the Hadoop version (e.g. set through ```SPARK_HADOOP_VERSION```) starts with 2.2.0 or higher then the build process will depend on Akka artifacts distributed by the Spark project compatible with Protobuf 2.5. Furthermore, the build process then uses the directory ```new-yarn``` (instead of ```yarn```), which supports the new YARN API. The build process should seamlessly work out of the box. 
+# Building Spark for Hadoop/YARN 2.2.x
 
 See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using the Maven process.
 
@@ -126,4 +126,3 @@ See [Building Spark with Maven](building-with-maven.html) for instructions on ho
 - The local directories used for spark will be the local directories configured for YARN (Hadoop Yarn config yarn.nodemanager.local-dirs). If the user specifies spark.local.dir, it will be ignored.
 - The --files and --archives options support specifying file names with the # similar to Hadoop. For example you can specify: --files localtest.txt#appSees.txt and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name appSees.txt and your application should use the name as appSees.txt to reference it when running on YARN.
 - The --addJars option allows the SparkContext.addJar function to work if you are using it with local files. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
-- YARN 2.2.x users cannot simply depend on the Spark packages without building Spark, as the published Spark artifacts are compiled to work with the pre 2.2 API. Those users must build Spark and publish it locally.  
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 1db255ca53..c1ef46a1cd 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -13,7 +13,7 @@ At a high level, every Spark application consists of a *driver program* that run
 
 A second abstraction in Spark is *shared variables* that can be used in parallel operations. By default, when Spark runs a function in parallel as a set of tasks on different nodes, it ships a copy of each variable used in the function to each task. Sometimes, a variable needs to be shared across tasks, or between tasks and the driver program. Spark supports two types of shared variables: *broadcast variables*, which can be used to cache a value in memory on all nodes, and *accumulators*, which are variables that are only "added" to, such as counters and sums.
 
-This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for [closures](http://www.scala-lang.org/node/133). Note that you can also run Spark interactively using the `spark-shell` script. We highly recommend doing that to follow along!
+This guide shows each of these features and walks through some samples. It assumes some familiarity with Scala, especially with the syntax for [closures](http://www.scala-lang.org/node/133). Note that you can also run Spark interactively using the `bin/spark-shell` script. We highly recommend doing that to follow along!
 
 # Linking with Spark
 
@@ -54,16 +54,16 @@ object for more advanced configuration.
 
 The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later.
 
-In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `spark-shell` on four cores, use
+In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `bin/spark-shell` on four cores, use
 
 {% highlight bash %}
-$ MASTER=local[4] ./spark-shell
+$ MASTER=local[4] ./bin/spark-shell
 {% endhighlight %}
 
 Or, to also add `code.jar` to its classpath, use:
 
 {% highlight bash %}
-$ MASTER=local[4] ADD_JARS=code.jar ./spark-shell
+$ MASTER=local[4] ADD_JARS=code.jar ./bin/spark-shell
 {% endhighlight %}
 
 ### Master URLs
@@ -95,7 +95,7 @@ If you want to run your application on a cluster, you will need to specify the t
 * `sparkHome`: The path at which Spark is installed on your worker machines (it should be the same on all of them).
 * `jars`: A list of JAR files on the local machine containing your application's code and any dependencies, which Spark will deploy to all the worker nodes. You'll need to package your application into a set of JARs using your build system. For example, if you're using SBT, the [sbt-assembly](https://github.com/sbt/sbt-assembly) plugin is a good way to make a single JAR with your code and dependencies.
 
-If you run `spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it.  This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed.
+If you run `bin/spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it.  This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./bin/spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed.
 
 # Resilient Distributed Datasets (RDDs)
 
@@ -366,9 +366,9 @@ res2: Int = 10
 # Where to Go from Here
 
 You can see some [example Spark programs](http://spark.incubator.apache.org/examples.html) on the Spark website.
-In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `run-example` script included in Spark; for example:
+In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `bin/run-example` script included in Spark; for example:
 
-    ./run-example org.apache.spark.examples.SparkPi
+    ./bin/run-example org.apache.spark.examples.SparkPi
 
 Each example program prints usage help when run without any arguments.
 
diff --git a/docs/spark-debugger.md b/docs/spark-debugger.md
index d6315d97f4..11c51d5cde 100644
--- a/docs/spark-debugger.md
+++ b/docs/spark-debugger.md
@@ -39,7 +39,7 @@ where `path/to/event-log` is where you want the event log to go relative to `$SP
 
 ### Loading the event log into the debugger
 
-1. Run a Spark shell with `MASTER=<i>host</i> ./spark-shell`.
+1. Run a Spark shell with `MASTER=<i>host</i> ./bin/spark-shell`.
 2. Use `EventLogReader` to load the event log as follows:
     {% highlight scala %}
 spark> val r = new spark.EventLogReader(sc, Some("path/to/event-log"))
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index f7f0b78908..2a186261b7 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -10,17 +10,13 @@ In addition to running on the Mesos or YARN cluster managers, Spark also provide
 
 # Installing Spark Standalone to a Cluster
 
-The easiest way to deploy Spark is by running the `./make-distribution.sh` script to create a binary distribution.
-This distribution can be deployed to any machine with the Java runtime installed; there is no need to install Scala.
-
-The recommended procedure is to deploy and start the master on one node first, get the master spark URL,
-then modify `conf/spark-env.sh` in the `dist/` directory before deploying to all the other nodes.
+To install Spark Standlone mode, you simply place a compiled version of Spark on each node on the cluster. You can obtain pre-built versions of Spark with each release or [build it yourself](index.html#building).
 
 # Starting a Cluster Manually
 
 You can start a standalone master server by executing:
 
-    ./bin/start-master.sh
+    ./sbin/start-master.sh
 
 Once started, the master will print out a `spark://HOST:PORT` URL for itself, which you can use to connect workers to it,
 or pass as the "master" argument to `SparkContext`. You can also find this URL on
@@ -28,7 +24,7 @@ the master's web UI, which is [http://localhost:8080](http://localhost:8080) by
 
 Similarly, you can start one or more workers and connect them to the master via:
 
-    ./spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT
+    ./bin/spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT
 
 Once you have started a worker, look at the master's web UI ([http://localhost:8080](http://localhost:8080) by default).
 You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS).
@@ -70,12 +66,12 @@ To launch a Spark standalone cluster with the launch scripts, you need to create
 
 Once you've set up this file, you can launch or stop your cluster with the following shell scripts, based on Hadoop's deploy scripts, and available in `SPARK_HOME/bin`:
 
-- `bin/start-master.sh` - Starts a master instance on the machine the script is executed on.
-- `bin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file.
-- `bin/start-all.sh` - Starts both a master and a number of slaves as described above.
-- `bin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script.
-- `bin/stop-slaves.sh` - Stops the slave instances that were started via `bin/start-slaves.sh`.
-- `bin/stop-all.sh` - Stops both the master and the slaves as described above.
+- `sbin/start-master.sh` - Starts a master instance on the machine the script is executed on.
+- `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file.
+- `sbin/start-all.sh` - Starts both a master and a number of slaves as described above.
+- `sbin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script.
+- `sbin/stop-slaves.sh` - Stops the slave instances that were started via `bin/start-slaves.sh`.
+- `sbin/stop-all.sh` - Stops both the master and the slaves as described above.
 
 Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine.
 
@@ -143,13 +139,45 @@ constructor](scala-programming-guide.html#initializing-spark).
 
 To run an interactive Spark shell against the cluster, run the following command:
 
-    MASTER=spark://IP:PORT ./spark-shell
+    MASTER=spark://IP:PORT ./bin/spark-shell
 
-Note that if you are running spark-shell from one of the spark cluster machines, the `spark-shell` script will
+Note that if you are running spark-shell from one of the spark cluster machines, the `bin/spark-shell` script will
 automatically set MASTER from the `SPARK_MASTER_IP` and `SPARK_MASTER_PORT` variables in `conf/spark-env.sh`.
 
 You can also pass an option `-c <numCores>` to control the number of cores that spark-shell uses on the cluster.
 
+# Launching Applications Inside the Cluster
+
+You may also run your application entirely inside of the cluster by submitting your application driver using the submission client. The syntax for submitting applications is as follows:
+
+
+    ./spark-class org.apache.spark.deploy.Client launch 
+       [client-options] \
+       <cluster-url> <application-jar-url> <main-class> \
+       [application-options]
+
+    cluster-url: The URL of the master node.
+    application-jar-url: Path to a bundled jar including your application and all dependencies. Currently, the URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. 
+    main-class: The entry point for your application.
+
+    Client Options:
+      --memory <count> (amount of memory, in MB, allocated for your driver program)
+      --cores <count> (number of cores allocated for your driver program)
+      --supervise (whether to automatically restart your driver on application or node failure)
+      --verbose (prints increased logging output)
+
+Keep in mind that your driver program will be executed on a remote worker machine. You can control the execution environment in the following ways:
+
+ * _Environment variables_: These will be captured from the environment in which you launch the client and applied when launching the driver program.
+ * _Java options_: You can add java options by setting `SPARK_JAVA_OPTS` in the environment in which you launch the submission client.
+ * _Dependencies_: You'll still need to call `sc.addJar` inside of your program to make your bundled application jar visible on all worker nodes.
+
+Once you submit a driver program, it will appear in the cluster management UI at port 8080 and
+be assigned an identifier. If you'd like to prematurely terminate the program, you can do so using
+the same client:
+
+    ./spark-class org.apache.spark.deploy.client.DriverClient kill <driverId>
+
 # Resource Scheduling
 
 The standalone cluster mode currently only supports a simple FIFO scheduler across applications.
@@ -167,6 +195,16 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
+In addition, you can configure `spark.deploy.defaultCores` on the cluster master process to change the
+default for applications that don't set `spark.cores.max` to something less than infinite.
+Do this by adding the following to `conf/spark-env.sh`:
+
+{% highlight bash %}
+export SPARK_JAVA_OPTS="-Dspark.deploy.defaultCores=<value>"
+{% endhighlight %}
+
+This is useful on shared clusters where users might not have configured a maximum number of cores
+individually.
 
 # Monitoring and Logging
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 6f97db6a3e..1c9ece6270 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -245,7 +245,7 @@ $ nc -lk 9999
 Then, in a different terminal, you can start NetworkWordCount by using
 
 {% highlight bash %}
-$ ./run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999
+$ ./bin/run-example org.apache.spark.streaming.examples.NetworkWordCount local[2] localhost 9999
 {% endhighlight %}
 
 This will make NetworkWordCount connect to the netcat server. Any lines typed in the terminal running the netcat server will be counted and printed on screen.
@@ -283,7 +283,7 @@ Time: 1357008430000 ms
 </td>
 </table>
 
-You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/`. They can be run in the similar manner using `./run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files.
+You can find more examples in `<Spark repo>/streaming/src/main/scala/org/apache/spark/streaming/examples/`. They can be run in the similar manner using `./bin/run-example org.apache.spark.streaming.examples....` . Executing without any parameter would give the required parameter list. Further explanation to run them can be found in comments in the files.
 
 # DStream Persistence
 Similar to RDDs, DStreams also allow developers to persist the stream's data in memory. That is, using `persist()` method on a DStream would automatically persist every RDD of that DStream in memory. This is useful if the data in the DStream will be computed multiple times (e.g., multiple operations on the same data). For window-based operations like `reduceByWindow` and `reduceByKeyAndWindow` and state-based operations like `updateStateByKey`, this is implicitly true. Hence, DStreams generated by window-based operations are automatically persisted in memory, without the developer calling `persist()`.