Merge remote-tracking branch 'apache-github/master' into standalone-driver

Conflicts:
	core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
	pom.xml

14 files changed, 348 insertions, 81 deletions

diff --git a/docs/README.md b/docs/README.md
index e3d6c9a5bc..dfcf753553 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -27,10 +27,10 @@ To mark a block of code in your markdown to be syntax highlighted by jekyll duri
 
 ## API Docs (Scaladoc and Epydoc)
 
-You can build just the Spark scaladoc by running `sbt doc` from the SPARK_PROJECT_ROOT directory.
+You can build just the Spark scaladoc by running `sbt/sbt doc` from the SPARK_PROJECT_ROOT directory.
 
 Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the SPARK_PROJECT_ROOT/pyspark directory.
 
-When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
+When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
 
 NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`.
diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb
index ef9912c808..431de909cb 100644
--- a/docs/_plugins/copy_api_dirs.rb
+++ b/docs/_plugins/copy_api_dirs.rb
@@ -26,8 +26,8 @@ if not (ENV['SKIP_API'] == '1' or ENV['SKIP_SCALADOC'] == '1')
   curr_dir = pwd
   cd("..")
 
-  puts "Running sbt doc from " + pwd + "; this may take a few minutes..."
-  puts `sbt doc`
+  puts "Running sbt/sbt doc from " + pwd + "; this may take a few minutes..."
+  puts `sbt/sbt doc`
 
   puts "Moving back into docs dir."
   cd("docs")
diff --git a/docs/api.md b/docs/api.md
index 11e2c15324..e86d07770a 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -3,7 +3,7 @@ layout: global
 title: Spark API documentation (Scaladoc)
 ---
 
-Here you can find links to the Scaladoc generated for the Spark sbt subprojects.  If the following links don't work, try running `sbt doc` from the Spark project home directory.
+Here you can find links to the Scaladoc generated for the Spark sbt subprojects.  If the following links don't work, try running `sbt/sbt doc` from the Spark project home directory.
 
 - [Spark](api/core/index.html)
 - [Spark Examples](api/examples/index.html)
diff --git a/docs/configuration.md b/docs/configuration.md
index 09342fedfc..1d6c3d1633 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -77,13 +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>, the maximum amount of CPU cores to request for the application from
-    across the cluster (not from each machine). The default will use all available cores
-    offered by the cluster manager.
+    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>
@@ -361,6 +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>
@@ -395,6 +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/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index 141d475ba6..de6a2b0a43 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -12,7 +12,7 @@ with these distributions:
 When compiling Spark, you'll need to 
 [set the SPARK_HADOOP_VERSION flag](index.html#a-note-about-hadoop-versions):
 
-    SPARK_HADOOP_VERSION=1.0.4 sbt assembly
+    SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly
 
 The table below lists the corresponding `SPARK_HADOOP_VERSION` code for each CDH/HDP release. Note that
 some Hadoop releases are binary compatible across client versions. This means the pre-built Spark
diff --git a/docs/index.md b/docs/index.md
index bf8d1c3375..86d574daaa 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -17,7 +17,7 @@ Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you n
 
 Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with it. To compile the code, go into the top-level Spark directory and run
 
-    sbt assembly
+    sbt/sbt assembly
 
 For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you will need to use this same version of Scala in your own program -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
 
@@ -56,12 +56,12 @@ Hadoop, you must build Spark against the same version that your cluster uses.
 By default, Spark links to Hadoop 1.0.4. You can change this by setting the
 `SPARK_HADOOP_VERSION` variable when compiling:
 
-    SPARK_HADOOP_VERSION=2.2.0 sbt assembly
+    SPARK_HADOOP_VERSION=2.2.0 sbt/sbt assembly
 
 In addition, if you wish to run Spark on [YARN](running-on-yarn.html), set
 `SPARK_YARN` to `true`:
 
-    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt assembly
+    SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
 Note that on Windows, you need to set the environment variables on separate lines, e.g., `set SPARK_HADOOP_VERSION=1.2.1`.
 
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 95537ef185..45ee166688 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 `bin/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. 
@@ -210,3 +162,269 @@ at each iteration.
 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 %}
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index 5d48cb676a..dc187b3efe 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -69,7 +69,7 @@ The script automatically adds the `bin/pyspark` package to the `PYTHONPATH`.
 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 assembly
+$ sbt/sbt assembly
 $ ./bin/pyspark
 {% endhighlight %}
 
diff --git a/docs/quick-start.md b/docs/quick-start.md
index 9b9261cfff..153081bdaa 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -12,7 +12,7 @@ See the [programming guide](scala-programming-guide.html) for a more complete re
 To follow along with this guide, you only need to have successfully built Spark on one machine. Simply go into your Spark directory and run:
 
 {% highlight bash %}
-$ sbt assembly
+$ sbt/sbt assembly
 {% endhighlight %}
 
 # Interactive Analysis with the Spark Shell
@@ -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 %}
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index a35e003cdc..717071d72c 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 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`.
@@ -25,7 +25,7 @@ The build process now also supports new YARN versions (2.2.x). See below.
 - The assembled jar can be installed into HDFS or used locally.
 - Your application code must be packaged into a separate JAR file.
 
-If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
+If you want to test out the YARN deployment mode, you can use the current Spark examples. A `spark-examples_{{site.SCALA_VERSION}}-{{site.SPARK_VERSION}}` file can be generated by running `sbt/sbt assembly`. NOTE: since the documentation you're reading is for Spark version {{site.SPARK_VERSION}}, we are assuming here that you have downloaded Spark {{site.SPARK_VERSION}} or checked it out of source control. If you are using a different version of Spark, the version numbers in the jar generated by the sbt package command will obviously be different.
 
 # Configuration
 
@@ -72,7 +72,7 @@ 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 assembly
+    $ SPARK_HADOOP_VERSION=2.0.5-alpha SPARK_YARN=true sbt/sbt assembly
 
     # Configure logging
     $ cp conf/log4j.properties.template conf/log4j.properties
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 3d0e8923d5..c1ef46a1cd 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -31,7 +31,7 @@ In addition, if you wish to access an HDFS cluster, you need to add a dependency
     artifactId = hadoop-client
     version = <your-hdfs-version>
 
-For other build systems, you can run `sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions).
+For other build systems, you can run `sbt/sbt assembly` to pack Spark and its dependencies into one JAR (`assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop*.jar`), then add this to your CLASSPATH. Set the HDFS version as described [here](index.html#a-note-about-hadoop-versions).
 
 Finally, you need to import some Spark classes and implicit conversions into your program. Add the following lines:
 
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index ecd642cc60..2a186261b7 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -195,6 +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