Merge branch 'master' into spark-1002-remove-jars

1 files changed, 3 insertions, 1 deletions

diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 3e7075c382..fe1bca789f 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -49,6 +49,9 @@ This is done through the following constructor:
 new SparkContext(master, appName, [sparkHome], [jars])
 {% endhighlight %}
 
+or through `new SparkContext(conf)`, which takes a [SparkConf](api/core/index.html#org.apache.spark.SparkConf)
+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
@@ -94,7 +97,6 @@ If you want to run your application on a cluster, you will need to specify the t
 
 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.
 
-
 # Resilient Distributed Datasets (RDDs)
 
 Spark revolves around the concept of a _resilient distributed dataset_ (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are currently two types of RDDs: *parallelized collections*, which take an existing Scala collection and run functions on it in parallel, and *Hadoop datasets*, which run functions on each record of a file in Hadoop distributed file system or any other storage system supported by Hadoop. Both types of RDDs can be operated on through the same methods.