Work in progress:

- Add job scheduling docs
- Rename some fair scheduler properties
- Organize intro page better
- Link to Apache wiki for "contributing to Spark"

1 files changed, 15 insertions, 11 deletions

diff --git a/docs/index.md b/docs/index.md
index d3aacc629f..1814cb19c8 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -21,7 +21,7 @@ Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with
 
 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/).
 
-# Testing the Build
+# 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
@@ -34,14 +34,16 @@ 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, Spark can be used interactively through modified versions of the Scala shell (`./spark-shell`) or
-Python interpreter (`./pyspark`). These are a great way to learn Spark.
+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.
 
-# Running on a Cluster
+# Launching on a Cluster
 
-Spark supports several options for deployment:
+The Spark [cluster mode overview](cluster-overview.html) explains the key concepts in running on a cluster.
+Spark can run both by itself, or over several existing cluster managers. It currently provides several
+options for deployment:
 
-* [Amazon EC2](ec2-scripts.html): our scripts let you launch a cluster in about 5 minutes
+* [Amazon EC2](ec2-scripts.html): our EC2 scripts let you launch a cluster in about 5 minutes
 * [Standalone Deploy Mode](spark-standalone.html): simplest way to deploy Spark on a private cluster
 * [Apache Mesos](running-on-mesos.html)
 * [Hadoop YARN](running-on-yarn.html)
@@ -91,19 +93,21 @@ In addition, if you wish to run Spark on [YARN](running-on-yarn.md), set
 
 **Deployment guides:**
 
-* [Running Spark on Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
+* [Cluster Overview](cluster-overview.html): overview of concepts and components when running on a cluster
+* [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
 * [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
-* [Running Spark on Mesos](running-on-mesos.html): deploy a private cluster using
+* [Mesos](running-on-mesos.html): deploy a private cluster using
     [Apache Mesos](http://incubator.apache.org/mesos)
-* [Running Spark on YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
+* [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
 
 **Other documents:**
 
 * [Configuration](configuration.html): customize Spark via its configuration system
 * [Tuning Guide](tuning.html): best practices to optimize performance and memory use
 * [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware
-* [Building Spark with Maven](building-with-maven.html): Build Spark using the Maven build tool
-* [Contributing to Spark](contributing-to-spark.html)
+* [Job Scheduling](job-scheduling.html): scheduling resources across and within Spark applications
+* [Building Spark with Maven](building-with-maven.html): build Spark using the Maven system
+* [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
 
 **External resources:**