Some tweaks to docs

docs/_layouts/global.html

@@ -53,11 +53,11 @@
                         </li>
                         
                         <li class="dropdown">
-                            <a href="#" class="dropdown-toggle" data-toggle="dropdown">API (Scaladoc)<b class="caret"></b></a>
+                            <a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a>
                             <ul class="dropdown-menu">
-                                <li><a href="api/core/index.html">Spark Scala/Java (Scaladoc)</a></li>
+                                <li><a href="api/core/index.html">Spark Java/Scala (Scaladoc)</a></li>
                                 <li><a href="api/pyspark/index.html">Spark Python (Epydoc)</a></li>
-                                <li><a href="api/streaming/index.html">Spark Streaming Scala/Java (Scaladoc) </a></li>
+                                <li><a href="api/streaming/index.html">Spark Streaming Java/Scala (Scaladoc) </a></li>
                             </ul>
                         </li>
 

docs/index.md

@@ -58,9 +58,9 @@ of `project/SparkBuild.scala`, then rebuilding Spark (`sbt/sbt clean compile`).
 
 * [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
 * [Spark Programming Guide](scala-programming-guide.html): an overview of Spark concepts, and details on the Scala API
-* [Streaming Programming Guide](streaming-programming-guide.html): an API preview of Spark Streaming
 * [Java Programming Guide](java-programming-guide.html): using Spark from Java
 * [Python Programming Guide](python-programming-guide.html): using Spark from Python
+* [Spark Streaming Guide](streaming-programming-guide.html): using the alpha release of Spark Streaming
 
 **API Docs:**
 

docs/streaming-programming-guide.md

@@ -7,9 +7,9 @@ title: Spark Streaming Programming Guide
 {:toc}
 
 # Overview
-A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collection of elements) representing a continuous stream of data. DStreams can created from live incoming data (such as data from a socket, Kafka, etc.) or it can be generated by transformation of existing DStreams using parallel operators like map, reduce, and window. The basic processing model is as follows: 
-(i) While a Spark Streaming driver program is running, the system receives data from various sources and and divides the data into batches. Each batch of data is treated as a RDD, that is a immutable and parallel collection of data. These input data RDDs are automatically persisted in memory (serialized by default) and replicated to two nodes for fault-tolerance. This sequence of RDDs is collectively referred to as an InputDStream.
-(ii) Data received by InputDStreams are processed processed using DStream operations. Since all data is represented as RDDs and all DStream operations as RDD operations, data is automatically recovered in the event of node failures.  
+A Spark Streaming application is very similar to a Spark application; it consists of a *driver program* that runs the user's `main` function and continuous executes various *parallel operations* on input streams of data. The main abstraction Spark Streaming provides is a *discretized stream* (DStream), which is a continuous sequence of RDDs (distributed collections of elements) representing a continuous stream of data. DStreams can be created from live incoming data (such as data from a socket, Kafka, etc.) or can be generated by transformong existing DStreams using parallel operators like `map`, `reduce`, and `window`. The basic processing model is as follows: 
+(i) While a Spark Streaming driver program is running, the system receives data from various sources and and divides it into batches. Each batch of data is treated as an RDD, that is, an immutable parallel collection of data. These input RDDs are saved in memory and replicated to two nodes for fault-tolerance. This sequence of RDDs is collectively called an InputDStream.
+(ii) Data received by InputDStreams are processed using DStream operations. Since all data is represented as RDDs and all DStream operations as RDD operations, data is automatically recovered in the event of node failures.  
 
 This guide shows some how to start programming with DStreams. 
 
@@ -20,7 +20,7 @@ The first thing a Spark Streaming program must do is create a `StreamingContext`
 new StreamingContext(master, jobName, batchDuration)
 {% endhighlight %}
 
-The `master` parameter is either the [Mesos master URL](running-on-mesos.html) (for running on a cluster)or the special "local" string (for local mode) that is used to create a Spark Context. For more information about this please refer to the [Spark programming guide](scala-programming-guide.html). The `jobName` is the name of the streaming job, which is the same as the jobName used in SparkContext. It is used to identify this job in the Mesos web UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such the cluster can keep up with the processing of the data streams. Starting with something conservative like 5 seconds maybe a good start. See [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion.
+The `master` parameter is a standard [Spark cluster URL](scala-programming-guide.html#master-urls) and can be "local" for local testing mode. The `jobName` is the name of the streaming job, which is the same as the jobName used in SparkContext. It is used to identify this job in the cluster UI. The `batchDuration` is the size of the batches (as explained earlier). This must be set carefully such the cluster can keep up with the processing of the data streams. Starting with something conservative like 5 seconds. See the [Performance Tuning](#setting-the-right-batch-size) section for a detailed discussion.
 
 This constructor creates a SparkContext object using the given `master` and `jobName` parameters. However, if you already have a SparkContext or you need to create a custom SparkContext by specifying list of JARs, then a StreamingContext can be created from the existing SparkContext, by using
 {% highlight scala %}
@@ -514,5 +514,5 @@ JavaPairDStream<String, Integer> wordCounts = words.map(
 
 
 # Where to Go from Here
-* Documentation - [Scala](api/streaming/index.html#spark.streaming.package) and [Java](api/streaming/index.html#spark.streaming.api.java.package)
+* API docs - [Scala](api/streaming/index.html#spark.streaming.package) and [Java](api/streaming/index.html#spark.streaming.api.java.package)
 * More examples - [Scala](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/mesos/spark/tree/master/examples/src/main/java/spark/streaming/examples)