Updates to documentation:

- Edited quick start and tuning guide to simplify them a little
- Simplified top menu bar
- Made private a SparkContext constructor parameter that was left as
  public
- Various small fixes

1 files changed, 45 insertions, 49 deletions

diff --git a/docs/quick-start.md b/docs/quick-start.md
index 51e60426b5..7d35fb01bb 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -1,35 +1,34 @@
 ---
 layout: global
-title: Spark Quick Start
+title: Quick Start
 ---
 
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-# Introduction
+This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's interactive Scala shell (don't worry if you don't know Scala -- you will need much for this), then show how to write standalone jobs in Scala and Java. See the [programming guide](scala-programming-guide.html) for a fuller reference.
 
-This document provides a quick-and-dirty look at Spark's API. See the [programming guide](scala-programming-guide.html) for a complete reference. To follow along with this guide, you only need to have successfully built Spark on one machine. Building Spark is as simple as running
+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/sbt package
 {% endhighlight %}
 
-from within the Spark directory.
+# Interactive Analysis with the Spark Shell
 
-# Interactive Data Analysis with the Spark Shell
+## Basics
 
-## Shell 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 Spark shell by executing `./spark-shell` in the Spark directory.
-
-Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDD's can be created from Hadoop InputFormat's (such as HDFS files) or by transforming other RDD's. Let's make a new RDD derived from the text of the README file in the Spark source 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:
 
 {% highlight scala %}
 scala> val textFile = sc.textFile("README.md")
 textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3
 {% endhighlight %}
 
-RDD's have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDD's. Let's start with a few actions:
+RDDs have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions:
 
 {% highlight scala %}
 scala> textFile.count() // Number of items in this RDD
@@ -39,11 +38,11 @@ scala> textFile.first() // First item in this RDD
 res1: String = # Spark
 {% endhighlight %}
 
-Now let's use a transformation. We will use the [filter](scala-programming-guide.html#transformations)() transformation to return a new RDD with a subset of the items in the file.
+Now let's use a transformation. We will use the [`filter`](scala-programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file.
 
 {% highlight scala %}
-scala> val sparkLinesOnly = textFile.filter(line => line.contains("Spark"))
-sparkLinesOnly: spark.RDD[String] = spark.FilteredRDD@7dd4af09
+scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
+linesWithSpark: spark.RDD[String] = spark.FilteredRDD@7dd4af09
 {% endhighlight %}
 
 We can chain together transformations and actions:
@@ -53,18 +52,18 @@ scala> textFile.filter(line => line.contains("Spark")).count() // How many lines
 res3: Long = 15
 {% endhighlight %}
 
-## Data flow
+## Transformations
 RDD transformations can be used for more complex computations. Let's say we want to find the line with the most words:
 
 {% highlight scala %}
-scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a < b) {b} else {a})
+scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
 res4: Long = 16
 {% endhighlight %}
 
-This first maps a line to an integer value, creating a new RDD. `reduce` is called on that RDD to find the largest line count. The arguments to [map](scala-programming-guide.html#transformations)() and [reduce](scala-programming-guide.html#actions)() are scala closures. We can easily include functions declared elsewhere, or include existing functions in our anonymous closures. For instance, we can use `Math.max()` to make this code easier to understand. 
+This first maps a line to an integer value, creating a new RDD. `reduce` is called on that RDD to find the largest line count. The arguments to `map` and `reduce` are Scala function literals (closures), and can use any language feature or Scala/Java library. For example, we can easily call functions declared elsewhere. We'll use `Math.max()` function to make this code easier to understand: 
 
 {% highlight scala %}
-scala> import java.lang.Math;
+scala> import java.lang.Math
 import java.lang.Math
 
 scala> textFile.map(line => line.split(" ").size).reduce((a, b) => Math.max(a, b))
@@ -74,36 +73,35 @@ res5: Int = 16
 One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily:
 
 {% highlight scala %}
-scala> val wordCountRDD = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((c1, c2) => c1 + c2)
-wordCountRDD: spark.RDD[(java.lang.String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
+scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)
+wordCounts: spark.RDD[(java.lang.String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
-Here, we combined the [flatMap](scala-programming-guide.html#transformations)(), [map](scala-programming-guide.html#transformations)() and [reduceByKey](scala-programming-guide.html#transformations)() transformations to create per-word counts in the file. To collect the word counts in our shell, we can use the [collect](scala-programming-guide.html#actions)() action:
+Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action:
 
 {% highlight scala %}
-scala> wordCountRDD.collect()
+scala> wordCounts.collect()
 res6: Array[(java.lang.String, Int)] = Array((need,2), ("",43), (Extra,3), (using,1), (passed,1), (etc.,1), (its,1), (`/usr/local/lib/libmesos.so`,1), (`SCALA_HOME`,1), (option,1), (these,1), (#,1), (`PATH`,,2), (200,1), (To,3),...
 {% endhighlight %}
 
 ## Caching
-Spark also supports pulling data sets into a cluster-wide cache. This is very useful when data is accessed iteratively, such as in machine learning jobs, or repeatedly, such as when small "hot data" is queried repeatedly. As a simple example, let's pull part of our file into memory:
-
+Spark also supports pulling data sets into a cluster-wide in-memory cache. This is very useful when data is accessed repeatedly, such as when querying a small "hot" dataset or when running an iterative algorithm like PageRank. As a simple example, let's mark our `linesWithSpark` dataset to be cached:
 
 {% highlight scala %}
-scala> val linesWithSparkCached = linesWithSpark.cache()
-linesWithSparkCached: spark.RDD[String] = spark.FilteredRDD@17e51082
-
-scala> linesWithSparkCached.count()
-res7: Long = 15
+scala> linesWithSpark.cache()
+res7: spark.RDD[String] = spark.FilteredRDD@17e51082
 
-scala> linesWithSparkCached.count()
+scala> linesWithSpark.count()
 res8: Long = 15
+
+scala> linesWithSpark.count()
+res9: Long = 15
 {% endhighlight %}
 
-It may seem silly to use a 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.
+It may seem silly to use a 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).
 
-# A Spark Job in Scala
-Now say we wanted to write custom job using the Spark API. We will walk through a simple job in both Scala (with sbt) and Java (with maven). If you using other build systems, please reference the Spark assembly jar in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:
+# A Standalone Job in Scala
+Now say we wanted to write a standalone job using the Spark API. We will walk through a simple job in both Scala (with sbt) and Java (with maven). If you using other build systems, please reference the Spark assembly JAR in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:
 
 {% highlight bash %}
 $ sbt/sbt publish-local
@@ -117,7 +115,7 @@ import spark.SparkContext
 import SparkContext._
 
 object SimpleJob extends Application {
-  val logFile = "/var/log/syslog" // Should be some log file on your system
+  val logFile = "/var/log/syslog" // Should be some file on your system
   val sc = new SparkContext("local", "Simple Job", "$YOUR_SPARK_HOME", 
     "target/scala-{{site.SCALA_VERSION}}/simple-project_{{site.SCALA_VERSION}}-1.0.jar")
   val logData = sc.textFile(logFile, 2).cache()
@@ -127,7 +125,7 @@ object SimpleJob extends Application {
 }
 {% endhighlight %}
 
-This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark Shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
+This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
 
 This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency:
 
@@ -141,7 +139,7 @@ scalaVersion := "{{site.SCALA_VERSION}}"
 libraryDependencies += "org.spark-project" %% "spark-core" % "{{site.SPARK_VERSION}}"
 {% endhighlight %}
 
-Of course, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a jar package containing the job's code, then use `sbt run` to execute our example job. 
+Of course, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the job's code, then use `sbt run` to execute our example job. 
 
 {% highlight bash %}
 $ find . 
@@ -152,21 +150,21 @@ $ find .
 ./src/main/scala
 ./src/main/scala/SimpleJob.scala
 
-$ sbt clean package
+$ sbt package
 $ sbt run
 ...
 Lines with a: 8422, Lines with b: 1836
 {% endhighlight %}
 
-This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation and consider using a distributed input source, such as HDFS.
+This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.
 
-# A Spark Job In Java
-Now say we wanted to write custom job using the Spark API. We will walk through a simple job in both Scala (with sbt) and Java (with maven). If you using other build systems, please reference the Spark assembly jar in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:
+# A Standalone Job In Java
+Now say we wanted to write a standalone job using the Java API. We will walk through doing this with Maven. If you using other build systems, please reference the Spark assembly JAR in the developer guide. The first step is to publish Spark to our local Ivy/Maven repositories. From the Spark directory:
 
 {% highlight bash %}
 $ sbt/sbt publish-local
 {% endhighlight %}
-Next, we'll create a very simple Spark job in Scala. So simple, in fact, that it's named `SimpleJob.java`:
+Next, we'll create a very simple Spark job, `SimpleJob.java`:
 
 {% highlight java %}
 /*** SimpleJob.java ***/
@@ -175,7 +173,7 @@ import spark.api.java.function.Function;
 
 public class SimpleJob {
   public static void main(String[] args) {
-    String logFile = "/var/log/syslog"; // Should be some log file on your system
+    String logFile = "/var/log/syslog"; // Should be some file on your system
     JavaSparkContext sc = new JavaSparkContext("local", "Simple Job", 
       "$YOUR_SPARK_HOME", "target/simple-project-1.0.jar");
     JavaRDD<String> logData = sc.textFile(logFile).cache();
@@ -188,15 +186,14 @@ public class SimpleJob {
       public Boolean call(String s) { return s.contains("b"); }
     }).count();
 
-    System.out.println(String.format(
-        "Lines with a: %s, Lines with b: %s", numAs, numBs));
+    System.out.println("Lines with a: " + numAs + ", lines with b: " + numBs);
   }
 }
 {% endhighlight %}
 
-This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark Shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the job, the directory where Spark is installed, and a name for the jar file containing the job's sources. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes.
+This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that like in the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide]("java-programming-guide") describes these differences in more detail.
 
-Our Maven `pom.xml` file will list Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.
+To build the job, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version.
 
 {% highlight xml %}
 <project>
@@ -216,7 +213,7 @@ Our Maven `pom.xml` file will list Spark as a dependency. Note that Spark artifa
 </project>
 {% endhighlight %}
 
-To make Maven happy, we lay out these files according to the canonical directory structure:
+We lay out these files according to the canonical Maven directory structure:
 {% highlight bash %}
 $ find .
 ./pom.xml
@@ -229,11 +226,10 @@ $ find .
 Now, we can execute the job using Maven:
 
 {% highlight bash %}
-$ mvn clean package
+$ mvn package
 $ mvn exec:java -Dexec.mainClass="SimpleJob"
 ...
 Lines with a: 8422, Lines with b: 1836
 {% endhighlight %}
 
-This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation and consider using a distributed input source, such as HDFS.
-
+This example only runs the job locally; for a tutorial on running jobs across several machines, see the [Standalone Mode](spark-standalone.html) documentation, and consider using a distributed input source, such as HDFS.