Adds liquid variables to docs templating system so that they can be used

throughout the docs: SPARK_VERSION, SCALA_VERSION, and MESOS_VERSION.

To use them, e.g. use {{site.SPARK_VERSION}}.

Also removes uses of {{HOME_PATH}} which were being resolved to ""
by the templating system anyway.

1 files changed, 7 insertions, 7 deletions

diff --git a/docs/quick-start.md b/docs/quick-start.md
index f9356afe9a..2e88fd863e 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -8,7 +8,7 @@ title: Spark Quick Start
 
 # Introduction
 
-This document provides a quick-and-dirty look at Spark's API. See the [programming guide]({{HOME_PATH}}/scala-programming-guide.html) for a complete reference. To follow along with this guide, you only need to have successfully [built spark]({{HOME_PATH}}) on one machine. Building Spark is as simple as running
+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
 
 {% highlight bash %}
 $ sbt/sbt package
@@ -29,7 +29,7 @@ scala> val textFile = sc.textFile("README.md")
 textFile: spark.RDD[String] = spark.MappedRDD@2ee9b6e3
 {% endhighlight %}
 
-RDD's have _[actions]({{HOME_PATH}}/scala-programming-guide.html#actions)_, which return values, and _[transformations]({{HOME_PATH}}/scala-programming-guide.html#transformations)_, which return pointers to new RDD's. Let's start with a few actions:
+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:
 
 {% highlight scala %}
 scala> textFile.count() // Number of items in this RDD
@@ -39,7 +39,7 @@ scala> textFile.first() // First item in this RDD
 res1: String = # Spark
 {% endhighlight %}
 
-Now let's use a transformation. We will use the [filter]({{HOME_PATH}}/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"))
@@ -61,7 +61,7 @@ scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a < b) {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]({{HOME_PATH}}/scala-programming-guide.html#transformations)() and [reduce]({{HOME_PATH}}/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](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. 
 
 {% highlight scala %}
 scala> import java.lang.Math;
@@ -78,7 +78,7 @@ scala> val wordCountRDD = textFile.flatMap(line => line.split(" ")).map(word =>
 wordCountRDD: spark.RDD[(java.lang.String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
-Here, we combined the [flatMap]({{HOME_PATH}}/scala-programming-guide.html#transformations)(), [map]({{HOME_PATH}}/scala-programming-guide.html#transformations)() and [reduceByKey]({{HOME_PATH}}/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]({{HOME_PATH}}/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 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:
 
 {% highlight scala %}
 scala> wordCountRDD.collect()
@@ -158,7 +158,7 @@ $ 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]({{HOME_PATH}}/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:
@@ -235,5 +235,5 @@ $ 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]({{HOME_PATH}}/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.