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, 8 insertions, 8 deletions

diff --git a/docs/configuration.md b/docs/configuration.md
index db90b5bc16..4270e50f47 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -23,7 +23,7 @@ the copy executable.
 Inside `spark-env.sh`, you can set the following environment variables:
 
 * `SCALA_HOME` to point to your Scala installation.
-* `MESOS_NATIVE_LIBRARY` if you are [running on a Mesos cluster]({{HOME_PATH}}running-on-mesos.html).
+* `MESOS_NATIVE_LIBRARY` if you are [running on a Mesos cluster](running-on-mesos.html).
 * `SPARK_MEM` to set the amount of memory used per node (this should be in the same format as the JVM's -Xmx option, e.g. `300m` or `1g`)
 * `SPARK_JAVA_OPTS` to add JVM options. This includes any system properties that you'd like to pass with `-D`.
 * `SPARK_CLASSPATH` to add elements to Spark's classpath.
@@ -53,9 +53,9 @@ there are at least four properties that you will commonly want to control:
   <td>
     Class to use for serializing objects that will be sent over the network or need to be cached
     in serialized form. The default of Java serialization works with any Serializable Java object but is
-    quite slow, so we recommend <a href="{{HOME_PATH}}tuning.html">using <code>spark.KryoSerializer</code>
+    quite slow, so we recommend <a href="tuning.html">using <code>spark.KryoSerializer</code>
     and configuring Kryo serialization</a> when speed is necessary. Can be any subclass of 
-    <a href="{{HOME_PATH}}api/core/index.html#spark.Serializer"><code>spark.Serializer</code></a>).
+    <a href="api/core/index.html#spark.Serializer"><code>spark.Serializer</code></a>).
   </td>
 </tr>
 <tr>
@@ -64,8 +64,8 @@ there are at least four properties that you will commonly want to control:
   <td>
     If you use Kryo serialization, set this class to register your custom classes with Kryo.
     You need to set it to a class that extends 
-    <a href="{{HOME_PATH}}api/core/index.html#spark.KryoRegistrator"><code>spark.KryoRegistrator</code></a>).
-    See the <a href="{{HOME_PATH}}tuning.html#data-serialization">tuning guide</a> for more details.
+    <a href="api/core/index.html#spark.KryoRegistrator"><code>spark.KryoRegistrator</code></a>).
+    See the <a href="tuning.html#data-serialization">tuning guide</a> for more details.
   </td>
 </tr>
 <tr>
@@ -81,8 +81,8 @@ there are at least four properties that you will commonly want to control:
   <td>spark.cores.max</td>
   <td>(infinite)</td>
   <td>
-    When running on a <a href="{{HOME_PATH}}spark-standalone.html">standalone deploy cluster</a> or a
-    <a href="{{HOME_PATH}}running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained"
+    When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a
+    <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained"
     sharing mode</a>, how many CPU cores to request at most. The default will use all available cores.
   </td>
 </tr>
@@ -98,7 +98,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>false</td>
   <td>
     If set to "true", runs over Mesos clusters in
-    <a href="{{HOME_PATH}}running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>,
+    <a href="running-on-mesos.html#mesos-run-modes">"coarse-grained" sharing mode</a>,
     where Spark acquires one long-lived Mesos task on each machine instead of one Mesos task per Spark task.
     This gives lower-latency scheduling for short queries, but leaves resources in use for the whole
     duration of the Spark job.