Fix markdown syntax issues that maruku flags, even though we use kram…

…down (but only those that do not affect kramdown's output)

docs/README.md

@@ -14,9 +14,10 @@ The markdown code can be compiled to HTML using the
 [Jekyll tool](http://jekyllrb.com).
 To use the `jekyll` command, you will need to have Jekyll installed. 
 The easiest way to do this is via a Ruby Gem, see the 
-[jekyll installation instructions](http://jekyllrb.com/docs/installation). 
-Compiling the site with Jekyll will create a directory called 
-_site containing index.html as well as the rest of the compiled files.
+[jekyll installation instructions](http://jekyllrb.com/docs/installation).
+If not already installed, you need to install `kramdown` with `sudo gem install kramdown`.
+Execute `jekyll` from the `docs/` directory. Compiling the site with Jekyll will create a directory called
+`_site` containing index.html as well as the rest of the compiled files.
 
 You can modify the default Jekyll build as follows:
 
@@ -44,6 +45,6 @@ You can build just the Spark scaladoc by running `sbt/sbt doc` from the SPARK_PR
 
 Similarly, you can build just the PySpark epydoc by running `epydoc --config epydoc.conf` from the SPARK_PROJECT_ROOT/pyspark directory. Documentation is only generated for classes that are listed as public in `__init__.py`.
 
-When you run `jekyll` in the docs directory, it will also copy over the scaladoc for the various Spark subprojects into the docs directory (and then also into the _site directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
+When you run `jekyll` in the `docs` directory, it will also copy over the scaladoc for the various Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a jekyll plugin to run `sbt/sbt doc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc.  The jekyll plugin also generates the PySpark docs using [epydoc](http://epydoc.sourceforge.net/).
 
 NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 jekyll`.

docs/cluster-overview.md

@@ -181,7 +181,7 @@ The following table summarizes terms you'll see used to refer to cluster concept
       <td>Distinguishes where the driver process runs. In "cluster" mode, the framework launches
         the driver inside of the cluster. In "client" mode, the submitter launches the driver
         outside of the cluster.</td>
-    <tr>
+    </tr>
     <tr>
       <td>Worker node</td>
       <td>Any node that can run application code in the cluster</td>

docs/configuration.md

@@ -318,7 +318,7 @@ Apart from these, the following properties are also available, and may be useful
     When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches
     objects to prevent writing redundant data, however that stops garbage collection of those
     objects. By calling 'reset' you flush that info from the serializer, and allow old
-    objects to be collected. To turn off this periodic reset set it to a value of <= 0.
+    objects to be collected. To turn off this periodic reset set it to a value &lt;= 0.
     By default it will reset the serializer every 10,000 objects.
   </td>
 </tr>

docs/mllib-decision-tree.md

@@ -95,7 +95,7 @@ The recursive tree construction is stopped at a node when one of the two conditi
 
 ### Practical limitations
 
-1. The tree implementation stores an Array[Double] of size *O(#features \* #splits \* 2^maxDepth)*
+1. The tree implementation stores an `Array[Double]` of size *O(#features \* #splits \* 2^maxDepth)*
    in memory for aggregating histograms over partitions. The current implementation might not scale
    to very deep trees since the memory requirement grows exponentially with tree depth.
 2. The implemented algorithm reads both sparse and dense data. However, it is not optimized for

docs/mllib-linear-methods.md

@@ -63,7 +63,7 @@ methods MLlib supports:
   <tbody>
     <tr>
       <td>hinge loss</td><td>$\max \{0, 1-y \wv^T \x \}, \quad y \in \{-1, +1\}$</td>
-      <td>$\begin{cases}-y \cdot \x & \text{if $y \wv^T \x <1$}, \\ 0 &
+      <td>$\begin{cases}-y \cdot \x &amp; \text{if $y \wv^T \x &lt;1$}, \\ 0 &amp;
 \text{otherwise}.\end{cases}$</td>
     </tr>
     <tr>

docs/mllib-naive-bayes.md

@@ -109,7 +109,7 @@ smoothing parameter `lambda` as input, and output a
 [NaiveBayesModel](api/pyspark/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be
 used for evaluation and prediction.
 
-<!--- TODO: Make Python's example consistent with Scala's and Java's. --->
+<!-- TODO: Make Python's example consistent with Scala's and Java's. -->
 {% highlight python %}
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.classification import NaiveBayes

docs/scala-programming-guide.md

@@ -48,12 +48,12 @@ how to access a cluster. To create a `SparkContext` you first need to build a `S
 that contains information about your application.
 
 {% highlight scala %}
-val conf = new SparkConf().setAppName(<app name>).setMaster(<master>)
+val conf = new SparkConf().setAppName(appName).setMaster(master)
 new SparkContext(conf)
 {% endhighlight %}
 
-The `<master>` parameter is a string specifying a [Spark, Mesos or YARN cluster URL](#master-urls)
-to connect to, or a special "local" string to run in local mode, as described below. `<app name>` is
+The `master` parameter is a string specifying a [Spark, Mesos or YARN cluster URL](#master-urls)
+to connect to, or a special "local" string to run in local mode, as described below. `appName` is
 a name for your application, which will be shown in the cluster web UI. It's also possible to set
 these variables [using a configuration file](cluster-overview.html#loading-configurations-from-a-file)
 which avoids hard-coding the master name in your application.
@@ -81,9 +81,8 @@ The master URL passed to Spark can be in one of the following formats:
 <table class="table">
 <tr><th>Master URL</th><th>Meaning</th></tr>
 <tr><td> local </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
-<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
+<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). </td></tr>
 <tr><td> local[*] </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
-</td></tr>
 <tr><td> spark://HOST:PORT </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
         cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
 </td></tr>

docs/sql-programming-guide.md

@@ -416,3 +416,4 @@ results = hiveCtx.hql("FROM src SELECT key, value").collect()
 {% endhighlight %}
 
 </div>
+</div>