Docs small fixes

Author: Jacek Laskowski <jacek@japila.pl>

Closes #8629 from jaceklaskowski/docs-fixes.

docs/building-spark.md

@@ -61,12 +61,13 @@ If you don't run this, you may see errors like the following:
 You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
 
 **Note:**
-* *For Java 8 and above this step is not required.*
-* *If using `build/mvn` and `MAVEN_OPTS` were not already set, the script will automate this for you.*
+
+* For Java 8 and above this step is not required.
+* If using `build/mvn` with no `MAVEN_OPTS` set, the script will automate this for you.
 
 # Specifying the Hadoop Version
 
-Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
+Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the `hadoop.version` property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
 
 <table class="table">
   <thead>
@@ -91,7 +92,7 @@ mvn -Dhadoop.version=1.2.1 -Phadoop-1 -DskipTests clean package
 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -Phadoop-1 -DskipTests clean package
 {% endhighlight %}
 
-You can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". Spark only supports YARN versions 2.2.0 and later.
+You can enable the `yarn` profile and optionally set the `yarn.version` property if it is different from `hadoop.version`. Spark only supports YARN versions 2.2.0 and later.
 
 Examples:
 
@@ -125,7 +126,7 @@ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -Dskip
 # Building for Scala 2.11
 To produce a Spark package compiled with Scala 2.11, use the `-Dscala-2.11` property:
 
-    dev/change-scala-version.sh 2.11
+    ./dev/change-scala-version.sh 2.11
     mvn -Pyarn -Phadoop-2.4 -Dscala-2.11 -DskipTests clean package
 
 Spark does not yet support its JDBC component for Scala 2.11.
@@ -163,11 +164,9 @@ the `spark-parent` module).
 
 Thus, the full flow for running continuous-compilation of the `core` submodule may look more like:
 
-```
- $ mvn install
- $ cd core
- $ mvn scala:cc
-```
+    $ mvn install
+    $ cd core
+    $ mvn scala:cc
 
 # Building Spark with IntelliJ IDEA or Eclipse
 
@@ -193,11 +192,11 @@ then ship it over to the cluster. We are investigating the exact cause for this.
 
 # Packaging without Hadoop Dependencies for YARN
 
-The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.
+The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with `yarn.application.classpath`.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.
 
 # Building with SBT
 
-Maven is the official recommendation for packaging Spark, and is the "build of reference".
+Maven is the official build tool recommended for packaging Spark, and is the *build of reference*.
 But SBT is supported for day-to-day development since it can provide much faster iterative
 compilation. More advanced developers may wish to use SBT.
 

docs/cluster-overview.md

@@ -5,18 +5,19 @@ title: Cluster Mode Overview
 
 This document gives a short overview of how Spark runs on clusters, to make it easier to understand
 the components involved. Read through the [application submission guide](submitting-applications.html)
-to submit applications to a cluster.
+to learn about launching applications on a cluster.
 
 # Components
 
-Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext
+Spark applications run as independent sets of processes on a cluster, coordinated by the `SparkContext`
 object in your main program (called the _driver program_).
+
 Specifically, to run on a cluster, the SparkContext can connect to several types of _cluster managers_
-(either Spark's own standalone cluster manager or Mesos/YARN), which allocate resources across
+(either Spark's own standalone cluster manager, Mesos or YARN), which allocate resources across
 applications. Once connected, Spark acquires *executors* on nodes in the cluster, which are
 processes that run computations and store data for your application.
 Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to
-the executors. Finally, SparkContext sends *tasks* for the executors to run.
+the executors. Finally, SparkContext sends *tasks* to the executors to run.
 
 <p style="text-align: center;">
   <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" />
@@ -33,9 +34,9 @@ There are several useful things to note about this architecture:
 2. Spark is agnostic to the underlying cluster manager. As long as it can acquire executor
    processes, and these communicate with each other, it is relatively easy to run it even on a
    cluster manager that also supports other applications (e.g. Mesos/YARN).
-3. The driver program must listen for and accept incoming connections from its executors throughout 
-   its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config 
-   section](configuration.html#networking)). As such, the driver program must be network 
+3. The driver program must listen for and accept incoming connections from its executors throughout
+   its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config
+   section](configuration.html#networking)). As such, the driver program must be network
    addressable from the worker nodes.
 4. Because the driver schedules tasks on the cluster, it should be run close to the worker
    nodes, preferably on the same local area network. If you'd like to send requests to the