[SPARK-6402][DOC] - Remove some refererences to shark in docs and ec2

EC2 script and job scheduling documentation still refered to Shark.
I removed these references.

I also removed a remaining `SHARK_VERSION` variable from `ec2-variables.sh`.

Author: Pierre Borckmans <[email protected]>

Closes apache#5083 from pierre-borckmans/remove_refererences_to_shark_in_docs and squashes the following commits:

4e90ffc [Pierre Borckmans] Removed deprecated SHARK_VERSION
caea407 [Pierre Borckmans] Remove shark reference from ec2 script doc
196c744 [Pierre Borckmans] Removed references to Shark

docs/ec2-scripts.md

@@ -5,7 +5,7 @@ title: Running Spark on EC2
 
 The `spark-ec2` script, located in Spark's `ec2` directory, allows you
 to launch, manage and shut down Spark clusters on Amazon EC2. It automatically
-sets up Spark, Shark and HDFS on the cluster for you. This guide describes 
+sets up Spark and HDFS on the cluster for you. This guide describes 
 how to use `spark-ec2` to launch clusters, how to run jobs on them, and how 
 to shut them down. It assumes you've already signed up for an EC2 account 
 on the [Amazon Web Services site](http://aws.amazon.com/).

docs/job-scheduling.md

@@ -14,8 +14,7 @@ runs an independent set of executor processes. The cluster managers that Spark r
 facilities for [scheduling across applications](#scheduling-across-applications). Second,
 _within_ each Spark application, multiple "jobs" (Spark actions) may be running concurrently
 if they were submitted by different threads. This is common if your application is serving requests
-over the network; for example, the [Shark](http://shark.cs.berkeley.edu) server works this way. Spark
-includes a [fair scheduler](#scheduling-within-an-application) to schedule resources within each SparkContext.
+over the network. Spark includes a [fair scheduler](#scheduling-within-an-application) to schedule resources within each SparkContext.
 
 # Scheduling Across Applications
 
@@ -52,8 +51,7 @@ an application to gain back cores on one node when it has work to do. To use thi
 
 Note that none of the modes currently provide memory sharing across applications. If you would like to share
 data this way, we recommend running a single server application that can serve multiple requests by querying
-the same RDDs. For example, the [Shark](http://shark.cs.berkeley.edu) JDBC server works this way for SQL
-queries. In future releases, in-memory storage systems such as [Tachyon](http://tachyon-project.org) will
+the same RDDs. In future releases, in-memory storage systems such as [Tachyon](http://tachyon-project.org) will
 provide another approach to share RDDs.
 
 ## Dynamic Resource Allocation

ec2/deploy.generic/root/spark-ec2/ec2-variables.sh

@@ -25,7 +25,6 @@ export MAPRED_LOCAL_DIRS="{{mapred_local_dirs}}"
 export SPARK_LOCAL_DIRS="{{spark_local_dirs}}"
 export MODULES="{{modules}}"
 export SPARK_VERSION="{{spark_version}}"
-export SHARK_VERSION="{{shark_version}}"
 export TACHYON_VERSION="{{tachyon_version}}"
 export HADOOP_MAJOR_VERSION="{{hadoop_major_version}}"
 export SWAP_MB="{{swap}}"