Updated run-example quickstart (commands, screenshots)

This closes #136

docs/run_example_quickstart.md

@@ -5,22 +5,20 @@ title: "Quick Start: Run K-Means Example"
 * This will be replaced by the TOC
 {:toc}
 
-This guide will demonstrate Flink's features by example. You will see how you can leverage Flink's Iteration-feature to find clusters in a dataset using [K-Means clustering](http://en.wikipedia.org/wiki/K-means_clustering). 
-On the way, you will see the compiler, the status interface and the result of the algorithm.
+This guide walks you through the steps of executing an example program ([K-Means clustering](http://en.wikipedia.org/wiki/K-means_clustering)) on Flink. On the way, you will see the a visualization of the program, the optimized execution plan, and track the progress of its execution.
 
+## Setup Flink
+Follow the [instructions](setup_quickstart.html) to setup Flink and enter the root directory of your Flink setup.
 
-##  Generate Input Data
+## Generate Input Data
 Flink contains a data generator for K-Means.
 
 ~~~bash
-# Download Flink
-wget {{ site.FLINK_DOWNLOAD_URL_HADOOP_1_STABLE }}
-tar xzf flink-*.tgz 
-cd flink-*
+# Assuming you are in the root directory of your Flink setup
 mkdir kmeans
 cd kmeans
 # Run data generator
-java -cp  ../examples/flink-java-examples-{{ site.FLINK_VERSION_STABLE }}-KMeans.jar org.apache.flink.example.java.clustering.util.KMeansDataGenerator 500 10 0.08
+java -cp ../examples/flink-java-examples-*-KMeans.jar org.apache.flink.examples.java.clustering.util.KMeansDataGenerator 500 10 0.08
 cp /tmp/points .
 cp /tmp/centers .
 ~~~
@@ -31,19 +29,18 @@ The generator has the following arguments:
 KMeansDataGenerator <numberOfDataPoints> <numberOfClusterCenters> [<relative stddev>] [<centroid range>] [<seed>]
 ~~~
 
-The _relative standard deviation_ is an interesting tuning parameter: it determines the closeness of the points to the centers.
+The _relative standard deviation_ is an interesting tuning parameter. It determines the closeness of the points to randomly generated centers.
 
-The `kmeans/` directory should now contain two files: `centers` and `points`.
+The `kmeans/` directory should now contain two files: `centers` and `points`. The `points` file contains the points to cluster and the `centers` file contains initial cluster centers.
 
 
-## Review Input Data
-Use the `plotPoints.py` tool to review the result of the data generator. [Download Python Script](quickstart/plotPoints.py)
+## Inspect the Input Data
+Use the `plotPoints.py` tool to review the generated data points. [Download Python Script](quickstart/plotPoints.py)
 
 ~~~ bash
-python plotPoints.py points points input
+python plotPoints.py points ./points input
 ~~~ 
 
-
 Note: You might have to install [matplotlib](http://matplotlib.org/) (`python-matplotlib` package on Ubuntu) to use the Python script.
 
 You can review the input data stored in the `input-plot.pdf`, for example with Evince (`evince input-plot.pdf`).
@@ -55,37 +52,39 @@ The following overview presents the impact of the different standard deviations
 |<img src="img/quickstart-example/kmeans003.png" alt="example1" style="width: 275px;"/>|<img src="img/quickstart-example/kmeans008.png" alt="example2" style="width: 275px;"/>|<img src="img/quickstart-example/kmeans015.png" alt="example3" style="width: 275px;"/>|
 
 
-## Run Clustering
-We are using the generated input data to run the clustering using a Flink job.
+## Start Flink
+Start Flink and the web job submission client on your local machine.
 
-	# go to the Flink-root directory
-	cd flink
-	# start Flink (use ./bin/start-cluster.sh if you're on a cluster)
-	./bin/start-local.sh
-	# Start Flink web client
-	./bin/start-webclient.sh
+~~~ bash
+# return to the Flink root directory
+cd ..
+# start Flink
+./bin/start-local.sh
+# Start the web client
+./bin/start-webclient.sh
+~~~
 
-## Review Flink Compiler
-The Flink webclient allows to submit Flink programs using a graphical user interface.
+## Inspect and Run the K-Means Example Program
+The Flink web client allows to submit Flink programs using a graphical user interface.
 
 <div class="row" style="padding-top:15px">
 	<div class="col-md-6">
 		<a data-lightbox="compiler" href="img/quickstart-example/run-webclient.png" data-lightbox="example-1"><img class="img-responsive" src="img/quickstart-example/run-webclient.png" /></a>
 	</div>
 	<div class="col-md-6">
-		1. <a href="http://localhost:8080/launch.html">Open webclient on localhost:8080</a> <br>
-		2. Upload the file. 
+		1. Open web client on  <a href="http://localhost:8080/launch.html">localhost:8080</a> <br>
+		2. Upload the K-Mean job JAR file. 
 			{% highlight bash %}
-			examples/flink-java-examples-{{ site.FLINK_VERSION_STABLE }}-KMeans.jar
+			./examples/flink-java-examples-*-KMeans.jar
 			{% endhighlight %} </br>
 		3. Select it in the left box to see how the operators in the plan are connected to each other. <br>
 		4. Enter the arguments in the lower left box:
 			{% highlight bash %}
-			file://<pathToGenerated>points file://<pathToGenerated>centers file://<pathToGenerated>result 10
+			file://<pathToFlink>/kmeans/points file://<pathToFlink>/kmeans/centers file://<pathToFlink>/kmeans/result 10
 			{% endhighlight %}
 			For example:
 			{% highlight bash %}
-			file:///tmp/flink/kmeans/points file:///tmp/flink/kmeans/centers file:///tmp/flink/kmeans/result 20
+			file:///tmp/flink/kmeans/points file:///tmp/flink/kmeans/centers file:///tmp/flink/kmeans/result 10
 			{% endhighlight %}
 	</div>
 </div>
@@ -96,7 +95,7 @@ The Flink webclient allows to submit Flink programs using a graphical user inter
 	</div>
 
 	<div class="col-md-6">
-		1. Press the <b>RunJob</b> to see the optimzer plan. <br>
+		1. Press the <b>RunJob</b> to see the optimizer plan. <br>
 		2. Inspect the operators and see the properties (input sizes, cost estimation) determined by the optimizer.
 	</div>
 </div>
@@ -107,18 +106,27 @@ The Flink webclient allows to submit Flink programs using a graphical user inter
 	</div>
 	<div class="col-md-6">
 		1. Press the <b>Continue</b> button to start executing the job. <br>
-		2. <a href="http://localhost:8080/launch.html">Open Flink's monitoring interface</a> to see the job's progress.<br>
-		3. Once the job has finished, you can analyize the runtime of the individual operators.
+		2. <a href="http://localhost:8080/launch.html">Open Flink's monitoring interface</a> to see the job's progress. (Due to the small input data, the job will finish really quick!)<br>
+		3. Once the job has finished, you can analyze the runtime of the individual operators.
 	</div>
 </div>
 
+## Shutdown Flink
+Stop Flink when you are done.
 
-## Analyze the Result
+~~~ bash
+# stop Flink
+./bin/stop-local.sh
+# Stop the Flink web client
+./bin/stop-webclient.sh
+~~~
 
-Use the [Python Script](quickstart/plotPoints.py) again to visualize the result
+## Analyze the Result
+Use the [Python Script](quickstart/plotPoints.py) again to visualize the result.
 
 ~~~bash
-python plotPoints.py result result result-pdf
+cd kmeans
+python plotPoints.py result ./result clusters
 ~~~
 
 The following three pictures show the results for the sample input above. Play around with the parameters (number of iterations, number of clusters) to see how they affect the result.