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Steps for Spinning AutoSpark on Ubuntu VCL nodes

For setting up master nodes

  • On your local machine, git clone this repository, come to this directory and run ./

  • Open the file using vi ~/.ssh/ and copy the keys. Paste the keys into [] -> log in using your unity id -> goto User Preferences -> General Preferences -> Public keys -> Paste the copied keys.

  • In the Spark_VCL directory, open main.yml file and edit at line 4 "unity-id" with your unity-id.

  • Now from the Spark_VCL directory, you might want to edit the at line 30 specifying the full path of data file on which you want to run spark jobs. After that, run ./ unity-id minutes. Replace "unity-id" with the one provided to you by NC State and replace the minutes with integer value for which you want to reserve the master node for (Multiples of 60). It will ask for unity-id password.

For setting up the slave nodes

  • Your Master node is setup. Now run ssh unity-id@ip_address. Replace "unity-id" with your unity-id and replace "ip_address" with the ip_address generated at the above step.

  • You will see a directory automatically generated as "Spark_VCL". Change to this directory, and open the main1.yml file and edit at line 3 "unity-id" with your unity-id.

  • On the master node, come to this directory and run ./

  • Now from the Spark_VCL directory, Run ./ unity-id. It will ask for unity-id password.

  • The above script will start a Prompt asking for different inputs like:

provider - vcl	#fixed input
name - 		# any arbitrary strings
count -		# No of slaves to be in the cluster or no. of VCL VMs you want to request.
duration -	# Duration for each node reservation
  • Change directory to cd AutoSpark/Ansible/playbooks and run ./ Check if any of these tasks are red, if yes then run it again else proceed.
  • After the successful completion of the above setup, you will have to run these commands from the home directory (cd ~).
source ~/.bashrc
hadoop namenode -format
hadoop fs -mkdir -p In
hadoop fs -mkdir -p Logs
hadoop fs -put <data_file_full_path> /user/<unity-id>/In/<file_name_at_destination>
	- <data_file_full_path> the data file which you want to load (generally ~/filename).
	- <unity-id> - unity-id
	- <file_name_at_destination> - filename

~/spark/spark_latest/bin/spark-submit --driver-memory 1 --executor-memory 2 --class 3 4 --master 5 6 7 8
	- 1 - Ram like (2G, 3G)
	- 2 - Ram like (2G, 3G)
	- 3 - Class of scala file - In our example, I have provided you with the lda.jar scala example. (lda.lda)
	- 4 - Jar target path - In our example, I have provided you with the lda.jar scala example. (lda/target/lda-0.0.1.jar)
	- 5 - Spark Url - Which is of the form spark://<master-ip>:7077
	- 6 - Data filename which you put on hdfs. (filename)
	- 7 - <master-ip> - current nodes ip address
	- 8 - <unity-id> - unity-id
	- NOTE: You can use your own examples but then you will have to transfer the data file and the scala code or jar files accordingly. Argument 3, 4 and 6 will change accordingly. And the executor memory and driver memory is set in the lda.scala code.
  • I packaged the lda example using maven and then zipped it. Zipped file can be found under Spark_VCL/AutoSpark/Spark_Jobs. And you can transfer the zipped file on master node using scp.
  • There are couple of import configurations which was done specific to the hardware and the kind of job you are running on Spark and Hadoop. You might have to modify main.yml and Autospark/Ansible/playbook/.sparkplaybook.yml. For example this configuration will start the spark executors and drivers with 6G of memory.
  • If you stop hadoop some time, then you will need to delete the tmp folders on namenodes and datanodes. You need to change directory using cd ~/Spark_VCL/AutoSpark/Ansible/playbooks and execute ./


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