Spark On AWS

Spark Demo on AWS

Recently, I had the pleasure of working with JT Halbert and Jason Morris on creating an AWS-based Spark demo using the Enron email dataset for the Apache Spark Maryland meetup.

I had worked on putting together a Spark demo for the Enron email dataset and provided all the ETL to put the text-based CMU Enron email set into Avro along with some utilties to make it easy to explore from spark-shell.

As part of this, I also wanted to try out my code on the AWS cluster but did not know how much it would cost to run. Estimates were available at on Amazon’s EMR pricing, which seemed very reasonable. Instance types were described on the EC2 instance types page. So I decided to just try it out.

First, I installed the AWS CLI following the Amazon CLI instructions with Python 2.6.5 or higher:

sudo apt-get install python-pip
sudo pip install awscli

I then used Jason’s to spin up a three-node cluster with a custom 1.2.1 Spark on m3.xlarge instances. Note: also downloads some datasets, which you may or may not want. Modify to suit your needs.

I ran for 232 minutes (not realizing that usage is charged by the hour so I could have run for 300 minutes for the same cost - 120 normalized instance hours). The total cost for this was $5.55 broken out as:

  • $0.30 in data transfer
  • $4.20 EC2
  • $1.05 EMR
  • 232 minutes (always charged by fraction of full hours)
  • 120 normalized instance hours
  • Instance type: m3.xlarge

This data was gathered from:

EMR AMI versions

The key line in the script for cluster creation is:

 aws emr create-cluster --name SparkCluster --ami-version 3.2 --instance-type m3.xlarge --instance-count 3  --ec2-attributes KeyName=$KEYNAME

The ami-version for EMR determines what Hadoop ecosystem is on the box. See AMI for EMR overview. So we were running on Hadoop 2.4.0.

Bottom line: AWS EMR and CLI are great utilities to launch a Spark cluster on demand!

Background on AWS setup

Enter access key id, secret access key when prompted or create and source that before running the script:

export AWS_ACCESS_KEY_ID=keyId
export AWS_SECRET_ACCESS_KEY=secretAccessKey (longer than key id)
export AWS_DEFAULT_REGION=us-east-1