Skip to content

stat-37601/spark-ec2

 
 

Repository files navigation

spark-ec2

This repository contains the set of scripts used to setup a Spark cluster on EC2. These scripts are intended to be used by the default Spark AMI and is not expected to work on other AMIs. If you wish to start a cluster using Spark, please refer to http://spark-project.org/docs/latest/ec2-scripts.html

Details

The Spark cluster setup is guided by the values set in ec2-variables.sh.setup.sh first performs basic operations like enabling ssh across machines, mounting ephemeral drives and also creates files named /root/spark-ec2/masters, and /root/spark-ec2/slaves. Following that every module listed in MODULES is initialized.

To add a new module, you will need to do the following:

a. Create a directory with the module's name

b. Optionally add a file named init.sh. This is called before templates are configured and can be used to install any pre-requisites.

c. Add any files that need to be configured based on the cluster setup to templates/. The path of the file determines where the configured file will be copied to. Right now the set of variables that can be used in a template are

  {{master_list}}
  {{active_master}}
  {{slave_list}}
  {{zoo_list}}
  {{cluster_url}}
  {{hdfs_data_dirs}}
  {{mapred_local_dirs}}
  {{spark_local_dirs}}
  {{default_spark_mem}}
  {{spark_worker_instances}}
  {{spark_worker_cores}}
  {{spark_master_opts}}

You can add new variables by modifying deploy_templates.py

d. Add a file named setup.sh to launch any services on the master/slaves. This is called after the templates have been configured. You can use the environment variables $SLAVES to get a list of slave hostnames and /root/spark-ec2/copy-dir to sync a directory across machines.

e. Modify https://github.com/mesos/spark/blob/master/ec2/spark_ec2.py to add your module to the list of enabled modules.

About

Private fork of Spark EC2 setup scripts.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Shell 54.2%
  • ApacheConf 41.2%
  • Python 4.6%