def refresh_code_on_machines():
""" Refresh the current version of the code on the machines. """
hostnames = [instance.public_dns_name for instance in get_all_instances()]
with settings(parallel=True, user='******',
key_filename='/Users/aismail/.ssh/ami-keypair.pem'):
execute('refresh_code', hosts=hostnames)
def run_experiment(url='https://raw.github.com/ami-lab/AmI-Platform/master/dumps/diana.txt',
name='cloud_experiment',
file_name='/tmp/experiment.txt',
experiment_profile='default_experiment.json'):
# Load up which machines are needed from the experiment profile
machines = json.load(open(experiment_profile, 'rt'))
# Only open new machines if it's necessary
opened_instances = get_all_instances()
if len(opened_instances) == 0:
# Open exactly the desired number of machines. Sometimes EC2 fails
# weirdly to open the requested number of machines (don't know why)
# so I'm putting in a retry mechanism.
machines_to_open = len(machines)
machines_opened = 0
machine_hostnames = []
while machines_opened < machines_to_open:
hostnames = execute('open_machines',
count=machines_to_open - machines_opened)['<local-only>']
machine_hostnames.extend(hostnames)
machines_opened += len(hostnames)
else:
hostnames = [instance.public_dns_name for instance in opened_instances]
# Attach tags to machines. This meta-data is used for provisioning and
# for the lifecycle management of machines as well.
for hostname, machine_meta_data in zip(hostnames, machines):
tag_instance(hostname, machine_meta_data)
execute('bootstrap_machines')
execute('provision_machines')
execute('copy_experiment', url=url, file_name=file_name, name=name)
execute('play_experiment', name=name)
def bootstrap_machines():
hostnames = [instance.public_dns_name for instance in get_all_instances()]
# Provision the machines in parallel. The manifest for each machine
# will be taken from env.hostname_to_manifest, because it's the only sane
# way I found in fab to do the provisioning in parallel.
with settings(parallel=True, user='******'):
execute('bootstrap_machine', hosts=hostnames)
def configure_hiera_for_machines():
""" Configures hiera for all machines launched by our experiment. """
hostnames = [instance.public_dns_name for instance in get_all_instances()]
execute('generate_hiera_datasources')
# NOTE: we would be better off executing configure_hiera_for_machine
# as root but I don't think it's OK to complicate ourselves by provisioning
# the keypair for the root user as well. So when we need, for example, to
# copy the files in a folder only accessible by root, we copy them in a
# folder accessible by the ami user, and then use sudo to move it around
# locally.
with settings(parallel=True):
execute('configure_hiera_for_machine', hosts=hostnames)
def provision_machines():
hostnames = [instance.public_dns_name for instance in get_all_instances()]
# Provision the machines in parallel. The manifest for each machine
# will be taken from env.hostname_to_manifest, because it's the only sane
# way I found in fab to do the provisioning in parallel.
with settings(parallel=True):
execute('provision_machine', hosts=hostnames)
# Determine the crunching hostnames - these are generally modules of
# type 'crunch' but there might be exceptions when we want to keep the
# code close to a machine, especially since we have all the code on all
# the machines.
crunching_hostnames = [instance.public_dns_name
for instance in get_all_instances()
if len(instance.tags.get('modules', '').strip()) > 0]
# For crunch nodes, generate settings_local.py files and services.txt files.
# Afterwards, run deploy task on each of them.
with settings(parallel=True):
execute('generate_services_file', hosts=crunching_hostnames)
execute('deploy_ami_services_on_crunch_node', hosts=crunching_hostnames)
def provision_machines():
hostnames = [instance.public_dns_name for instance in get_all_instances()]
# Provision the machines in parallel. The manifest for each machine
# will be taken from env.hostname_to_manifest, because it's the only sane
# way I found in fab to do the provisioning in parallel.
with settings(parallel=True, user='******',
key_filename='/Users/aismail/.ssh/ami-keypair.pem'):
execute('provision_machine', hosts=hostnames)
crunch_hostnames = [instance.public_dns_name for instance in
get_instances_by_tags({'type': 'crunch'})]
# For crunch nodes, generate settings_local.py files and services.txt files.
# Afterwards, run deploy task on each of them.
with settings(parallel=True, user='******',
key_filename='/Users/aismail/.ssh/ami-keypair.pem'):
execute('generate_settings_local_file', hosts=crunch_hostnames)
execute('generate_services_file', hosts=crunch_hostnames)
execute('deploy_ami_services_on_crunch_node', hosts=crunch_hostnames)
def generate_hiera_datasources():
""" Hiera datasources are hierarchical configurations to be applied by
puppet automatically in its template files. We need them for provisioning
purposes. """
instances = get_all_instances()
local('rm -rf /tmp/hiera')
local('mkdir -p /tmp/hiera/node')
common_context = {
'mongo_hostname': get_instance_by_tags({'Name': 'measurements'}).public_dns_name,
'redis_hostname': get_instance_by_tags({'Name': 'sessions'}).public_dns_name,
'kestrel_hostname': get_instance_by_tags({'Name': 'queues'}).public_dns_name
}
render_template('admin/templates/common.json',
common_context,
'/tmp/hiera/common.json')
for instance in instances:
context = {'hostname': instance.public_dns_name}
render_template('admin/templates/node.json',
context,
'/tmp/hiera/node/%s.json' % instance.private_dns_name)
def refresh_code_on_machines():
""" Refresh the current version of the code on the machines. """
hostnames = [instance.public_dns_name for instance in get_all_instances()]
with settings(parallel=True):
execute('refresh_code', hosts=hostnames)
