def get_scaling_group(group, config_data):
"""This function checks and gets active servers in scaling group
:param group: group name
:param config_data: json configuration data
:returns: scalingGroup if server state is active else null
"""
group_id = common.get_group_value(config_data, group, 'group_id')
if group_id is None:
logger.error('Unable to get group_id from json file')
return
scalingGroup = cloudmonitor.scaling_group_servers(group_id)
if scalingGroup is None:
return
# Check active server(s) in scaling group
if len(scalingGroup.get_state()['active']) == 0:
return
else:
logger.info('Server(s) in scaling group: %s' %
', '.join(['(%s, %s)'
% (cloudmonitor.get_server_name(s_id), s_id)
for s_id in scalingGroup.get_state()['active']]))
logger.info('Current Active Servers: ' +
str(scalingGroup.get_state()['active_capacity']))
return scalingGroup
def get_scaling_group(group, config_data):
"""This function checks and gets active servers in scaling group
:param group: group name
:param config_data: json configuration data
:returns: scalingGroup if server state is active else null
"""
group_id = common.get_group_value(config_data, group, 'group_id')
if group_id is None:
logger.error('Unable to get group_id from json file')
return
scalingGroup = cloudmonitor.scaling_group_servers(group_id)
if scalingGroup is None:
return
# Check active server(s) in scaling group
if len(scalingGroup.get_state()['active']) == 0:
return
else:
logger.info('Server(s) in scaling group: %s' % ', '.join([
'(%s, %s)' % (cloudmonitor.get_server_name(s_id), s_id)
for s_id in scalingGroup.get_state()['active']
]))
logger.info('Current Active Servers: ' +
str(scalingGroup.get_state()['active_capacity']))
return scalingGroup
def autoscale(group, config_data, args):
"""This function executes scale up or scale down policy
:param group: group name
:param config_data: json configuration data
:param args: user provided arguments
"""
au = pyrax.autoscale
scalingGroup = get_scaling_group(group, config_data)
if scalingGroup is None:
return 1
check_type = common.get_group_value(config_data, group, 'check_type')
if check_type is None:
return 1
check_config = common.get_group_value(config_data, group, 'check_config')
if check_config is None:
return 1
for s_id in scalingGroup.get_state()['active']:
rv = cloudmonitor.add_cm_check(s_id, check_type, check_config)
logger.info('Cluster Mode Enabled: %s' % str(args['cluster']))
if args['cluster']:
rv = is_node_master(scalingGroup)
if rv is None:
# Not a master, no need to proceed further
return
if rv == 1:
# Cluster state unknown return error.
return 1
# Gather cluster statistics
check_type = common.get_group_value(config_data, group, 'check_type')
if check_type is None:
check_type = 'agent.load_average'
metric_name = common.get_group_value(config_data, group, 'metric_name')
if metric_name is None:
metric_name = '1m'
logger.info('Gathering Monitoring Data')
results = []
cm = pyrax.cloud_monitoring
# Get all CloudMonitoring entities on the account
entities = cm.list_entities()
# Shuffle entities so the sample uses different servers
entities = random.sample(entities, len(entities))
for ent in entities:
# Check if the entity is also in the scaling group
if ent.agent_id in scalingGroup.get_state()['active']:
ent_checks = ent.list_checks()
# Loop through checks to find checks of the correct type
for check in ent_checks:
if check.type == check_type:
data = check.get_metric_data_points(metric_name,
int(time.time())-300,
int(time.time()),
points=2)
if len(data) > 0:
point = len(data)-1
logger.info('Found metric for: ' + ent.name +
', value: ' + str(data[point]['average']))
results.append(float(data[point]['average']))
break
# Restrict number of data points to save on API calls
if len(results) >= args['max_sample']:
logger.info('--max-sample value of ' + str(args['max_sample']) +
' reached, not gathering any more statistics')
break
if len(results) == 0:
logger.error('No data available')
return 1
else:
average = sum(results)/len(results)
scale_up_threshold = common.get_group_value(config_data, group,
'scale_up_threshold')
if scale_up_threshold is None:
scale_up_threshold = 0.6
scale_down_threshold = common.get_group_value(config_data, group,
'scale_down_threshold')
if scale_down_threshold is None:
scale_down_threshold = 0.4
logger.info('Cluster average for ' + check_type +
'(' + metric_name + ') at: ' + str(average))
if average > scale_up_threshold:
try:
logger.info('Above Threshold - Scaling Up')
scale_policy_id = common.get_group_value(config_data, group,
'scale_up_policy')
scale_policy = scalingGroup.get_policy(scale_policy_id)
if not args['dry_run']:
common.webhook_call(config_data, group, 'scale_up', 'pre')
scale_policy.execute()
common.webhook_call(config_data, group, 'scale_up', 'post')
else:
logger.info('Scale up prevented by --dry-run')
logger.info('Scale up policy executed ('
+ scale_policy_id + ')')
except Exception, e:
logger.warning('Scale up: %s' % str(e))
'scale_up_policy')
scale_policy = scalingGroup.get_policy(scale_policy_id)
if not args['dry_run']:
common.webhook_call(config_data, group, 'scale_up', 'pre')
scale_policy.execute()
common.webhook_call(config_data, group, 'scale_up', 'post')
else:
logger.info('Scale up prevented by --dry-run')
logger.info('Scale up policy executed ('
+ scale_policy_id + ')')
except Exception, e:
logger.warning('Scale up: %s' % str(e))
elif average < scale_down_threshold:
try:
logger.info('Below Threshold - Scaling Down')
scale_policy_id = common.get_group_value(config_data, group,
'scale_down_policy')
scale_policy = scalingGroup.get_policy(scale_policy_id)
if not args['dry_run']:
common.webhook_call(config_data, group, 'scale_down', 'pre')
scale_policy.execute()
common.webhook_call(config_data, group, 'scale_down', 'post')
else:
logger.info('Scale down prevented by --dry-run')
logger.info('Scale down policy executed (' +
scale_policy_id + ')')
except Exception, e:
logger.warning('Scale down: %s' % str(e))
else:
logger.info('Cluster within target paramters')
def autoscale(group, config_data, args):
"""This function executes scale up or scale down policy
:param group: group name
:param config_data: json configuration data
:param args: user provided arguments
"""
au = pyrax.autoscale
scalingGroup = get_scaling_group(group, config_data)
if scalingGroup is None:
return 1
check_type = common.get_group_value(config_data, group, 'check_type')
if check_type is None:
return 1
check_config = common.get_group_value(config_data, group, 'check_config')
if check_config is None:
return 1
for s_id in scalingGroup.get_state()['active']:
rv = cloudmonitor.add_cm_check(s_id, check_type, check_config)
logger.info('Cluster Mode Enabled: %s' % str(args['cluster']))
if args['cluster']:
rv = is_node_master(scalingGroup)
if rv is None:
# Not a master, no need to proceed further
return
if rv == 1:
# Cluster state unknown return error.
return 1
# Gather cluster statistics
check_type = common.get_group_value(config_data, group, 'check_type')
if check_type is None:
check_type = 'agent.load_average'
metric_name = common.get_group_value(config_data, group, 'metric_name')
if metric_name is None:
metric_name = '1m'
logger.info('Gathering Monitoring Data')
results = []
cm = pyrax.cloud_monitoring
# Get all CloudMonitoring entities on the account
entities = cm.list_entities()
# Shuffle entities so the sample uses different servers
entities = random.sample(entities, len(entities))
for ent in entities:
# Check if the entity is also in the scaling group
if ent.agent_id in scalingGroup.get_state()['active']:
ent_checks = ent.list_checks()
# Loop through checks to find checks of the correct type
for check in ent_checks:
if check.type == check_type:
data = check.get_metric_data_points(metric_name,
int(time.time()) - 300,
int(time.time()),
points=2)
if len(data) > 0:
point = len(data) - 1
logger.info('Found metric for: ' + ent.name +
', value: ' + str(data[point]['average']))
results.append(float(data[point]['average']))
break
# Restrict number of data points to save on API calls
if len(results) >= args['max_sample']:
logger.info('--max-sample value of ' + str(args['max_sample']) +
' reached, not gathering any more statistics')
break
if len(results) == 0:
logger.error('No data available')
return 1
else:
average = sum(results) / len(results)
scale_up_threshold = common.get_group_value(config_data, group,
'scale_up_threshold')
if scale_up_threshold is None:
scale_up_threshold = 0.6
scale_down_threshold = common.get_group_value(config_data, group,
'scale_down_threshold')
if scale_down_threshold is None:
scale_down_threshold = 0.4
logger.info('Cluster average for ' + check_type + '(' + metric_name +
') at: ' + str(average))
if average > scale_up_threshold:
try:
logger.info('Above Threshold - Scaling Up')
scale_policy_id = common.get_group_value(config_data, group,
'scale_up_policy')
scale_policy = scalingGroup.get_policy(scale_policy_id)
if not args['dry_run']:
common.webhook_call(config_data, group, 'scale_up', 'pre')
scale_policy.execute()
common.webhook_call(config_data, group, 'scale_up', 'post')
else:
logger.info('Scale up prevented by --dry-run')
logger.info('Scale up policy executed (' + scale_policy_id +
')')
except Exception, e:
logger.warning('Scale up: %s' % str(e))
'scale_up_policy')
scale_policy = scalingGroup.get_policy(scale_policy_id)
if not args['dry_run']:
common.webhook_call(config_data, group, 'scale_up', 'pre')
scale_policy.execute()
common.webhook_call(config_data, group, 'scale_up', 'post')
else:
logger.info('Scale up prevented by --dry-run')
logger.info('Scale up policy executed (' + scale_policy_id +
')')
except Exception, e:
logger.warning('Scale up: %s' % str(e))
elif average < scale_down_threshold:
try:
logger.info('Below Threshold - Scaling Down')
scale_policy_id = common.get_group_value(config_data, group,
'scale_down_policy')
scale_policy = scalingGroup.get_policy(scale_policy_id)
if not args['dry_run']:
common.webhook_call(config_data, group, 'scale_down', 'pre')
scale_policy.execute()
common.webhook_call(config_data, group, 'scale_down', 'post')
else:
logger.info('Scale down prevented by --dry-run')
logger.info('Scale down policy executed (' + scale_policy_id +
')')
except Exception, e:
logger.warning('Scale down: %s' % str(e))
else:
logger.info('Cluster within target paramters')
