def autoscale_rule_create(cmd, client, autoscale_name, resource_group_name, condition,
scale, profile_name=DEFAULT_PROFILE_NAME, cooldown=5, source=None,
timegrain="avg 1m"):
from azure.mgmt.monitor.models import ScaleRule, ScaleAction, ScaleDirection
autoscale_settings = client.get(resource_group_name, autoscale_name)
profile = next(x for x in autoscale_settings.profiles if x.name == profile_name)
condition.metric_resource_uri = source or autoscale_settings.target_resource_uri
condition.statistic = timegrain.statistic
condition.time_grain = timegrain.time_grain
rule = ScaleRule(
metric_trigger=condition,
scale_action=ScaleAction(
direction=scale.direction,
type=scale.type,
cooldown='PT{}M'.format(cooldown),
value=scale.value)
)
profile.rules.append(rule)
autoscale_settings = client.create_or_update(resource_group_name, autoscale_name, autoscale_settings)
profile = next(x for x in autoscale_settings.profiles if x.name == profile_name)
# determine if there are unbalanced rules
scale_out_rule_count = len([x for x in profile.rules if x.scale_action.direction == ScaleDirection.increase])
scale_in_rule_count = len([x for x in profile.rules if x.scale_action.direction == ScaleDirection.decrease])
if scale_out_rule_count and not scale_in_rule_count:
logger.warning("Profile '%s' has rules to scale out but none to scale in. "
"Recommend creating at least 1 scale in rule.", profile_name)
elif scale_in_rule_count and not scale_out_rule_count:
logger.warning("Profile '%s' has rules to scale in but none to scale out. "
"Recommend creating at least 1 scale out rule.", profile_name)
return rule
def __call__(self, parser, namespace, values, option_string=None):
from azure.mgmt.monitor.models import ScaleAction, ScaleType
if len(values) == 1:
# workaround because CMD.exe eats > character... Allows condition to be
# specified as a quoted expression
values = values[0].split(' ')
if len(values) != 2:
from knack.util import CLIError
raise CLIError('usage error: --scale {in,out,to} VALUE[%]')
dir_val = values[0]
amt_val = values[1]
scale_type = None
if dir_val == 'to':
scale_type = ScaleType.exact_count.value
elif str(amt_val).endswith('%'):
scale_type = ScaleType.percent_change_count.value
amt_val = amt_val[:-1] # strip off the percent
else:
scale_type = ScaleType.change_count.value
scale = ScaleAction(
direction=get_autoscale_scale_direction_map()[dir_val],
type=scale_type,
cooldown=None, # this will be filled in later
value=amt_val)
namespace.scale = scale
def create_rule_instance(params):
rule = params.copy()
rule['metric_resource_uri'] = rule.get('metric_resource_uri', self.target)
rule['time_grain'] = timedelta(minutes=rule.get('time_grain', 0))
rule['time_window'] = timedelta(minutes=rule.get('time_window', 0))
rule['cooldown'] = timedelta(minutes=rule.get('cooldown', 0))
return ScaleRule(metric_trigger=MetricTrigger(**rule), scale_action=ScaleAction(**rule))
def shutdown_scaleset_rule(queue_uri: str) -> ScaleRule:
return ScaleRule(
# Scale in if there are 0 or more messages in the queue (aka: every time)
metric_trigger=MetricTrigger(
metric_name="ApproximateMessageCount",
metric_resource_uri=queue_uri,
# Check every 10 minutes
time_grain=timedelta(minutes=5),
# The average amount of messages there are in the pool queue
time_aggregation=TimeAggregationType.AVERAGE,
statistic=MetricStatisticType.SUM,
# Over the past 10 minutes
time_window=timedelta(minutes=5),
operator=ComparisonOperationType.GREATER_THAN_OR_EQUAL,
threshold=0,
divide_per_instance=False,
),
scale_action=ScaleAction(
direction=ScaleDirection.DECREASE,
type=ScaleType.CHANGE_COUNT,
value=1,
cooldown=timedelta(minutes=5),
),
)
def create_auto_scale_profile(
queue_uri: str,
min: int,
max: int,
default: int,
scale_out_amount: int,
scale_out_cooldown: int,
scale_in_amount: int,
scale_in_cooldown: int,
) -> AutoscaleProfile:
return AutoscaleProfile(
name=str(uuid.uuid4()),
capacity=ScaleCapacity(minimum=min, maximum=max, default=max),
# Auto scale tuning guidance:
# https://docs.microsoft.com/en-us/azure/architecture/best-practices/auto-scaling
rules=[
ScaleRule(
metric_trigger=MetricTrigger(
metric_name="ApproximateMessageCount",
metric_resource_uri=queue_uri,
# Check every 15 minutes
time_grain=timedelta(minutes=15),
# The average amount of messages there are in the pool queue
time_aggregation=TimeAggregationType.AVERAGE,
statistic=MetricStatisticType.COUNT,
# Over the past 15 minutes
time_window=timedelta(minutes=15),
# When there's more than 1 message in the pool queue
operator=ComparisonOperationType.GREATER_THAN_OR_EQUAL,
threshold=1,
divide_per_instance=False,
),
scale_action=ScaleAction(
direction=ScaleDirection.INCREASE,
type=ScaleType.CHANGE_COUNT,
value=scale_out_amount,
cooldown=timedelta(minutes=scale_out_cooldown),
),
),
# Scale in
ScaleRule(
# Scale in if no work in the past 20 mins
metric_trigger=MetricTrigger(
metric_name="ApproximateMessageCount",
metric_resource_uri=queue_uri,
# Check every 10 minutes
time_grain=timedelta(minutes=10),
# The average amount of messages there are in the pool queue
time_aggregation=TimeAggregationType.AVERAGE,
statistic=MetricStatisticType.SUM,
# Over the past 10 minutes
time_window=timedelta(minutes=10),
# When there's no messages in the pool queue
operator=ComparisonOperationType.EQUALS,
threshold=0,
divide_per_instance=False,
),
scale_action=ScaleAction(
direction=ScaleDirection.DECREASE,
type=ScaleType.CHANGE_COUNT,
value=scale_in_amount,
cooldown=timedelta(minutes=scale_in_cooldown),
),
),
],
)
def autoscale_rule_create(cmd,
client,
autoscale_name,
resource_group_name,
condition,
scale,
profile_name=DEFAULT_PROFILE_NAME,
cooldown=5,
source=None,
timegrain="avg 1m"):
from azure.mgmt.monitor.models import ScaleRule, ScaleAction, ScaleDirection
from azure.mgmt.core.tools import parse_resource_id, resource_id
autoscale_settings = client.get(resource_group_name, autoscale_name)
profile = _identify_profile(autoscale_settings.profiles, profile_name)
condition.metric_resource_uri = source or autoscale_settings.target_resource_uri
condition.statistic = timegrain.statistic
condition.time_grain = timegrain.time_grain
def preprocess_for_spring_cloud_service():
try:
result = parse_resource_id(autoscale_settings.target_resource_uri)
if result['namespace'].lower(
) == 'microsoft.appplatform' and result['type'].lower(
) == 'spring':
if condition.metric_namespace is None:
condition.metric_namespace = "Microsoft.AppPlatform/Spring"
logger.warning(
'Set metricNamespace to Microsoft.AppPlatform/Spring')
if source is None:
condition.metric_resource_uri = resource_id(
subscription=result['subscription'],
resource_group=result['resource_group'],
namespace=result['namespace'],
type=result['type'],
name=result['name'])
logger.warning(
'Set metricResourceUri to Spring Cloud service')
except KeyError:
pass
preprocess_for_spring_cloud_service()
rule = ScaleRule(metric_trigger=condition,
scale_action=ScaleAction(
direction=scale.direction,
type=scale.type,
cooldown='PT{}M'.format(cooldown),
value=scale.value))
profile.rules.append(rule)
autoscale_settings = client.create_or_update(resource_group_name,
autoscale_name,
autoscale_settings)
# determine if there are unbalanced rules
scale_out_rule_count = len([
x for x in profile.rules
if x.scale_action.direction == ScaleDirection.increase
])
scale_in_rule_count = len([
x for x in profile.rules
if x.scale_action.direction == ScaleDirection.decrease
])
if scale_out_rule_count and not scale_in_rule_count:
logger.warning(
"Profile '%s' has rules to scale out but none to scale in. "
"Recommend creating at least 1 scale in rule.", profile_name)
elif scale_in_rule_count and not scale_out_rule_count:
logger.warning(
"Profile '%s' has rules to scale in but none to scale out. "
"Recommend creating at least 1 scale out rule.", profile_name)
return rule
