def __call__(self, parser, namespace, values, option_string=None):
from azure.mgmt.monitor.models import MetricTrigger
if len(values) == 1:
# workaround because CMD.exe eats > character... Allows condition to be
# specified as a quoted expression
values = values[0].split(' ')
name_offset = 0
try:
metric_name = ' '.join(values[name_offset:-4])
operator = get_autoscale_operator_map()[values[-4]]
threshold = int(values[-3])
aggregation = get_autoscale_aggregation_map()[values[-2].lower()]
window = period_type(values[-1])
except (IndexError, KeyError):
from knack.util import CLIError
raise CLIError('usage error: --condition METRIC {==,!=,>,>=,<,<=} '
'THRESHOLD {avg,min,max,total,count} PERIOD')
condition = MetricTrigger(
metric_name=metric_name,
metric_resource_uri=None, # will be filled in later
time_grain=None, # will be filled in later
statistic=None, # will be filled in later
time_window=window,
time_aggregation=aggregation,
operator=operator,
threshold=threshold
)
namespace.condition = condition
def __call__(self, parser, namespace, values, option_string=None):
from azure.mgmt.monitor.models import MetricTrigger
if len(values) == 1:
# workaround because CMD.exe eats > character... Allows condition to be
# specified as a quoted expression
values = values[0].split(' ')
name_offset = 0
try:
metric_name = ' '.join(values[name_offset:-4])
operator = get_autoscale_operator_map()[values[-4]]
threshold = int(values[-3])
aggregation = get_autoscale_aggregation_map()[values[-2].lower()]
window = period_type(values[-1])
except (IndexError, KeyError):
from knack.util import CLIError
raise CLIError('usage error: --condition METRIC {==,!=,>,>=,<,<=} '
'THRESHOLD {avg,min,max,total,count} PERIOD')
condition = MetricTrigger(
metric_name=metric_name,
metric_resource_uri=None, # will be filled in later
time_grain=None, # will be filled in later
statistic=None, # will be filled in later
time_window=window,
time_aggregation=aggregation,
operator=operator,
threshold=threshold)
namespace.condition = condition
def exitAggregation(self, ctx):
aggregation = get_autoscale_aggregation_map()[ctx.getText().strip()]
self.parameters['time_aggregation'] = aggregation
