def test_detector_with_assign_combinator():
""" We should correctly generate a detector comprised of two assignment
functions
"""
cpu_util_string = 'cpu.utilization'
sum_string = 'utilization_sum'
count_string = 'utilization_count'
mean_string = 'utilization_mean'
sum_data = Data(cpu_util_string).sum()
count_data = Data(cpu_util_string).count()
utilization_sum = Assign(sum_string, sum_data)
utilization_count = Assign(count_string, count_data)
mean_data = Div(Ref(sum_string), Ref(count_string))
utilization_mean = Assign(mean_string, mean_data)
detect = Detect(When(GT(Ref(mean_string), 50))).publish(label='detector')
program = Program(utilization_sum, utilization_count, utilization_mean,
detect)
detector = Detector().with_program(program)
assert detector.options["programText"] == "{0}\n{1}\n{2}\n{3}".format(
str(utilization_sum), str(utilization_count), str(utilization_mean),
str(detect))
assert program.statements.pop() == detect
assert program.statements.pop() == utilization_mean
assert program.statements.pop() == utilization_count
assert program.statements.pop() == utilization_sum
def fun(name, threshold):
program = Program(
Detect(GT(Data("cpu.utilization"), threshold)).publish(label=name))
rule = (Rule().for_label(name).with_severity(
Severity.Info).with_notifications(
EmailNotification("*****@*****.**")))
return (Detector(session=session).with_name(name).with_program(
program).with_rules(rule))
def bad_helper(p):
p.add_statements(Data("I shouldn't exist").publish(label='Y'))
return Program(Detect(LT(p.find_label('Z'), 10)).publish(label='foo'))
def helper(p):
return Program(Detect(LT(p.find_label('Z'), 10)).publish(label='foo'))
def detector_helper(prog):
d = prog.find_label('A')
return Program(Detect(LT(d, 1)).publish(alert_label))
from signal_analog.detectors import Detector, Rule, EmailNotification, Severity
from signal_analog.flow import Data, Detect, Filter, Program
from signal_analog.combinators import And, LT
"""
Example 1: from scratch
This is useful when you want to monitor something that isn't tied to an
existing chart or dashboard.
"""
alert_label = 'CPU is too low for 75% of the last 2 minutes!'
filters = And(Filter('app', 'my-app'), Filter('env', 'test'))
data = Data('cpu.utilization', filter=filters).publish(label='A')
cpu_too_low = Detect(When(LT(data, 50), '2m', 0.75)).publish(alert_label)
program = Program(cpu_too_low)
info_rule = Rule()\
.for_label(alert_label)\
.with_severity(Severity.Info)\
.with_notifications(EmailNotification('*****@*****.**'))
detector = Detector()\
.with_name('TEST: example detector')\
.with_program(program)\
.with_rules(info_rule)
"""
Example 2: from an existing chart
def program(threshold):
return Program(
Detect(GT(Data('cpu.utilization'),
threshold)).publish(label=label))