def post_processing(self, axol_task_value):
clusters = {'api': [], 'web': []}
clusters = CommonMath.derive_clusters(clusters=clusters,
map_value='current_usage',
axol_task_value=axol_task_value)
for cluster in clusters:
clusters[cluster] = CommonMath.map_deviation(
integer_list=clusters[cluster])
clusters[cluster]['source'] = self.source
clusters[cluster]['name'] = cluster
self.query([
insert(data_object=clusters, table_space='axol_metrics.clusters')
])
return axol_task_value
def calculate_new_fields(self, response_object): def calculate_current_usage(response_object): disk_list = [] for attr in response_object.__dict__: if re.search(r'disk.*', attr): usage = getattr(response_object, attr) disk_list.append(int(usage['percent_used'])) response_object.current_usage = float(max(disk_list)) return response_object def calculate_usage_for_health(response_object): response_object.normalized_indicator = response_object.current_usage / 100 response_object.multiplier = 1 response_object.threshold_red = 80 response_object.scale = 50 return response_object if response_object.error == None: response_object = calculate_current_usage( response_object=response_object ) response_object = calculate_usage_for_health( response_object=response_object ) response_object.health_indicator = CommonMath.adaptive_filtration( normalized_indicator=response_object.normalized_indicator, multiplier=response_object.multiplier, threshold_red=response_object.threshold_red, scale=response_object.scale ) self.threshold_validation(response_object) return response_object
def calculate_new_fields(self, response_object):
def calculate_current_usage(response_object):
n = float(response_object.one_minute) * 100
n = float(n) / float(response_object.number_of_processing_units)
if n == 0:
n = 1
response_object.current_usage = n
return response_object
def calculate_usage_for_health(response_object):
x = (2 * float(response_object.one_minute) +
3 * float(response_object.five_minute)) / 5
response_object.normalized_indicator = x
response_object.multiplier = response_object.number_of_processing_units
response_object.threshold_red = 100
response_object.scale = 0
return response_object
if response_object.error == None:
response_object = calculate_usage_for_health(
response_object=response_object)
response_object = calculate_current_usage(
response_object=response_object)
response_object.health_indicator = CommonMath.adaptive_filtration(
normalized_indicator=response_object.normalized_indicator,
multiplier=response_object.multiplier,
threshold_red=response_object.threshold_red,
scale=response_object.scale)
response_object = self.threshold_validation(response_object)
return response_object
def run_task(self, task):
print 'RUN TASK 1 ##############'
print task
for server in task.value:
for _type in self.sources:
key = '%s_%s' % (_type, str(server))
try:
cache_data = DW.cache_key_get(key)
if type(eval(str(cache_data))) is dict:
try:
task.value[server][_type] = GenericDataObject(eval(str(cache_data)))
if task.value[server][_type].warnings[server] != None:
print 'RED FLAG: %s %s' % (server, _type)
print task.value[server][_type].warnings
else:
task.value[server][_type].health_indicator = CommonMath.adaptive_filtration(
task.value[server][_type].normalized_indicator,
task.value[server][_type].multiplier,
task.value[server][_type].threshold_red,
task.value[server][_type].scale
)
except Exception, e:
print 'ERROR 1: %s' % e
except Exception, e:
CommonLogger.log(e, 'create_health_metrics', 'run_task-<%s-%s>' % (server, _type))
print 'ERROR 2: %s' % e
return jsonify({'response': {'error': str(e)}})
def run_task(self, task):
print 'RUN TASK 1 ##############'
print task
for server in task.value:
for _type in self.sources:
key = '%s_%s' % (_type, str(server))
try:
cache_data = DW.cache_key_get(key)
if type(eval(str(cache_data))) is dict:
try:
task.value[server][_type] = GenericDataObject(
eval(str(cache_data)))
if task.value[server][_type].warnings[
server] != None:
print 'RED FLAG: %s %s' % (server, _type)
print task.value[server][_type].warnings
else:
task.value[server][
_type].health_indicator = CommonMath.adaptive_filtration(
task.value[server]
[_type].normalized_indicator,
task.value[server][_type].multiplier,
task.value[server]
[_type].threshold_red,
task.value[server][_type].scale)
except Exception, e:
print 'ERROR 1: %s' % e
except Exception, e:
CommonLogger.log(e, 'create_health_metrics',
'run_task-<%s-%s>' % (server, _type))
print 'ERROR 2: %s' % e
return jsonify({'response': {'error': str(e)}})
def calculate_new_fields(self, response_object):
def calculate_current_usage(response_object):
disk_list = []
for attr in response_object.__dict__:
if re.search(r'disk.*', attr):
usage = getattr(response_object, attr)
disk_list.append(int(usage['percent_used']))
response_object.current_usage = float(max(disk_list))
return response_object
def calculate_usage_for_health(response_object):
response_object.normalized_indicator = response_object.current_usage / 100
response_object.multiplier = 1
response_object.threshold_red = 80
response_object.scale = 50
return response_object
if response_object.error == None:
response_object = calculate_current_usage(
response_object=response_object)
response_object = calculate_usage_for_health(
response_object=response_object)
response_object.health_indicator = CommonMath.adaptive_filtration(
normalized_indicator=response_object.normalized_indicator,
multiplier=response_object.multiplier,
threshold_red=response_object.threshold_red,
scale=response_object.scale)
self.threshold_validation(response_object)
return response_object
def calculate_new_fields(self, response_object): def calculate_current_usage(response_object): n = float(response_object.memused) / 1000 n = int(n) + 0 response_object.current_usage = n return response_object def calculate_usage_for_health(response_object): response_object.normalized_indicator = ( float(response_object.memused) / float(response_object.memtotal) ) / 1 + 0 response_object.multiplier = 1 response_object.threshold_red = 90 response_object.scale = response_object.memtotal / 200000 return response_object if response_object.error == None: response_object = calculate_usage_for_health( response_object=response_object ) response_object = calculate_current_usage( response_object=response_object ) response_object.health_indicator = CommonMath.adaptive_filtration( normalized_indicator=response_object.normalized_indicator, multiplier=response_object.multiplier, threshold_red=response_object.threshold_red, scale=response_object.scale ) self.threshold_validation(response_object) return response_object
def fudge_data(time_list_2):
deviation_pos_list = []
deviation_neg_list = []
variance_list = []
mean_list = []
time_list = []
int_list = []
deviation_amount = []
n = 0
init_data_list = [
1675, 3223, 2017, 1501, 2067, 2500, 1000, 2972, 1975, 2104, 2100, 2973,
1980
]
deviation = CommonMath.map_deviation(init_data_list)
deviation_amount = [0 for i in range(len(init_data_list))]
mean_list = init_data_list
int_list = range(len(init_data_list))
time_list = time_list_2[:len(init_data_list)]
time_list_2 = time_list_2[:len(init_data_list)]
variance_list = [0 for i in range(len(init_data_list))]
deviation_neg_list = [0 for i in range(len(init_data_list))]
deviation_pos_list = [0 for i in range(len(init_data_list))]
return deviation_amount, mean_list, int_list, time_list, time_list_2, variance_list, deviation_neg_list, deviation_pos_list
def post_processing(self, axol_task_value):
clusters = {'api': [], 'web': []}
try:
clusters = CommonMath.derive_clusters(
clusters=clusters,
map_value='current_usage',
axol_task_value=axol_task_value
)
except Exception, e:
print 'ERROR POST-PROC: %s' % e
def post_processing(self, axol_task_value):
clusters = {'api': [], 'web': []}
clusters = CommonMath.derive_clusters(
clusters=clusters,
map_value='current_usage',
axol_task_value=axol_task_value
)
for cluster in clusters:
clusters[cluster] = CommonMath.map_deviation(
integer_list=clusters[cluster]
)
clusters[cluster]['source'] = self.source
clusters[cluster]['name'] = cluster
self.query([
insert(
data_object=clusters,
table_space='axol_metrics.clusters'
)]
)
return axol_task_value
def fudge_data(time_list_2): deviation_pos_list = [] deviation_neg_list = [] variance_list = [] mean_list = [] time_list = [] int_list = [] deviation_amount = [] n = 0 init_data_list = [ 1675, 3223, 2017, 1501, 2067, 2500, 1000, 2972, 1975, 2104, 2100, 2973, 1980 ] deviation = CommonMath.map_deviation(init_data_list) deviation_amount = [0 for i in range(len(init_data_list))] mean_list = init_data_list int_list = range(len(init_data_list)) time_list = time_list_2[:len(init_data_list)] time_list_2 = time_list_2[:len(init_data_list)] variance_list = [0 for i in range(len(init_data_list))] deviation_neg_list = [0 for i in range(len(init_data_list))] deviation_pos_list = [0 for i in range(len(init_data_list))] return deviation_amount, mean_list, int_list, time_list, time_list_2, variance_list, deviation_neg_list, deviation_pos_list
return response_object
def post_processing(self, axol_task_value):
clusters = {'api': [], 'web': []}
try:
clusters = CommonMath.derive_clusters(
clusters=clusters,
map_value='current_usage',
axol_task_value=axol_task_value
)
except Exception, e:
print 'ERROR POST-PROC: %s' % e
try:
for cluster in clusters:
clusters[cluster] = CommonMath.map_deviation(
integer_list=clusters[cluster]
)
clusters[cluster]['source'] = self.source
clusters[cluster]['name'] = cluster
except Exception, e:
print 'ERROR POST-PROC 2: %s' % e
try:
self.query([
insert(
data_object=clusters,
table_space='axol_metrics.clusters'
)]
)
except Exception, e:
print 'ERROR POST-PROC 3: %s' % e
return axol_task_value
