def __init__(self, *args, **kargs):
super(engine, self).__init__(*args, **kargs)
self.storage = get_storage(
namespace='events',
account=Account(
user="******",
group="root"
)
)
self.manager = PerfData()
self.perf_data = PerfDataUtils()
class engine(Engine):
etype = 'consolidation'
def __init__(self, *args, **kargs):
super(engine, self).__init__(*args, **kargs)
self.storage = get_storage(
namespace='events',
account=Account(
user="******",
group="root"
)
)
self.manager = PerfData()
self.perf_data = PerfDataUtils()
def pre_run(self):
self.storage = get_storage(
namespace='object',
account=Account(user="******", group="root")
)
self.manager = PerfData()
def fetch(self, serie, _from, _to):
self.logger.debug("*Start fetch*")
t_serie = serie.copy()
timewindow = {'start': _from, 'stop': _to, 'timezone': gmtime()}
if (len(t_serie['metrics']) > 1
and t_serie['aggregate_method'].lower() == 'none'):
self.logger.debug(
'More than one metric in serie, performing an aggregation'
)
self.logger.debug('serie:'.format(t_serie))
self.logger.debug('aggregation: average - 60s')
t_serie['aggregate_method'] = 'average'
t_serie['aggregate_interval'] = 60
if t_serie['aggregate_method'].lower() == 'none':
self.logger.debug('serie:'.format(t_serie))
timeserie = {'aggregation': 'NONE'}
results = self.perf_data.perfdata(
metric_id=t_serie['metrics'], timewindow=timewindow,
timeserie=timeserie
)
else:
self.logger.debug('serie:', t_serie)
timeserie = {
'aggregation': t_serie['aggregate_method'],
'period': {'second': t_serie['aggregate_interval']}
}
results = self.perf_data.perfdata(
metric_id=t_serie['metrics'], timewindow=timewindow,
timeserie=timeserie
)
formula = t_serie['formula']
finalserie = self.metric_raw(results, formula)
self.logger.debug("*End fetch*")
return finalserie
def metric_raw(self, results, formula):
#nmetric = results[1]
metrics, _ = results
# Build points dictionnary
points = {}
length = False
for m in metrics:
cid = m['meta']['data_id']
mid = 'metric_' + hashlib.md5(cid).hexdigest()
mname = self.retreive_metric_name(cid)
# Replace metric name in formula by the unique id
formula = formula.replace(mname, mid)
self.logger.debug("Metric {0} - {1}".format(mname, mid))
points[mid] = m['points']
# Make sure we treat the same amount of points by selecting
# The metric with less points.
if not length or len(m['points']) < length:
length = len(m['points'])
self.logger.debug('formula: {}'.format(formula))
#self.logger.debug('points: {}'.format(points))
mids = points.keys()
finalSerie = []
# Now loop over all points to calculate the final serie
for i in range(length):
data = {}
ts = 0
for j in range(len(mids)):
mid = mids[j]
# Get point value at timestamp "i" for metric "mid"
data[mid] = points[mid][i][1]
# Set timestamp
ts = points[mid][i][0]
# import data in math context
math = Formulas(data)
# Evaluate the mathematic formula
pointval = math.evaluate(formula)
# Add computed point in the serie
finalSerie.append([ts * 1000, pointval])
# Remove variables values from math context
math.reset()
self.logger.debug('finalserie: {}'.format(finalSerie))
return finalSerie, points[mid]
def retreive_metric_name(self, name):
'''
This method allow to slice data from an existing one.
TODO: improve this method with the Context ID.
'''
if name is None:
return None
li = name.split('/')
for i in range(4):
li.pop(0)
name = '/'+'/'.join(li)
return name
def consume_dispatcher(self, event, *args, **kargs):
self.logger.debug("Start metrics consolidation")
t_serie = event.copy()
self.logger.debug('\n\n\n\n----->serie: {}'.format(t_serie))
if not t_serie:
# Show error message
self.logger.error('No record found.')
# Test Settings
_from = 1425394522
_to = 1425402296
perf_data_array = []
_, points = self.fetch(t_serie, _from, _to)
# This method allow us to update an metric or a list of metrics
self.manager.put(metric_id=t_serie['_id'], points=points)
# Publish the consolidation metrics
# metric_name = 'metric_name' # Change the value with UI data
for t, v in points:
#c_event['timestamp'] = t
perf_data_array.append(
{
'metric': t_serie['_id'], 'value': v,
'unit': t_serie['_id'], 'min': None,
'max': None, 'warn': None, 'crit': None, 'type': 'GAUGE'
}
)
conso_event = forger(
timestamp=t,
component='conso',
connector='Engine',
connector_name='consolidation',
event_type='perf',
source_type='component',
perf_data_array=perf_data_array
)
self.logger.debug('Publishing {}'.format(conso_event))
publish(publisher=self.amqp, event=conso_event)
perf_data_array = [] # reset the perf_data_array data
# Update crecords informations
event_id = t_serie['_id']
self.crecord_task_complete(event_id)
