class PerfdataModule(MigrationModule):
def __init__(self, *args, **kwargs):
super(PerfdataModule, self).__init__(*args, **kwargs)
self.manager = PerfData()
def init(self):
pass
def update(self):
if self.get_version('perfdata') < 1:
self.logger.info('Migrating to version 1')
self.update_to_version_1()
self.set_version('perfdata', 1)
def update_to_version_1(self):
storage = self.manager[PerfData.PERFDATA_STORAGE]
nan = float('nan')
oneweek = 3600 * 24 * 7
for document in storage.find_elements():
metric_id = document['i']
values = document['v']
t = document['t']
points = list(
(t + int(ts), nan if values[ts] is None else values[ts])
for ts in values
)
rightvalues = {
key: values[key] for key in values if int(key) < oneweek
}
document['v'] = rightvalues
storage.put_element(
element=document, cache=False
)
self.manager.put(metric_id=metric_id, points=points, cache=False)
class PerfDataTest(TestCase):
def setUp(self):
self.perfdata = PerfData(data_scope='test')
def test_put_get_data(self):
timewindow = TimeWindow()
metric_id = 'test_manager'
self.perfdata.remove(metric_id=metric_id, with_meta=True)
count = self.perfdata.count(metric_id=metric_id)
self.assertEqual(count, 0)
tv0 = (int(timewindow.start()), None)
tv1 = (int(timewindow.start() + 1), 0)
tv2 = (int(timewindow.stop()), 2)
tv3 = (int(timewindow.stop() + 1000000), 3)
# set values with timestamp without order
points = [tv0, tv2, tv1, tv3]
meta = {'plop': None}
self.perfdata.put(
metric_id=metric_id,
points=points,
meta=meta
)
data, _meta = self.perfdata.get(
metric_id=metric_id,
timewindow=timewindow,
with_meta=True
)
self.assertEqual(meta, _meta[0][PerfData.META_VALUE])
self.assertEqual([tv0, tv1, tv2], data)
# remove 1 data at stop point
_timewindow = get_offset_timewindow(timewindow.stop())
self.perfdata.remove(
metric_id=metric_id,
timewindow=_timewindow
)
data, _meta = self.perfdata.get(
metric_id=metric_id,
timewindow=timewindow,
with_meta=True
)
self.assertEqual(meta, _meta[0][PerfData.META_VALUE])
self.assertEqual(data, [tv0, tv1])
# get data on timewindow
data, _meta = self.perfdata.get(
metric_id=metric_id,
timewindow=timewindow,
with_meta=True
)
self.assertEqual(meta, _meta[0][PerfData.META_VALUE])
# get all data
data, _meta = self.perfdata.get(
metric_id=metric_id,
with_meta=True
)
self.assertEqual(meta, _meta[0][PerfData.META_VALUE])
self.assertEqual(len(data), 3)
# remove all data
self.perfdata.remove(
metric_id=metric_id,
with_meta=True
)
data, _meta = self.perfdata.get(
metric_id=metric_id,
with_meta=True
)
self.assertIsNone(_meta)
self.assertEqual(len(data), 0)
class PerfdataModule(MigrationModule):
def __init__(self, *args, **kwargs):
super(PerfdataModule, self).__init__(*args, **kwargs)
self.manager = PerfData()
def init(self):
pass
def update_mongo2influxdb(self):
"""Convert mongo data to influxdb data."""
mongostorage = MongoStorage(table='periodic_perfdata')
count = mongostorage._backend.count()
if count: # if data have to be deleted
for document in mongostorage._backend.find():
data_id = document['i']
timestamp = int(document['t'])
values = document['v']
points = [(timestamp + int(ts), values[ts]) for ts in values]
perfdata.put(metric_id=data_id, points=points)
mongostorage.remove_elements(ids=document['_id'])
def update(self):
# FIXME
if self.get_version('perfdata') < 1 and False:
self.logger.info(u'Migrating to version 1')
self.update_to_version_1()
self.set_version('perfdata', 1)
if self.get_version('perfdata') < 2:
self.logger.info(u'Migrating to version 2')
self.update_mongo2influxdb()
self.set_version('perfdata', 2)
def update_to_version_1(self):
storage = self.manager[PerfData.PERFDATA_STORAGE]
nan = float('nan')
oneweek = 3600 * 24 * 7
for document in storage.find_elements():
metric_id = document['i']
values = document['v']
t = document['t']
points = list(
(t + int(ts), nan if values[ts] is None else values[ts])
for ts in values
)
rightvalues = {
key: values[key] for key in values if int(key) < oneweek
}
document['v'] = rightvalues
storage.put_element(
element=document, cache=False
)
self.manager.put(metric_id=metric_id, points=points, cache=False)
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)
class engine(Engine):
etype = 'perfdata'
def __init__(self, *args, **kargs):
super(engine, self).__init__(*args, **kargs)
self.perfdata = PerfData()
def work(self, event, *args, **kargs):
# Get perfdata
perf_data = event.get('perf_data')
perf_data_array = event.get('perf_data_array', [])
if perf_data_array is None:
perf_data_array = []
# Parse perfdata
if perf_data:
self.logger.debug(' + perf_data: {0}'.format(perf_data))
try:
perf_data_array += self.perfdata.parse_perfdata(perf_data)
except Exception as err:
self.logger.error(
"Impossible to parse perfdata from: {0} ({1})"
.format(event, err)
)
self.logger.debug(' + perf_data_array: {0}'.format(perf_data_array))
# Add status informations
event_type = event.get('event_type')
handled_event_types = ['check', 'selector', 'sla']
if event_type is not None and event_type in handled_event_types:
self.logger.debug('Add status informations')
state = int(event.get('state', 0))
state_type = int(event.get('state_type', 0))
state_extra = 0
# Multiplex state
cps_state = state * 100 + state_type * 10 + state_extra
perf_data_array.append(
{
"metric": "cps_state",
"value": cps_state
}
)
event['perf_data_array'] = perf_data_array
# remove perf_data_keys where values are None
for index, perf_data in enumerate(event['perf_data_array']):
perf_data_array_with_Nones = event['perf_data_array'][index]
event['perf_data_array'][index] = {
name: perf_data_array_with_Nones[name]
for name in perf_data_array_with_Nones
if perf_data_array_with_Nones[name] is not None
}
self.logger.debug('perf_data_array: {0}'.format(perf_data_array))
event = deepcopy(event)
# Metrology
timestamp = event['timestamp'] if 'timestamp' in event else None
if timestamp is not None:
perf_data_array = event.get('perf_data_array', [])
for perf_data in perf_data_array:
perf_data = perf_data.copy()
event_with_metric = deepcopy(event)
event_with_metric['type'] = 'metric'
event_with_metric[Context.NAME] = perf_data.pop('metric')
metric_id = self.perfdata.context.get_entity_id(
event_with_metric
)
value = perf_data.pop('value', None)
self.perfdata.put(
metric_id=metric_id, points=[(timestamp, value)],
meta=perf_data, cache=True
)
return event