def query_graphite_targets(self): """ Queries arrival rate and service time data for all targets stored in self.targets (attached to LeptoidScaler) and extracts data from Graphite's response. Returns: list of leptoid.ServiceQueues with utilization populated. """ # Retrieve arrival rates and service times from Graphite. raw_rates = graphite.call_graphite(self.targets['arrival_rates'], self.api_params) raw_times = graphite.call_graphite(self.targets['service_times'], self.api_params) arrival_rates = graphite.extract_time_series(raw_rates) service_times = graphite.extract_time_series(raw_times) return generate_service_queues(arrival_rates, service_times)
def test_data_extraction(self):
""" Using a Mock() to check whether data is extracted correctly. """
fake_pickle = [{
'start': 1000,
'values': arange(10),
'name': "Knewton.Staging.Webservice-KRS.i-deadbeef"}]
namespace_data = g.extract_time_series(fake_pickle)
tser = namespace_data['staging']['kbs.KRS']['i-deadbeef']
# Data should be in a dict, with a pandas.TimeSeries containing
# data.
self.assertTrue(isinstance(namespace_data, dict))
self.assertTrue(isinstance(tser, TimeSeries))
self.assertTrue(len(tser) == 10)
def test_data_extraction(self):
""" Using a Mock() to check whether data is extracted correctly. """
fake_pickle = [{
'start': 1000,
'values': arange(10),
'name': "Knewton.Staging.Webservice-KRS.i-deadbeef"
}]
namespace_data = g.extract_time_series(fake_pickle)
tser = namespace_data['staging']['kbs.KRS']['i-deadbeef']
# Data should be in a dict, with a pandas.TimeSeries containing
# data.
self.assertTrue(isinstance(namespace_data, dict))
self.assertTrue(isinstance(tser, TimeSeries))
self.assertTrue(len(tser) == 10)
