def test_float_mix(self):
expected = 60781.6245372199
data_series = [60785.9962, 899.4, 78.66, 69.58, 4.93795,
587.195486, 96.7694536, 5.13755964,
33.333333334, 60786.5624872199]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_10000_values_processing(self):
expected = 28001.068
data_series = [uniform(-10000, 10000) for _ in range(10000)]
data_series.insert(randrange(len(data_series)), 15000.569)
data_series.insert(randrange(len(data_series)), -13000.499)
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_processing_100_values_100_times(self):
expected = 35911.3134
for _ in range(1, 100):
data_series = [uniform(-10000, 10000) for _ in range(100)]
data_series.insert(randrange(len(data_series)), 16956.3334)
data_series.insert(randrange(len(data_series)), -18954.98)
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def data_event(self, executor):
self.logger.debug("Event received")
if executor.terminated:
self._push_to_db(executor)
else:
workload = '.'.join(executor.current_workload.split('.')[1:6])
if 'metrics' not in executor.metadata:
executor.metadata['metrics'] = {}
steady_state = True
metrics = {}
for metric in ('lat_ns.mean', 'iops', 'bw'):
metrics[metric] = {}
for io_type in ('read', 'write'):
metrics[metric][io_type] = {}
series = self._lookup_prior_data(executor, metric, io_type)
series = self._convert_timestamps_to_samples(
executor, series)
steady = self._evaluate_prior_data(
series, executor.steady_state_samples)
self.logger.debug("Steady state for %s %s: %s" %
(io_type, metric, steady))
metrics[metric][io_type]['series'] = series
metrics[metric][io_type]['steady_state'] = steady
treated_data = DataTreatment.data_treatment(series)
metrics[metric][io_type]['slope'] = \
math.slope(treated_data['slope_data'])
metrics[metric][io_type]['range'] = \
math.range_value(treated_data['range_data'])
average = math.average(treated_data['average_data'])
metrics[metric][io_type]['average'] = average
metrics_key = '%s.%s.%s' % (workload, io_type, metric)
executor.metadata['metrics'][metrics_key] = average
if not steady:
steady_state = False
if 'report_data' not in executor.metadata:
executor.metadata['report_data'] = {}
if 'steady_state' not in executor.metadata:
executor.metadata['steady_state'] = {}
executor.metadata['report_data'][workload] = metrics
executor.metadata['steady_state'][workload] = steady_state
workload_name = executor.current_workload.split('.')[1]
if steady_state and not workload_name.startswith('_'):
executor.terminate_current_run()
def steady_state(data_series):
"""
This function seeks to detect steady state given on a measurement
window given the data series of that measurement window following
the pattern : [[x1,y1], [x2,y2], ..., [xm,ym]]. m represents the number
of points recorded in the measurement window, x which represents the
time, and y which represents the Volume performance variable being
tested e.g. IOPS, latency...
The function returns a boolean describing wether or not steady state
has been reached with the data that is passed to it.
"""
logger = logging.getLogger('storperf.utilities.steady_state')
# Pre conditioning the data to match the algorithms
treated_data = DataTreatment.data_treatment(data_series)
# Calculating useful values invoking dedicated functions
slope_value = math.slope(treated_data['slope_data'])
range_value = math.range_value(treated_data['range_data'])
average_value = math.average(treated_data['average_data'])
if (slope_value is not None and range_value is not None
and average_value is not None):
# Verification of the Steady State conditions following the SNIA
# definition
range_condition = abs(range_value) <= 0.20 * abs(average_value)
slope_condition = abs(slope_value) <= 0.10 * abs(average_value)
steady_state = range_condition and slope_condition
logger.debug("Range %s <= %s?" % (abs(range_value),
(0.20 * abs(average_value))))
logger.debug("Slope %s <= %s?" % (abs(slope_value),
(0.10 * abs(average_value))))
logger.debug("Steady State? %s" % steady_state)
else:
steady_state = False
return steady_state
def test_float_series_5_decimals(self):
expected = 8956208.84494
data_series = [12.78496, 55.91275, 668.94378,
550396.5671, 512374.9999, 8956221.6299]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_negative_positive_mix(self):
expected = 58.859500000000004
data_series = [6.85698, -2.8945, 0, -0.15, 55.965]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_single_element(self):
expected = 0
data_series = [2.265]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_float_integer_mix(self):
expected = 460781.05825
data_series = [460785.9962, 845.634, 24.1, 69.58, 89, 4.93795]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_negative_values(self):
expected = 596.78163
data_series = [-4.655, -33.3334, -596.78422, -0.00259, -66.785]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_float_series_10_decimals(self):
expected = 5984.507397972699
data_series = [1.1253914785, 5985.6327894512,
256.1875693287, 995.8497623415]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_empty_series(self):
expected = None
data_series = []
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_integer_series(self):
expected = 11946
data_series = [5, 351, 847, 2, 1985, 18,
96, 389, 687, 1, 11947, 758, 155]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_float_series_4_decimals(self):
expected = 122985.3241
data_series = [39.4785, 896.7845, 11956.3654,
44.2398, 6589.7134, 0.3671, 122985.6912]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_float_series_3_decimals(self):
expected = 992.181
data_series = [4.562, 12.582, 689.452,
135.162, 996.743, 65.549, 36.785]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_float_series_2_decimals(self):
expected = 5693.47
data_series = [51.36, 78.40, 1158.24, 5.50, 0.98, 5694.45]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
def test_float_series_1_decimal(self):
expected = 778595.5
data_series = [736.4, 9856.4, 684.2, 0.3, 0.9, 778595.8]
actual = Range.range_value(data_series)
self.assertEqual(expected, actual)
