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)