def using_exponentially_decaying_sample(cls, prefix=""):
"""
Uses an exponentially decaying sample of 1028 elements, which offers
a 99.9% confidence level with a 5% margin of error assuming a normal
distribution, and an alpha factor of 0.015, which heavily biases
the sample to the past 5 minutes of measurements.
"""
sample = ExponentiallyDecayingSample(1028, 0.015)
return HistogramMetricReporter(sample, prefix=prefix)
def test_100_out_of_10_elements(self):
population = [i for i in range(0, 10)]
sample = ExponentiallyDecayingSample(100, 0.99)
for i in population:
sample.update(i)
self.assertEqual(sample.size(), 10)
self.assertEqual(len(sample.get_values()), 10,
'Should have 10 elements')
self.assertEqual(
len(set(sample.get_values()).difference(set(population))), 0,
'Should only have elements from the population')
def test_ewma_overflow(self):
"""Long pauses on metric input should not overflow weight."""
_time = [10000]
def wtime():
return _time[0]
sample = ExponentiallyDecayingSample(100, 0.99, wall_time=wtime)
for i in xrange(100):
sample.update(random.normalvariate(0, 10))
_time[0] += 10000
self.assertEqual(sample.size(), 100)
self.assertEqual(len(sample.get_values()), 100,
'Should have 100 elements')
def test_ewma_sample_load(self):
_time = [10000]
def wtime():
return _time[0]
sample = ExponentiallyDecayingSample(100, 0.99, wall_time=wtime)
sample.RESCALE_THRESHOLD = 100
sample.clear()
for i in xrange(10000000):
sample.update(random.normalvariate(0, 10))
_time[0] += 1
self.assertEqual(sample.size(), 100)
self.assertEqual(len(sample.get_values()), 100,
'Should have 100 elements')