def test_exponential_growth(self, base, rate):
growth = exponential_growth(base, rate)
growth_seq = list(islice(growth, 10))
self.assertThat(growth_seq, HasLength(10))
self.assertThat(growth_seq, AllMatch(IsInstance(float)))
self.assertEqual(growth_seq, sorted(growth_seq))
self.assertEqual((base * rate), growth_seq[0])
self.assertEqual((base * (rate ** 10)), growth_seq[-1])
def gen_retry_intervals(base=0.01, rate=2.5, maximum=10.0):
"""Generate retry intervals based on an exponential series.
Once any interval exceeds `maximum` the interval generated will forever be
`maximum`; this effectively disconnects from the exponential series. All
intervals will be subject to "jitter" as a final step.
The defaults seem like reasonable coefficients for a capped, full-jitter,
exponential back-off series, and were derived by experimentation at the
command-line. Real-world experience may teach us better values.
"""
# An exponentially growing series...
intervals = exponential_growth(base, rate)
# from which we stop pulling one we've hit a maximum...
intervals = takewhile((lambda i: i < maximum), intervals)
# and thereafter return the maximum value indefinitely...
intervals = chain(intervals, repeat(maximum))
# and to which we add some randomness.
return full_jitter(intervals)