def test_histogram_checkpoint_empty():
hist = DecayingExponentialHistogram(0.01, 1.05, ONE_DAY)
assert hist.get_checkpoint() == {
"total_weight": 0,
"bucket_weights": {},
"reference_time": 0,
}
def test_histogram_from_checkpoint():
hist = DecayingExponentialHistogram(0.01, 1.05, ONE_DAY)
hist.from_checkpoint({
"total_weight": 1,
"bucket_weights": {
0: 10000
},
"reference_time": 123
})
assert hist.reference_time == 123
def test_histogram_checkpoint_single_bucket():
hist = DecayingExponentialHistogram(0.01, 1.05, ONE_DAY)
now = time.mktime((2020, 4, 15, 21, 34, 0, 2, 0, 0))
hist.add_sample(0.001, 1, now)
assert hist.get_checkpoint() == {
"total_weight": hist.total_weight,
"bucket_weights": {
0: hist.total_weight
},
"reference_time": (now // ONE_DAY) * ONE_DAY,
}
def test_histogram_max_decay():
min_value = 10.0 * 1024 * 1024 # 10 MiB
hist = DecayingExponentialHistogram(min_value, 1.05, ONE_DAY)
now = time.mktime((2020, 4, 15, 21, 34, 0, 2, 0, 0))
old_max = 900.0 * 1024 * 1024
new_max = 100.0 * 1014 * 1024
hist.add_sample(old_max, 1, now)
assert hist.get_percentile(1.0) == approx(old_max, rel=0.01)
ts = now
for i in range(60):
ts += ONE_DAY
hist.add_sample(new_max, 1, ts)
if i < 53:
assert hist.get_percentile(1.0) == approx(old_max, rel=0.01)
else:
# old max decayed too much, the new value prevails
assert hist.get_percentile(1.0) == approx(new_max, rel=0.15)
def test_histogram_percentile_large2():
hist = DecayingExponentialHistogram(0.001, 1.05, ONE_DAY)
now = time.mktime((2020, 4, 15, 21, 34, 0, 2, 0, 0))
values = [0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.42, 0.51, 0.7, 1.2, 1.4]
values.sort()
for i in values:
hist.add_sample(i, 1, now)
assert hist.get_percentile(0.5) == approx(percentile(values, 0.5),
abs=0.02)
assert hist.get_percentile(0.75) == approx(percentile(values, 0.75),
abs=0.15)
assert hist.get_percentile(0.9) == approx(percentile(values, 0.9),
abs=0.05)
def test_histogram_percentile():
hist = DecayingExponentialHistogram(0.01, 1.05, ONE_DAY)
now = time.mktime((2020, 4, 15, 21, 34, 0, 2, 0, 0))
hist.add_sample(1, 1, now)
hist.add_sample(2, 1, now)
hist.add_sample(3, 1, now)
assert hist.get_percentile(0.5) == approx(2, rel=0.1)
hist = DecayingExponentialHistogram(0.01, 1.05, ONE_DAY)
for i in range(1, 11):
hist.add_sample(i, 1, now)
assert hist.get_percentile(0.5) == approx(5, rel=0.1)
assert hist.get_percentile(1.0) == approx(10, rel=0.2)
def test_histogram_decay():
hist = DecayingExponentialHistogram(0.01, 1.05, ONE_DAY)
now = time.time()
hist.add_sample(10, 1, now)
expected_ref_time = (now // ONE_DAY) * ONE_DAY
assert hist.reference_time == expected_ref_time
assert hist.get_percentile(0.5) == approx(10, rel=0.1)
hist.add_sample(10, 1, now + 60)
assert hist.reference_time == expected_ref_time
assert hist.get_percentile(0.5) == approx(10, rel=0.2)
hist.add_sample(1, 1, now + ONE_DAY)
assert hist.reference_time == expected_ref_time
hist.add_sample(1, 1, now + ONE_DAY + 1)
assert hist.reference_time == expected_ref_time
assert hist.get_percentile(0.5) == approx(1, rel=0.2)
def test_histogram_empty():
hist = DecayingExponentialHistogram(0.01, 1.05, ONE_DAY)
assert hist.is_empty()
assert hist.get_percentile(0.9) == 0
def test_histogram_save_load_checkpoint():
now = time.mktime((2020, 4, 19, 21, 34, 0, 2, 0, 0))
start = time.time()
hist = DecayingExponentialHistogram(0.001, 1.05, ONE_DAY)
hist.add_sample(0.5, 1, now)
checkpoint = hist.get_checkpoint()
for i in range(100):
hist = DecayingExponentialHistogram(0.001, 1.05, ONE_DAY)
hist.from_checkpoint(checkpoint)
hist.add_sample(0.1 * i, 1, now + (i * 60))
hist.add_sample(0.1 * i, 1, now + (i * 60))
hist.add_sample(0.1 * i, 1, now + (i * 60))
hist.get_percentile(0.9)
checkpoint = hist.get_checkpoint()
delta = time.time() - start
# just for performance test, ignore
assert delta < 10.0
def test_histogram_percentile_large1():
hist = DecayingExponentialHistogram(0.001, 1.05, ONE_DAY)
now = time.mktime((2020, 4, 15, 21, 34, 0, 2, 0, 0))
values = [
0.00356826,
0.003571567,
0.00357581,
0.00357730,
0.003580761,
0.00359196,
0.003596697,
0.0035993,
0.00361249,
0.003615085,
0.003617967,
0.003621645,
0.003624581,
0.003628672,
0.00363173,
0.003633299,
0.00363402,
0.00363437,
0.003634643,
0.003634752,
0.003641667,
0.003641921,
0.003644168,
0.00365697,
0.00365703,
0.003660491,
0.00366627,
0.00366672,
0.00366859,
0.003671890,
0.003674470,
0.003674653,
0.003678485,
0.0036906,
0.003692064,
0.003692147,
0.003692247,
0.00369417,
0.003694798,
0.003697279,
0.00370017,
0.00370552,
0.003706270,
0.003708455,
0.00371141,
0.00371142,
0.00371282,
0.00371546,
0.003717293,
0.00372156,
0.00372266,
0.00372328,
0.003731720,
0.00373524,
0.003738866,
0.003739554,
0.003740255,
0.003740514,
0.003742887,
0.003743114,
0.00374609,
0.003748427,
0.00375339,
0.003754504,
0.003759066,
0.003762933,
0.003763028,
0.003770094,
0.003772507,
0.003773164,
0.003784076,
0.00378883,
0.003791513,
0.00379167,
0.003797098,
0.0037978,
0.003799059,
0.003800420,
0.003803184,
0.00380564,
0.00380581,
0.00381127,
0.00381342,
0.00381523,
0.003815614,
0.00382284,
0.003827261,
0.00382847,
0.003831219,
0.003836552,
0.003837597,
0.00383817,
0.003838523,
0.003843782,
0.00384448,
0.003847642,
0.00384810,
0.003849126,
0.003852803,
0.003858588,
0.003859859,
0.00386054,
0.003861486,
0.00386349,
0.00448897,
0.004490609,
0.00449483,
0.00449538,
0.00449559,
0.00449842,
0.00449850,
0.00449870,
0.00449938,
0.00450397,
0.0045061,
0.00451622,
0.00451764,
0.00451884,
0.00452040,
0.00452098,
0.004521002,
0.00452261,
0.0045254,
]
values.sort()
for i in values:
hist.add_sample(i, 1, now)
assert hist.get_percentile(0.5) == approx(percentile(values, 0.5),
abs=0.02)
assert hist.get_percentile(0.75) == approx(percentile(values, 0.75),
abs=0.02)
assert hist.get_percentile(0.9) == approx(percentile(values, 0.9),
abs=0.02)