Files
bugsink/performance/some_script.py
Klaas van Schelven bf9c40c0e2 Rewrite comment
2024-01-04 20:19:06 +01:00

115 lines
3.9 KiB
Python

import random
import time
from datetime import datetime, timezone
from bugsink.period_counter import _prev_tup, PeriodCounter
from performance.bursty_data import generate_bursty_data, buckets_to_points_in_time
# this file is the beginning of an approach to getting a handle on performance.
class passed_time(object):
def __enter__(self):
self.t0 = time.time()
return self
def __exit__(self, type, value, traceback):
self.elapsed = (time.time() - self.t0) * 1_000 # miliseconds is a good unit for timeing things
def print_thoughts_about_prev_tup():
v = (2020, 1, 1, 10, 10)
with passed_time() as t:
for i in range(1_000):
v = _prev_tup(v)
print(f"""## _prev_tup()
1_000 iterations of _prev_tup in {t.elapsed:.3f}ms. The main thing we care about is not this little
private helper though, but PeriodCounter.inc(). Let's test that next.
""")
def print_thoughts_about_inc():
random.seed(42)
pc = PeriodCounter()
# make sure the pc has some data before we start
for point in buckets_to_points_in_time(
generate_bursty_data(num_buckets=350, expected_nr_of_bursts=10),
datetime(2020, 10, 15, tzinfo=timezone.utc),
datetime(2021, 10, 15, 10, 5, tzinfo=timezone.utc),
10_000,
):
pc.inc(point)
points = buckets_to_points_in_time(
generate_bursty_data(num_buckets=25, expected_nr_of_bursts=5),
datetime(2021, 10, 15, 10, 5, tzinfo=timezone.utc),
datetime(2021, 10, 16, 10, 5, tzinfo=timezone.utc),
1000)
with passed_time() as t:
for point in points:
pc.inc(point)
print(f"""## PeriodCounter.inc()
1_000 iterations of PeriodCounter.inc() in {t.elapsed:.3f}ms. We care about evaluation of some event more though. Let's
test that next.
""")
def print_thoughts_about_event_evaluation():
random.seed(42)
pc = PeriodCounter()
def noop():
pass
# Now, let's add some event-listeners. These are chosen to match a typical setup of quota for a given Issue or
# Project. In this setup, the monthly maximum is spread out in a way that the smaller parts are a bit more than just
# splitting things equally. Why? We want some flexibility for bursts of activity without using up the entire budget
# for a longer time all at once.
pc.add_event_listener("day", 30, 10_000, noop, noop, event_state=False) # 1 month rolling window
pc.add_event_listener("hour", 24, 1_000, noop, noop, event_state=False) # 1 day rolling window
pc.add_event_listener("minute", 60, 200, noop, noop, event_state=False) # 1 hour rolling window
# make sure the pc has some data before we start. we pick a 1-month period to match the listeners in the above.
for point in buckets_to_points_in_time(
generate_bursty_data(num_buckets=350, expected_nr_of_bursts=10),
datetime(2021, 10, 15, tzinfo=timezone.utc),
datetime(2021, 11, 15, 10, 5, tzinfo=timezone.utc),
10_000,
):
pc.inc(point)
# now we start the test: we generate a bursty data-set for a 1-day period, and see how long it takes to evaluate
points = buckets_to_points_in_time(
generate_bursty_data(num_buckets=25, expected_nr_of_bursts=5),
datetime(2021, 11, 15, 10, 5, tzinfo=timezone.utc),
datetime(2021, 11, 16, 10, 5, tzinfo=timezone.utc),
1000)
with passed_time() as t:
for point in points:
pc.inc(point)
print(f"""## PeriodCounter.inc()
1_000 iterations of PeriodCounter.inc() in {t.elapsed:.3f}ms. (when 3 event-listeners are active). I'm not sure exactly
what a good performance would be here, but I can say the following: this means when a 1,000 events happen in a second,
the period-counter uses up 10% of the budget. A first guess would be: this is good enough.""")
print_thoughts_about_prev_tup()
print_thoughts_about_inc()
print_thoughts_about_event_evaluation()