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Remove old script (it's now a command)
This commit is contained in:
Klaas van Schelven
@@ -1,145 +0,0 @@
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import random
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import time
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from datetime import datetime, timezone
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from bugsink.period_counter import _prev_tup, PeriodCounter
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from performance.bursty_data import generate_bursty_data, buckets_to_points_in_time
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from projects.models import Project
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from issues.models import Issue
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from events.models import Event
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# this file is the beginning of an approach to getting a handle on performance.
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class passed_time(object):
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def __enter__(self):
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self.t0 = time.time()
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return self
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def __exit__(self, type, value, traceback):
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self.elapsed = (time.time() - self.t0) * 1_000 # miliseconds is a good unit for timeing things
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def print_thoughts_about_prev_tup():
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v = (2020, 1, 1, 10, 10)
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with passed_time() as t:
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for i in range(1_000):
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v = _prev_tup(v)
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print(f"""## _prev_tup()
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1_000 iterations of _prev_tup in {t.elapsed:.3f}ms. The main thing we care about is not this little
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private helper though, but PeriodCounter.inc(). Let's test that next.
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""")
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def print_thoughts_about_inc():
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random.seed(42)
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pc = PeriodCounter()
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# make sure the pc has some data before we start
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for point in buckets_to_points_in_time(
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generate_bursty_data(num_buckets=350, expected_nr_of_bursts=10),
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datetime(2020, 10, 15, tzinfo=timezone.utc),
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datetime(2021, 10, 15, 10, 5, tzinfo=timezone.utc),
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10_000,
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):
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pc.inc(point)
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points = buckets_to_points_in_time(
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generate_bursty_data(num_buckets=25, expected_nr_of_bursts=5),
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datetime(2021, 10, 15, 10, 5, tzinfo=timezone.utc),
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datetime(2021, 10, 16, 10, 5, tzinfo=timezone.utc),
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1000)
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with passed_time() as t:
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for point in points:
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pc.inc(point)
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print(f"""## PeriodCounter.inc()
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1_000 iterations of PeriodCounter.inc() in {t.elapsed:.3f}ms. We care about evaluation of some event more though. Let's
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test that next.
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""")
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def print_thoughts_about_event_evaluation():
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random.seed(42)
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pc = PeriodCounter()
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def noop():
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pass
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# Now, let's add some event-listeners. These are chosen to match a typical setup of quota for a given Issue or
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# Project. In this setup, the monthly maximum is spread out in a way that the smaller parts are a bit more than just
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# splitting things equally. Why? We want some flexibility for bursts of activity without using up the entire budget
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# for a longer time all at once.
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pc.add_event_listener("day", 30, 10_000, noop, noop, initial_event_state=False) # 1 month rolling window
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pc.add_event_listener("hour", 24, 1_000, noop, noop, initial_event_state=False) # 1 day rolling window
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pc.add_event_listener("minute", 60, 200, noop, noop, initial_event_state=False) # 1 hour rolling window
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# make sure the pc has some data before we start. we pick a 1-month period to match the listeners in the above.
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for point in buckets_to_points_in_time(
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generate_bursty_data(num_buckets=350, expected_nr_of_bursts=10),
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datetime(2021, 10, 15, tzinfo=timezone.utc),
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datetime(2021, 11, 15, 10, 5, tzinfo=timezone.utc),
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10_000,
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):
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pc.inc(point)
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# now we start the test: we generate a bursty data-set for a 1-day period, and see how long it takes to evaluate
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points = buckets_to_points_in_time(
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generate_bursty_data(num_buckets=25, expected_nr_of_bursts=5),
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datetime(2021, 11, 15, 10, 5, tzinfo=timezone.utc),
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datetime(2021, 11, 16, 10, 5, tzinfo=timezone.utc),
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1000)
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with passed_time() as t:
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for point in points:
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pc.inc(point)
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print(f"""## PeriodCounter.inc()
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1_000 iterations of PeriodCounter.inc() in {t.elapsed:.3f}ms. (when 3 event-listeners are active). I'm not sure exactly
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what a good performance would be here, but I can say the following: this means when a 1,000 events happen in a second,
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the period-counter uses up 3% of the budget. A first guess would be: this is good enough.""")
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def print_thoughts_about_pc_registry():
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# as a first approach, let's focus on a 'typical' (whatever that means) local setup (not hosted), for a small team.
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# maybe 10 people would work on max 10 projects. let's assume we have 10k per-project limits for events set up. and
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# let's assume 100 issues per project (far from inbox-zero, approach bug-sewer territory)
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#
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projects = [Project.objects.create(name="project %s" % i) for i in range(10)]
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issues_by_project = {}
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for p in projects:
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issues_by_project[p.id] = [Issue.objects.create(project=p, hash="hash %d" % i) for i in range(100)]
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# now we have 10 projects, each with 100 issues. let's create 10k events for each project.
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for p in projects:
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points = buckets_to_points_in_time(
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generate_bursty_data(num_buckets=350, expected_nr_of_bursts=10),
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datetime(2020, 10, 15, tzinfo=timezone.utc),
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datetime(2021, 10, 15, 10, 5, tzinfo=timezone.utc),
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10_000,
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)
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for point in points:
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# note: because we use such minimal (non-data-containing) events here, the setup in the below may actually
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# not be representative of real world performance. having said that: this immediately triggers the thought
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# that for real initialization only timestamps and issue_ids are needed, and that we should adjust the code
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# accordingly
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Event.objects.create(project=p, issue=random.choice(issues_by_project[p.id]), server_side_timestamp=point)
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print_thoughts_about_prev_tup()
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print_thoughts_about_inc()
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print_thoughts_about_event_evaluation()
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