def test_normal_rate_long_duration(self) -> None:
# 1 event/s for 3m, 9m durations
histograms = []
for _ in range(6 * 3):
hist = DurationsHistogram(bucket_size=10)
hist.incr(duration=9 * 60, count=10)
histograms.append(hist)
durations = BucketedDurationsHistograms(timestamp=0, width=10, histograms=histograms)
assert excessive_event_duration(project_id=1, durations=durations)
def test_low_rate_long_duration(self) -> None:
# 1 event/m for 3m, 9m durations
histograms = []
durations = None
for i in range(6 * 3):
hist = DurationsHistogram(bucket_size=10)
if i % 6 == 0:
hist.incr(duration=9 * 60, count=1)
histograms.append(hist)
durations = BucketedDurationsHistograms(timestamp=0, width=10, histograms=histograms)
assert durations is not None
assert not excessive_event_duration(project_id=1, durations=durations)
def excessive_event_duration(project_id: int, durations: BucketedDurationsHistograms) -> bool:
"""Whether the project's symbolication requests are taking too long to process."""
total_histogram = DurationsHistogram(bucket_size=durations.histograms[0].bucket_size)
for histogram in durations.histograms:
total_histogram.incr_from(histogram)
try:
p75_duration = total_histogram.percentile(0.75)
except ValueError:
return False
events_per_minute = total_histogram.total_count() / (durations.total_time() / 60)
# Note, We had these tagged with tags={"project_id": project_id} during our initial
# evaluation, however the cardinality for this is really too high to leave that on
# forever in production.
metrics.gauge("symbolication.lpq.computation.durations.p75", p75_duration)
metrics.gauge("symbolication.lpq.computation.durations.events_per_minutes", events_per_minute)
if events_per_minute > 15 and p75_duration > 6 * 60:
return True
else:
return False
def test_no_durations(self) -> None:
durations = BucketedDurationsHistograms(
timestamp=0, width=10, histograms=[DurationsHistogram()] * 5 * 6
)
assert not excessive_event_duration(project_id=1, durations=durations)