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Add more avg_over_time test cases with extreme values

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These tests were initially created by @crush-on-anechka. I modified
them slightly.

Signed-off-by: beorn7 <beorn@grafana.com>
This commit is contained in:
beorn7 2025-06-05 21:52:50 +02:00
parent ff67596a82
commit 64431830b8
1 changed files with 73 additions and 0 deletions
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73
promql/promqltest/testdata/functions.test vendored
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@ -1013,6 +1013,79 @@ eval instant at 1m sum_over_time(metric[2m])
eval instant at 1m avg_over_time(metric[2m])
{} 0.5
# More tests for extreme values.
clear
# All positive values with varying magnitudes.
load 5s
metric1 1e10 1e-6 1e-6 1e-6 1e-6 1e-6
metric2 5.30921651659898 0.961118537914768 1.62091361305318 0.865089463758091 0.323055185914577 0.951811357687154
metric3 1.78264e50 0.76342771 1.9592258 7.69805412 458.90154
metric4 0.76342771 1.9592258 7.69805412 1.78264e50 458.90154
metric5 1.78264E+50 0.76342771 1.9592258 2.258E+220 7.69805412 458.90154
eval instant at 55s avg_over_time(metric1[1m])
{} 1.6666666666666675e+09
eval instant at 55s avg_over_time(metric2[1m])
{} 1.67186744582113
eval instant at 55s avg_over_time(metric3[1m])
{} 3.56528E+49
eval instant at 55s avg_over_time(metric4[1m])
{} 3.56528E+49
eval instant at 55s avg_over_time(metric5[1m])
{} 3.76333333333333E+219
# Contains negative values; result is dominated by a very large value.
load 5s
metric6 -1.78264E+50 0.76342771 1.9592258 2.258E+220 7.69805412 -458.90154
metric7 -1.78264E+50 0.76342771 1.9592258 -2.258E+220 7.69805412 -458.90154
metric8 -1.78264E+215 0.76342771 1.9592258 2.258E+220 7.69805412 -458.90154
metric9 -1.78264E+215 0.76342771 1.9592258 2.258E+220 7.69805412 1.78264E+215 -458.90154
metric10 -1.78264E+219 0.76342771 1.9592258 2.3757689E+217 -2.3757689E+217 2.258E+220 7.69805412 1.78264E+219 -458.90154
eval instant at 55s avg_over_time(metric6[1m])
{} 3.76333333333333E+219
eval instant at 55s avg_over_time(metric7[1m])
{} -3.76333333333333E+219
eval instant at 55s avg_over_time(metric8[1m])
{} 3.76330362266667E+219
eval instant at 55s avg_over_time(metric9[1m])
{} 3.225714285714286e+219
# Interestingly, before PR #16569, this test failed with a result of
# 3.225714285714286e+219, so the incremental calculation combined with
# Kahan summation (in the improved way as introduced by PR #16569)
# seems to be more accurate than the simple mean calculation with
# Kahan summation.
eval instant at 55s avg_over_time(metric10[1m])
{} 2.5088888888888888e+219
# Large values of different magnitude, combined with small values. The
# large values, however, all cancel each other exactly, so that the actual
# average here is determined by only the small values. Therefore, the correct
# outcome is -44.848083237000004.
load 5s
metric11 -2.258E+220 -1.78264E+219 0.76342771 1.9592258 2.3757689E+217 -2.3757689E+217 2.258E+220 7.69805412 1.78264E+219 -458.90154
# Thus, the result here is very far off! With the different orders of
# magnitudes involved, even Kahan summation cannot cope.
# Interestingly, with the simple mean calculation (still including
# Kahan summation) prior to PR #16569, the result is 0. That's
# arguably more accurate, but it still shows that Kahan summation
# doesn't work well in this situation. To solve this properly, we
# needed to do something like sorting the values (which is hard given
# how the PromQL engine works). The question is how practically
# relevant this scenario is.
eval instant at 55s avg_over_time(metric11[1m])
{} -1.881783551706252e+203
# {} -44.848083237000004 <- This is the correct value.
# Test per-series aggregation on dense samples.
clear
load 1ms