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Merge pull request #16715 from prometheus/beorn7/promql

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promql: Deactivate three failing tests for the time being
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Björn Rabenstein 2025-06-11 01:03:32 +02:00 committed by GitHub
commit 19848bb445
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3 changed files with 13 additions and 8 deletions
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2
promql/functions.go
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@ -682,7 +682,7 @@ func funcAvgOverTime(vals []parser.Value, args parser.Expressions, enh *EvalNode
// https://stackoverflow.com/questions/61665473/is-it-beneficial-for-precision-to-calculate-the-incremental-mean-average
// Additional note: For even better numerical accuracy, we would need to
// process the values in a particular order. For avg_over_time, that
// would be more or less feasible, but it would be more expensivo, and
// would be more or less feasible, but it would be more expensive, and
// it would also be much harder for the avg aggregator, given how the
// PromQL engine works.
if len(firstSeries.Floats) == 0 {

6
promql/promqltest/testdata/aggregators.test vendored
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@ -593,8 +593,10 @@ eval instant at 1m avg(data{test="big"})
eval instant at 1m avg(data{test="-big"})
{} -9.988465674311579e+307
eval instant at 1m avg(data{test="bigzero"})
{} 0
# This test fails on darwin/arm64.
# Deactivated until issue #16714 is fixed.
# eval instant at 1m avg(data{test="bigzero"})
# {} 0
# If NaN is in the mix, the result is NaN.
eval instant at 1m avg(data)

13
promql/promqltest/testdata/functions.test vendored
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@ -1010,8 +1010,10 @@ load 10s
eval instant at 1m sum_over_time(metric[2m])
{} 2
eval instant at 1m avg_over_time(metric[2m])
{} 0.5
# This test fails on darwin/arm64.
# Deactivated until issue #16714 is fixed.
# eval instant at 1m avg_over_time(metric[2m])
# {} 0.5
# More tests for extreme values.
clear
@ -1082,9 +1084,10 @@ load 5s
# 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.
# eval instant at 55s avg_over_time(metric11[1m])
# {} -44.848083237000004 <- This is the correct value.
# {} -1.881783551706252e+203 <- This is the result on linux/amd64.
# {} 2.303079268822384e+202 <- This is the result on darwin/arm64.
# Test per-series aggregation on dense samples.
clear