promql: Add test cases for direct mean calculation

These demonstrate that direct mean calculation has some merits after
all.

Signed-off-by: beorn7 <beorn@grafana.com>

promql/promqltest/testdata/aggregators.test

@@ -663,6 +663,63 @@ eval instant at 1m avg by (group) (data{test="nan"})
 
 clear
 
+# Demonstrate robustness of direct mean calculation vs. incremental mean calculation.
+# The tests below are prone to small inaccuracies with incremental mean calculation.
+# The exact number of aggregated values that trigger an inaccuracy depends on the
+# hardware.
+# See also discussion in https://github.com/prometheus/prometheus/issues/16714
+load 5m
+  foo{idx="0"} 52
+  foo{idx="1"} 52
+  foo{idx="2"} 52
+  foo{idx="3"} 52
+  foo{idx="4"} 52
+  foo{idx="5"} 52
+  foo{idx="6"} 52
+  foo{idx="7"} 52
+  foo{idx="8"} 52
+  foo{idx="9"} 52
+  foo{idx="10"} 52
+  foo{idx="11"} 52
+
+eval instant at 0 avg(foo) - 52
+  {} 0
+
+eval instant at 0 avg(topk(11, foo)) - 52
+  {} 0
+
+eval instant at 0 avg(topk(10, foo)) - 52
+  {} 0
+
+eval instant at 0 avg(topk(9, foo)) - 52
+  {} 0
+
+eval instant at 0 avg(topk(8, foo)) - 52
+  {} 0
+
+# The following tests do not utilize the tolerance built into the
+# testing framework but rely on the exact equality implemented in
+# PromQL. They currently pass, but we should keep in mind that this is
+# not a hard requirement, and generally it is a bad idea in practice
+# to rely on exact equality like this in alerting rules etc.
+
+eval instant at 0 avg(foo) == 52
+  {} 52
+
+eval instant at 0 avg(topk(11, foo)) == 52
+  {} 52
+
+eval instant at 0 avg(topk(10, foo)) == 52
+  {} 52
+
+eval instant at 0 avg(topk(9, foo)) == 52
+  {} 52
+
+eval instant at 0 avg(topk(8, foo)) == 52
+  {} 52
+
+clear
+
 # Test that aggregations are deterministic.
 # Commented because it is flaky in range mode.
 #load 10s

promql/promqltest/testdata/functions.test

@@ -1089,6 +1089,45 @@ load 5s
 #   {} -1.881783551706252e+203 <- This is the result on linux/amd64.
 #   {} 2.303079268822384e+202 <- This is the result on darwin/arm64.
 
+# Demonstrate robustness of direct mean calculation vs. incremental mean calculation.
+# The tests below are prone to small inaccuracies with incremental mean calculation.
+# The exact number of aggregated values that trigger an inaccuracy depends on the
+# hardware.
+# See also discussion in https://github.com/prometheus/prometheus/issues/16714
+clear
+load 10s
+  foo 52+0x100
+
+eval instant at 10m avg_over_time(foo[100s]) - 52
+  {} 0
+
+eval instant at 10m avg_over_time(foo[110s]) - 52
+  {} 0
+
+eval instant at 10m avg_over_time(foo[120s]) - 52
+  {} 0
+
+eval instant at 10m avg_over_time(foo[130s]) - 52
+  {} 0
+
+# The following tests do not utilize the tolerance built into the
+# testing framework but rely on the exact equality implemented in
+# PromQL. They currently pass, but we should keep in mind that this is
+# not a hard requirement, and generally it is a bad idea in practice
+# to rely on exact equality like this in alerting rules etc.
+
+eval instant at 10m avg_over_time(foo[100s]) == 52
+  {} 52
+
+eval instant at 10m avg_over_time(foo[110s]) == 52
+  {} 52
+
+eval instant at 10m avg_over_time(foo[120s]) == 52
+  {} 52
+
+eval instant at 10m avg_over_time(foo[130s]) == 52
+  {} 52
+
 # Test per-series aggregation on dense samples.
 clear
 load 1ms