Merge pull request #14464 from prometheus/beorn7/histogram

promql: Add NHCB tests

promql/promqltest/testdata/histograms.test

@@ -73,22 +73,32 @@ eval instant at 50m histogram_count(testhistogram3)
 	{start="positive"} 110
 	{start="negative"} 20
 
+# Classic way of accessing the count still works.
+eval instant at 50m testhistogram3_count
+	testhistogram3_count{start="positive"} 110
+	testhistogram3_count{start="negative"} 20
+
 # Test histogram_sum.
 eval instant at 50m histogram_sum(testhistogram3)
 	{start="positive"} 330
 	{start="negative"} 80
 
-# Test histogram_avg.
+# Classic way of accessing the sum still works.
+eval instant at 50m testhistogram3_sum
+	testhistogram3_sum{start="positive"} 330
+	testhistogram3_sum{start="negative"} 80
+
+# Test histogram_avg. This has no classic equivalent.
 eval instant at 50m histogram_avg(testhistogram3)
 	{start="positive"} 3
 	{start="negative"} 4
 
-# Test histogram_stddev.
+# Test histogram_stddev. This has no classic equivalent.
 eval instant at 50m histogram_stddev(testhistogram3)
 	{start="positive"} 2.8189265757336734
 	{start="negative"} 4.182715937754936
 
-# Test histogram_stdvar.
+# Test histogram_stdvar. This has no classic equivalent.
 eval instant at 50m histogram_stdvar(testhistogram3)
 	{start="positive"} 7.946347039377573
 	{start="negative"} 17.495112615949154
@@ -103,137 +113,282 @@ eval instant at 50m histogram_fraction(0, 0.2, rate(testhistogram3[5m]))
 	{start="positive"} 0.6363636363636364
 	{start="negative"} 0
 
-# Test histogram_quantile.
+# In the classic histogram, we can access the corresponding bucket (if
+# it exists) and divide by the count to get the same result.
+
+eval instant at 50m testhistogram3_bucket{le=".2"} / ignoring(le) testhistogram3_count
+	{start="positive"} 0.6363636363636364
+	
+eval instant at 50m rate(testhistogram3_bucket{le=".2"}[5m]) / ignoring(le) rate(testhistogram3_count[5m])
+	{start="positive"} 0.6363636363636364
+
+# Test histogram_quantile, native and classic.
+
+eval instant at 50m histogram_quantile(0, testhistogram3)
+	{start="positive"} 0
+	{start="negative"} -0.25
 
 eval instant at 50m histogram_quantile(0, testhistogram3_bucket)
 	{start="positive"} 0
 	{start="negative"} -0.25
 
+eval instant at 50m histogram_quantile(0.25, testhistogram3)
+	{start="positive"} 0.055
+	{start="negative"} -0.225
+
 eval instant at 50m histogram_quantile(0.25, testhistogram3_bucket)
 	{start="positive"} 0.055
 	{start="negative"} -0.225
 
+eval instant at 50m histogram_quantile(0.5, testhistogram3)
+	{start="positive"} 0.125
+	{start="negative"} -0.2
+
 eval instant at 50m histogram_quantile(0.5, testhistogram3_bucket)
 	{start="positive"} 0.125
 	{start="negative"} -0.2
 
+eval instant at 50m histogram_quantile(0.75, testhistogram3)
+	{start="positive"} 0.45
+	{start="negative"} -0.15
+
 eval instant at 50m histogram_quantile(0.75, testhistogram3_bucket)
 	{start="positive"} 0.45
 	{start="negative"} -0.15
 
+eval instant at 50m histogram_quantile(1, testhistogram3)
+	{start="positive"} 1
+	{start="negative"} -0.1
+
 eval instant at 50m histogram_quantile(1, testhistogram3_bucket)
 	{start="positive"} 1
 	{start="negative"} -0.1
 
 # Quantile too low.
+
+eval_warn instant at 50m histogram_quantile(-0.1, testhistogram)
+	{start="positive"} -Inf
+	{start="negative"} -Inf
+
 eval_warn instant at 50m histogram_quantile(-0.1, testhistogram_bucket)
 	{start="positive"} -Inf
 	{start="negative"} -Inf
 
 # Quantile too high.
+
+eval_warn instant at 50m histogram_quantile(1.01, testhistogram)
+	{start="positive"} +Inf
+	{start="negative"} +Inf
+
 eval_warn instant at 50m histogram_quantile(1.01, testhistogram_bucket)
 	{start="positive"} +Inf
 	{start="negative"} +Inf
 
 # Quantile invalid.
+
+eval_warn instant at 50m histogram_quantile(NaN, testhistogram)
+	{start="positive"} NaN
+	{start="negative"} NaN
+
 eval_warn instant at 50m histogram_quantile(NaN, testhistogram_bucket)
 	{start="positive"} NaN
 	{start="negative"} NaN
 
 # Quantile value in lowest bucket.
+
+eval instant at 50m histogram_quantile(0, testhistogram)
+	{start="positive"} 0
+	{start="negative"} -0.2
+
 eval instant at 50m histogram_quantile(0, testhistogram_bucket)
 	{start="positive"} 0
 	{start="negative"} -0.2
 
 # Quantile value in highest bucket.
+
+eval instant at 50m histogram_quantile(1, testhistogram)
+	{start="positive"} 1
+	{start="negative"} 0.3
+
 eval instant at 50m histogram_quantile(1, testhistogram_bucket)
 	{start="positive"} 1
 	{start="negative"} 0.3
 
 # Finally some useful quantiles.
+
+eval instant at 50m histogram_quantile(0.2, testhistogram)
+	{start="positive"} 0.048
+	{start="negative"} -0.2
+
 eval instant at 50m histogram_quantile(0.2, testhistogram_bucket)
 	{start="positive"} 0.048
 	{start="negative"} -0.2
 
+eval instant at 50m histogram_quantile(0.5, testhistogram)
+	{start="positive"} 0.15
+	{start="negative"} -0.15
+
 eval instant at 50m histogram_quantile(0.5, testhistogram_bucket)
 	{start="positive"} 0.15
 	{start="negative"} -0.15
 
+eval instant at 50m histogram_quantile(0.8, testhistogram)
+	{start="positive"} 0.72
+	{start="negative"} 0.3
+
 eval instant at 50m histogram_quantile(0.8, testhistogram_bucket)
 	{start="positive"} 0.72
 	{start="negative"} 0.3
 
 # More realistic with rates.
+
+eval instant at 50m histogram_quantile(0.2, rate(testhistogram[5m]))
+	{start="positive"} 0.048
+	{start="negative"} -0.2
+
 eval instant at 50m histogram_quantile(0.2, rate(testhistogram_bucket[5m]))
 	{start="positive"} 0.048
 	{start="negative"} -0.2
 
+eval instant at 50m histogram_quantile(0.5, rate(testhistogram[5m]))
+	{start="positive"} 0.15
+	{start="negative"} -0.15
+
 eval instant at 50m histogram_quantile(0.5, rate(testhistogram_bucket[5m]))
 	{start="positive"} 0.15
 	{start="negative"} -0.15
 
+eval instant at 50m histogram_quantile(0.8, rate(testhistogram[5m]))
+	{start="positive"} 0.72
+	{start="negative"} 0.3
+
 eval instant at 50m histogram_quantile(0.8, rate(testhistogram_bucket[5m]))
 	{start="positive"} 0.72
 	{start="negative"} 0.3
 
 # Want results exactly in the middle of the bucket.
+
+eval instant at 7m histogram_quantile(1./6., testhistogram2)
+	{} 1
+
 eval instant at 7m histogram_quantile(1./6., testhistogram2_bucket)
 	{} 1
 
+eval instant at 7m histogram_quantile(0.5, testhistogram2)
+	{} 3
+
 eval instant at 7m histogram_quantile(0.5, testhistogram2_bucket)
 	{} 3
 
+eval instant at 7m histogram_quantile(5./6., testhistogram2)
+	{} 5
+
 eval instant at 7m histogram_quantile(5./6., testhistogram2_bucket)
 	{} 5
 
+eval instant at 47m histogram_quantile(1./6., rate(testhistogram2[15m]))
+	{} 1
+
 eval instant at 47m histogram_quantile(1./6., rate(testhistogram2_bucket[15m]))
 	{} 1
 
+eval instant at 47m histogram_quantile(0.5, rate(testhistogram2[15m]))
+	{} 3
+
 eval instant at 47m histogram_quantile(0.5, rate(testhistogram2_bucket[15m]))
 	{} 3
 
+eval instant at 47m histogram_quantile(5./6., rate(testhistogram2[15m]))
+	{} 5
+
 eval instant at 47m histogram_quantile(5./6., rate(testhistogram2_bucket[15m]))
 	{} 5
 
-# Aggregated histogram: Everything in one.
+# Aggregated histogram: Everything in one. Note how native histograms
+# don't require aggregation by le.
+
+eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])))
+	{} 0.075
+
 eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le))
 	{} 0.075
 
+eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])))
+	{} 0.1277777777777778
+
 eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le))
 	{} 0.1277777777777778
 
 # Aggregated histogram: Everything in one. Now with avg, which does not change anything.
+
+eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds[5m])))
+	{} 0.075
+
 eval instant at 50m histogram_quantile(0.3, avg(rate(request_duration_seconds_bucket[5m])) by (le))
 	{} 0.075
 
+eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds[5m])))
+	{} 0.12777777777777778
+
 eval instant at 50m histogram_quantile(0.5, avg(rate(request_duration_seconds_bucket[5m])) by (le))
 	{} 0.12777777777777778
 
 # Aggregated histogram: By instance.
+
+eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])) by (instance))
+	{instance="ins1"} 0.075
+	{instance="ins2"} 0.075
+
 eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
 	{instance="ins1"} 0.075
 	{instance="ins2"} 0.075
 
+eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])) by (instance))
+	{instance="ins1"} 0.1333333333
+	{instance="ins2"} 0.125
+
 eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, instance))
 	{instance="ins1"} 0.1333333333
 	{instance="ins2"} 0.125
 
 # Aggregated histogram: By job.
+
+eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])) by (job))
+	{job="job1"} 0.1
+	{job="job2"} 0.0642857142857143
+
 eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
 	{job="job1"} 0.1
 	{job="job2"} 0.0642857142857143
 
+eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])) by (job))
+	{job="job1"} 0.14
+	{job="job2"} 0.1125
+
 eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job))
 	{job="job1"} 0.14
 	{job="job2"} 0.1125
 
 # Aggregated histogram: By job and instance.
+
+eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds[5m])) by (job, instance))
+	{instance="ins1", job="job1"} 0.11
+	{instance="ins2", job="job1"} 0.09
+	{instance="ins1", job="job2"} 0.06
+	{instance="ins2", job="job2"} 0.0675
+
 eval instant at 50m histogram_quantile(0.3, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
 	{instance="ins1", job="job1"} 0.11
 	{instance="ins2", job="job1"} 0.09
 	{instance="ins1", job="job2"} 0.06
 	{instance="ins2", job="job2"} 0.0675
 
+eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds[5m])) by (job, instance))
+	{instance="ins1", job="job1"} 0.15
+	{instance="ins2", job="job1"} 0.1333333333333333
+	{instance="ins1", job="job2"} 0.1
+	{instance="ins2", job="job2"} 0.1166666666666667
+
 eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bucket[5m])) by (le, job, instance))
 	{instance="ins1", job="job1"} 0.15
 	{instance="ins2", job="job1"} 0.1333333333333333
@@ -241,18 +396,32 @@ eval instant at 50m histogram_quantile(0.5, sum(rate(request_duration_seconds_bu
 	{instance="ins2", job="job2"} 0.1166666666666667
 
 # The unaggregated histogram for comparison. Same result as the previous one.
+
+eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds[5m]))
+	{instance="ins1", job="job1"} 0.11
+	{instance="ins2", job="job1"} 0.09
+	{instance="ins1", job="job2"} 0.06
+	{instance="ins2", job="job2"} 0.0675
+
 eval instant at 50m histogram_quantile(0.3, rate(request_duration_seconds_bucket[5m]))
 	{instance="ins1", job="job1"} 0.11
 	{instance="ins2", job="job1"} 0.09
 	{instance="ins1", job="job2"} 0.06
 	{instance="ins2", job="job2"} 0.0675
 
+eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds[5m]))
+	{instance="ins1", job="job1"} 0.15
+	{instance="ins2", job="job1"} 0.13333333333333333
+	{instance="ins1", job="job2"} 0.1
+	{instance="ins2", job="job2"} 0.11666666666666667
+
 eval instant at 50m histogram_quantile(0.5, rate(request_duration_seconds_bucket[5m]))
 	{instance="ins1", job="job1"} 0.15
 	{instance="ins2", job="job1"} 0.13333333333333333
 	{instance="ins1", job="job2"} 0.1
 	{instance="ins2", job="job2"} 0.11666666666666667
 
+# All NHCBs summed into one.
 eval instant at 50m sum(request_duration_seconds)
 	{} {{schema:-53 count:250 custom_values:[0.1 0.2] buckets:[100 90 60]}}
 
@@ -303,11 +472,13 @@ load_with_nhcb 5m
 eval instant at 50m histogram_quantile(0.2, rate(empty_bucket[5m]))
 	{instance="ins1", job="job1"} NaN
 
-# Load a duplicate histogram with a different name to test failure scenario on multiple histograms with the same label set
+# Load a duplicate histogram with a different name to test failure scenario on multiple histograms with the same label set.
 # https://github.com/prometheus/prometheus/issues/9910
 load_with_nhcb 5m
-	request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.1"}	0+1x10
+	request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.1"}	 0+1x10
-	request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.2"}	0+3x10
+	request_duration_seconds2_bucket{job="job1", instance="ins1", le="0.2"}	 0+3x10
-	request_duration_seconds2_bucket{job="job1", instance="ins1", le="+Inf"}	0+4x10
+	request_duration_seconds2_bucket{job="job1", instance="ins1", le="+Inf"} 0+4x10
 
-eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*_bucket$"})
+eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*_bucket"})
+
+eval_fail instant at 50m histogram_quantile(0.99, {__name__=~"request_duration_seconds\\d*"})