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488466246f
The test at line 1283 for avg_over_time(nhcb_metric[13m]) incorrectly expected counter_reset_hint:gauge in the result. However, the actual avg_over_time implementation does not explicitly set the CounterResetHint to GaugeType on its output histogram. With the new counter reset hint comparison logic added to the promqltest framework (which compares hints when explicitly specified in expected results), this incorrect expectation was now being caught. This fix removes the incorrect counter_reset_hint:gauge from the expected result, allowing the test to correctly verify the avg_over_time behavior without asserting a specific hint value that the function does not set. The counter reset hint comparison logic works as designed: if the expected histogram has UnknownCounterReset (the default when not specified), no comparison is performed. Only when a hint is explicitly specified in the test expectation will it be compared against the actual result. Fixes the test failure introduced by the counter reset hint comparison feature in promqltest. Signed-off-by: Aviral Garg <aviralg2106@gmail.com> Signed-off-by: aviralgarg05 <gargaviral99@gmail.com>
1874 lines
72 KiB
Text
1874 lines
72 KiB
Text
# Minimal valid case: an empty histogram.
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load 5m
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empty_histogram {{}}
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eval instant at 1m empty_histogram
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{__name__="empty_histogram"} {{}}
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eval instant at 1m histogram_count(empty_histogram)
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{} 0
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eval instant at 1m histogram_sum(empty_histogram)
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{} 0
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eval instant at 1m histogram_avg(empty_histogram)
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{} NaN
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eval instant at 1m histogram_fraction(-Inf, +Inf, empty_histogram)
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{} NaN
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eval instant at 1m histogram_fraction(0, 8, empty_histogram)
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{} NaN
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clear
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# buckets:[1 2 1] means 1 observation in the 1st bucket, 2 observations in the 2nd and 1 observation in the 3rd (total 4).
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load 5m
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single_histogram {{schema:0 sum:5 count:4 buckets:[1 2 1]}}
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# histogram_count extracts the count property from the histogram.
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eval instant at 1m histogram_count(single_histogram)
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{} 4
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# histogram_sum extracts the sum property from the histogram.
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eval instant at 1m histogram_sum(single_histogram)
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{} 5
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# histogram_avg calculates the average from sum and count properties.
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eval instant at 1m histogram_avg(single_histogram)
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{} 1.25
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# We expect half of the values to fall in the range 1 < x <= 2.
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eval instant at 1m histogram_fraction(1, 2, single_histogram)
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{} 0.5
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# We expect all values to fall in the range 0 < x <= 8.
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eval instant at 1m histogram_fraction(0, 8, single_histogram)
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expect no_info
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{} 1
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# Median is 1.414213562373095 (2**2**-1, or sqrt(2)) due to
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# exponential interpolation, i.e. the "midpoint" within range 1 < x <=
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# 2 is assumed where the bucket boundary would be if we increased the
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# resolution of the histogram by one step.
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eval instant at 1m histogram_quantile(0.5, single_histogram)
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expect no_info
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{} 1.414213562373095
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clear
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# Repeat the same histogram 10 times.
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load 5m
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multi_histogram {{schema:0 sum:5 count:4 buckets:[1 2 1]}}x10 {{schema:0 sum:5 count:4 buckets:[1 2 1]}}+{{}}x10
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eval instant at 5m histogram_count(multi_histogram)
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{} 4
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eval instant at 5m histogram_sum(multi_histogram)
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{} 5
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eval instant at 5m histogram_avg(multi_histogram)
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{} 1.25
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eval instant at 5m histogram_fraction(1, 2, multi_histogram)
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{} 0.5
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# See explanation for exponential interpolation above.
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eval instant at 5m histogram_quantile(0.5, multi_histogram)
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{} 1.414213562373095
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# Each entry should look the same as the first.
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eval instant at 50m histogram_count(multi_histogram)
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{} 4
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eval instant at 50m histogram_sum(multi_histogram)
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{} 5
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eval instant at 50m histogram_avg(multi_histogram)
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{} 1.25
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eval instant at 50m histogram_fraction(1, 2, multi_histogram)
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{} 0.5
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# See explanation for exponential interpolation above.
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eval instant at 50m histogram_quantile(0.5, multi_histogram)
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{} 1.414213562373095
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clear
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# Accumulate the histogram addition for 10 iterations, offset is a bucket position where offset:0 is always the bucket
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# with an upper limit of 1 and offset:1 is the bucket which follows to the right. Negative offsets represent bucket
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# positions for upper limits <1 (tending toward zero), where offset:-1 is the bucket to the left of offset:0.
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load 5m
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incr_histogram {{schema:0 sum:4 count:4 buckets:[1 2 1]}}+{{sum:2 count:1 buckets:[1] offset:1}}x10
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eval instant at 5m histogram_count(incr_histogram)
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{} 5
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eval instant at 5m histogram_sum(incr_histogram)
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{} 6
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eval instant at 5m histogram_avg(incr_histogram)
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{} 1.2
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# We expect 3/5ths of the values to fall in the range 1 < x <= 2.
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eval instant at 5m histogram_fraction(1, 2, incr_histogram)
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{} 0.6
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# See explanation for exponential interpolation above.
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eval instant at 5m histogram_quantile(0.5, incr_histogram)
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{} 1.414213562373095
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eval instant at 50m incr_histogram
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{__name__="incr_histogram"} {{count:14 sum:24 buckets:[1 12 1]}}
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eval instant at 50m histogram_count(incr_histogram)
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{} 14
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eval instant at 50m histogram_sum(incr_histogram)
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{} 24
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eval instant at 50m histogram_avg(incr_histogram)
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{} 1.7142857142857142
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# We expect 12/14ths of the values to fall in the range 1 < x <= 2.
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eval instant at 50m histogram_fraction(1, 2, incr_histogram)
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{} 0.8571428571428571
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# See explanation for exponential interpolation above.
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eval instant at 50m histogram_quantile(0.5, incr_histogram)
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{} 1.414213562373095
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# Per-second average rate of increase should be 1/(5*60) for count and buckets, then 2/(5*60) for sum.
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eval instant at 50m rate(incr_histogram[10m])
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expect no_warn
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{} {{count:0.0033333333333333335 sum:0.006666666666666667 offset:1 buckets:[0.0033333333333333335]}}
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# Calculate the 50th percentile of observations over the last 10m.
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# See explanation for exponential interpolation above.
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eval instant at 50m histogram_quantile(0.5, rate(incr_histogram[10m]))
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expect no_warn
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{} 1.414213562373095
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clear
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# Schema represents the histogram resolution, different schema have compatible bucket boundaries, e.g.:
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# 0: 1 2 4 8 16 32 64 (higher resolution)
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# -1: 1 4 16 64 (lower resolution)
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#
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# Histograms can be merged as long as the histogram to the right is same resolution or higher.
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load 5m
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low_res_histogram {{schema:-1 sum:4 count:1 buckets:[1] offset:1}}+{{schema:0 sum:4 count:4 buckets:[2 2] offset:1}}x1
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eval instant at 5m low_res_histogram
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{__name__="low_res_histogram"} {{schema:-1 count:5 sum:8 offset:1 buckets:[5]}}
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eval instant at 5m histogram_count(low_res_histogram)
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{} 5
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eval instant at 5m histogram_sum(low_res_histogram)
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{} 8
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eval instant at 5m histogram_avg(low_res_histogram)
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{} 1.6
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# We expect all values to fall into the lower-resolution bucket with the range 1 < x <= 4.
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eval instant at 5m histogram_fraction(1, 4, low_res_histogram)
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{} 1
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clear
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# z_bucket:1 means there is one observation in the zero bucket and z_bucket_w:0.5 means the zero bucket has the range
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# 0 < x <= 0.5. Sum and count are expected to represent all observations in the histogram, including those in the zero bucket.
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load 5m
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single_zero_histogram {{schema:0 z_bucket:1 z_bucket_w:0.5 sum:0.25 count:1}}
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eval instant at 1m histogram_count(single_zero_histogram)
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{} 1
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eval instant at 1m histogram_sum(single_zero_histogram)
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{} 0.25
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eval instant at 1m histogram_avg(single_zero_histogram)
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{} 0.25
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# When only the zero bucket is populated, or there are negative buckets, the distribution is assumed to be equally
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# distributed around zero; i.e. that there are an equal number of positive and negative observations. Therefore the
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# entire distribution must lie within the full range of the zero bucket, in this case: -0.5 < x <= +0.5.
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eval instant at 1m histogram_fraction(-0.5, 0.5, single_zero_histogram)
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{} 1
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# Half of the observations are estimated to be zero, as this is the midpoint between -0.5 and +0.5.
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eval instant at 1m histogram_quantile(0.5, single_zero_histogram)
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{} 0
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clear
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# Let's turn single_histogram upside-down.
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load 5m
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negative_histogram {{schema:0 sum:-5 count:4 n_buckets:[1 2 1]}}
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eval instant at 1m histogram_count(negative_histogram)
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{} 4
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eval instant at 1m histogram_sum(negative_histogram)
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{} -5
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eval instant at 1m histogram_avg(negative_histogram)
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{} -1.25
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# We expect half of the values to fall in the range -2 < x <= -1.
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eval instant at 1m histogram_fraction(-2, -1, negative_histogram)
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{} 0.5
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# Exponential interpolation works the same as for positive buckets, just mirrored.
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eval instant at 1m histogram_quantile(0.5, negative_histogram)
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{} -1.414213562373095
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clear
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# Two histogram samples.
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load 5m
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two_samples_histogram {{schema:0 sum:4 count:4 buckets:[1 2 1]}} {{schema:0 sum:-4 count:4 n_buckets:[1 2 1]}}
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# We expect to see the newest sample.
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eval instant at 5m histogram_count(two_samples_histogram)
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{} 4
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eval instant at 5m histogram_sum(two_samples_histogram)
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{} -4
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eval instant at 5m histogram_avg(two_samples_histogram)
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{} -1
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eval instant at 5m histogram_fraction(-2, -1, two_samples_histogram)
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{} 0.5
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# See explanation for exponential interpolation above.
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eval instant at 5m histogram_quantile(0.5, two_samples_histogram)
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{} -1.414213562373095
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clear
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# Add two histograms with negated data.
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load 5m
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balanced_histogram {{schema:0 sum:4 count:4 buckets:[1 2 1]}}+{{schema:0 sum:-4 count:4 n_buckets:[1 2 1]}}x1
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eval instant at 5m histogram_count(balanced_histogram)
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{} 8
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eval instant at 5m histogram_sum(balanced_histogram)
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{} 0
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eval instant at 5m histogram_avg(balanced_histogram)
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{} 0
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eval instant at 5m histogram_fraction(0, 4, balanced_histogram)
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{} 0.5
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# If the quantile happens to be located in a span of empty buckets, the actually returned value is the lower bound of
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# the first populated bucket after the span of empty buckets.
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eval instant at 5m histogram_quantile(0.5, balanced_histogram)
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{} 0.5
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clear
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# Add histogram to test sum(last_over_time) regression
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load 5m
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incr_sum_histogram{number="1"} {{schema:0 sum:0 count:0 buckets:[1]}}+{{schema:0 sum:1 count:1 buckets:[1]}}x10
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incr_sum_histogram{number="2"} {{schema:0 sum:0 count:0 buckets:[1]}}+{{schema:0 sum:2 count:1 buckets:[1]}}x10
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eval instant at 50m histogram_sum(sum(incr_sum_histogram))
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{} 30
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eval instant at 50m histogram_sum(sum(last_over_time(incr_sum_histogram[5m])))
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{} 30
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clear
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# Apply rate function to histogram.
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load 15s
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histogram_rate {{schema:1 count:12 sum:18.4 z_bucket:2 z_bucket_w:0.001 buckets:[1 2 0 1 1] n_buckets:[1 2 0 1 1]}}+{{schema:1 count:9 sum:18.4 z_bucket:1 z_bucket_w:0.001 buckets:[1 1 0 1 1] n_buckets:[1 1 0 1 1]}}x100
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eval instant at 5m rate(histogram_rate[45s])
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expect no_warn
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{} {{schema:1 count:0.6 sum:1.2266666666666652 z_bucket:0.06666666666666667 z_bucket_w:0.001 buckets:[0.06666666666666667 0.06666666666666667 0 0.06666666666666667 0.06666666666666667] n_buckets:[0.06666666666666667 0.06666666666666667 0 0.06666666666666667 0.06666666666666667]}}
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eval range from 5m to 5m30s step 30s rate(histogram_rate[45s])
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expect no_warn
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{} {{schema:1 count:0.6 sum:1.2266666666666652 z_bucket:0.06666666666666667 z_bucket_w:0.001 buckets:[0.06666666666666667 0.06666666666666667 0 0.06666666666666667 0.06666666666666667] n_buckets:[0.06666666666666667 0.06666666666666667 0 0.06666666666666667 0.06666666666666667]}}x1
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clear
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# Apply count and sum function to histogram.
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load 10m
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histogram_count_sum_2 {{schema:0 count:24 sum:100 z_bucket:4 z_bucket_w:0.001 buckets:[2 3 0 1 4] n_buckets:[2 3 0 1 4]}}x1
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eval instant at 10m histogram_count(histogram_count_sum_2)
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{} 24
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eval instant at 10m histogram_sum(histogram_count_sum_2)
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{} 100
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clear
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# Apply stddev and stdvar function to histogram with {1, 2, 3, 4} (low res).
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load 10m
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histogram_stddev_stdvar_1 {{schema:2 count:4 sum:10 buckets:[1 0 0 0 1 0 0 1 1]}}x1
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eval instant at 10m histogram_stddev(histogram_stddev_stdvar_1)
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{} 1.0787993180043811
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eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_1)
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{} 1.163807968526718
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clear
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# Apply stddev and stdvar function to histogram with {1, 1, 1, 1} (high res).
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load 10m
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histogram_stddev_stdvar_2 {{schema:8 count:10 sum:10 buckets:[1 2 3 4]}}x1
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eval instant at 10m histogram_stddev(histogram_stddev_stdvar_2)
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{} 0.0048960313898237465
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eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_2)
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{} 2.3971123370139447e-05
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clear
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# Apply stddev and stdvar function to histogram with {-50, -8, 0, 3, 8, 9}.
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load 10m
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histogram_stddev_stdvar_3 {{schema:3 count:7 sum:62 z_bucket:1 buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ] n_buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ]}}x1
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eval instant at 10m histogram_stddev(histogram_stddev_stdvar_3)
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{} 42.94723640026
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eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_3)
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{} 1844.4651144196398
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clear
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# Apply stddev and stdvar function to histogram with {-100000, -10000, -1000, -888, -888, -100, -50, -9, -8, -3}.
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load 10m
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histogram_stddev_stdvar_4 {{schema:0 count:10 sum:-112946 z_bucket:0 n_buckets:[0 0 1 1 1 0 1 1 0 0 3 0 0 0 1 0 0 1]}}x1
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eval instant at 10m histogram_stddev(histogram_stddev_stdvar_4)
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{} 27556.344499842
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eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_4)
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{} 759352122.1939945
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clear
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# Apply stddev and stdvar function to histogram with {-10x10}.
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load 10m
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histogram_stddev_stdvar_5 {{schema:0 count:10 sum:-100 z_bucket:0 n_buckets:[0 0 0 0 10]}}x1
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eval instant at 10m histogram_stddev(histogram_stddev_stdvar_5)
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{} 1.3137084989848
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eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_5)
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{} 1.725830020304794
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clear
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# Apply stddev and stdvar function to histogram with {-50, -8, 0, 3, 8, 9, NaN}.
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load 10m
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histogram_stddev_stdvar_6 {{schema:3 count:7 sum:NaN z_bucket:1 buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ] n_buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ]}}x1
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eval instant at 10m histogram_stddev(histogram_stddev_stdvar_6)
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{} NaN
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eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_6)
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{} NaN
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clear
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# Apply stddev and stdvar function to histogram with {-50, -8, 0, 3, 8, 9, Inf}.
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load 10m
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histogram_stddev_stdvar_7 {{schema:3 count:7 sum:Inf z_bucket:1 buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ] n_buckets:[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ]}}x1
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eval instant at 10m histogram_stddev(histogram_stddev_stdvar_7)
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{} Inf
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eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_7)
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{} Inf
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clear
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# Apply quantile function to histogram with all positive buckets with zero bucket.
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load 10m
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histogram_quantile_1 {{schema:0 count:12 sum:100 z_bucket:2 z_bucket_w:0.001 buckets:[2 3 0 1 4]}}x1
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eval instant at 10m histogram_quantile(1.001, histogram_quantile_1)
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expect warn
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{} Inf
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eval instant at 10m histogram_quantile(1, histogram_quantile_1)
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expect no_warn
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{} 16
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# The following quantiles are within a bucket. Exponential
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# interpolation is applied (rather than linear, as it is done for
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# classic histograms), leading to slightly different quantile values.
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eval instant at 10m histogram_quantile(0.99, histogram_quantile_1)
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expect no_warn
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{} 15.67072476139083
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eval instant at 10m histogram_quantile(0.9, histogram_quantile_1)
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expect no_warn
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{} 12.99603834169977
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eval instant at 10m histogram_quantile(0.6, histogram_quantile_1)
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expect no_warn
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{} 4.594793419988138
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eval instant at 10m histogram_quantile(0.5, histogram_quantile_1)
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expect no_warn
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{} 1.5874010519681994
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# Linear interpolation within the zero bucket after all.
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eval instant at 10m histogram_quantile(0.1, histogram_quantile_1)
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expect no_warn
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{} 0.0006
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eval instant at 10m histogram_quantile(0, histogram_quantile_1)
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expect no_warn
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{} 0
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|
|
eval instant at 10m histogram_quantile(-1, histogram_quantile_1)
|
|
expect warn
|
|
{} -Inf
|
|
|
|
clear
|
|
|
|
# Apply quantile function to histogram with all negative buckets with zero bucket.
|
|
load 10m
|
|
histogram_quantile_2 {{schema:0 count:12 sum:100 z_bucket:2 z_bucket_w:0.001 n_buckets:[2 3 0 1 4]}}x1
|
|
|
|
eval instant at 10m histogram_quantile(1.001, histogram_quantile_2)
|
|
expect warn
|
|
{} Inf
|
|
|
|
eval instant at 10m histogram_quantile(1, histogram_quantile_2)
|
|
expect no_warn
|
|
{} 0
|
|
|
|
# Again, the quantile values here are slightly different from what
|
|
# they would be with linear interpolation. Note that quantiles
|
|
# ending up in the zero bucket are linearly interpolated after all.
|
|
eval instant at 10m histogram_quantile(0.99, histogram_quantile_2)
|
|
expect no_warn
|
|
{} -0.00006
|
|
|
|
eval instant at 10m histogram_quantile(0.9, histogram_quantile_2)
|
|
expect no_warn
|
|
{} -0.0006
|
|
|
|
eval instant at 10m histogram_quantile(0.5, histogram_quantile_2)
|
|
expect no_warn
|
|
{} -1.5874010519681996
|
|
|
|
eval instant at 10m histogram_quantile(0.1, histogram_quantile_2)
|
|
expect no_warn
|
|
{} -12.996038341699768
|
|
|
|
eval instant at 10m histogram_quantile(0, histogram_quantile_2)
|
|
expect no_warn
|
|
{} -16
|
|
|
|
eval instant at 10m histogram_quantile(-1, histogram_quantile_2)
|
|
expect warn
|
|
{} -Inf
|
|
|
|
clear
|
|
|
|
# Apply quantile function to histogram with both positive and negative
|
|
# buckets with zero bucket.
|
|
# First positive buckets with exponential interpolation.
|
|
load 10m
|
|
histogram_quantile_3 {{schema:0 count:24 sum:100 z_bucket:4 z_bucket_w:0.001 buckets:[2 3 0 1 4] n_buckets:[2 3 0 1 4]}}x1
|
|
|
|
eval instant at 10m histogram_quantile(1.001, histogram_quantile_3)
|
|
expect warn
|
|
{} Inf
|
|
|
|
eval instant at 10m histogram_quantile(1, histogram_quantile_3)
|
|
expect no_warn
|
|
{} 16
|
|
|
|
eval instant at 10m histogram_quantile(0.99, histogram_quantile_3)
|
|
expect no_warn
|
|
{} 15.34822590920423
|
|
|
|
eval instant at 10m histogram_quantile(0.9, histogram_quantile_3)
|
|
expect no_warn
|
|
{} 10.556063286183155
|
|
|
|
eval instant at 10m histogram_quantile(0.7, histogram_quantile_3)
|
|
expect no_warn
|
|
{} 1.2030250360821164
|
|
|
|
# Linear interpolation in the zero bucket, symmetrically centered around
|
|
# the zero point.
|
|
eval instant at 10m histogram_quantile(0.55, histogram_quantile_3)
|
|
expect no_warn
|
|
{} 0.0006
|
|
|
|
eval instant at 10m histogram_quantile(0.5, histogram_quantile_3)
|
|
expect no_warn
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_quantile(0.45, histogram_quantile_3)
|
|
expect no_warn
|
|
{} -0.0006
|
|
|
|
# Finally negative buckets with mirrored exponential interpolation.
|
|
eval instant at 10m histogram_quantile(0.3, histogram_quantile_3)
|
|
expect no_warn
|
|
{} -1.2030250360821169
|
|
|
|
eval instant at 10m histogram_quantile(0.1, histogram_quantile_3)
|
|
expect no_warn
|
|
{} -10.556063286183155
|
|
|
|
eval instant at 10m histogram_quantile(0.01, histogram_quantile_3)
|
|
expect no_warn
|
|
{} -15.34822590920423
|
|
|
|
eval instant at 10m histogram_quantile(0, histogram_quantile_3)
|
|
expect no_warn
|
|
{} -16
|
|
|
|
eval instant at 10m histogram_quantile(-1, histogram_quantile_3)
|
|
expect warn
|
|
{} -Inf
|
|
|
|
clear
|
|
|
|
# Try different schemas. (The interpolation logic must not depend on the schema.)
|
|
clear
|
|
load 1m
|
|
var_res_histogram{schema="-1"} {{schema:-1 sum:6 count:5 buckets:[0 5]}}
|
|
var_res_histogram{schema="0"} {{schema:0 sum:4 count:5 buckets:[0 5]}}
|
|
var_res_histogram{schema="+1"} {{schema:1 sum:4 count:5 buckets:[0 5]}}
|
|
|
|
eval instant at 1m histogram_quantile(0.5, var_res_histogram)
|
|
{schema="-1"} 2.0
|
|
{schema="0"} 1.4142135623730951
|
|
{schema="+1"} 1.189207
|
|
|
|
eval instant at 1m histogram_fraction(0, 2, var_res_histogram{schema="-1"})
|
|
{schema="-1"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(0, 1.4142135623730951, var_res_histogram{schema="0"})
|
|
{schema="0"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(0, 1.189207, var_res_histogram{schema="+1"})
|
|
{schema="+1"} 0.5
|
|
|
|
# The same as above, but one bucket "further to the right".
|
|
clear
|
|
load 1m
|
|
var_res_histogram{schema="-1"} {{schema:-1 sum:6 count:5 buckets:[0 0 5]}}
|
|
var_res_histogram{schema="0"} {{schema:0 sum:4 count:5 buckets:[0 0 5]}}
|
|
var_res_histogram{schema="+1"} {{schema:1 sum:4 count:5 buckets:[0 0 5]}}
|
|
|
|
eval instant at 1m histogram_quantile(0.5, var_res_histogram)
|
|
{schema="-1"} 8.0
|
|
{schema="0"} 2.82842712474619
|
|
{schema="+1"} 1.6817928305074292
|
|
|
|
eval instant at 1m histogram_fraction(0, 8, var_res_histogram{schema="-1"})
|
|
{schema="-1"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(0, 2.82842712474619, var_res_histogram{schema="0"})
|
|
{schema="0"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(0, 1.6817928305074292, var_res_histogram{schema="+1"})
|
|
{schema="+1"} 0.5
|
|
|
|
# And everything again but for negative buckets.
|
|
clear
|
|
load 1m
|
|
var_res_histogram{schema="-1"} {{schema:-1 sum:6 count:5 n_buckets:[0 5]}}
|
|
var_res_histogram{schema="0"} {{schema:0 sum:4 count:5 n_buckets:[0 5]}}
|
|
var_res_histogram{schema="+1"} {{schema:1 sum:4 count:5 n_buckets:[0 5]}}
|
|
|
|
eval instant at 1m histogram_quantile(0.5, var_res_histogram)
|
|
{schema="-1"} -2.0
|
|
{schema="0"} -1.4142135623730951
|
|
{schema="+1"} -1.189207
|
|
|
|
eval instant at 1m histogram_fraction(-2, 0, var_res_histogram{schema="-1"})
|
|
{schema="-1"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(-1.4142135623730951, 0, var_res_histogram{schema="0"})
|
|
{schema="0"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(-1.189207, 0, var_res_histogram{schema="+1"})
|
|
{schema="+1"} 0.5
|
|
|
|
clear
|
|
load 1m
|
|
var_res_histogram{schema="-1"} {{schema:-1 sum:6 count:5 n_buckets:[0 0 5]}}
|
|
var_res_histogram{schema="0"} {{schema:0 sum:4 count:5 n_buckets:[0 0 5]}}
|
|
var_res_histogram{schema="+1"} {{schema:1 sum:4 count:5 n_buckets:[0 0 5]}}
|
|
|
|
eval instant at 1m histogram_quantile(0.5, var_res_histogram)
|
|
{schema="-1"} -8.0
|
|
{schema="0"} -2.82842712474619
|
|
{schema="+1"} -1.6817928305074292
|
|
|
|
eval instant at 1m histogram_fraction(-8, 0, var_res_histogram{schema="-1"})
|
|
{schema="-1"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(-2.82842712474619, 0, var_res_histogram{schema="0"})
|
|
{schema="0"} 0.5
|
|
|
|
eval instant at 1m histogram_fraction(-1.6817928305074292, 0, var_res_histogram{schema="+1"})
|
|
{schema="+1"} 0.5
|
|
|
|
|
|
# Apply fraction function to empty histogram.
|
|
load 10m
|
|
histogram_fraction_1 {{}}x1
|
|
|
|
eval instant at 10m histogram_fraction(3.1415, 42, histogram_fraction_1)
|
|
{} NaN
|
|
|
|
clear
|
|
|
|
# Apply fraction function to histogram with positive and zero buckets.
|
|
load 10m
|
|
histogram_fraction_2 {{schema:0 count:12 sum:100 z_bucket:2 z_bucket_w:0.001 buckets:[2 3 0 1 4]}}x1
|
|
|
|
eval instant at 10m histogram_fraction(0, +Inf, histogram_fraction_2)
|
|
{} 1
|
|
|
|
eval instant at 10m histogram_fraction(-Inf, 0, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-0.001, 0, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(0, 0.001, histogram_fraction_2)
|
|
{} 0.16666666666666666
|
|
|
|
# Note that this result and the one above add up to 1.
|
|
eval instant at 10m histogram_fraction(0.001, inf, histogram_fraction_2)
|
|
{} 0.8333333333333334
|
|
|
|
# We are in the zero bucket, resulting in linear interpolation
|
|
eval instant at 10m histogram_fraction(0, 0.0005, histogram_fraction_2)
|
|
{} 0.08333333333333333
|
|
|
|
# Demonstrate that the inverse operation with histogram_quantile yields
|
|
# the original value with the non-trivial result above.
|
|
eval instant at 10m histogram_quantile(0.08333333333333333, histogram_fraction_2)
|
|
{} 0.0005
|
|
|
|
eval instant at 10m histogram_fraction(-inf, -0.001, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(1, 2, histogram_fraction_2)
|
|
{} 0.25
|
|
|
|
# More non-trivial results with interpolation involved below, including
|
|
# some round-trips via histogram_quantile to prove that the inverse
|
|
# operation leads to the same results.
|
|
|
|
eval instant at 10m histogram_fraction(0, 1.5, histogram_fraction_2)
|
|
{} 0.4795739585136224
|
|
|
|
eval instant at 10m histogram_fraction(1.5, 2, histogram_fraction_2)
|
|
{} 0.10375937481971091
|
|
|
|
eval instant at 10m histogram_fraction(1, 8, histogram_fraction_2)
|
|
{} 0.3333333333333333
|
|
|
|
eval instant at 10m histogram_fraction(0, 6, histogram_fraction_2)
|
|
{} 0.6320802083934297
|
|
|
|
eval instant at 10m histogram_quantile(0.6320802083934297, histogram_fraction_2)
|
|
{} 6
|
|
|
|
eval instant at 10m histogram_fraction(1, 6, histogram_fraction_2)
|
|
{} 0.29874687506009634
|
|
|
|
eval instant at 10m histogram_fraction(1.5, 6, histogram_fraction_2)
|
|
{} 0.15250624987980724
|
|
|
|
eval instant at 10m histogram_fraction(-2, -1, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-2, -1.5, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-8, -1, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-6, -1, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-6, -1.5, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(42, 3.1415, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(0, 0, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(0.000001, 0.000001, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(42, 42, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-3.1, -3.1, histogram_fraction_2)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(3.1415, NaN, histogram_fraction_2)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(NaN, 42, histogram_fraction_2)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(NaN, NaN, histogram_fraction_2)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(-Inf, +Inf, histogram_fraction_2)
|
|
{} 1
|
|
|
|
# Apply fraction function to histogram with negative and zero buckets.
|
|
load 10m
|
|
histogram_fraction_3 {{schema:0 count:12 sum:100 z_bucket:2 z_bucket_w:0.001 n_buckets:[2 3 0 1 4]}}x1
|
|
|
|
eval instant at 10m histogram_fraction(0, +Inf, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-Inf, 0, histogram_fraction_3)
|
|
{} 1
|
|
|
|
eval instant at 10m histogram_fraction(-0.001, 0, histogram_fraction_3)
|
|
{} 0.16666666666666666
|
|
|
|
eval instant at 10m histogram_fraction(0, 0.001, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-0.0005, 0, histogram_fraction_3)
|
|
{} 0.08333333333333333
|
|
|
|
eval instant at 10m histogram_fraction(-inf, -0.0005, histogram_fraction_3)
|
|
{} 0.9166666666666666
|
|
|
|
eval instant at 10m histogram_quantile(0.9166666666666666, histogram_fraction_3)
|
|
{} -0.0005
|
|
|
|
eval instant at 10m histogram_fraction(0.001, inf, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-inf, -0.001, histogram_fraction_3)
|
|
{} 0.8333333333333334
|
|
|
|
eval instant at 10m histogram_fraction(1, 2, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(1.5, 2, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(1, 8, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(1, 6, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(1.5, 6, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-2, -1, histogram_fraction_3)
|
|
{} 0.25
|
|
|
|
eval instant at 10m histogram_fraction(-2, -1.5, histogram_fraction_3)
|
|
{} 0.10375937481971091
|
|
|
|
eval instant at 10m histogram_fraction(-8, -1, histogram_fraction_3)
|
|
{} 0.3333333333333333
|
|
|
|
eval instant at 10m histogram_fraction(-inf, -6, histogram_fraction_3)
|
|
{} 0.36791979160657035
|
|
|
|
eval instant at 10m histogram_quantile(0.36791979160657035, histogram_fraction_3)
|
|
{} -6
|
|
|
|
eval instant at 10m histogram_fraction(-6, -1, histogram_fraction_3)
|
|
{} 0.29874687506009634
|
|
|
|
eval instant at 10m histogram_fraction(-6, -1.5, histogram_fraction_3)
|
|
{} 0.15250624987980724
|
|
|
|
eval instant at 10m histogram_fraction(42, 3.1415, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(0, 0, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(0.000001, 0.000001, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(42, 42, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-3.1, -3.1, histogram_fraction_3)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(3.1415, NaN, histogram_fraction_3)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(NaN, 42, histogram_fraction_3)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(NaN, NaN, histogram_fraction_3)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(-Inf, +Inf, histogram_fraction_3)
|
|
{} 1
|
|
|
|
clear
|
|
|
|
# Apply fraction function to histogram with both positive, negative and zero buckets.
|
|
load 10m
|
|
histogram_fraction_4 {{schema:0 count:24 sum:100 z_bucket:4 z_bucket_w:0.001 buckets:[2 3 0 1 4] n_buckets:[2 3 0 1 4]}}x1
|
|
|
|
eval instant at 10m histogram_fraction(0, +Inf, histogram_fraction_4)
|
|
{} 0.5
|
|
|
|
eval instant at 10m histogram_fraction(-Inf, 0, histogram_fraction_4)
|
|
{} 0.5
|
|
|
|
eval instant at 10m histogram_fraction(-0.001, 0, histogram_fraction_4)
|
|
{} 0.08333333333333333
|
|
|
|
eval instant at 10m histogram_fraction(0, 0.001, histogram_fraction_4)
|
|
{} 0.08333333333333333
|
|
|
|
eval instant at 10m histogram_fraction(-0.0005, 0.0005, histogram_fraction_4)
|
|
{} 0.08333333333333333
|
|
|
|
eval instant at 10m histogram_fraction(-inf, 0.0005, histogram_fraction_4)
|
|
{} 0.5416666666666666
|
|
|
|
eval instant at 10m histogram_quantile(0.5416666666666666, histogram_fraction_4)
|
|
{} 0.0005
|
|
|
|
eval instant at 10m histogram_fraction(-inf, -0.0005, histogram_fraction_4)
|
|
{} 0.4583333333333333
|
|
|
|
eval instant at 10m histogram_quantile(0.4583333333333333, histogram_fraction_4)
|
|
{} -0.0005
|
|
|
|
eval instant at 10m histogram_fraction(0.001, inf, histogram_fraction_4)
|
|
{} 0.4166666666666667
|
|
|
|
eval instant at 10m histogram_fraction(-inf, -0.001, histogram_fraction_4)
|
|
{} 0.4166666666666667
|
|
|
|
eval instant at 10m histogram_fraction(1, 2, histogram_fraction_4)
|
|
{} 0.125
|
|
|
|
eval instant at 10m histogram_fraction(1.5, 2, histogram_fraction_4)
|
|
{} 0.051879687409855414
|
|
|
|
eval instant at 10m histogram_fraction(1, 8, histogram_fraction_4)
|
|
{} 0.16666666666666666
|
|
|
|
eval instant at 10m histogram_fraction(1, 6, histogram_fraction_4)
|
|
{} 0.14937343753004825
|
|
|
|
eval instant at 10m histogram_fraction(1.5, 6, histogram_fraction_4)
|
|
{} 0.07625312493990366
|
|
|
|
eval instant at 10m histogram_fraction(-2, -1, histogram_fraction_4)
|
|
{} 0.125
|
|
|
|
eval instant at 10m histogram_fraction(-2, -1.5, histogram_fraction_4)
|
|
{} 0.051879687409855456
|
|
|
|
eval instant at 10m histogram_fraction(-8, -1, histogram_fraction_4)
|
|
{} 0.16666666666666666
|
|
|
|
eval instant at 10m histogram_fraction(-6, -1, histogram_fraction_4)
|
|
{} 0.14937343753004817
|
|
|
|
eval instant at 10m histogram_fraction(-6, -1.5, histogram_fraction_4)
|
|
{} 0.07625312493990362
|
|
|
|
eval instant at 10m histogram_fraction(42, 3.1415, histogram_fraction_4)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(0, 0, histogram_fraction_4)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(0.000001, 0.000001, histogram_fraction_4)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(42, 42, histogram_fraction_4)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(-3.1, -3.1, histogram_fraction_4)
|
|
{} 0
|
|
|
|
eval instant at 10m histogram_fraction(3.1415, NaN, histogram_fraction_4)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(NaN, 42, histogram_fraction_4)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(NaN, NaN, histogram_fraction_4)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_fraction(-Inf, +Inf, histogram_fraction_4)
|
|
{} 1
|
|
|
|
eval instant at 10m histogram_sum(scalar(histogram_fraction(-Inf, +Inf, sum(histogram_fraction_4))) * histogram_fraction_4)
|
|
{} 100
|
|
|
|
# Apply multiplication and division operator to histogram.
|
|
load 10m
|
|
histogram_mul_div {{schema:0 count:30 sum:33 z_bucket:3 z_bucket_w:0.001 buckets:[3 3 3] n_buckets:[6 6 6]}}x1
|
|
float_series_3 3+0x1
|
|
float_series_0 0+0x1
|
|
|
|
eval instant at 10m histogram_mul_div*3
|
|
expect no_info
|
|
{} {{schema:0 count:90 sum:99 z_bucket:9 z_bucket_w:0.001 buckets:[9 9 9] n_buckets:[18 18 18]}}
|
|
|
|
eval instant at 10m histogram_mul_div*-1
|
|
expect no_info
|
|
{} {{schema:0 count:-30 sum:-33 z_bucket:-3 z_bucket_w:0.001 buckets:[-3 -3 -3] n_buckets:[-6 -6 -6]}}
|
|
|
|
eval instant at 10m -histogram_mul_div
|
|
expect no_info
|
|
{} {{schema:0 count:-30 sum:-33 z_bucket:-3 z_bucket_w:0.001 buckets:[-3 -3 -3] n_buckets:[-6 -6 -6]}}
|
|
|
|
eval instant at 10m histogram_mul_div*-3
|
|
expect no_info
|
|
{} {{schema:0 count:-90 sum:-99 z_bucket:-9 z_bucket_w:0.001 buckets:[-9 -9 -9] n_buckets:[-18 -18 -18]}}
|
|
|
|
eval instant at 10m 3*histogram_mul_div
|
|
expect no_info
|
|
{} {{schema:0 count:90 sum:99 z_bucket:9 z_bucket_w:0.001 buckets:[9 9 9] n_buckets:[18 18 18]}}
|
|
|
|
eval instant at 10m histogram_mul_div*float_series_3
|
|
expect no_info
|
|
{} {{schema:0 count:90 sum:99 z_bucket:9 z_bucket_w:0.001 buckets:[9 9 9] n_buckets:[18 18 18]}}
|
|
|
|
eval instant at 10m float_series_3*histogram_mul_div
|
|
expect no_info
|
|
{} {{schema:0 count:90 sum:99 z_bucket:9 z_bucket_w:0.001 buckets:[9 9 9] n_buckets:[18 18 18]}}
|
|
|
|
eval instant at 10m histogram_mul_div/3
|
|
expect no_info
|
|
{} {{schema:0 count:10 sum:11 z_bucket:1 z_bucket_w:0.001 buckets:[1 1 1] n_buckets:[2 2 2]}}
|
|
|
|
eval instant at 10m histogram_mul_div/-3
|
|
expect no_info
|
|
{} {{schema:0 count:-10 sum:-11 z_bucket:-1 z_bucket_w:0.001 buckets:[-1 -1 -1] n_buckets:[-2 -2 -2]}}
|
|
|
|
eval instant at 10m histogram_mul_div/float_series_3
|
|
expect no_info
|
|
{} {{schema:0 count:10 sum:11 z_bucket:1 z_bucket_w:0.001 buckets:[1 1 1] n_buckets:[2 2 2]}}
|
|
|
|
eval instant at 10m histogram_mul_div*0
|
|
expect no_info
|
|
{} {{schema:0 count:0 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 0] n_buckets:[0 0 0]}}
|
|
|
|
eval instant at 10m 0*histogram_mul_div
|
|
expect no_info
|
|
{} {{schema:0 count:0 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 0] n_buckets:[0 0 0]}}
|
|
|
|
eval instant at 10m histogram_mul_div*float_series_0
|
|
expect no_info
|
|
{} {{schema:0 count:0 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 0] n_buckets:[0 0 0]}}
|
|
|
|
eval instant at 10m float_series_0*histogram_mul_div
|
|
expect no_info
|
|
{} {{schema:0 count:0 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 0] n_buckets:[0 0 0]}}
|
|
|
|
eval instant at 10m histogram_mul_div/0
|
|
expect no_info
|
|
{} {{schema:0 count:Inf sum:Inf z_bucket_w:0.001 z_bucket:Inf}}
|
|
|
|
eval instant at 10m histogram_mul_div/float_series_0
|
|
expect no_info
|
|
{} {{schema:0 count:Inf sum:Inf z_bucket_w:0.001 z_bucket:Inf}}
|
|
|
|
eval instant at 10m histogram_mul_div*0/0
|
|
expect no_info
|
|
{} {{schema:0 count:NaN sum:NaN z_bucket_w:0.001 z_bucket:NaN}}
|
|
|
|
eval instant at 10m histogram_mul_div*histogram_mul_div
|
|
expect info
|
|
|
|
eval instant at 10m histogram_mul_div/histogram_mul_div
|
|
expect info
|
|
|
|
eval instant at 10m float_series_3/histogram_mul_div
|
|
expect info
|
|
|
|
eval instant at 10m 0/histogram_mul_div
|
|
expect info
|
|
|
|
clear
|
|
|
|
# Apply binary operators to mixed histogram and float samples.
|
|
# TODO:(NeerajGartia21) move these tests to their respective locations when tests from engine_test.go are be moved here.
|
|
|
|
load 10m
|
|
histogram_sample {{schema:0 count:24 sum:100 z_bucket:4 z_bucket_w:0.001 buckets:[2 3 0 1 4] n_buckets:[2 3 0 1 4]}}x1
|
|
float_sample 0x1
|
|
|
|
eval instant at 10m float_sample+histogram_sample
|
|
expect info
|
|
|
|
eval instant at 10m histogram_sample+float_sample
|
|
expect info
|
|
|
|
eval instant at 10m float_sample-histogram_sample
|
|
expect info
|
|
|
|
eval instant at 10m histogram_sample-float_sample
|
|
expect info
|
|
|
|
# Counter reset only noticeable in a single bucket.
|
|
load 5m
|
|
reset_in_bucket {{schema:0 count:4 sum:5 buckets:[1 2 1]}} {{schema:0 count:5 sum:6 buckets:[1 1 3]}} {{schema:0 count:6 sum:7 buckets:[1 2 3]}}
|
|
|
|
eval instant at 10m increase(reset_in_bucket[15m])
|
|
expect no_warn
|
|
{} {{count:9 sum:10.5 buckets:[1.5 3 4.5]}}
|
|
|
|
# The following two test the "fast path" where only sum and count is decoded.
|
|
eval instant at 10m histogram_count(increase(reset_in_bucket[15m]))
|
|
expect no_warn
|
|
{} 9
|
|
|
|
eval instant at 10m histogram_sum(increase(reset_in_bucket[15m]))
|
|
expect no_warn
|
|
{} 10.5
|
|
|
|
clear
|
|
|
|
# Test native histograms with custom buckets.
|
|
load 5m
|
|
custom_buckets_histogram {{schema:-53 sum:5 count:4 custom_values:[5 10] buckets:[1 2 1]}}x10
|
|
|
|
eval instant at 5m histogram_fraction(5, 10, custom_buckets_histogram)
|
|
{} 0.5
|
|
|
|
eval instant at 5m histogram_quantile(0.5, custom_buckets_histogram)
|
|
{} 7.5
|
|
|
|
eval instant at 5m sum(custom_buckets_histogram)
|
|
{} {{schema:-53 sum:5 count:4 custom_values:[5 10] buckets:[1 2 1]}}
|
|
|
|
clear
|
|
|
|
# Test 'this native histogram metric is not a counter' warning for rate
|
|
load 30s
|
|
some_metric {{schema:0 sum:1 count:1 buckets:[1] counter_reset_hint:gauge}} {{schema:0 sum:2 count:2 buckets:[2] counter_reset_hint:gauge}} {{schema:0 sum:3 count:3 buckets:[3] counter_reset_hint:gauge}}
|
|
|
|
# Test the case where we only have two points for rate
|
|
eval instant at 30s rate(some_metric[1m])
|
|
expect warn msg: PromQL warning: this native histogram metric is not a counter: "some_metric"
|
|
{} {{count:0.03333333333333333 sum:0.03333333333333333 buckets:[0.03333333333333333]}}
|
|
|
|
# Test the case where we have more than two points for rate
|
|
eval instant at 1m rate(some_metric[1m30s])
|
|
expect warn msg: PromQL warning: this native histogram metric is not a counter: "some_metric"
|
|
{} {{count:0.03333333333333333 sum:0.03333333333333333 buckets:[0.03333333333333333]}}
|
|
|
|
clear
|
|
|
|
# Test rate() over mixed exponential and custom buckets.
|
|
load 30s
|
|
some_metric {{schema:0 sum:1 count:1 buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
# Start and end with exponential, with custom in the middle.
|
|
eval instant at 1m rate(some_metric[1m30s])
|
|
expect warn msg: PromQL warning: vector contains a mix of histograms with exponential and custom buckets schemas for metric name "some_metric"
|
|
# Should produce no results.
|
|
|
|
# Start and end with custom, with exponential in the middle.
|
|
eval instant at 1m30s rate(some_metric[1m30s])
|
|
expect warn msg: PromQL warning: vector contains a mix of histograms with exponential and custom buckets schemas for metric name "some_metric"
|
|
# Should produce no results.
|
|
|
|
# Start with custom, end with exponential. Return the exponential histogram divided by 48.
|
|
# (The 1st sample is the NHCB with count:1. It is mostly ignored with the exception of the
|
|
# count, which means the rate calculation extrapolates until the count hits 0.)
|
|
eval instant at 1m rate(some_metric[1m])
|
|
expect no_warn
|
|
{} {{count:0.08333333333333333 sum:0.10416666666666666 counter_reset_hint:gauge buckets:[0.020833333333333332 0.041666666666666664 0.020833333333333332]}}
|
|
|
|
# Start with exponential, end with custom. Return the custom buckets histogram divided by 30.
|
|
# (With the 2nd sample having a count of 1, the extrapolation to zero lands exactly at the
|
|
# left boundary of the range, so no extrapolation limitation needed.)
|
|
eval instant at 30s rate(some_metric[1m])
|
|
expect no_warn
|
|
{} {{schema:-53 sum:0.03333333333333333 count:0.03333333333333333 custom_values:[5 10] buckets:[0.03333333333333333]}}
|
|
|
|
clear
|
|
|
|
# Histogram with constant buckets.
|
|
load 1m
|
|
const_histogram {{schema:0 sum:1 count:1 buckets:[1 1 1]}} {{schema:0 sum:1 count:1 buckets:[1 1 1]}} {{schema:0 sum:1 count:1 buckets:[1 1 1]}} {{schema:0 sum:1 count:1 buckets:[1 1 1]}} {{schema:0 sum:1 count:1 buckets:[1 1 1]}}
|
|
|
|
# There is no change to the bucket count over time, thus rate is 0 in each bucket.
|
|
# However native histograms do not represent empty buckets, so here the zeros are implicit.
|
|
eval instant at 5m rate(const_histogram[5m])
|
|
expect no_warn
|
|
{} {{schema:0 sum:0 count:0}}
|
|
|
|
# Zero buckets mean no observations, thus the denominator in the average is 0
|
|
# leading to 0/0, which is NaN.
|
|
eval instant at 5m histogram_avg(rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} NaN
|
|
|
|
# Zero buckets mean no observations, so count is 0.
|
|
eval instant at 5m histogram_count(rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} 0.0
|
|
|
|
# Zero buckets mean no observations and empty histogram has a sum of 0 by definition.
|
|
eval instant at 5m histogram_sum(rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} 0.0
|
|
|
|
# Zero buckets mean no observations, thus the denominator in the fraction is 0,
|
|
# leading to 0/0, which is NaN.
|
|
eval instant at 5m histogram_fraction(0.0, 1.0, rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} NaN
|
|
|
|
# Workaround to calculate the observation count corresponding to NaN fraction.
|
|
eval instant at 5m histogram_count(rate(const_histogram[5m])) == 0.0 or histogram_fraction(0.0, 1.0, rate(const_histogram[5m])) * histogram_count(rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} 0.0
|
|
|
|
# Zero buckets mean no observations, so there is no value that observations fall below,
|
|
# which means that any quantile is a NaN.
|
|
eval instant at 5m histogram_quantile(1.0, rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} NaN
|
|
|
|
# Zero buckets mean no observations, so there is no standard deviation.
|
|
eval instant at 5m histogram_stddev(rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} NaN
|
|
|
|
# Zero buckets mean no observations, so there is no standard variance.
|
|
eval instant at 5m histogram_stdvar(rate(const_histogram[5m]))
|
|
expect no_warn
|
|
{} NaN
|
|
|
|
clear
|
|
|
|
# Test mixing exponential and custom buckets.
|
|
load 6m
|
|
metric{series="exponential"} {{sum:4 count:3 buckets:[1 2 1]}} _ {{sum:4 count:3 buckets:[1 2 1]}}
|
|
metric{series="other-exponential"} {{sum:3 count:2 buckets:[1 1 1]}} _ {{sum:3 count:2 buckets:[1 1 1]}}
|
|
metric{series="custom"} _ {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric{series="other-custom"} _ {{schema:-53 sum:15 count:2 custom_values:[5 10] buckets:[0 2]}} {{schema:-53 sum:15 count:2 custom_values:[5 10] buckets:[0 2]}}
|
|
|
|
# T=0: only exponential
|
|
# T=6: only custom
|
|
# T=12: mixed, should be ignored and emit a warning
|
|
eval range from 0 to 12m step 6m sum(metric)
|
|
expect warn
|
|
{} {{sum:7 count:5 buckets:[2 3 2]}} {{schema:-53 sum:16 count:3 custom_values:[5 10] buckets:[1 2]}} _
|
|
|
|
eval range from 0 to 12m step 6m avg(metric)
|
|
expect warn
|
|
{} {{sum:3.5 count:2.5 buckets:[1 1.5 1]}} {{schema:-53 sum:8 count:1.5 custom_values:[5 10] buckets:[0.5 1]}} _
|
|
|
|
clear
|
|
|
|
# Test mismatched custom bucket boundaries.
|
|
load 6m
|
|
metric{series="1"} _ {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric{series="2"} {{schema:-53 sum:1 count:1 custom_values:[10] buckets:[1]}} _ {{schema:-53 sum:1 count:1 custom_values:[5] buckets:[1]}}
|
|
metric{series="3"} {{schema:-53 sum:1 count:1 custom_values:[2 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
eval range from 0 to 12m step 6m sum(metric)
|
|
expect no_warn
|
|
{} {{schema:-53 count:2 sum:2 custom_values:[10] buckets:[2]}} {{schema:-53 sum:2 count:2 custom_values:[5 10] buckets:[2]}} {{schema:-53 count:3 sum:3 custom_values:[5] buckets:[3]}}
|
|
|
|
eval range from 0 to 12m step 6m avg(metric)
|
|
expect no_warn
|
|
{} {{schema:-53 count:1 sum:1 custom_values:[10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 count:1 sum:1 custom_values:[5] buckets:[1]}}
|
|
|
|
# Test mismatched boundaries with additional aggregation operators
|
|
eval range from 0 to 12m step 6m count(metric)
|
|
{} 2 2 3
|
|
|
|
eval range from 0 to 12m step 6m group(metric)
|
|
{} 1 1 1
|
|
|
|
eval range from 0 to 12m step 6m count(limitk(1, metric))
|
|
{} 1 1 1
|
|
|
|
eval range from 0 to 12m step 6m limitk(3, metric)
|
|
metric{series="1"} _ {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric{series="2"} {{schema:-53 sum:1 count:1 custom_values:[10] buckets:[1]}} _ {{schema:-53 sum:1 count:1 custom_values:[5] buckets:[1]}}
|
|
metric{series="3"} {{schema:-53 sum:1 count:1 custom_values:[2 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
eval range from 0 to 12m step 6m limit_ratio(1, metric)
|
|
metric{series="1"} _ {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric{series="2"} {{schema:-53 sum:1 count:1 custom_values:[10] buckets:[1]}} _ {{schema:-53 sum:1 count:1 custom_values:[5] buckets:[1]}}
|
|
metric{series="3"} {{schema:-53 sum:1 count:1 custom_values:[2 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
# Test mismatched schemas with and/or
|
|
eval range from 0 to 12m step 6m metric{series="1"} and ignoring(series) metric{series="2"}
|
|
metric{series="1"} _ _ {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
eval range from 0 to 12m step 6m metric{series="1"} or ignoring(series) metric{series="2"}
|
|
metric{series="1"} _ {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric{series="2"} {{schema:-53 sum:1 count:1 custom_values:[10] buckets:[1]}} _ _
|
|
|
|
# Test mismatched boundaries with arithmetic binary operators
|
|
eval range from 0 to 12m step 6m metric{series="2"} + ignoring (series) metric{series="3"}
|
|
expect info msg:PromQL info: mismatched custom buckets were reconciled during addition
|
|
{} {{schema:-53 count:2 sum:2 custom_values:[10] buckets:[2]}} _ {{schema:-53 count:2 sum:2 custom_values:[5] buckets:[2]}}
|
|
|
|
eval range from 0 to 12m step 6m metric{series="2"} - ignoring (series) metric{series="3"}
|
|
expect info msg:PromQL info: mismatched custom buckets were reconciled during subtraction
|
|
{} {{schema:-53 custom_values:[10] counter_reset_hint:gauge}} _ {{schema:-53 custom_values:[5] counter_reset_hint:gauge}}
|
|
|
|
clear
|
|
|
|
# Test mismatched boundaries with comparison binary operators
|
|
load 6m
|
|
metric1 {{schema:-53 sum:1 count:1 custom_values:[2] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric2 {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
eval range from 0 to 6m step 6m metric1 == metric2
|
|
expect no_info
|
|
metric1{} _ {{schema:-53 count:1 sum:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
eval range from 0 to 6m step 6m metric1 != metric2
|
|
expect no_info
|
|
metric1{} {{schema:-53 sum:1 count:1 custom_values:[2] buckets:[1]}} _
|
|
|
|
eval range from 0 to 6m step 6m metric2 > metric2
|
|
expect info
|
|
|
|
clear
|
|
|
|
load 6m
|
|
nhcb_metric {{schema:-53 sum:1 count:1 custom_values:[5] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
# If evaluating at 12m, the first two NHCBs have the same custom values
|
|
# while the 3rd one has different ones.
|
|
|
|
eval instant at 12m sum_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
expect info msg: PromQL info: mismatched custom buckets were reconciled during aggregation
|
|
{} {{schema:-53 count:3 sum:3 custom_values:[5] buckets:[3]}}
|
|
|
|
eval instant at 12m avg_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
expect info msg: PromQL info: mismatched custom buckets were reconciled during aggregation
|
|
{} {{schema:-53 count:1 sum:1 custom_values:[5] buckets:[1]}}
|
|
|
|
eval instant at 12m last_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
nhcb_metric{} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
eval instant at 12m count_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 3
|
|
|
|
eval instant at 12m present_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 1
|
|
|
|
eval instant at 12m changes(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 1
|
|
|
|
eval instant at 12m delta(nhcb_metric[13m])
|
|
expect warn msg: PromQL warning: this native histogram metric is not a gauge: "nhcb_metric"
|
|
{} {{schema:-53 custom_values:[5]}}
|
|
|
|
eval instant at 12m increase(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} {{schema:-53 custom_values:[5]}}
|
|
|
|
eval instant at 12m rate(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} {{schema:-53 custom_values:[5] }}
|
|
|
|
eval instant at 12m resets(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 0
|
|
|
|
# Now doing the same again, but at 18m, where the first NHCB has
|
|
# different custom_values compared to the other two.
|
|
|
|
eval instant at 18m sum_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} {{schema:-53 count:3 sum:3 custom_values:[5] buckets:[3]}}
|
|
|
|
eval instant at 18m avg_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} {{schema:-53 count:1 sum:1 custom_values:[5] buckets:[1]}}
|
|
|
|
eval instant at 18m last_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
nhcb_metric{} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
|
|
eval instant at 18m count_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 3
|
|
|
|
eval instant at 18m present_over_time(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 1
|
|
|
|
eval instant at 18m changes(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 1
|
|
|
|
eval instant at 18m delta(nhcb_metric[13m])
|
|
expect warn msg: PromQL warning: this native histogram metric is not a gauge: "nhcb_metric"
|
|
expect info msg: PromQL info: mismatched custom buckets were reconciled during subtraction
|
|
{} {{schema:-53 custom_values:[5]}}
|
|
|
|
eval instant at 18m increase(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} {{schema:-53 custom_values:[5]}}
|
|
|
|
eval instant at 18m rate(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} {{schema:-53 custom_values:[5]}}
|
|
|
|
eval instant at 18m resets(nhcb_metric[13m])
|
|
expect no_warn
|
|
{} 0
|
|
|
|
clear
|
|
|
|
load 1m
|
|
metric{group="just-floats", series="1"} 2
|
|
metric{group="just-floats", series="2"} 3
|
|
metric{group="just-exponential-histograms", series="1"} {{sum:3 count:4 buckets:[1 2 1]}}
|
|
metric{group="just-exponential-histograms", series="2"} {{sum:2 count:3 buckets:[1 1 1]}}
|
|
metric{group="just-custom-histograms", series="1"} {{schema:-53 sum:1 count:1 custom_values:[2] buckets:[1]}}
|
|
metric{group="just-custom-histograms", series="2"} {{schema:-53 sum:3 count:4 custom_values:[2] buckets:[7]}}
|
|
metric{group="floats-and-histograms", series="1"} 2
|
|
metric{group="floats-and-histograms", series="2"} {{sum:2 count:3 buckets:[1 1 1]}}
|
|
metric{group="exponential-and-custom-histograms", series="1"} {{sum:2 count:3 buckets:[1 1 1]}}
|
|
metric{group="exponential-and-custom-histograms", series="2"} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric{group="mismatched-custom-histograms", series="1"} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}}
|
|
metric{group="mismatched-custom-histograms", series="2"} {{schema:-53 sum:1 count:1 custom_values:[10] buckets:[1]}}
|
|
|
|
eval instant at 0 sum by (group) (metric)
|
|
expect warn
|
|
{group="just-floats"} 5
|
|
{group="just-exponential-histograms"} {{sum:5 count:7 buckets:[2 3 2]}}
|
|
{group="just-custom-histograms"} {{schema:-53 sum:4 count:5 custom_values:[2] buckets:[8]}}
|
|
{group="mismatched-custom-histograms"} {{schema:-53 count:2 sum:2 custom_values:[10] buckets:[2]}}
|
|
|
|
clear
|
|
|
|
# Test native histograms with sum, count, avg.
|
|
load 10m
|
|
histogram_sum{idx="0"} {{schema:0 count:25 sum:1234.5 z_bucket:4 z_bucket_w:0.001 buckets:[1 2 0 1 1] n_buckets:[2 4 0 0 1 9]}}x1
|
|
histogram_sum{idx="1"} {{schema:0 count:41 sum:2345.6 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
|
histogram_sum{idx="2"} {{schema:0 count:41 sum:1111.1 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
|
histogram_sum{idx="3"} {{schema:1 count:0}}x1
|
|
histogram_sum_float{idx="0"} 42.0x1
|
|
|
|
eval instant at 10m sum(histogram_sum)
|
|
expect no_warn
|
|
{} {{schema:0 count:107 sum:4691.2 z_bucket:14 z_bucket_w:0.001 buckets:[3 8 2 5 3 2 2] n_buckets:[2 6 8 4 15 9 0 0 0 10 10 4]}}
|
|
|
|
eval instant at 10m sum({idx="0"})
|
|
expect warn
|
|
|
|
eval instant at 10m sum(histogram_sum{idx="0"} + ignoring(idx) histogram_sum{idx="1"} + ignoring(idx) histogram_sum{idx="2"} + ignoring(idx) histogram_sum{idx="3"})
|
|
expect no_warn
|
|
{} {{schema:0 count:107 sum:4691.2 z_bucket:14 z_bucket_w:0.001 buckets:[3 8 2 5 3 2 2] n_buckets:[2 6 8 4 15 9 0 0 0 10 10 4]}}
|
|
|
|
eval instant at 10m count(histogram_sum)
|
|
expect no_warn
|
|
{} 4
|
|
|
|
eval instant at 10m avg(histogram_sum)
|
|
expect no_warn
|
|
{} {{schema:0 count:26.75 sum:1172.8 z_bucket:3.5 z_bucket_w:0.001 buckets:[0.75 2 0.5 1.25 0.75 0.5 0.5] n_buckets:[0.5 1.5 2 1 3.75 2.25 0 0 0 2.5 2.5 1]}}
|
|
|
|
clear
|
|
|
|
# Test native histograms with sum_over_time, avg_over_time.
|
|
load 1m
|
|
histogram_sum_over_time {{schema:0 count:25 sum:1234.5 z_bucket:4 z_bucket_w:0.001 buckets:[1 2 0 1 1] n_buckets:[2 4 0 0 1 9]}} {{schema:0 count:41 sum:2345.6 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}} {{schema:0 count:41 sum:1111.1 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}} {{schema:1 count:0}}
|
|
|
|
eval instant at 3m sum_over_time(histogram_sum_over_time[4m:1m])
|
|
{} {{schema:0 count:107 sum:4691.2 z_bucket:14 z_bucket_w:0.001 buckets:[3 8 2 5 3 2 2] n_buckets:[2 6 8 4 15 9 0 0 0 10 10 4]}}
|
|
|
|
eval instant at 3m avg_over_time(histogram_sum_over_time[4m:1m])
|
|
{} {{schema:0 count:26.75 sum:1172.8 z_bucket:3.5 z_bucket_w:0.001 buckets:[0.75 2 0.5 1.25 0.75 0.5 0.5] n_buckets:[0.5 1.5 2 1 3.75 2.25 0 0 0 2.5 2.5 1]}}
|
|
|
|
clear
|
|
|
|
# Test native histograms with sub operator.
|
|
load 10m
|
|
histogram_sub_1{idx="0"} {{schema:0 count:41 sum:2345.6 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
|
histogram_sub_1{idx="1"} {{schema:0 count:11 sum:1234.5 z_bucket:3 z_bucket_w:0.001 buckets:[0 2 1] n_buckets:[0 0 3 2]}}x1
|
|
histogram_sub_2{idx="0"} {{schema:0 count:41 sum:2345.6 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
|
histogram_sub_2{idx="1"} {{schema:1 count:11 sum:1234.5 z_bucket:3 z_bucket_w:0.001 buckets:[0 2 1] n_buckets:[0 0 3 2]}}x1
|
|
histogram_sub_3{idx="0"} {{schema:1 count:11 sum:1234.5 z_bucket:3 z_bucket_w:0.001 buckets:[0 2 1] n_buckets:[0 0 3 2]}}x1
|
|
histogram_sub_3{idx="1"} {{schema:0 count:41 sum:2345.6 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
|
|
|
eval instant at 10m histogram_sub_1{idx="0"} - ignoring(idx) histogram_sub_1{idx="1"}
|
|
{} {{schema:0 count:30 sum:1111.1 z_bucket:2 z_bucket_w:0.001 buckets:[1 1 0 2 1 1 1] n_buckets:[0 1 1 0 7 0 0 0 0 5 5 2]}}
|
|
|
|
eval instant at 10m histogram_sub_2{idx="0"} - ignoring(idx) histogram_sub_2{idx="1"}
|
|
{} {{schema:0 count:30 sum:1111.1 z_bucket:2 z_bucket_w:0.001 buckets:[1 0 1 2 1 1 1] n_buckets:[0 -2 2 2 7 0 0 0 0 5 5 2]}}
|
|
|
|
eval instant at 10m histogram_sub_3{idx="0"} - ignoring(idx) histogram_sub_3{idx="1"}
|
|
{} {{schema:0 count:-30 sum:-1111.1 z_bucket:-2 z_bucket_w:0.001 buckets:[-1 0 -1 -2 -1 -1 -1] n_buckets:[0 2 -2 -2 -7 0 0 0 0 -5 -5 -2]}}
|
|
|
|
clear
|
|
|
|
# Test native histograms with last_over_time subquery
|
|
load 2m
|
|
http_request_duration_seconds{pod="nginx-1"} {{schema:0 count:3 sum:14.00 buckets:[1 2]}}x20
|
|
|
|
eval range from 0s to 60s step 15s last_over_time({__name__="http_request_duration_seconds"} @ start()[1h:1m] offset 1m16s)
|
|
{__name__="http_request_duration_seconds", pod="nginx-1"} {{count:3 sum:14 buckets:[1 2]}}x4
|
|
|
|
clear
|
|
|
|
# Test native histogram quantile and fraction when the native histogram with exponential
|
|
# buckets has NaN observations.
|
|
load 1m
|
|
histogram_nan{case="100% NaNs"} {{schema:0 count:0 sum:0}} {{schema:0 count:3 sum:NaN}}
|
|
histogram_nan{case="20% NaNs"} {{schema:0 count:0 sum:0}} {{schema:0 count:15 sum:NaN buckets:[12]}}
|
|
|
|
eval instant at 1m histogram_quantile(1, histogram_nan)
|
|
expect info msg: PromQL info: input to histogram_quantile has NaN observations, result is NaN for metric name "histogram_nan"
|
|
{case="100% NaNs"} NaN
|
|
{case="20% NaNs"} NaN
|
|
|
|
eval instant at 1m histogram_quantile(0.81, histogram_nan)
|
|
expect info msg: PromQL info: input to histogram_quantile has NaN observations, result is NaN for metric name "histogram_nan"
|
|
{case="100% NaNs"} NaN
|
|
{case="20% NaNs"} NaN
|
|
|
|
eval instant at 1m histogram_quantile(0.8, histogram_nan{case="100% NaNs"})
|
|
expect info msg: PromQL info: input to histogram_quantile has NaN observations, result is NaN for metric name "histogram_nan"
|
|
{case="100% NaNs"} NaN
|
|
|
|
eval instant at 1m histogram_quantile(0.8, histogram_nan{case="20% NaNs"})
|
|
expect info msg: PromQL info: input to histogram_quantile has NaN observations, result is skewed higher for metric name "histogram_nan"
|
|
{case="20% NaNs"} 1
|
|
|
|
eval instant at 1m histogram_quantile(0.4, histogram_nan{case="100% NaNs"})
|
|
expect info msg: PromQL info: input to histogram_quantile has NaN observations, result is NaN for metric name "histogram_nan"
|
|
{case="100% NaNs"} NaN
|
|
|
|
# histogram_quantile and histogram_fraction equivalence if quantile is not NaN
|
|
eval instant at 1m histogram_quantile(0.4, histogram_nan{case="20% NaNs"})
|
|
expect info msg: PromQL info: input to histogram_quantile has NaN observations, result is skewed higher for metric name "histogram_nan"
|
|
{case="20% NaNs"} 0.7071067811865475
|
|
|
|
eval instant at 1m histogram_fraction(-Inf, 0.7071067811865475, histogram_nan)
|
|
expect info msg: PromQL info: input to histogram_fraction has NaN observations, which are excluded from all fractions for metric name "histogram_nan"
|
|
{case="100% NaNs"} 0.0
|
|
{case="20% NaNs"} 0.4
|
|
|
|
eval instant at 1m histogram_fraction(-Inf, +Inf, histogram_nan)
|
|
expect info msg: PromQL info: input to histogram_fraction has NaN observations, which are excluded from all fractions for metric name "histogram_nan"
|
|
{case="100% NaNs"} 0.0
|
|
{case="20% NaNs"} 0.8
|
|
|
|
clear
|
|
|
|
# Tests to demonstrate how an extrapolation below zero is prevented for both float counters and native counter histograms.
|
|
# Note that the float counter behaves the same as the histogram count after `increase`.
|
|
|
|
load 1m
|
|
metric{type="histogram"} {{schema:0 count:15 sum:25 buckets:[5 10]}} {{schema:0 count:2490 sum:75 buckets:[15 2475]}}x55
|
|
metric{type="counter"} 15 2490x55
|
|
|
|
# End of range coincides with sample. Zero point of count is reached within the range.
|
|
# Note that the 2nd bucket has an exaggerated increase of 2479.939393939394 (although
|
|
# it has a value of only 2475 at the end of the range).
|
|
eval instant at 55m increase(metric[90m])
|
|
expect no_warn
|
|
{type="histogram"} {{count:2490 sum:50.303030303030305 counter_reset_hint:gauge buckets:[10.06060606060606 2479.939393939394]}}
|
|
{type="counter"} 2490
|
|
|
|
# End of range does not coincide with sample. Zero point of count is reached within the range.
|
|
# The 2nd bucket again has an exaggerated increase, but it is less obvious because of the
|
|
# right-side extrapolation.
|
|
eval instant at 54m30s increase(metric[90m])
|
|
expect no_warn
|
|
{type="histogram"} {{count:2512.9166666666665 sum:50.76599326599326 counter_reset_hint:gauge buckets:[10.153198653198652 2502.7634680134674]}}
|
|
{type="counter"} 2512.9166666666665
|
|
|
|
# End of range coincides with sample. Zero point of count is reached outside of (i.e. before) the range.
|
|
# This means no change of extrapolation is required for the histogram count (and neither for the float counter),
|
|
# however, the 2nd bucket's extrapolation will reach zero within the range. The overestimation is visible
|
|
# easily here because the last sample in the range coincides with the boundary, where the 2nd bucket has
|
|
# a value of 2475 but has increased by 2476.2045454545455 according to the returned result.
|
|
eval instant at 55m increase(metric[55m15s])
|
|
expect no_warn
|
|
{type="histogram"} {{count:2486.25 sum:50.227272727272734 counter_reset_hint:gauge buckets:[10.045454545454547 2476.2045454545455]}}
|
|
{type="counter"} 2486.25
|
|
|
|
# End of range does not coincide with sample. Zero point of count is reached outside of (i.e. before) the range.
|
|
# This means no change of extrapolation is required for the histogram count (and neither for the float counter),
|
|
# however, the 2nd bucket's extrapolation will reach zero within the range.
|
|
eval instant at 54m30s increase(metric[54m45s])
|
|
expect no_warn
|
|
{type="histogram"} {{count:2509.375 sum:50.69444444444444 counter_reset_hint:gauge buckets:[10.13888888888889 2499.236111111111]}}
|
|
{type="counter"} 2509.375
|
|
|
|
# Try the same, but now extract just the histogram count via `histogram_count`.
|
|
eval instant at 55m histogram_count(increase(metric[90m]))
|
|
expect no_warn
|
|
{type="histogram"} 2490
|
|
|
|
eval instant at 54m30s histogram_count(increase(metric[90m]))
|
|
expect no_warn
|
|
{type="histogram"} 2512.9166666666665
|
|
|
|
eval instant at 55m histogram_count(increase(metric[55m15s]))
|
|
expect no_warn
|
|
{type="histogram"} 2486.25
|
|
|
|
eval instant at 54m30s histogram_count(increase(metric[54m45s]))
|
|
expect no_warn
|
|
{type="histogram"} 2509.375
|
|
|
|
clear
|
|
|
|
# Test counter reset hint adjustment in subtraction and aggregation, including _over_time.
|
|
load 5m
|
|
metric{id="1"} {{schema:0 sum:4 count:4 buckets:[1 2 1]}}x10
|
|
metric{id="2"} {{schema:0 sum:4 count:4 buckets:[1 2 1]}}x10
|
|
|
|
# Unary minus turns counters into gauges.
|
|
eval instant at 5m -metric
|
|
expect no_warn
|
|
expect no_info
|
|
{id="1"} {{count:-4 sum:-4 counter_reset_hint:gauge buckets:[-1 -2 -1]}}
|
|
{id="2"} {{count:-4 sum:-4 counter_reset_hint:gauge buckets:[-1 -2 -1]}}
|
|
|
|
# Subtraction results in gauges, even if the result is not negative.
|
|
eval instant at 5m metric - 0.5 * metric
|
|
expect no_warn
|
|
expect no_info
|
|
{id="1"} {{count:2 sum:2 counter_reset_hint:gauge buckets:[0.5 1 0.5]}}
|
|
{id="2"} {{count:2 sum:2 counter_reset_hint:gauge buckets:[0.5 1 0.5]}}
|
|
|
|
# Subtraction results in gauges, now with actually negative result.
|
|
eval instant at 5m metric - 2 * metric
|
|
expect no_warn
|
|
expect no_info
|
|
{id="1"} {{count:-4 sum:-4 counter_reset_hint:gauge buckets:[-1 -2 -1]}}
|
|
{id="2"} {{count:-4 sum:-4 counter_reset_hint:gauge buckets:[-1 -2 -1]}}
|
|
|
|
# sum and avg of counters yield a counter.
|
|
eval instant at 5m sum(metric)
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:8 sum:8 counter_reset_hint:not_reset buckets:[2 4 2]}}
|
|
|
|
eval instant at 5m avg(metric)
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:4 sum:4 counter_reset_hint:not_reset buckets:[1 2 1]}}
|
|
|
|
clear
|
|
|
|
# Note that with all the series below, we never get counter_reset_hint:reset
|
|
# as a result because of of https://github.com/prometheus/prometheus/issues/15346 .
|
|
# Therefore, all the tests only look at the hints gauge, not_reset, and unknown.
|
|
load 1m
|
|
metric{type="gauge"} {{sum:4 count:4 counter_reset_hint:gauge buckets:[1 2 1]}}+{{sum:2 count:3 counter_reset_hint:gauge buckets:[1 1 1]}}x10
|
|
metric{type="counter"} {{sum:6 count:5 buckets:[2 2 1]}}+{{sum:2 count:3 buckets:[1 1 1]}}x10
|
|
metric{type="counter_with_reset"} {{sum:6 count:5 buckets:[2 2 1]}}+{{sum:2 count:3 buckets:[1 1 1]}}x5 {{sum:4 count:4 buckets:[1 2 1]}}+{{sum:2 count:3 buckets:[1 1 1]}}x5
|
|
mixed {{sum:6 count:5 buckets:[2 2 1]}}+{{sum:2 count:3 buckets:[1 1 1]}}x4 {{sum:4 count:4 counter_reset_hint:gauge buckets:[1 2 1]}} {{sum:6 count:5 buckets:[2 2 1]}}+{{sum:2 count:3 buckets:[1 1 1]}}x4 {{sum:4 count:4 buckets:[1 2 1]}}+{{sum:2 count:3 buckets:[1 1 1]}}x5
|
|
|
|
# Mix of gauge and not_reset results in gauge.
|
|
eval instant at 3m sum(metric)
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:41 sum:34 counter_reset_hint:gauge buckets:[14 15 12]}}
|
|
|
|
eval instant at 3m avg(metric)
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:13.666666666666668 sum:11.333333333333334 counter_reset_hint:gauge buckets:[4.666666666666667 5 4]}}
|
|
|
|
eval instant at 5m sum_over_time(mixed[3m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:35 sum:30 counter_reset_hint:gauge buckets:[12 13 10]}}
|
|
|
|
eval instant at 5m avg_over_time(mixed[3m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:11.666666666666666 sum:10 counter_reset_hint:gauge buckets:[4 4.333333333333334 3.333333333333333]}}
|
|
|
|
# Mix of gauge, not_reset, and unknown results in gauge.
|
|
eval instant at 6m sum(metric)
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:49 sum:38 counter_reset_hint:gauge buckets:[16 18 15]}}
|
|
|
|
eval instant at 6m avg(metric)
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:16.333333333333332 sum:12.666666666666666 counter_reset_hint:gauge buckets:[5.333333333333334 6 5]}}
|
|
|
|
eval instant at 14m sum_over_time(mixed[10m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:93 sum:82 counter_reset_hint:gauge buckets:[31 36 26]}}
|
|
|
|
eval instant at 14m avg_over_time(mixed[10m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:9.3 sum:8.2 counter_reset_hint:gauge buckets:[3.1 3.6 2.6]}}
|
|
|
|
# Only not_reset results in not_reset.
|
|
eval instant at 3m sum(metric{type=~"counter.*"})
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:28 sum:24 counter_reset_hint:not_reset buckets:[10 10 8]}}
|
|
|
|
eval instant at 3m avg(metric{type=~"counter.*"})
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:14 sum:12 counter_reset_hint:not_reset buckets:[5 5 4]}}
|
|
|
|
eval instant at 3m sum_over_time(mixed[3m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:33 sum:30 counter_reset_hint:not_reset buckets:[12 12 9]}}
|
|
|
|
eval instant at 3m avg_over_time(mixed[3m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:11 sum:10 counter_reset_hint:not_reset buckets:[4 4 3]}}
|
|
|
|
# Mix of not_reset and unknown results in unknown.
|
|
eval instant at 6m sum(metric{type=~"counter.*"})
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:27 sum:22 counter_reset_hint:unknown buckets:[9 10 8]}}
|
|
|
|
eval instant at 6m avg(metric{type=~"counter.*"})
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:13.5 sum:11 counter_reset_hint:unknown buckets:[4.5 5 4]}}
|
|
|
|
eval instant at 15m sum_over_time(mixed[10m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:105 sum:90 counter_reset_hint:unknown buckets:[35 40 30]}}
|
|
|
|
eval instant at 15m avg_over_time(mixed[10m])
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{count:10.5 sum:9 counter_reset_hint:unknown buckets:[3.5 4 3]}}
|
|
|
|
# To finally test the warning about a direct counter reset collisions, we can
|
|
# utilize the HistogramStatsIterator (by calling histogram_count()). This
|
|
# special iterator does counter reset detection on the fly and therefore
|
|
# is able to create the counter reset hint "reset", which we can then mix
|
|
# with the "not_reset" hint in the test and provoke the warning.
|
|
eval instant at 6m histogram_count(sum(metric))
|
|
expect warn msg:PromQL warning: conflicting counter resets during histogram aggregation
|
|
expect no_info
|
|
{} 49
|
|
|
|
eval instant at 6m histogram_count(avg(metric))
|
|
expect warn msg:PromQL warning: conflicting counter resets during histogram aggregation
|
|
expect no_info
|
|
{} 16.333333333333332
|
|
|
|
eval instant at 14m histogram_count(sum_over_time(mixed[10m]))
|
|
expect warn msg:PromQL warning: conflicting counter resets during histogram aggregation
|
|
expect no_info
|
|
{} 93
|
|
|
|
eval instant at 14m histogram_count(avg_over_time(mixed[10m]))
|
|
expect warn msg:PromQL warning: conflicting counter resets during histogram aggregation
|
|
expect no_info
|
|
{} 9.3
|
|
|
|
# In the following two tests, the first sample has hint "not_reset"
|
|
# and the second has "reset". This tests if the conflict is detected
|
|
# between the first two samples, too.
|
|
eval instant at 11m histogram_count(sum_over_time(mixed[2m]))
|
|
expect warn msg:PromQL warning: conflicting counter resets during histogram aggregation
|
|
expect no_info
|
|
{} 21
|
|
|
|
eval instant at 11m histogram_count(avg_over_time(mixed[2m]))
|
|
expect warn msg:PromQL warning: conflicting counter resets during histogram aggregation
|
|
expect no_info
|
|
{} 10.5
|
|
|
|
# Test histogram_quantile annotations.
|
|
load 1m
|
|
nonmonotonic_bucket{le="0.1"} 0+2x10
|
|
nonmonotonic_bucket{le="1"} 0+1x10
|
|
nonmonotonic_bucket{le="10"} 0+5x10
|
|
nonmonotonic_bucket{le="100"} 0+4x10
|
|
nonmonotonic_bucket{le="1000"} 0+9x10
|
|
nonmonotonic_bucket{le="+Inf"} 0+8x10
|
|
myHistogram1{abe="0.1"} 0+2x10
|
|
myHistogram2{le="Hello World"} 0+2x10
|
|
mixedHistogram{le="0.1"} 0+2x10
|
|
mixedHistogram{le="1"} 0+3x10
|
|
mixedHistogram{} {{schema:0 count:10 sum:50 buckets:[1 2 3]}}
|
|
|
|
eval instant at 1m histogram_quantile(0.5, nonmonotonic_bucket)
|
|
expect info msg: PromQL info: input to histogram_quantile needed to be fixed for monotonicity (see https://prometheus.io/docs/prometheus/latest/querying/functions/#histogram_quantile) for metric name "nonmonotonic_bucket"
|
|
{} 8.5
|
|
|
|
eval instant at 1m histogram_quantile(0.5, myHistogram1)
|
|
expect warn msg: PromQL warning: bucket label "le" is missing or has a malformed value of "" for metric name "myHistogram1"
|
|
|
|
eval instant at 1m histogram_quantile(0.5, myHistogram2)
|
|
expect warn msg: PromQL warning: bucket label "le" is missing or has a malformed value of "Hello World" for metric name "myHistogram2"
|
|
|
|
eval instant at 1m histogram_quantile(0.5, mixedHistogram)
|
|
expect warn msg: PromQL warning: vector contains a mix of classic and native histograms for metric name "mixedHistogram"
|
|
|
|
clear
|
|
|
|
# A counter reset only in a bucket. Sub-queries still need to detect
|
|
# it via explicit counter reset detection. This test also runs it with
|
|
# histogram_count in the expression to make sure that the
|
|
# HistogramStatsIterator is not used. (The latter fails to correctly
|
|
# do the counter resets because Seek is used with sub-queries. And the
|
|
# explicit counter reset detection done with sub-queries cannot access
|
|
# the buckets anymore, if HistogramStatsIterator is used.)
|
|
load 1m
|
|
h{} {{schema:0 count:1 sum:10 buckets:[1]}}+{{}}x20 {{schema:0 count:1 sum:10 buckets:[0 1]}}+{{}}x20
|
|
|
|
# Both evals below should yield the same value for the count.
|
|
eval instant at 41m histogram_count(increase(h[40m:9m]))
|
|
{} 1.4814814814814814
|
|
|
|
eval instant at 41m increase(h[40m:9m])
|
|
{} {{count:1.4814814814814814 sum:14.814814814814813 counter_reset_hint:gauge offset:1 buckets:[1.4814814814814814]}}
|
|
|
|
clear
|
|
|
|
load 1m
|
|
reset{timing="late"} {{schema:0 sum:1 count:0 buckets:[1 1 1]}} {{schema:0 sum:1 count:2 buckets:[1 1 1]}} {{schema:0 sum:1 count:3 buckets:[1 1 1]}} {{schema:0 sum:1 count:2 buckets:[1 1 1]}}
|
|
reset{timing="early"} {{schema:0 sum:1 count:3 buckets:[1 1 1]}} {{schema:0 sum:1 count:2 buckets:[1 1 1]}} {{schema:0 sum:1 count:2 buckets:[1 1 1]}} {{schema:0 sum:1 count:3 buckets:[1 1 1]}}
|
|
|
|
# Trigger an annotation about conflicting counter resets by going through the
|
|
# HistogramStatsIterator, which creates counter reset hints on the fly.
|
|
eval instant at 5m histogram_count(sum_over_time(reset{timing="late"}[5m]))
|
|
expect warn msg: PromQL warning: conflicting counter resets during histogram aggregation
|
|
{timing="late"} 7
|
|
|
|
eval instant at 5m histogram_count(sum(reset))
|
|
expect warn msg: PromQL warning: conflicting counter resets during histogram aggregation
|
|
{} 5
|
|
|
|
eval instant at 5m histogram_count(avg(reset))
|
|
expect warn msg: PromQL warning: conflicting counter resets during histogram aggregation
|
|
{} 2.5
|
|
|
|
# No annotation with the right timing.
|
|
eval instant at 30s histogram_count(sum(reset))
|
|
expect no_warn
|
|
{} 3
|
|
|
|
eval instant at 30s histogram_count(avg(reset))
|
|
expect no_warn
|
|
{} 1.5
|
|
|
|
# Ensure that the annotation does not happen with rate.
|
|
eval instant at 5m histogram_count(rate(reset{timing="late"}[5m]))
|
|
expect no_warn
|
|
{timing="late"} 0.0175
|
|
|
|
clear
|
|
|
|
# Test edge cases of HistogramStatsIterator detection.
|
|
# We access the same series multiple times within the same expression,
|
|
# once with and once without HistogramStatsIterator. The results here
|
|
# at least prove that we do not use HistogramStatsIterator where we
|
|
# should not.
|
|
load 1m
|
|
histogram {{schema:0 count:10 sum:50 counter_reset_hint:gauge buckets:[1 2 3 4]}}
|
|
|
|
eval instant at 1m histogram_count(histogram unless histogram_quantile(0.5, histogram) < 3)
|
|
{} 10
|
|
|
|
eval instant at 1m histogram_quantile(0.5, histogram unless histogram_count(histogram) == 0)
|
|
{} 3.1748021039363987
|
|
|
|
clear
|
|
|
|
# Regression test for:
|
|
# https://github.com/prometheus/prometheus/issues/14172
|
|
# https://github.com/prometheus/prometheus/issues/15177
|
|
load 1m
|
|
mixed_metric1 1 2 3 {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:8 count:6 buckets:[1 4 1]}} 4 5 {{schema:0 sum:18 count:10 buckets:[3 4 3]}}
|
|
mixed_metric2 1 2 3 {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:8 count:6 buckets:[1 4 1]}}
|
|
|
|
# The order of the float vs native histograms is preserved.
|
|
eval range from 0 to 8m step 1m mixed_metric1
|
|
mixed_metric1{} 1 2 3 {{count:4 sum:5 buckets:[1 2 1]}} {{count:6 sum:8 buckets:[1 4 1]}} 4 5 {{schema:0 sum:18 count:10 buckets:[3 4 3]}} {{schema:0 sum:18 count:10 buckets:[3 4 3]}}
|
|
|
|
eval range from 0 to 5m step 1m mixed_metric2
|
|
mixed_metric2 1 2 3 {{count:4 sum:5 buckets:[1 2 1]}} {{count:6 sum:8 buckets:[1 4 1]}} {{count:6 sum:8 buckets:[1 4 1]}}
|
|
|
|
clear
|
|
|
|
# Test native histograms with custom buckets, reconciling mismatched bounds.
|
|
load 1m
|
|
nhcb_add_buckets {{schema:-53 sum:55 count:15 custom_values:[2 4 6] buckets:[1 2 5 7]}} {{schema:-53 sum:555 count:450 custom_values:[1 2 3 4 5 6 7 8] buckets:[10 20 30 40 50 60 70 80 90]}}
|
|
|
|
eval instant at 1m irate(nhcb_add_buckets[2m]) * 60
|
|
expect no_warn
|
|
expect info msg: PromQL info: mismatched custom buckets were reconciled during subtraction
|
|
{} {{schema:-53 sum:500 count:435 custom_values:[2 4 6] buckets:[29 68 105 233]}}
|
|
|
|
load 1m
|
|
nhcb_remove_buckets {{schema:-53 sum:55 count:45 custom_values:[1 2 3 4 5 6 7 8] buckets:[1 2 3 4 5 6 7 8 9]}} {{schema:-53 sum:5560 count:1000 custom_values:[3 5 7] buckets:[100 200 300 400]}}
|
|
|
|
eval instant at 1m irate(nhcb_remove_buckets[2m]) * 60
|
|
expect no_warn
|
|
expect info msg: PromQL info: mismatched custom buckets were reconciled during subtraction
|
|
{} {{schema:-53 sum:5505 count:955 custom_values:[3 5 7] buckets:[94 191 287 383]}}
|
|
|
|
clear
|
|
|
|
# Test native histograms with custom buckets, reconciling mismatched bounds, with counter reset in one bucket.
|
|
load 1m
|
|
nhcb_add_bucket {{schema:-53 sum:55 count:15 custom_values:[2 4 6] buckets:[1 2 5 7]}} {{schema:-53 sum:56 count:15 custom_values:[2 3 4 6] buckets:[1 0 1 5 8]}}
|
|
|
|
eval instant at 1m irate(nhcb_add_bucket[2m]) * 60
|
|
expect no_warn
|
|
expect no_info
|
|
{} {{schema:-53 sum:56 count:15 custom_values:[2 3 4 6] buckets:[1 0 1 5 8] counter_reset_hint:gauge}}
|