mirror of
https://github.com/prometheus/prometheus.git
synced 2025-12-05 17:41:21 +01:00
1875 lines
72 KiB
Plaintext
1875 lines
72 KiB
Plaintext
# Minimal valid case: an empty histogram.
|
|
load 5m
|
|
empty_histogram {{}}
|
|
|
|
eval instant at 1m empty_histogram
|
|
{__name__="empty_histogram"} {{}}
|
|
|
|
eval instant at 1m histogram_count(empty_histogram)
|
|
{} 0
|
|
|
|
eval instant at 1m histogram_sum(empty_histogram)
|
|
{} 0
|
|
|
|
eval instant at 1m histogram_avg(empty_histogram)
|
|
{} NaN
|
|
|
|
eval instant at 1m histogram_fraction(-Inf, +Inf, empty_histogram)
|
|
{} NaN
|
|
|
|
eval instant at 1m histogram_fraction(0, 8, empty_histogram)
|
|
{} NaN
|
|
|
|
clear
|
|
|
|
# buckets:[1 2 1] means 1 observation in the 1st bucket, 2 observations in the 2nd and 1 observation in the 3rd (total 4).
|
|
load 5m
|
|
single_histogram {{schema:0 sum:5 count:4 buckets:[1 2 1]}}
|
|
|
|
# histogram_count extracts the count property from the histogram.
|
|
eval instant at 1m histogram_count(single_histogram)
|
|
{} 4
|
|
|
|
# histogram_sum extracts the sum property from the histogram.
|
|
eval instant at 1m histogram_sum(single_histogram)
|
|
{} 5
|
|
|
|
# histogram_avg calculates the average from sum and count properties.
|
|
eval instant at 1m histogram_avg(single_histogram)
|
|
{} 1.25
|
|
|
|
# We expect half of the values to fall in the range 1 < x <= 2.
|
|
eval instant at 1m histogram_fraction(1, 2, single_histogram)
|
|
{} 0.5
|
|
|
|
# We expect all values to fall in the range 0 < x <= 8.
|
|
eval instant at 1m histogram_fraction(0, 8, single_histogram)
|
|
expect no_info
|
|
{} 1
|
|
|
|
# Median is 1.414213562373095 (2**2**-1, or sqrt(2)) due to
|
|
# exponential interpolation, i.e. the "midpoint" within range 1 < x <=
|
|
# 2 is assumed where the bucket boundary would be if we increased the
|
|
# resolution of the histogram by one step.
|
|
eval instant at 1m histogram_quantile(0.5, single_histogram)
|
|
expect no_info
|
|
{} 1.414213562373095
|
|
|
|
clear
|
|
|
|
# Repeat the same histogram 10 times.
|
|
load 5m
|
|
multi_histogram {{schema:0 sum:5 count:4 buckets:[1 2 1]}}x10 {{schema:0 sum:5 count:4 buckets:[1 2 1]}}+{{}}x10
|
|
|
|
eval instant at 5m histogram_count(multi_histogram)
|
|
{} 4
|
|
|
|
eval instant at 5m histogram_sum(multi_histogram)
|
|
{} 5
|
|
|
|
eval instant at 5m histogram_avg(multi_histogram)
|
|
{} 1.25
|
|
|
|
eval instant at 5m histogram_fraction(1, 2, multi_histogram)
|
|
{} 0.5
|
|
|
|
# See explanation for exponential interpolation above.
|
|
eval instant at 5m histogram_quantile(0.5, multi_histogram)
|
|
{} 1.414213562373095
|
|
|
|
|
|
# Each entry should look the same as the first.
|
|
eval instant at 50m histogram_count(multi_histogram)
|
|
{} 4
|
|
|
|
eval instant at 50m histogram_sum(multi_histogram)
|
|
{} 5
|
|
|
|
eval instant at 50m histogram_avg(multi_histogram)
|
|
{} 1.25
|
|
|
|
eval instant at 50m histogram_fraction(1, 2, multi_histogram)
|
|
{} 0.5
|
|
|
|
# See explanation for exponential interpolation above.
|
|
eval instant at 50m histogram_quantile(0.5, multi_histogram)
|
|
{} 1.414213562373095
|
|
|
|
clear
|
|
|
|
# Accumulate the histogram addition for 10 iterations, offset is a bucket position where offset:0 is always the bucket
|
|
# with an upper limit of 1 and offset:1 is the bucket which follows to the right. Negative offsets represent bucket
|
|
# positions for upper limits <1 (tending toward zero), where offset:-1 is the bucket to the left of offset:0.
|
|
load 5m
|
|
incr_histogram {{schema:0 sum:4 count:4 buckets:[1 2 1]}}+{{sum:2 count:1 buckets:[1] offset:1}}x10
|
|
|
|
eval instant at 5m histogram_count(incr_histogram)
|
|
{} 5
|
|
|
|
eval instant at 5m histogram_sum(incr_histogram)
|
|
{} 6
|
|
|
|
eval instant at 5m histogram_avg(incr_histogram)
|
|
{} 1.2
|
|
|
|
# We expect 3/5ths of the values to fall in the range 1 < x <= 2.
|
|
eval instant at 5m histogram_fraction(1, 2, incr_histogram)
|
|
{} 0.6
|
|
|
|
# See explanation for exponential interpolation above.
|
|
eval instant at 5m histogram_quantile(0.5, incr_histogram)
|
|
{} 1.414213562373095
|
|
|
|
|
|
eval instant at 50m incr_histogram
|
|
{__name__="incr_histogram"} {{count:14 sum:24 buckets:[1 12 1]}}
|
|
|
|
eval instant at 50m histogram_count(incr_histogram)
|
|
{} 14
|
|
|
|
eval instant at 50m histogram_sum(incr_histogram)
|
|
{} 24
|
|
|
|
eval instant at 50m histogram_avg(incr_histogram)
|
|
{} 1.7142857142857142
|
|
|
|
# We expect 12/14ths of the values to fall in the range 1 < x <= 2.
|
|
eval instant at 50m histogram_fraction(1, 2, incr_histogram)
|
|
{} 0.8571428571428571
|
|
|
|
# See explanation for exponential interpolation above.
|
|
eval instant at 50m histogram_quantile(0.5, incr_histogram)
|
|
{} 1.414213562373095
|
|
|
|
# Per-second average rate of increase should be 1/(5*60) for count and buckets, then 2/(5*60) for sum.
|
|
eval instant at 50m rate(incr_histogram[10m])
|
|
expect no_warn
|
|
{} {{count:0.0033333333333333335 sum:0.006666666666666667 offset:1 buckets:[0.0033333333333333335]}}
|
|
|
|
# Calculate the 50th percentile of observations over the last 10m.
|
|
# See explanation for exponential interpolation above.
|
|
eval instant at 50m histogram_quantile(0.5, rate(incr_histogram[10m]))
|
|
expect no_warn
|
|
{} 1.414213562373095
|
|
|
|
clear
|
|
|
|
# Schema represents the histogram resolution, different schema have compatible bucket boundaries, e.g.:
|
|
# 0: 1 2 4 8 16 32 64 (higher resolution)
|
|
# -1: 1 4 16 64 (lower resolution)
|
|
#
|
|
# Histograms can be merged as long as the histogram to the right is same resolution or higher.
|
|
load 5m
|
|
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
|
|
|
|
eval instant at 5m low_res_histogram
|
|
{__name__="low_res_histogram"} {{schema:-1 count:5 sum:8 offset:1 buckets:[5]}}
|
|
|
|
eval instant at 5m histogram_count(low_res_histogram)
|
|
{} 5
|
|
|
|
eval instant at 5m histogram_sum(low_res_histogram)
|
|
{} 8
|
|
|
|
eval instant at 5m histogram_avg(low_res_histogram)
|
|
{} 1.6
|
|
|
|
# We expect all values to fall into the lower-resolution bucket with the range 1 < x <= 4.
|
|
eval instant at 5m histogram_fraction(1, 4, low_res_histogram)
|
|
{} 1
|
|
|
|
clear
|
|
|
|
# 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
|
|
# 0 < x <= 0.5. Sum and count are expected to represent all observations in the histogram, including those in the zero bucket.
|
|
load 5m
|
|
single_zero_histogram {{schema:0 z_bucket:1 z_bucket_w:0.5 sum:0.25 count:1}}
|
|
|
|
eval instant at 1m histogram_count(single_zero_histogram)
|
|
{} 1
|
|
|
|
eval instant at 1m histogram_sum(single_zero_histogram)
|
|
{} 0.25
|
|
|
|
eval instant at 1m histogram_avg(single_zero_histogram)
|
|
{} 0.25
|
|
|
|
# When only the zero bucket is populated, or there are negative buckets, the distribution is assumed to be equally
|
|
# distributed around zero; i.e. that there are an equal number of positive and negative observations. Therefore the
|
|
# entire distribution must lie within the full range of the zero bucket, in this case: -0.5 < x <= +0.5.
|
|
eval instant at 1m histogram_fraction(-0.5, 0.5, single_zero_histogram)
|
|
{} 1
|
|
|
|
# Half of the observations are estimated to be zero, as this is the midpoint between -0.5 and +0.5.
|
|
eval instant at 1m histogram_quantile(0.5, single_zero_histogram)
|
|
{} 0
|
|
|
|
clear
|
|
|
|
# Let's turn single_histogram upside-down.
|
|
load 5m
|
|
negative_histogram {{schema:0 sum:-5 count:4 n_buckets:[1 2 1]}}
|
|
|
|
eval instant at 1m histogram_count(negative_histogram)
|
|
{} 4
|
|
|
|
eval instant at 1m histogram_sum(negative_histogram)
|
|
{} -5
|
|
|
|
eval instant at 1m histogram_avg(negative_histogram)
|
|
{} -1.25
|
|
|
|
# We expect half of the values to fall in the range -2 < x <= -1.
|
|
eval instant at 1m histogram_fraction(-2, -1, negative_histogram)
|
|
{} 0.5
|
|
|
|
# Exponential interpolation works the same as for positive buckets, just mirrored.
|
|
eval instant at 1m histogram_quantile(0.5, negative_histogram)
|
|
{} -1.414213562373095
|
|
|
|
clear
|
|
|
|
# Two histogram samples.
|
|
load 5m
|
|
two_samples_histogram {{schema:0 sum:4 count:4 buckets:[1 2 1]}} {{schema:0 sum:-4 count:4 n_buckets:[1 2 1]}}
|
|
|
|
# We expect to see the newest sample.
|
|
eval instant at 5m histogram_count(two_samples_histogram)
|
|
{} 4
|
|
|
|
eval instant at 5m histogram_sum(two_samples_histogram)
|
|
{} -4
|
|
|
|
eval instant at 5m histogram_avg(two_samples_histogram)
|
|
{} -1
|
|
|
|
eval instant at 5m histogram_fraction(-2, -1, two_samples_histogram)
|
|
{} 0.5
|
|
|
|
# See explanation for exponential interpolation above.
|
|
eval instant at 5m histogram_quantile(0.5, two_samples_histogram)
|
|
{} -1.414213562373095
|
|
|
|
clear
|
|
|
|
# Add two histograms with negated data.
|
|
load 5m
|
|
balanced_histogram {{schema:0 sum:4 count:4 buckets:[1 2 1]}}+{{schema:0 sum:-4 count:4 n_buckets:[1 2 1]}}x1
|
|
|
|
eval instant at 5m histogram_count(balanced_histogram)
|
|
{} 8
|
|
|
|
eval instant at 5m histogram_sum(balanced_histogram)
|
|
{} 0
|
|
|
|
eval instant at 5m histogram_avg(balanced_histogram)
|
|
{} 0
|
|
|
|
eval instant at 5m histogram_fraction(0, 4, balanced_histogram)
|
|
{} 0.5
|
|
|
|
# If the quantile happens to be located in a span of empty buckets, the actually returned value is the lower bound of
|
|
# the first populated bucket after the span of empty buckets.
|
|
eval instant at 5m histogram_quantile(0.5, balanced_histogram)
|
|
{} 0.5
|
|
|
|
clear
|
|
|
|
# Add histogram to test sum(last_over_time) regression
|
|
load 5m
|
|
incr_sum_histogram{number="1"} {{schema:0 sum:0 count:0 buckets:[1]}}+{{schema:0 sum:1 count:1 buckets:[1]}}x10
|
|
incr_sum_histogram{number="2"} {{schema:0 sum:0 count:0 buckets:[1]}}+{{schema:0 sum:2 count:1 buckets:[1]}}x10
|
|
|
|
eval instant at 50m histogram_sum(sum(incr_sum_histogram))
|
|
{} 30
|
|
|
|
eval instant at 50m histogram_sum(sum(last_over_time(incr_sum_histogram[5m])))
|
|
{} 30
|
|
|
|
clear
|
|
|
|
# Apply rate function to histogram.
|
|
load 15s
|
|
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
|
|
|
|
eval instant at 5m rate(histogram_rate[45s])
|
|
expect no_warn
|
|
{} {{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]}}
|
|
|
|
eval range from 5m to 5m30s step 30s rate(histogram_rate[45s])
|
|
expect no_warn
|
|
{} {{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
|
|
|
|
clear
|
|
|
|
# Apply count and sum function to histogram.
|
|
load 10m
|
|
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
|
|
|
|
eval instant at 10m histogram_count(histogram_count_sum_2)
|
|
{} 24
|
|
|
|
eval instant at 10m histogram_sum(histogram_count_sum_2)
|
|
{} 100
|
|
|
|
clear
|
|
|
|
# Apply stddev and stdvar function to histogram with {1, 2, 3, 4} (low res).
|
|
load 10m
|
|
histogram_stddev_stdvar_1 {{schema:2 count:4 sum:10 buckets:[1 0 0 0 1 0 0 1 1]}}x1
|
|
|
|
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_1)
|
|
{} 1.0787993180043811
|
|
|
|
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_1)
|
|
{} 1.163807968526718
|
|
|
|
clear
|
|
|
|
# Apply stddev and stdvar function to histogram with {1, 1, 1, 1} (high res).
|
|
load 10m
|
|
histogram_stddev_stdvar_2 {{schema:8 count:10 sum:10 buckets:[1 2 3 4]}}x1
|
|
|
|
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_2)
|
|
{} 0.0048960313898237465
|
|
|
|
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_2)
|
|
{} 2.3971123370139447e-05
|
|
|
|
clear
|
|
|
|
# Apply stddev and stdvar function to histogram with {-50, -8, 0, 3, 8, 9}.
|
|
load 10m
|
|
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
|
|
|
|
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_3)
|
|
{} 42.94723640026
|
|
|
|
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_3)
|
|
{} 1844.4651144196398
|
|
|
|
clear
|
|
|
|
# Apply stddev and stdvar function to histogram with {-100000, -10000, -1000, -888, -888, -100, -50, -9, -8, -3}.
|
|
load 10m
|
|
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
|
|
|
|
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_4)
|
|
{} 27556.344499842
|
|
|
|
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_4)
|
|
{} 759352122.1939945
|
|
|
|
clear
|
|
|
|
# Apply stddev and stdvar function to histogram with {-10x10}.
|
|
load 10m
|
|
histogram_stddev_stdvar_5 {{schema:0 count:10 sum:-100 z_bucket:0 n_buckets:[0 0 0 0 10]}}x1
|
|
|
|
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_5)
|
|
{} 1.3137084989848
|
|
|
|
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_5)
|
|
{} 1.725830020304794
|
|
|
|
clear
|
|
|
|
# Apply stddev and stdvar function to histogram with {-50, -8, 0, 3, 8, 9, NaN}.
|
|
load 10m
|
|
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
|
|
|
|
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_6)
|
|
{} NaN
|
|
|
|
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_6)
|
|
{} NaN
|
|
|
|
clear
|
|
|
|
# Apply stddev and stdvar function to histogram with {-50, -8, 0, 3, 8, 9, Inf}.
|
|
load 10m
|
|
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
|
|
|
|
eval instant at 10m histogram_stddev(histogram_stddev_stdvar_7)
|
|
{} Inf
|
|
|
|
eval instant at 10m histogram_stdvar(histogram_stddev_stdvar_7)
|
|
{} Inf
|
|
|
|
clear
|
|
|
|
# Apply quantile function to histogram with all positive buckets with zero bucket.
|
|
load 10m
|
|
histogram_quantile_1 {{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_quantile(1.001, histogram_quantile_1)
|
|
expect warn
|
|
{} Inf
|
|
|
|
eval instant at 10m histogram_quantile(1, histogram_quantile_1)
|
|
expect no_warn
|
|
{} 16
|
|
|
|
# The following quantiles are within a bucket. Exponential
|
|
# interpolation is applied (rather than linear, as it is done for
|
|
# classic histograms), leading to slightly different quantile values.
|
|
eval instant at 10m histogram_quantile(0.99, histogram_quantile_1)
|
|
expect no_warn
|
|
{} 15.67072476139083
|
|
|
|
eval instant at 10m histogram_quantile(0.9, histogram_quantile_1)
|
|
expect no_warn
|
|
{} 12.99603834169977
|
|
|
|
eval instant at 10m histogram_quantile(0.6, histogram_quantile_1)
|
|
expect no_warn
|
|
{} 4.594793419988138
|
|
|
|
eval instant at 10m histogram_quantile(0.5, histogram_quantile_1)
|
|
expect no_warn
|
|
{} 1.5874010519681994
|
|
|
|
# Linear interpolation within the zero bucket after all.
|
|
eval instant at 10m histogram_quantile(0.1, histogram_quantile_1)
|
|
expect no_warn
|
|
{} 0.0006
|
|
|
|
eval instant at 10m histogram_quantile(0, histogram_quantile_1)
|
|
expect no_warn
|
|
{} 0
|
|
|
|
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] counter_reset_hint:gauge 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}}
|