# 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) {} 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) {} 1.414213562373095 clear # Repeat the same histogram 10 times. load 5m multi_histogram {{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]) {} {{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])) {} 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]) {} {{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]) {} {{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) {} 42.947236400258 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_warn instant at 10m histogram_quantile(1.001, histogram_quantile_1) {} Inf eval instant at 10m histogram_quantile(1, histogram_quantile_1) {} 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) {} 15.67072476139083 eval instant at 10m histogram_quantile(0.9, histogram_quantile_1) {} 12.99603834169977 eval instant at 10m histogram_quantile(0.6, histogram_quantile_1) {} 4.594793419988138 eval instant at 10m histogram_quantile(0.5, histogram_quantile_1) {} 1.5874010519681994 # Linear interpolation within the zero bucket after all. eval instant at 10m histogram_quantile(0.1, histogram_quantile_1) {} 0.0006 eval instant at 10m histogram_quantile(0, histogram_quantile_1) {} 0 eval_warn instant at 10m histogram_quantile(-1, histogram_quantile_1) {} -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_warn instant at 10m histogram_quantile(1.001, histogram_quantile_2) {} Inf eval instant at 10m histogram_quantile(1, histogram_quantile_2) {} 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) {} -0.00006 eval instant at 10m histogram_quantile(0.9, histogram_quantile_2) {} -0.0006 eval instant at 10m histogram_quantile(0.5, histogram_quantile_2) {} -1.5874010519681996 eval instant at 10m histogram_quantile(0.1, histogram_quantile_2) {} -12.996038341699768 eval instant at 10m histogram_quantile(0, histogram_quantile_2) {} -16 eval_warn instant at 10m histogram_quantile(-1, histogram_quantile_2) {} -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_warn instant at 10m histogram_quantile(1.001, histogram_quantile_3) {} Inf eval instant at 10m histogram_quantile(1, histogram_quantile_3) {} 16 eval instant at 10m histogram_quantile(0.99, histogram_quantile_3) {} 15.34822590920423 eval instant at 10m histogram_quantile(0.9, histogram_quantile_3) {} 10.556063286183155 eval instant at 10m histogram_quantile(0.7, histogram_quantile_3) {} 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) {} 0.0006 eval instant at 10m histogram_quantile(0.5, histogram_quantile_3) {} 0 eval instant at 10m histogram_quantile(0.45, histogram_quantile_3) {} -0.0006 # Finally negative buckets with mirrored exponential interpolation. eval instant at 10m histogram_quantile(0.3, histogram_quantile_3) {} -1.2030250360821169 eval instant at 10m histogram_quantile(0.1, histogram_quantile_3) {} -10.556063286183155 eval instant at 10m histogram_quantile(0.01, histogram_quantile_3) {} -15.34822590920423 eval instant at 10m histogram_quantile(0, histogram_quantile_3) {} -16 eval_warn instant at 10m histogram_quantile(-1, histogram_quantile_3) {} -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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{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 {} {{schema:0 count:Inf sum:Inf z_bucket_w:0.001 z_bucket:Inf}} eval instant at 10m histogram_mul_div/float_series_0 {} {{schema:0 count:Inf sum:Inf z_bucket_w:0.001 z_bucket:Inf}} eval instant at 10m histogram_mul_div*0/0 {} {{schema:0 count:NaN sum:NaN z_bucket_w:0.001 z_bucket:NaN}} eval_info instant at 10m histogram_mul_div*histogram_mul_div eval_info instant at 10m histogram_mul_div/histogram_mul_div eval_info instant at 10m float_series_3/histogram_mul_div eval_info instant at 10m 0/histogram_mul_div 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_info instant at 10m float_sample+histogram_sample eval_info instant at 10m histogram_sample+float_sample eval_info instant at 10m float_sample-histogram_sample eval_info instant at 10m histogram_sample-float_sample # 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]) {} {{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])) {} 9 eval instant at 10m histogram_sum(increase(reset_in_bucket[15m])) {} 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 gauge' 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_warn instant at 30s rate(some_metric[1m]) {} {{count:0.03333333333333333 sum:0.03333333333333333 buckets:[0.03333333333333333]}} # Test the case where we have more than two points for rate eval_warn instant at 1m rate(some_metric[1m]) {} {{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_warn instant at 1m rate(some_metric[1m]) # Should produce no results. # Start and end with custom, with exponential in the middle. eval_warn instant at 1m30s rate(some_metric[1m]) # Should produce no results. # Start with custom, end with exponential. eval_warn instant at 1m rate(some_metric[1m]) # Should produce no results. # Start with exponential, end with custom. eval_warn instant at 30s rate(some_metric[1m]) # Should produce no results. 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]) {} {{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])) {} NaN # Zero buckets mean no observations, so count is 0. eval instant at 5m histogram_count(rate(const_histogram[5m])) {} 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])) {} 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])) {} 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])) {} 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])) {} NaN # Zero buckets mean no observations, so there is no standard deviation. eval instant at 5m histogram_stddev(rate(const_histogram[5m])) {} NaN # Zero buckets mean no observations, so there is no standard variance. eval instant at 5m histogram_stdvar(rate(const_histogram[5m])) {} 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_warn range from 0 to 12m step 6m sum(metric) {} {{sum:7 count:5 buckets:[2 3 2]}} {{schema:-53 sum:16 count:3 custom_values:[5 10] buckets:[1 2]}} _ eval_warn range from 0 to 12m step 6m avg(metric) {} {{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 incompatible custom bucket schemas. 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:[2] buckets:[1]}} _ {{schema:-53 sum:1 count:1 custom_values:[2] buckets:[1]}} metric{series="3"} {{schema:-53 sum:1 count:1 custom_values:[5 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]}} # T=0: incompatible, should be ignored and emit a warning # T=6: compatible # T=12: incompatible followed by compatible, should be ignored and emit a warning eval_warn range from 0 to 12m step 6m sum(metric) {} _ {{schema:-53 sum:2 count:2 custom_values:[5 10] buckets:[2]}} _ eval_warn range from 0 to 12m step 6m avg(metric) {} _ {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} _ 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="incompatible-custom-histograms", series="1"} {{schema:-53 sum:1 count:1 custom_values:[5 10] buckets:[1]}} metric{group="incompatible-custom-histograms", series="2"} {{schema:-53 sum:1 count:1 custom_values:[2] buckets:[1]}} eval_warn instant at 0 sum by (group) (metric) {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]}} 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) {} {{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_warn instant at 10m sum({idx="0"}) 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"}) {} {{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) {} 4 eval instant at 10m avg(histogram_sum) {} {{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