It run correctly

and this?
avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m]))

All these run correctly

To check the data drift, I need to calculate the avg of the last query by time bucket and calculate the variation by minute of the new metric

I ran the test, but there was no result. I need to calculate a variation of average: avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m])). The variation per minute. I tried using delta, but encountered an error.

try to break it into parts and understand what produces the error
for example:
increase(test12_model_custom:Glucose_bucket[1m])
increase(test12_model_custom:Glucose_sum[1m])
increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m])
and so on

Hi @<1673501397007470592:profile|RelievedDuck3>

how can I configure my alerts to be notified when the distribution of my metrics (variables) changes on my heatmaps?

This can be done inside grafana, here is a simple example:
None
Specifically you need to create a new metric that is the distance of current distribution (i.e. heatmap) from the previous window), then on the distance metric, create an alert
basically instead of the heatmap value, you take the current bucket value and subtract it from the same bucket X minutes ago

• create average over time per bucket, for example: histogram_avg(buckets_seconds[1d]))None
  2, then subtract two histograms and sum the abs difference
  sum(abs(buckets_value - histogram_avg(buckets_value[1d])))
  None
  None

Thank you very much for your help. I will test it.

I used this PromQL query: 100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m]) to visualize the distribution of the variable (in my case called Glucose). So according to your explanation, I should calculate a new metric: sum(abs(test12_model_custom:Glucose_bucket - histogram_avg(test12_model_custom:Glucose_bucket[1m]))). I set up the alert rule on this metric by defining a threshold to trigger the alert. Did I understand correctly?

Or the new metric should be: sum(abs((100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m])) - histogram_avg((100 * increase(test12_model_custom:Glucose_bucket[1m]) / increase(test12_model_custom:Glucose_sum[1m]))[1m])))?

When I calculated the average, I got this result. Now, with this new metric, I need to calculate the variation per minute. I tried increase, rate, delta, but no result, just an error: bad_data: 1:110: parse error: ranges only allowed for vector selectors: delta(avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m]))[1m])

I ran the test, but there was no result.

what do you mean by no result, no data after the new query?

I set up the alert rule on this metric by defining a threshold to trigger the alert. Did I understand correctly?

Yes exactly!

Or the new metric should...

basically combining the two, yes looks good.

Now, when I add delta to calculate the variation of this: error: bad_data: 1:110: parse error: ranges only allowed for vector selectors

This means your avg is already a scalar (i.e. not a vector) which means you can (as you said) have the alert based on that

Alternatively, can I directly define my alert on avg(...)

Now, when I add delta to calculate the variation of this: error: bad_data: 1:110: parse error: ranges only allowed for vector selectors

I feel like to do this, I need to create a recording rule from the metric avg(...) at the Prometheus level and then query increase(). However, this approach requires me to interact directly with Prometheus.