Use native prometheus aggregations instead of stats package about go-commons

[rsevilla87]

More tests:

Query: max_over_time(rate(node_forks_total[2m])[5h:])

# Using an instant query
$ curl -s 'http://demo.robustperception.io:9090/api/v1/query?query=max_over_time%28rate%28node_forks_total%5B2m%5D%29%5B5h%3A%5D%29&time=1691492544'    | jq .
{
  "status": "success",
  "data": {
    "resultType": "vector",
    "result": [
      {
        "metric": {
          "env": "demo",
          "instance": "demo.do.prometheus.io:9100",
          "job": "node"
        },
        "value": [
          1691492544,
          "2.400068573387811"
        ]
      }
    ]
  }
}

And now using the prometheus GUI to perform a query_range and verify that value is present among the plotted datapoints

Both values match, hence, I think we replace the kube-burner report's expressions by these ones.

---

Practical example

in a hypothetical workload that lasted 3721 seconds and finished at Tue Aug 8 01:57:42 we could calculate the average node cpu usage over the workload duration with an promql query like:

GET
query=avg_over_time(sum(irate(node_cpu_seconds_total{mode!="idle", mode!="steal"}[2m]))[3721s:])&timestamp=1691452662

[smalleni]

@vishnuchalla @rsevilla87 @jtaleric FYI

[rsevilla87]

mm, this is interesting, I've digged a little in the avg_over_time() functions and I think we may use them by combining instant queries and using a time range with a colon notation in order to get the aggregated values up to the passed timestamp:

Example:

$ curl -s 'http://demo.robustperception.io:9090/api/v1/query?query=avg_over_time%28rate%28node_cpu_seconds_total%7Bmode%3D%22idle%22%7D%5B2m%5D%29%5B10m%3A%5D%29&time=1691492504'    | jq .
{
  "status": "success",
  "data": {
    "resultType": "vector",
    "result": [
      {
        "metric": {
          "cpu": "0",
          "env": "demo",
          "instance": "demo.do.prometheus.io:9100",
          "job": "node",
          "mode": "idle"
        },
        "value": [
          1691492504,
          "0.8361428571421476"
        ]
      }
    ]
  }
}

The above urlencoded query is actually avg_over_time(rate(node_cpu_seconds_total{mode="idle"}[2m])[10m:])

[rsevilla87]

I still have to verify whether this value is valid or not

[vishnuchalla]

Yes we can fetch the aggregations over time and also within in a given time range

curl -kH "Authorization: Bearer TOKEN" 'PROM_URL/api/v1/query?query=avg_over_time(sum(irate(node_cpu_seconds_total[2h]))[2h:1h])'
{"status":"success","data":{"resultType":"vector","result":[{"metric":{},"value":[1691553906.635,"103.71599999999302"]}]}}

But the only thing missing from the existing function is the step (sample collection interval), which I don't think we need here while calculating aggregations. Correct me If I am wrong!

[rsevilla87]

With the two previous PRs I think we can close this one