1aced0a130
as we will use distinct Influx and Grafana instances for the different stages/environments.
17 lines
791 B
Plaintext
17 lines
791 B
Plaintext
import "date"
|
|
|
|
data = from(bucket: "poseidon")
|
|
|> range(start: date.truncate(t: v.timeRangeStart, unit: 1m), stop: date.truncate(t: v.timeRangeStop, unit: 1m))
|
|
|> filter(fn: (r) => r._field == "duration")
|
|
|> filter(fn: (r) => (not exists r.environment_id) or contains(value: r["environment_id"], set: ${environment_ids:json}))
|
|
|> keep(columns: ["_time", "_value", "status"])
|
|
|
|
all = data |> set(key: "status", value: "all")
|
|
|
|
result = union(tables: [data, all])
|
|
|> aggregateWindow(every: 1m, fn: count, createEmpty: true)
|
|
|
|
if int(v: v.windowPeriod) > int(v: 1m)
|
|
then result |> aggregateWindow(every: duration(v: int(v: v.windowPeriod) * 2), fn: mean, createEmpty: true)
|
|
else result |> aggregateWindow(every: duration(v: int(v: v.windowPeriod) * 5), fn: mean, createEmpty: false)
|