Question: Best practise for transforming timeseries data into logs with time-ranges

[Paul Werner]

Hey everyone,
today we encountered an use-case, where machine-status-data saves as timeseries in influx should be transformed into log-like data with a start and stop timestamp.

f.ex.

array.from(rows: [
  {_time: 2021-01-01T00:00:00Z, _value: true},
  {_time: 2021-01-01T00:01:00Z, _value: true},
  {_time: 2021-01-01T00:02:00Z, _value: false},
  {_time: 2021-01-01T00:03:00Z, _value: false},
  {_time: 2021-01-01T00:04:00Z, _value: false},
  {_time: 2021-01-01T00:05:00Z, _value: true},
  {_time: 2021-01-01T00:06:00Z, _value: true},
])

should result in:

array.from(rows: [
  {_start: 2021-01-01T00:00:00Z, _stop: 2021-01-01T00:01:00Z, _value: true},
  {_time: 2021-01-01T00:02:00Z, _stop: 2021-01-01T00:04:00Z, _value: false},
  {_time: 2021-01-01T00:05:00Z, _stop: 2021-01-01T00:06:00Z, _value: true},
])

my first suggestion was to use a function like monitor.changeState. #3582 suggested it is not implemented.
As a workaround / idea to start, we had tried using the following statement, which is no quite performat, when quering larger datasets.
We are using the differnce to determine, if the boolean value has been changed and in which direction. We are using aggregate Sum to keep the latest value, when its been "active" within the whole range. (an no value_changes has occur)
Our api then aggregate this data to the wanted format. { _measurement: string; _field: string; start: Date; stop: Date; _value: boolean; }
Aggregation is not an option, because we need the precise time-stamps.

from(bucket: "bucket-name")
    |> range(start: -5h, stop: now())
    // additional filtering here...
    |> map(fn: (r) => ({r with rowId: 1, change: int(v: r._value) }))
    |> difference(initialZero: true, keepFirst: true, columns: ["change"])
    |> sort(columns: ["_time"], desc: true)
    |> cumulativeSum(columns: ["rowId"])
    |> sort(columns: ["_time"], desc: false)
    |> filter(fn: (r) => (r._value == true and r.rowId == 1) or r.change != 0)

@UlrichThiess

[Paul Werner]

we also thought about running this query regulary with a smaller range and store the result into an other database.
is this apropiate? or did someone would suggest a better solution?

@UlrichThiess i tagged you because of a previous conversation :)

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[Paul Werner]

any updates or suggestions?

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[Paul Werner]

problem noch nicht gelöst.

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[Paul Werner]

problem noch nicht gelöst.

...

[Ulrich]

import "array"

data = array.from(rows: [
  {_time: 2021-01-01T00:00:00Z, _value: true},
  {_time: 2021-01-01T00:01:00Z, _value: true},
  {_time: 2021-01-01T00:02:00Z, _value: false},
  {_time: 2021-01-01T00:03:00Z, _value: false},
  {_time: 2021-01-01T00:04:00Z, _value: false},
  {_time: 2021-01-01T00:05:00Z, _value: true},
  {_time: 2021-01-01T00:06:00Z, _value: true},
])

// Markieren Sie den Start eines neuen Segments
marked = data
  |> map(fn: (r, index) => ({
      r with
      segmentStart: if index == 0 or r._value != array.get(arr: data, i: index - 1)._value then r._time else 0
    })
  )

// Zusammenfassen der Segmente
result = marked
  |> reduce(
      identity: {start: 0, stop: 0, value: false, init: true},
      fn: (r, accumulator) => ({
        start: if accumulator.init then r.segmentStart else accumulator.start,
        stop: r._time,
        value: r._value,
        init: if r.segmentStart != 0 then false else accumulator.init
      })
    )
  |> filter(fn: (r) => r.start != 0)

result

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