I’m wondering how to optimally issue queries for a groups of measurements. My situation is like this:
- there are many (up to hundreds of thousands) individual measurements, perhaps storing lots of data
- occasionally I want to group some (‘some’ might mean 10k) of them
- and do some queries (sum of data, average, etc.)
- one measurement might be in 0, 1 or more groups at any given time
- when group gets defined, I would like to see the aggregated data from before group creation time (it would be ideal to see the aggregated history immediately, but it is OK to wait a bit before history gets aggregated for given new group)
Groups can be added and removed dynamically over lifespan of the measurement, so tagging isn’t really viable here, I’m afraid. Unless I can add/remove tags on the fly?
There are few obvious solutions, like querying them all one by one and then aggregating myself. Or having giant
where id = 1 or id = 2 or id = 3 or .. kind of monster. I don’t think that they will be performant, however.
How things like that should be modelled? So far I’m using Influx only, maybe Kapacitor will help me here?