I’d like to use Druid as a time series DB and have some beginner’s questions, sorry if the questions have been asked before.
I have several year’s worth of sensor data (sampled at 1 Hertz, a couple of dozen sensors for now, but may scale up to couple of hundreds later) that I’d like to store and make available for interactive analysis. I.e. a dashboard that allows to query a couple of days worth of data from random timefrimes out of the several year with second-granularity.
Here are the questions:
-would you recommend Druid for this usecase? If not, what would you recommend?
-should I store sensor values (mostly floats) as metric or dimension? I am mostly interested in displaying the raw value.
-on visualization: I have tried Superset but ran into issues with gaps in sensor data which seem to prevent aggregations (e.g. max value in 10s buckets) from working; I read that resampling can fix this, but isn’t this resource intensive? Is there a way to deal with this at ingestion (e.g. treat nulls as 0 for aggregation)?
-I have previously used Graphite/Grafana, which seems to adapt aggregations based on the query (i.e. zooming out of a graph requests aggregates over larger time frames than zooming in); does Druid/Superset have a similar support or do you have
recommendation on how to set up this kind of functionality?
-should I store derived metrics such as aggregations with different granularities at ingestions for this purpose (i.e. zooming in and out without overloading the front-end)? Do you have an example?
Thanks for reading this far and sorry if the questions are too basic. I’d also appreciate links to helpful resources.