Hi.
I starting with Timescale and need to figure out what is a good way to distribute my time series data in the database.
In a common SQL database I usually store all time series data payload in one big table inside three fields: | ts_id | delivery_date_z | value | And currently in 90% of the use cases each curve (time series) is accessed as a whole. So all values with equal ts_id are returned at once.
95% of my time series are forward curves that are created at least every few hours. E.g. the expected price, for a commodity, for each hour of the day, for the next 5 years (44k values at once). Or the expected end-of-year price for another commodity for this year (1 value at once). And everything between this extremes.
My question is: Does it make sense to store different curves in different hyper tables? Lets say all curves with a hourly granularity into table A and all curves with a yearly granularity into table B. Or maybe to distribute the data in another way. Lets say by curve types, or by the approx count of grid points in a curve. Or maybe even to use only one hyper table?