I imagined there would be more literature on this, but I'm having trouble finding any. I have a lot of non-algebraically-aggregatable time series data (that is to say, points for which no function exists that I could use to aggregate them to a higher granularity-- stuff like unique active users, unique contributors, etc... where knowing the amount I had every minute of some hour does not tell me how many I had total during the hour). Currently, I'm just storing and presenting all of this data in UTC. The problem is that many of my clients find this confusing-- understandably so. Because the data is non-algebraically-aggregatable, there's no way to get from UTC data for 1 day midnight- midnight to, say, PST data from midnight to midnight. Recalculation would need to be done from raw data.
So:
- Recalculation from raw data is prohibitively expensive for some complicated analytics graphs
- We could store all data for all time zones, but this would increase the amount of data we store x24.
All of that said, how do other people deal with this issue? Here's how Google Analytics does it, but this seems insufficient for my use case because I know if I open the multiple timezone can of worms, clients will ask for more than one. This will also take a lot of work that doesn't seem worth the effort as just adding timezone support won't be extremely noticeable or a huge win. What I'm really hoping for is some clever design solution that just presents the UTC data in some intuitive enough way that it's no longer confusing for people in other timezones. Has anyone dealt with similar problems and come upon a solution I'm missing?