Support aggregation of many time-series in CDF

Thank you for this reminder @RobersoSN!

The current limit was set in order to ensure appropriate performance from the service. We believe we might be able to increase it somewhat but have concerns about the impact to expectations and latency we are trying to sort out.

Have a couple of proposals in the queue and have reached out directly to you about this.

Thanks @Thomas Sjølshagen, looking forward to seeing your proposals implemented.

Are you impaced by synthetic TimeSeries for your addition tech @Eric Stein-Beldring? 

@RobersoSN unfortunately no, since the backend we use to run calculations are separate from the synthetic time series capabilities in Cognite Data Fusion. 

That said, we are working on the backend functionality that will make this possible for us to build a better frontend experience for this type of use case. Regardless, I believe @Thomas Sjølshagen’s proposals for using synthetic time series will probably be a more efficient and scalable solution to this problem that you’re describing (at least in the near-term). 

Hi @Eric Stein-Beldring, just want to confirm that this is an interesting feature for our company. Operations engineers want to be able to see a syntethic time serie summarizing data points from several time series in a chart. I think being able to specifiy this in the Cognite Chart url could work great. Example

This URL would give a chart showing a time serie with 10% of time serie 1 + 100 % of time serie 2, in the last year.
https://charts.cogniteapp.com/prod?cluster=example&endTime=now&startTime=365d-ago&timeserieExternalIds=0.1,timeserie_1_id+1.0,timeserie2_id

@Ola Øyan sorry, your comment slipped past me. Thank you for the added input – I agree that being able to specify time series granularity like this would certainly be valuable. We’ve recently been discussing ideas in the team about how we would provide the option to provide this granularity preference for time series and calculations (I know you and I have talked about the latter as well).