Suggestions for further improvement: Cap the amount of cells and essentially partition. Using 2 machines could conceivably double performance after scaling up

Suggestions for further improvement: Cap the amount of cells and essentially partition. Using 2 machines could conceivably double performance after scaling up stops working. So a 1bn row sum could conceivably ask for 5 200m sums then sum those. Maintain a float32 and float64 version. Compute the float32 first and return to user, then update the result. You could conceivably run some kind of ml model or even regular logic to predict the API call your formula engine will need to make, eg. If the customer status typing a column of sum range, you could optimistically fetch that part as soon as you see that element. Optimistically caching is obviously high variance and resource draining. The 10000iq version is coordination free horizontal scaling with scyladb algorithm