Okay here you’ll find some examples of working with instances in DATs. Initial big picture ideas to hold onto are that your column headers are how you match values from a DAT to an instance.
The first couple of examples show how you can drive instances from a DAT, then there’s a look at how you can manipulate those / update those values directly in a table. Following are some exploratory ideas about other ways to drive your instances with updating json data. Everything from putting in a point attribute to pulling it from storage with a script CHOP.
900 instances still runs smoothly for me on a little ultra-book’s processor on battery power, but your mileage will vary. My general advice is to imagine that you need to reserve at least 20% of your project time for optimization. If you’re doing something for you, or for a lower-stakes situation you can likely skip this. If, this is client work or something that’s mission critical for success, I would make sure you reserved more time than you expect for optimization and edge case handling - what happens when you get a json blob with no data, or if the keys have changed; what are your upper and lower limits for data throughput, if your maxing out, do you need to break this into another touch process and how do you want to handle the data exchange; and so so many other considerations.
I’m going to look at your project specifically next, but I wanted to make sure that you had some solid examples that weren’t domain specific so you could explore the concepts.
dat-instancing.toe (24.6 KB)