Hello,
I am currently trying to use python to essentially backup features from a "Tracking" layer to a hosted feature layer (I'm gonna call this layer the "repository layer") for extra storage. Originally I was using a notebook to create a spatial data frame of the tracking layer, compare it against the global ids in the repository layer, and append those features if those global ids aren't in there.
My current issue is that as of a few months ago, my script stopped working. After some troubleshooting I realized that the issue lies in trying to load my entire layer into a spatial dataframe. This worked previously, but now it only lets me load up to 180 features, if I try any more than that, it gives me a JSON Decode Error.

Here is what my troubleshooting currently looks like. I'm wondering if there is a way to get around this, if i should avoid dataframes entirely and if I should try an entirely different approach. I was testing around with Returning IDs Only in my query and trying to use those ids to have a similar functionality, but I don't even know where to start with that. Any help would be GREATLY appreciated!