Hi All, I was searching around for documentation of the performance differences of having a sharded, single schema, dynamic field set up vs. a multi-core, static multi-schema setup (which I currently have), but I have not had much luck finding what I am looking for. I understand commits and optimizes will be more intensive in a single core since there is more data (though I would offset by sharding heavily), but I am particularly curious about the search performance implications.
I am interested in moving to the dynamic field setup in order to implement a better global search, but I want to make sure I understood the drawbacks of hitting those datasets individually and globally after they are merged (NOTE: I would have a global field signifying the dataset type, which could then be added to the filter query in order to create the subset for individual dataset queries). Some background about the data: it is extremely variable. Some documents contain only 2 or 3 sentences, and some are 20 page extracted PDFs. There would probably only be about 100-150 unique fields. Any input is greatly appreciated! Thanks, Briggs Thompson