Some classes of applications, including browsers, generate an ever-growing set of data: visits to pages, plays of songs and videos, purchases, messages.
It's routine for these applications to have approaches to scaling by minimizing the 'working' data set: expiration of old data, archiving of subsets of the data, or similar.
The simplest approach to scale is to more carefully constrain queries, doing most of the work on only a subset of the candidates — e.g., evaluating, ranking, and extracting only history entries with any visit within the last year, rather than ranking all history entries and applying a limit after ranking. Sometimes this kind of bounding is sufficient.
When it isn't, we need other mechanisms for reducing the working set.
Data in Mentat lives in these four places: