Mentat is not interested in that. Just like Datascript, Mentat speaks Datalog for querying and takes additions and retractions as input to a transaction. It is less concerned with exposing consistent database states outside transaction boundaries, because that's less important here, and dropping some of these requirements allows us to leverage SQLite itself. By abstracting away the storage schema, and by exposing change listeners outside the database (not via triggers), we hope to make domain schemas stable, and allow both the data store itself and embedding applications to use better architectures, meeting performance goals in a way that allows future evolution.
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Teams learn to fear and avoid schema changes, and eventually they ship a table called metadata, with three TEXT columns, so they never have to write a migration again. Writing a robust SQL schema migration is hard, particularly if a bad migration has ever escaped into the wild! Mentat is intended to be a flexible relational (not key-value, not document-oriented) store that makes it easy to describe, grow, and reuse your domain schema.
In a SQL database, the same schema definition defines everything from high-level domain relationships through to numeric field sizes in the same smear of keywords. They both have limitations - the Mentat schema allows only for an open world (it's possible to declare friendships with people whose email isn't known), and requires validation code to enforce email string correctness - but we think that even such a tiny SQL example is harder to understand and obscures important domain decisions.
That decision pushes storage complexity into application code.