nanograph-ops

Manage graph database migrations using a schema-first workflow with .pg and .gq files. This approach enforces linting and structured migration steps to prevent stale schemas and ensure data integrity during updates.

Inspect graph metadata by running nanograph describe --format json at the project root. This checks the current graph database state and readiness before executing any operational changes or mutations.

A mutation-first workflow with parameterized queries prevents incorrect CLI usage and avoids stale schemas. It ensures safe graph mutations by validating changes before they are applied to the database.

Track CDC visibility by running post-change practices like describe, doctor, and changes commands. These operations verify graph database state and monitor data capture events after mutations are applied.

Yes, day-to-day operations support storage generation, compact, and embed functions for local or embedded graphs. These features manage storage efficiency and generate vector embeddings directly within the graph database.

Limitations include risks of stale schemas and incorrect CLI usage if operational guidelines are bypassed. Skipping the post-change checklist or avoiding linting steps can lead to unsafe graph mutations and data inconsistencies.