surrealdb-expert

To manage document and graph data in a multi-model database, use SurrealDB 3.0 to define schemas and coordinate cross-store queries. It natively supports document, graph, vector, and key-value models within a single database engine using SurrealQL.

Yes, SurrealDB 3.0 supports setting up authentication and custom API endpoints natively. You use SurrealQL syntax with DEFINE ACCESS for authentication and DEFINE API to create custom endpoints for your applications.

Implementing vector indexing in SurrealDB 3.0 involves using HNSW vector indexing. You define vector indexes through SurrealQL syntax to efficiently store and query vector embeddings alongside document and graph data.

Defining schemas and computed fields in SurrealQL 3.0 involves using specific syntax to establish table structures and record references. You can create dynamic computed fields to automatically calculate values based on other record data.

Yes, SurrealDB 3.0 works for polyglot persistence architectures by consolidating multiple data models into a single engine. It eliminates the need for separate databases by handling document, graph, vector, and key-value stores simultaneously.

Security hardening in SurrealDB 3.0 involves configuring proper authentication and permissions using DEFINE ACCESS. You should apply strict access controls and network security best practices to protect cross-store data.