What is SurrealDB?

SurrealDB is a modern database that blends the best parts of SQL and NoSQL into one system. It can store data as tables, documents, or graphs and lets you run real-time queries that update instantly as data changes.

Let's break it down

  • SurrealDB: the name of the database product.
  • Modern database: a newer type of data storage built for today’s web and app needs.
  • Blends the best parts of SQL and NoSQL: it lets you write familiar SQL-style commands while also handling flexible, schema-less data like NoSQL databases.
  • One system: you don’t need separate tools for tables, documents, or graphs.
  • Store data as tables, documents, or graphs: you can keep data in rows/columns (tables), key-value pairs (documents), or nodes/edges (graphs).
  • Real-time queries: queries that automatically push updates to your app whenever the underlying data changes.

Why does it matter?

Because it reduces the number of different databases and services you need to learn and maintain, letting developers build faster, more interactive applications with less code and fewer moving parts.

Where is it used?

  • Real-time dashboards that show live sensor or business metrics.
  • Collaborative editing tools (like shared text editors or whiteboards) where multiple users see changes instantly.
  • IoT platforms that need to store device data and relationships while pushing updates to devices.
  • Gaming leaderboards that must update scores and rankings in real time for all players.

Good things about it

  • Multi-model support (SQL, document, graph) in a single engine.
  • Built-in live queries for automatic real-time updates.
  • Simple, SQL-like query language that many developers already know.
  • Can run as a serverless function, embedded library, or full-scale server.
  • Strong ACID guarantees for reliable transactions.

Not-so-good things

  • Still a relatively new project, so the community and documentation are smaller than for established databases.
  • Fewer third-party tools, drivers, and integrations are available today.
  • Learning how to use the hybrid model efficiently can take time.
  • Performance may not match highly specialized databases when handling massive, ultra-large datasets.