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Last Updated: 08-21-2026 03:26 PM (UTC)

SurrealDB

With 32,800 GitHub stars, 180 contributors, and version 3.2 shipping in July 2026, SurrealDB eliminates the database zoo by unifying document, graph, relational, time-series, geospatial, and key-value data models into a single Rust binary queried through SurrealQL — an intuitive SQL-like language that handles graph traversals, record links, subqueries, and computed fields without switching between multiple database engines. Purpose-built for AI applications, it integrates vector indexing, full-text search, and hybrid retrieval that blends semantic similarity with graph and relational intelligence for context-aware RAG pipelines and recommendation engines. Real-time subscriptions and event-driven triggers push live data changes to connected clients without requiring external message brokers like Kafka. Multi-row, multi-table ACID transactions guarantee consistency while incrementally computed views deliver pre-calculated analytics without batch processing. Role-based access control with record-level permissions, JWT authentication, and multi-tenant isolation enables backend-as-a-service usage where client applications connect directly with fine-grained security. SDKs for JavaScript, Python, Go, Rust, .NET, and Java connect via WebSocket or HTTP APIs. Storage and compute separation allows deployment as an embedded library, a single-node server, or a highly-scalable distributed cluster with TiKV or FoundationDB backends. Deploy via Docker with persistent volumes on any Linux host. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. Source-available licensed.

SurrealDB
SurrealDB
SurrealDB
SurrealDB
SurrealDB

Benefits

  • Six Data Models One Engine
  • Document, graph, relational, time-series, geospatial, and key-value models queried through a single language, eliminating the operational overhead of running PostgreSQL, Neo4j, Redis, and InfluxDB separately.
  • AI-Native Vector Search
  • Integrated vector indexing with full-text and hybrid retrieval combines semantic similarity, graph traversals, and relational filters in unified queries for RAG pipelines and recommendation engines.
  • Real-Time Event Architecture
  • Built-in WebSocket subscriptions and event triggers push live data changes to connected clients instantly without requiring external message brokers, pub-sub systems, or change data capture tools.
  • Single Binary Flexible Deployment
  • A single Rust binary runs embedded in applications, as a standalone server, or as a distributed cluster with TiKV or FoundationDB backends supporting horizontal scaling and high availability.

Features

  • SurrealQL Query Language
  • SQL-like syntax with native graph traversals, record links, subqueries, computed fields, permissions, and schema enforcement supporting both structured and schemaless data in unified queries.
  • ACID Transactions
  • Multi-row, multi-table transactions with serializable isolation guarantee consistency across complex operations spanning documents, graph edges, and relational records simultaneously.
  • Backend-as-a-Service Mode
  • End-user authentication with JWT, RBAC, record-level permissions, and multi-tenant isolation enable direct client connections from browsers and mobile apps without middleware.
  • Computed Materialized Views
  • Incrementally computed views pre-calculate aggregations, joins, and analytics that update automatically as underlying data changes, eliminating batch ETL pipelines.
  • Multi-Language SDKs
  • Official SDKs for JavaScript, Python, Go, Rust, .NET, and Java with WebSocket and HTTP transport, connection pooling, and type-safe query builders.