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surrealdb/surrealdb

A scalable, distributed, collaborative, document-graph database, for the realtime web

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TINIX TRENDING·Chủ đề: backend-as-a-service

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TINIX TRENDING·Chủ đề: serverless

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TINIX TRENDING·Chủ đề: document-database

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🦀 SurrealDB: One Database to Rule Them All

A scalable, distributed, collaborative, document-graph database built in Rust for the realtime web.

SurrealDB is a multi-model database built in Rust designed to unify multiple data models into a single engine. Created by the SurrealDB team, it combines document, graph, relational, time-series, geospatial, and key-value data types with powerful search and retrieval functionalities (full-text, vector, hybrid) and real-time, event-driven capabilities. It can run embedded in-app, in the browser via WebAssembly, at the edge, self-hosted as a single node, or in a distributed cluster in the cloud.

✨ Highlights

  • Multi-model engine: Supports document, graph, relational, time-series, geospatial, and key-value data natively.
  • SurrealQL: An intuitive SQL-like query language for all data models.
  • Real-time collaboration: Functions as both a database and an API backend service with real-time capabilities.
  • Backend-as-a-service: Includes end-user authentication, reducing the need for server-side components.
  • Flexible deployment: Runs embedded, in-browser (WASM), edge, self-hosted, or in a distributed cloud cluster.
  • Powerful search: Full-text, vector, and hybrid search capabilities built-in.
  • Single Rust binary: Easy deployment and minimal setup across environments.
  • Language SDKs: Official support for Rust, JavaScript, Python, .NET, PHP, and more.

💡 Why it matters

SurrealDB eliminates the need to stitch together multiple databases and backend services by offering a unified engine that handles diverse data types and real-time interactions. This simplification reduces development time, infrastructure complexity, and operational overhead.

It is especially well-suited for data-intensive applications requiring multiple data models, real-time features like recommendation engines or fraud detection, AI agent data layers, knowledge graphs, and embedded or edge systems.


#MultiModelDatabase #RustLang #RealtimeDatabase #TiniX

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