SurrealDB
SurrealDB is a scalable, distributed multi-model database that unifies vectors, graphs, documents, time-series and files to enable real-time AI applications with built-in authentication, permissions, and WebSocket capabilities.
https://surrealdb.com/?ref=aipure
Product Information
Updated:Jun 16, 2025
SurrealDB Monthly Traffic Trends
SurrealDB experienced a 58.1K visits with a -18.0% decline in traffic. Despite hosting a developer meetup in New York City titled "Unlock Smarter Asset Insights with Graph-Powered Financial Data Analysis" and launching SurrealDB 2.0 with advanced features, the decline suggests that ongoing market competition and the complexity of integrating new updates might have impacted user engagement.
What is SurrealDB
SurrealDB is an innovative multi-model database designed for tomorrow's serverless, jamstack, single-page, and traditional applications. It allows developers to store and manage data in various models including relational, document, graph, time-series, vector & search, and key-value - all in one unified platform. Built with modern applications in mind, SurrealDB provides a comprehensive solution that eliminates the need for complex backend servers and APIs while offering enterprise-grade features out of the box.
Key Features of SurrealDB
SurrealDB is a multi-model database that unifies multiple database paradigms including document, graph, vector, relational, and time-series data models into a single platform. It features built-in authentication, real-time data streaming, and supports both edge computing and cloud deployment. With its SQL-like query language (SurrealQL), it enables complex data operations while providing scalability from edge devices to petabyte clusters, making it particularly suitable for AI applications and modern web development.
Multi-model Database Architecture: Combines document, graph, vector, time-series, and relational database capabilities in one platform, eliminating the need for multiple specialized databases
Advanced Security Features: Offers row-level permissions, custom access rules, and integration with OAuth, SAML, and LDAP for comprehensive security control
Vector and Hybrid Search: Native support for AI model integration with vector embeddings and full-text search capabilities, making it ideal for AI-powered applications
Real-time Event Streaming: Built-in support for real-time data synchronization and event-driven notifications for building responsive applications
Use Cases of SurrealDB
AI and Machine Learning Applications: Perfect for applications requiring vector storage and search, integration with LLM models, and real-time AI processing
Backend-as-a-Service (BaaS): Serves as a complete backend solution for modern web and mobile applications, reducing the need for complex server architectures
Edge Computing Solutions: Can be embedded in edge devices while maintaining synchronization with cloud deployments, ideal for IoT and distributed systems
Real-time Analytics Platforms: Suitable for building real-time analytics and recommendation systems with its event streaming and processing capabilities
Pros
Versatile multi-model architecture reduces infrastructure complexity
Strong built-in security features and authentication system
Excellent support for modern AI and real-time applications
Flexible deployment options from edge to cloud
Cons
Relatively new technology with a developing ecosystem
Learning curve associated with its unique query language (SurrealQL)
May not match specialized databases in specific use cases
How to Use SurrealDB
Install SurrealDB: Install a standalone instance on your system using the provided installation script. For macOS, use Homebrew. For Linux/Windows, follow the installation instructions from the official documentation.
Start the Database: Choose and specify which storage engine to use by providing the engine as the connection URL protocol. You can run it as an embedded database or deploy it as a scalable cluster.
Create Records: Use the CREATE statement to add new records to the database. You can create records with fields and initialize them with values. Tables can be either schemafull or schemaless.
Define Schema (Optional): If using schemafull tables, define your schema using SurrealQL, which is similar to standard SQL. This sets up table structures and constraints.
Set Up Authentication: Configure authentication and access rules. SurrealDB supports row and field-level permissions, and can integrate with OAuth, SAML and LDAP.
Query Data: Use the SELECT statement to retrieve data. SurrealDB's SELECT includes additional features inspired by NoSQL databases and supports JOIN-less deep document fetching.
Create Relationships: Use the RELATE statement to create connections between records. This enables graph database functionality for modeling complex relationships.
Update Records: Use UPDATE or UPSERT statements to modify existing records. UPSERT will create a record if it doesn't exist.
Implement Real-time Features: Set up real-time queries using WebSocket connections. Clients can subscribe to queries and receive live push updates for data changes.
Integrate with Development Stack: Use official SDKs available for various programming languages including Python, JavaScript, Rust, Go, Node.js, and WebAssembly to connect your application to SurrealDB.
SurrealDB FAQs
SurrealDB is a multi-model database that combines document, graph, relational, and vector capabilities in a single platform. It's designed to handle various data types including vectors, graphs, documents, time-series and files, making it particularly suitable for AI applications.
SurrealDB Video
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Analytics of SurrealDB Website
SurrealDB Traffic & Rankings
58.2K
Monthly Visits
#499362
Global Rank
#8846
Category Rank
Traffic Trends: Mar 2025-May 2025
SurrealDB User Insights
00:01:27
Avg. Visit Duration
2.57
Pages Per Visit
39.51%
User Bounce Rate
Top Regions of SurrealDB
SE: 10.61%
DE: 9.97%
US: 9.72%
ID: 8.6%
GB: 6.66%
Others: 54.44%
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