Pinecone
Managed vector database for AI applications
DingoDB
Distributed multi-modal vector database with MySQL compatibility
Open-source distributed vector database combining real-time strong consistency, relational semantics, and vector search with MySQL protocol compatibility and horizontal scalability.
Overview
What is DingoDB?
DingoDB is a distributed multi-modal vector database that unifies relational and vector semantics into a single platform. Designed for enterprise-grade applications, it delivers real-time strong consistency, horizontal scalability, and elastic scaling capabilities while maintaining MySQL protocol compatibility.
Who Should Use It?
DingoDB targets developers and organizations building AI-powered applications that require both structured data management and vector search capabilities. Teams seeking to avoid complex multi-database architectures will benefit from its unified SQL interface that handles traditional queries and vector operations seamlessly.
Core Capabilities
The platform supports scalar-vector hybrid retrieval, enabling sophisticated queries that combine traditional database filters with semantic search. Built-in high availability eliminates external dependencies, reducing deployment complexity. Automatic data sharding with dynamic splitting and merging adapts to workload changes without manual intervention. Real-time index optimization runs transparently in the background, ensuring consistent query performance. For large-scale deployments, cold-hot tiered storage minimizes memory footprint while maintaining search responsiveness.
DingoDB provides flexible access through SQL, SDK, and API interfaces, treating both tables and vectors as first-class data models. Licensed under Apache 2.0, it offers comprehensive multi-language support for diverse development environments.
Highlights
Scalar-vector hybrid retrieval with unified SQL interface and MySQL compatibility
Built-in high availability without external dependencies or complex orchestration
Automatic elastic data sharding with dynamic splitting and merging
Real-time index optimization for both scalar and vector data types
Pros
- Unified platform eliminates need for separate relational and vector databases
- MySQL protocol compatibility enables easy integration with existing tools
- Automatic sharding and index optimization reduce operational overhead
- Cold-hot tiered storage supports massive datasets with controlled memory usage
Considerations
- Primary implementation in Java may limit performance compared to native alternatives
- Relatively smaller community compared to established vector database solutions
- Documentation and ecosystem maturity still developing
- Multi-modal architecture may introduce complexity for simple use cases
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Fit guide
Great for
- AI applications requiring both structured data and semantic search capabilities
- Teams seeking MySQL-compatible vector database for easier migration
- Enterprise deployments needing built-in high availability and auto-scaling
- Organizations wanting to consolidate relational and vector workloads
Not ideal when
- Projects requiring only vector search without relational data management
- Teams preferring lightweight, single-purpose vector stores
- Applications demanding maximum performance over feature breadth
- Environments with strict constraints against JVM-based systems
How teams use it
E-commerce Product Search
Combine semantic similarity search on product descriptions with structured filters for price, category, and inventory using unified SQL queries
Customer Support Knowledge Base
Enable hybrid retrieval across support tickets and documentation, filtering by metadata while ranking results by semantic relevance
Real-Time Recommendation Engine
Deliver personalized recommendations by joining user profiles with vector embeddings of content, maintaining strong consistency across distributed nodes
Multi-Tenant SaaS Analytics
Scale automatically across tenants with elastic sharding while providing each customer isolated semantic search over their structured datasets
Tech snapshot
Java99%
FreeMarker1%
Shell1%
Dockerfile1%
Frequently asked questions
Does DingoDB require external components for high availability?
No. DingoDB provides built-in high availability configurations without requiring external components like ZooKeeper or etcd, reducing deployment complexity and operational overhead.
Can I use existing MySQL tools and clients with DingoDB?
Yes. DingoDB offers MySQL protocol compatibility, allowing you to connect using standard MySQL clients, drivers, and tools while accessing both relational and vector capabilities.
How does DingoDB handle data growth and scaling?
DingoDB automatically splits and merges data shards based on configurable size thresholds, enabling elastic horizontal scaling without manual intervention as your dataset grows.
What vector index types does DingoDB support?
DingoDB supports various vector index types and can dynamically switch between memory-based and disk-based indexes depending on data scale, optimizing for both performance and resource consumption.
Can I perform transactions across scalar and vector data?
Yes. DingoDB supports distributed transaction processing that spans both scalar and vector data, ensuring consistency across hybrid queries and updates.
Project at a glance
Active- Stars
- 1,692
- Watchers
- 1,692
- Forks
- 266
LicenseApache-2.0
Repo age4 years old
Last commit7 hours ago
Primary languageJava
Last synced 4 hours ago