Introduction to DingoDB
DingoDB is an innovative open-source distributed multimodal vector database. Developed by DataCanvas, it combines features such as online strong consistency, relational and vector semantics, which makes it a unique and versatile database solution. It’s designed to cater to the demanding needs of enterprises, offering exceptional scalability and easy integration with existing systems. DingoDB supports multiple programming languages and is completely compatible with the MySQL protocol, ensuring flexibility and ease of use for users.
Key Features of DingoDB
Comprehensive Access Interface
DingoDB stands out by providing a comprehensive range of access interfaces. Whether developers prefer SQL, SDK, or API, DingoDB has flexible modes to support diverse needs. It also features Table and Vector as principal data models, enhancing the capability to process data efficiently.
Built-in Data High Availability
To save users from the hassle of deploying external components, DingoDB comes equipped with built-in high availability features. This significantly lowers the costs related to deployment and ongoing system maintenance, enhancing overall operational efficiency without compromising on performance.
Fully Automatic Elastic Data Sharding
DingoDB boasts a sophisticated data sharding mechanism that is fully automatic. It dynamically configures shard sizes and performs automatic splitting and merging, providing optimal resource allocation. This feature is particularly beneficial for businesses anticipating significant expansion, as it allows for seamless adaptability.
Scalar-Vector Hybrid Retrieval
Offering both traditional and vector index types, DingoDB delivers an exceptional hybrid retrieval experience. It seamlessly merges scalar and vector operations and supports distributed transaction processing, ensuring excellent data retrieval capabilities that meet industry standards.
Built-in Real-Time Index Optimization
A standout feature of DingoDB is its ability to perform real-time index optimization. This built-in feature continuously optimizes both scalar and vector indices behind the scenes, ensuring efficient data retrieval without any noticeable delays for the user.
Getting Started with DingoDB
For those interested in exploring DingoDB, comprehensive documentation is available here. It guides users on installation, whether opting for Docker or Ansible, and provides insights on utilizing DingoDB effectively.
Development and Contribution
Developers interested in contributing to DingoDB can use IntelliJ IDEA, which supports Java and Gradle, for an efficient development experience. The project encourages collaboration through GitHub submissions, adhering to best practices such as single commit squashing and rigorous code testing.
Supporting and Collaborating
DingoDB is sponsored by DataCanvas and appreciates community involvement, inviting feedback and contributions. For performance optimization, the project recommends the YourKit Java Profiler, acknowledging its utility for performance-intensive applications.
Contact Information
For technical inquiries or business-related questions, stakeholders can reach out through the provided WeChat and Official Account QR codes, ensuring open channels of communication for all users.
DingoDB is licensed under the Apache License Version 2.0, fostering an open and collaborative community environment.