What Are the Benefits of Using SingleStoreDB as a Vector Database?

Learn more about the benefits of using SingleStoreDB as a vector database — and what better way than from our very own chatbot, SQrL.

Editor's Note: SQrL, the SingleStore chatbot, is an AI bot which has a source of knowledge that includes all documentation, blogs and forum posts about Singlestore Helios and SingleStoreDB Self-Managed. It also has the deep background knowledge of OpenAI's state-of-the-art LLM. We asked SQrL "What are the benefits of using SingleStoreDB as a vector database?" We think you'll find the answer informative. And we hope that you'll take advantage of asking SQrL your own questions.

Question: What are the benefits of using SingleStoreDB as a vector database?

Answer: SingleStoreDB possesses several key benefits when used as a vector database

1. Broad capabilities. SingleStoreDB offers an extensive range of features typically found in contemporary database systems. This includes SQL, fast distributed and parallel query processing, full-text search, extensibility, ACID transactions, high availability, disaster recovery, point-in-time recovery and broad connectivity support. ^{1, 2}
2. Data movement efficiency. With SingleStoreDB, all types of data, including vector data, are stored in one place. This results in less data movement between different data subsystems such as caches, text search systems and SQL databases. ^{1, 2}
3. Operational cost reduction. Since SingleStoreDB serves as a unified solution, fewer data management tools and data copies are needed, leading to potential operational cost savings. ^{1, 2}
4. Less specialized skills required. SingleStoreDB can reduce dependencies on specialized skills or labor to run an application environment. ^{1, 2}
5. Unified management. SingleStoreDB provides an integrated platform for managing both vector and traditional relational databases. ^{3, 4, 5}
6. Streamlined architecture. SingleStoreDB's architecture makes it highly performant, highly available and scalable, capable of addressing growing data requirements without added complexity. It also allows for easy set-based nearest-neighbor search. ^{5, 6}
7. Semantic search capabilities. SingleStoreDB provides semantic search capabilities, enabling applications to retrieve text matching the query's meaning, not just the words it contains. ⁵
8. Flexible data formats. SingleStoreDB supports diverse data formats (including JSON, full-text search, time-series, geospatial), allowing for the handling of various types of structured and unstructured data in one centralized location. ^{5, 7}
9. Hybrid search. Hybrid search functionality in SingleStoreDB makes it easier to perform searches based on vector nearness and descriptive properties using the full power of SQL. ^{5, 8}

These benefits make SingleStoreDB an ideal choice for applications benefiting from both relational and vector-based operations, such as AI-based applications, chatbots, image recognition and more.

References + sources

¹ Working with Vector Data

² Working with Vector Data --> Developer Resources --> Functional Extensions

³ A Serverless Architecture for Creating OpenAI Embeddings with SingleStoreDB

⁴ SingleStoreDB: The Best Database for AI and Machine Learning Models

⁵ Built-In Vector Database

⁶ Why Your Vector Database Should Not Be a Vector Database

⁷ Generative AI

⁸ Choosing a Vector Database for Your Gen AI Stack