SingleStore vs MongoDB Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare SingleStore and MongoDB, let's first explore the concept of vector databases.
A vector database is specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. These vectors encode complex information, such as the semantic meaning of text, the visual features of images, or product attributes. By enabling efficient similarity searches, vector databases play a pivotal role in AI applications, allowing for more advanced data analysis and retrieval.
Common use cases for vector databases include e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, and natural language processing (NLP) tasks. They also play a crucial role in Retrieval Augmented Generation (RAG), a technique that enhances the performance of large language models (LLMs) by providing external knowledge to reduce issues like AI hallucinations.
There are many types of vector databases available in the market, including:
- Purpose-built vector databases such as Milvus, Zilliz Cloud (fully managed Milvus)
- Vector search libraries such as Faiss and Annoy.
- Lightweight vector databases such as Chroma and Milvus Lite.
- Traditional databases with vector search add-ons capable of performing small-scale vector searches.
SingleStore is a distributed, relational, SQL database management system and MongoDB is a NoSQL database that stores data in JSON-like documents. Both have vector search as an add-on. This post compares their vector search capabilities.
SingleStore: Overview and Core Technology
SingleStore has made vector search possible by putting it in the database itself, so you don’t need separate vector databases in your tech stack. Vectors can be stored in regular database tables and searched with standard SQL queries. For example, you can search similar product images while filtering by price range or explore document embeddings while limiting results to specific departments. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ for vector index and dot product and Euclidean distance for similarity matching. This is super useful for applications like recommendation systems, image recognition and AI chatbots where similarity matching is fast.
At its core SingleStore is built for performance and scale. The database distributes the data across multiple nodes so you can handle large scale vector data operations. As your data grows you can just add more nodes and you’re good to go. The query processor can combine vector search with SQL operations so you don’t need to make multiple separate queries. Unlike vector only databases SingleStore gives you these capabilities as part of a full database so you can build AI features without managing multiple systems or dealing with complex data transfers.
For vector indexing SingleStore has two options. The first is exact k-nearest neighbors (kNN) search which finds the exact set of k nearest neighbors for a query vector. But for very large datasets or high concurrency SingleStore also supports Approximate Nearest Neighbor (ANN) search using vector indexing. ANN search can find k near neighbors much faster than exact kNN search sometimes by orders of magnitude. There’s a trade off between speed and accuracy - ANN is faster but may not return the exact set of k nearest neighbors. For applications with billions of vectors that need interactive response times and don’t need absolute precision ANN search is the way to go.
The technical implementation of vector indices in SingleStore has specific requirements. These indices can only be created on columnstore tables and must be created on a single column that stores the vector data. The system currently supports Vector Type(dimensions[, F32]) format, F32 is the only supported element type. This structured approach makes SingleStore great for applications like semantic search using vectors from large language models, retrieval-augmented generation (RAG) for focused text generation and image matching based on vector embeddings. By combining these with traditional database features SingleStore allows developers to build complex AI applications using SQL syntax while maintaining performance and scale.
MongoDB: Overview and Core Technology
MongoDB Atlas Vector Search is a feature that allows you to do vector similarity searches on data stored in MongoDB Atlas. You can index and query high-dimensional vector embeddings along with your document data and do AI and machine learning right in the database.
At its core, Atlas Vector Search uses the Hierarchical Navigable Small World (HNSW) algorithm for indexing and searching vector data. This creates a multi-level graph of the vector space so you can do Approximate Nearest Neighbor (ANN) searches. It’s a balance of speed and accuracy for large scale vector search. Atlas Vector Search also supports Exact Nearest Neighbors (ENN) searches which prioritizes accuracy over performance for queries of up to 10,000 documents.
One of the big advantages of Atlas Vector Search is its integration with MongoDB’s flexible document model. You can store vector embeddings along with other document data so you can search more contextually and precisely. You can query any kind of data that can be embedded up to 4096 dimensions. Atlas Vector Search allows you to combine vector similarity searches with traditional document filtering. For example, a semantic search for products could be filtered by category, price range or availability.
Atlas Vector Search also supports hybrid search, combining vector search with full text search for more granular results. This is different from Atlas Search which is focused on keyword based search. The platform integrates with popular AI services and tools so you can use it with embedding models from providers like OpenAI, VoyageAI and many others listed on Hugging Face. It also supports open-source frameworks like LangChain and LlamaIndex for building applications that use Large Language Models (LLMs).
To ensure scalability and performance, MongoDB Atlas provides Search Nodes, which provides dedicated infrastructure for Atlas Search and Vector Search workloads. This allows you to have optimized compute resources and independent scaling of search needs so you get better performance at scale.
By having these capabilities in the MongoDB ecosystem, Atlas Vector Search is a full solution for developers building AI powered applications, recommendation systems or advanced search features. No need for a separate vector database, you can use MongoDB’s scalability and rich features along with vector search.
Key Differences
Search Methodology and Algorithms
SingleStore has multiple vector search options to fit different use cases. For exact results, it has exact k-nearest neighbors (kNN) search. For speed over exactness, SingleStore has Approximate Nearest Neighbor (ANN) search with multiple index types: FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ. It supports both dot product and Euclidean distance for similarity matching so developers have flexibility in how they measure vector similarity.
MongoDB Atlas Vector Search takes a more focused approach and uses HNSW (Hierarchical Navigable Small World) as its primary search method. For smaller datasets up to 10,000 documents, MongoDB has Exact Nearest Neighbors (ENN) search. For larger scale, it switches to ANN search to maintain performance. This simplifies the decision for developers while still providing search capability.
Data Handling and Structure
SingleStore uses a structured approach based on columnstore tables. Vector data must be in the format: Vector Type(dimensions[, F32]). This structured approach allows SingleStore to combine traditional SQL with vector operations efficiently. It works well for applications with a clear data schema where SQL operations are the primary requirement.
MongoDB takes a more relaxed approach with its document based storage. It supports vectors up to 4096 dimensions and you can mix vector data with any document structure. This flexibility makes MongoDB good for applications with semi-structured and unstructured data where schema might change over time.
Scalability and Performance
SingleStore scales through data distribution across multiple nodes. As your data grows, you can add more nodes to maintain performance. It combines vector search with SQL in a single query reducing complexity and improving performance. This architecture makes SingleStore good for high performance vector operations within a traditional database.
MongoDB scales through dedicated Search Nodes for vector search workloads. This separation of search infrastructure from main database operations allows for independent scaling of search. It’s optimized for document based operations with integrated vector search so it’s good for applications that need to balance traditional document storage with vector search features.
Integration and Ecosystem
SingleStore’s strength is in its SQL based approach. It works seamlessly with existing SQL tools and workflows so it’s a great choice for organizations with existing SQL expertise and infrastructure. Applications that require strong SQL integration can use SingleStore’s vector capability without significant architectural changes.
MongoDB has broad integration with popular AI services like OpenAI and VoyageAI. It supports modern AI frameworks like LangChain and LlamaIndex and works with various embedding models. It also has built-in support for hybrid search combining vector and full-text search. This rich ecosystem makes MongoDB a good choice for AI driven applications.
When to Choose SingleStore
SingleStore is for companies that use SQL and need to handle structured data at scale. It’s perfect for enterprise applications where exact vector matching matters, like financial analysis platforms, real-time recommendation engines or large image similarity search systems that need precise results. It’s for when you need to combine traditional database operations with vector search and your team has SQL expertise and your infrastructure is built around relational databases.
When to Choose MongoDB
MongoDB is the obvious choice when your app needs flexible data structures and seamless integration with modern AI services. It’s great for applications like content recommendation systems, semantic document search or AI powered chatbots that need to combine vector search with unstructured data. It’s for when you need to rapidly prototype and iterate on your vector search implementation, need hybrid search or plan to integrate with many AI services and embedding models.
Conclusion
Both SingleStore and MongoDB are great for vector search but serve different needs in the modern app landscape. SingleStore’s strength is its SQL first approach, precise vector operations and handling structured data at scale, so it’s great for enterprise environments where SQL expertise is plentiful. MongoDB’s flexibility, AI service integration and document based approach makes it perfect for modern apps that need to combine vector search with multiple data types and AI capabilities. Your choice should be based on your use case, existing tech stack, team expertise and whether you need precise SQL based operations or flexibility and ease of AI integration.
Read this to get an overview of SingleStore and MongoDB but to evaluate these you need to evaluate based on your use case. One tool that can help with that is VectorDBBench, an open-source benchmarking tool for vector database comparison. In the end, thorough benchmarking with your own datasets and query patterns will be key to making a decision between these two powerful but different approaches to vector search in distributed database systems.
Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
VectorDBBench is an open-source benchmarking tool for users who need high-performance data storage and retrieval systems, especially vector databases. This tool allows users to test and compare different vector database systems like Milvus and Zilliz Cloud (the managed Milvus) using their own datasets and find the one that fits their use cases. With VectorDBBench, users can make decisions based on actual vector database performance rather than marketing claims or hearsay.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Download VectorDBBench from its GitHub repository to reproduce our benchmark results or obtain performance results on your own datasets.
Take a quick look at the performance of mainstream vector databases on the VectorDBBench Leaderboard.
Read the following blogs to learn more about vector database evaluation.
Further Resources about VectorDB, GenAI, and ML
Chloe WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- SingleStore: Overview and Core Technology
- MongoDB: Overview and Core Technology
- Key Differences
- Search Methodology and Algorithms
- Data Handling and Structure
- Scalability and Performance
- Integration and Ecosystem
- When to Choose SingleStore
- When to Choose MongoDB
- Conclusion
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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