SingleStore vs Apache Cassandra Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare SingleStore and Apache Cassandra, 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 Apache Cassandra is a NoSQL database. Both have vector search as an add-on. This post compares their vector search capabilities.
SingleStore: Overview and Core Technology
SingleStore has built vector search into the database itself, so you don’t have to add a separate vector database to your tech stack. Vector search in SingleStore works alongside regular database operations, storing vectors in regular database tables. You can combine vector search with regular SQL queries - search through vectors while also filtering by dates, categories or any other data fields. For example you can find similar product images while filtering by price range or search through document embeddings while limiting results to specific departments.
SingleStore’s vector functionality supports semantic search and nearest neighbor queries. The system can process vector similarity using both dot product and Euclidean distance calculations which are required for matching similar items in AI applications. This is useful for applications like recommendation systems, image recognition and AI chatbots where finding similar items quickly is important.
Performance and scalability are at the heart of SingleStore’s design. The database distributes data across multiple nodes so it can handle large amounts of vector data. This means you can add more nodes as your data grows. SingleStore’s query processor can also combine vector search with SQL operations - you don’t have to do multiple separate queries to get what you want.
Unlike databases that only focus on vector operations, SingleStore gives you these capabilities as part of the full database. So you can build AI features without managing multiple databases or moving data between systems. The database handles both vector and regular data types, complex queries that combine both and scales as your application grows - all in familiar SQL that you already know.
Apache Cassandra: Overview and Core Technology
Apache Cassandra is an open-source, distributed NoSQL database known for its scalability and availability. Cassandra's features include a masterless architecture for availability, scalability, tunable consistency, and a flexible data model. With the release of Cassandra 5.0, it now supports vector embeddings and vector similarity search through its Storage-Attached Indexes (SAI) feature. While this integration allows Cassandra to handle vector data, it's important to note that vector search is implemented as an extension of Cassandra's existing architecture rather than a native feature.
Cassandra's vector search functionality is built on its existing architecture. It allows users to store vector embeddings alongside other data and perform similarity searches. This integration enables Cassandra to support AI-driven applications while maintaining its strengths in handling large-scale, distributed data.
A key component of Cassandra's vector search is the use of Storage-Attached Indexes (SAI). SAI is a highly-scalable and globally-distributed index that adds column-level indexes to any vector data type column. It provides high I/O throughput for databases to use Vector Search as well as other search indexing. SAI offers extensive indexing functionality, capable of indexing both queries and content (including large inputs like documents, words, and images) to capture semantics.
Vector Search is the first instance of validating the extensibility of SAI, leveraging its new modularity. This combination of Vector Search and SAI enhances Cassandra's capabilities in handling AI and machine learning workloads, making it a strong contender in the vector database space.
Key Differences
Search Methodology
SingleStore has vector search as a native database feature and supports both dot product and Euclidean distance for similarity matching. You can write SQL queries that combine vector operations with regular database filters.
Cassandra has vector search through its Storage-Attached Indexes (SAI) feature. This adds vector capabilities to Cassandra’s existing architecture so you can do vector operations along with its regular NoSQL functionality.
Data
SingleStore stores vectors in database tables like any other data type. This means you can do vector searches while filtering by regular columns. For example, finding similar products within a certain price range.
Cassandra stores vector data through its columnar storage model. It can store and index vector embeddings but the integration is newer and built as an extension rather than a core feature.
Scalability and Performance
Both have strong scalability features but different approaches:
SingleStore distributes data across nodes and can scale horizontally by adding more nodes as your data grows. Its query processor optimizes combined vector and SQL operations in a single pass.
Cassandra’s masterless architecture is great for horizontal scaling and high availability. SAI is designed for high I/O throughput, which is good for vector search across distributed environments.
Integration and Ecosystem
SingleStore integrates vector search with standard SQL. If your team already knows SQL you won’t need to learn new query languages or manage separate systems for vector and regular data.
Cassandra’s vector capabilities are within its NoSQL ecosystem. While this means you’ll need to learn CQL, it gives you flexibility in data modeling and consistency options.
Ease of Use
SingleStore is more familiar to teams with SQL backgrounds. Vector operations use standard SQL syntax so there’s less of a learning curve for developers who already know relational databases.
Cassandra has a steeper learning curve if you’re coming from a SQL background as it has its own query language and data modeling concepts. But its documentation and community support is extensive.
Cost
SingleStore pricing is based on your data volume, compute requirements and whether you choose cloud or self-hosted.
Cassandra is open-source and free to use but you’ll need to factor in infrastructure costs and potentially hiring specialized expertise for maintenance.
Security Features
Both have robust security options: SingleStore has standard database security features like role-based access control, encryption at rest and in transit and audit logging.
Cassandra has similar security capabilities through authentication, authorization and encryption and more through various distributions.
When to Choose SingleStore
SingleStore is for companies that need to combine SQL with vector search in one system. It’s great for companies that have structured data and vector embeddings, like e-commerce sites that need real-time product recommendations while tracking inventory, or content management systems that need to search across documents while managing user permissions and metadata. It’s for scenarios where you need ACID compliance with vector operations, so it’s perfect for applications where data integrity is key.
When to Choose Apache Cassandra
Apache Cassandra is for companies that need horizontal scalability and high availability for their vector search. It’s great for use cases with massive amounts of unstructured or semi-structured data, like social media analytics platforms, IoT applications processing sensor data with vector representations, or large-scale logging systems that need to search across distributed datasets. The open source and masterless architecture makes it perfect for companies with NoSQL expertise and those looking to minimize licensing costs.
Conclusion
Your choice between SingleStore and Apache Cassandra depends on your technical requirements and constraints. SingleStore is more integrated with SQL syntax and ACID compliance so it’s great for companies with SQL expertise and applications that need strong consistency. Cassandra has better horizontal scalability and high availability with its distributed architecture so it’s perfect for companies with massive datasets across multiple regions. Consider your team’s expertise, existing infrastructure, scalability needs and budget constraints when making this decision as both have different advantages that may be more or less valuable depending on your use case.
Read this to get an overview of SingleStore and Apache Cassandra 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
- Apache Cassandra: Overview and Core Technology
- Key Differences
- When to Choose SingleStore
- When to Choose Apache Cassandra
- 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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