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Couchbase vs Apache Cassandra: Choosing the Right Vector Database for Your AI Apps

Couchbase vs Apache Cassandra: Choosing the Right Vector Database for Your AI Apps

Sep 30, 202410 min read

What is a Vector Database?

Before we compare Couchbase and 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.

Couchbase is distributed multi-model NoSQL document-oriented database with vector search capabilities as an add-on. Apache Cassandra is a traditional database with vector search capabilities as an add-on.

Couchbase: Overview and Core Technology

Couchbase is a distributed, open-source, NoSQL database that can be used to build applications for cloud, mobile, AI, and edge computing. It combines the strengths of relational databases with the versatility of JSON. Couchbase also provides the flexibility to implement vector search despite not having native support for vector indexes. Developers can store vector embeddings—numerical representations generated by machine learning models—within Couchbase documents as part of their JSON structure. These vectors can be used in similarity search use cases, such as recommendation systems or retrieval-augmented generation both based on semantic search, where finding data points close to each other in a high-dimensional space is important.

One approach to enabling vector search in Couchbase is by leveraging Full Text Search (FTS). While FTS is typically designed for text-based search, it can be adapted to handle vector searches by converting vector data into searchable fields. For instance, vectors can be tokenized into text-like data, allowing FTS to index and search based on those tokens. This can facilitate approximate vector search, providing a way to query documents with vectors that are close in similarity.

Alternatively, developers can store the raw vector embeddings in Couchbase and perform the vector similarity calculations at the application level. This involves retrieving documents and computing metrics such as cosine similarity or Euclidean distance between vectors to identify the closest matches. This method allows Couchbase to serve as a storage solution for vectors while the application handles the mathematical comparison logic.

For more advanced use cases, some developers integrate Couchbase with specialized libraries or algorithms (like FAISS or HNSW) that enable efficient vector search. These integrations allow Couchbase to manage the document store while the external libraries perform the actual vector comparisons. In this way, Couchbase can still be part of a solution that supports vector search.

By using these approaches, Couchbase can be adapted to handle vector search functionality, making it a flexible option for various AI and machine learning tasks that rely on similarity searches.

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 between Couchbase and Apache Cassandra for Vector Search

Search Methodology:

Couchbase and Apache Cassandra take different approaches to vector search. Couchbase doesn't have native vector search support but offers workarounds. It allows adapting Full Text Search (FTS) for vector searches by converting vector data into searchable fields. Alternatively, developers can store raw vector embeddings and perform similarity calculations at the application level. For advanced use cases, Couchbase can be integrated with specialized libraries.

Apache Cassandra, with its 5.0 release, introduced vector search support through Storage-Attached Indexes (SAI). This feature allows Cassandra to perform vector similarity searches directly within its architecture. SAI provides column-level indexes for vector data types, enabling efficient vector search capabilities.

Data Handling:

Couchbase combines elements of relational databases with JSON versatility. It can store vector embeddings within JSON documents, making it flexible for various data types. This approach allows Couchbase to handle structured, semi-structured, and unstructured data effectively.

Cassandra, known for its flexible data model, now supports vector embeddings alongside other data types. Its columnar storage model allows for efficient handling of structured and semi-structured data. With the addition of vector search capabilities, Cassandra can now manage vector data within its existing architecture.

Scalability and Performance:

Both databases are designed for scalability, but their approaches differ. Couchbase uses a distributed architecture that can provide good scalability for general database operations. However, for vector search, performance may vary depending on the implementation method chosen.

Cassandra is renowned for its scalability and availability, featuring a masterless architecture. The integration of vector search through SAI is designed to maintain this scalability. SAI is described as highly-scalable and globally-distributed, suggesting that Cassandra's vector search capabilities can scale effectively with large datasets.

Flexibility and Customization:

Couchbase offers flexibility in implementing vector search, allowing developers to choose between adapting FTS, performing application-level calculations, or integrating with external libraries. This flexibility can be advantageous for teams with specific requirements or existing workflows.

Cassandra's vector search is more integrated into its core functionality through SAI. While this might offer less flexibility in implementation methods, it provides a more standardized approach to vector search within the Cassandra ecosystem.

Integration and Ecosystem:

Couchbase can be integrated with various tools and frameworks, especially those in the NoSQL ecosystem. For vector search, it may require integration with specialized libraries, which can be both a strength (in terms of customization) and a challenge (in terms of complexity).

Cassandra's vector search capabilities are built into its architecture, potentially offering a more streamlined integration experience within its ecosystem. The SAI feature is designed to work seamlessly with Cassandra's existing functionalities.

Ease of Use:

Implementing vector search in Couchbase may require more setup and custom development, as it's not a native feature. This could lead to a steeper learning curve for teams new to vector search implementations.

Cassandra's integrated approach with SAI might offer an easier entry point for vector search capabilities, especially for teams already familiar with Cassandra. However, the overall complexity of Cassandra's distributed architecture should be considered.

Cost Considerations:

The cost implications for both systems will depend on specific implementation details and scale. Couchbase's cost for vector search may include additional expenses for any external libraries or services used in the implementation.

For Cassandra, the vector search capabilities are included in the core functionality from version 5.0, which might lead to more predictable costs within the Cassandra ecosystem.

When to Choose Couchbase:

Couchbase is more suitable for projects that require a flexible NoSQL database with the ability to implement custom vector search solutions. It's a good choice for applications where vector search is not the primary focus but needs to be integrated alongside other data types. Couchbase is well-suited for scenarios where developers want control over the vector search implementation, allowing integration with specialized libraries. It's also a strong option for projects that need to combine JSON document flexibility with vector search capabilities, such as recommendation systems or retrieval-augmented generation based on semantic search. Couchbase can be particularly useful in situations where the vector search requirements might evolve or change, as its flexible approach allows for different implementation methods.

When to Choose Apache Cassandra:

Apache Cassandra is the better option for projects that require a scalable, distributed database with integrated vector search capabilities. With its 5.0 release featuring Storage-Attached Indexes (SAI), Cassandra is well-suited for large-scale AI and machine learning workloads that need efficient vector similarity searches. It's an excellent choice for applications that demand high availability and scalability while also requiring vector search functionality. Cassandra is particularly advantageous for use cases where vector embeddings need to be stored and searched alongside other data types within the same database system. Its masterless architecture makes it ideal for globally distributed applications that need to perform vector searches across large datasets. Cassandra's integrated approach to vector search can be beneficial for teams looking for a more standardized solution without the need for external libraries or custom implementations.

Conclusion

When choosing between Couchbase and Apache Cassandra for vector search, consider your project's specific needs and your team's expertise. Couchbase offers flexibility in implementing vector search through various methods like adapting Full Text Search or integrating external libraries. It's a good choice if you need a versatile database that can handle vector data alongside other types and want control over the implementation. Cassandra, with its recent 5.0 release, provides integrated vector search capabilities through Storage-Attached Indexes (SAI). This makes Cassandra suitable for large-scale, distributed applications that require native vector search functionality. Cassandra's approach might be more straightforward to implement but less flexible than Couchbase's custom solutions. Your decision should be based on factors such as the scale of your data, the importance of native vector search support, your team's familiarity with each system, and whether you need a general-purpose database or a specialized solution for distributed, high-availability scenarios with vector search capabilities.

While this article provides an overview of Couchbase and Cassandra, it's key to evaluate these databases based on your specific use case. One tool that can assist in this process is VectorDBBench, an open-source benchmarking tool designed for comparing vector database performance. Ultimately, thorough benchmarking with specific datasets and query patterns will be essential in making an informed decision between these two powerful, yet distinct, 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 designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems such as Milvus and Zilliz Cloud (the managed Milvus) using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.

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 Williams
Chloe Williams is a technical writer at Zilliz.

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