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Couchbase vs Clickhouse Choosing the Right Vector Database for Your AI Apps

Couchbase vs Clickhouse Choosing the Right Vector Database for Your AI Apps

Oct 01, 20249 min read

What is a Vector Database?

Before we compare Couchbase and Clickhouse, 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. Clickhouse is an open-source column-oriented database with vector search 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.

Clickhouse: Overview and Core Technology

ClickHouse is an open-source real-time OLAP database known for its full SQL support and high-speed query processing. It excels at handling analytical queries due to its fully parallelized query pipeline, allowing it to perform vector search operations quickly. Its high levels of compression, customizable through codecs, enable ClickHouse to store and query large datasets effectively. One of its key strengths is that it can handle multi-TB datasets without being constrained by memory, making it a powerful tool for users dealing with large-scale vector data. It also supports filtering and aggregation on metadata, allowing developers to perform complex queries on both vectors and their associated metadata.

ClickHouse integrates vector search functionality through its SQL capabilities, where vector distance operations are treated like any other SQL function. This allows seamless combination with traditional filtering and aggregation, making it ideal for use cases where vector data needs to be queried alongside metadata or other information. Additionally, experimental features like Approximate Nearest Neighbour (ANN) indices offer faster, though approximate, matching capabilities. ClickHouse also supports exact matching through a linear scan over rows, with its parallelized processing ensuring high speed and efficiency.

ClickHouse is an excellent option for vector search when combining vector matching with metadata filtering or aggregation is important. It's especially useful for very large vector datasets that need to be processed in parallel across multiple CPU cores. ClickHouse is also advantageous when SQL support is necessary, and the vector dataset is too large to rely on memory-only indices. Additionally, if you already have related data in ClickHouse or wish to avoid learning another tool for managing millions of vectors, ClickHouse can save you both time and resources. Its strengths lie in fast, parallelized exact matching and handling large datasets, making it suitable for users with advanced search requirements.

ClickHouse stands out as a versatile platform for vector search, particularly when dealing with large datasets that require parallelized processing and when combining vector searches with SQL-based filtering and aggregation. While it may not be as specialized for small, memory-bound datasets or high-QPS scenarios as dedicated vector databases, its ability to handle complex queries, including metadata, makes it a powerful option for developers familiar with SQL who need high-speed vector search capabilities.

Key Differences

Search Methodology:

Couchbase adapts its Full Text Search (FTS) for vector searches by converting vector data into searchable fields. This allows for approximate vector search. Alternatively, raw vector embeddings can be stored and compared at the application level.

ClickHouse treats vector distance operations as SQL functions, enabling seamless integration with traditional queries. It offers both exact matching through linear scans and approximate matching using experimental Approximate Nearest Neighbor (ANN) indices.

Data Handling:

Couchbase is a NoSQL database that combines relational database strengths with JSON versatility. It can store vector embeddings within JSON documents, providing flexibility for various data types.

ClickHouse is an OLAP database with full SQL support. It can handle structured data efficiently and supports storing vector data alongside metadata, allowing for complex queries that combine both.

Scalability and Performance:

Couchbase is distributed and designed for cloud, mobile, AI, and edge computing. It can scale horizontally but may require additional setup for efficient vector search at scale.

ClickHouse excels at handling multi-TB datasets without memory constraints. Its fully parallelized query pipeline allows for fast processing of large-scale vector data across multiple CPU cores.

Flexibility and Customization:

Couchbase offers flexibility in implementing vector search, allowing developers to use external libraries for more advanced use cases.

ClickHouse provides customizable compression through codecs and supports complex queries combining vector operations with SQL-based filtering and aggregation.

Integration and Ecosystem:

Couchbase can integrate with specialized vector search libraries, making it adaptable to various AI and machine learning tasks.

ClickHouse's SQL support makes it easier to integrate with existing data ecosystems and tools that work with SQL databases.

Ease of Use:

Couchbase may require more setup and custom coding to implement vector search functionality effectively.

ClickHouse offers a more straightforward approach for developers familiar with SQL, as vector operations can be treated like any other SQL function.

Cost Considerations:

Couchbase may have lower upfront costs but could require more development time to implement vector search capabilities.

ClickHouse might have higher initial setup costs but could save time and resources in the long run, especially if you're already using it for other data storage needs.

Security Features:

Both Couchbase and ClickHouse offer standard security features like encryption and access control.

When to Choose Couchbase

Couchbase is a good choice when you need a flexible NoSQL database that can handle various data types, including vector embeddings. It's well-suited for applications that require real-time data access, such as mobile and web apps, where you want to store and query both structured and unstructured data. Choose Couchbase if you need to implement vector search alongside other database functionalities, especially in distributed environments. It's also a strong option if you're building applications for cloud, mobile, AI, or edge computing that benefit from Couchbase's scalability and versatility. If your team is already familiar with JSON document structures and you want the option to integrate with specialized vector search libraries, Couchbase can provide the flexibility you need.

When to Choose ClickHouse

ClickHouse is the better option when you're dealing with large-scale vector datasets and need high-speed analytical processing. It's particularly suitable for scenarios where you need to combine vector search operations with complex SQL queries, metadata filtering, and aggregations. Choose ClickHouse if you're working with multi-TB datasets and need a solution that can handle vector search without being constrained by memory. It's also a great fit if your team is comfortable with SQL and you want to leverage its full SQL support for vector operations. ClickHouse shines in use cases that require fast, parallelized processing of vector data across multiple CPU cores, such as real-time analytics on large datasets. If you already have related data in ClickHouse or want to avoid learning a new tool for managing millions of vectors, ClickHouse can be a time and resource-saving choice.

Conclusion

In conclusion, both Couchbase and ClickHouse offer unique strengths for vector search, catering to different use cases and requirements. Your choice between the two should depend on your specific needs, existing infrastructure, and team expertise. Couchbase provides flexibility and adaptability, making it suitable for diverse applications that require both traditional database functionalities and vector search capabilities. On the other hand, ClickHouse excels in handling large-scale vector datasets with its powerful analytical processing and SQL integration. Consider factors such as the size of your dataset, the complexity of your queries, your team's familiarity with SQL, and your need for scalability when making your decision. Ultimately, both technologies can be effective tools for vector search, and the best choice will align with your project's unique demands and your organization's technical landscape.

While this article provides an overview of Couchbase and Clickhouse, 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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