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

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

Nov 30, 20248 min read

What is a Vector Database?

Before we compare Couchbase and Redis, 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 a distributed multi-model NoSQL document-oriented database and Redis is an in-memory database. Both have vector search capabilities added on. This post compares their vector search capabilities.

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.

Redis: Overview and Core Technology

Redis was originally known for its in-memory data storage and has added vector search capabilities through the Redis Vector Library which is now part of Redis Stack. This allows Redis to do vector similarity search while keeping its speed and performance.

The vector search in Redis is built on top of its existing infrastructure, using in-memory processing for fast query execution. Redis uses FLAT and HNSW (Hierarchical Navigable Small World) algorithms for approximate nearest neighbor search which allows for fast and accurate search in high dimensional vector spaces.

One of the main strengths of Redis vector search is that it can combine vector similarity search with traditional filtering on other attributes. This hybrid search allows developers to create complex queries that consider both semantic similarity and specific metadata criteria, so it’s versatile for many AI driven applications.

The Redis Vector Library provides a simple interface for developers to work with vector data in Redis. It has features like flexible schema design, custom vector queries and extensions for LLM related tasks like semantic caching and session management. This makes it easier for AI/ML engineers and data scientists to integrate Redis into their AI workflow, especially for real-time data processing and retrieval.

Key Differences

When you need vector search for AI applications, both Couchbase and Redis offer different ways to get there. Let’s see how they handle this:

Redis Takes the Direct Route

Redis has vector search built in to its core with Redis Stack. It’s like having a specialized tool for the job. When you want to find similar vectors, Redis uses tried and true algorithms (HNSW and FLAT) for this. So:

You can start searching vectors without extra setup
Search happens in memory so it’s fast
You can mix vector search with regular filters (e.g. combine a product’s features with its visual similarity)

Couchbase Takes the Flexible Route

Couchbase doesn’t have vector search built in but gives you ways to add it. You can:

Use Full Text Search (FTS) by converting vectors into searchable text
Store vectors in JSON and do the math in your app
Connect Couchbase with vector search tools like FAISS

Data Management Style

Redis stores data in memory first so it’s fast but you need to plan your memory usage carefully. It works well when you need quick searches and your data fits in memory.

Couchbase stores data on disk first and uses memory for caching. This helps with larger datasets but might not be as fast as Redis for pure vector operations.

Scaling

Redis scales by adding more memory and splitting data across nodes. It’s easy but can get expensive as your data grows.

Couchbase is a distributed system that can handle both memory and disk storage. This can be more cost effective for large datasets but requires more setup work.

Getting Started and Running

Redis with vector search is easier to get started with. The commands are straightforward and there’s good documentation for vector operations.

Couchbase requires more initial setup for vector search since you’re either adapting existing features or connecting external tools. But once set up, it fits in with larger apps.

Costs to Consider

Redis: Memory is the main cost. You need enough RAM for your vectors and indexes.

Couchbase: More flexible with storage costs since it uses both memory and disk but might need more CPU for vector ops.

Integration with Other Tools

Redis is good for AI workflows and works well with Python which is common in AI development.

Couchbase connects well with enterprise systems and has more options for complex data modeling.

When to Choose Couchbase

Couchbase works best for enterprise applications that need both vector search and complex data handling. It's a good fit when you have large datasets that won't fit in memory, need strong data consistency, or want to combine vector search with regular database operations. Choose Couchbase if you're building applications that need to handle multiple data types, require flexible scaling options, and can benefit from its strong support for distributed systems. This makes it suitable for companies building large-scale AI applications where data persistence and complex querying are as important as vector search capabilities.

When to Choose Redis

Redis shines in applications that need fast vector search and real-time processing. It's the better choice when your data can fit in memory and you need quick vector similarity searches, like in recommendation systems or real-time image similarity search. Redis works particularly well for applications that need low latency, such as live personalization features, semantic search in chat applications, or AI-powered content recommendations. Its built-in vector search capabilities make it easier to implement and maintain these features without additional infrastructure.

Conclusion

Your choice between Couchbase and Redis should match your specific needs. Couchbase offers flexibility and strong enterprise features, making it good for complex, large-scale applications. Redis provides built-in vector search and fast performance, making it ideal for real-time applications. Consider your data size, search speed requirements, and scaling needs when making your choice. Remember that success with either technology depends on how well it fits your specific use case, team expertise, and infrastructure requirements.

Read this to get an overview of Couchbase and Redis 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 Williams
Chloe Williams is a technical writer at Zilliz.

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