Redis vs Neo4j: Choosing the Right Vector Database for Your Needs
As AI and data-driven technologies advance, selecting an appropriate vector database for your application is becoming increasingly important. Redis and Vearch are two options in this space. This article compares these technologies to help you make an informed decision for your project.
What is a Vector Database?
Before we compare Redis and Neo4j, 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), and Weaviate
- 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.
Redis is an in-memory database and Neo4j is a graph database. Both have vector search as an add-on. This post compares their vector search capabilities.
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.
Neo4J: The Basics
Neo4j’s vector search allows developers to create vector indexes to search for similar data across their graph. These indexes work with node properties that contain vector embeddings - numerical representations of data like text, images or audio that capture the meaning of the data. The system supports vectors up to 4096 dimensions and cosine and Euclidean similarity functions.
The implementation uses Hierarchical Navigable Small World (HNSW) graphs to do fast approximate k-nearest neighbor searches. When querying a vector index, you specify how many neighbors you want to retrieve and the system returns matching nodes ordered by similarity score. These scores are 0-1 with higher being more similar. The HNSW approach works well by keeping connections between similar vectors and allowing the system to quickly jump to different parts of the vector space.
Creating and using vector indexes is done through the query language. You can create indexes with the CREATE VECTOR INDEX command and specify parameters like vector dimensions and similarity function. The system will validate that only vectors of the configured dimensions are indexed. Querying these indexes is done with the db.index.vector.queryNodes procedure which takes an index name, number of results and query vector as input.
Neo4j’s vector indexing has performance optimizations like quantization which reduces memory usage by compressing the vector representations. You can tune the index behavior with parameters like max connections per node (M) and number of nearest neighbors tracked during insertion (ef_construction). While these parameters allow you to balance between accuracy and performance, the defaults work well for most use cases. The system also supports relationship vector indexes from version 5.18, so you can search for similar data on relationship properties.
This allows developers to build AI powered applications. By combining graph queries with vector similarity search applications can find related data based on semantic meaning not exact matches. For example a movie recommendation system could use plot embedding vectors to find similar movies, while using the graph structure to ensure the recommendations come from the same genre or era as the user prefers.
Key Differences
When choosing between Redis and Neo4j for vector search, understanding the differences will help you make the right decision for your use case. Let’s compare these technologies across the key aspects that matter most for vector search.
Search Methodology
Redis uses both FLAT and HNSW (Hierarchical Navigable Small World) algorithms for vector similarity search. FLAT is good for smaller datasets where accuracy is key, HNSW is fast approximate nearest neighbor search for larger datasets.
Neo4j only uses HNSW for vector search, supports vectors up to 4096 dimensions with cosine and Euclidean similarity functions. That might seem limited compared to Redis’s dual approach, but Neo4j’s HNSW is well optimized and works for most use cases.
Data Handling
Redis stores vectors in memory, so it’s super fast for read operations. It supports hybrid queries that combine vector similarity search with attribute filtering. For example, you can search for similar product images while filtering by price range and category.
Neo4j takes a graph-first approach, stores vectors as properties on nodes or relationships. This is powerful for connected data where relationships between entities matter. You can combine vector similarity search with graph traversal queries, so you can do complex operations like finding similar products recommended by users in your social network.
Scalability and Performance
Redis’s in-memory architecture is super fast but can be expensive when dealing with large datasets since all data must fit in memory. It offers horizontal scaling through Redis Cluster, so you can split your vector data across multiple nodes.
Neo4j offers both horizontal and vertical scaling. Its native graph architecture means it’s optimized for connected data at scale. Neo4j’s vector indexes use quantization to reduce memory usage, which can be more cost effective for large datasets.
Integration and Ecosystem
Redis integrates well with popular machine learning frameworks and has client libraries for multiple programming languages. Redis Stack has additional modules for time series data, search and JSON support.
Neo4j has strong integration with popular data science tools like Python’s data science stack. Cypher query language is designed for graph operations so it’s powerful for applications that need both vector search and graph capabilities.
Ease of Use
Redis has a simpler learning curve for basic vector search operations. Command syntax is straightforward and Redis Stack documentation has examples for vector search implementation.
Neo4j requires learning Cypher query language, which takes more time initially. But Cypher’s expressiveness can make complex queries more readable:
Cost Considerations
Redis requires more memory since it’s an in-memory database which can increase infrastructure costs for large datasets. But its performance benefits might offset those costs in use cases where speed is key.
Neo4j has lower memory requirements due to its storage architecture and quantization features. It has community and enterprise editions, the enterprise edition has additional features like advanced security and clustering.
Security Features
Both have security features. Redis has ACLs, SSL/TLS encryption, role-based access control. Neo4j Enterprise has fine-grained access control and advanced auth.
When to use Redis for Vector Search
Use Redis when real-time vector search performance is your top priority especially in applications that need instant responses like recommendation engines, real-time fraud detection or live semantic search features. It’s great when your dataset can fit in memory and you need to do high-throughput vector similarity search with attribute filtering. Good for applications like e-commerce product recommendations, content matching systems or AI powered chatbots that need immediate responses.
When to use Neo4j for Vector Search
Use Neo4j when your application needs to understand and use relationships between entities and vector similarity search. It’s great for applications like knowledge graphs, social networks or complex recommendation systems where the relationships between items are as important as the vector similarities. The combination of graph traversal with vector search is good for use cases like drug discovery, social recommendation engines or fraud detection systems that need to analyze patterns in connected data.
Conclusion
Your choice between Redis and Neo4j for vector search depends on your performance requirements, data structure and application needs. Redis is the fastest and simplest for real-time vector search operations while Neo4j is the combination of graph capabilities with vector search features. Use Redis when millisecond response times and simple vector similarity search is a must and use Neo4j when you need to combine vector search with complex relationship analysis in your data model. Remember both can do vector search, it's just matching their strengths to your use case.
While this article provides an overview of Redis and Neo4j, 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 WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- Redis: Overview and Core Technology
- Neo4J: The Basics
- Key Differences
- When to use Redis for Vector Search
- When to use Neo4j for Vector Search
- 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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