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Redis vs MyScale: Choosing the Right Vector Database for Your Needs

Redis vs MyScale: Choosing the Right Vector Database for Your Needs

Oct 06, 20249 min read

As AI and data-driven technologies advance, selecting an appropriate vector database for your application is becoming increasingly important. Redis and MyScale 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 MyScale, 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 MyScale is traditional database. Both have vector search capabilities 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.

MyScale: Overview and Tech

MyScale is a cloud based database built on top of the open source ClickHouse database, designed for AI and machine learning workloads. It can handle structured and vector data and real time analytics and machine learning. MyScale is focused on time series, vector search and full text search so it’s good for real time processing and AI driven insights. By using ClickHouse architecture, MyScale is high performance and scalable for AI.

One of the key features of MyScale is native SQL support which simplifies AI driven queries by integrating vector search, full text search and traditional SQL queries in one system. This reduces the need for multiple tools and makes it scalable for AI. MyScale supports and manages analytical processing of both structured and vectorized data on one platform using OLAP database architecture to operate on vectorized data. Developers can interact with MyScale using SQL so it’s accessible to all programmers familiar with relational databases.

MyScale has multiple vector index types and similarity metrics to support different use cases. It supports common distance metrics like Euclidean distance (L2), inner product (IP) and cosine similarity. The database has multiple indexing algorithms: MSTG (Multi-Scale Tree Graph), ScaNN, IVFFLAT, IVFPQ, IVFSQ and HNSW, each with its own set of parameters to tune. MyScale’s proprietary MSTG vector engine uses NVMe SSDs to increase data density so it outperforms specialized vector databases in both performance and cost.

By combining the functionality of an SQL database, vector database and full text search engine into one system MyScale reduces infrastructure and maintenance costs. This unification allows for joint data queries and analytics and a single data foundation for AI applications. MyScale also has MyScale Telemetry for full observability of LLM systems so you can monitor and debug efficiently. As data gets more complex MyScale is a future proof solution that can handle newer data modalities and database sizes while keeping computing performance and integration between different data types.

Key Differences

Redis vs MyScale: Which Vector Search Tool is Right for You

When choosing a vector search tool, developers and engineers have many factors to consider. Let’s compare Redis and MyScale across the key areas to help you decide.

Search Methodology

Redis uses FLAT and HNSW (Hierarchical Navigable Small World) for approximate nearest neighbor search. Both are fast and accurate in high dimensional spaces. Redis also supports hybrid search, combining vector similarity with attribute filtering.

MyScale has multiple vector index types and similarity metrics. It supports common distance metrics like Euclidean distance (L2), inner product (IP), and cosine similarity. MyScale has multiple indexing algorithms: MSTG (Multi-Scale Tree Graph), ScaNN, IVFFLAT, IVFPQ, IVFSQ, and HNSW. Each algorithm has tunable parameters to fine tune for specific use cases.

Data

Redis is great at in-memory data and now has vector search through the Redis Vector Library. It has flexible schema and can work with structured and unstructured data.

MyScale is built on ClickHouse architecture and can handle structured and vector data, time series and full-text search. It can process structured and vectorized data in real-time using OLAP database architecture.

Scalability and Performance

Redis is known for its in-memory speed, which means fast query execution for vector search. Its architecture allows to scale vector search operations.

MyScale uses ClickHouse high-performance architecture and is designed for AI and machine learning workloads. It uses NVMe SSDs to increase data density and potentially outperform specialized vector databases in performance and cost.

Flexibility and Customization

Redis has a simple interface for vector data and allows custom vector queries. It also has extensions for LLM related tasks like semantic caching and session management.

MyScale has native SQL which allows to have integrated vector search, full-text search and traditional SQL queries in one system. This flexibility allows to have complex queries that combine different data types and search methods.

Integration and Ecosystem

Redis has a big ecosystem and integrates well with many tools and frameworks. The Vector Library is part of Redis Stack so it’s a good fit for projects already using Redis.

MyScale is an SQL database, vector database and full-text search engine in one. This unified approach can simplify infrastructure and reduce the need for multiple tools in AI driven applications.

Ease of Use

Redis is known to be developer friendly with good documentation and easy setup. The Vector Library extends this to vector search operations.

MyScale uses SQL syntax so it’s familiar to developers with relational database experience. But it has many features and algorithms, so it may require a steeper learning curve to use optimally.

Cost

Redis is cost effective for small datasets that fit in memory. For larger datasets, costs will increase as more memory is needed.

MyScale uses NVMe SSDs and unified approach may be cost effective for big data, potentially reducing infrastructure and maintenance costs.

Security

Redis has various security features: access control, encryption, authentication options.

MyScale being built on ClickHouse likely has similar security features but need to be checked with the vendor.

When to Choose Each

Redis is best for projects that need high speed in-memory processing and real-time vector search. It’s great for applications that need low latency, like recommendation systems, real-time fraud detection or content based image retrieval. Redis is perfect for scenarios where data fits in memory and you need to combine vector similarity search with attribute filtering. It’s also a good choice if you already use Redis in your stack and want to add vector search without introducing a new database.

MyScale is better for projects that need integrated SQL, vector and full-text search, especially for large scale AI and machine learning workloads. It’s good for applications that need to process and analyze both structured and unstructured data in real-time, like advanced analytics platforms, complex search engines or AI driven business intelligence tools. MyScale’s ability to handle time series data alongside vector search makes it great for applications that need to analyze trends over time and also perform similarity searches. Its SQL based interface is also good for teams with strong SQL skills.

Conclusion

Redis has high speed in-memory vector search and can combine vector similarity and attribute filtering. It’s known for simplicity, large ecosystem and great performance for in-memory datasets. MyScale is a unified platform for SQL, vector and full-text search with strong scalability for large AI and ML workloads. It can handle diverse data types and complex queries. Choose between Redis and MyScale based on your use case, data volume, query complexity and existing infrastructure. Consider the size of your dataset, need for SQL integration, your team’s expertise and the real-time requirement for your application. Both have vector search capabilities but their strengths are for different types of projects and requirements.

While this article provides an overview of Redis and MyScale, 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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