Weaviate vs ClickHouse: 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. Weaviate and ClickHouse 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 Weaviate 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), 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.
Weaviate is a purpose-built vector database and ClickHouse is an open-source column-oriented database with vector search capabilities as an add-on. This post compares their vector search capabilities.
Weaviate: Overview and Core Technology
Weaviate is an open-source vector database designed to simplify AI application development. It offers built-in vector and hybrid search capabilities, easy integration with machine learning models, and a focus on data privacy. These features aim to help developers of various skill levels create, iterate, and scale AI applications more efficiently.
One of Weaviate's strengths is its fast and accurate similarity search. It uses HNSW (Hierarchical Navigable Small World) indexing to enable vector search on large datasets. Weaviate also supports combining vector searches with traditional filters, allowing for powerful hybrid queries that leverage both semantic similarity and specific data attributes.
Key features of Weaviate include:
- PQ compression for efficient storage and retrieval
- Hybrid search with an alpha parameter for tuning between BM25 and vector search
- Built-in plugins for embeddings and reranking, which ease development
Weaviate is an entry point for developers to try out vector search. It offers a developer-friendly approach with a simple setup and well-documented APIs. Deep integration with the GenAI ecosystem makes it suitable for small projects or proof-of-concept work. The target audience for Weaviate are software engineers building AI applications, data engineers working with large datasets and data scientists deploying machine learning models. Weaviate simplifies semantic search, recommendation systems, content classification and other AI features.
Weaviate is designed to scale horizontally so it can handle large datasets and high query loads by distributing data across multiple nodes in a cluster. It supports multi-modal data, works with various data types (text, images, audio, video) depending on the vectorization modules used. Weaviate provides both RESTful and GraphQL APIs for flexibility in how developers interact with the database.
However, for large-scale production environments, there are several considerations to keep in mind:
- Limited enterprise-grade security features
- Potential scalability challenges with multi-billion vector datasets
- Manual management required for newly released tiered storage options
- Horizontal scale-up requires assistance from Weaviate engineers and cannot be done automatically
This last point is particularly noteworthy, as it means organizations need to plan ahead and allocate time for scaling operations, ensuring they don't approach their system limits without proper preparation.
ClickHouse: Overview and Core
ClickHouse is an open-source OLAP database for real-time analytics with full SQL support and fast query processing. It’s great for analytical queries because of fully parallelized query pipeline and can do vector search fast. It has high compression (customizable through codecs) so can store and query big datasets. One of its main advantages is that it can handle multi-TB datasets without being memory bound so it’s a great tool for users with large vector data. Also supports filtering and aggregation on metadata, so you can query vectors and their metadata.
ClickHouse has vector search functionality through SQL where vector distance operations are just like any other SQL function. So you can combine it with traditional filtering and aggregation. Great for use cases where you need to query vector data along with metadata or other information. Also has experimental Approximate Nearest Neighbour (ANN) indices for faster (but approximate) matching. And exact matching through linear scan over rows with parallel processing for speed and efficiency.
ClickHouse is great for vector search when you need to combine vector matching with metadata filtering or aggregation. Especially for very large vector datasets that need to be processed in parallel across multiple CPU cores. ClickHouse is also good when you need SQL support and your vector dataset is too big to fit in memory-only indices. Also if you already have related data in ClickHouse or don’t want to learn another tool to manage millions of vectors, ClickHouse can save you time and resources. Fast parallelized exact matching and handling big datasets is what ClickHouse is good for, so it’s for advanced search users.
ClickHouse is a general purpose platform for vector search, especially for large datasets that need parallel processing and when you combine vector search with SQL-based filtering and aggregation. Not as good as specialized vector databases for small memory-bound datasets or high-QPS scenarios but can handle complex queries including metadata so great for developers who know SQL and need fast vector search.
Key Differences: Weaviate vs ClickHouse for Vector Search
When choosing a vector search tool, it’s good to know the differences between Weaviate and ClickHouse. Both have strengths for different use cases, so let’s compare them across a few key aspects.
Search Methodology
Weaviate uses HNSW (Hierarchical Navigable Small World) indexing for fast and accurate similarity searches. Also supports hybrid queries, combining vector searches with traditional filters. So you can search based on semantic similarity and specific data attributes.
ClickHouse has vector search through SQL. It treats vector distance operations as any other SQL function, so you can combine them with traditional filtering and aggregation. ClickHouse also has experimental Approximate Nearest Neighbor (ANN) indices for faster but approximate matching and exact matching through linear scans over rows with parallel processing.
Data
Weaviate supports multi-modal data, works with various data types: text, images, audio, video depending on the vectorization modules used. Has built-in plugins for embeddings and reranking which makes development easier.
ClickHouse is designed for real-time analytics with full SQL support. It can handle structured data efficiently and supports filtering and aggregation on metadata. So you can query vectors alongside other data types.
Scalability and Performance
Weaviate is designed to scale horizontally, data is distributed across multiple nodes in a cluster. But for multi-billion vector datasets there are scalability challenges. Horizontal scale up requires Weaviate engineers assistance and can’t be done automatically.
ClickHouse can handle multi-TB datasets without being memory bound. It uses fully parallelized query pipelines and can process large vector datasets across multiple CPU cores. So it’s perfect for very large vector datasets scenarios.
Flexibility and Customization
Weaviate has a developer friendly approach with well documented APIs. Has both RESTful and GraphQL APIs so developers have flexibility on how to interact with the database.
ClickHouse has full SQL support which is good for users already familiar with SQL. You can do complex queries that combines vector search with SQL based filtering and aggregation.
Integration and Ecosystem
Weaviate has deep integration with the GenAI ecosystem, so it’s good for small projects or proof-of-concept work. Works well with various machine learning models and AI applications.
ClickHouse, being a general purpose analytical database, may have more integration options with data processing and analytics tools. But may not have as many AI specific integrations as Weaviate.
Ease of Use
Weaviate tries to simplify AI application development,and has a simple setup process which is good for developers of all skill levels. Documentation and APIs are user friendly.
ClickHouse may have a steeper learning curve for those not familiar with SQL or analytical databases. But for users with SQL knowledge it’s a powerful and familiar tool for vector search.
Cost
Both are open-source but operational costs may vary. Weaviate’s manual management of tiered storage options and scalability challenges with very large datasets may impact long term costs.
ClickHouse can handle large datasets efficiently without being memory bound, so it may save costs in data intensive scenarios.
Security
Weaviate has limited enterprise grade security features which may be a concern for large production environments.
ClickHouse security features are not mentioned in the provided info so you’ll need to research this if it’s a requirement for your use case.
When to Use Each
Weaviate is best for AI focused applications that need semantic search, recommendation systems or content classification. It’s perfect for projects where setup and integration with machine learning models is key. Weaviate excels with multi-modal data (text, images, audio, video) and when you need a mix of vector and traditional search. Developer friendly and GraphQL API makes it great for prototyping and smaller AI projects.
ClickHouse is best when dealing with massive vector datasets, especially in the multi-terabyte range. It’s perfect for when you need to combine vector search with complex SQL queries, filtering and aggregations on metadata. ClickHouse excels when you need real-time analytics on vector data alongside other structured data. It can handle large data without being memory bound so it’s great for organizations with big data processing needs and those who prefer to work with SQL for vector operations.
Summary
Weaviate stands out for its AI focused design, built-in vector search, easy ML model integration and developer experience. Its strength is in simplifying AI application development and handling multi-modal data. ClickHouse is great for massive datasets, powerful SQL based vector operations and vector data alongside traditional analytics. Choose between these based on your use case, data size, team expertise and performance requirements. Use Weaviate for AI focused projects with diverse data types and ClickHouse for large scale analytical workloads with vector search.
While this article provides an overview of Weaviate 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 WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- Weaviate: Overview and Core Technology
- ClickHouse: Overview and Core
- Key Differences: Weaviate vs ClickHouse for Vector Search
- Search Methodology
- When to Use Each
- Summary
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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