Weaviate vs Aerospike: 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 Aerospike 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 Aerospike, 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 Aerospike is a distributed, scalable NoSQL 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.
What is Aerospike? An Overview
Aerospike is a NoSQL database for high-performance real-time applications. It has added support for vector indexing and searching so it’s suitable for vector database use cases. The vector capability is called Aerospike Vector Search (AVS) and is in Preview. You can request early access from Aerospike.
AVS only supports Hierarchical Navigable Small World (HNSW) indexes for vector search. When updates or inserts are made in AVS, record data including the vector is written to the Aerospike Database (ASDB) and is immediately visible. For indexing, each record must have at least one vector in the specified vector field of an index. You can have multiple vectors and indexes for a single record so you can search on the same data in different ways. Aerospike recommends assigning upserted records to a specific set so you can monitor and operate on them.
AVS has a unique way of building the index, it’s concurrent across all AVS nodes. While vector record updates are written directly to ASDB, index records are processed asynchronously from an indexing queue. This is done in batches and distributed across all AVS nodes, so it uses all the CPU cores in the AVS cluster and is scalable. Ingestion performance is highly dependent on host memory and storage layer configuration.
For each item in the indexing queue, AVS processes the vector for indexing, builds the clusters for each vector and commits those to ASDB. An index record contains a copy of the vector itself and the clusters for that vector at a given layer of the HNSW graph. Indexing uses vector extensions (AVX) for single instruction, multiple data parallel processing.
AVS queries during ingestion to “pre-hydrate” the index cache because records in the clusters are interconnected. These queries are not counted as query requests but show up as reads against the storage layer. This way, the cache is populated with relevant data and can improve query performance. This shows how AVS handles vector data and builds indexes for similarity search so it can scale for high-dimensional vector searches.
Key Differences
When building AI applications Weaviate vs Aerospike Vector Search (AVS) can make a big difference to your project. Both have different approaches to vector search and different strengths for different use cases. Let’s dive into the differences to help you make a decision.
Search Methodology
Both use HNSW (Hierarchical Navigable Small World) indexing for vector search but their implementation is different. Weaviate stands out with its hybrid search that combines vector similarity with traditional filters. With an alpha parameter, you can fine tune the balance between BM25 and vector search results. Aerospike’s vector search implementation (currently in Preview) uses specialized hardware instructions (AVX) for parallel processing which could give performance advantages for specific workloads.
Data
Both have different philosophies on data. Weaviate has multi-modal data handling out of the box, supports text, images, audio, video through various vectorization modules and uses PQ compression for storage and retrieval. Aerospike, built on top of a NoSQL database, allows multiple vectors and indexes per record so you can have different search approaches on the same data. One key difference is that Aerospike makes updates and inserts visible in the main database immediately and processes indexes asynchronously.
Scalability and Performance
The platforms have different approaches to growth and performance. Weaviate offers horizontal scaling through data distribution across cluster nodes but you need to engineer the scaling yourself - there’s no auto-scaling. So you need to plan ahead for growth. Aerospike takes a different approach, does concurrent index building across all nodes and processes indexing queues in batches across the cluster. It uses all available CPU cores and pre-hydrates index caches during ingestion to boost query performance, but this depends heavily on host memory and storage configuration.
Integration and Flexibility
The platforms have different approaches to integration and customization. Weaviate has both REST and GraphQL APIs and built-in plugins for embeddings and reranking so it’s highly accessible for various development approaches. Aerospike focuses on high-performance real-time applications and has vector search integrated into its existing database features. This is particularly useful for organizations already using Aerospike for other database needs.
Ease of Use
Developer experience is very different between the two. Weaviate is all about developer experience with its simple setup and comprehensive documentation and is a great entry point for vector search projects. It’s perfect for PoC and small projects. Aerospike requires more initial configuration, especially around memory and storage and its vector search feature is currently in Preview, so you need to request early access which might slow down your development.
Production
Each has different trade-offs for production. Weaviate has some limitations in enterprise security features and might struggle with multi-billion vector datasets. Requires manual management for tiered storage and engineer assistance for scaling but has strong ecosystem integration. Aerospike is designed for enterprise-grade performance, has robust scaling but vector search is in Preview. Complex initial setup but suitable for large scale deployments.
When to Choose
Choose Weaviate for projects that need quick vector search, multi-modal data, GraphQL, or smaller to medium sized datasets with hybrid search, while Aerospike is for organisations already using Aerospike databases, teams with distributed systems experience, very large scale deployments or applications that need hardware optimised processing with immediate consistency for vector updates.
Conclusion
Weaviate wins on developer experience, multi-modal support and hybrid search, so is great for teams that need quick implementation and flexibility, while Aerospike wins on enterprise grade performance, scaling and immediate consistency, so is better for large scale production - ultimately your choice should fit your use cases, technical expertise and scaling requirements, and remember both platforms are evolving with new features and capabilities.
The choice between Weaviate and Aerospike depends on your specific use case, the nature of your data, and your future scalability needs. Both technologies continue to evolve, so it's worth keeping an eye on their development as you make your decision. Remember that in some cases, a hybrid approach using both technologies might be the optimal solution, leveraging the strengths of each for different aspects of your application. As with any technology decision, it's advisable to conduct thorough testing with your specific datasets and use cases before making a final choice.
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
- What is a Vector Database?
- Weaviate: Overview and Core Technology
- What is Aerospike**? An Overview**
- Key Differences
- When to Choose
- 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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