Vespa vs Aerospike Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Vespa 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)
- 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.
Vespa is a purpose-built vector database. Aerospike is a distributed, scalable NoSQL database with vector search capabilities as an add-on. This post compares their vector search capabilities.
Vespa: Overview and Core Technology
Vespa is a powerful search engine and vector database that can handle multiple types of searches all at once. It's great at vector search, text search, and searching through structured data. This means you can use it to find similar items (like images or products), search for specific words in text, and filter results based on things like dates or numbers - all in one go. Vespa is flexible and can work with different types of data, from simple numbers to complex structures.
One of Vespa's standout features is its ability to do vector search. You can add any number of vector fields to your documents, and Vespa will search through them quickly. It can even handle special types of vectors called tensors, which are useful for representing things like multi-part document embeddings. Vespa is smart about how it stores and searches these vectors, so it can handle really large amounts of data without slowing down.
Vespa is built to be super fast and efficient. It uses its own special engine written in C++ to manage memory and do searches, which helps it perform well even when dealing with complex queries and lots of data. It's designed to keep working smoothly even when you're adding new data or handling a lot of searches at the same time. This makes it great for big, real-world applications that need to handle a lot of traffic and data.
Another cool thing about Vespa is that it can automatically scale up to handle more data or traffic. You can add more computers to your Vespa setup, and it will automatically spread the work across them. This means your search system can grow as your needs grow, without you having to do a lot of complicated setup. Vespa can even adjust itself automatically to handle changes in how much data or traffic you have, which can help save on costs. This makes it a great choice for businesses that need a search system that can grow with them over time.
Aerospike: Overview and Core Technology
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 for 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
Search Methodology
Vespa and Aerospike have different approaches to vector search which can impact performance and efficiency depending on the type of queries you need to run.
Vespa: Vespa supports multiple search types in one engine, vector search, text search and structured data filtering. Vector search is highly optimized for real-time and large scale. Vespa allows you to index various types of vectors (including tensor based vectors) and retrieve similar items quickly. It uses advanced algorithms to search through these vectors and support complex queries on high dimensional data.
Aerospike: Aerospike’s vector search is based on Hierarchical Navigable Small World (HNSW) indexing. This is a graph based search algorithm optimized for high dimensional data, particularly for nearest neighbor search. Aerospike’s vector search is in preview, it’s built for real-time applications so any updates to vectors are immediately visible for search. However, it only supports HNSW, so it may not have the variety of search algorithms that Vespa has for more specific use cases.
Data Types
When it comes to different data types, both have flexibility but there are big differences in how they handle structured, semi-structured and unstructured data.
Vespa: Vespa is very flexible in handling structured, semi-structured and unstructured data. It can handle multiple data types in one document, so you can blend text, numeric values and vector data together. You can store and search through different data types in one query, e.g. mix vector search with structured filtering (e.g. price range, publication date).
Aerospike: As a NoSQL database Aerospike is optimized for real-time storage of structured and semi-structured data. While it now supports vector search, its primary focus is on fast writes and retrieval of structured data. Aerospike’s data model is simple but effective for high throughput applications. For vector search you need to manage vector data as part of records but it doesn’t have Vespa’s ability to handle different data types in one query.
Scalability and Performance
Both Vespa and Aerospike are built for scalability but they do it differently.
Vespa: Vespa is designed to scale for large scale high traffic applications. It can automatically distribute data and processing across multiple nodes and adjust resource allocation dynamically. This self-scaling feature makes Vespa a good choice for businesses that expect high growth or fluctuating traffic loads.
Aerospike: Aerospike is known for its scalability especially for real-time workloads. It uses a distributed architecture where data is partitioned across nodes and both reads and writes are optimized for low latency access. But vector search performance is more dependent on memory and storage layer configuration, which requires careful setup to get maximum throughput.
Flexibility and Customization
Customization is key when building complex search solutions and both have different levels of flexibility.
Vespa: Vespa is very flexible. You can define complex schemas with custom fields including various vector types. You can also define powerful queries including custom ranking, filtering and combining multiple search types (text, vector, numeric etc). This makes Vespa suitable for applications that require complex data modeling and advanced search customization.
Aerospike: Aerospike’s flexibility is focused on the data model. You can define vector fields within records and customize how they are indexed and queried. But compared to Vespa, Aerospike’s customization options for search logic and ranking are limited. It’s optimized for simple real-time applications rather than highly customizable search queries.
Integration and Ecosystem
Integration with other tools and ecosystems can be a big factor when choosing between these two.
Vespa: Vespa integrates with various tools and frameworks, including popular machine learning libraries and search engines. It has built-in support for complex data pipelines so it’s suitable for use cases like recommendation engines, image search and large scale content search. It also has RESTful APIs, so integration with external systems is relatively straightforward.
Aerospike: Aerospike is more focused on real-time data storage and search so it integrates well with applications that require low latency access to large datasets. It has connectors for popular ecosystems, including Python, Java and Go. But compared to Vespa it may require more custom development to integrate with machine learning frameworks or other complex data systems.
Ease of Use
For developers the learning curve, setup complexity and ongoing maintenance is important.
Vespa: Vespa can be hard to set up and configure especially for those new to distributed systems or advanced search engines. But it has comprehensive documentation and an active community that can help with setup and troubleshooting. Once set up Vespa’s flexible query system and self-scaling makes maintenance more manageable.
Aerospike: Aerospike is generally easier to set up and maintain especially for developers familiar with NoSQL databases. Its primary focus on real-time performance makes it a good choice for projects that require speed without much complexity in the search logic. The vector search functionality while useful may still require more effort to configure compared to Vespa’s fully-featured vector capabilities.
Cost
Cost is a big factor when choosing between these two especially if you plan to scale fast.
Vespa: While Vespa performs and scales well, the cost structure can vary depending on the resources used especially when scaling horizontally. As it’s open-source there are no licensing fees but operational costs (e.g. hardware, cloud instances) and maintenance can add up. There are also managed services for Vespa which can incur additional costs.
Aerospike: Aerospike has a more straightforward pricing model with options for open-source and enterprise versions. The enterprise version has advanced features and support, which is important for mission critical applications. As with Vespa, scaling can lead to operational costs, especially when vector search is integrated into high throughput environments.
Security Features
Security is always a concern when dealing with sensitive data, especially when using these databases in production.
Vespa: Vespa has various security features including authentication and access control, but you need to configure extra to secure data in transit and at rest. As open-source security features are available , they may need to be customized based on your requirements.
Aerospike: Aerospike has robust security features including authentication, encryption at rest and secure communication over TLS. The enterprise version also has additional security capabilities for compliance with standards like GDPR so it’s a good choice for high security applications.
When to Choose Each
Vespa: Vespa is for applications that need multi-modal search, combining vector search, full-text search and structured data filtering in one query. Use Vespa for large distributed data systems that need real-time search, scalability and dynamic query customization. Examples are recommendation engines, semantic content search or advanced e-commerce search where multiple data types and complex ranking models are involved.
Aerospike: Aerospike is for real-time, low-latency applications that need high throughput and simple search logic. It’s for scenarios where high-speed data ingestion and retrieval is key, such as financial transaction systems or ad-tech platforms. Aerospike’s vector search (although still in preview) is for use cases that need efficient nearest-neighbor search in structured or semi-structured data, as long as speed is more important than customization.
Summary
Vespa and Aerospike are different. Vespa is flexible, multi-modal and scalable for data-rich applications, Aerospike is for real-time and simple for high-speed workloads. The choice between them depends on your use case, data types and the balance between search complexity and performance. Think it through.
Read this to get an overview of Vespa and Aerospike 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 WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- Vespa: Overview and Core Technology
- Aerospike: Overview and Core Technology
- Key Differences
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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