OpenSearch vs Aerospike: Selecting the Right Database for GenAI Applications
As AI-driven applications evolve, the importance of vector search capabilities in supporting these advancements cannot be overstated. This blog post will discuss two prominent databases with vector search capabilities: OpenSearch and Aerospike. Each provides robust capabilities for handling vector search, an essential feature for applications such as recommendation engines, image retrieval, and semantic search. Our goal is to provide developers and engineers with a clear comparison, aiding in the decision of which database best aligns with their specific requirements.
What is a Vector Database?
Before we compare OpenSearch vs 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.
Both OpenSearch and Aerospike are traditional databases that have evolved to include vector search capabilities as an add-on.
What is OpenSearch? An Overview
OpenSearch is a robust, open-source search and analytics suite that manages a diverse array of data types, from structured, semi-structured, to unstructured data. Launched in 2021 as a community-driven fork from Elasticsearch and Kibana, this OpenSearch suite includes the OpenSearch data store and search engine, OpenSearch Dashboards for advanced data visualization, and Data Prepper for efficient server-side data collection.
Built on the solid foundation of Apache Lucene, OpenSearch enables highly scalable and efficient full-text searches (keyword search), making it ideal for handling large datasets. With its latest releases, OpenSearch has significantly expanded its search capabilities to include vector search through additional plugins, which is essential for building AI-driven applications. OpenSearch now supports an array of machine learning-powered search methods, including traditional lexical searches, k-nearest neighbors (k-NN), semantic search, multimodal search, neural sparse search, and hybrid search models. These enhancements integrate neural models directly into the search framework, allowing for on-the-fly embedding generation and search at the point of data ingestion. This integration not only streamlines processes but also markedly improves search relevance and efficiency.
Recent updates have further advanced OpenSearch's functionality, introducing features such as disk-optimized vector search, binary quantization, and byte vector encoding in k-NN searches. These additions, along with improvements in machine learning task processing and search query performance, reaffirm OpenSearch as a cutting-edge tool for developers and enterprises aiming to fully leverage their data. Supported by a dynamic and collaborative community, OpenSearch continues to evolve, offering a comprehensive, scalable, and adaptable search and analytics platform that stands out as a top choice for developers needing advanced search capabilities in their applications.
What is Aerospike? An Overview
Aerospike is a high-performance NoSQL database renowned for its ability to manage extremely high volumes of data with minimal latency. At its core, Aerospike employs a unique hybrid memory architecture that merges in-memory and on-disk storage, ensuring rapid data access and updates essential in dynamic digital environments.
To expand its offerings, Aerospike has introduced the Aerospike Vector Search (AVS), an extension tailored for AI and machine learning applications. With this extension, Aerospike can perform similarity-based vector searches over extensive datasets, which is indispensable for developing modern AI applications such as recommendation engines, retrieval augmented generation, semantic search, and dynamic content curation.
Aerospike Vector Search (AVS) incorporates a variety of vector search functionalities that are vital for modern AI applications, which depend on machine learning models to rapidly interpret and process large data volumes. Built upon Aerospike's robust database infrastructure, AVS leverages the database's inherent performance strengths while introducing advanced capabilities:
- Multi-model: Flexibility well beyond NoSQL, including key-value, document, graph, and vector. All powered by a single integrated database engine at its core.
- Real-time updates: Ingestion at scale prevents hierarchical navigable small world (HNSW) index from getting stale, ensuring search results with fresh context.
- Hybrid search: Combine your vector search with additional criteria – filtering, relationship examination, and more – powered by Aerospike’s multi-model architecture.
- Millisecond latency: Manage very large data sets with the ability to store and retrieve billions of records within milliseconds.
- Parallel vector ingestion: Fast, innovative approach to ingesting and updating complex HNSW index structures from tens of thousands of sources.
- Flexible configuration: Flexibility to control your costs, including through separation of compute and storage and working with large or small models.
Comparing OpenSearch and Aerospike: Key Differences for GenAI
When selecting a database technology for AI-driven applications, understanding the differences between OpenSearch and Aerospike can help developers make informed choices based on their specific needs. Here's a detailed comparison of both platforms across several critical factors:
Search Methodology
OpenSearch: Continuing its legacy from Elasticsearch, OpenSearch builds on Apache Lucene and has further expanded its search capabilities by incorporating vector search, including support for k-nearest neighbors (k-NN), semantic search, multimodal search, and neural sparse search. These enhancements facilitate more complex, AI-driven search scenarios, improving relevance and efficiency by integrating neural models for on-the-fly embedding generation.
Aerospike: With the introduction of Aerospike Vector Search (AVS), Aerospike now supports advanced vector search capabilities, essential for AI applications like recommendation systems and semantic search. AVS combines Aerospike’s high-performance model with modern search features, including real-time updates to HNSW indices, hybrid searches that combine vector search with traditional querying, and parallel vector ingestion for scalability.
Data Handling
OpenSearch: Manages a wide range of data types, from structured to unstructured, enhancing its utility in handling diverse datasets. The latest updates enhance its ability to process and search large datasets efficiently, making it even more powerful for complex analytical tasks.
Aerospike: Known for its hybrid memory architecture that effectively handles high volumes of data across key-value, document, graph, and now vector models. The AVS extension enriches its data handling capabilities, particularly for AI-driven applications requiring rapid processing of complex data structures.
Scalability and Performance
OpenSearch: Highly scalable, leveraging its Lucene foundation to handle extensive datasets with improved search query performance and disk-optimized vector search. These features make it well-suited for enterprise-scale applications requiring efficient data retrieval and real-time analytics.
Aerospike: Excels in performance and scalability, with AVS enhancing these aspects by supporting real-time updates and millisecond latency in vector searches. Its unique data handling and storage methods ensure that performance does not degrade, even with very large datasets.
Flexibility and Customization
OpenSearch: Offers significant flexibility in data modeling and queries, supported by a robust set of APIs and plugins for customization. The integration of various search methodologies allows for tailored solutions that meet specific application needs.
Aerospike: AVS brings enhanced flexibility with its multi-model support and hybrid search capabilities, allowing users to combine different data retrieval methods. Its flexible configuration options help optimize costs and performance according to application requirements.
Integration and Ecosystem
OpenSearch: Continues to benefit from a broad ecosystem, integrating seamlessly with a variety of tools and frameworks for data processing and visualization. The community-driven approach ensures continual improvements and support.
Aerospike: While traditionally focused on high-performance key-value storage, its ecosystem is expanding with the integration of vector and multi-model capabilities. However, it might not yet match the breadth of integrations available with OpenSearch.
Ease of Use
OpenSearch: Maintains a comprehensive documentation and community support structure, though its advanced features can pose a learning curve. The inclusion of new search capabilities and data prepper features may require additional learning for optimal use.
Aerospike: The addition of AVS and its multi-model capabilities might increase the complexity, but Aerospike is generally known for its straightforward setup and maintenance, especially in high-throughput environments.
Cost Considerations
OpenSearch: Operational costs vary based on deployment strategies, with options for on-premise and cloud hosting. Managed services like Amazon OpenSearch Service offer ease of use but at a higher cost.
Aerospike: Offers a cost-effective solution in its Community Edition, with the Enterprise Edition providing additional features. The flexibility in configuration and separation of compute and storage can help manage costs effectively.
Security Features
OpenSearch: Strong security features remain a key aspect, with robust encryption, role-based access control, and audit logging to meet stringent security and compliance requirements.
Aerospike: Continues to provide comprehensive security options, including enhanced capabilities with AVS to ensure secure handling and processing of sensitive data.
This comparison shows that both OpenSearch and Aerospike have evolved to address modern data challenges, especially in AI and machine learning contexts, offering powerful and flexible solutions tailored to diverse application needs.
When to choose OpenSearch and Aerospike
When deciding between OpenSearch and Aerospike, the choice largely depends on the specific use cases, requirements for search capabilities, performance needs, and system architecture. Here’s a guide to when you might choose each technology:
Choose OpenSearch for GenAI when:
- Complex Searches: Your application needs advanced text searching capabilities like full-text, fuzzy, or contextual searches.
- Analytics & Visualization: You require tools for visualizing and analyzing data in real time.
- Machine Learning Enhancements: You're incorporating machine learning to improve search relevance and analytics.
- Cloud Scalability: You plan on using cloud resources extensively and need a system that scales efficiently.
Choose Aerospike for GenAI when:
- Peak Performance: Your application demands ultra-fast data access and handling, especially under high loads.
- Large Data Volumes: You need to manage huge amounts of data with minimal latency.
- AI with Vector Search: You're using vector search for AI applications like recommendation systems.
- Cost Efficiency: You seek a system that is both powerful and economical in operation, particularly in environments with stringent performance and reliability requirements.
When to Choose a Specialized Vector Database?
While OpenSearch and Aerospike offer vector search capabilities through an extension, they are not optimized for large-scale, high-performance vector search tasks. If your application relies on fast, accurate similarity searches over millions or billions of high-dimensional vectors, such as in image recognition, e-commerce recommendations, or NLP tasks, specialized vector databases like Milvus and Zilliz Cloud (the managed Milvus) are a better fit. These databases are built to handle vector data at scale, using advanced Approximate Nearest Neighbor (ANN) algorithms (e.g., HNSW, IVF ) and offering advanced features like hybrid search (including hybrid sparse and dense search, multimodal search, vector search with metadata filtering, and hybrid dense and full-text search), real-time ingestion, and distributed scalability for high-performance in dynamic environments.
On the other hand, general-purpose systems like OpenSearch and Aerospike are suitable when vector search is not the primary focus, and you’re handling structured or semi-structured data with smaller vector datasets or moderate performance requirements. If you already use these systems and want to avoid the overhead of introducing new infrastructure, vector search plugins can extend their capabilities and provide a cost-effective solution for simpler, lower-scale vector search tasks.
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?
- What is OpenSearch? An Overview
- What is Aerospike? An Overview
- Comparing OpenSearch and Aerospike: Key Differences for GenAI
- When to choose OpenSearch and Aerospike
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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