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SingleStore vs Elasticsearch Choosing the Right Vector Database for Your AI Apps

SingleStore vs Elasticsearch Choosing the Right Vector Database for Your AI Apps

Dec 17, 20247 min read

What is a Vector Database?

Before we compare SingleStore and Elasticsearch, 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.

SingleStore is a distributed, relational, SQL database management systemand Elasticsearch is a search engine based on Apache Lucene. Both have vector search as an add-on. This post compares their vector search capabilities.

SingleStore: Overview and Core Technology

SingleStore has made vector search possible by putting it in the database itself, so you don’t need separate vector databases in your tech stack. Vectors can be stored in regular database tables and searched with standard SQL queries. For example, you can search similar product images while filtering by price range or explore document embeddings while limiting results to specific departments. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ for vector index and dot product and Euclidean distance for similarity matching. This is super useful for applications like recommendation systems, image recognition and AI chatbots where similarity matching is fast.

At its core SingleStore is built for performance and scale. The database distributes the data across multiple nodes so you can handle large scale vector data operations. As your data grows you can just add more nodes and you’re good to go. The query processor can combine vector search with SQL operations so you don’t need to make multiple separate queries. Unlike vector only databases SingleStore gives you these capabilities as part of a full database so you can build AI features without managing multiple systems or dealing with complex data transfers.

For vector indexing SingleStore has two options. The first is exact k-nearest neighbors (kNN) search which finds the exact set of k nearest neighbors for a query vector. But for very large datasets or high concurrency SingleStore also supports Approximate Nearest Neighbor (ANN) search using vector indexing. ANN search can find k near neighbors much faster than exact kNN search sometimes by orders of magnitude. There’s a trade off between speed and accuracy - ANN is faster but may not return the exact set of k nearest neighbors. For applications with billions of vectors that need interactive response times and don’t need absolute precision ANN search is the way to go.

The technical implementation of vector indices in SingleStore has specific requirements. These indices can only be created on columnstore tables and must be created on a single column that stores the vector data. The system currently supports Vector Type(dimensions[, F32]) format, F32 is the only supported element type. This structured approach makes SingleStore great for applications like semantic search using vectors from large language models, retrieval-augmented generation (RAG) for focused text generation and image matching based on vector embeddings. By combining these with traditional database features SingleStore allows developers to build complex AI applications using SQL syntax while maintaining performance and scale.

Elasticsearch: Overview and Core Technology

Elasticsearch is an open source search engine built on top of the Apache Lucene library. It’s known for real time indexing and full text search so it’s a go to search for heavy applications and log analytics. Elasticsearch lets you search and analyse large amounts of data fast and efficiently.

Elasticsearch was built for search and analytics, with features like fuzzy searching, phrase matching and relevance ranking. It’s great for scenarios where complex search queries and real time data retrieval is required. With the rise of AI applications, Elasticsearch has added vector search capabilities so it can do similarity search and semantic search, which is required for AI use cases like image recognition, document retrieval and Generative AI.

Vector Search

Vector search is integrated in Elasticsearch through Apache Lucene. Lucene organises data into immutable segments that are merged periodically, vectors are added to the segments the same way as other data structures. The process involves buffering vectors in memory at index time, then serializing these buffers as part of segments when needed. Segments are merged periodically for optimization, and searches combine vector hits across all segments.

For vector indexing, Elasticsearch uses the HNSW (Hierarchical Navigable Small World) algorithm which creates a graph where similar vectors are connected to each other. This is chosen for its simplicity, strong benchmark performance and ability to handle incremental updates without requiring complete retraining of the index. The system performs vector searches typically in tens or hundreds of milliseconds, much faster than brute force approaches.

Elasticsearch’s technical architecture is one of its biggest strengths. The system supports lock free searching even during concurrent indexing and maintains strict consistency across different fields when updating documents. So if you update both vector and keyword fields, searches will see either all old values or all new values, data consistency is guaranteed. While the system can scale beyond available RAM, performance optimizes when vector data fits in memory.

Beyond the core vector search capabilities, Elasticsearch provides practical integration features that makes it super valuable. Vector searches can be combined with traditional Elasticsearch filters, so you can do hybrid search that mixes vector similarity with full text search results. The vector search is fully compatible with Elasticsearch’s security features, aggregations and index sorting, so it’s a complete solution for modern search use cases.

Key Differences

Search Technology and Implementation

SingleStore has multiple vector index options: FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, HNSW_PQ. It supports exact k-nearest neighbors (kNN) and Approximate Nearest Neighbor (ANN) search methods with dot product and Euclidean distance for similarity matching.

Elasticsearch uses the HNSW algorithm for vector search, implemented through Apache Lucene. This creates a graph where similar vectors connect to each other, so searches are usually done in milliseconds.

Data Management and Storage

SingleStore integrates vector search into its SQL database. You can store vectors in regular tables and query them with standard SQL, combining vector searches with regular database operations in a single query. But you can only create vector indices on columnstore tables and must use Vector Type(dimensions[, F32]) format.

Elasticsearch handles vectors through Lucene's segment-based architecture. Vectors are buffered in memory during indexing and then serialized into segments. The system keeps consistency across different fields during updates, so searches see either all old or all new values.

Scalability

SingleStore distributes data across multiple nodes for large scale vector operations. You can add more nodes as your data grows. It shines when combining vector search with SQL operations.

Elasticsearch has distributed architecture for scaling through sharding and replication. While it performs best when vector data fits in memory, it can scale beyond available RAM. Segment-based architecture helps manage large datasets.

Features

SingleStore's main advantage is its SQL integration, so it’s great for applications that need to combine vector search with regular database operations. It’s good for recommendation systems and AI chatbots that need both vector similarity matching and structured data queries.

Elasticsearch is great at combining vector search with its existing search. You can mix vector similarity with full-text search results and use regular Elasticsearch filters. It also integrates well with its security features, aggregations and index sorting.

When to Use Each

SingleStore: For SQL and Vector Together

SingleStore is best when you need to build applications that combine SQL and vector operations. If you’re working on recommendation systems that need to consider both user preferences (as vectors) and business rules (as SQL constraints), or if you’re building AI applications that need to combine vector similarity with structured data queries. It works well when you need to scale horizontally and still do complex SQL along with vector search.

Elasticsearch: For Search-First Apps

Elasticsearch is best when search is the main focus and vector capabilities are an add-on to existing search. It’s the right choice for apps that need powerful full text search along with semantic similarity, like content recommendation systems or document retrieval platforms. It works well when you need to combine keyword search, filters and vector similarity in a single query, so it’s great for apps that need to blend traditional search with AI powered semantic understanding.

Summary

The choice between SingleStore and Elasticsearch boils down to your use case - SingleStore has strong SQL with vector capabilities and Elasticsearch has robust search with vector support. Your decision should be based on whether you need a primary database with vector capabilities (SingleStore) or a search engine with vector search (Elasticsearch). Consider your existing tech stack, the types of queries you’ll run most often and whether you need more traditional database operations or search functionality. Both can do vector search but they are strong in different areas, so are suitable for different use cases.

Chloe Williams
Chloe Williams is a technical writer at Zilliz.

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