This write-up talks about how DISKANN and vector indexing are benfitting the users with the release of SQL Server 2025.
Table Of Contents
Microsoft’s SQL Server 2025 introduces a significant advancement in data querying with native vector search capabilities, moving beyond traditional keyword and full-text searches. This feature allows you to perform semantic searches on your data, finding results based on contextual meaning rather than just exact word matches. This is powered by a new VECTOR data type and the use of the DiskANN algorithm for creating efficient vector indexes.
How Vector Search Works
The process of vector search in SQL Server 2025 can be broken down into three main steps:
Vectorization (Creating Embeddings): The first step is to convert your data—which can be text, images, or other data types—into numerical representations called vectors or embeddings. These high-dimensional vectors are created using machine learning models (like those from OpenAI or Ollama) that capture the semantic meaning and relationships of the data. For example, the words “car” and “bus” would have very similar vector representations because they are both types of vehicles. SQL Server 2025 simplifies this with functions like AI_GENERATE_EMBEDDINGS.
Storage: The generated vectors are stored in a new VECTOR column type in your SQL Server database. This allows you to keep the embeddings alongside the original data they represent, eliminating the need for a separate vector database.
Search: To find similar data points, you use the VECTOR_SEARCH function, which compares a query vector to the vectors in your table. Instead of a linear scan of every vector in the table, which would be computationally expensive for large datasets, SQL Server uses an efficient indexing method to quickly find the most similar vectors. This is where the DiskANN algorithm comes into play.
The Role of DiskANN and Vector Indexes
For large datasets, an exact nearest neighbor (ENN) search, which calculates the distance to every vector, is not feasible. This is where approximate nearest neighbor (ANN) search algorithms are essential. SQL Server 2025 leverages DiskANN, a specialized ANN algorithm developed by Microsoft.
DiskANN is a graph-based vector indexing algorithm optimized for disk storage and memory efficiency. Instead of holding the entire index in memory, it’s designed to minimize random disk reads, making it highly effective for large-scale datasets that may not fit entirely in memory. It constructs a graph where each node represents a vector, and edges connect similar vectors.
Here’s how it works:
Index Creation: You create a vector index on your VECTOR column using the CREATE VECTOR INDEX statement. This process builds the DiskANN graph structure. It requires a CLUSTERED PRIMARY KEY on an integer column in the table.
Approximate Search: When you execute a VECTOR_SEARCH query, SQL Server’s query processor uses the DiskANN index to traverse the graph efficiently. It starts at a specific entry point and “walks” through the connected nodes, moving toward the vectors that are most similar to your query vector. This process quickly narrows down the search space, returning a set of approximate nearest neighbors with high accuracy. While this method isn’t guaranteed to find the absolute closest vectors (hence the “approximate”), it provides a high recall rate (typically above 95%) with a significant performance boost over an exact search.
This new capability allows for powerful use cases such as:
Semantic Search: Finding documents or products that are conceptually similar to a query, even if they don’t share any keywords.
Recommendation Engines: Identifying items that are similar to a user’s past purchases or viewed products.
Fraud Detection: Flagging new claims that exhibit patterns semantically similar to past fraudulent cases.
Conclusion
By integrating DiskANN, SQL Server 2025 makes advanced AI-powered search directly accessible within the database engine, allowing developers to build intelligent applications using familiar T-SQL syntax without relying on external services or complex, custom infrastructure.
