SQL Server 2025 Benchmarking with Vector Database

Hi @anup bharti

Thanks for the detailed explanation. Just to add this doesn’t fully cover benchmarking scenarios. Currently, there’s no official large vector dataset or dedicated tool (like VectorDBBench) for SQL Server 2025. In practice, benchmarking is usually done by:

• Using public datasets (SIFT, GloVe) and loading via Python
• Writing custom scripts (Python/k6/JMeter) to measure QPS & latency
• Testing under concurrent load with realistic query patterns

Also consider tuning areas like memory, MAXDOP, storage (NVMe), and dataset size scaling for meaningful results.

If you share your target scale (10M vs 100M), we can suggest a more concrete setup.

Thanks,
Akhil.

The provided information does not describe any official, ready‑made large benchmark database (for example, 10M–100M vectors), nor any specific benchmarking framework (such as VectorDBBench) tailored for SQL Server 2025 vector workloads.

What is covered in the context is how vector search and vector indexes work, and the mechanics needed to build a custom benchmark:

1. Vector storage and search model

• SQL Server 2025, Azure SQL Database, Azure SQL Managed Instance, and SQL database in Microsoft Fabric support a native vector data type for dense embeddings.
• Approximate nearest‑neighbor search is implemented via vector indexes based on the DiskANN algorithm, which is optimized for large‑scale vector search with high QPS and low latency while balancing memory, CPU, and I/O usage.
• Approximate search is performed by creating a vector index with CREATE VECTOR INDEX and querying with the VECTOR_SEARCH function.

2. Enabling vector features and index creation

• In SQL Server 2025, vector features (including VECTOR_SEARCH) are preview features and require enabling PREVIEW_FEATURES using ALTER DATABASE SCOPED CONFIGURATION before use.
• Vector indexes are created with CREATE VECTOR INDEX on a vector column, specifying a distance metric such as COSINE, EUCLIDEAN, or DOT.
• Latest‑version vector indexes:
  • Are fully DML‑capable (support INSERT, UPDATE, DELETE, MERGE without making the table read‑only).
  • Apply predicates iteratively during vector search.
  • Are optimizer‑driven: the query optimizer chooses between DiskANN index and kNN search.
  • Use advanced quantization for storage efficiency and performance.
• Latest‑version vector indexes require at least 100 rows before index creation.

3. Measuring index creation time

• Index creation is done with CREATE VECTOR INDEX; index build time can be measured externally (for example, by timing the CREATE VECTOR INDEX statement) since the syntax and behavior are standard DDL.
• After creation, DML operations automatically maintain the vector index in real time, so subsequent benchmarks can include mixed read/write workloads if desired.

4. Benchmarking similarity search performance

• Approximate similarity search is executed via VECTOR_SEARCH. For latest‑version vector indexes, approximate search must use SELECT TOP (N) WITH APPROXIMATE syntax and omit the legacy TOP_N parameter.
• For earlier, deprecated index versions, VECTOR_SEARCH uses a TOP_N parameter; using TOP_N with latest‑version indexes results in an error.
• Example workflows in the documentation show:
  • Creating a table with a vector column.
  • Inserting at least 100 rows of embeddings (for example, via AI_GENERATE_EMBEDDINGS or external libraries).
  • Creating a vector index.
  • Running VECTOR_SEARCH queries to perform similarity search.
• EF Core integration demonstrates how VECTOR_SEARCH can be invoked via LINQ with VectorSearch() and HasVectorIndex(), which translates to VECTOR_SEARCH in SQL. This can be used to benchmark from an application layer while still relying on the same underlying SQL primitives.

5. Data type and storage considerations for benchmarking

• The vector type is designed for dense vectors; sparse vectors are not natively supported.
• Embeddings can be stored as float32 or float16 equivalents; using half‑precision (2‑byte) floats can significantly reduce storage and memory usage with typically minimal impact on similarity quality, which is important when benchmarking large datasets.
• When modeling embeddings from multiple columns, either:
  • Store separate embeddings per column, or
  • Concatenate column values and generate a single embedding, depending on schema and workload design.

6. DML behavior and workload design

• With latest‑version vector indexes, DML operations are fully supported:
  • INSERTed rows become immediately searchable without index rebuild.
  • DELETE removes rows from both the table and vector search results.
  • UPDATE of vector columns updates the index so future searches use the new embedding.
  • MERGE can combine insert/update/delete in one statement, with the vector index automatically updated.
• This allows constructing realistic mixed workloads (read + write) for benchmarking, not just static read‑only search.

7. Platform and performance‑tuning specifics

• The context does not provide a comparison between Windows and Linux for SQL Server 2025 vector workloads, nor platform‑specific tuning guidance.
• It also does not list general performance‑tuning best practices beyond the vector‑specific behaviors described above.

To run a benchmark with the available information:

• Create a table with a vector column sized for the embedding dimension.
• Populate it with sufficient rows (at least 100 for index creation; more for realistic scale) using embeddings generated by AI_GENERATE_EMBEDDINGS or an external embedding generator.
• Enable preview features (for SQL Server 2025) and create a latest‑version vector index with an appropriate metric.
• Measure:
  • CREATE VECTOR INDEX execution time for index build.
  • Latency and QPS of VECTOR_SEARCH queries using SELECT TOP (N) WITH APPROXIMATE on representative query vectors.
  • Impact of DML operations on search performance if mixed workloads are required.

No official large prebuilt benchmark database, no dedicated benchmarking framework, and no OS‑level recommendations are described in the provided material.

References:

• Vector search and vector indexes in the SQL Database Engine
• VECTOR_SEARCH (Transact-SQL) (Preview)
• Vector search in the SQL Server EF Core Provider
• CREATE VECTOR INDEX (Transact-SQL) (Preview)
• Vector and embeddings: Frequently asked questions (FAQ)
• Vector Indexes - Microsoft Q&A I have a database table with a Vector Column in SQL Server 2025 but unable to use the Create Vector Index Command