Accelerate Similarity Search with Automatic HeatWave Vector Index

MySQL HeatWave is the only fully managed MySQL database service that combines transactions, analytics, machine learning, and GenAI services, without ETL. It also includes the capabilities to query data in object store and provides the best price performance in the industry. MySQL HeatWave is available on Oracle OCI, Amazon AWS, and Microsoft Azure.

When building AI applications such as chatbots, RAG pipelines, semantic search, or personalized recommendations, you need vector similarity search that’s fast, accurate, and scalable. Embedding it directly into your existing data stack simplifies development and improves performance.

Traditionally, this meant managing separate vector databases, tuning indexes by hand, wiring up multiple tools, and duplicating data. MySQL HeatWave eliminates that overhead by integrating high-performance vector search, GenAI, OLTP, and analytics into a single, unified platform.

MySQL HeatWave: Intelligent Vector Search

With MySQL HeatWave, everything you need to build and scale AI applications is available in a single service:

• In-memory scan vector search: Fast and accurate in-memory full table scan for smaller or mid-sized datasets.
• Vector Index: Efficient Approximate Nearest Neighbor (ANN) search for large-scale collections or datasets with highly selective predicates with adaptive accuracy based on your requirements.
• GenAI integration: Built-in large language models (LLMs) to generate vector embeddings and power RAG pipelines—all within the database.
• HeatWave Vector Store: Easily manage unstructured documents and their associated metadata.
• Transactional + Analytics workloads: Combine OLTP and real-time analytics on both transactional and document data, including data stored in object storage.

No need for separate ETL processes, additional infrastructure, or complex operations. MySQL HeatWave lets you power your entire AI stack within a single, integrated service.

Why HeatWave Vector Index—And Why Now?

Organizations adopt MySQL HeatWave to consolidate OLTP and OLAP workloads, improve performance, and reduce infrastructure overhead. As GenAI adoption accelerates, they are increasingly storing semantic vectors alongside transactional data and running similarity searches via in-memory scan in HeatWave.

As data volumes grow and queries become more complex, HeatWave Vector Index improves query latency with its high-performance, low-latency approximate nearest neighbor (ANN) search. This helps scaling AI workloads without added complexity.

3 Common Challenges as AI Workloads Grow:

1. Scaling Challenges with Exact Search: As data collections grow to tens or hundreds of millions of vectors, running full-scan searches quickly becomes inefficient and costly.
2. Low latency is Essential: Applications that use RAG, semantic search, or GenAI require responses within milliseconds. Even small delays can impact user satisfaction.
3. AI Shouldn’t Create Complex Operations: Teams want easy solutions—not the hassle of managing a separate vector database or performing manual tuning. Simplicity and automation are key.

The Solution: HeatWave Vector Index

HeatWave Vector Index brings approximate nearest neighbor (ANN) search speeds with flexible, adaptive accuracy directly within the same MySQL HeatWave service handling your transactions, analytics, and AI workloads.

What Makes HeatWave Vector Index Different?

While many platforms offer vector search, few offer the depth of integration, automation, and precision that MySQL HeatWave provides:

With HeatWave Vector Index, organizations can scale AI-driven applications without additional operational overhead, silos, or complexity.

Where HeatWave Vector Index Delivers Value

Here are the core use cases suited for HeatWave Vector Index, along with the unique advantages it brings:

How It Works: Fast, Accurate, and Effortless

1. Run a query with the clause—ORDER BY VECTOR_DISTANCE(…) LIMIT N—using standard SQL.
2. HeatWave automatically recognizes if a vector index is available—or creates one in the background based on your query patterns.
3. Before execution, HeatWave estimates the recall (e.g., Recall@100 ≥ 99%) to maintain accuracy.
4. If the index cannot meet your precision target, HeatWave transparently falls back to an exact full-scan search to ensure no results are missed.

End-to-End GenAI Pipeline Coverage

HeatWave Vector Index is natively integrated with HeatWave Vector Store and GenAI capabilities so you can:

• Store and manage a wide range of documents (PDF, DOCX, TXT, more)
• Generate embeddings directly inside the database using LLMs or external APIs
• Run vector similarity searches using standard SQL
• Seamlessly build RAG pipelines—no extra infrastructure or coordination needed
• Ensure security and compliance with OCI IAM, audit logging, and encryption

Summary

HeatWave Vector Index delivers fast, accurate vector similarity search directly inside MySQL HeatWave, no separate systems or manual tuning needed. It combines ANN performance with adaptive accuracy, all within a single service that also supports OLTP, analytics, vector store, and GenAI workloads. With built-in security and automation, it’s the simplest way to run scalable AI applications on your data.