Changes in MySQL HeatWave 9.5.0 (2025-10-21, General Availability)

Note

These release notes were created with the assistance of MySQL HeatWave GenAI.

• MySQL HeatWave AutoML

• MySQL HeatWave GenAI

• MySQL HeatWave Lakehouse

• MySQL HeatWave

MySQL HeatWave AutoML

• MySQL HeatWave AutoML now supports enhanced log anomaly detection capabilities with the introduction of semantic feature embeddings. This update enriches the feature space by extracting semantic features from logs using embedding models and combining them with existing statistical TF-IDF features, ultimately improving the accuracy and effectiveness of log anomaly detection.

  For more information, see Anomaly Detection for Logs. (WL #17097)

• MySQL HeatWave AutoML now supports advanced recommendation capabilities with the introduction of Deep Recommendation models, a deep learning-based approach to building embedding models for personalized recommendations. This new feature leverages PyTorch to construct a deep learning pipeline, enabling faster similarity searches between you and products through the generation of user and item embeddings. With this update, MySQL HeatWave AutoML lays the groundwork for delivering highly accurate and scalable personalized recommendations, enhancing the overall user experience. (WL #16656)

MySQL HeatWave GenAI

• MySQL HeatWave GenAI now supports hybrid search, combining the strengths of semantic and keyword-based search. With hybrid search, you can effectively retrieve data that may not be covered by semantic search, such as queries or datasets with specific product names, stock keeping units (SKUs), or brand names, leading to higher quality search results.

  For more information, see Retrieve Context and Generate Content Using Hybrid Search. (WL #16950)

• MySQL HeatWave GenAI now supports automatic creation of Vector Indexes using advanced index structures like Hierarchical Navigable Small World (HNSW) for frequently queried vector columns in vector store and embedding tables, enabling accelerated similarity search queries, and rapid retrieval of relevant results while balancing accuracy and speed.

  For more information, see Automatic Vector Index Creation. (WL #16429, WL #16881)

MySQL HeatWave Lakehouse

• MySQL HeatWave Lakehouse now automatically detects temporal data formats, eliminating the need to manually specify common date and time patterns. This streamlines data onboarding and simplifies integration of temporal data from diverse sources, making your Lakehouse workflows more efficient.

  For more information, see About Lakehouse Auto Parallel Load Schema Inference. (WL #17151)

• MySQL HeatWave Lakehouse now supports enhanced CSV parsing capabilities, allowing you to ignore quote and escape characters by setting their values to empty strings. This update enables the successful loading of CSV files containing JSON columns or special characters, previously resulting in parsing errors. With this improvement, you can efficiently handle complex CSV data, ensuring seamless integration with MySQL HeatWave Lakehouse tables.

  For more information, see Lakehouse External Table SQL Syntax and Lakehouse External Table JSON Syntax. (WL #17074)

• MySQL HeatWave Lakehouse now enables flexible data ingestion from Parquet files with enhanced support for column re-mapping. You can load columns into Lakehouse tables by choosing from three column mapping modes: by order, name (case-sensitive), or name (case-insensitive). This new functionality gives you greater control over the data loading process.

  For more information, see Lakehouse External Table JSON Syntax. (WL #16965)

• MySQL HeatWave Lakehouse now supports reading Delta Lake tables, introducing a new dialect format called "delta". This enhancement enables you to load Delta Lake tables as Lakehouse tables. With this update, MySQL HeatWave Lakehouse provides an efficient way to work with Delta Lake tables, making it easier to manage and analyze data in a Lakehouse environment. (WL #16685)

MySQL HeatWave

• MySQL HeatWave now supports bulk loading of tables with generated columns, enhancing its data import capabilities. This update allows for seamless handling of both stored and virtual generated columns, enabling you to efficiently load data from CSV files while leveraging the benefits of generated columns in their database design. With this enhancement, MySQL HeatWave provides improved flexibility and performance for managing complex data sets, making it an even more powerful tool for data analytics and management.

  For more information, see Bulk Ingest Data. (WL #17016)

• MySQL HeatWave now supports materialized views, introducing a new level of efficiency in data management. With this update, you can create materialized views using the CREATE MATERIALIZED VIEW statement, allowing for faster query performance and improved data analysis. The materialized view is populated lazily during the execution of a query that references it directly, ensuring that data is always up-to-date and optimized for performance. This new feature enhances the overall functionality of MySQL HeatWave, providing you with more powerful tools to manage and analyze your data. (WL #16886)

• MySQL HeatWave now supports using stored functions inside MySQL HeatWave queries, with a few limitations. This enhancement enables you to execute complex queries with improved performance.

  For more information, see Other Limitations. (WL #16724)