Oracle announced significant enhancements to MySQL HeatWave, including support for vector store, generative AI, new in-database machine learning features, MySQL Autopilot enhancements, new HeatWave Lakehouse capabilities, support for JavaScript, acceleration of JSON queries, and support for new analytic operators. Currently in private preview, the vector store will enable customers to leverage the power of large language models (LLMs) with their proprietary data to get answers that are more accurate than using models which have been trained on public data only. With generative AI and vector store capabilities, customers can interact with MySQL HeatWave in natural language and efficiently search documents in various file formats in HeatWave Lakehouse.
“Today’s enhancements to MySQL HeatWave are another significant step on our journey to address pressing customer data, analytics, and AI issues,” said Edward Screven, chief corporate architect, Oracle. “We’ve previously added real-time analytics with the best price-performance in the industry, automated machine learning, lakehouse, and multicloud capabilities to HeatWave. Now vector store and generative AI bring the power of LLMs to customers, providing them with an intuitive way to interact with data in their enterprise and get the accurate answers that they need for their business.”
For customers looking to perform analytics, transaction processing, machine learning, and generative AI across a variety of data types and sources, additional capabilities have been added to MySQL HeatWave—for both MySQL-compatible and non-MySQL workloads.
Generative AI and vector store (private preview)
The vector store ingests documents in a variety of formats such as PDF and stores them as embeddings generated via an encoder model. For a given user query, the vector store identifies the most similar documents by performing a similarity search over the stored embeddings and the embedded query. These documents are used to augment the prompt given to the LLM so that it provides a more contextual answer.
MySQL HeatWave AutoML
MySQL HeatWave provides in-database machine learning with a fully automated pipeline for training models. Customers don’t need to move data to a separate machine learning service; they can easily and securely apply machine learning training, inference, and explanation to data stored inside MySQL HeatWave. The following new capabilities have been added:
- Support for HeatWave Lakehouse: Customers can now leverage HeatWave AutoML for training, inference, and explanations on data in object storage in addition to data in the MySQL database—and use a much wider set of data for machine learning.
- Text column support: Enables customers to perform machine learning tasks – anomaly detection, forecasting, classification, regression, and recommender system – on text columns, further broadening the corpus of data on which customers can leverage HeatWave AutoML.
- Enhanced recommender system: With support for Bayesian Personalized Ranking (BPR), HeatWave AutoML can now consider both implicit feedback (past purchases, browsing behavior) and explicit feedback (ratings, likes) to generate personalized recommendations. As an example, analysts can predict items a user will like, users who will like a specific item, and ratings items will receive.
- Training Progress monitor: Customers can now monitor the progress of the model training with HeatWave AutoML, allowing them to better manage resources.
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MySQL Autopilot
MySQL Autopilot is a built-in capability of MySQL HeatWave that uses machine learning-powered automation to help improve performance and scalability without requiring database tuning expertise. It learns from the execution of queries to improve the execution plan of future queries. The latest enhancements to MySQL Autopilot include:
- MySQL Autopilot indexing (limited availability): Helps customers eliminate the time-consuming tasks of creating optimal indexes for their OLTP workloads and maintaining those over time as workloads evolve. MySQL Autopilot automatically determines the indexes customers should create or drop from their tables to optimize their OLTP throughput, using machine learning to make a prediction based on individual application workloads. In addition, Autopilot indexing predicts the expected improvement with the recommended indexes without creating those indexes and without incurring compute or storage overhead on the users’ tenancy.
- Auto compression: Helps customers determine the optimal compression algorithm for each column, which improves load and query performance with faster data compression and decompression. By reducing memory usage, customers can cut costs by up to 25 percent.
- Adaptive query execution: Helps customers optimize the execution plan of a query after the query has started to execute, improving the performance of ad hoc queries by up to 25 percent. It uses information obtained from the partial execution of the query to adjust data structures and system resources and then independently optimizes query execution for each HeatWave node based on actual data distribution at run time.
- Auto load and unload: Autopilot automatically loads the columns being used in an application workload to HeatWave and automatically unload tables that were never or rarely queried. This helps free up memory and reduce costs for customers, without having to manually perform this task.
SOURCE: PRNewswire
