MySQL HeatWave Features
Enterprise-grade transaction processing
- Compatibility with MySQL, with full InnoDB support and ACID compliance, provides data consistency and isolation for every transaction.
- Hypergraph optimizer provides cost-based query optimization for improved performance of complex transactional and mixed workloads.
- Enterprise Thread Pool scales for thousands of concurrent connections using multithreaded scheduling for the most demanding OLTP environments.
- Autopilot Indexing automatically determines secondary indexes to optimize OLTP throughput, using machine learning (ML) to make a prediction based on individual application workloads. It eliminates the time-consuming tasks of creating optimal indexes for OLTP performance and maintaining them as workloads evolve.
Read the MySQL HeatWave technical brief (PDF)
Maximum availability and built-in security
Achieve 99.99% uptime with group replication, automatic failover, read scalability via replicas, continuous backups, automated patching, and robust security, including full encryption and granular role-based access controls.
Fully automated operations
Provisioning, scaling, performance optimization, patching, backups, and recovery are automatically managed, delivering peak efficiency and reduced operational burden.
The only MySQL cloud service built on MySQL Enterprise Edition
Use a service developed, managed, and supported by the MySQL team that provides the latest features and security updates without delays. MySQL Enterprise support is included with no extra cost.
Asymmetric encryption with key generation and digital signatures
Server-side asymmetric encryption lets developers and DBAs increase the protection of confidential data using both public and private keys. They can also implement digital signatures to confirm the identity of people signing documents. Developers can encrypt data without modifying applications.
Data masking and deidentification
Data masking and deidentification hide and replace real data values with substitutes; selective masking, random data substitution, blurring, and other functions are also available. With data masking and deidentification in MySQL HeatWave, customers reduce the risk of a data breach by hiding sensitive data, which can be used in nonproduction systems, such as development and test environments. These data masking functions are available when queries are executed on the MySQL Database node or the MySQL HeatWave cluster.
Connection Control: Built-in defense against brute-force attacks
MySQL HeatWave comes with Connection Control, a security feature designed to defend against brute-force attacks. With cyberthreats constantly evolving, protecting your data starts with securing access at the source. Connection Control adds an extra layer of security by automatically slowing the response time for repeated failed login attempts from the same host. This mechanism can significantly reduce the effectiveness of automated attacks that rely on rapid-fire credential guessing.
High performance, in-memory query accelerator
MySQL HeatWave features an in-memory, massively parallel, hybrid columnar query-processing engine. It implements state-of-the-art algorithms for distributed query processing that provide very high performance.
Architected for massive scale and performance
MySQL HeatWave massively partitions data across a cluster of nodes that can be operated in parallel, providing excellent internodal scalability. Each node within a cluster and each core within a node can process partitioned data in parallel. An intelligent query scheduler overlaps computation with network communication tasks to achieve scalability across thousands of cores.
Optimized for the cloud
Query processing is optimized for commodity servers in the cloud. The sizes of the partitions are optimized to fit the cache of the underlying shapes. The overlap of computation with communication tasks is optimized for the available network bandwidth. Various analytics processing primitives use the hardware instructions of the underlying virtual machines.
Optimized for high transaction rates and connections
Autopilot improves the performance of the MySQL HeatWave thread pool, providing a mechanism to optimally use hardware resources for better performance. As a result, MySQL HeatWave delivers high throughput for OLTP workloads and prevents it from dropping at high levels of transactions and concurrency.
Real-time analytics without ETL
MySQL HeatWave lets you run real-time analytics on data in MySQL Database and object storage without extract, transform, and load (ETL) duplication. Eliminate complex, time-consuming integration with separate analytics database and lakehouse services.
Run real-time analytics
Analytics queries access the most current information as updates from transactions automatically replicate in real time to the MySQL HeatWave analytics cluster. There’s no need to index the data before running analytics queries. Developers and DBAs can also take advantage of MySQL HeatWave for real-time analytics on JSON documents stored in MySQL Database and object storage, accelerating analytics queries on the documents by orders of magnitude.
Make no changes to MySQL applications
MySQL HeatWave is a native MySQL solution. Current MySQL applications work without changes.
Use the same BI and data visualization tools
MySQL HeatWave supports the same business intelligence (BI) and data visualization tools as MySQL Database, including Oracle Analytics Cloud, Tableau, and Looker.
Improve security
Data at rest and in transit between MySQL Database and the nodes of the MySQL HeatWave cluster is always encrypted. There’s no risk of data being compromised during ETL since data isn’t transferred between data stores.
A modern data platform with GenAI and ML
MySQL HeatWave provides integrated generative AI and ML capabilities at no additional cost.
MySQL HeatWave GenAI
MySQL HeatWave GenAI provides integrated, automated, and secure generative AI with in-database large language models (LLMs); an automated, in-database vector store; scale-out vector processing; and the ability to have contextual conversations in natural language—allowing you to use generative AI without AI expertise, data movement, or additional cost.
In-database LLMs
Use the built-in LLMs in all Oracle Cloud Infrastructure (OCI) regions, OCI Dedicated Region, Oracle Alloy, Amazon Web Services (AWS), and Microsoft Azure—and obtain consistent results with predictable performance across deployments. Help reduce infrastructure costs by eliminating the need to provision GPUs.
In-database vector store
Perform retrieval-augmented generation across LLMs and your proprietary documents housed in MySQL HeatWave Vector Store to get more accurate and contextually relevant answers—without moving data to a separate vector database.
Automated generation of embeddings
Leverage the automated pipeline to help discover, ingest, and generate embeddings for proprietary documents in MySQL HeatWave Vector Store, making it easier for developers and analysts without AI expertise to use the vector store.
Scale-out vector processing
Vector processing is parallelized across up to 512 MySQL HeatWave cluster nodes and executed at memory bandwidth, helping deliver fast results with a reduced likelihood of accuracy loss.
Learn more about MySQL HeatWave GenAI
HeatWave AutoML
With in-database ML, you don’t need to move data to a separate ML service. You can easily and securely apply ML training, inference, and explanation to data stored both inside MySQL and in the object store. As a result, you can accelerate ML initiatives, increase security, and reduce costs.
ML lifecycle automation for saving time and effort
MySQL HeatWave AutoML automates the ML lifecycle, including algorithm selection, intelligent data sampling for model training, feature selection, and hyperparameter optimization—saving data analysts and data scientists significant time and effort. It provides options and flexibility for experienced users to customize the ML pipeline as needed. MySQL HeatWave AutoML supports anomaly detection, forecasting, classification, regression, and recommender system tasks.
Recommender system for personalized recommendations
By considering both implicit feedback (past purchases, browsing behavior, and so forth) and explicit feedback (ratings, likes, and so forth), the MySQL HeatWave AutoML recommender system can generate personalized recommendations. Ecommerce sites, for instance, can predict items that a user will like, users who will like a specific item, and ratings that items will receive. Given a user, the sites can also obtain a list of similar users and, given a specific item, obtain a list of similar items.
Explainable ML models
All the models trained by MySQL HeatWave AutoML are explainable. Predictions with an explanation of the results help improve regulatory compliance, fairness, repeatability, and trust as well as reduce casuality.
MySQL HeatWave Lakehouse
MySQL HeatWave Lakehouse lets you quickly query data in object storage, MySQL databases, or a combination of both. Query processing is done entirely within the MySQL HeatWave engine, so you can take advantage of MySQL HeatWave Lakehouse for non–MySQL workloads as well as MySQL–compatible workloads.
Scale-out architecture for data management and query processing
MySQL HeatWave’s massively partitioned architecture allows a scale-out architecture for MySQL HeatWave Lakehouse. Query processing and data management operations, such as loading and reloading data, scale with the size of data. Customers can query up to half a petabyte of data in object storage with MySQL HeatWave Lakehouse without copying it to the MySQL database instance. The MySQL HeatWave cluster scales to 512 nodes.
ML-powered automation for increasing performance and saving time
Reduce database administration overhead and improve performance with MySQL HeatWave Autopilot:
Auto schema inference automatically infers the mapping of file data to the corresponding schema definition for all supported file types, including CSV. As a result, customers don’t need to manually define and update the schema mapping of files, saving time and effort.
Adaptive data sampling intelligently samples the files in object storage to supply MySQL HeatWave Autopilot with information for making automation predictions. Using adaptive data sampling, MySQL HeatWave Autopilot can scan and make predictions, such as schema mapping on a 400 TB file, in less than a minute.
Available in public clouds and your data center
You can deploy MySQL HeatWave on OCI, AWS, or Azure. You can replicate data from on-premises OLTP applications to HeatWave MySQL to get near real-time analytics and process vector data in the cloud. You also can use HeatWave MySQL in your data center with OCI Dedicated Region.
MySQL HeatWave on AWS delivers a native experience for AWS customers. The console, control plane, and data plane reside in AWS.