MySQL HeatWave Database Service Features

One MySQL cloud database service for OLTP, OLAP, and ML

MySQL HeatWave is the only service that enables developers, database administrators, and data analysts to run OLTP, OLAP, and machine learning (ML) workloads directly from MySQL Database.

Eliminate ETL

Eliminate the complex, time-consuming, and costly ETL process and integration with a separate analytics database and a separate ML service.

Deliver real-time analytics

Analytics queries always access the most current information as updates from transactions automatically replicate in real time to the HeatWave analytics cluster. There’s no need to index the data before running analytics queries.

Real-time analytics on JSON documents

Developers and DBAs can take advantage of HeatWave for real-time analytics on JSON documents stored in MySQL Database, accelerating analytics queries on the documents by orders of magnitude.

Fully automated in-database machine learning

With HeatWave AutoML, you can quickly and easily build, train, deploy, and explain machine learning models within MySQL HeatWave. No need to move the data to a separate ML cloud service, and no need to be an ML expert.

Improve security

Data at rest and in transit between MySQL Database and the nodes of the HeatWave cluster is always encrypted. There’s no risk of data being compromised during ETL since data isn’t transferred between data stores.

No changes to MySQL applications

HeatWave is a native MySQL solution. Current MySQL applications work without changes.

Use existing business intelligence (BI), data visualization, and ML tools

HeatWave supports the same BI and data visualization tools as MySQL Database, including Oracle Analytics Cloud, Tableau, and Looker. HeatWave AutoML is integrated with popular notebooks such as Jupyter and Apache Zeppelin.

Available in public clouds and your data center

Deploy MySQL HeatWave on OCI, AWS, or Azure. Replicate data from on-premises OLTP applications to MySQL HeatWave to get near real-time analytics without ETL. You can also use MySQL HeatWave in your data center with OCI Dedicated Region.


High performance, in-memory query accelerator

HeatWave is 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

HeatWave massively partitions data across a cluster of nodes, which can be operated in parallel. This provides excellent internodal scalability. Each node within a cluster and each core within a node can process partitioned data in parallel. HeatWave has an intelligent query scheduler that overlaps computation with network communication tasks to achieve very high scalability across thousands of cores.

Optimized for the cloud

Query processing in HeatWave has been optimized for commodity servers in the cloud. The sizes of the partitions have been optimized to fit the cache of the underlying shapes. The overlap of computation with communication is optimized for the network bandwidth available. Various analytics processing primitives use the hardware instructions of the underlying virtual machines (VMs).

Optimized for high transaction rates and connections

Oracle MySQL 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 higher throughput for OLTP workloads and prevents the throughput from dropping at high levels of transactions and concurrency.


MySQL HeatWave Lakehouse

MySQL HeatWave includes MySQL HeatWave Lakehouse, letting users query half a petabyte of data in object storage—in a variety of file formats, such as CSV, Parquet, Avro, and export files from other databases. The query processing is done entirely in the HeatWave engine, enabling customers to take advantage of HeatWave for non-MySQL workloads in addition to MySQL-compatible workloads. With HeatWave Lakehouse, MySQL HeatWave provides one cloud database service for transaction processing, real-time analytics across data warehouses and data lakes, and machine learning—without ETL across cloud services.

Fast lakehouse analytics and machine learning on all data

Customers can query data in various formats in object storage, transactional data in MySQL databases, or a combination of both using standard SQL commands. Querying the data in object storage is as fast as querying the databases, as demonstrated by a 10 TB TPC-H benchmark.

With HeatWave AutoML, customers can use data in object storage, the database, or both to automatically build, train, deploy, and explain ML models—without moving the data to a separate ML cloud service.

Scale-out architecture for data management and query processing

HeatWave’s massively partitioned architecture enables a scale-out architecture for MySQL HeatWave Lakehouse. Query processing and data management operations, such as loading/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. The HeatWave cluster scales to 512 nodes.

Increase performance and save time with machine learning–powered automation

MySQL Autopilot capabilities, such as auto provisioning, auto query plan improvement, and auto parallel loading, have been enhanced for MySQL HeatWave Lakehouse, further reducing database administration overhead and improving performance. New MySQL Autopilot capabilities are also available for MySQL HeatWave Lakehouse.

  • 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 derive information used by MySQL Autopilot to make predictions for automation. Using adaptive data sampling, MySQL Autopilot can scan and make predictions, such as schema mapping on a 400 TB file in less than one minute.
  • Adaptive data flow lets MySQL HeatWave Lakehouse dynamically adapt to the performance of the underlying object store in any region to improve overall performance, price-performance, and availability.

In-database machine learning with AutoML

HeatWave AutoML includes everything users need to build, train, deploy, and explain machine learning models within MySQL HeatWave, at no additional cost.

No need for a separate machine learning service

With in-database machine learning in MySQL HeatWave, 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 both inside MySQL and in the object store with HeatWave Lakehouse. As a result, they can accelerate ML initiatives, increase security, and reduce costs.

Save time and effort with machine learning lifecycle automation

HeatWave AutoML automates the machine learning 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. Aspects of the machine learning pipeline can be customized, including algorithm selection, feature selection, and hyperparameter optimization. HeatWave AutoML supports anomaly detection, forecasting, classification, regression, and recommender system tasks, including on text columns.

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 HeatWave AutoML recommender system can generate personalized recommendations. Analysts, for instance, can predict items that a user will like, users who will like a specific item, and ratings that items will receive. They can also, given a user, obtain a list of similar users, and given a specific item, obtain a list of similar items.

Interactive MySQL HeatWave AutoML console

The interactive console lets business analysts build, train, run, and explain ML models using a visual interface—without using SQL commands or any coding. The console also makes it easy to explore what-if scenarios to evaluate business assumptions—for example, “How would investing 30% more in paid social media advertising affect both revenue and profit?”

Explainable ML models

All the models trained by HeatWave AutoML are explainable. HeatWave AutoML delivers predictions with an explanation of the results, helping organizations with regulatory compliance, fairness, repeatability, causality, and trust.

Use current skills

Developers and data analysts can build machine learning models using familiar SQL commands; they don’t have to learn new tools and languages. Additionally, HeatWave AutoML is integrated with popular notebooks such as Jupyter and Apache Zeppelin.


Generative AI with MySQL HeatWave vector store

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 trained on only public data. 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.

The vector store ingests documents in a variety of formats, including 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 against 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 Autopilot: Built-in machine learning–powered automation

MySQL Autopilot provides workload-aware, machine learning–powered automation. It improves performance and scalability without requiring database tuning expertise, increases the productivity of developers and DBAs, and helps eliminate human errors. MySQL Autopilot automates many of the most important and often challenging aspects of achieving high query performance at scale—including provisioning, data loading, query execution, and failure handling. MySQL Autopilot is available at no additional charge for MySQL HeatWave customers.

MySQL Autopilot provides numerous capabilities for both HeatWave and OLTP, including

  • Auto provisioning predicts the number of HeatWave nodes required for running a workload by adaptive sampling of table data on which analytics is required. This means developers and DBAs no longer need to manually estimate the optimal size of their cluster.
  • Auto thread pooling lets the database service process more transactions for a given hardware configuration, delivering higher throughput for OLTP workloads and preventing it from dropping at high levels of transactions and concurrency.
  • Auto shape prediction continuously monitors the OLTP workload, including throughput and buffer pool hit rate, to recommend the right compute shape at any given time—allowing customers to always get the best price-performance.
  • Auto encoding determines the optimal representation of columns being loaded into HeatWave, taking the queries into consideration. This optimal representation provides the best query performance and minimizes the size of the cluster to minimize costs.
  • Auto query plan improvement learns various statistics from the execution of queries and improves the execution plan of future queries. This improves the performance of the system as more queries are run.
  • Adaptive query optimization uses various statistics to adjust data structures and system resources after query execution has started—independently optimizing query execution for each node based on actual data distribution at runtime. This helps improve the performance of ad hoc queries by up to 25%.
  • Auto data placement predicts the column on which tables should be partitioned in memory to achieve the best performance for queries. It also predicts the expected gain in query performance with the new column recommendation. This minimizes data movement across nodes due to suboptimal choices that can be made by operators when manually selecting the column.
  • Auto compression determines 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%.
  • Indexing (limited availability) automatically determines the indexes that customers should create or drop from their tables to optimize OLTP throughput, using machine learning to make a prediction based on individual application workloads. That helps customers eliminate the time-consuming tasks of creating optimal indexes for their OLTP workloads and maintaining those over time as workloads evolve.

Real-time elasticity

Real-time elasticity enables customers to increase or decrease the size of their HeatWave cluster by any number of nodes without incurring any downtime or read-only time.

Consistent high performance, even at peak times, and reduced costs with no downtime

The resizing operation takes only a few minutes, during which time HeatWave remains online, available for all operations. Once resized, data is downloaded from object storage, automatically rebalanced among all available cluster nodes, and becomes immediately available for queries. As a result, customers benefit from consistently high performance, even at peak times, and lower costs by downsizing their HeatWave cluster when appropriate—without incurring any downtime or read-only time.

With efficient data reloading from object storage, customers can also pause and resume their HeatWave cluster to reduce costs.

No overprovisioned instances

Customers can expand or reduce their HeatWave cluster to any number of nodes. They aren’t constrained to overprovisioned and costly instances forced by rigid sizing models offered by other cloud database providers. With HeatWave customers pay only for the exact resources they use.


Fully managed database service

Improve productivity by automating time-consuming tasks such as high-availability management, patching, upgrades, and backup with a fully managed database service. Accelerate application development with instant provisioning of resources.

Built, managed, and supported by the MySQL engineering team

Developers can deliver modern, cloud native database applications with immediate access to the latest features from the MySQL team. MySQL security patches are automatically applied to limit exposure to security vulnerabilities. MySQL HeatWave is 100% compatible with on-premises MySQL for a seamless transition to the cloud without changes to applications.

MySQL HeatWave interactive console: Manage resources, run queries, and monitor performance

Developers and DBAs can easily create and manage MySQL Database and HeatWave nodes. Within the console, they can access MySQL Autopilot capabilities, such as auto-provisioning, to determine the optimal configuration of their HeatWave cluster. They can view and administer the tables loaded in MySQL HeatWave as well as rapidly build and run queries.

The console also lets developers and DBAs monitor the performance of the MySQL Database node and the HeatWave cluster. They can monitor the use of various hardware resources and diverse query execution metrics.

OCI Database Management for MySQL HeatWave

OCI Database Management helps prevent outages in applications by providing diagnostics capabilities that help ensure the quick resolution of performance bottlenecks. The service can be used to proactively detect and identify the root cause of MySQL HeatWave performance issues.


Advanced security

Advanced security features let customers implement additional security measures to protect data throughout its lifecycle and help comply with regulatory requirements.

Asymmetric encryption with key generation and digital signatures

Server-side asymmetric encryption enables developers and DBAs to 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 current applications. They get the tools they need for encryption, key generation, and digital signatures.

Hide your data

Data masking and deidentification hides and replaces real data values with substitutes (selective masking, random data substitution, blurring, and other functions are available). With data masking and deidentification in MySQL HeatWave, customers reduce the risk of a data breach by hiding sensitive data, which can then 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 HeatWave cluster.

Block unauthorized database activities

The MySQL HeatWave database firewall monitors database threats, automatically creates an allowlist of approved SQL statements, and blocks unauthorized database activity. It provides real-time protection against database-specific attacks, such as SQL injections.

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