MySQL HeatWave Database Service

MySQL HeatWave is the only service that enables developers and database administrators to run OLTP and OLAP workloads directly from their MySQL Database.

Eliminate ETL

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

Deliver real-time analytics

Analytics queries always access the most up-to-date data 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.

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 databases.

No changes to MySQL applications

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

Use existing business intelligence (BI) and data visualization tools

HeatWave supports the same BI and data visualization tools as MySQL Database, including Oracle Analytics Cloud, Tableau, and Looker.

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.

• Read the ebook (PDF)

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.

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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 native, 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 inside MySQL HeatWave. 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.

Interactive MySQL HeatWave AutoML console

The new interactive console lets business analysts build, train, run, and explain ML models using the 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?”

Faster, less expensive, and more accurate than Redshift ML

Benchmarks demonstrate that, on average, HeatWave AutoML produces more accurate results than Amazon Redshift ML, trains models up to 25X faster at 1% of the cost, and scales as more nodes are added.

• See the benchmark details

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.

• Read the HeatWave AutoML technical brief (PDF)

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.

• See the top 10 reasons to use MySQL HeatWave for MySQL Community Edition users (PDF)

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.

MySQL HeatWave is faster and less expensive, as demonstrated by multiple standard industry benchmarks, including TPC-H, TPC-DS, and CH-benCHmark.

Analytics: Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, and Amazon Aurora are slower and more expensive

• Snowflake: 6.8X slower; 5X more expensive
• Amazon Redshift with AQUA: 6.8X slower; 2X more expensive
• Google BigQuery: 9X slower; 4X more expensive
• Azure Synapse: 3X slower; 5X more expensive
• Amazon Aurora: 1400X slower; 2X more expensive

Mixed workloads: Amazon Aurora is slower and more expensive

Most real-world applications have a mix of OLTP and complex OLAP queries. For such workloads, MySQL HeatWave is much faster and costs a fraction of Amazon Aurora. Using the industry standard CH-benCHmark on a 100 GB dataset for OLAP queries, Amazon Aurora is 18X slower, provides 110X less throughput, and is 2.4X more expensive than MySQL HeatWave. For OLTP queries, Amazon Aurora has the same performance as MySQL HeatWave and is 2.4X more expensive.

• See the benchmark details on OCI and AWS

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.

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.

• Read the MySQL HeatWave technical brief (PDF)