One MySQL Database service for transactions, analytics, and machine learning (ML). Real-time, secure analytics without the complexity, latency, and cost of extract, transform, and load (ETL) duplication. Available on Oracle Cloud Infrastructure (OCI), Amazon Web Services (AWS), and Microsoft Azure.
Simplicity of transaction and real-time analytics in one service
Eliminate the cost and complexity of separate analytics database, ML, and ETL services. Avoid the latency and security risks of data movement between data stores.
Unmatched price-performance
MySQL HeatWave is 6.5X faster than Amazon Redshift at half the cost, 7X faster than Snowflake at one-fifth the cost, and 1,400X faster than Amazon Aurora at half the cost.
Ready for the distributed cloud
Deploy MySQL HeatWave on OCI, AWS, Azure, or in your data center.
MySQL HeatWave customers significantly improve productivity while reducing costs, deliver a better customer experience, scale to onboard more clients, and accelerate time to market.
Digital agency from Germany consolidates data processing and analytics with MySQL HeatWave on AWS for 90X faster complex queries than RDS, doubling click-through rates for marketing campaigns with greater scalability and less administration.
The multicloud tech leader consolidated data processing and analytics with MySQL HeatWave on AWS for 20X faster query performance, more scalability, and less administration than MariaDB on RDS. All with no code changes for real-time reporting.
This medical device manufacturer consolidated data processing and analytics with MySQL HeatWave on AWS for 50X faster complex queries than RDS for real-time insights to improve diabetes self-monitoring.
This K-12 educational SaaS provider in Brazil achieves real-time analytics with 300X faster complex query execution at 85% lower cost than Google BigQuery while supporting three million users—all to enhance student performance.
The Brazilian metaverse startup migrated all its data to MySQL HeatWave from AWS EC2. Within 3 hours, it achieved 5X better database performance for an event with more than one million visitors with greater security and at the half the cost.
This Japanese video game company gained real-time insights by adding HeatWave to MySQL Database Service, helping it meet its goal of continuously improving joyful entertainment for customers around the world.
Migrate to MySQL HeatWave on OCI or AWS
MySQL HeatWave is the only service that enables developers and database administrators to run OLTP and OLAP workloads directly from their MySQL Database.
Eliminate the complex, time-consuming, expensive ETL process and integration with a separate analytics database.
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.
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.
HeatWave is a native MySQL solution. Current MySQL applications work without changes.
HeatWave supports the same BI and data visualization tools as MySQL Database, including Oracle Analytics Cloud, Tableau, and Looker.
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 Oracle Dedicated Region Cloud@Customer.
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.
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.
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).
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 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. It uses advanced techniques to automate HeatWave, further improving performance and scalability and making it easier to use—saving developers and DBAs significant time. MySQL Autopilot is available at no additional charge for MySQL HeatWave customers.
MySQL Autopilot provides numerous capabilities for HeatWave and OLTP.
HeatWave AutoML includes everything users need to build, train, deploy, and explain machine learning models within MySQL HeatWave, at no additional cost.
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.
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.
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.
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.
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.
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.
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.
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.
Advanced security features let customers implement additional security measures to protect data throughout its lifecycle and help comply with regulatory requirements.
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.
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.
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.
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.
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.
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.
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.
MySQL HeatWave expands to include MySQL HeatWave Lakehouse, letting users process and query hundreds of terabytes of data in the object store—in a variety of file formats, such as CSV, Parquet, and Aurora/Redshift backups. With MySQL HeatWave Lakehouse, MySQL HeatWave provides one service for transaction processing, analytics across data warehouses and data lakes, and machine learning—without ETL across cloud services.
As demonstrated by a 400 TB TPC-H benchmark, the query performance of MySQL HeatWave Lakehouse is 17X faster than Snowflake and 6X faster than Amazon Redshift. Loading data into MySQL HeatWave Lakehouse is also significantly faster. The load performance of MySQL HeatWave Lakehouse is 8X faster than Amazon Redshift and 2.7X faster than Snowflake, as demonstrated by the 400 TB TPC-H benchmark.
Customers can query transactional data in MySQL databases, data in various formats in object storage, or a combination of both using standard MySQL commands. Querying the data in the database is as fast as querying data in the object store, as demonstrated by 10 TB and 30 TB TPC-H benchmarks.
The massively partitioned architecture of HeatWave enables a scale-out architecture for MySQL HeatWave Lakehouse. Query processing and data management operations, such as loading/reloading data or node recovery, scale with the size of data. Customers can query up to 400 TB of data with MySQL HeatWave Lakehouse, and the HeatWave cluster scales to 512 nodes.
MySQL Autopilot capabilities such as auto provisioning and auto query plan improvement have been enhanced for MySQL HeatWave Lakehouse, which further reduces database administration overhead and improves performance. New MySQL Autopilot capabilities are also available for MySQL HeatWave Lakehouse.
Key capabilities |
Available on OCI |
Available on AWS |
---|---|---|
Fully managed service | yes |
yes |
OLTP and OLAP in MySQL | yes |
yes |
Query acceleration for analytics and mixed workloads | yes |
yes |
Data compression | yes |
yes |
Machine learning–powered automation (MySQL Autopilot for HeatWave and OLTP)* | yes |
yes |
Advanced security* | yes |
yes |
In-database machine learning (HeatWave AutoML) | yes |
yes |
Scale-out data management | yes |
Coming soon |
Interactive query and data management console | Coming soon | yes |
Performance and workload monitoring from the console | Coming soon | yes |
Adding HeatWave to any MySQL shape | Coming soon | yes |
* Auto thread pooling and auto shape prediction in MySQL Autopilot as well as the MySQL HeatWave database firewall will be available soon on OCI.
See documented performance comparisons that show how MySQL HeatWave is 6.5X faster than Amazon Redshift at half the cost, 7X faster than Snowflake at one-fifth the cost, and 1400X faster than Amazon Aurora at half the cost.
See the performance and learn more about the benchmark setup configuration
In an interview with theCUBE, Oracle Senior Vice President Nipun Agarwal calls the new capabilities of MySQL HeatWave on AWS “impressive.”
Discover why, according to Futurum Research, “a robust HeatWave warning clearly remains in effect across the cloud database landscape.”
Find out why according to IDC, HeatWave AutoML is "a game changer for application developers and a broad range of data analysts and scientists."
In this in-depth analysis, Wikibon discusses the TCO advantages of MySQL HeatWave over its competitors, praising it as an “unprecedented breakthrough in query processing and machine learning.”
IDC
“MySQL HeatWave on AWS is a very compelling solution not just for analytics but also for OLTP and mixed workloads, as may be seen in publicly available benchmarks. For any developers working with MySQL on AWS, Oracle has just dropped a big productivity boost on your doorstep without the big price tag.”
Futurum
“The bottom line is we believe the competition just got outplayed on every measurable metric imaginable. It also represents a wake-up call for the industry and a rude awakening to the database cloud competition as they all must now respond to the MySQL HeatWave innovation juggernaut.”
Wikibon
“The performance results put Amazon Redshift and Snowflake to shame. While Aurora, Amazon’s own modified MySQL cloud database service is limited to 128TB database sizes, MySQL HeatWave Lakehouse supports cloud databases in excess of 400TB, demonstrating continued innovation at a compelling price point for customers worldwide.”
Moor Insights & Strategy
“Oracle introduced MySQL HeatWave and they did send shockwaves because they named and shamed basically every database company out there and my favorite is what they talked about with Snowflake...You can spend $80K on HeatWave and that would cost you $420K to run on Snowflake.”
Nipun Agarwal, Oracle Senior Vice President, MySQL HeatWave Development
MySQL HeatWave is the only MySQL-based service that combines transaction processing, real-time analytics, and machine learning within one single database. All these MySQL HeatWave capabilities, which are built, managed, and continuously supported by the MySQL HeatWave development team, are now available on AWS. All components of the MySQL HeatWave service on AWS, namely the service console, control plane, and data plane, are built and optimized for AWS.
Product |
Comparison price (/vCPU)* |
Unit price |
Unit |
MySQL Database—Standard - AMD E4 - Compute |
OCPU per hour |
||
MySQL Database—Standard - AMD E4 - Memory |
Gigabyte per hour |
||
MySQL Database—Standard - Intel X9 - Compute |
OCPU per hour |
||
MySQL Database—Standard - Intel X9 - Memory |
Gigabyte per hour |
||
MySQL Database—Optimized - Intel X9 - Compute |
OCPU per hour |
||
MySQL Database—Optimized - Intel X9 - Memory |
Gigabyte per hour |
||
MySQL Database—Storage |
Gigabyte storage capacity per month |
||
MySQL Database—Backup Storage |
Gigabyte storage capacity per month |
||
MySQL HeatWave—Standard |
Node per hour |
||
MySQL Database for HeatWave—Standard |
Node per hour |
||
MySQL Database for HeatWave—Bare Metal Standard |
Node per hour |
SCENARIO
A municipality is launching a new application to conduct various surveys and wants to run real-time analytics on the data. 50GB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 528,16
SCENARIO
A marketing agency wants to analyze advertising campaign performance in real-time. 1 TB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 829,24
SCENARIO
A telecommunications company wants to analyze its customers’ communication patterns in real-time. 10 TB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 5467,53
Currently available from North America
ECPU (Elastic CPU) per hour is defined as a combination of the total CPU hours used by MySQL Database and a measure of work done by the MySQL Database and HeatWave. HeatWave capacity per hour is defined as a unit of 16 gigabyte memory hours allocated in MySQL HeatWave.
Product |
Unit price |
Unit |
HeatWave—AWS |
HeatWave capacity per hour |
|
MySQL Database—AWS—ECPU |
ECPU per hour |
|
MySQL Database—AWS—storage |
Gigabyte storage capacity per month |
|
MySQL Database—AWS—backup storage |
Gigabyte storage capacity per month |
|
MySQL Database—AWS—outbound data transfer—inter AWS region |
Gigabyte of data transferred |
|
MySQL Database—AWS—outbound data transfer—to internet |
Gigabyte of data transferred |
SCENARIO
A municipality is launching a new application to conduct various surveys and wants to run real-time analytics on the data. 50GB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 116
SCENARIO
A marketing agency wants to analyze advertising campaign performance in real-time. 1 TB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 2,028
SCENARIO
A telecommunications company wants to analyze its customers’ communication patterns in real-time. 10 TB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 16,486
HeatWave is a massively parallel, high performance, in-memory query accelerator that increases MySQL performance by orders of magnitude for analytics and mixed workloads—without any changes to existing applications.
Join the conversation by visiting the MySQL HeatWave customer forum.
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