One MySQL cloud database service for transactions, real-time analytics across data warehouses and data lakes, and machine learning (ML)—without the complexity, latency, risks, and cost of extract, transform, and load (ETL) duplication. Available on Oracle Cloud Infrastructure (OCI), Amazon Web Services (AWS), and Microsoft Azure.
Watch “The Future of Scale-Out Data Processing with HeatWave Lakehouse” CloudWorld keynote.
Simplicity of transactions, real-time analytics across data warehouses and data lakes, and ML in one cloud database service
Eliminate the cost and complexity of separate analytics database, lakehouse, ML, and ETL cloud services. Query data in MySQL, in object storage, or across both. Avoid the latency and security risks of data movement between data stores.
Unmatched performance and price-performance
MySQL HeatWave is 4.2X faster than Amazon Redshift at one-fifth the cost, 3.3X faster than Snowflake at one-eighth the cost, and 1,400X faster than Amazon Aurora at half the cost. The price-performance of MySQL HeatWave Lakehouse for query processing is 8X better than Redshift, 18X better than Databricks, 22X better than Snowflake, and 30X better than Google BigQuery.
Ready for the distributed cloud
Deploy MySQL HeatWave on OCI, AWS, Azure, or in your data center.
MariaDB discontinued several products; get help to migrate to MySQL HeatWave.
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.
See how MySQL HeatWave enables digital marketing agency customers to send the right offer to the right prospect via the right channel at the right time—and provides real-time campaign performance analytics to make the best decisions.
Learn more about real-time marketing analytics with MySQL HeatWave
Discover why numerous fast-growing, cloud native organizations migrate to MySQL HeatWave to overcome their growing pains—improving performance, scalability, security, and productivity while reducing costs.
The technology that fintechs rely on often determines their ability to deliver an innovative solution with the performance, scalability, security, reliability, and cost efficiency that will sway customers. Learn why fintech startups migrate to MySQL HeatWave.
For ISVs delivering SaaS applications, selecting the right cloud platform is crucial since it represents the foundation on which their applications are built and has a large impact on how well they can serve customers. See why MySQL HeatWave has become a popular choice for ISVs.
MySQL HeatWave is the only service that enables developers and database administrators to run OLTP and OLAP workloads directly from 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.
Developers and DBAs can take advantage of HeatWave for real-time analytics on JSON documents stored in MySQL Database, accelerating analytics queries by orders of magnitude on the documents.
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 OCI Dedicated Region.
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 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
HeatWave AutoML includes everything users need to build, train, deploy, and explain machine learning models within MySQL HeatWave, at no additional cost.
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.
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.
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.
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?”
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.
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.
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.
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 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.
With efficient data reloading from object storage, customers can also pause and resume their HeatWave cluster to reduce costs.
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 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.
As demonstrated by a 500 TB TPC-H benchmark, the query performance of MySQL HeatWave Lakehouse is
The data load performance of MySQL HeatWave Lakehouse is
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.
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.
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.
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 |
yes |
Interactive query and data management console | Coming soon | yes |
Performance and workload monitoring from the console | Coming soon | yes |
Interactive MySQL HeatWave AutoML console | Coming soon | yes |
Adding HeatWave to any MySQL shape | Coming soon | yes |
MySQL HeatWave Lakehouse | yes |
Limited availability |
* Auto thread pooling and auto shape prediction in MySQL Autopilot as well as the MySQL HeatWave database firewall will be available soon on OCI.
As demonstrated by a 500 TB TPC-H benchmark, the query performance of MySQL HeatWave Lakehouse is
See the performance details and learn more about the benchmark setup configuration
Learn why Constellation Research says the addition of lakehouse support has made MySQL HeatWave “the cloud-native data platform for all data processing needs of an enterprise.”
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.”
NAND Research
“HeatWave is the only cloud data lakehouse service to query data in object storage and in the database at the same speed, which has never been achieved before...Oracle simplifies the experience for users, removing the need to keep multiple copies of data across multiple object stores, paying for data movement and pipelines, all while shuffling critical data around.”
Wikibon
“MySQL HeatWave, now with Lakehouse, may be the most significant open source cloud database innovation in the last decade….MySQL HeatWave just took a giant leap by increasing the scale-out processing by a factor of 8x to 512 nodes. The ability of HeatWave to load and query data on such a massive number of nodes in parallel is the first in the industry. Expect it to spur a market focus on much lower cost/performance, accelerated innovation, and increased competition.”
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 $420K to run on Snowflake.”
Nipun Agarwal, Oracle Senior Vice President, MySQL HeatWave Development
With support for Generative AI, users can interact with MySQL HeatWave in natural language. Both the user queries and the response from the system are generated in natural language using a Large Language Model (LLM).
Explore the MySQL HeatWave ISV catalog.
Product |
Comparison price (/vCPU)* |
Unit price |
Unit |
MySQL Database—Standard - AMD E4 - Compute |
OCPU per hour |
||
MySQL Database—Standard - AMD E4 - Memory |
Gigabyte per hour |
||
MySQL Database—Standard - Intel X9 - Compute |
OCPU per hour |
||
MySQL Database—Standard - Intel X9 - Memory |
Gigabyte per hour |
||
MySQL Database—Optimized - Intel X9 - Compute |
OCPU per hour |
||
MySQL Database—Optimized - Intel X9 - Memory |
Gigabyte per hour |
||
MySQL Database—Storage |
Gigabyte storage capacity per month |
||
MySQL Database—Backup Storage |
Gigabyte storage capacity per month |
||
HeatWave—Standard |
Node per hour |
||
MySQL Database for HeatWave—Standard |
Node per hour |
||
MySQL Database for HeatWave—Bare Metal Standard |
Node per hour |
||
Oracle Cloud Infrastructure - HeatWave |
HeatWave capacity per hour |
||
Oracle Cloud Infrastructure - HeatWave - Storage |
Gigabyte storage capacity per month |
SCENARIO
A marketing agency wants to analyze advertising campaign performance in real-time. 1 TB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 564.97
SCENARIO
A telecommunications company wants to analyze its customers’ communication patterns in real-time. 10 TB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 4,666.39
SCENARIO
An automotive company wants to obtain real-time telemetry analytics. 30 TB of data.
SPECS
ESTIMATED MONTHLY COST
US$ 10,704.15
Currently available from North America, Europe, Japan, and India.
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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