MySQL HeatWave Database Service

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.

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 by orders of magnitude on the documents.

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)
• Learn more about MySQL HeatWave on AWS (PDF)

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.

• Discover more about HeatWave

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?”

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)

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

• Snowflake: 3.3X slower; 8X more expensive
• Amazon Redshift: 4.2X slower; 5X more expensive
• Google BigQuery: 5.6X slower; 5X more expensive
• Azure Synapse: 3X slower; 5X more expensive
• Amazon Aurora: 1,400X 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

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.

• Read the MySQL HeatWave technical brief (PDF)