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MySQL Autopilot for MySQL HeatWave

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DateWednesday, August 11, 2021

Edward Screven, Chief Corporate Architect, Oracle

Oracle Announces MySQL Autopilot for MySQL HeatWave Service

Machine learning techniques to automate HeatWave making it easier to use and further improve performance and scalability


Oracle has made available MySQL Autopilot, a new component of MySQL HeatWave service, the in-memory query acceleration engine for MySQL Database Service in Oracle Cloud Infrastructure (OCI). MySQL Autopilot uses advanced machine learning techniques to automate HeatWave making it easier to use and further improve performance and scalability.


Oracle claims 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 sample data, collect statistics on data and queries, and build machine learning models using Oracle AutoML to model memory usage, network load and execution time.


These machine learning models are then used by MySQL Autopilot to execute its core capabilities making the HeatWave query optimizer increasingly intelligent as more queries are executed, resulting in continually improving system performance over time—a capability, which, the company says, is not available on Amazon Aurora, Amazon Redshift, Snowflake, or other MySQL-based database services.



MySQL Autopilot includes the following capabilities:


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 that customers no longer need to manually estimate the optimal size of their cluster. No other database service provides this capability.


Auto parallel load can optimize the load time and memory usage by predicting the optimal degree of parallelism for each table being loaded into HeatWave. No other cloud vendor offers this capability.


Auto data placement predicts the column on which tables should be partitioned in-memory to help 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. No other database service provides this capability.


Auto encoding can determine 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 can improve the execution plan of future queries. This improves the performance of the system as more queries are run. No other database service provides this capability.


Auto query time estimation can estimate the execution time of a query prior to executing the query. This provides a prediction of how long a query will take, enabling customers to decide if the duration of the query is too long and instead run a different query.


Auto change propagation intelligently determines the optimal time when changes in MySQL Database should be propagated to the HeatWave Scale-out Data Management layer. This helps ensure that changes are being propagated at the right optimal cadence. No other cloud vendor offers this capability.


Auto scheduling can determine which queries in the queue are short running and prioritize them over long running queries in an intelligent way to reduce overall wait time. Most other databases use the First In, First Out (FIFO) mechanism for scheduling.


Auto error recovery provisions new nodes and reloads necessary data if one or more HeatWave nodes is unresponsive due to software or hardware failure.


Edward Screven, Chief Corporate Architect, Oracle, said; “Oracle’s MySQL Database Service with HeatWave is the only MySQL database that efficiently supports both OLTP and OLAP, enabling users to run mixed workloads or real-time analytics against their MySQL database with 10 to 1,000 times better performance and less than half the cost compared to other analytical or MySQL-based databases.


MySQL HeatWave is one of the fastest growing cloud services on OCI and an increasing number of customers are moving their MySQL workloads to HeatWave. Today, we are announcing a number of innovations which are the result of years of research and advanced development at Oracle. The combination of these innovations delivers massive improvements in automation, performance and cost—further distancing HeatWave from other database cloud services.”


As part of this announcement, Oracle is also introducing MySQL Scale-out Data Management, which can improve the performance of reloading data into HeatWave by up to 100 times. HeatWave now supports a cluster size of 64 nodes—up from 24 nodes—and is capable of processing up to 32 TB of data—up from 12 TB. These new enhancements further strengthen the price/performance advantages of HeatWave relative to its primary competitors.


MySQL HeatWave is also incorporated into the Oracle lake house. OCI Data Catalog is the single catalog for the lake house, including data from MySQL Database Service as well as Oracle Autonomous Database and OCI Object Storage. Lake house users can discover MySQL data through the catalog and move it or analyze it as needed. Several other OCI services such as Oracle Analytics Cloud and Oracle Cloud Data Integration service are also integrated with MySQL HeatWave.


The new features introduced in the latest MySQL HeatWave release are available now on OCI across all 30 Oracle Cloud Regions.





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