As a result, they can accelerate ML initiatives, increase security, and reduce costs. They can easily and securely apply machine learning training, inference, and explanation to data stored inside MySQL HeatWave. With native, in-database machine learning in MySQL HeatWave, customers don’t need to move data to a separate machine learning service. No need for a separate machine learning service HeatWave ML includes everything users need to build, train, deploy, and explain machine learning models within MySQL HeatWave, at no additional cost.

See all MySQL Autopilot capabilities (PDF).

No other database service provides this capability. This minimizes data movement across nodes due to suboptimal choices that can be made by operators when manually selecting the column. It also predicts the expected gain in query performance with the new column recommendation.

Auto data placement predicts the column on which tables should be partitioned in memory to achieve the best performance for queries.

This improves the performance of the system as more queries are run.

Auto query plan improvement learns various statistics from the execution of queries and improves the execution plan of future queries.

This optimal representation provides the best query performance and minimizes the size of the cluster to minimize costs.

Auto encoding determines the optimal representation of columns being loaded into HeatWave, taking the queries into consideration.

Auto shape prediction continuously monitors the OLTP workload, including throughput and buffer pool hit rate, to recommend the right compute shape at any given time-allowing customers to always get the best price performance.

Auto thread pooling lets the database service process more transactions for a given hardware configuration, delivering higher throughput for OLTP workloads and preventing it from dropping at high levels of transactions and concurrency.This means developers and DBAs no longer need to manually estimate the optimal size of their cluster.

Auto provisioning predicts the number of HeatWave nodes required for running a workload by adaptive sampling of table data on which analytics is required.MySQL Autopilot provides numerous capabilities for HeatWave and OLTP. MySQL Autopilot is available at no additional charge for MySQL HeatWave customers. 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 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: machine learning–powered automation Various analytics processing primitives use the hardware instructions of the underlying virtual machines (VMs). The overlap of computation with communication is optimized for the network bandwidth available. The sizes of the partitions have been optimized to fit the cache of the underlying shapes. Query processing in HeatWave has been optimized for commodity servers in the cloud. HeatWave has an intelligent query scheduler that overlaps computation with network communication tasks to achieve very high scalability across thousands of cores.

Each node within a cluster and each core within a node can process partitioned data in parallel. This provides excellent internodal scalability. HeatWave massively partitions data across a cluster of nodes, which can be operated in parallel. Architected for massive scale and performance It implements state-of-the-art algorithms for distributed query processing that provide very high performance. HeatWave is an in-memory, massively parallel, hybrid columnar query-processing engine.

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