MySQL Autopilot: Machine Learning–powered automation
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 delivers machine learning based automation.
System Setup
- 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.
- 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.
Data Load
- Auto parallel loading optimizes the load time and memory usage by predicting the optimal degree of parallelism for each table being loaded into HeatWave.
- Auto encoding determines the optimal representation of columns being loaded into HeatWave taking queries into consideration. This optimal representation provides the best query performance and minimizes the size of the cluster to minimize the cost.
- Auto data placement predicts the column on which tables should be partitioned in-memory to achieve the best performance for queries. It also predicts the expected gain in query performance with the new column recommendation.
- Auto unload determines unused or rarely used tables in HeatWave, and predicts the memory saved from unloading the tables.
Query Execution
- Auto query plan improvement learns various statistics from the execution of queries and improves the execution plan of future queries. This improves the performance of the system as more queries are run.
- Auto query time estimation estimates the execution time of a query prior to executing the query, allowing quick tryout and test on different queries
- Auto change propagation intelligently determines the optimal time when changes in MySQL Database should be propagated to the HeatWave storage layer. This ensures that changes are being propagated at the right optimal cadence.
- Auto scheduling determines which queries in the queue are short running and prioritizes them over long running queries in an intelligent way to reduce overall wait time.
- 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.
Failure Handling
- Auto error recovery provisions new nodes and reloads necessary data from the HeatWave storage layer if one or more HeatWave nodes is unresponsive due to software or hardware failure