1.5 MySQL Autopilot

MySQL Autopilot automates many of the most important and often challenging aspects of achieving exceptional query performance at scale, including cluster provisioning, loading data, query processing, and failure handling. It uses advanced techniques to sample data, collect statistics on data and queries, and build machine learning models to model memory usage, network load, and execution time. The machine learning models are used by MySQL Autopilot to execute its core capabilities. MySQL Autopilot makes the HeatWave query optimizer increasingly intelligent as more queries are executed, resulting in continually improving system performance.

System Setup

• Auto Provisioning

  Estimates the number of HeatWave nodes required by sampling the data, which means that manual cluster size estimations are not necessary. For MySQL HeatWave on OCI, see HeatWave Cluster Size Estimates. For MySQL HeatWave on AWS, see Estimating Cluster Size with MySQL Autopilot. For HeatWave in Oracle Database Service for Azure (ODSA), see the Provisioning HeatWave Nodes.

• Auto Shape Prediction

  For MySQL HeatWave on AWS, the Auto Shape Prediction feature in MySQL Autopilot uses MySQL statistics for the workload to assess the suitability of the current shape. Auto Shape Prediction provides prompts to upsize the shape and improve system performance, or to downsize the shape if the system is under-utilized. See: Autopilot Shape Advisor.

Data Load

• Auto Parallel Load

  Optimizes load time and memory usage by predicting the optimal degree of parallelism for each table loaded into HeatWave. See: Section 2.2.3, “Loading Data Using Auto Parallel Load”.

• Auto Encoding

  Determines the optimal encoding for string column data, which minimizes the required cluster size and improves query performance. See: Section 2.8.2, “Auto Encoding”.

• Auto Data Placement

  Recommends how tables should be partitioned in memory to achieve the best query performance, and estimates the expected performance improvement. See: Section 2.8.3, “Auto Data Placement”.

• Auto Data Compression

  HeatWave and HeatWave Lakehouse can compress data stored in memory using different compression algorithms. To minimize memory usage while providing the best query performance, auto compression dynamically determines the compression algorithm to use for each column based on its data characteristics. Auto compression employs an adaptive sampling technique during the data loading process, and automatically selects the optimal compression algorithm without user intervention. Algorithm selection is based on the compression ratio and the compression and decompression rates, which balance the memory needed to store the data in HeatWave with query execution time. See: Section 2.2.6, “Data Compression”.

Query Execution

• Auto Query Plan Improvement

  Uses statistics from previously executed queries to improve future query execution plans. See: Section 2.3.4, “Auto Query Plan Improvement”.

• Adaptive Query Execution

  Adaptive query optimization automatically improves query performance and memory consumption, and mitigates skew-related performance issues as well as out of memory errors. It uses various statistics to adjust data structures and system resources after query execution has started. It independently optimizes query execution for each HeatWave node based on actual data distribution at runtime. This helps improve the performance of ad hoc queries by up to 25%. The HeatWave optimizer generates a physical query plan based on statistics collected by Autopilot. During query execution, each HeatWave node executes the same query plan. With adaptive query execution, each individual HeatWave node adjusts the local query plan based on statistics such as cardinality and distinct value counts of intermediate relations collected locally in real-time. This allows each HeatWave node to tailor the data structures that it needs, resulting in better query execution time, lower memory usage, and improved data skew-related performance.

• Auto Query Time Estimation

  Estimates query execution time, allowing you to determine how a query might perform without having to run the query. Runtime estimates are provided by the Advisor Query Insights feature. See: Section 2.8.4, “Query Insights”.

• Auto Change Propagation

  For MySQL HeatWave on OCI, Auto Change Propagation intelligently determines the optimal time when changes to data on the MySQL DB System should be propagated to the HeatWave Storage Layer.

• Auto Scheduling

  Prioritizes queries in an intelligent way to reduce overall query execution wait times. See: Section 2.3.3, “Auto Scheduling”.

• Auto Thread Pooling

  Queues incoming transactions to give sustained throughput during high transaction concurrency. Where multiple clients are running queries concurrently, Auto Thread Pooling applies workload-aware admission control to eliminate resource contention caused by too many waiting transactions. Auto Thread Pooling automatically manages the settings for the thread pool control variables thread_pool_size, thread_pool_max_transactions_limit, and thread_pool_query_threads_per_group. For details of how the thread pool works, see Thread Pool Operation.

Failure Handling

• Auto Error Recovery

  For MySQL HeatWave on OCI, Auto Error Recovery recovers a failed node or provisions a new one and reloads data from the HeatWave storage layer when a HeatWave node becomes unresponsive due to a software or hardware failure. See: HeatWave Cluster Failure and Recovery.

  For MySQL HeatWave on AWS, Auto Error Recovery recovers a failed node and reloads data from the MySQL DB System when a HeatWave node becomes unresponsive due to a software failure.