HeatWave ML 查询现在被监控并记录在 Performance Schema 表
rpd_query_stats和rpd_exec_stats. 如果单个 HeatWave ML 查询包含多个子查询，则rpd_query_stats在rpd_exec_stats.-
新功能已添加到 HeatWave ML 以帮助您管理模型：
当您
ML_TRAIN在训练数据集上运行例程时，您现在可以指定一个模型句柄用于模型而不是生成的模型。notes表中添加了 一个新列MODEL_CATALOG，您可以使用它来记录有关模型目录中模型的注释。model_metadata表中 的新列MODEL_CATALOG记录了模型的元数据，例如训练分数、训练时间和有关训练数据集的信息。如果在训练过程中发生错误或您取消训练操作，HeatWave ML 会在此列中记录错误状态。
HeatWave ML 现在支持将 ONNX（开放式神经网络交换）格式的预训练模型上传到模型目录。
ML_MODEL_IMPORT您可以使用提供将模型存储在 MySQL 表中所需的转换 的存储过程加载它们 。-
ML_EXPLAIN除了默认的排列重要性模型和预测解释器之外， 新的存储过程还可以让您为 HeatWave ML 训练各种模型解释器和预测解释器：部分依赖模型解释器显示更改一列或多列的值将如何更改模型预测的值。
The SHAP model explainer produces global feature importance values based on Shapley values.
The Fast SHAP model explainer is a subsampling version of the SHAP model explainer which usually has a faster runtime.
The Permutation Importance prediction explainer explains the prediction for a single row or table.
The SHAP prediction explainer uses feature importance values to explain the prediction for a single row or table.
When you use the
ML_EXPLAIN_TABLEandML_EXPLAIN_ROWstored procedures to generate explanations for a prediction, you can now use the SHAP prediction explainer as an alternative to the default Permutation Importance prediction explainer. SHAP produces feature importance values (explanations) based on Shapley values. HeatWave ML now supports timeseries forecasting using the existing stored procedures
ML_TRAIN,ML_PREDICT_TABLE, andML_SCORE. You can create a forecast for a single column (a univariate endogenous variable) with a numeric data type. The forecasting task is specified as a JSON object when you call theML_TRAINstored procedure.
HeatWave uses dictionary encoding to compress string columns (CHAR, VARCHAR, TEXT). These dictionaries are built for each string column with the
RAPID_COLUMN=ENCODING=SORTEDkeyword. HeatWave now supports 8.5 billion dictionary entries (up from 4 billion), which means HeatWave can now encode string columns with number of distinct value (NDV) up to 8.5 billion.MySQL HeatWave now uses zone maps to exclude data chunks that are not relevant for a query. The zone map stores per chunk statistics for the minimum and maximum values of primary key columns. Queries using point and range filters to filter on values can now get accelerated by HeatWave by an order of magnitude. This is particularly useful for improving range queries in OLAP and mixed workloads.
A new hypergraph-based MySQL optimizer is introduced for HeatWave to provide a holistic cost model across MySQL and HeatWave, create better query plans based on statistics used in Autopilot, reduce compilation time, eliminate the need of query hints for join order, and improve join query performance. With the new optimizer, HeatWave can now run all 22 TPC-H queries without straight join hints. Before 8.0.31, a straight join hint is needed for 10 out of 22 TPC-H to reach peak performance.
现在允许在将 RAPID 定义为辅助引擎的表上使用
ALTER TABLE、RENAME TABLE和 等 DDL 语句。TRUNCATE TABLE如果对当时加载到 HeatWave 集群的表成功执行 DDL 操作，HeatWave 会自动从 InnoDB 重新加载该表。请注意，如果 DDL 操作使表的结构与 HeatWave 不兼容，则该表将从 HeatWave 集群中卸载。