Predictive Query Processing Using Input Variable Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing database management systems face inefficiencies in selecting access paths for query statement execution due to unknown input variable values during bind time processing, leading to suboptimal performance variations during runtime execution.
Innovation Solution
The method involves predicting input variable values associated with query statements and using these predictions to select the most optimal access paths for runtime execution, incorporating predictive models trained with historical data to improve access path scoring and selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If access path selection is performed during bind time processing without knowing input variable values, then query processing can proceed, but performance varies suboptimally during runtime execution
Solution Approach 1:
The system performs preliminary actions by collecting historical input variable values and training predictive models during bind time, enabling more accurate access path selection. The predictive models are trained in advance using historical data, allowing the system to make informed predictions about future input values and select optimal access paths before actual query execution occurs.
Solution Approach 2:
The system implements feedback mechanisms by using actual runtime input variable values to update and retrain predictive models. The performance data from executed queries feeds back into the model training process, continuously improving prediction accuracy and access path selection over time. This closed-loop feedback ensures the system adapts to changing data patterns and query workloads.
2Measurement precision
If predictive models are used to predict input variable values, then access path selection accuracy improves, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the database management system automatically collects historical data, trains predictive models, and integrates them into the query optimization process without requiring external intervention. The system self-manages the complexity of model training and maintenance, making predictive capabilities available through standard SQL interfaces while handling the underlying complexity internally.
3Productivity
If historical input variable values are collected and used for training predictive models, then future query performance improves, but data storage and processing requirements increase
Solution Approach 1:
The system applies parameter changes by transforming historical input variable values into predictive model parameters and statistics. Instead of storing and processing all raw historical data, the system converts the data into condensed model representations that capture the essential patterns and relationships. This parameter transformation reduces storage requirements while maintaining the ability to make accurate predictions for future queries.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: receiving an incoming query statement, wherein the incoming query statement comprises a query statement expression that includes an input variable; predicting an input variable value associated to the input variable; selecting an access path for runtime execution of the query statement in dependence on the predicted input variable value; and performing runtime execution of the query statement using the selected access path.