Neural Network Query Performance Prediction
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Solution Overview
Problem
Database query performance varies significantly between testing and production environments, leading to unforeseen drops in application response times and usability, due to differences in data volume and schema, which are resource-intensive to synchronize and often go unnoticed until scaled up, and expert review is unfeasible in large-scale systems.
Innovation Solution
A neural network-based machine learning system is trained to predict query performance by tokenizing and encoding database queries as numerical vectors, allowing it to analyze contextual information and identify patterns in performant and non-performant queries, triggering alerts and optimizations as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database environments are synchronized to have similar data volume and schema, then query performance predictability is improved, but resource consumption and time required for synchronization increase
Solution Approach 1:
The patent replaces the mechanical approach of physically synchronizing database environments with a computational approach using neural networks. The system trains a neural network model on query performance data from both development and production environments, then uses the trained model to predict and adjust query performance characteristics across environments without requiring actual data synchronization. This substitutes the mechanical synchronization process with an information-processing-based solution.
Solution Approach 2:
The patent introduces a neural network model as an intermediary between the development and production database environments. This intermediary learns the performance characteristics and patterns from both environments and enables performance prediction and optimization without direct synchronization. The neural network acts as a mediator that captures the essential performance relationships without requiring the environments to be identical.
2Manufacturing precision
If expert database administrators review every database query before deployment, then query performance quality is improved, but productivity and scalability deteriorate
Solution Approach 1:
The patent implements a self-service system where the neural network automatically reviews, evaluates, and optimizes database queries without requiring expert database administrator intervention. The system uses trained models to autonomously predict query performance, identify problematic queries, and suggest or apply optimizations. This automation enables the system to serve itself, freeing administrators from manual query review while maintaining or improving performance quality.
Solution Approach 2:
The patent replaces the manual expert review process with an automated neural network-based system. Instead of relying on human administrators to manually analyze and optimize queries, the system uses machine learning models trained on historical query performance data to automatically evaluate and optimize queries. This substitution of human mechanical review with computational automation resolves the contradiction between quality and productivity.
3Measurement precision
If neural network model complexity is increased to improve query performance prediction accuracy, then prediction precision is improved, but device complexity and training resources increase
Solution Approach 1:
The patent applies partial action by using ensembles of multiple simpler neural network models rather than a single highly complex model. The system trains and evaluates multiple models with different architectures and complexity levels, then combines their predictions or selects the best-performing subset. This approach achieves high prediction accuracy through the collective effort of multiple models while avoiding the excessive complexity and resource requirements of a single ultra-complex model.
Data Source
AI summary
Techniques are described herein for leveraging recurrent neural networks for query processing. In some embodiments, a query analytic system determines a sequence of tokens for at least a portion of a query and determines a vector representation for each token. The query analytic system further generates, using a neural network based on the sequence of tokens, a performance prediction associated with executing at least the portion of the query, wherein the neural network assigns at least a first weight for at least a first token in the sequence of tokens based at least in part on at least a second token that preceded the token in the sequence. The query analytic system further triggers a responsive action, such as triggering an alert and/or tuning the query, based at least in part on the performance prediction.