Dynamic Database Query Resource Allocation via ML Classification
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Solution Overview
Problem
Database systems face challenges in efficiently managing varying workload access requests, leading to increased complexity and costs due to the need to process both high and low workload queries concurrently, which affects performance and resource allocation.
Innovation Solution
Implementing a dynamic query size determination method that classifies database queries based on their workload and allocates appropriate computing resources, using machine learning classifiers and timeouts to optimize resource utilization and prevent misclassification impacts on other queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If database systems process both high and low workload access requests concurrently, then the system can handle diverse query types, but the complexity and cost of maintaining data storage and management technologies increases
Solution Approach 1:
The patent segments the database query processing system into two distinct configurations: a first configuration optimized for high workload access requests and a second configuration optimized for low workload access requests. This segmentation allows each configuration to be specialized for its specific query type, improving overall system efficiency while managing complexity through modular design.
Solution Approach 2:
The patent implements dynamic reconfiguration capability that allows the database system to switch between the first and second configurations based on the type of access request being processed. This dynamic adaptation enables the system to optimize resource allocation in real-time, handling diverse query types efficiently without maintaining permanently complex infrastructure.
2Adaptability or versatility
If database systems process both high and low workload access requests concurrently, then the system can handle diverse query types, but the cost of maintaining the information increases
Solution Approach 1:
By segmenting the system into specialized configurations for high and low workload requests, each configuration can be optimized for its specific purpose, reducing wasted computational resources and lowering overall maintenance costs compared to a single general-purpose system.
Solution Approach 2:
The patent changes system parameters (configuration state) based on the query workload type. By adjusting the system configuration to match the query demands, the system avoids the energy waste of using overly powerful resources for simple queries or insufficient resources for complex queries, thereby reducing maintenance costs.
3Productivity
If computing resources are allocated statically to database queries, then resource allocation is simple, but query performance deteriorates due to mismatched resource allocation
Solution Approach 1:
The patent implements dynamic resource allocation where the computing resources are automatically adjusted based on the determined query size. Small queries are allocated fewer resources while large queries receive more resources, optimizing execution efficiency. The dynamic nature of this allocation mechanism manages complexity through automated decision-making based on query characteristics.
Solution Approach 2:
The system uses feedback from query size determination to adjust resource allocation dynamically. By measuring query characteristics and using that information to allocate appropriate computing resources, the system achieves optimal performance without requiring complex manual configuration or over-provisioning of resources.
Data Source
AI summary
Database queries may be performed using resources based on a determined size of the database query. Database query size may be dynamically determined for a database query when the query is received. The database query may be assigned to resources used for database queries of the determined size. In some embodiments, timeouts may be applied to reassign database queries to different resources if the performance of the database query exceeds a timeout threshold.