Automated Query Scoring for Distributed Database Resource Control
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Solution Overview
Problem
Inefficient database queries in enterprise networks lead to disproportionate resource usage, slowing down other users and causing resource issues due to poor query practices, particularly in Hadoop-based systems where disk operations are time-consuming, especially for small data volumes.
Innovation Solution
An automated software module analyzes user queries for patterns of good or poor practices, assigns scores, and implements enforcement mechanisms such as throttling or notifications to improve query efficiency, using machine learning to optimize database structures and partitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated query analysis and enforcement mechanisms are implemented, then query efficiency and resource usage are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The system automatically analyzes queries, assigns quality scores, and enforces optimization rules without requiring manual intervention from database administrators or users. The automated enforcement mechanisms including throttling and notifications operate autonomously to improve query efficiency while reducing operational complexity.
Solution Approach 2:
The system implements continuous feedback loops where query performance is monitored, quality scores are assigned based on best practices compliance, and enforcement actions are taken based on these scores. This feedback mechanism enables the system to self-regulate and improve query efficiency automatically.
2Productivity
If machine learning models are used to optimize database structures, then query performance is improved, but training time and computational resources increase
Solution Approach 1:
The machine learning models are trained in advance on historical query data to learn optimal database structures and query patterns. This preliminary training enables the models to quickly evaluate and optimize new queries without requiring extensive training time during operational use.
Solution Approach 2:
The system applies machine learning optimization selectively to queries that would benefit most from it, rather than applying full ML processing to all queries. This partial application reduces the overall computational burden and training requirements while maintaining query performance improvements.
Data Source
AI summary
Systems and methods automatically optimize database queries for a distributed database management platform of an enterprise. A server system of a distributed database management platform receives data queries from users associated with the enterprise for data stored in the distributed database management platform. The server system assigns a score to each of the data queries, with the score for a data query being indicative of a quality of the data query. The server system generates, based on the scores for the data queries, a general score for at least one user group. The server system automatically undertakes a computing query optimization action for the at least one user group based on the general score for the at least one user group.