Database Query Testing System for Efficiency Optimization
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Solution Overview
Problem
Current database systems lack efficient methods to automatically detect and correct inefficient queries before they cause significant user experience issues, particularly in complex scenarios or large user bases, leading to slow websites and apps.
Innovation Solution
A computer-implemented system that mirrors a primary database to a secondary database, creates a testing database, and evaluates queries by identifying predicates, determining common values, creating test queries, and running them on the secondary database to assess resource utilization, providing recommendations for efficiency improvements when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually identify and correct inefficient queries after user reports, then query efficiency can be improved, but user experience deteriorates during the time lag and the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system performs preliminary actions by automatically detecting and correcting inefficient queries before they are deployed to production environments. The query testing system evaluates queries against test databases, identifies inefficiencies, and prevents deployment of problematic queries, thereby eliminating the time lag associated with manual post-deployment detection and correction.
Solution Approach 2:
The query testing system enables self-service by automatically detecting inefficient queries without requiring user reports or manual intervention. The system autonomously analyzes query performance, identifies optimization opportunities, and implements corrections, making the process independent of external triggers and significantly reducing detection time.
2Reliability
If complex queries are run on large user bases, then comprehensive data analysis is achieved, but resource utilization increases and user experience deteriorates due to slow performance
Solution Approach 1:
The system applies partial action by first evaluating queries on a reduced test database before full deployment. The query testing system uses a subset of data (test database) to assess query performance and resource utilization, allowing comprehensive analysis to be performed partially on smaller datasets to predict full-scale behavior without immediately consuming full production resources.
Solution Approach 2:
Resource utilization is preliminarily assessed through query testing on test databases before queries are executed on production systems with large user bases. This preliminary evaluation identifies resource-intensive queries in advance, allowing optimization or rejection before they consume excessive production resources, thereby ensuring reliability without excessive energy use.
3Ease of operation
If inefficient queries are deployed to production databases, then query functionality is provided, but user experience deteriorates due to slow website and app performance
Solution Approach 1:
The system performs preliminary quality assurance by automatically testing queries before deployment to production databases. The query testing system evaluates query efficiency and performance characteristics in advance, ensuring that only optimized queries are deployed. This maintains ease of operation by automating the testing process while preventing performance deterioration through pre-deployment validation.
Solution Approach 2:
The query testing system implements feedback by automatically analyzing query performance metrics and providing information about efficiency issues before production deployment. This feedback loop enables developers to identify and correct performance problems without manual testing, maintaining simple deployment processes while ensuring high productivity through automated performance verification.
Data Source
AI summary
Methods and systems for database query efficiency improvement are disclosed. In one embodiment, a method includes mirroring a primary database to a secondary database; creating a testing database comprising the schema; receiving a query; running the query on the testing database; and evaluating the query by: identifying predicates in the query; determining most common values for each column name by querying the secondary database; creating, for each column name, a list comprising at least one of the most common values; creating a test predicate comprising one of the column names and an entry for the list corresponding to the column name; creating a test query comprising one or more test predicates; determining a resource utilization of the query by running each of the test queries on the secondary database; and providing, to a user interface for display, an efficiency improvement recommendation when the resource utilization exceeds a threshold.