Benchmark Software System for Database Performance Testing
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Solution Overview
Problem
Existing performance testing methods for database management systems (DBMS) fail to generate realistic data and queries, leading to ineffective and non-repeatable testing, as they rely on randomly generated data and queries that do not account for system loading or simulate real-world usage.
Innovation Solution
A system comprising a data generator, query generator, and query driver that creates realistic data and queries, with configurable complexity and loading parameters, allowing for repeatable and realistic performance testing of DBMS applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If random data and queries are generated for performance testing, then testing can be conducted without privacy concerns, but the testing effectiveness and repeatability deteriorate
Solution Approach 1:
The patent creates synthetic test data that copies the structural and statistical properties of real data without containing actual sensitive information. The data generator produces artificial datasets that replicate the schema, data types, relationships, and distribution patterns of production data, enabling effective performance testing while maintaining privacy protection.
Solution Approach 2:
The system transforms real data characteristics into configurable parameters that control synthetic data generation. By adjusting parameters such as data volume, distribution patterns, relationship densities, and constraint configurations, the system can generate test data with specific properties tailored to different testing scenarios while maintaining structural fidelity to the original system.
2Productivity
If random queries are applied to test DBMS performance, then testing can be conducted quickly, but the testing repeatability and realism deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-generating comprehensive test datasets and query workloads before actual performance testing begins. The data generator creates complete synthetic datasets with all necessary relationships and constraints established in advance, and the query generator prepares batches of realistic queries based on predefined templates and distributions, enabling consistent and repeatable testing execution.
Solution Approach 2:
The patent creates a universal testing framework that can generate diverse yet consistent test scenarios across different DBMS configurations. The same data generator and query generator can produce comparable test workloads for different database systems, versions, and configurations, enabling standardized performance evaluation and meaningful comparisons across multiple testing environments.
3Measurement precision
If realistic data is generated for performance testing, then testing accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex task of generating realistic test data into distinct functional components: schema definition, data generation, relationship creation, constraint application, and query generation. Each component handles a specific aspect of the testing preparation, making the overall system more manageable and maintainable while producing high-fidelity test datasets that accurately reflect production environments.
Data Source
AI summary
A system for and a method of testing the performance of a database management system. The system and method utilize a data table generator, a query generator, and a query driver system that are configured to generate test data, generate a series of test queries, and execute the queries against the data in a controlled and measurable manner such that the performance of the database management system can be tested in a configurable, repeatable, and consistent manner to measure the impact of system software and configuration changes.