Automated Database Performance Testing for Schema and Code Changes
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Solution Overview
Problem
Existing database performance testing methods fail to detect performance hits from subtle changes in database schema and code, often missed in code reviews and integration tests, and require extensive setup and resources for load testing.
Innovation Solution
An automated system generates and executes test scenarios using a directed acyclic graph to identify dependencies between execution units and tables, allowing concurrent execution on primary and secondary threads to measure performance, capturing real-time snapshots for validation and troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional database performance testing methods are used, then setup resources and time are reduced, but performance issues from subtle schema and code changes are not detected
Solution Approach 1:
The system automatically generates test scenarios by analyzing the directed acyclic graph of execution units and tables, eliminating the need for manual test scenario creation and maintenance. The system self-configures based on the database schema and code dependencies, providing autonomous performance testing without requiring extensive manual setup.
Solution Approach 2:
The system pre-generates test scenarios by analyzing the directed acyclic graph structure before actual performance testing begins. By预先 identifying execution units, tables, and their dependencies, the system prepares comprehensive test cases that cover subtle schema and code changes, enabling accurate performance issue detection without extensive runtime setup.
2Reliability
If comprehensive load testing is performed to detect all performance issues, then detection capability is improved, but resource requirements and setup time increase
Solution Approach 1:
The system segments the database system into execution units and tables, representing their dependencies as a directed acyclic graph. This segmentation allows the testing system to focus resources on specific execution paths and data relationships, achieving comprehensive performance coverage with reduced overall resource requirements compared to full-system load testing.
Solution Approach 2:
The system dynamically adjusts testing parameters based on the analyzed dependency graph, optimizing resource allocation for each test scenario. By changing test parameters such as concurrency levels, data volumes, and execution sequences according to the specific execution unit and table relationships, the system achieves high reliability performance testing with efficient resource utilization.
3Measurement precision
If manual test scenario maintenance is performed, then test accuracy is maintained, but time and effort for updates increase
Solution Approach 1:
The system automatically generates and updates test scenarios by re-analyzing the directed acyclic graph when database schema or code changes occur. This self-service capability eliminates manual test scenario maintenance while preserving accuracy, as the system adapts test cases based on the current execution unit and table dependencies without requiring human intervention.
Solution Approach 2:
The system implements feedback mechanisms that monitor changes in the database schema and code, automatically triggering regeneration of the directed acyclic graph and associated test scenarios. This feedback loop ensures test scenario accuracy is maintained through continuous adaptation to system changes, replacing manual maintenance with automated detection and update cycles.
4Measurement precision
If concurrent execution on multiple threads is implemented, then performance measurement accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments execution control into primary and secondary threads with clearly defined responsibilities. The primary thread manages overall test scenario coordination and result aggregation, while secondary threads execute specific execution units concurrently. This segmentation of execution control reduces the perceived complexity by organizing concurrent operations into structured, manageable thread roles based on the directed acyclic graph dependencies.
Data Source
AI summary
In one embodiment, a non-transitory computer-readable media embodies software operable when executed to identify dependencies between execution units (EUs) associated with a database and a first table associated with the database, identify a combination of operation types to be tested, generate a first test scenario, which includes identifying a first EU that directly or indirectly depends on the first table based on the dependencies and identifying a first set of EUs that (a) directly or indirectly depend on the first table, based on the dependencies, and (b) match the combination of operation types to be tested, execute the first test scenario, which includes executing the first EU on a primary thread and executing one or more of the first set of EUs on secondary threads, wherein the primary thread and the secondary threads are concurrently executed, and determine a first performance measurement associated with executing the first test scenario.