Database Query Result Signatures for Post-Deployment Validation
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Solution Overview
Problem
Existing database systems lack comprehensive validation after deployment, leading to undetected inconsistencies that result in incorrect query results being returned to users, which can negatively impact user environments.
Innovation Solution
A database engine generates signatures for query results using a per-row hash function, allowing for automatic detection of inconsistencies by comparing signatures from different versions of the database engine program code, and generating reports or performing remediation when mismatches are found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If validation testing is performed only during development stages, then development time and resources are reduced, but inconsistencies in deployed database systems remain undetected
Solution Approach 1:
The patent performs validation testing in advance during development stages by executing test queries against development instances and storing expected results. This preliminary action ensures that when the same queries are executed against production instances, any inconsistencies can be quickly detected by comparing results against the pre-established expected outcomes, eliminating the need for time-consuming post-deployment validation.
Solution Approach 2:
The patent creates copies of development instances and their associated test queries, expected results, and validation configurations. These copies are stored in a validation database and used to automatically validate production instances. By using copies rather than recreating validation environments, the system enables rapid, repeated validation without consuming additional development resources or time.
2Reliability
If comprehensive validation testing is performed continuously, then inconsistency detection capability increases, but system overhead and resource consumption increase
Solution Approach 1:
The patent extracts only the essential validation components from the development environment and stores them in a compact validation database. Specifically, it extracts test query definitions, expected result signatures, and validation configurations, separating them from the full development instance. This extraction reduces validation system overhead while maintaining detection capability, as only the minimal necessary data is retained and compared during validation.
Solution Approach 2:
The patent transforms detailed query results into simplified parameter representations, specifically generating signatures (hashes) of the results. Instead of storing and comparing complete query result sets, the system stores and compares these condensed signatures. This parameter transformation dramatically reduces the resources required for validation while preserving the ability to detect inconsistencies, as any change in the actual result will produce a different signature.
3Productivity
If validation tests are not performed after deployment, then operational efficiency is maintained, but incorrect query results may be returned to users without detection
Solution Approach 1:
The patent implements self-service validation where the production database instance automatically validates itself against the stored expected results from development testing. The system executes the same test queries against the production instance, compares the results against pre-stored expected signatures, and automatically generates inconsistency reports if mismatches are detected. This self-validation mechanism maintains operational efficiency by not requiring external validation teams while ensuring reliability through continuous automatic checking.
Solution Approach 2:
The patent establishes a feedback loop where the results of production query execution are automatically compared against expected results, and any inconsistencies trigger automated feedback in the form of inconsistency reports. This feedback mechanism ensures that incorrect query results are detected and reported promptly, maintaining reliability without compromising operational efficiency, as the feedback process is automated and does not require manual intervention or system shutdown.
Data Source
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AI summary
The automatic detection of inconsistencies in a database system is described. A first signature and a second signature are received. The first signature is a signature of a result of a first execution of the query against a database by a first version of database engine program code. The second signature is a signature of a result of a second execution of the query by a second version of the database engine program code. A determination is made of whether the first signature and the second signature match. In response to the first signature and the second signature failing to match, an inconsistency report regarding at least one of the first or second versions of the database engine program code is generated and remediation regarding at least one of the first or second versions of the database engine program code is performed.