Database Query Signature Verification for Post-Deployment Remediation
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Solution Overview
Problem
Existing database management systems (DBMS) lack effective post-deployment validation, 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, storing them in a key-value store for comparison with subsequent executions, and a correctness verifier detects mismatches to generate inconsistency reports and perform remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If validation testing is performed only during software development stages, then development time and resources are reduced, but undetected inconsistencies lead to incorrect query results being returned to users
Solution Approach 1:
The patent implements preliminary validation by generating signatures of query results during development and storing them in advance. These pre-generated signatures serve as reference values for future comparisons, enabling automatic detection of inconsistencies without requiring extensive re-testing during deployment or operation.
Solution Approach 2:
The system establishes a feedback mechanism where query results are continuously validated by comparing current execution signatures against stored reference signatures. When mismatches are detected, the system generates inconsistency reports and can trigger remediation actions, creating a closed-loop validation system that operates after deployment.
2Measurement precision
If signature-based validation is implemented for all query executions, then detection capability of inconsistencies is increased, but system overhead increases
Solution Approach 1:
The patent extracts only the essential validation information (signatures) from complete query result sets. Instead of validating entire result sets or executing complex comparison logic, the system generates and compares compact signature representations, significantly reducing the computational overhead while maintaining detection capability.
Solution Approach 2:
The system transforms query results into a different parameter representation (signatures) that preserves the essential characteristics needed for validation while reducing data volume. This parameter transformation enables efficient comparison operations and reduces the computational burden of validation.
3Productivity
If no post-deployment validation is performed, then system operation speed is maintained, but inconsistencies pass undetected and impact user environments
Solution Approach 1:
The patent creates a copy of the validation mechanism that operates independently from the main query execution path. The signature generation and comparison processes run as separate validation operations, allowing the primary system to maintain its operation speed while the copied validation process checks for inconsistencies in the background.
Data Source
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.