Backup Integrity Validation via Selective Hash Comparison
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Solution Overview
Problem
Existing data backup systems face challenges in ensuring the integrity of backed-up data, particularly when the target backup location becomes writable due to system errors, leading to potential data corruption.
Innovation Solution
A system and method that validate the integrity of data backups by comparing hashes of specific files at the source and target locations, identifying a subset of files to validate prior to the validation process, and storing the validation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the target backup location is made writable to allow system updates and maintenance, then ease of operation is improved, but data integrity is compromised due to potential corruption
Solution Approach 1:
The system performs preliminary actions by establishing write permissions and performing backup operations before validation occurs. The validation process then serves as a subsequent check to ensure the preliminary actions did not compromise data integrity, allowing the system to maintain writability while detecting corruption that may have occurred during write operations.
2Reliability
If validation is performed on all files in the backup, then data integrity is ensured, but processing time and computational resources increase significantly
Solution Approach 1:
The system applies partial action by validating only a selected subset of files rather than performing excessive validation on all files. This selective approach uses validation metadata to identify which files require validation, reducing processing time while still providing sufficient integrity checks for critical files.
Solution Approach 2:
The validation process is segmented into different categories of files based on validation metadata. Critical files are validated with full integrity checks, while non-critical files may use simplified validation or be excluded from validation entirely, allowing the system to balance thoroughness with processing efficiency.
3Productivity
If a subset of files is selected for validation to reduce processing time, then validation efficiency is improved, but the risk of undetected corruption increases
Solution Approach 1:
The system applies local quality by differentiating validation intensity based on file importance. Critical files receive comprehensive validation with full hash checking, while non-critical files receive minimal or no validation. This localized approach to validation quality maintains high detection capability for important files while improving overall processing efficiency.
Solution Approach 2:
The system uses validation metadata as feedback to guide the selection of files for validation. Files are tagged with metadata indicating their criticality and validation status, which feeds back into future validation decisions. This feedback mechanism ensures that files with higher corruption risk or greater importance are prioritized for validation, maintaining detection capability while optimizing resource usage.
Data Source
AI summary
A system can determine that an integrity of a data backup is to undergo a validation process, wherein the data backup is located at a target location, and wherein the data backup comprises a backup of source data at a source location. The system can identify a subset of files of the source data to validate in the data backup, wherein the subset of files is determined prior to performing the determining that the integrity of the data backup is to undergo the validation process. The system can, as part of validation of the subset of files according to the validation process, validate respective first hashes of respective files of the subset of files relative to respective second hashes of respective corresponding second files of the data backup, to produce a validation result. The system can store the validation result.