Cache Validation Component for Data Corruption Remediation
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Solution Overview
Problem
Existing data corruption issues in distributed storage systems are difficult to identify and remediate, leading to resource-intensive processes that often fail to update underlying data structures, resulting in widespread corruption.
Innovation Solution
The use of cache memory for content validation and error remediation, where a validation component interfaces with the cache to detect and correct corruption in real-time, updating data structures in both cache and permanent storage to prevent further corruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ad-hoc corruption identification methods are used, then specific corruption cases can be tracked to individual clients or services, but the process is resource intensive and fails to consistently identify the root cause or prevent widespread corruption
Solution Approach 1:
A validation component is introduced as an intermediary between the cache memory and the permanent data storage. This validation component automatically validates data structures when data is read from cache or written to permanent storage, preventing corruption without requiring resource-intensive ad-hoc tracking of individual corruption cases. The intermediary validates data proactively rather than reactively identifying corruption after it occurs.
Solution Approach 2:
The system performs preliminary validation of data structures before data is written to permanent storage and before data is read from cache memory. By validating data structures in advance (preliminarily), the system prevents corruption from occurring in the first place, rather than spending resources trying to identify and fix corruption after it has already happened.
2Reliability
If corruption remediation services address issues one by one, then individual corruption cases can be fixed, but the underlying invalid data structure is not updated allowing corruption to recur and spread
Solution Approach 1:
The validation component provides continuous feedback about the validity of data structures to both the cache memory and permanent data storage systems. When invalidity is detected, the validation component notifies the relevant storage systems so they can update their data structures. This feedback mechanism ensures that the root cause of corruption is addressed systematically rather than just treating individual symptoms.
Solution Approach 2:
The validation component serves multiple functions simultaneously: it validates data structures for both cache memory and permanent storage, identifies corruption, and notifies both storage systems to update their data structures. This multi-functional approach consolidates what would otherwise require separate complex remediation services for each storage system and corruption type.
3Reliability
If validation processing is performed on all data access operations, then data integrity is maintained, but processing latency increases
Solution Approach 1:
The validation component performs partial validation by focusing on validating data structures rather than re-validating entire data contents. When data is read from cache or written to permanent storage, the system validates the structural integrity (metadata, schemas, formats) rather than performing exhaustive content verification, reducing validation overhead while maintaining data integrity.
Data Source
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AI summary
The present disclosure relates to processing operations that enable use of cache memory for content validation and error remediation of content. Functionality of the cache is extended to enable interfacing between a cache and a validation component, which is configured to validate data read directly from the cache. Corrupted content may be updated and propagated to a permanent data store associated with an application/service, system, etc. Use of the cache may be optimal for targeting recent and/or heavily accessed content as well as a means for offloading data processing operations from a permanent data storage. Examples described herein extend to those in which data associated with an electronic file is already stored in cache memory as well as those in which update occurs directly to a permanent data storage and subsequently forwarded for validation processing.