Job Invalidation System for Data Integrity in Processing
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Solution Overview
Problem
In data-processing environments, errors or incorrect data can propagate through jobs, leading to unreliable output, which is not detected until after execution, causing delays or incorrect reports, and there is a need for a method to identify and invalidate jobs producing invalid data to prevent further errors.
Innovation Solution
A system and method for invalidating jobs in a computing system, including user interfaces and back-end operations to detect anomalous results, trace the root of errors, and selectively invalidate jobs and their descendants, using statistical analysis and dependency information to identify and flag suspect jobs for review and invalidation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If jobs are executed in a data-processing system without validation, then productivity is improved by continuous processing, but reliability deteriorates as errors propagate through the system
Solution Approach 1:
The system performs preliminary validation by checking data quality metrics and anomaly detection before jobs complete execution. This allows the system to identify potentially erroneous output early, preventing error propagation to downstream jobs while maintaining continuous processing of valid jobs.
Solution Approach 2:
The system implements feedback mechanisms where job output quality is monitored and fed back into the scheduling system. When anomalies are detected in job output, this feedback triggers invalidation of the job and its descendants, allowing the system to adapt processing based on real-time data quality information.
2Device complexity
If error detection is performed after job execution, then device complexity is reduced by simple processing, but loss of time increases due to delayed error detection
Solution Approach 1:
The system performs partial validation by checking specific data quality metrics and anomaly indicators rather than comprehensive validation of all job output. This selective approach detects errors earlier than post-execution checking while avoiding the complexity of exhaustive pre-validation of all possible error conditions.
3Reliability
If all descendant jobs are invalidated when an error is detected, then reliability is improved by preventing error propagation, but productivity deteriorates due to reprocessing requirements
Solution Approach 1:
The system segments the job dependency graph to identify and invalidate only the specific subset of descendant jobs affected by erroneous data. Rather than blanket invalidation of all downstream jobs, the system traces data dependencies to precisely identify which jobs require reprocessing, minimizing unnecessary work while maintaining data integrity.
4Reliability
If manual review of jobs is required before invalidation, then reliability is improved by human judgment, but ease of operation deteriorates due to increased operational burden
Solution Approach 1:
The system performs self-service by automatically detecting anomalies in job output using statistical analysis and data quality metrics. This automated detection reduces operator workload by pre-identifying suspicious jobs that require review, allowing operators to focus their judgment on cases where the system has already flagged potential issues rather than manually reviewing all jobs.
Data Source
AI summary
A computing system can provide user interfaces and back-end operations to facilitate review and invalidation of executed jobs. The system can provide an interface that allows the operator to review quality-control information about a completed job. Once the operator identifies a job as invalid, the operator can be presented with further options, such as whether to invalidate only the reviewed job or the job and all its descendants. The operator can also review antecedent jobs to an invalid job (e.g., in order to trace the root of the problem) and can selectively invalidate antecedent jobs.