Parallel Data Transport Job Scheduling for Version Conflict Prevention
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Solution Overview
Problem
Existing technologies face challenges in ensuring the latest version of data is transported between computer systems, leading to potential overwrites or retrieval of outdated data.
Innovation Solution
The implementation of an automated process that instantiates multiple jobs to move data units between computer systems, with a mechanism to determine if an overlap exists between data units before executing each job, ensuring that only the latest version of data is moved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is transported between computer systems using traditional methods, then data movement is achieved, but there is a risk of transporting outdated versions and overwriting newer data
Solution Approach 1:
The system performs preliminary actions by checking for overlapping data units and determining the latest version before initiating data transport. The job scheduling mechanism proactively identifies and prevents potential version conflicts before they occur, ensuring that only the latest data versions are transported between systems.
Solution Approach 2:
The system implements feedback mechanisms through automated job scheduling that monitors data unit overlaps and version status. This feedback loop continuously tracks data movements across multiple jobs, adjusting scheduling decisions based on real-time information about data versions and transport status to prevent outdated data from being transported.
2Productivity
If multiple data transport jobs are executed simultaneously, then productivity is improved, but data version conflicts and overwrites may occur
Solution Approach 1:
The system segments the data transport process into individual jobs, each handling specific data units. By dividing the overall transport task into discrete, manageable job units, the system can schedule and execute multiple jobs in parallel while maintaining control over each job's data version integrity through the overlap checking mechanism.
Solution Approach 2:
The job scheduling system dynamically adjusts the execution of multiple data transport jobs based on real-time conditions. It monitors overlapping data units and automatically coordinates job execution to prevent conflicts, allowing maximum parallelization when safe and sequential execution when needed to maintain version consistency.
3Reliability
If automated job scheduling is implemented to prevent data version conflicts, then data reliability is improved, but system complexity increases
Solution Approach 1:
The automated job scheduling system operates autonomously to manage data transport jobs and prevent version conflicts. It self-manages the complexity of coordinating multiple jobs, checking for overlaps, determining latest versions, and scheduling execution without requiring manual intervention, thereby containing complexity within the automated system while simplifying user interaction.
Data Source
AI summary
Embodiments of the present disclosure include techniques for moving data. In one embodiment, computer systems import and/or export data. The computer systems may be application servers coupled to a content management system accessible over a plurality of tenant frontends. The data movement may use a plurality of jobs executing in parallel to move packages of content between the systems. In one embodiment, before a job is executed, a check is made of other jobs to ensure any overlap does not cause data conflicts.