Parallel Job Coordination for Snapshot Overwrite Conflicts
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Solution Overview
Problem
Conventional systems lack effective coordination of data management jobs such as materializer and bootstrap jobs, leading to conflicts, inefficiencies, and data inconsistencies, particularly in AI-driven platforms that require high-quality datasets.
Innovation Solution
A job coordination system that intelligently coordinates bootstrap and materialization jobs in parallel, allowing preemptive and remedial actions based on user preferences for data freshness or accuracy, ensuring data integrity and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bootstrap jobs and materialization jobs are run in parallel without coordination, then productivity increases, but data integrity deteriorates due to snapshot overwriting conflicts
Solution Approach 1:
The patent introduces a coordination system that acts as an intermediary between bootstrap jobs and materialization jobs. This coordinator manages snapshot naming and job scheduling, preventing direct conflicts while enabling parallel execution. The intermediary resolves the contradiction by orchestrating access to shared resources without sacrificing productivity or data integrity.
Solution Approach 2:
The patent segments the job execution process into coordinated phases with distinct snapshot naming conventions. By dividing the workflow into structured segments (bootstrap phase, materialization phase) with clear boundaries and naming protocols, the system enables parallel execution while maintaining data integrity through organized resource management.
2Reliability
If manual scheduling procedures are used to coordinate jobs, then data integrity is maintained, but productivity decreases due to sequential execution
Solution Approach 1:
The patent implements dynamic job coordination that adapts to real-time system state. Rather than rigid sequential scheduling, the system dynamically determines job execution order and snapshot management based on current conditions, enabling parallel execution when safe and maintaining data integrity through intelligent, flexible coordination.
Solution Approach 2:
The coordination system incorporates feedback mechanisms that monitor job progress and snapshot states. This feedback enables the system to make intelligent decisions about job scheduling and snapshot management, allowing parallel execution while preventing conflicts through real-time awareness of system state and adaptive coordination.
3Loss of energy
If snapshot overwriting is allowed without coordination, then resource efficiency improves, but data accuracy deteriorates due to premature overwriting
Solution Approach 1:
The patent implements preliminary actions through structured snapshot naming conventions and pre-execution checks. Before allowing snapshot creation or overwriting, the system performs preliminary validation to ensure data accuracy is maintained. This preliminary action prevents premature overwriting while enabling efficient resource utilization through coordinated snapshot management.
Data Source
AI summary
Systems and methods for intelligently running jobs in parallel are disclosed. An example method is performed by one or more processors of a job coordination system and includes receiving a transmission over a communications network from a computing device associated with a user of the system, the transmission including a request to perform a bootstrap job on one or more data assets, and the transmission further including a user preference indicating whether the bootstrap job is to be a primary job or a secondary job to a materialization job running in parallel on the one or more data assets, selectively performing one or more preemptive actions based on whether a snapshot for the secondary job would detrimentally overwrite a snapshot for the primary job, and selectively performing one or more remedial actions based on whether the snapshot for the primary job will detrimentally overwrite the snapshot for the secondary job.