Intelligent Pipeline Manager for Automated Workflow Failure Recovery
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Solution Overview
Problem
Creating and executing workflows of analytic jobs for large datasets is time-consuming due to potential faults and delays in detecting job failures, requiring manual intervention and rerunning of portions of the workflow, which can lead to significant delays of days.
Innovation Solution
An intelligent pipeline manager system that monitors and manages workflow jobs, detects failures, and automatically recovers or aborts workflows by determining substitute jobs based on previous job results and metadata, using an orchestration engine and intelligent decision-making system to dynamically adjust and correct the workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and intervention is used for workflow execution, then the system can detect and respond to job failures, but the detection delay and manual response time cause significant workflow delays
Solution Approach 1:
The system implements self-service through automated failure detection mechanisms that continuously monitor job execution status and automatically trigger recovery procedures without human intervention. The workflow manager detects failures by checking job status indicators and autonomously initiates rerunning of failed jobs, eliminating the need for manual monitoring and response.
Solution Approach 2:
The system employs feedback mechanisms where the workflow manager continuously receives status information from executing jobs and uses this feedback to determine whether failures have occurred. Based on this feedback loop, the system automatically adjusts workflow execution by rerunning failed jobs, creating a closed-loop control system that responds dynamically to execution conditions.
2Adaptability or versatility
If workflows are manually created and adjusted for large datasets, then the workflow can be customized to handle specific data processing needs, but the creation process becomes time-consuming and requires expert intervention
Solution Approach 1:
The system enables non-expert users to create customized workflows through self-service interfaces that guide users through workflow definition without requiring deep technical knowledge. The workflow manager automatically handles the complexity of job sequencing and dependency management, allowing users to focus on specifying their data processing requirements rather than technical implementation details.
Solution Approach 2:
The system performs preliminary actions by pre-defining common workflow patterns and job templates that can be selected and customized for specific data processing tasks. This allows users to start with pre-configured workflows and modify them as needed, rather than building workflows from scratch, significantly reducing creation time while maintaining adaptability.
3Reliability
If analytic jobs are rerun manually after failure, then the workflow can recover from errors, but the manual rerunning process delays workflow completion by days
Solution Approach 1:
The workflow manager implements self-service recovery by automatically detecting failed jobs and rerunning them without human intervention. When a job failure is detected through status monitoring, the system autonomously triggers the rerun process, maintaining workflow continuity and eliminating the need for manual recovery actions.
Solution Approach 2:
The system ensures continuity of useful action by immediately rerunning failed jobs rather than pausing workflow execution. This maintains the continuous flow of data processing through the workflow pipeline, preventing interruptions that would otherwise delay overall workflow completion and preserving productivity.
Data Source
AI summary
A system includes a processor and a non-transitory computer-readable medium. The non-transitory computer-readable medium comprises instructions executable by the processor to cause the system to perform a method. The method comprises receiving a first job to execute and executing the first job. A plurality of data associated with the first job is determined. The plurality of data comprises data associated with (i) a second job executed immediately prior to the first job, (ii) a third job executed immediately after the first job, (iii) a determination of whether the first job failed or executed successfully and (iv) a type of data associated with the first job. The determined plurality of data is stored.