Distributed Data Pipeline Job Scheduling and Priority Allocation
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Solution Overview
Problem
Data processing pipelines face challenges in identifying and managing component failures due to complexity, leading to unnoticed failures and difficulty in resource allocation.
Innovation Solution
Implementing a system that includes a scheduling component to manage job queues, a pipeline allocation component to determine resource availability and allocate jobs, and a priority allocation component to adjust job priorities based on resource availability and execution constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data processing pipelines are composed of hundreds or thousands of components to handle complex jobs, then the system can process more diverse and complex data operations, but the complexity of identifying and managing component failures increases significantly
Solution Approach 1:
The patent segments the complex data processing pipeline into distributed processing clusters, each with its own scheduler and resource management. This segmentation allows independent management and failure isolation of individual clusters while maintaining overall system versatility. Each cluster can be scaled and managed separately, reducing the complexity of monitoring and managing thousands of components as a single monolithic system.
2Productivity
If more processing components are added to handle increased job load, then productivity increases, but the difficulty of detecting and measuring component failures increases
Solution Approach 1:
The patent implements feedback mechanisms through schedulers that continuously monitor resource availability, job queue states, and component health in each distributed processing cluster. This feedback enables automatic detection of failures and triggers appropriate responses such as job reassignment or cluster scaling, making failure detection manageable even as system size increases.
Solution Approach 2:
The scheduler acts as an intermediary between the job queue and processing components, abstracting away the complexity of individual component management. It provides a unified interface for monitoring system state and coordinating failure responses across multiple clusters, reducing the difficulty of detecting and managing failures in large-scale systems.
3Productivity
If distributed processing clusters are scaled horizontally to handle overload, then processing capacity increases, but resource allocation complexity increases
Solution Approach 1:
The patent creates universal scheduling components that can manage multiple distributed processing clusters with heterogeneous resources. The scheduler is designed to handle different resource types (CPU, GPU, storage) and job requirements through a unified allocation mechanism, reducing the complexity of managing resources across scaled clusters while maintaining increased processing capacity.
Data Source
AI summary
Techniques are disclosed for executing a data processing pipeline. The techniques may include receiving a job at a data pipeline queue, setting up one or more distributed processing environments, and allocating the job to one of the distributed processing environments. The techniques may further include receiving the allocated job at a job queue within the distributed processing environment, increasing a priority level of the job, and executing the job within the distributed processing environment. The techniques can further include providing a retry pipeline at the data processing pipeline, and re-executing the job at a stage following a failure of at least one of its components. The techniques may decrement the retry budget as the job is re-executed.