Stream Processing Pipeline Stage Deployment via Directed Graph Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In stream processing infrastructures, data flow from downstream to upstream stages is difficult to manage efficiently, leading to increased data re-input at the first stage, which causes performance deterioration due to increased network load, parsing load, and reduced fault tolerance.
Innovation Solution
A data stream processing method that generates a directed graph representing processes as nodes and data relationships as edges, calculates node degrees based on edge weights, and deploys processes at pipeline stages to minimize data re-input, thereby optimizing data flow and reducing performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data flows from downstream to upstream stages in a stream processing pipeline, then data can be re-input at the first stage, but this increases network load and parsing load causing performance deterioration
Solution Approach 1:
The patent segments the stream processing pipeline into multiple stages with designated data input/output points. By dividing the pipeline structure, data can be efficiently routed at specific stages rather than requiring complete re-input at the first stage, reducing unnecessary network and parsing loads while maintaining flow flexibility.
Solution Approach 2:
The patent introduces an intermediary mechanism (the directed graph-based deployment system) that mediates data flow between stages. This intermediary optimizes data routing decisions, allowing data to be reused across stages without full re-processing, thereby reducing the harmful effects of network and parsing loads while preserving adaptability.
2Adaptability or versatility
If data is re-input at the first stage from downstream stages, then data can be reprocessed, but fault tolerance is reduced
Solution Approach 1:
By segmenting the pipeline into distinct stages with controlled data input/output points, the system allows selective reprocessing at appropriate stages rather than forcing all data through the first stage. This segmentation maintains fault tolerance by preventing unnecessary data flow through potential failure points while preserving reprocessing capability where needed.
3Ease of operation
If processes are manually deployed at pipeline stages, then service designers can control data flow, but this increases device complexity and manual intervention requirements
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically determines optimal process deployment at pipeline stages using directed graph analysis. The system calculates node degrees and weights to autonomously optimize data flow without requiring manual service designer intervention, thereby reducing deployment complexity while maintaining operational effectiveness.
Solution Approach 2:
The patent changes the deployment parameter from manual configuration to automated calculation based on graph-theoretic metrics (node degree, edge weights). This parameter transformation enables the system to automatically adapt process placement to optimize data flow, reducing both manual intervention and overall system complexity.
Data Source
AI summary
A computer-implemented data stream processing method includes generating, calculating, and deploying. The generating includes generating a directed graph in which processes in a stream processing infrastructure are represented by nodes and data input/output relationships between the nodes are represented by edges. The calculating includes calculating a degree of each of the nodes based on a weight of each of the edges. The deploying includes deploying, based on the calculated degree of each of the nodes, the processes represented by the nodes at stages of a pipeline in the stream processing infrastructure.