Mapping Non-Distributed Data Pipeline Components to Distributed Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions lack a framework for converting legacy components from non-distributed environments to distributed environments, leading to performance issues due to the inability of single machines to handle increasing data and complex computations, making it inefficient to scale technological solutions.
Innovation Solution
A method and system that identifies bottleneck components in a non-distributed data pipeline, converts their data and code formats to be compatible with a distributed environment, installs necessary libraries, and measures performance to optimize memory usage and processing time, using techniques like memory-driven and performance-driven approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a non-distributed environment is used to process data pipeline components, then the system structure is simple and ease of operation is maintained, but productivity decreases and memory usage becomes insufficient when handling increasing amounts of data
Solution Approach 1:
The patent segments the data pipeline into multiple components (data sources, processing components, sinks) that can be independently mapped and converted from non-distributed to distributed environments. This allows selective distribution of only bottleneck components while maintaining simplicity for non-critical parts, resolving the contradiction between productivity improvement and system complexity.
Solution Approach 2:
The patent introduces an environment mapping system as an intermediary that automatically converts non-distributed pipeline components to distributed equivalents. This mediator handles the complexity of distribution architecture, allowing users to maintain simple operation modes while achieving improved productivity through automatic component segmentation and mapping to appropriate distributed infrastructure.
2Reliability
If a single machine processes all data pipeline components, then device complexity is low and ease of operation is maintained, but memory usage becomes insufficient and processing time increases for bottleneck components
Solution Approach 1:
The patent changes the deployment parameter of bottleneck components from single-machine to multi-machine distributed environments. By identifying components with insufficient memory resources or excessive processing times and redistributing them across multiple machines with different resource characteristics, the system achieves improved reliability and performance consistency while utilizing available memory resources effectively.
3Productivity
If bottleneck components are identified and converted to distributed environment, then productivity and memory usage improve, but device complexity and conversion effort increase
Solution Approach 1:
The environment mapping system implements self-service by automatically profiling pipeline components, identifying bottlenecks, and converting them to distributed equivalents without requiring manual intervention. The system autonomously determines which components benefit from distribution, performs the conversion, and validates results, thereby improving productivity while minimizing conversion effort and complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the mapping system continuously monitors pipeline performance, identifies new bottlenecks, and iteratively converts additional components to distributed environments. This feedback-driven approach ensures that productivity improvements are achieved systematically while conversion effort is optimized by focusing only on components that actually benefit from distribution.
Data Source
AI summary
Embodiments of the present invention disclose a method, a computer program product, and a computer system for mapping components of non-distributed environments to distributed environments. A computer receives a data pipeline configured for a non-distributed environment and identifies one or more bottleneck components of the data pipeline. In addition, the computer converts data used in the pipeline to a format compatible with a distributed environment and installs the necessary computing libraries necessary for operating the pipeline within the distributed environment. The computer further converts the code of the pipeline to a code that is compatible with the distributed environment and optimizes components of the pipeline for use in the distributed environment.