Sub-flow Operator Dilation in Data Flow Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data integration applications face challenges in operating data processing engines in a truly integrated fashion due to incompatible runtime systems, different development paradigms, and the need for manual coding to bridge gaps between engines, which complicates optimizations and transaction processing across engine domains.
Innovation Solution
A computer-implemented method and system that dilates sub-flow operators in a data flow by traversing the data flow to locate sub-flow operators, retrieving and injecting them, allowing for the creation and reuse of sub-flows as iconic representations, enabling users to model logical data transformations and bridge disparate systems without requiring expertise in each engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate systems and specialized code are used for each runtime system, then compatibility with different engines is achieved, but device complexity and development difficulty increase
Solution Approach 1:
The patent implements a universal data flow representation that can execute across multiple runtime systems (SQL engines, specialized engines, etc.) without requiring separate systems. The data flow with operators and parameters serves as a multi-functional framework that adapts to different engine types through standardized interfaces, eliminating the need for specialized code for each engine pair.
Solution Approach 2:
The patent introduces an intermediary compilation and deployment framework that translates high-level data flow definitions into engine-specific executions. This mediator layer handles the complexity of interfacing with different runtime systems, allowing developers to work with a unified data flow model while the intermediary manages the diversity of underlying engines.
2Adaptability or versatility
If manual coding is used to bridge gaps between engines, then data can be passed between systems, but productivity and development speed decrease
Solution Approach 1:
The patent enables reuse of data flow definitions across multiple runtime systems through copying and deployment of the same high-level data flow to different targets. Instead of manually coding integrations for each engine pair, the unified data flow can be copied and deployed to various combinations of sources, transformations, and targets, dramatically improving productivity.
Solution Approach 2:
The patent performs preliminary compilation of the data flow into an executable form that can be deployed to multiple runtime systems in advance. This pre-compilation step creates a portable representation that can be quickly instantiated across different engines without requiring manual integration coding at deployment time.
3Productivity
If optimizations are attempted across runtime engine boundaries, then processing efficiency may improve, but complexity of managing different development paradigms increases
Solution Approach 1:
The patent segments the data integration system into distinct components: a high-level data flow definition layer, a compilation layer, and runtime execution layers. This segmentation allows optimizations to be applied at appropriate levels without managing the entire system's complexity. Each runtime engine operates independently in its own segment while adhering to standardized interfaces.
Solution Approach 2:
The patent uses parameter-based configuration to adapt the unified data flow to different runtime systems. By changing parameters and settings in the data flow definition, the same logical flow can be optimized for different engine types without restructuring the entire system or managing complex paradigm differences.
4Reliability
If specialized engines are used for specific tasks, then processing quality improves, but ease of operation and engine selection difficulty increase
Solution Approach 1:
The patent creates a universal data flow interface that works with multiple specialized engines through standardized operations. Developers don't need to know which specialized engine to use for each task because the unified data flow framework handles engine selection and compatibility, allowing specialized engines to be used for quality processing while maintaining ease of operation.
Data Source
AI summary
A method for dilating a sub-flow operator in a data flow is provided. The data flow is traversed to locate sub-flow operators. The data flow bound to a sub-flow operator is retrieved. The sub-flow operators are then injected into the data flow.