Microservice Configuration Planning for Distributed Transaction Trade-Offs
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Solution Overview
Problem
Existing microservice development for distributed transactions is time-consuming due to difficulties in constructing a distributed transaction function that meets required performance, and there is a need for a system that can propose a suitable combination of performance, data store, and architecture tailored to the industry type.
Innovation Solution
A microservice development support system that includes an input unit for receiving industry type, a graph selection unit for selecting a trade-off relationship graph, a combination processing unit for obtaining feasible performance levels, and an output unit for providing a microservice configuration plan with a data store and library function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a specific database, architecture, and distributed transaction management server are introduced to develop microservices for distributed transactions, then the distributed transaction function can be provided, but the development time increases significantly
Solution Approach 1:
The system pre-stores multiple architecture patterns and their associated configurations in a database before the development process. When a developer selects an industry type, the system retrieves pre-configured architecture patterns that match the requirements, eliminating the need for developers to create these configurations from scratch and significantly reducing development time.
Solution Approach 2:
The system allows dynamic selection and adjustment of architecture parameters (such as data store types, library functions, and configuration settings) based on the selected industry type. This parameterization approach enables rapid configuration of distributed transaction systems without requiring custom development for each industry scenario.
2Reliability
If trial and error is performed to improve performance to satisfy required performance, then the required performance can be achieved, but the development time increases
Solution Approach 1:
The system pre-calculates and stores optimal performance configurations for different industry types in the architecture pattern database. When developers select an industry type, they receive pre-optimized performance settings, eliminating the need for time-consuming trial and error performance tuning.
Solution Approach 2:
The system incorporates performance evaluation mechanisms that provide feedback on configuration effectiveness. This feedback loop allows the system to automatically adjust and optimize performance parameters based on measured results, reducing the iterative trial and error process.
3Adaptability or versatility
If combination optimization is performed to find suitable architecture combinations, then the optimal architecture can be obtained, but the development time increases
Solution Approach 1:
The system pre-computes and stores optimized architecture combinations for various industry types in the database. When a developer selects an industry type, the system retrieves the pre-optimized architecture combination, eliminating the need for time-consuming combination optimization and examination processes.
Solution Approach 2:
The system creates a universal architecture pattern library that can be applied across multiple industry types. The pre-stored architecture patterns serve multiple purposes and can be adapted to different industry scenarios, reducing the need for custom architecture development for each case.
Data Source
AI summary
An object of the present invention is to reduce load of microservice development. A microservice development support system includes an input unit that receives an industry type for providing a microservice, a graph selection unit that selects a trade-off relationship graph based on the received industry type, a combination processing unit that obtains a feasibility of a microservice by a combination of levels of required performance items included in the selected trade-off relationship graph, and an output unit that outputs a data store and a library function corresponding to the combination of the levels of the required performance items as a microservice configuration plan when the obtained feasibility satisfies a predetermined criterion.