Design And Implementation Of Data Verification System Based On Microservices And Rule Engine

With the rapid development of information technology,enterprises are experiencing explosive growth in application data.As the team gets bigger,it will encounter data quality problems such as data inconsistency and dirty data reading.In the face of multiple sources of data,traditional data management and service solutions are now difficult to meet the needs of large-scale and high-complexity enterprise applications.As a general policy and rule management framework,the rule engine provides new ideas for solving problems.The existing technology is limited by a single application architecture or a specific rule engine framework,resulting in a high degree of system coupling,uncustomizable rule processing,and difficult to withstand multi-tasking and highthroughput application requirements.In this regard,a microservice architecture is adopted to design and implement a data verification system based on the Drools rule engine,which mainly includes functions such as rule customization,data adaptation,and load balancing based on service gateways.The rule customization service is implemented based on the Json To Drl algorithm,which solves the current problem of manually writing rule files.Through dynamic reflection and interface abstraction,the adaptation service is completed.Based on this,the timing scheduling strategy is further designed and implemented.Using the microservice gateway design idea,the service discovery and registration function is completed,and a dynamic load balancing strategy is implemented to complete the data verification task of multiple machines.The system supports multi-tasking to perform tens of millions of data verification at the same time,and has flexible component decoupling and rapid deployment capabilities.System testing and analysis were carried out in a single machine environment and a distributed environment.The results show that the system has a good experience in operational interaction,is compatible with heterogeneous relational databases,and supports timing scheduling functions.Comparing the task running results under stand-alone and microservice deployment,it is verified that the execution efficiency of the system under microservice deployment has been significantly improved.