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This article introduces the relevant knowledge of "what are the advantages of the risk control engine project". In the course of the operation of the actual case, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Project characteristics

Real-time risk control, response within 100ms can be achieved in special situations.

Visual rule editor, rich operators, flexible calculation rules

Support Chinese, make it easier to use

Custom rule engine, more flexible, supporting complex and changeable scenarios

Plug-in design for quick access to other data capability platforms

NoSQL, easy to scale, high performance

Simple configuration, out of the box!

Technology selection

Springboot: I was born in java, so I choose Springboot as a matter of course, which is convenient for myself and other Java users to expand.

Mybatis + tkMapper: persistence layer framework. TkMapper provides mapper universal template function to reduce the generation of repetitive code.

Mysql: the relational database in this project is mainly used to store meta-information of risk model.

MongoDB: used to store event JSON, provide basic statistical calculations (e.g. max, min, sum, avg,), complex statistical concepts (sd,variance, etc … Is calculated in memory

ES: provides data query and rule hit report services.

Redis: provides caching support, and Engine uses publish / subscribe feature to listen for updates of relevant configurations on the management side.

Groovy: rule engine, risk control rules are finally generated groovy scripts, real-time editing, dynamic generation, immediate effect.

Swagger: Rest API Management

Architecture diagram

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