Drools rule engine practice

Part 1 Rules Engine

Rule engine: The full name is business rule management system, or BRMS in English. The main idea of ​​the rule engine is to separate the business decision part of the application and use predefined semantic modules to write business decisions (business rules), which are configured and managed by users or developers when needed. It should be noted that the rule engine is not a specific technical framework, but refers to a type of system, namely the business rule management system.

Java open source rule engines include: Drools, Easy Rules, Mandarax, IBM ILOG . The most widely used and open source one is Drools .

Main application scenarios: For some systems with complex business rules and frequently changing business rules, it is more suitable to use rule engines, as follows:

Risk control decision system-------risk loan, risk assessment

Anti-fraud projects ----- bank loans, credit verification, anti-money laundering

Real-time anti-fraud platform - mobile payment, credit card consumption

Drools rule engine composition

The drools rule engine consists of the following parts:

• Working Memory
• Rules
• Inference Engine

◆Pattern Match (matcher) matches a specific rule, which is completed by it

◆Agenda

◆Execution Engine

Drools rule engine concepts

Working Memory: Working memory. The drools rule engine will obtain data from the Working Memory and perform pattern matching with the rules defined in the rule file, so the application we develop only needs to insert our data into the Working Memory. For example, in this case, we call kieSession.insert(order) to insert the order object into the working memory.

Fact: In the drools rule application, the object after inserting a common JavaBean into the Working Memory is a Fact object. For example, the Order object in this case belongs to a Fact object. The Fact object is the bridge or channel for data interaction between our application and the rule engine.

Rules: rule base. All the rules defined in the rule file will be loaded into the rule base.

Pattern Matcher: Matcher that performs pattern matching on all rules in Rule Base and Fact objects in Working Memory. Successfully matched rules will be activated and placed in Agenda.

Agenda: Agenda is used to store rules that are activated after pattern matching by the matcher.

Drools basic syntax

Rules file composition

A rule usually consists of three parts: attribute, condition (LHS) and result (RHS)

Syntax structure:

 rule "ruleName" attributes when LHS then RHS end

rule: keyword, indicating the start of a rule. The parameter is the unique name of the rule.

attributes: rule attributes, which are the parameters between rule and when and are optional.

when: Keyword followed by the conditional part of the rule.

LHS(Left Hand Side): is the generic name for the conditional part of a rule. It consists of zero or more conditional elements. If LHS is empty, it will be treated as a conditional element that is always true.

then: keyword followed by the result part of the rule.

RHS (Right Hand Side): is the general name for the consequence or action part of a rule.

end: keyword, indicating the end of a rule.

Part 2: Rule comparison operators

Part 3 Drools rule attributes

The properties provided in Drools are as follows (partial properties)

Part 4 Project Application

In order to achieve real-time monitoring of the video usage quality of set-top box users, the provincial set-top box soft probe platform uses real-time big data to perform quasi-real-time indicator statistics, and pushes the indicator results to Kafka. The platform's newly added alarm engine consumes Kafka data in real time and realizes real-time alarm judgment through pre-customized expert rules. The final alarm result is notified and fed back to the alarm platform in real time, realizing the life cycle from alarm to alarm recovery.

Part 5 Rule Examples

 rule "播放成功率告警" no-loop true when $fact:Fact() eval($fact.getPlay() > 0.0) eval($fact.getPlay() < MapUtils.getDoubleValue($fact, "playThreshold", 0.96)) then RuleResult fr = new RuleResult("播放成功率告警"); fr.setRuleCode(900001); fr.setActualTime(MapUtils.getString($fact, "actualTime")); fr.setAlarmTime(RuleUtil.getAlarmTime(MapUtils.getString($fact, "actualTime"), MapUtils.getString($fact, "900001"))); //说明当前是要告警的fr.setAlarmStatus("1"); fr.setAlarmTitle("指标播放成功率异常"); fr.setLocateInfo("指标播放成功率异常"); fr.setAlarmText(RuleUtil.playAlarmText($fact, MapUtils.getDoubleValue($fact, "playThreshold", 0.96))); insert(fr); end rule "播放成功率告警结束" no-loop true when $fact:Fact() eval($fact.getPlay() >= MapUtils.getDoubleValue($fact, "playThreshold", 0.96)) eval(MapUtils.getLongValue($fact, "900001") > 0) eval(RuleUtil.checkAlarmTime($fact.getActualTime(), MapUtils.getLongValue($fact, "900001_time"), MapUtils.getLongValue($fact, "reThreshold", 900000))) then RuleResult fr = new RuleResult("播放成功率告警恢复"); fr.setRuleCode(900001); fr.setActualTime(MapUtils.getString($fact, "actualTime")); fr.setAlarmTime(MapUtils.getString($fact, "900001")); fr.setAlarmStatus("0"); fr.setMsg("告警恢复"); fr.setAlarmTitle("指标播放成功率异常"); fr.setLocateInfo("指标播放成功率异常"); logger.info("播放告警恢复了{}", $fact); insert(fr);