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12.6 Implementing business rules with a rule engine
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For complex business rules, a rule engine is often used to handle rule evaluation. Using a rule engine separates the rule evaluation concern from the core business logic. However, all core modules that need business rule support still have to embed code to populate the engine with business objects and invoke the rule evaluation. In this section, we examine a solution that allows the core modules to be completely oblivious to the business rule implementation; they no longer have to embed any business rule code. This creates an isolation layer between the rule engine and the business logic, allowing independent evolution of the two. At a high level, the rule engine based solution is similar to our earlier solution in section 12.5, except that here we employ a rule engine to carry out the rule evaluation.
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12.6.1 An overview of the rule engine Rule engines consist of two parts:
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A mechanism for expressing the rules and facts A fast implementation for evaluating the rules and taking appropriate action
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Many of the rule engines are implementations of a well-known Rete algorithm.1 Others use a backward-reasoning algorithm. Using these algorithms, the engines can evaluate the rules several times faster than the procedural evaluation of equivalent if-then statements. Rules relate the facts pertaining to the current situation to corresponding actions. The set of facts is also called a knowledge base, or working memory. When it is run, the engine evaluates the rules based on the facts, and then triggers the actions associated with the rules that evaluate to true. The languages that the engines use to express the rules and facts vary widely from LISP-like syntax to pseudo-English. They all, however, express the if-then rules in some engine-specific way. New languages on the horizon, such as RuleML, offer the possibility of a standard way to express rules independently of the rule engine. Many rule engines are available, ranging from commercial offerings to free or open source projects. A few of them are implemented in Java. With these engines, Java objects are loaded into an engine s working memory as facts, and the engine can create facts based on those objects as well as invoke actions expressed in Java on those objects. 12.6.2 Using a rule engine Implementing business rules using a rule engine involves the following steps:
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Expressing the rules Usually this step involves writing a separate file with rules written in a language understood by the engine. Some engines may also support programmatic construction of the rules. Expressing the facts These facts pertain to the state of the business objects under consideration. For example, in a banking application, facts would include accounts in a current transaction and the withdrawal amount. Evaluating the rules Once the engine is loaded with rules and facts, firing the engine causes the actual evaluation, which ultimately triggers the
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See http://herzberg.ca.sandia.gov/jess/docs/61/rete.html for a simple explanation of the Rete algorithm.
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Figure 12.2 The rule engine collaborates with the core business logic through a shared knowledge base known as working memory. Business objects pertaining to current facts are put into the rule engine s working memory before the engine evaluates the rules.
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actions associated with the rules that evaluated to true. During this evaluation process, the engine may ascertain other derived facts based on these basic facts and the rules, which help the engine decide what actions to take. For instance, when examining an account, the engine may determine that it would fall below the minimum balance if the requested amount of money were withdrawn; this potential violation of the minimum balance rule may lead to aborting the transaction. Figure 12.2 shows the collaboration between business objects and the rule engine. While this figure shows the structural view of a system using a rule engine, figure 12.3 shows the behavioral view of these steps in a sequence diagram. At the beginning of this section, we discussed the three steps involved in using a rule engine. In figure 12.3, the call to initializeRules() corresponds to the first step of expressing the rules. This step is needed only once to initialize the rule engine before it can be used. The next two steps are executed each time an operation is called by the core business logic execution. In step 2, expressing the facts, we store the facts into the rule engine s working memory (also referred
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