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## Chapter 2. User Guide

## 2.1. The Basics

### 2.1.1. Stateless Knowledge Session

So where do we get started? There are so many use cases and so much functionality in a rule engine such as Drools that it becomes beguiling. Have no fear my intrepid adventurer, the complexity is layered and you can ease yourself into with simple use cases.

Stateless session, not utilising inference, forms the simplest use case. A stateless session can be called like a function passing it some data and then receiving some results back. Some common use cases for stateless sessions are, but not limited to:

- Validation

  - Is this person eligible for a mortgage?

- Calculation

  - Compute a mortgage premium.

- Routing and Filtering

  - Filter incoming messages, such as emails, into folders.

  - Send incoming messages to a destination.

So let's start with a very simple example using a driving license application.

    public class Applicant {

        private String name;

        private int age;

        private boolean valid;

        // getter and setter methods here

    }

Now that we have our data model we can write our first rule. We assume that the application uses rules to reject invalid applications. As this is a simple validation use case we will add a single rule to disqualify any applicant younger than 18.

    package com.company.license

    rule "Is of valid age"
    when
        $a : Applicant( age < 18 )
    then
        $a.setValid( false );
    end

To make the engine aware of data, so it can be processed against the rules, we have to insert the data, much like with a database. When the Applicant instance is inserted into the engine it is evaluated against the constraints of the rules, in this case just two constraints for one rule. We say two because the type Applicant is the first object type constraint, and `age < 18` is the second field constraint. An object type constraint plus its zero or more field constraints is referred to as a pattern. When an inserted instance satisfies both the object type constraint and all the field constraints, it is said to be matched. The `$a` is a binding variable which permits us to reference the matched object in the consequence. There its properties can be updated. The dollar character ('$') is optional, but it helps to differentiate variable names from field names. The process of matching patterns against the inserted data is, not surprisingly, often referred to as pattern matching.

Let's assume that the rules are in the same folder as the classes, so we can use the classpath resource loader to build our first `KnowledgeBase`. A Knowledge Base is what we call our collection of compiled definitions, such as rules and processes, which are compiled using the `KnowledgeBuilder`. Both the KnowledgeBuilder and KnowledgeBase can be created from the factories KnowledgeBuilderFactory and KnowledgeBaseFactory.

    KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    kbuilder.add( ResourceFactory.newClassPathResource( "licenseApplication.drl",

                  getClass() ), ResourceType.DRL );

    if ( kbuilder.hasErrors() ) {

        System.err.println( kbuilder.getErrors().toString() );

    }

    KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();

    kbase.addKnowledgePackages( kbuilder.getKnowledgePackages() );

The above code snippet looks on the classpath for the `licenseApplication.drl` file, using the method `newClassPathResource()`. The resource type is DRL, short for "Drools Rule Language". Once the DRL file has been added we can check the Knowledge Builder object for any errors. If there are no errors, we can add the resulting packages to our Knowledge Base. Now we are ready to build our session and execute against some data:

    StatelessKnowledgeSession ksession = kbase.newStatelessKnowledgeSession();

    Applicant applicant = new Applicant( "Mr John Smith", 16 );

    assertTrue( applicant.isValid() );

    ksession.execute( applicant );

    assertFalse( applicant.isValid() );

The preceding code executes the data against the rules. Since the applicant is under the age of 18, the application is marked as invalid.

So far we've only used a single instance, but what if we want to use more than one? We can execute against any object implementing Iterable, such as a collection. Let's add another class called `Application`, which has the date of the application, and we'll also move the boolean valid field to the `Application` class.

    public class Applicant {

        private String name;

        private int age;

        // getter and setter methods here

    }

    public class Application {

        private Date dateApplied;

        private boolean valid;

        // getter and setter methods here

    }

We can also add another rule to validate that the application was made within a period of time.

    package com.company.license

    rule "Is of valid age"
    when
        Applicant( age < 18 )
        $a : Application()
    then
        $a.setValid( false );
    end

    rule "Application was made this year"
    when
        $a : Application( dateApplied > "01-jan-2009" )
    then
        $a.setValid( false );
    end

Unfortunately a Java array does not implement the `Iterable` interface, so we have to use the JDK converter method `Arrays.asList(...)`. The code shown below executes against an iterable list, where all collection elements are inserted before any matched rules are fired.

    StatelessKnowledgeSession ksession = kbase.newStatelessKnowledgeSession();

    Applicant applicant = new Applicant( "Mr John Smith", 16 );

    Application application = new Application();

    assertTrue( application.isValid() );

    ksession.execute( Arrays.asList( new Object[] { application, applicant } ) );

    assertFalse( application.isValid() );

The two execute methods `execute(Object object)` and `execute(Iterable objects)` are actually convenience methods for the interface `BatchExecutor`'s method `execute(Command command)`.

A `CommandFactory` is used to create commands, so that the following is equivalent to `execute(Iterable it)`:

    ksession.execute( CommandFactory.newInsertIterable( new Object[] { application, applicant } ) );

Batch Executor and Command Factory are particularly useful when working with multiple Commands and with output identifiers for obtaining results.

    List<Command> cmds = new ArrayList<Command>();

    cmds.add( CommandFactory.newInsert( new Person( "Mr John Smith" ), "mrSmith" );

    cmds.add( CommandFactory.newInsert( new Person( "Mr John Doe" ), "mrDoe" );

    BatchExecutionResults results = ksession.execute( CommandFactory.newBatchExecution( cmds ) );

    assertEquals( new Person( "Mr John Smith" ), results.getValue( "mrSmith" ) );

`CommandFactory` supports many other Commands that can be used in the `BatchExecutor` like `StartProcess`, `Query`, and `SetGlobal`.

### 2.1.2. Stateful Knowledge Session

Stateful Sessions are longer lived and allow iterative changes over time. Some common use cases for Stateful Sessions are, but not limited to:

- Monitoring

  - Stock market monitoring and analysis for semi-automatic buying.

- Diagnostics

  - Fault finding, medical diagnostics

- Logistics

  - Parcel tracking and delivery provisioning

- Compliance

  - Validation of legality for market trades.

In contrast to a Stateless Session, the `dispose()` method must be called afterwards to ensure there are no memory leaks, as the Knowledge Base contains references to Stateful Knowledge Sessions when they are created. `StatefulKnowledgeSession` also supports the `BatchExecutor` interface, like `StatelessKnowledgeSession`, the only difference being that the `FireAllRules` command is not automatically called at the end for a Stateful Session.

We illustrate the monitoring use case with an example for raising a fire alarm. Using just four classes, we represent rooms in a house, each of which has one sprinkler. If a fire starts in a room, we represent that with a single `Fire` instance.

    public class Room {

        private String name

        // getter and setter methods here

    }

    public class Sprinkler {

        private Room room;

        private boolean on;

        // getter and setter methods here

    }

    public class Fire {

        private Room room;

        // getter and setter methods here

    }

    public class Alarm {

    }

In the previous section on Stateless Sessions the concepts of inserting and matching against data were introduced. That example assumed that only a single instance of each object type was ever inserted and thus only used literal constraints. However, a house has many rooms, so rules must express relationships between objects, such as a sprinkler being in a certain room. This is best done by using a binding variable as a constraint in a pattern. This "join" process results in what is called cross products, which are covered in the next section.

When a fire occurs an instance of the `Fire` class is created, for that room, and inserted into the session. The rule uses a binding on the `room` field of the `Fire` object to constrain matching to the sprinkler for that room, which is currently off. When this rule fires and the consequence is executed the sprinkler is turned on.

    rule "When there is a fire turn on the sprinkler"
    when
        Fire($room : room)
        $sprinkler : Sprinkler( room == $room, on == false )
    then
        modify( $sprinkler ) { setOn( true ) };
        System.out.println( "Turn on the sprinkler for room " + $room.getName() );
    end

Whereas the Stateless Session uses standard Java syntax to modify a field, in the above rule we use the `modify` statement, which acts as a sort of "with" statement. It may contain a series of comma separated Java expressions, i.e., calls to setters of the object selected by the `modify` statement's control expression. This modifies the data, and makes the engine aware of those changes so it can reason over them once more. This process is called inference, and it's essential for the working of a Stateful Session. Stateless Sessions typically do not use inference, so the engine does not need to be aware of changes to data. Inference can also be turned off explicitly by using the sequential mode.

So far we have rules that tell us when matching data exists, but what about when it does not exist? How do we determine that a fire has been extinguished, i.e., that there isn't a `Fire` object any more? Previously the constraints have been sentences according to Propositional Logic, where the engine is constraining against individual instances. Drools also has support for First Order Logic that allows you to look at sets of data. A pattern under the keyword `not` matches when something does not exist. The rule given below turns the sprinkler off as soon as the fire in that room has disappeared.

    rule "When the fire is gone turn off the sprinkler"
    when
        $room : Room( )
        $sprinkler : Sprinkler( room == $room, on == true )
        not Fire( room == $room )
    then
        modify( $sprinkler ) { setOn( false ) };
        System.out.println( "Turn off the sprinkler for room " + $room.getName() );
    end

While there is one sprinkler per room, there is just a single alarm for the building. An `Alarm` object is created when a fire occurs, but only one `Alarm` is needed for the entire building, no matter how many fires occur. Previously `not` was introduced to match the absence of a fact; now we use its complement `exists` which matches for one or more instances of some category.

    rule "Raise the alarm when we have one or more fires"
    when
        exists Fire()
    then
        insert( new Alarm() );
        System.out.println( "Raise the alarm" );
    end

Likewise, when there are no fires we want to remove the alarm, so the `not` keyword can be used again.

    rule "Cancel the alarm when all the fires have gone"
    when
        not Fire()
        $alarm : Alarm()
    then
        retract( $alarm );
        System.out.println( "Cancel the alarm" );
    end

Finally there is a general health status message that is printed when the application first starts and after the alarm is removed and all sprinklers have been turned off.

    rule "Status output when things are ok"
    when
        not Alarm()
        not Sprinkler( on == true )
    then
        System.out.println( "Everything is ok" );
    end

The above rules should be placed in a single DRL file and saved to some directory on the classpath and using the file name `fireAlarm.drl`, as in the Stateless Session example. We can then build a Knowledge Base, as before, just using the new name `fireAlarm.drl`. The difference is that this time we create a Stateful Session from the Knowledge Base, whereas before we created a Stateless Session.

    KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    kbuilder.add( ResourceFactory.newClassPathResource( "fireAlarm.drl", getClass() ),

                  ResourceType.DRL );

    if ( kbuilder.hasErrors() ) {

        System.err.println( kbuilder.getErrors().toString() );

    }

    kbase.addKnowledgePackages( kbuilder.getKnowledgePackages() );

    StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession();

With the session created it is now possible to iteratively work with it over time. Four `Room` objects are created and inserted, as well as one `Sprinkler` object for each room. At this point the engine has done all of its matching, but no rules have fired yet. Calling `ksession.fireAllRules()` allows the matched rules to fire, but without a fire that will just produce the health message.

    String[] names = new String[]{"kitchen", "bedroom", "office", "livingroom"};

    Map<String,Room> name2room = new HashMap<String,Room>();

    for( String name: names ){

        Room room = new Room( name );

        name2room.put( name, room );

        ksession.insert( room );

        Sprinkler sprinkler = new Sprinkler( room );

        ksession.insert( sprinkler );

    }

    ksession.fireAllRules();

    > Everything is ok

We now create two fires and insert them; this time a reference is kept for the returned `FactHandle`. A Fact Handle is an internal engine reference to the inserted instance and allows instances to be retracted or modified at a later point in time. With the fires now in the engine, once `fireAllRules()` is called, the alarm is raised and the respective sprinklers are turned on.

    Fire kitchenFire = new Fire( name2room.get( "kitchen" ) );

    Fire officeFire = new Fire( name2room.get( "office" ) );

    FactHandle kitchenFireHandle = ksession.insert( kitchenFire );

    FactHandle officeFireHandle = ksession.insert( officeFire );

    ksession.fireAllRules();

    > Raise the alarm
    > Turn on the sprinkler for room kitchen
    > Turn on the sprinkler for room office

After a while the fires will be put out and the `Fire` instances are retracted. This results in the sprinklers being turned off, the alarm being cancelled, and eventually the health message is printed again.

    ksession.retract( kitchenFireHandle );

    ksession.retract( officeFireHandle );

    ksession.fireAllRules();

    > Cancel the alarm
    > Turn off the sprinkler for room office
    > Turn off the sprinkler for room kitchen
    > Everything is ok

Everyone still with me? That wasn't so hard and already I'm hoping you can start to see the value and power of a declarative rule system.

### 2.1.3. Methods versus Rules

People often confuse methods and rules, and new rule users regular ask, "How do I call a rule?" After the last section, you are now feeling like a rule expert and the answer to that is obvious, but let's summarize the differences nonetheless.

    public void helloWorld(Person person) {

        if ( person.getName().equals( "Chuck" ) ) {

            System.out.println( "Hello Chuck" );

        }

    }

- Methods are called directly.

- Specific instances are passed.

- One call results in a single execution.

    rule "Hello World"
        when
            Person( name == "Chuck" )
        then
            System.out.println( "Hello Chuck" );
            end

- Rules execute by matching against any data as long it is inserted into the engine.

- Rules can never be called directly.

- Specific instances cannot be passed to a rule.

- Depending on the matches, a rule may fire once or several times, or not at all.

### 2.1.4. Cross Products

Earlier the term "cross product" was mentioned, which is the result of a join. Imagine for a moment that the data from the fire alarm example were used in combination with the following rule where there ar no field constraints:

    rule
    when
        $room : Room()
        $sprinkler : Sprinkler()
    then
        System.out.println( "room:" + $room.getName() +
                            " sprinkler:" + $sprinkler.getRoom().getName() );
    end

In SQL terms this would be like doing `select * from Room, Sprinkler` and every row in the Room table would be joined with every row in the Sprinkler table resulting in the following output:

    room:office sprinkler:office
    room:office sprinkler:kitchen
    room:office sprinkler:livingroom
    room:office sprinkler:bedroom
    room:kitchen sprinkler:office
    room:kitchen sprinkler:kitchen
    room:kitchen sprinkler:livingroom
    room:kitchen sprinkler:bedroom
    room:livingroom sprinkler:office
    room:livingroom sprinkler:kitchen
    room:livingroom sprinkler:livingroom
    room:livingroom sprinkler:bedroom
    room:bedroom sprinkler:office
    room:bedroom sprinkler:kitchen
    room:bedroom sprinkler:livingroom
    room:bedroom sprinkler:bedroom

These cross products can obviously become huge, and they may very well contain spurious data. The size of cross products is often the source of performance problems for new rule authors. From this it can be seen that it's always desirable to constrain the cross products, which is done with the variable constraint.

    rule
    when
        $room : Room()
        $sprinkler : Sprinkler( room == $room )
    then
        System.out.println( "room:" + $room.getName() +
                            " sprinkler:" + $sprinkler.getRoom().getName() );
    end

This results in just four rows of data, with the correct Sprinkler for each Room. In SQL (actually HQL) the corresponding query would be `select * from Room, Sprinkler where Room == Sprinkler.room`.

    room:office sprinkler:office
    room:kitchen sprinkler:kitchen
    room:livingroom sprinkler:livingroom
    room:bedroom sprinkler:bedroom

## 2.2. Inference

### 2.2.1. Bus Pass Example

Inference has a bad name these days, as something not relevant to business use cases and just too complicated to be useful. It is true that contrived and complicated examples occur with inference, but that should not detract from the fact that simple and useful ones exist too. But more than this, correct use of inference can crate more agile and less error prone businesses with easier to maintain software.

So what is inference? Something is inferred when we gain knowledge of something from using previous knowledge. For example, given a Person fact with an age field and a rule that provides age policy control, we can infer whether a Person is an adult or a child and act on this.

    rule "Infer Adult"
    when
      $p : Person( age >= 18 )
    then
      insert( new IsAdult( $p ) )
    end

due to the preceding rule, every Person who is 18 or over will have an instance of IsAdult inserted for them. This fact is special in that it is known as a relation. We can use this inferred relation in any rule:

    $p : Person()
    IsAdult( person == $p )

So now we know what inference is, and have a basic example, how does this facilitate good rule design and maintenance?

Let's take a government department that are responsible for issuing ID cards when children become adults, henceforth referred to as ID department. They might have a decision table that includes logic like this, which says when an adult living in London is 18 or over, issue the card:

However the ID department does not set the policy on who an adult is. That's done at a central government level. If the central government were to change that age to 21, this would initiate a change management process. Someone would have to liaise with the ID department and make sure their systems are updated, in time for the law going live.

This change management process and communication between departments is not ideal for an agile environment, and change becomes costly and error prone. Also the card department is managing more information than it needs to be aware of with its "monolithic" approach to rules management which is "leaking" information better placed elsewhere. By this I mean that it doesn't care what explicit "age >= 18" information determines whether someone is an adult, only that they are an adult.

In contrast to this, let's pursue an approach where we split (de-couple) the authoring responsibilities, so that both the central government and the ID department maintain their own rules.

It's the central government's job to determine who is an adult. If they change the law they just update their central repository with the new rules, which others use:

The IsAdult fact, as discussed previously, is inferred from the policy rules. It encapsulates the seemingly arbitrary piece of logic "age >= 18" and provides semantic abstractions for its meaning. Now if anyone uses the above rules, they no longer need to be aware of explicit information that determines whether someone is an adult or not. They can just use the inferred fact:

While the example is very minimal and trivial it illustrates some important points. We started with a monolithic and leaky approach to our knowledge engineering. We created a single decision table that had all possible information in it and that leaks information from central government that the ID department did not care about and did not want to manage.

We first de-coupled the knowledge process so each department was responsible for only what it needed to know. We then encapsulated this leaky knowledge using an inferred fact IsAdult. The use of the term IsAdult also gave a semantic abstraction to the previously arbitrary logic "age >= 18".

So a general rule of thumb when doing your knowledge engineering is:

- Bad

  - Monolithic

  - Leaky

- Good

  - De-couple knowledge responsibilities

  - Encapsulate knowledge

  - Provide semantic abstractions for those encapsulations

## 2.3. Truth Maintenance with Logical Objects

### 2.3.1. Overview

After regular inserts you have to retract facts explicitly. With logical assertions, the fact that was asserted will be automatically retracted when the conditions that asserted it in the first place are no longer true. Actually, it's even cleverer then that, because it will be retracted only if there isn't any single condition that supports the logical assertion.

Normal insertions are said to be stated, i.e., just like the intuitive meaning of "stating a fact" implies. Using a `HashMap` and a counter, we track how many times a particular equality is stated; this means we count how many different instances are equal.

When we logically insert an object during a RHS execution we are said to justify it, and it is considered to be justified by the firing rule. For each logical insertion there can only be one equal object, and each subsequent equal logical insertion increases the justification counter for this logical assertion. A justification is removed by the LHS of the creating rule becoming untrue, and the counter is decreased accordingly. As soon as we have no more justifications the logical object is automatically retracted.

If we try to logically insert an object when there is an equal stated object, this will fail and return null. If we state an object that has an existing equal object that is justified we override the Fact; how this override works depends on the configuration setting `WM_BEHAVIOR_PRESERVE`. When the property is set to discard we use the existing handle and replace the existing instance with the new Object, which is the default behavior; otherwise we override it to stated but we create an new `FactHandle`.

This can be confusing on a first read, so hopefully the flow charts below help. When it says that it returns a new `FactHandle`, this also indicates the `Object` was propagated through the network.

Figure 2.1. Stated Insertion

Figure 2.2. Logical Insertion

#### 2.3.1.1. Bus Pass Example With Inference and TMS

The previous example was issuing ID cards to over 18s, in this example we now issue bus passes, either a child or adult pass.

    rule "Issue Child Bus Pass" when
      $p : Person( age < 16 )
    then
      insert(new ChildBusPass( $p ) );
    end

    rule "Issue Adult Bus Pass" when
      $p : Person( age >= 16 )
    then
      insert(new AdultBusPass( $p ) );
    end

As before the above example is considered monolithic, leaky and providing poor separation of concerns.

As before we can provide a more robust application with a separation of concerns using inference. Notice this time we don't just insert the inferred object, we use "insertLogical":

    rule "Infer Child" when
        $p : Person( age < 16 )
    then
        insertLogical( new IsChild( $p ) )
    end
    rule "Infer Adult" when
        $p : Person( age >= 16 )
    then
        insertLogical( new IsAdult( $p ) )
    end

A "insertLogical" is part of the Drools Truth Maintenance System (TMS). Here the fact is logically inserted, this fact is dependant on the truth of the "when" clause. It means that when the rule becomes false the fact is automatically retracted. This works particularly well as the two rules are mutually exclusive. So in the above rules if the person is under 16 it inserts an IsChild fact, once the person is 16 or over the IsChild fact is automatically retracted and the IsAdult fact inserted.

We can now bring back in the code to issue the passes, these two can also be logically inserted, as the TMS supports chaining of logical insertions for a cascading set of retracts.

    rule "Issue Child Bus Pass" when
      $p : Person( )
           IsChild( person == $p )
    then
        insertLogical(new ChildBusPass( $p ) );
    end

    rule "Issue Adult Bus Pass" when
      $p : Person( age >= 16 )
           IsAdult( person =$p )
    then
        insertLogical(new AdultBusPass( $p ) );
    end

Now when the person changes from being 15 to 16, not only is the IsChild fact automatically retracted, so is the person's ChildBusPass fact. For bonus points we can combine this with the 'not' conditional element to handle notifications, in this situation a request for the returning of the pass. So when the TMS automatically retracts the ChildBusPass object, this rule triggers and sends a request to the person:

    rule "Return ChildBusPass Request "when
      $p : Person( )
           not( ChildBusPass( person == $p ) )
    then
        requestChildBusPass( $p );
    end

#### 2.3.1.2. Lazy Truth Maintenance

You no longer need to enable or disable truth maintenance, via the kbase configuration. It is now handled automatically and turned on only when needed. This was done along with the code changes so that all entry points use the same code, previous to this the default entry point and named entry points used different code, to avoid TMS overhead for event processing.

#### 2.3.1.3. Important note: Equality for Java objects

It is important to note that for Truth Maintenance (and logical assertions) to work at all, your Fact objects (which may be JavaBeans) must override equals and hashCode methods (from java.lang.Object) correctly. As the truth maintenance system needs to know when two different physical objects are equal in value, both equals and hashCode must be overridden correctly, as per the Java standard.

Two objects are equal if and only if their equals methods return true for each other and if their hashCode methods return the same values. See the Java API for more details (but do keep in mind you MUST override both equals and hashCode).

TMS behaviour is not affected by theruntime configuration of Identity vs Equality, TMS is always equality.

## 2.4. Decision Tables in Spreadsheets

Decision tables are a "precise yet compact" (ref. Wikipedia) way of representing conditional logic, and are well suited to business level rules.

Drools supports managing rules in a spreadsheet format. Supported formats are Excel (XLS), and CSV, which means that a variety of spreadsheet programs (such as Microsoft Excel, OpenOffice.org Calc amongst others) can be utilized. It is expected that web based decision table editors will be included in a near future release.

Decision tables are an old concept (in software terms) but have proven useful over the years. Very briefly speaking, in Drools decision tables are a way to generate rules driven from the data entered into a spreadsheet. All the usual features of a spreadsheet for data capture and manipulation can be taken advantage of.

### 2.4.1. When to Use Decision Tables

Consider decision tables as a course of action if rules exist that can be expressed as rule templates and data: each row of a decision table provides data that is combined with a template to generate a rule.

Many businesses already use spreadsheets for managing data, calculations, etc. If you are happy to continue this way, you can also manage your business rules this way. This also assumes you are happy to manage packages of rules in `.xls` or `.csv` files. Decision tables are not recommended for rules that do not follow a set of templates, or where there are a small number of rules (or if there is a dislike towards software like Excel or OpenOffice.org). They are ideal in the sense that there can be control over what parameters of rules can be edited, without exposing the rules directly.

Decision tables also provide a degree of insulation from the underlying object model.

### 2.4.2. Overview

Here are some examples of real world decision tables (slightly edited to protect the innocent).

In the above examples, the technical aspects of the decision table have been collapsed away (using a standard spreadsheet feature).

The rules start from row 17, with each row resulting in a rule. The conditions are in columns C, D, E, etc., the actions being off-screen. The values in the cells are quite simple, and their meaning is indicated by the headers in Row 16. Column B is just a description. It is customary to use color to make it obvious what the different areas of the table mean.

## Note

Note that although the decision tables look like they process top down, this is not necessarily the case. Ideally, rules are authored without regard for the order of rows, simply because this makes maintenance easier, as rows will not need to be shifted around all the time.

As each row is a rule, the same principles apply. As the rule engine processes the facts, any rules that match may fire. (Some people are confused by this. It is possible to clear the agenda when a rule fires and simulate a very simple decision table where only the first match effects an action.) Also note that you can have multiple tables on one spreadsheet. This way, rules can be grouped where they share common templates, yet at the end of the day they are all combined into one rule package. Decision tables are essentially a tool to generate DRL rules automatically.

### 2.4.3. How Decision Tables Work

The key point to keep in mind is that in a decision table each row is a rule, and each column in that row is either a condition or action for that rule.

The spreadsheet looks for the RuleTable keyword to indicate the start of a rule table (both the starting row and column). Other keywords are also used to define other package level attributes (covered later). It is important to keep the keywords in one column. By convention the second column ("B") is used for this, but it can be any column (convention is to leave a margin on the left for notes). In the following diagram, C is actually the column where it starts. Everything to the left of this is ignored.

If we expand the hidden sections, it starts to make more sense how it works; note the keywords in column C.

Now the hidden magic which makes it work can be seen. The RuleSet keyword indicates the name to be used in the rule package that will encompass all the rules. This name is optional, using a default, but it must have the RuleSet keyword in the cell immediately to the right.

The other keywords visible in Column C are Import and Sequential which will be covered later. The RuleTable keyword is important as it indicates that a chunk of rules will follow, based on some rule templates. After the RuleTable keyword there is a name, used to prefix the names of the generated rules. The sheet name and row numbers are appended to guarantee unique rule names.

## Warning

The RuleTable name combined with the sheet name must be unique across all spreadsheet files in the same KnowledgeBase. If that's not the case, some rules might have the same name and only 1 of them will be applied. To show such ignored rules, raise the severity of such rule name conflicts.

The column of RuleTable indicates the column in which the rules start; columns to the left are ignored.

## Note

In general the keywords make up name-value pairs.

Referring to row 14 (the row immediately after RuleTable), the keywords CONDITION and ACTION indicate that the data in the columns below are for either the LHS or the RHS parts of a rule. There are other attributes on the rule which can also be optionally set this way.

Row 15 contains declarations of ObjectTypes. The content in this row is optional, but if this option is not in use, the row must be left blank; however this option is usually found to be quite useful. When using this row, the values in the cells below (row 16) become constraints on that object type. In the above case, it generates `Person(age=="42")` and `Cheese(type=="stilton")`, where 42 and "stilton" come from row 18. In the above example, the "==" is implicit; if just a field name is given the translator assumes that it is to generate an exact match.

## Note

An ObjectType declaration can span columns (via merged cells), meaning that all columns below the merged range are to be combined into one set of constraints within a single pattern matching a single fact at a time, as opposed to non-merged cells containing the same ObjectType, but resulting in different patterns, potentially matching different or identical facts.

Row 16 contains the rule templates themselves. They can use the "$param" placeholder to indicate where data from the cells below should be interpolated. (For multiple insertions, use "$1", "$2", etc., indicating parameters from a comma-separated list in a cell below.) Row 17 is ignored; it may contain textual descriptions of the column's purpose.

Rows 18 and 19 show data, which will be combined (interpolated) with the templates in row 15, to generate rules. If a cell contains no data, then its template is ignored. (This would mean that some condition or action does not apply for that rule row.) Rule rows are read until there is a blank row. Multiple RuleTables can exist in a sheet. Row 20 contains another keyword, and a value. The row positions of keywords like this do not matter (most people put them at the top) but their column should be the same one where the RuleTable or RuleSet keywords should appear. In our case column C has been chosen to be significant, but any other column could be used instead.

In the above example, rules would be rendered like the following (as it uses the "ObjectType" row):

    //row 18
    rule "Cheese_fans_18"
    when
        Person(age=="42")
        Cheese(type=="stilton")
    then
        list.add("Old man stilton");
    end

## Note

The constraints `age=="42"` and `type=="stilton"` are interpreted as single constraints, to be added to the respective ObjectType in the cell above. If the cells above were spanned, then there could be multiple constraints on one "column".

## Warning

Very large decision tables may have very large memory requirements.

### 2.4.4. Spreadsheet Syntax

#### 2.4.4.1. Spreadsheet Structure

There are two types of rectangular areas defining data that is used for generating a DRL file. One, marked by a cell labelled `RuleSet`, defines all DRL items except rules. The other one may occur repeatedly and is to the right and below a cell whose contents begin with `RuleTable`. These areas represent the actual decision tables, each area resulting in a set of rules of similar structure.

A Rule Set area may contain cell pairs, one below the `RuleSet` cell and containing a keyword designating the kind of value contained in the other one that follows in the same row.

The columns of a Rule Table area define patterns and constraints for the left hand sides of the rules derived from it, actions for the consequences of the rules, and the values of individual rule attributes. Thus, a Rule Table area should contain one or more columns, both for conditions and actions, and an arbitrary selection of columns for rule attributes, at most one column for each of these. The first four rows following the row with the cell marked with `RuleTable` are earmarked as header area, mostly used for the definition of code to construct the rules. It is any additional row below these four header rows that spawns another rule, with its data providing for variations in the code defined in the Rule Table header.

All keywords are case insensitive.

Only the first worksheet is examined for decision tables.

#### 2.4.4.2. Rule Set Entries

Entries in a Rule Set area may define DRL constructs (except rules), and specify rule attributes. While entries for constructs may be used repeatedly, each rule attribute may be given at most once, and it applies to all rules unless it is overruled by the same attribute being defined within the Rule Table area.

Entries must be given in a vertically stacked sequence of cell pairs. The first one contains a keyword and the one to its right the value, as shown in the table below. This sequence of cell pairs may be interrupted by blank rows or even a Rule Table, as long as the column marked by `RuleSet` is upheld as the one containing the keyword.

Table 2.1. Entries in the Rule Set area

Keyword | Value | Usage
--- | --- | ---
RuleSet | The package name for the generated DRL file. Optional, the default is `rule_table`. | Must be First entry.
Sequential | "true" or "false". If "true", then salience is used to ensure that rules fire from the top down. | Optional, at most once. If omitted, no firing order is imposed.
EscapeQuotes | "true" or "false". If "true", then quotation marks are escaped so that they appear literally in the DRL. | Optional, at most once. If omitted, quotation marks are escaped.
Import | A comma-separated list of Java classes to import. | Optional, may be used repeatedly.
Variables | Declarations of DRL globals, i.e., a type followed by a variable name. Multiple global definitions must be separated with a comma. | Optional, may be used repeatedly.
Functions | One or more function definitions, according to DRL syntax. | Optional, may be used repeatedly.
Queries | One or more query definitions, according to DRL syntax. | Optional, may be used repeatedly.
Declare | One or more declarative types, according to DRL syntax. | Optional, may be used repeatedly.

## Warning

In some locales, MS Office, LibreOffice and OpenOffice will encode a double quoth `"` differently, which will cause a compilation error. The difference is often hard to see. For example: `“A”` will fail, but `"A"` will work.

For defining rule attributes that apply to all rules in the generated DRL file you can use any of the entries in the following table. Notice, however, that the proper keyword must be used. Also, each of these attributes may be used only once.

Table 2.2. Rule attribute entries in the Rule Set area

Keyword | Initial | Value
--- | --- | ---
PRIORITY | P | An integer defining the "salience" value for the rule. Overridden by the "Sequential" flag.
DURATION | D | A long integer value defining the "duration" value for the rule.
TIMER | T | A timer definition. See "Timers and Calendars".
CALENDARS | E | A calendars definition. See "Timers and Calendars".
NO-LOOP | U | A Boolean value. "true" inhibits looping of rules due to changes made by its consequence.
LOCK-ON-ACTIVE | L | A Boolean value. "true" inhibits additional activations of all rules with this flag set within the same ruleflow or agenda group.
AUTO-FOCUS | F | A Boolean value. "true" for a rule within an agenda group causes activations of the rule to automatically give the focus to the group.
ACTIVATION-GROUP | X | A string identifying an activation (or XOR) group. Only one rule within an activation group will fire, i.e., the first one to fire cancels any existing activations of other rules within the same group.
AGENDA-GROUP | G | A string identifying an agenda group, which has to be activated by giving it the "focus", which is one way of controlling the flow between groups of rules.
RULEFLOW-GROUP | R | A string identifying a rule-flow group.

#### 2.4.4.3. Rule Tables

All Rule Tables begin with a cell containing "RuleTable", optionally followed by a string within the same cell. The string is used as the initial part of the name for all rules derived from this Rule Table, with the row number appended for distinction. (This automatic naming can be overridden by using a NAME column.) All other cells defining rules of this Rule Table are below and to the right of this cell.

The next row defines the column type, with each column resulting in a part of the condition or the consequence, or providing some rule attribute, the rule name or a comment. The table below shows which column headers are available; additional columns may be used according to the table showing rule attribute entries given in the preceding section. Note that each attribute column may be used at most once. For a column header, either use the keyword or any other word beginning with the letter given in the "Initial" column of these tables.

Table 2.3. Column Headers in the Rule Table

Keyword | Initial | Value | Usage
--- | --- | --- | ---
NAME | N | Provides the name for the rule generated from that row. The default is constructed from the text following the RuleTable tag and the row number. | At most one column
DESCRIPTION | I | A text, resulting in a comment within the generated rule. | At most one column
CONDITION | C | Code snippet and interpolated values for constructing a constraint within a pattern in a condition. | At least one per rule table
ACTION | A | Code snippet and interpolated values for constructing an action for the consequence of the rule. | At least one per rule table
METADATA | @ | Code snippet and interpolated values for constructing a metadata entry for the rule. | Optional, any number of columns

Given a column headed CONDITION, the cells in successive lines result in a conditional element.

- Text in the first cell below CONDITION develops into a pattern for the rule condition, with the snippet in the next line becoming a constraint. If the cell is merged with one or more neighbours, a single pattern with multiple constraints is formed: all constraints are combined into a parenthesized list and appended to the text in this cell. The cell may be left blank, which means that the code snippet in the next row must result in a valid conditional element on its own.

  To include a pattern without constraints, you can write the pattern in front of the text for another pattern.

  The pattern may be written with or without an empty pair of parentheses. A "from" clause may be appended to the pattern.

  If the pattern ends with "eval", code snippets are supposed to produce boolean expressions for inclusion into a pair of parentheses after "eval".

- Text in the second cell below CONDITION is processed in two steps.

  1. The code snippet in this cell is modified by interpolating values from cells farther down in the column. If you want to create a constraint consisting of a comparison using "==" with the value from the cells below, the field selector alone is sufficient. Any other comparison operator must be specified as the last item within the snippet, and the value from the cells below is appended. For all other constraint forms, you must mark the position for including the contents of a cell with the symbol `$param`. Multiple insertions are possible by using the symbols `$1`, `$2`, etc., and a comma-separated list of values in the cells below.

     A text according to the pattern `forall(`delimiter`){`snippet`}` is expanded by repeating the snippet once for each of the values of the comma-separated list of values in each of the cells below, inserting the value in place of the symbol `$` and by joining these expansions by the given delimiter. Note that the forall construct may be surrounded by other text.

  2. If the cell in the preceding row is not empty, the completed code snippet is added to the conditional element from that cell. A pair of parentheses is provided automatically, as well as a separating comma if multiple constraints are added to a pattern in a merged cell.

     If the cell above is empty, the interpolated result is used as is.

- Text in the third cell below CONDITION is for documentation only. It should be used to indicate the column's purpose to a human reader.

- From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the conditional element or constraint for this rule.

Given a column headed ACTION, the cells in successive lines result in an action statement.

- Text in the first cell below ACTION is optional. If present, it is interpreted as an object reference.

- Text in the second cell below ACTION is processed in two steps.

  1. The code snippet in this cell is modified by interpolating values from cells farther down in the column. For a singular insertion, mark the position for including the contents of a cell with the symbol `$param`. Multiple insertions are possible by using the symbols `$1`, `$2`, etc., and a comma-separated list of values in the cells below.

     A method call without interpolation can be achieved by a text without any marker symbols. In this case, use any non-blank entry in a row below to include the statement.

     The forall construct is available here, too.

  2. If the first cell is not empty, its text, followed by a period, the text in the second cell and a terminating semicolon are stringed together, resulting in a method call which is added as an action statement for the consequence.

     If the cell above is empty, the interpolated result is used as is.

- Text in the third cell below ACTION is for documentation only. It should be used to indicate the column's purpose to a human reader.

- From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the action statement for this rule.

## Note

Using `$1` instead of `$param` works in most cases, but it will fail if the replacement text contains a comma: then, only the part preceding the first comma is inserted. Use this "abbreviation" judiciously.

Given a column headed METADATA, the cells in successive lines result in a metadata annotation for the generated rules.

- Text in the first cell below METADATA is ignored.

- Text in the second cell below METADATA is subject to interpolation, as described above, using values from the cells in the rule rows. The metadata marker character `@` is prefixed automatically, and thus it should not be included in the text for this cell.

- Text in the third cell below METADATA is for documentation only. It should be used to indicate the column's purpose to a human reader.

- From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the metadata annotation for this rule.

#### 2.4.4.4. Examples

The various interpolations are illustrated in the following example.

Example 2.1. Interpolating cell data

If the template is `Foo(bar == $param)` and the cell is `42`, then the result is `Foo(bar == 42)`.

If the template is `Foo(bar < $1, baz == $2)` and the cell contains `42,43`, the result will be `Foo(bar < 42, baz ==43)`.

The template `forall(&&){bar != $}` with a cell containing `42,43` results in `bar != 42 && bar != 43`.

The next example demonstrates the joint effect of a cell defining the pattern type and the code snippet below it.

This spreadsheet section shows how the `Person` type declaration spans 2 columns, and thus both constraints will appear as `Person(age == ..., type == ...)`. Since only the field names are present in the snippet, they imply an equality test.

In the following example the marker symbol `$param` is used.

The result of this column is the pattern `Person(age == "42"))`. You may have noticed that the marker and the operator "==" are redundant.

The next example illustrates that a trailing insertion marker can be omitted.

Here, appending the value from the cell is implied, resulting in `Person(age < "42")).`

You can provide the definition of a binding variable, as in the example below. .

Here, the result is `c: Cheese(type == "stilton").` Note that the quotes are provided automatically. Actually, anything can be placed in the object type row. Apart from the definition of a binding variable, it could also be an additional pattern that is to be inserted literally.

A simple construction of an action statement with the insertion of a single value is shown below.

The cell below the ACTION header is left blank. Using this style, anything can be placed in the consequence, not just a single method call. (The same technique is applicable within a CONDITION column as well.)

Below is a comprehensive example, showing the use of various column headers. It is not an error to have no value below a column header (as in the NO-LOOP column): here, the attribute will not be applied in any of the rules.

And, finally, here is an example of Import, Variables and Functions.

Multiple package names within the same cell must be separated by a comma. Also, the pairs of type and variable names must be comma-separated. Functions, however, must be written as they appear in a DRL file. This should appear in the same column as the "RuleSet" keyword; it could be above, between or below all the rule rows.

## Note

It may be more convenient to use Import, Variables, Functions and Queries repeatedly rather than packing several definitions into a single cell.

### 2.4.5. Creating and integrating Spreadsheet based Decision Tables

The API to use spreadsheet based decision tables is in the drools-decisiontables module. There is really only one class to look at: `SpreadsheetCompiler`. This class will take spreadsheets in various formats, and generate rules in DRL (which you can then use in the normal way). The `SpreadsheetCompiler` can just be used to generate partial rule files if it is wished, and assemble it into a complete rule package after the fact (this allows the separation of technical and non-technical aspects of the rules if needed).

To get started, a sample spreadsheet can be used as a base. Alternatively, if the plug-in is being used (Rule Workbench IDE), the wizard can generate a spreadsheet from a template (to edit it an xls compatible spreadsheet editor will need to be used).

### 2.4.6. Managing Business Rules in Decision Tables

#### 2.4.6.1. Workflow and Collaboration

Spreadsheets are well established business tools (in use for over 25 years). Decision tables lend themselves to close collaboration between IT and domain experts, while making the business rules clear to business analysts, it is an ideal separation of concerns.

Typically, the whole process of authoring rules (coming up with a new decision table) would be something like:

1. Business analyst takes a template decision table (from a repository, or from IT)

2. Decision table business language descriptions are entered in the table(s)

3. Decision table rules (rows) are entered (roughly)

4. Decision table is handed to a technical resource, who maps the business language (descriptions) to scripts (this may involve software development of course, if it is a new application or data model)

5. Technical person hands back and reviews the modifications with the business analyst.

6. The business analyst can continue editing the rule rows as needed (moving columns around is also fine etc).

7. In parallel, the technical person can develop test cases for the rules (liaising with business analysts) as these test cases can be used to verify rules and rule changes once the system is running.

#### 2.4.6.2. Using spreadsheet features

Features of applications like Excel can be used to provide assistance in entering data into spreadsheets, such as validating fields. Lists that are stored in other worksheets can be used to provide valid lists of values for cells, like in the following diagram.

Some applications provide a limited ability to keep a history of changes, but it is recommended to use an alternative means of revision control. When changes are being made to rules over time, older versions are archived (many open source solutions exist for this, such as Subversion or Git).

### 2.4.7. Rule Templates

Related to decision tables (but not necessarily requiring a spreadsheet) are "Rule Templates" (in the drools-templates module). These use any tabular data source as a source of rule data - populating a template to generate many rules. This can allow both for more flexible spreadsheets, but also rules in existing databases for instance (at the cost of developing the template up front to generate the rules).

With Rule Templates the data is separated from the rule and there are no restrictions on which part of the rule is data-driven. So whilst you can do everything you could do in decision tables you can also do the following:

- store your data in a database (or any other format)

- conditionally generate rules based on the values in the data

- use data for any part of your rules (e.g. condition operator, class name, property name)

- run different templates over the same data

As an example, a more classic decision table is shown, but without any hidden rows for the rule meta data (so the spreadsheet only contains the raw data to generate the rules).

See the `ExampleCheese.xls` in the examples download for the above spreadsheet.

If this was a regular decision table there would be hidden rows before row 1 and between rows 1 and 2 containing rule metadata. With rule templates the data is completely separate from the rules. This has two handy consequences - you can apply multiple rule templates to the same data and your data is not tied to your rules at all. So what does the template look like?

    1  template header
    2  age
    3  type
    4  log
    5
    6  package org.drools.examples.templates;
    7
    8  global java.util.List list;
    9
    10 template "cheesefans"
    11
    12 rule "Cheese fans_@{row.rowNumber}"
    13 when
    14    Person(age == @{age})
    15    Cheese(type == "@{type}")
    16 then
    17    list.add("@{log}");
    18 end
    19
    20 end template

Annotations to the preceding program listing:

- Line 1: All rule templates start with `template header`.

- Lines 2-4: Following the header is the list of columns in the order they appear in the data. In this case we are calling the first column `age`, the second `type` and the third `log`.

- Line 5: An empty line signifies the end of the column definitions.

- Lines 6-9: Standard rule header text. This is standard rule DRL and will appear at the top of the generated DRL. Put the package statement and any imports and global and function definitions into this section.

- Line 10: The keyword `template` signals the start of a rule template. There can be more than one template in a template file, but each template should have a unique name.

- Lines 11-18: The rule template - see below for details.

- Line 20: The keywords `end template` signify the end of the template.

The rule templates rely on MVEL to do substitution using the syntax @{token_name}. There is currently one built-in expression, @{row.rowNumber} which gives a unique number for each row of data and enables you to generate unique rule names. For each row of data a rule will be generated with the values in the data substituted for the tokens in the template. With the example data above the following rule file would be generated:

    package org.drools.examples.templates;

    global java.util.List list;

    rule "Cheese fans_1"
    when
      Person(age == 42)
      Cheese(type == "stilton")
    then
      list.add("Old man stilton");
    end

    rule "Cheese fans_2"
    when
      Person(age == 21)
      Cheese(type == "cheddar")
    then
      list.add("Young man cheddar");
    end

The code to run this is simple:

    DecisionTableConfiguration dtableconfiguration =

        KnowledgeBuilderFactory.newDecisionTableConfiguration();

    dtableconfiguration.setInputType( DecisionTableInputType.XLS );

    KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    kbuilder.add( ResourceFactory.newClassPathResource( getSpreadsheetName(),

                                                        getClass() ),

                  ResourceType.DTABLE,

                  dtableconfiguration );

## 2.5. Templates

If you discover that you have a group of rules following the same arrangement of patterns, constraints and actions on the RHS, differing only in constants or names for objects or fields, you might think of employing Drools's rule template feature for generating the actual rules. You would write a rule template file, containing the textual skeleton of your rule and use the Drools template compiler in combination with a collection of objects providing the actual values for the "flesh" of the rules for their instantiation.

The mechanism is very similar to what a macro processor does. The major advantage proffered by template expansion is that it's nicely integrated in the overall handling of Knowledge Resources.

## Caution

This is an experimental feature. In particular, the API is subject to change.

### 2.5.1. The Rule Template File

A rule template file begins with a header defining the placeholders, or formal template parameters for the strings that are to be inserted during instantiation. After the first line, which invariably contains `template header`, you should write a number of lines, each of which contains a single parameter name.

Example 2.2. Rule template file: template header

    template header
    parameter-name-1
    ...
    parameter-name-n
    ...

The template header is followed by the text that is to be replicated and interpolated with the actual parameters. It may begin with a `package` statement, followed by some additional lines. These may be sectioned into one or more templates, each of them between a pair of matching `template` and `end template` statements. The `template` takes an argument, which puts a name to the template. The name can be a simple unquoted name or an arbitrary string enclosed in double quotes. The template text between these lines may contain one or more rules, constituting the "raw material" for the expansion.

Example 2.3. Rule template file: templates

    template header
    parameter-name-1
    ...
    parameter-name-n
    package ...     // optional
    header text    // optional
    template template-name
    ...
    // template text
    ...
    end template
    ...

The resulting text will begin with the package line and the header text following it, if present. Then, each template text will be expanded individually, yielding one set of rules for each of the actual parameter sets. Therefore, the structure of the template sections affect the order of the generated rules, since the generator iterates over the sections and then over the set of actual parameters.

Any interpolation takes place between a pair of `template` and `end template` statements, when this template is expanded. The template text is scanned for occurrences of parameter expansions written according to:

    @{parameter-name}

The name between '@{' and '}' should be one of the parameter names defined in the template header. The substitution is effected anywhere, even within string literals.

An important parameter is available without having to be included in the data source providing the actual values. The parameter substitution `@{row.rowNumber}` expands to the integers 0, 1, 2, etc., providing a unique distinction for the instantiation derived from a parameter set. You would use this as part of each rule name, because, without this precaution, there would be duplicate rule names. (You are, of course, free to use your own identification included as an extra parameter.)

### 2.5.2. Expanding a Template

To expand a template, you must prepare a data source. This can be a spreadsheet, as explained in the previous section. Here, we'll concentrate on expansion driven by Java objects. There are two straightforward ways of supplying values for a fixed set of names: Java objects, in the JavaBeans style, and Maps. Both of them can be arranged in a `Collection`, whose elements will be processed during the expansion, resulting in an instantiation for each element.

#### 2.5.2.1. Instantiation from Java Objects

You may use a Java object that provides getter methods corresponding to all of the parameter names of your template file. If, for instance, you have defined a header

    template header
    type
    limit
    word

the following Java class could be used:

    public class ParamSet {

        //...

        public ParamSet( String t, int l, boolean w ) {

            //...

        }

        public String  getType(){...}

        public int     getLimit(){...}

        public boolean isWord(){...}

    }

Although interpolation is pure text manipulation, the actual values supplied may be of any type, just as long as this type provides a reasonable `toString()` method. (For simple types, the eponymous static method of the related class from `java.lang` is used.)

Assuming that we have created a `Collection<ParamSet>` for a template file `template.drl`, we can now proceed to request its expansion.

    Collection<ParamSet> paramSets = new ArrayList<ParamSet>();

    // populate paramSets

    paramSets.add( new ParamSet( "Foo", 42, true ) );

    paramSets.add( new ParamSet( "Bar", 13, false ) );

    ObjectDataCompiler converter = new ObjectDataCompiler();

    InputStream templateStream =

        this.getClass().getResourceAsStream( "template.drl" );

    String drl = converter.compile( objs, templateStream );

The resulting string contains the expanded rules text. You could write it to a file and proceed as usual, but it's also possible to feed this to a `KnowledgeBuilder` and continue with the resulting Knowledge Packages.

    KnowledgeBase kieBaseModel = KnowledgeBaseFactory.newKnowledgeBase();

    KnowledgeBuilder kBuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    Reader rdr = new StringReader( drl );

    kBuilder.add( ResourceFactory.newReaderResource( rdr ), ResourceType.DRL );

    if( kBuilder.hasErrors() ){

        // ...

        throw new IllegalStateException( "DRL errors" );

    }

    kieBaseModel.addKnowledgePackages( kBuilder.getKnowledgePackages() );

#### 2.5.2.2. Instantiation from Maps

A `Map` that provides the values for substituting template parameters should have a (string) key set matching all of the parameter names. Again, values could be from any class, as long as they provide a good `toString()` method. The expansion would use the same approach, just differing in the way the map collection is composed.

    Collection<Map<String,Object>> paramMaps = new ArrayList<Map<String,Object>>();

    // populate paramMaps

    ObjectDataCompiler converter = new ObjectDataCompiler();

    InputStream templateStream =

        this.getClass().getResourceAsStream( "template.drl" );

    String drl = converter.compile( objs, templateStream );

### 2.5.3. Example

The following example illustrates template expansion. It is based on simple objects of class `Item` containing a couple of integer fields and an `enum` field of type `ItemCode`.

    public class Item {

        // ...

        public Item( String n, int p, int w, ItemCode c ){...}

        public String   getName() {...}

        public int      getWeight() {...}

        public int      getPrice() {...}

        public ItemCode getCode() {...}

    }

    public enum ItemCode {

        LOCK,

        STOCK,

        BARREL;

    }

The rule template contains a single rule. Notice that the field name for the range test is a parameter, which enables us to instantiate the template for different fields.

    template header
    field
    lower
    upper
    codes

    package range;
    template "inRange"
    rule "is in range @{row.rowNumber}"
    when
        Item( $name : name, $v : @{field} >= @{lower} && <= @{upper}, $code : code @{codes} )
    then
        System.out.println( "Item " + $name + " @{field} in range: " + $v + " code: " + $code );
    end
    end template

The next code snippet is from the application, where several parameter sets have to be set up. First, there is class `ParamSet`, for storing a set of actual parameters.

    public class ParamSet {

        //...

        private EnumSet<ItemCode> codeSet;

        public ParamSet( String f, int l, int u, EnumSet<ItemCode> cs ){...}

        public String getField() { return field; }

        public int getLower() { return lower; }

        public int getUpper() { return upper; }

        public String getCodes(){

            StringBuilder sb = new StringBuilder();

            String conn = "";

            for( ItemCode ic: codeSet ){

                 sb.append( conn ).append( " == ItemCode." ).append( ic );

                 conn = " ||";

            }

            return sb.toString();

        }

    }

Note that the method `getCodes()` does returns the `EnumSet<ItemCode>` field value as a `String` value representing a multiple restriction, i.e., a test for one out of a list of values.

The task of expanding a template, passing the resulting DRL text to a Knowledge Builder and adding the resulting Knowledge Packages to a Knowledge Base is generic. The utility class `Expander` takes care of this, using a Knowledge Base, the `InputStream` with the rule template and the collection of parameter sets.

    public class Expander {

        public void expand( KnowledgeBase kieBaseModel, InputStream is, Collection<?> act )

            throws Exception {

            ObjectDataCompiler converter = new ObjectDataCompiler();

            String drl = converter.compile( act, is );

            KnowledgeBuilder kBuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

            Reader rdr = new StringReader( drl );

            kBuilder.add( ResourceFactory.newReaderResource( rdr ), ResourceType.DRL );

            if( kBuilder.hasErrors() ){

                for( KnowledgeBuilderError err: kBuilder.getErrors() ){

                    System.err.println( err.toString() );

                }

                throw new IllegalStateException( "DRL errors" );

            }

            kieBaseModel.addKnowledgePackages( kBuilder.getKnowledgePackages() );

        }

    }

We are now all set to prepare the Knowledge Base with some generated rules. First, we define several parameter sets, constructed as `ParamSet` objects, and add them to a `List`, which is passed to the `expand` method shown above. Then we launch a stateful session, insert a few `Item`, and watch what happens.

    Collection<ParamSet> cfl = new ArrayList<ParamSet>();

    cfl.add( new ParamSet( "weight",  10,  99, EnumSet.of( ItemCode.LOCK, ItemCode.STOCK ) ) );

    cfl.add( new ParamSet( "price",   10,  50, EnumSet.of( ItemCode.BARREL ) ) );

    KnowledgeBase kieBaseModel = KnowledgeBaseFactory.newKnowledgeBase();

    Expander ex = new Expander();

    InputStream dis = new FileInputStream( new File( "rangeTemp.drl" ) );

    ex.expand( kieBaseModel, dis, cfl );

    StatefulKnowledgeSession session = kieBaseModel.newStatefulKnowledgeSession();

    session.insert( new Item( "A", 130,  42, ItemCode.LOCK ) );

    session.insert( new Item( "B",  44, 100, ItemCode.STOCK ) );

    session.insert( new Item( "C", 123, 180, ItemCode.BARREL ) );

    session.insert( new Item( "D",  85,   9, ItemCode.LOCK ) );

    session.fireAllRules();

Notice that the two resulting rules deal with different fields, one with an item's weight, the other one with its price. - Below is the output.

    Item E price in range: 25 code: BARREL
    Item A weight in range: 42 code: LOCK

## 2.6. More on building and deploying

### 2.6.1. Knowledge Base by Configuration Using Changesets

So far, the programmatic API has been used to build a Knowledge Base. Quite often it's more desirable to do this via configuration. To facilitate this, Drools supports the "Changeset" feature. The file `changeset.xml` contains a list of resources, and it may also point recursively to another changeset XML file. Currently there is no XML schema for the changeset XML, but we hope to add one soon. A few examples will be shown to give you the gist of things. A resource approach is employed that uses a prefix to indicate the protocol. All the protocols provided by `java.net.URL`, such as "file" and "http", are supported, as well as an additional "classpath". Currently the type attribute must always be specified for a resource, as it is not inferred from the file name extension. Here is a simple example that points to a http location for some rules.

    <change-set xmlns='http://drools.org/drools-5.0/change-set'

                 xmlns:xs='http://www.w3.org/2001/XMLSchema-instance'

                 xs:schemaLocation='http://drools.org/drools-5.0/change-set http://anonsvn.jboss.org/repos/labs/labs/jbossrules/trunk/drools-api/src/main/resources/change-set-1.0.0.xsd' >

       <add>

           <resource source='http:org/domain/myrules.drl' type='DRL' />

       </add>

     </change-set>

To use the above XML, the code is almost identical as before, except we change the resource type to `CHANGE_SET`.

    KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

    kbuilder.add( ResourceFactory.newClasspathResource( "myChangeSet.xml", getClass() ),

                  ResourceType.CHANGE_SET );

    if ( kbuilder.hasErrors() ) {

        System.err.println( builder.getErrors().toString() );

    }

Changesets can include any number of resources, and they even support additional configuration information, which currently is only needed for decision tables. The example below is expanded to load the rules from a http URL location, and an Excel decision table from the classpath.

    <change-set xmlns='http://drools.org/drools-5.0/change-set'

                 xmlns:xs='http://www.w3.org/2001/XMLSchema-instance'

                 xs:schemaLocation='http://drools.org/drools-5.0/change-set.xsd http://anonsvn.jboss.org/repos/labs/labs/jbossrules/trunk/drools-api/src/main/resources/change-set-1.0.0.xsd' >

       <add>

           <resource source='http:org/domain/myrules.drl' type='DRL' />

           <resource source='classpath:data/IntegrationExampleTest.xls' type="DTABLE">

               <decisiontable-conf input-type="XLS" worksheet-name="Tables_2" />

           </resource>

       </add>

     </change-set>

It is also possible to specify a directory, to add the contents of that directory. It is expected that all the files are of the specified type, since type is not yet inferred from the file name extensions.

    <change-set xmlns='http://drools.org/drools-5.0/change-set'

                 xmlns:xs='http://www.w3.org/2001/XMLSchema-instance'

                 xs:schemaLocation='http://drools.org/drools-5.0/change-set.xsd http://anonsvn.jboss.org/repos/labs/labs/jbossrules/trunk/drools-api/src/main/resources/change-set-1.0.0.xsd' >

       <add>

           <resource source='file://myfolder/' type='DRL' />

       </add>

     </change-set>

### 2.6.2. Knowledge Agent

The Knowledge Agent provides automatic loading, caching and re-loading of resources and is configured from a properties files. The Knowledge Agent can update or rebuild this Knowledge Base as the resources it uses are changed. The strategy for this is determined by the configuration given to the factory, but it is typically pull-based using regular polling. We hope to add push-based updates and rebuilds in future versions.

    KnowledgeAgent kagent = KnowledgeAgentFactory.newKnowledgeAgent( "MyAgent" );

    kagent.applyChangeSet( ResourceFactory.newUrlResource( url ) );

    KnowledgeBase kbase = kagent.getKnowledgeBase();

A `KnowledgeAgent` object will continuously scan all the added resources, using a default polling interval of 60 seconds and, when some last modification date is updated, it will applied the changes into the cached Knowledge Base using the new resources. Note that the previous `KnowledgeBase` reference will still exist and you'll have to call `getKnowledgeBase()` to access the newly built `KnowledgeBase`. If a directory is specified as part of the change set, the entire contents of that directory will be scanned for changes. The way modifications are applied depends on `drools.agent.newInstance` property present in the KnowledgeAgentConfiguration object passed to the agent.

For polling to occur, the polling and notifier services must be started:

    ResourceFactory.getResourceChangeNotifierService().start();

    ResourceFactory.getResourceChangeScannerService().start();

#### 2.6.2.1. Knowledge Agent and Custom ClassLoaders

Because Knowledge Agent could scan and process remote resources, it could ends up failing when compiling or executing rules, queries, functions, etc. that use classes outside the agent's classloader. If this is your case, you could take 2 approach: use a custom classloader for agent's kbuilder or force the agent to use the same classloader that its kbase has.

##### 2.6.2.1.1. Custom ClassLoaders from KnowledgeBuilder

Knowledge Agent uses KnowledgeBuilder internally in order to compile managed resources. If you need to pass custom configuration to these compilers you could pass a KnowledgeBuilderConfiguration object to KnowledgeAgentFactory.newKnowledgeAgent(). This object will be used in every builder the agent creates. Using a KnowledgeBuilderConfiguration you can specify a custom classloader.

## Important

The usage of a KnowledgeBuilder to specify the CL the agent must use is discouraged. See the next section for the best solution to this problem.

## Warning

If the agent is handling binary resources (packages), then using a KnowledgeBuilder to specify the CL that must be used will not work. In this case the only solution is to pass a kbase that uses the custom CL. See next section for more details.

##### 2.6.2.1.2. Reuse KnowledgeBase ClassLoader

Most of the times, the classloader you want to use in the compilation process of remote resources is the same needed in the agent's kbase, so the rules could be executed. If you want to use this approach, you will need to setup the desired ClassLoader to the agent kbase and use the "drools.agent.useKBaseClassLoaderForCompiling" property of KnowledgeAgentConfiguration object.

This approach lets you modify agent's kbuilder classloader in runtime by modifying the classloader the agent's kbase uses. This will serve also when not using incremental change set processing (see the section below). When the kbase is recreated its configuration is reused, so the classloader is maintained.

    KnowledgeBaseConfiguration kbaseConfig =

        KnowledgeBaseFactory.newKnowledgeBaseConfiguration(null, customClassLoader);

    KnowledgeBase kbase =

        KnowledgeBaseFactory.newKnowledgeBase(kbaseConfig); //kbase with custom classloader

    KnowledgeAgentConfiguration aconf =

        KnowledgeAgentFactory.newKnowledgeAgentConfiguration();

    aconf.setProperty("drools.agent.newInstance", "false"); //incremental change set processing enabled

    aconf.setProperty("drools.agent.useKBaseClassLoaderForCompiling", "true");

    KnowledgeAgent kagent = KnowledgeAgentFactory.newKnowledgeAgent(

                    "test agent", kbase, aconf);

#### 2.6.2.2. Incremental Change Set Processing

Knowledge Agent can process change sets in two different ways: recreating the knowledge base every time a new change set is processed or applying the change set in the cached knowledge base without destroying it. This behavior is controlled by the "newInstance" property of the KnowledgeAgentConfiguration object passed to the Agent's constructor.

When "newInstance" is set to true (the default value), the agent will destroy the cached Knowledge Base it contains and populate a new one containing the change set modifications. When "newInstance" is set to "false" change sets are applied directly to the cached Knowledge Base. The rule that were not modified in the change sets' resources are not replaced in the Knowledge Base, the modified or deleted rules are modified or deleted from the cached Knowledge Base. Functions, Queries and Definition Types are always replaced in the cached Knowledge Base whether they are modified or not.

The following code snippet creates a new Knowledge Agent with its "newInstance" property set to false

    KnowledgeAgentConfiguration aconf = KnowledgeAgentFactory.newKnowledgeAgentConfiguration();

    aconf.setProperty("drools.agent.newInstance", "false");

    KnowledgeAgent kagent = KnowledgeAgentFactory.newKnowledgeAgent("test agent", null, aconf);

#### 2.6.2.3. Remote HTTP resource caching

A note on remote HTTP Url Resources: if your knowledge agent is "pulling" resources from a http(s) URL, then you might rightly be concerned if that resource (remote web server) suddenly disappears. To survive a restart when a resource is no longer available remotely (eg the remote server is being restarted) then you can set a System Property: drools.resource.urlcache to a directory that has write permissions for the application: the Knowledge Agent will cache copies of the remote resources in that local directory.

For example, using the java command line: -Ddrools.resource.urlcache=/users/someone/KnowledgeCache - will keep local copies of the resources (rules, packages etc) in that directory, for the agent to use should it be restarted (when a remote resource becomes available, and is updated, it will automatically update the local cache copy).

## 2.7. Logging

One way to illuminate the black box that is a rule engine, is to play with the logging level.

Everything is logged to SLF4J, which is a simple logging facade that can delegate any log to Logback, Apache Commons Logging, Log4j or java.util.logging. Add a dependency to the logging adaptor for your logging framework of choice. If you're not using any logging framework yet, you can use Logback by adding this Maven dependency:

        <dependency>

          <groupId>ch.qos.logback</groupId>

          <artifactId>logback-classic</artifactId>

          <version>1.x</version>

        </dependency>

## Note

If you're developing for an ultra light environment, use `slf4j-nop` or `slf4j-simple` instead.

Configure the logging level on the package `org.drools`. For example:

In Logback, configure it in your `logback.xml` file:

    <configuration>

        <logger name="org.drools" level="debug"/>

        ...

    <configuration>

In Log4J, configure it in your `log4j.xml` file:

    <log4j:configuration xmlns:log4j="http://jakarta.apache.org/log4j/">

        <category name="org.drools">

          <priority value="debug" />

        </category>

        ...

    </log4j:configuration>

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