Shadow proxies are no longer needed. Shadow proxies protected the engine from information change on facts, which if occurred outside of the engine's control it could not be modified or retracted.

You no longer need to confine one PackageBuilder to one package namespace. Just keeping adding your DRLs for any namespace and getPackages() returns an array of Packages for each of the used namespaces.

Package[] packages = pkgBuilder.getPackages();

It is now possible to attach a RuleBase to a PackageBuilder, this means that rules are built and added to the rulebase at the same time. PackageBuilder uses the Package instances of the actual RuleBase as it's source, removing the need for additional Package creation and merging that happens in the existing approach.

RuleBase ruleBase = RuleBaseFactory.newRuleBase();
PackageBuilder pkgBuilder = new PackageBuilder( ruleBase, null );

Stateful sessions can now saved and resumed at a later date. Pre-loaded data sessions can now be created. Pluggable strategies can be used for user object persistence, i.e. hibernate or identity maps.

Drools now supports a new base construct called Type Declaration. This construct fulfils two purposes: the ability to declare fact metadata, and the ability to dynamically generate new fact types local to the rule engine. The Guvnor modelling tool uses this underneath. One example of the construct is:

declare StockTick
  @role( event )
  @timestamp( timestampAttr )

  companySymbol : String
  stockPrice : double
  timestampAttr : long
end

To declare and associate fact metadata, just use the @ symbol for each metadata ID you want to declare. Example:

declare StockTick
  @role( event )
end

To activate the dynamic bean generation, just add fields and types to your type declaration:

declare Person
  name : String
  age : int
end

A series of DSL improvements were implemented, including a completely new parser and the ability to declare matching masks for matching variables. For instance, one can constrain a phone number field to a 2-digit country code + 3-digit area code + 8-digit phone number, all connected by a "-" (dash), by declaring the DSL map like: The phone number is {number:\d{2}-\d{3}-\d{8}} Any valid java regexp may be used in the variable mask.

Drools now supports "fireUntilHalt()" feature, that starts the engine in a reactive mode, where rules will be continually fired, until a halt() call is made. This is specially useful for CEP scenarios that require what is commonly known as "active queries".

Drools ReteOO algorithm now supports an option to start the rule base in a multi-thread mode, where Drools ReteOO network is split into multiple partitions and rules are then evaluated concurrently by multiple threads. This is also a requirement for CEP where there usually are several independent rules running concurrently, with near realtime performance/throughput requirements and the evaluation of one can not interfere with the evaluation of others.

Drools now supports XSD models. Remember though the XSD model is generated as pojos local to the Drools classloader. A helper class is there to assist in the creation of the model in the packagebuilder. Once the data model is generated you'll typically use the JAXB dataloader to insert data.

Drools now supports two data loaders, Smooks and JAXB. Smooks is an open source data transformation tool for ETL and JAXB a standard sun data mapping tool. Unit tests showing Smooks can be found here and JAXB here.

In addition to the ability of configuring options in drools through configuration files, system properties and by setting properties through the API setProperty() method, Drools-API now supports type safe configuration. We didn't want to add specific methods for each possible configuration methods for two reasons: it polutes the API and every time a new option is added to Drools, the API would have to change. This way, we followed a modular, class based configuration, where a new Option class is added to the API for each possible configuration, keeping the API stable, but flexible at the same time. So, in order to set configuration options now, you just need to use the enumerations or factories provided for each option. For instance, if you want to configure the knowledge base for assert behavior "equality" and to automatically remove identities from pattern matchings, you would just use the enums:

KnowledgeBaseConfiguration config = KnowledgeBaseFactory.newKnowledgeBaseConfiguration();
config.setOption( AssertBehaviorOption.EQUALITY );
config.setOption( RemoveIdentitiesOption.YES );

For options that don't have a predefined constant or can assume multiple values, a factory method is provided. For instance, to configure the alpha threshold to 5, just use the "get" factory method:

config.setOption( AlphaThresholdOption.get(5) );

As you can see, the same setOption() method is used for the different possible configurations, but they are still type safe.

There are times when it is necessary to collect sets or lists of values that are derived from the facts attributes, but are not facts themselves. In such cases, it was not possible to use the collect CE. So, Drools now has two accumulate functions for such cases: collectSet for collecting sets of values (i.e., with no duplicate values) and collectList for collecting lists of values (i.e., allowing duplicate values):

# collect the set of unique names in the working memory
$names : Set() from accumulate( Person( $n : name, $s : surname ),
                        collectSet( $n + " " + $s ) )

# collect the list of alarm codes from the alarms in the working memory
$codes : List() from accumulate( Alarm( $c : code, $s : severity ),
                         collectList( $c + $s ) )

Facts that implement support for property changes as defined in the Javabean(tm) spec, now can be annotated so that the engine register itself to listen for changes on fact properties. The boolean parameter that was used in the insert() method in the Drools 4 API is deprecated and does not exist in the drools-api module.

declare Person
	@propertyChangeSupport
end

Batch Executor allows for the scripting of of a Knowledge session using Commands, which can also re, both the StatelessKnowledgeSession and StatefulKnowledgeSession implement this interface Commands are created using the CommandFactory and executed using the "execute" method, such as the following insert Command:

ksession.execute( CommandFactory.newInsert( person ) ); 

Typically though you will want to execute a batch of commands, this can be achieved via the composite Command BatchExecution. BatchExecutionResults is now used to handle the results, some commands can specify "out" identifiers which it used to add the result to the BatchExecutionResult. Handily querries can now be executed and results added to the BatchExecutionResult. Further to this results are scoped to this execute call and return via the BatchExecutionResults:

List<Command> cmds = new ArrayList<Command>();
cmds.add( CommandFactory.newSetGlobal( "list1", new ArrayList(), true ) );
cmds.add( CommandFactory.newInsert( new Person( "jon", 102 ), "person" ) );
cmds.add( CommandFactory.newQuery( "Get People" "getPeople" );

BatchExecutionResults results = ksession.execute( CommandFactory.newBatchExecution( cmds ) );
results.getValue( "list1" ); // returns the ArrayList
results.getValue( "person" ); // returns the inserted fact Person
results.getValue( "Get People" );// returns the query as a QueryResults instance.
end

The CommandFactory details the supported commands, all of which can marshalled using XStream and the BatchExecutionHelper. This can be combined with the pipeline to automate the scripting of a session.

Action executeResultHandler = PipelineFactory.newExecuteResultHandler();
Action assignResult = PipelineFactory.newAssignObjectAsResult();
assignResult.setReceiver( executeResultHandler );
Transformer outTransformer = PipelineFactory.newXStreamToXmlTransformer( BatchExecutionHelper.newXStreamMarshaller() );
outTransformer.setReceiver( assignResult );
KnowledgeRuntimeCommand batchExecution = PipelineFactory.newBatchExecutor();
batchExecution.setReceiver( outTransformer );
Transformer inTransformer = PipelineFactory.newXStreamFromXmlTransformer( BatchExecutionHelper.newXStreamMarshaller() );
inTransformer.setReceiver( batchExecution );
Pipeline pipeline = PipelineFactory.newStatelessKnowledgeSessionPipeline( ksession );
pipeline.setReceiver( inTransformer ); 

Using the above for a rulset that updates the price of a Cheese fact, given the following xml to insert a Cheese instance using an out-identifier:

<batch-execution>
<insert out-identifier='outStilton'>
  <org.drools.Cheese>
    <type>stilton</type>
    <price>25</price>
    <oldPrice>0</oldPrice>
  </org.drools.Cheese>
</insert>
</batch-execution>

We then get the following BatchExecutionResults:

<batch-execution-results>
 <result identifier='outStilton'>
   <org.drools.Cheese>
     <type>stilton</type>
     <oldPrice>0</oldPrice>       
     <price>30</price>
   </org.drools.Cheese>
 </result>
</batch-execution-results>

The MarshallerFactory is used to marshal and unmarshal StatefulKnowledgeSessions. At the simplest it can be used as follows:

// ksession is the StatefulKnowledgeSession
// kbase is the KnowledgeBase
ByteArrayOutputStream baos = new ByteArrayOutputStream();
Marshaller marshaller = MarshallerFactory.newMarshaller( kbase );
marshaller.marshall( baos, ksession );
baos.close();

However with marshalling you need more flexibility when dealing with referenced user data. To achieve this we have the ObjectMarshallingStrategy interface. Two implementations are provided, but the user can implement their own. The two supplied are IdentityMarshallingStrategy and SerializeMarshallingStrategy. SerializeMarshallingStrategy is the default, as used in the example above and it just calls the Serializable or Externalizable methods on a user instance. IdentityMarshallingStrategy instead creates an int id for each user object and stores them in a Map the id is written to the stream. When unmarshalling it simply looks to the IdentityMarshallingStrategy map to retrieve the instance. This means that if you use the IdentityMarshallingStrategy it's stateful for the life of the Marshaller instance and will create ids and keep references to all objects that it attempts to marshal.

ByteArrayOutputStream baos = new ByteArrayOutputStream();
Marshaller marshaller = MarshallerFactory.newMarshaller( kbase, new ObjectMarshallingStrategy[] { MarshallerFactory.newIdentityMarshallingStrategy() } );
marshaller.marshall( baos, ksession );
baos.close(); 

For added flexability we can't assume that a single strategy is suitable for this we have added the ObjectMarshallingStrategyAcceptor interface that each ObjectMarshallingStrategy has. The Marshaller has a chain of strategies and when it attempts to read or write a user object it iterates the strategies asking if they accept responsability for marshalling the user object. One one implementation is provided the ClassFilterAcceptor. This allows strings and wild cards to be used to match class names. The default is "*.*", so in the above the IdentityMarshallingStrategy is used which has a default "*.*" acceptor. But lets say we want to serialise all classes except for one given package, where we will use identity lookup, we could do the following:

ByteArrayOutputStream baos = new ByteArrayOutputStream();
ObjectMarshallingStrategyAcceptor identityAceceptor = MarshallerFactory.newClassFilterAcceptor( new String[] { "org.domain.pkg1.*" } );
ObjectMarshallingStrategy identityStratetgy = MarshallerFactory.newIdentityMarshallingStrategy( identityAceceptor );
Marshaller marshaller = MarshallerFactory.newMarshaller( kbase, new ObjectMarshallingStrategy[] { identityStratetgy, MarshallerFactory.newSerializeMarshallingStrategy() } );
marshaller.marshall( baos, ksession );
baos.close();

The KnowlegeAgent is created by the KnowlegeAgentFactory. The KnowlegeAgent provides automatic loading, caching and re-loading, of resources and is configured from a properties files. The KnowledgeAgent can update or rebuild this KnowlegeBase 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. The Following example constructs an agent that will build a new KnowledgeBase from the files specified in the path String. It will poll those files every 30 seconds to see if they are updated. If new files are found it will construct a new KnowledgeBase, instead of updating the existing one, due to the "newInstance" set to "true" (however currently only the value of "true" is supported and is hard coded into the engine):

// Set the interval on the ResourceChangeScannerService if you are to use it and default of 60s is not desirable.
ResourceChangeScannerConfiguration sconf = ResourceFactory.getResourceChangeScannerService().newResourceChangeScannerConfiguration();
sconf.setProperty( "drools.resource.scanner.interval",
                  "30" ); // set the disk scanning interval to 30s, default is 60s
ResourceFactory.getResourceChangeScannerService().configure( sconf );
KnowledgeBase kbase = KnowledgeBaseFactory.newKnowledgeBase();
KnowledgeAgentConfiguration aconf = KnowledgeAgentFactory.newKnowledgeAgentConfiguration();
aconf.setProperty( "drools.agent.scanDirectories",
                  "true" ); // we want to scan directories, not just files, turning this on turns on file scanning
aconf.setProperty( "drools.agent.newInstance",
                  "true" ); // resource changes results in a new instance of the KnowledgeBase being built,
                            // this cannot currently be set to false for incremental building
    
KnowledgeAgent kagent = KnowledgeAgentFactory.newKnowledgeAgent( "test agent", // the name of the agent
                                                                kbase, // the KnowledgeBase to use, the Agent will also monitor any exist knowledge definitions
                                                                aconf );
kagent.applyChangeSet( ResourceFactory.newUrlResource( url ) ); // resource to the change-set xml for the resources to add

KnowledgeAgents can take a empty KnowledgeBase or a populated one. If a populated KnowledgeBase is provided, the KnowledgeAgent will iterate KnowledgeBase and subscribe to the Resource that it finds. While it is possible for the KnowledgeBuilder to build all resources found in a directory, that information is lost by the KnowledgeBuilder so those directories will not be continuously scanned. Only directories specified as part of the applyChangeSet(Resource) method are monitored.

Timers:

A timer node can be added which causes the execution of the node to wait for a specific period. Currently just uses JDK defaults of initial delay and repeat delay, more complex timers will be available in further milestones.

Human Task:

Processes can include tasks that need to be executed by human actors. Human tasks include parameters like taskname, priority, description, actorId, etc. The process engine can easily be integrated with existing human task component (like for example a WS-HumanTask implementation) using our pluggable work items (see below). Swimlanes and assignment rules are also supported.

The palette in the screenshot shows the two new components, and the workflow itself shows the human task in use. It also shows two "work items" which is explained in the next section:

Figure 2.9. Human task

Domain Specific Work Items are pluggable nodes that users create to facilitate custom task execution. They provide an api to specify a new icon in the palette and gui editor for the tasks properties, if no editor gui is supplied then it defaults to a text based key value pair form. The api then allows execution behaviour for these work items to be specified. By default the Email and Log work items are provided. The Drools flow Manual has been updated on how to implement these.

The below image shows three different work items in use in a workflow, "Blood Pressure", "BP Medication", "Notify GP":

Figure 2.10. Work items

This one ows a new "Notification" work item:

Figure 2.11. Notification

Drools 4.0 used Xstream to store it's content, which was not easily human writeable. Drools 5.0 introduced the ePDL which is a XML specific to our process language, it also allows for domain specific extensions which has been talked about in detail in this blog posting "Drools Extensible Process Definition Language (ePDL) and the Semantic Module Framework (SMF)". An example of the XML language, with a DSL extension in red, is shown below.

<process  name="process name" id="process name" package-name="org.domain"
xmlns="http://drools.org/drools-4.0/process"
xmlns:mydsl="http://domain/org/mydsl"
xmlns:xs="http://www.w3.org/2001/XMLSchema-instance"
xs:schemaLocation="http://drools.org/drools-4.0/process drools-processes-4.0.xsd" >
<nodes>
  <start id="0" />

  <action id="1" dialect="java">
      list.add( "action node was here" ); 
  </action>

    <mydsl:logger id="2" type="warn">
        This is my message         
    <mydsl:logger>

  <end id="3" />
</nodes>

<connections>
  <connection from="0 to="1" />
  <connection from="1" to="2" />
  <connection from="2" to="3" />
</connections>

</process>

The underlying nodes for the framework are completely pluggable making it simple to extend and to implement other execution models. We already have a partial implementation for OSWorkflow and are working with Deigo to complete this to provide a migration path for OSWorkflow users. Other enhancements include exception scopes, the ability to include on-entry and on-exit actions on various node types, integration with our binary persistence mechanism to persist the state of long running processes, etc. Check out the Drools Flow documentation to learn more.

Human task management is very important in the context of processes. While we allow users to plug in any task component they prefer, we have developed a human task management component that supports the entire life cycle of human tasks based on the WS-HumanTask specification.

As a result, new node types and properties have been added to the Drools Flow editor in Eclipse. You can also find examples of these new features in the integration tests (e.g. ProcessExceptionHandlerTest, ProcessTimerTest, etc.).

Our pluggable work item approach allows you to plug in domain-specific work in your process in a declarative manner. We plan to build a library of common work items and already provide an implementation for sending emails, finding files, archiving, executing system commands, logging and human tasks.

Improved support for persistence (JPA) and transactions (JTA).

// create a new JPA-based session and specify the JPA entity manager factory
Environment env = KnowledgeBaseFactory.newEnvironment();
env.set( EnvironmentName.ENTITY_MANAGER_FACTORY, Persistence.createEntityManagerFactory( "emf-name" ) );
env.set( EnvironmentName.TRANSACTION_MANAGER, TransactionManagerServices.getTransactionManager() );
        
StatefulKnowledgeSession ksession = JPAKnowledgeService.newStatefulKnowledgeSession( kbase, null, env ); // KnowledgeSessionConfiguration may be null, and a default will be used
int sessionId = ksession.getId();

// if no transaction boundary is specified, the method invocation is executed in a new transaction automatically
ProcessInstance processInstance = ksession.startProcess( "org.drools.test.TestProcess" );

// the users can also specify the transaction boundary themselves
UserTransaction ut = (UserTransaction) new InitialContext().lookup( "java:comp/UserTransaction" );
ut.begin();
ksession.insert( new Person( "John Doe" ) );
ksession.startProcess( "org.drools.test.TestProcess" );
ksession.fireAllRules();
ut.commit();

Support direct access to process variables in both MVEL and Java in code constraints and actions, so if you have a variable called "person" in your process, you can now describe constraints like:

    * [Java code constraint] return person.getAge() > 20;
    * [MVEL action] System.out.println(person.name);

Events are (from a rules engine perspective) a special type of fact that has a few special characteristics:

Any fact type can assume an event role, and its corresponding event semantics, by simply declaring the metadata for it.

# existing bean assuming an event role
import org.drools.test.StockTick
declare StockTick
  @role( event )
end

# generated bean assuming an event role
declare Alarm
  @role( event )
  type : String
  timestamp : long
end

A new key "from entry-point" has been added to allow a pattern in a rule to listen on a stream, which avoids the overhead of having to insert the object into the working memory where it is potentially reasoned over by all rules.

$st : StockTick( company == "ACME", price > 10 ) from entry-point "stock stream"

WorkingMemoryEntryPoint entry = wm.getWorkingMemoryEntryPoint( "stock stream" );
entry.insert( ticker );

StreamTest shows a unit for this.

Event correlation and time based constraint support are requirements of event processing, and are completely supported by Drools 5.0. The new, out of the box, time constraint operators can be seen in these test case rules: test_CEP_TimeRelationalOperators.drl

As seen in the test above, Drools supports both: primitive events, that are point in time occurrences with no duration, and compound events, that are events with distinct start and end timestamps.

The complete list of operators are:

Drools 5.0 adds support for reasoning over sliding windows of events. For instance:

StockTick( symbol == "RHAT" ) over window:time( 60 )

The above example will only pattern match the RHAT stock ticks that happened in the last 60 clock ticks, discarding any event older than that.

Enabling full event processing capabilities requires the ability to configure and interact with a session clock. Drools adds support for time reasoning and session clock configuration, allowing it to not only run real time event processing but also simulations, what-if scenarios and post-processing audit by replaying a scenario.

SessionConfiguration conf = new SessionConfiguration();
conf.setClockType( ClockType.PSEUDO_CLOCK );
StatefulSession session = ruleBase.newStatefulSession( conf );

Since events usually have strong temporal relationships, it is possible to infer a logical time window when events can possibly match. The engine uses that capability to calculate when an event is no longer capable of matching any rule anymore and automatically retracts that event.

Drools adopted a simplified syntax for time units, based on the ISO 8601 syntax for durations. This allows users to easily add temporal constraints to the rules writing time in well known units. Example:

SomeEvent( this after[1m,1h30m] $anotherEvent )

The above pattern will match if SomeEvent happens between 1 minute (1m) and 1 hour and 30 minutes after $anotherEvent.

added the ability to define a per-type event expiration policy. In the example bellow, the StockTick events will expire 10 minutes after they enter the system:

declare StockTick @role( event ) @expires( 10m ) end

Drools 3.0.x had only one working memory type that worked like a stateful working memory. Drools 4.0.x introduces separate APIs for Stateful and Stateless working memories that are called now Rule Sessions. In Drools 3.0.x, the code to create a working memory was:

WorkingMemory wm = rulebase.newWorkingMemory();

In Drools 4.0.x it must be changed to:

StatefulSession wm = rulebase.newStatefulSession();

The StatefulSession object has the same behavior as the Drools 3.0.x WorkingMemory (it even extends the WorkingMemory interface), so there should be no other problems with this fix.

Drools 4.0.x now supports pluggable dialects and has built-in support for Java and MVEL scripting language. In order to avoid keyword conflicts, the working memory actions were renamed as showed bellow:

The Working Memory actions in rule consequences were also changed in a similar way to the change made in the API. The following table summarizes the change:

Drools 3.0.x did not had native support for primitive types and consequently, it auto-boxed all primitives in it's respective wrapper classes. That way, any use of a boxed variable binding required a manual unbox.

Drools 4.0.x has full support for primitive types and does not wrap values anymore. So, all previous unwrap method calls must be removed from the DRL.

rule "Primitive int manual unbox"
when
    $c : Cheese( $price : price )
then
    $c.setPrice( $price.intValue() * 2 )
end

The above rule in 4.0.x would be:

rule "Primitive support"
when
    $c : Cheese( $price : price )
then
    $c.setPrice( $price * 2 )
end

GEF is the Eclipse Graphical Editing Framework, which is used for graph viewing components in the plug-in.

If you don't have GEF installed, you can install it using the built in update mechanism (or downloading GEF from the Eclipse.org website not recommended). JBoss IDE has GEF already, as do many other "distributions" of Eclipse, so this step may be redundant for some people.

Open the Help->Software updates...->Available Software->Add Site... from the help menu. Location is:

http://download.eclipse.org/tools/gef/updates/releases/

Next you choose the GEF plug-in:

Press next, and agree to install the plug-in (an Eclipse restart may be required). Once this is completed, then you can continue on installing the rules plug-in.

To install from the zip file, download and unzip the file. Inside the zip you will see a plug-in directory, and the plug-in jar itself. You place the plug-in jar into your Eclipse applications plug-in directory, and restart Eclipse.

Download the Drools Eclipse IDE plugin from the link below. Unzip the downloaded file in your main eclipse folder (do not just copy the file there, extract it so that the feature and plugin jars end up in the features and plugin directory of eclipse) and (re)start Eclipse.

http://www.jboss.org/drools/downloads.html

To check that the installation was successful, try opening the Drools perspective: Click the 'Open Perspective' button in the top right corner of your Eclipse window, select 'Other...' and pick the Drools perspective. If you cannot find the Drools perspective as one of the possible perspectives, the installation probably was unsuccessful. Check whether you executed each of the required steps correctly: Do you have the right version of Eclipse (3.4.x)? Do you have Eclipse GEF installed (check whether the org.eclipse.gef_3.4.*.jar exists in the plugins directory in your eclipse root folder)? Did you extract the Drools Eclipse plugin correctly (check whether the org.drools.eclipse_*.jar exists in the plugins directory in your eclipse root folder)? If you cannot find the problem, try contacting us (e.g. on irc or on the user mailing list), more info can be found no our homepage here:

http://www.jboss.org/drools/

A Drools runtime is a collection of jars on your file system that represent one specific release of the Drools project jars. To create a runtime, you must point the IDE to the release of your choice. If you want to create a new runtime based on the latest Drools project jars included in the plugin itself, you can also easily do that. You are required to specify a default Drools runtime for your Eclipse workspace, but each individual project can override the default and select the appropriate runtime for that project specifically.

3.1.3.4.1. Defining a Drools runtime

You are required to define one or more Drools runtimes using the Eclipse preferences view. To open up your preferences, in the menu Window select the Preferences menu item. A new preferences dialog should show all your preferences. On the left side of this dialog, under the Drools category, select "Installed Drools runtimes". The panel on the right should then show the currently defined Drools runtimes. If you have not yet defined any runtimes, it should like something like the figure below.

To define a new Drools runtime, click on the add button. A dialog as shown below should pop up, requiring the name for your runtime and the location on your file system where it can be found.

In general, you have two options:

1. If you simply want to use the default jars as included in the Drools Eclipse plugin, you can create a new Drools runtime automatically by clicking the "Create a new Drools 5 runtime ..." button. A file browser will show up, asking you to select the folder on your file system where you want this runtime to be created. The plugin will then automatically copy all required dependencies to the specified folder. After selecting this folder, the dialog should look like the figure shown below.

2. If you want to use one specific release of the Drools project, you should create a folder on your file system that contains all the necessary Drools libraries and dependencies. Instead of creating a new Drools runtime as explained above, give your runtime a name and select the location of this folder containing all the required jars.

After clicking the OK button, the runtime should show up in your table of installed Drools runtimes, as shown below. Click on checkbox in front of the newly created runtime to make it the default Drools runtime. The default Drools runtime will be used as the runtime of all your Drools project that have not selected a project-specific runtime.

You can add as many Drools runtimes as you need. For example, the screenshot below shows a configuration where three runtimes have been defined: a Drools 4.0.7 runtime, a Drools 5.0.0 runtime and a Drools 5.0.0.SNAPSHOT runtime. The Drools 5.0.0 runtime is selected as the default one.

Note that you will need to restart Eclipse if you changed the default runtime and you want to make sure that all the projects that are using the default runtime update their classpath accordingly.

3.1.3.4.2. Selecting a runtime for your Drools project

Whenever you create a Drools project (using the New Drools Project wizard or by converting an existing Java project to a Drools project using the "Convert to Drools Project" action that is shown when you are in the Drools perspective and you right-click an existing Java project), the plugin will automatically add all the required jars to the classpath of your project.

When creating a new Drools project, the plugin will automatically use the default Drools runtime for that project, unless you specify a project-specific one. You can do this in the final step of the New Drools Project wizard, as shown below, by deselecting the "Use default Drools runtime" checkbox and selecting the appropriate runtime in the drop-down box. If you click the "Configure workspace settings ..." link, the workspace preferences showing the currently installed Drools runtimes will be opened, so you can add new runtimes there.

You can change the runtime of a Drools project at any time by opening the project properties (right-click the project and select Properties) and selecting the Drools category, as shown below. Check the "Enable project specific settings" checkbox and select the appropriate runtime from the drop-down box. If you click the "Configure workspace settings ..." link, the workspace preferences showing the currently installed Drools runtimes will be opened, so you can add new runtimes there. If you deselect the "Enable project specific settings" checkbox, it will use the default runtime as defined in your global preferences.