Signing to jboss.org will give you access to the JBoss wiki, forums and JIRA. Go to https://www.jboss.org/ and click "Register".

The only form you need to sign is the contributor agreement, which is fully automated via the web. As the image below says "This establishes the terms and conditions for your contributions and ensures that source code can be licensed appropriately"

https://cla.jboss.org/

To be able to interact with the core development team you will need to use JIRA, the issue tracker. This ensures that all requests are logged and allocated to a release schedule and all discussions captured in one place. Bug reports, bug fixes, feature requests and feature submissions should all go here. General questions should be undertaken at the mailing lists.

Minor code submissions, like format or documentation fixes do not need an associated JIRA issue created.

https://issues.jboss.org/browse/DROOLS

https://issues.jboss.org/browse/JBPM

With the contributor agreement signed and your requests submitted to JIRA you should now be ready to code :) Create a GitHub account and fork any of the Drools, jBPM or Guvnor repositories. The fork will create a copy in your own GitHub space which you can work on at your own pace. If you make a mistake, don’t worry blow it away and fork again. Note each GitHub repository provides you the clone (checkout) URL, GitHub will provide you URLs specific to your fork.

https://github.com/kiegroup

When writing tests, try and keep them minimal and self contained. We prefer to keep the DRL fragments within the test, as it makes for quicker reviewing. If there are a large number of rules then using a String is not practical so then by all means place them in separate DRL files instead to be loaded from the classpath. If your tests need to use a model, please try to use those that already exist for other unit tests; such as Person, Cheese or Order. If no classes exist that have the fields you need, try and update fields of existing classes before adding a new class.

There are a vast number of tests to look over to get an idea, MiscTest is a good place to start.

https://github.com/kiegroup/drools/blob/master/drools-compiler/src/test/java/org/drools/integrationtests/MiscTest.java

When you commit, make sure you use the correct conventions. The commit must start with the JIRA issue id, such as DROOLS-1946. This ensures the commits are cross referenced via JIRA, so we can see all commits for a given issue in the same place. After the id the title of the issue should come next. Then use a newline, indented with a dash, to provide additional information related to this commit. Use an additional new line and dash for each separate point you wish to make. You may add additional JIRA cross references to the same commit, if it’s appropriate. In general try to avoid combining unrelated issues in the same commit.

Don’t forget to rebase your local fork from the primary branch and then push your commits back to your fork.

With your code rebased from primary branch and pushed to your personal GitHub area, you can now submit your work as a pull request. If you look at the top of the page in GitHub for your work area there will be a "Pull Request" button. Selecting this will then provide a gui to automate the submission of your pull request.

The pull request then goes into a queue for everyone to see and comment on. Below you can see a typical pull request. The pull requests allow for discussions and it shows all associated commits and the diffs for each commit. The discussions typically involve code reviews which provide helpful suggestions for improvements, and allows for us to leave inline comments on specific parts of the code. Don’t be disheartened if we don’t merge straight away, it can often take several revisions before we accept a pull request. Luckily GitHub makes it very trivial to go back to your code, do some more commits and then update your pull request to your latest and greatest.

It can take time for us to get round to responding to pull requests, so please be patient. Submitted tests that come with a fix will generally be applied quite quickly, where as just tests will often way until we get time to also submit that with a fix. Don’t forget to rebase and resubmit your request from time to time, otherwise over time it will have merge conflicts and core developers will general ignore those.

Drools engine provides an Eclipse-based IDE, which is optional. However, the Eclipse-based IDE will be deprecated in a future release.

The latest working version of the Eclipse-based IDE is 7.46.0.Final and can be used with the recent versions of Drools.

A simple way to get started is to download and install the Eclipse plug-in - this will also require the Eclipse GEF framework to be installed (see below, if you don’t have it installed already). This will provide you with all the dependencies you need to get going: you can simply create a new rule project and everything will be done for you. Refer to the chapter on Business Central and IDE for detailed instructions on this. Installing the Eclipse plug-in is generally as simple as unzipping a file into your Eclipse plug-in directory.

Use of the Eclipse plug-in is not required. Rule files are just textual input (or spreadsheets as the case may be) and the IDE (also known as Business Central) is just a convenience. People have integrated the Drools engine in many ways, there is no "one size fits all".

Alternatively, you can download the binary distribution, and include the relevant JARs in your projects classpath.

You can use the sample projects in Business Central to explore business assets as a reference for the rules or other assets that you create in your own Drools projects.

A decision requirements diagram (DRD) is a visual representation of your DMN model. Use the DMN designer in Business Central to design the DRD for the traffic violations project and to define the decision logic of the DRD components.

DMN data types determine the structure of the data that you use within a table, column, or field in a DMN boxed expression for defining decision logic. You can use default DMN data types (such as string, number, or boolean) or you can create custom data types to specify additional fields and constraints that you want to implement for the boxed expression values. Use the DMN designer’s Data Types tab in Business Central to define the custom data types for the traffic violations project.

The following tables list the tDriver, tViolation, and tFine custom data types that you will create for this project.

After you create the DMN custom data types, assign them to the appropriate DMN Input Data and DMN Decision nodes in the traffic violations DRD.

You have assigned the custom data types to your DRD’s input and decision nodes.

To calculate the fine and to decide whether the driver is to be suspended or not, you can define the traffic violations DMN decision logic using a DMN decision table and context boxed expression.

Use the test scenarios designer in Business Central to test the DMN decision requirements diagrams (DRDs) and define decision logic for the traffic violations project.

Directly interacting with the REST endpoints of KIE Server provides the most separation between the calling code and the decision logic definition. The calling code is completely free of direct dependencies, and you can implement it in an entirely different development platform such as Node.js or .NET. The examples in this section demonstrate Nix-style curl commands but provide relevant information to adapt to any REST client.

When you use a REST endpoint of KIE Server, the best practice is to define a domain object POJO Java class, annotated with standard KIE Server marshalling annotations. For example, the following code is using a domain object Person class that is annotated properly:

For more information about the KIE Server REST API, see KIE Server REST API for KIE containers and business assets.

KIE is the shared core for Drools and jBPM. It provides a unified methodology and programming model for building, deploying and utilizing resources.

The process of researching an integration knowledge solution for Drools and jBPM has simply used the "kiegroup" group name. This name permeates GitHub accounts and Maven POMs. As scopes broadened and new projects were spun KIE, an acronym for Knowledge Is Everything, was chosen as the new group name. The KIE name is also used for the shared aspects of the system; such as the unified build, deploy and utilization.

KIE currently consists of the following subprojects:

OptaPlanner, a local search and optimization tool, has been spun off from Drools Planner and is now a top level project with Drools and jBPM. This was a natural evolution as Optaplanner, while having strong Drools integration, has long been independent of Drools.

From the Polymita acquisition, along with other things, comes the powerful Dashboard Builder which provides powerful reporting capabilities. Dashboard Builder is currently a temporary name and after the 6.0 release a new name will be chosen. Dashboard Builder is completely independent of Drools and jBPM and will be used by many projects at JBoss, and hopefully outside of JBoss :)

UberFire is the new base Business Central project, spun off from the ground up rewrite. UberFire provides Eclipse-like workbench capabilities, with panels and pages from plugins. The project is independent of Drools and jBPM and anyone can use it as a basis of building flexible and powerful workbenches like Business Central. UberFire will be used for console and workbench development throughout JBoss.

It was determined that the Guvnor brand leaked too much from its intended role; such as the authoring metaphors, like Decision Tables, being considered Guvnor components instead of Drools components. This wasn’t helped by the monolithic projects structure used in 5.x for Guvnor. In 6.0 Guvnor’s focus has been narrowed to encapsulate the set of UberFire plugins that provide the basis for building a web based IDE. Such as Maven integration for building and deploying, management of Maven repositories and activity notifications via inboxes. Drools and jBPM build Business Central distributions using Uberfire as the base and including a set of plugins, such as Guvnor, along with their own plugins for things like decision tables, guided editors, BPMN2 designer, human tasks. Business Central is called business-central.

KIE-WB is the uber workbench that combined all the Guvnor, Drools and jBPM plugins. The jBPM-WB is ghosted out, as it doesn’t actually exist, being made redundant by KIE-WB.

The different aspects, or life cycles, of working with KIE system, whether it’s Drools or jBPM, can typically be broken down into the following:

With Red Hat Process Automation Manager, you can set up a development environment to develop business applications, a runtime environment to run those applications to support decisions, or both.

You can also cluster both development and runtime environments. A clustered development or runtime environment consists of a unified group or cluster of two or more servers. The primary benefit of clustering Red Hat Process Automation Manager development environments is high availability and enhanced collaboration, while the primary benefit of clustering Red Hat Process Automation Manager runtime environments is high availability and load balancing. High availability decreases the chance of data loss when a single server fails. When a server fails, another server fills the gap by providing a copy of the data that was on the failed server. When the failed server comes online again, it resumes its place in the cluster.

Drools supports several assets that you can use to define business decisions for your decision service. Each decision-authoring asset has different advantages, and you might prefer to use one or a combination of multiple assets depending on your goals and needs.

The following table highlights the main decision-authoring assets supported in Drools projects to help you decide or confirm the best method for defining decisions in your decision service.

When you define business decisions, you can also consider using Red Hat build of Kogito for your cloud-native decision services. For more information about getting started with Red Hat build of Kogito microservices, see Getting started with Red Hat build of Kogito in Drools.

As you develop a Drools project, you need to be able to track the versions of your project with a version-controlled repository, manage your project assets in a stable environment, and build your project for testing and deployment. You can use Business Central for all of these tasks, or use a combination of Business Central and external tools and repositories. Drools supports Git repositories for project version control, Apache Maven for project management, and a variety of Maven-based, Java-based, or custom-tool-based build options.

The following options are the main methods for Drools project versioning, storage, and building:

After you develop, test, and build your Drools project, you can deploy the project to begin using the business assets you have created. You can deploy a Drools project to a configured KIE Server, to an embedded Java application, or into a Red Hat OpenShift Container Platform environment for an enhanced containerized implementation.

The following options are the main methods for Drools project deployment:

After you build and deploy your Drools project to KIE Server or other environment, you can execute the deployed assets for testing or for runtime consumption. You can also execute assets locally in addition to or instead of executing them after deployment.

The following options are the main methods for Drools asset execution:

The following scenarios illustrate common variations of Drools installation, asset authoring, project storage, project deployment, and asset execution in a decision management architecture. Each section summarizes the methods and tools used and the advantages for the given architecture. The examples are basic and are only a few of the many combinations you might consider, depending on your specific goals and needs with Drools.

6.0 introduces a new configuration and convention approach to building KIE bases, instead of using the programmatic builder approach in 5.x. The builder is still available to fall back on, as it’s used for the tooling integration.

Building now uses Maven, and aligns with Maven practices. A KIE project or module is simply a Maven Java project or module; with an additional metadata file META-INF/kmodule.xml. The kmodule.xml file is the descriptor that selects resources to KIE bases and configures those KIE bases and sessions. There is also alternative XML support via Spring and OSGi BluePrints.

While standard Maven can build and package KIE resources, it will not provide validation at build time. There is a Maven plugin which is recommended to use to get build time validation. The plugin also generates many classes, making the runtime loading faster too.

The example project layout and Maven POM descriptor is illustrated in the screenshot

KIE uses defaults to minimise the amount of configuration. With an empty kmodule.xml being the simplest configuration. There must always be a kmodule.xml file, even if empty, as it’s used for discovery of the JAR and its contents.

Maven can either 'mvn install' to deploy a KieModule to the local machine, where all other applications on the local machine use it. Or it can 'mvn deploy' to push the KieModule to a remote Maven repository. Building the Application will pull in the KieModule and populate the local Maven repository in the process.

JARs can be deployed in one of two ways. Either added to the classpath, like any other JAR in a Maven dependency listing, or they can be dynamically loaded at runtime. KIE will scan the classpath to find all the JARs with a kmodule.xml in it. Each found JAR is represented by the KieModule interface. The terms classpath KieModule and dynamic KieModule are used to refer to the two loading approaches. While dynamic modules support side by side versioning, classpath modules do not. Further once a module is on the classpath, no other version may be loaded dynamically.

Detailed references for the API are included in the next sections, the impatient can jump straight to the examples section, which is fairly self-explanatory on the different use cases.

The best way to learn the new build system is by example. The source project "drools-examples-api" contains a number of examples, and can be found at GitHub:

https://github.com/kiegroup/drools/tree/6.0.x/drools-examples-api

Each example is described below, the order starts with the simplest (most of the options are defaulted) and working its way up to more complex use cases.

The Deploy use cases shown below all involve mvn install. Remote deployment of JARs in Maven is well covered in Maven literature. Utilize refers to the initial act of loading the resources and providing access to the KIE runtimes. Whereas Run refers to the act of interacting with those runtimes.

Rule assets in Drools are built from executable rule models by default with the standard kie-maven-plugin plugin. Executable rule models are embedded models that provide a Java-based representation of a rule set for execution at build time. The executable model is a more efficient alternative to the standard asset packaging in previous versions of Drools and enables KIE containers and KIE bases to be created more quickly, especially when you have large lists of DRL (Drools Rule Language) files and other Drools assets.

If you do not use the kie-maven-plugin plugin or if the required drools-model-compiler dependency is missing from your project, then rule assets are built without executable models. Therefore, to generate the executable model during build time, ensure that the kie-maven-plugin plugin and drools-model-compiler dependency are added in your project pom.xml file.

Executable rule models provide the following specific advantages for your projects:

The KIE scanner in Drools monitors your Maven repository for new SNAPSHOT versions of your Drools project and then deploys the latest version of the project to a specified KIE container. You can use a KIE scanner in a development environment to maintain your Drools project deployments more efficiently as new versions become available.

The KIE engine is a platform for the modelling and execution of business behavior, using a multitude of declarative abstractions and metaphors, like rules, processes, decision tables and etc.

Many times, the authoring of these metaphors is done by third party groups, be it a different group inside the same company, a group from a partner company, or even anonymous third parties on the internet.

Rules and Processes are designed to execute arbitrary code in order to do their job, but in such cases it might be necessary to constrain what they can do. For instance, it is unlikely a rule should be allowed to create a classloader (what could open the system to an attack) and certainly it should not be allowed to make a call to System.exit().

The Java Platform provides a very comprehensive and well defined security framework that allows users to define policies for what a system can do. The KIE platform leverages that framework and allow application developers to define a specific policy to be applied to any execution of user provided code, be it in rules, processes, work item handlers and etc.

A stateless KIE session is a session that does not use inference to make iterative changes to facts over time. In a stateless KIE session, data from a previous invocation of the KIE session (the previous session state) is discarded between session invocations, whereas in a stateful KIE session, that data is retained. A stateless KIE session behaves similarly to a function in that the results that it produces are determined by the contents of the KIE base and by the data that is passed into the KIE session for execution at a specific point in time. The KIE session has no memory of any data that was passed into the KIE session previously.

Stateless KIE sessions are commonly used for the following use cases:

For example, consider the following driver’s license data model and sample DRL rule:

The Is of valid age rule disqualifies any applicant younger than 18 years old. When the Applicant object is inserted into the Drools engine, the Drools engine evaluates the constraints for each rule and searches for a match. The "objectType" constraint is always implied, after which any number of explicit field constraints are evaluated. The variable $a is a binding variable that references the matched object in the rule consequence.

In this example, the sample rule and all other files in the ~/resources folder of the Drools project are built with the following code:

This code compiles all the rule files found on the class path and adds the result of this compilation, a KieModule object, in the KieContainer.

Finally, the StatelessKieSession object is instantiated from the KieContainer and is executed against specified data:

In a stateless KIE session configuration, the execute() call acts as a combination method that instantiates the KieSession object, adds all the user data and executes user commands, calls fireAllRules(), and then calls dispose(). Therefore, with a stateless KIE session, you do not need to call fireAllRules() or call dispose() after session invocation as you do with a stateful KIE session.

In this case, the specified applicant is under the age of 18, so the application is declined.

For a more complex use case, see the following example. This example uses a stateless KIE session and executes rules against an iterable list of objects, such as a collection.

A stateful KIE session is a session that uses inference to make iterative changes to facts over time. In a stateful KIE session, data from a previous invocation of the KIE session (the previous session state) is retained between session invocations, whereas in a stateless KIE session, that data is discarded.

Stateful KIE sessions are commonly used for the following use cases:

For example, consider the following fire alarm data model and sample DRL rules:

For the When there is a fire turn on the sprinkler rule, when a fire occurs, the instances of the Fire class are created for that room and inserted into the KIE session. The rule adds a constraint for the specific room matched in the Fire instance so that only the sprinkler for that room is checked. When this rule is executed, the sprinkler activates. The other sample rules determine when the alarm is activated or deactivated accordingly.

Whereas a stateless KIE session relies on standard Java syntax to modify a field, a stateful KIE session relies on the modify statement in rules to notify the Drools engine of changes. The Drools engine then reasons over the changes and assesses impact on subsequent rule executions. This process is part of the Drools engine ability to use inference and truth maintenance and is essential in stateful KIE sessions.

In this example, the sample rules and all other files in the ~/resources folder of the Drools project are built with the following code:

This code compiles all the rule files found on the class path and adds the result of this compilation, a KieModule object, in the KieContainer.

Finally, the KieSession object is instantiated from the KieContainer and is executed against specified data:

With the data added, the Drools engine completes all pattern matching but no rules have been executed, so the configured verification message appears. As new data triggers rule conditions, the Drools engine executes rules to activate the alarm and later to cancel the alarm that has been activated:

In this case, a reference is kept for the returned FactHandle object. A fact handle is an internal engine reference to the inserted instance and enables instances to be retracted or modified later.

As this example illustrates, the data and results from previous stateful KIE sessions (the activated alarm) affect the invocation of subsequent sessions (alarm cancellation).

In use cases with large amounts of KIE runtime data and high system activity, KIE sessions might be created and disposed very frequently. A high turnover of KIE sessions is not always time consuming, but when the turnover is repeated millions of times, the process can become a bottleneck and require substantial clean-up effort.

For these high-volume cases, you can use KIE session pools instead of many individual KIE sessions. To use a KIE session pool, you obtain a KIE session pool from a KIE container, define the initial number of KIE sessions in the pool, and create the KIE sessions from that pool as usual:

In this example, the KIE session pool starts with 10 KIE sessions in it, but you can specify the number of KIE sessions that you need. This integer value is the number of KIE sessions that are only initially created in the pool. If required by the running application, the number of KIE sessions in the pool can dynamically grow beyond that value.

After you define a KIE session pool, the next time you use the KIE session as usual and call dispose() on it, the KIE session is reset and pushed back into the pool instead of being destroyed.

KIE session pools typically apply to stateful KIE sessions, but KIE session pools can also affect stateless KIE sessions that you reuse with multiple execute() calls. When you create a stateless KIE session directly from a KIE container, the KIE session continues to internally create a new KIE session for each execute() invocation. Conversely, when you create a stateless KIE session from a KIE session pool, the KIE session internally uses only the specific KIE sessions provided by the pool.

When you finish using a KIE session pool, you can call the shutdown() method on it to avoid memory leaks. Alternatively, you can call dispose() on the KIE container to shut down all the pools created from the KIE container.

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

Consider a government ID department that is responsible for issuing ID cards when children become adults. 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:

The Drools engine supports the following fact equality modes that determine how the Drools engine stores and compares inserted facts:

As an illustration of fact equality modes, consider the following example facts:

In identity mode, facts p1 and p2 are different instances of a Person class and are treated as separate objects because they have separate identities. In equality mode, facts p1 and p2 are treated as the same object because they are composed the same way. This difference in behavior affects how you can interact with fact handles.

For example, assume that you insert facts p1 and p2 into the Drools engine and later you want to retrieve the fact handle for p1. In identity mode, you must specify p1 to return the fact handle for that exact object, whereas in equality mode, you can specify p1, p2, or new Person("John", 45) to return the fact handle.

To set the fact equality mode, use one of the following options:

Each rule has an integer salience attribute that determines the order of execution. Rules with a higher salience value are given higher priority when ordered in the activation queue. The default salience value for rules is zero, but the salience can be negative or positive.

For example, the following sample DRL rules are listed in the Drools engine stack in the order shown:

The RuleB rule is listed second, but it has a higher salience value than the RuleA rule and is therefore executed first.

An agenda group is a set of rules bound together by the same agenda-group rule attribute. Agenda groups partition rules on the Drools engine agenda. At any one time, only one group has a focus that gives that group of rules priority for execution before rules in other agenda groups. You determine the focus with a setFocus() call for the agenda group. You can also define rules with an auto-focus attribute so that the next time the rule is activated, the focus is automatically given to the entire agenda group to which the rule is assigned.

Each time the setFocus() call is made in a Java application, the Drools engine adds the specified agenda group to the top of the rule stack. The default agenda group "MAIN" contains all rules that do not belong to a specified agenda group and is executed first in the stack unless another group has the focus.

For example, the following sample DRL rules belong to specified agenda groups and are listed in the Drools engine stack in the order shown:

For this example, the rules in the "report" agenda group must always be executed first and the rules in the "calculation" agenda group must always be executed second. Any remaining rules in other agenda groups can then be executed. Therefore, the "report" and "calculation" groups must receive the focus to be executed in that order, before other rules can be executed:

You can also use the clear() method to cancel all the activations generated by the rules belonging to a given agenda group before each has had a chance to be executed:

An activation group is a set of rules bound together by the same activation-group rule attribute. In this group, only one rule can be executed. After conditions are met for a rule in that group to be executed, all other pending rule executions from that activation group are removed from the agenda.

For example, the following sample DRL rules belong to the specified activation group and are listed in the Drools engine stack in the order shown:

For this example, if the first rule in the "report" activation group is executed, the second rule in the group and all other executable rules on the agenda are removed from the agenda.

The Drools engine supports the following rule execution modes that determine how and when the Drools engine executes rules:

Although you should avoid using both fireAllRules() and fireUntilHalt() calls, especially from different threads, the Drools engine can handle such situations safely using thread-safety logic and an internal state machine. If a fireAllRules() call is in progress and you call fireUntilHalt(), the Drools engine continues to run in passive mode until the fireAllRules() operation is complete and then starts in active mode in response to the fireUntilHalt() call. However, if the Drools engine is running in active mode following a fireUntilHalt() call and you call fireAllRules(), the fireAllRules() call is ignored and the Drools engine continues to run in active mode until you call halt(). For more details about thread-safety and the internal state machine, see Improved multi-threading behaviour.

For added thread safety in active mode, the Drools engine supports a submit() method that you can use to group and perform operations on a KIE session in a thread-safe, atomic action:

Thread safety and atomic operations are also helpful from a client-side perspective. For example, you might need to insert more than one fact at a given time, but require the Drools engine to consider the insertions as an atomic operation and to wait until all the insertions are complete before evaluating the rules again.

The Drools engine supports the following fact propagation modes that determine how the Drools engine progresses inserted facts through the engine network in preparation for rule execution:

By default, the Phreak rule algorithm in the Drools engine uses lazy fact propagation for improved rule evaluation overall. However, in few cases, this lazy propagation behavior can alter the expected result of certain rule executions that may require immediate or eager propagation.

For example, the following rule uses a specified query with a ? prefix to invoke the query in pull-only or passive fashion:

For this example, the rule should be executed only when a String that satisfies the query is inserted before the Integer, such as in the following example commands:

However, due to the default lazy propagation behavior in Phreak, the Drools engine does not detect the insertion sequence of the two facts in this case, so this rule is executed regardless of String and Integer insertion order. For this example, immediate propagation is required for the expected rule evaluation.

To alter the Drools engine propagation mode to achieve the expected rule evaluation in this case, you can add the @Propagation(<type>) tag to your rule and set <type> to LAZY, IMMEDIATE, or EAGER.

In the same example rule, the immediate propagation annotation enables the rule to be evaluated only when a String that satisfies the query is inserted before the Integer, as expected:

The Drools engine supports an AgendaFilter object in the filter interface that you can use to allow or deny the evaluation of specified rules during agenda evaluation. You can specify an agenda filter as part of a fireAllRules() call.

The following example code permits only rules ending with the string "Test" to be evaluated and executed. All other rules are filtered out of the Drools engine agenda.

When the Drools engine starts, all rules are considered to be unlinked from pattern-matching data that can trigger the rules. At this stage, the Phreak algorithm in the Drools engine does not evaluate the rules. The insert, update, and delete actions are queued, and Phreak uses a heuristic, based on the rule most likely to result in execution, to calculate and select the next rule for evaluation. When all the required input values are populated for a rule, the rule is considered to be linked to the relevant pattern-matching data. Phreak then creates a goal that represents this rule and places the goal into a priority queue that is ordered by rule salience. Only the rule for which the goal was created is evaluated, and other potential rule evaluations are delayed. While individual rules are evaluated, node sharing is still achieved through the process of segmentation.

Unlike the tuple-oriented Rete, the Phreak propagation is collection oriented. For the rule that is being evaluated, the Drools engine accesses the first node and processes all queued insert, update, and delete actions. The results are added to a set, and the set is propagated to the child node. In the child node, all queued insert, update, and delete actions are processed, adding the results to the same set. The set is then propagated to the next child node and the same process repeats until it reaches the terminal node. This cycle creates a batch process effect that can provide performance advantages for certain rule constructs.

The linking and unlinking of rules happens through a layered bit-mask system, based on network segmentation. When the rule network is built, segments are created for rule network nodes that are shared by the same set of rules. A rule is composed of a path of segments. In case a rule does not share any node with any other rule, it becomes a single segment.

A bit-mask offset is assigned to each node in the segment. Another bit mask is assigned to each segment in the path of the rule according to these requirements:

The same bit-mask technique is used to track modified nodes, segments, and rules. This tracking ability enables an already linked rule to be unscheduled from evaluation if it has been modified since the evaluation goal for it was created. As a result, no rules can ever evaluate partial matches.

This process of rule evaluation is possible in Phreak because, as opposed to a single unit of memory in Rete, Phreak has three layers of contextual memory with node, segment, and rule memory types. This layering enables much more contextual understanding during the evaluation of a rule.

The following examples illustrate how rules are organized and evaluated in this three-layered memory system in Phreak.

Example 1: A single rule (R1) with three patterns: A, B and C. The rule forms a single segment, with bits 1, 2, and 4 for the nodes. The single segment has a bit offset of 1.

Example 2: Rule R2 is added and shares pattern A.

Pattern A is placed in its own segment, resulting in two segments for each rule. Those two segments form a path for their respective rules. The first segment is shared by both paths. When pattern A is linked, the segment becomes linked. The segment then iterates over each path that the segment is shared by, setting the bit 1 to on. If patterns B and C are later turned on, the second segment for path R1 is linked, and this causes bit 2 to be turned on for R1. With bit 1 and bit 2 turned on for R1, the rule is now linked and a goal is created to schedule the rule for later evaluation and execution.

When a rule is evaluated, the segments enable the results of the matching to be shared. Each segment has a staging memory to queue all inserts, updates, and deletes for that segment. When R1 is evaluated, the rule processes pattern A, and this results in a set of tuples. The algorithm detects a segmentation split, creates peered tuples for each insert, update, and delete in the set, and adds them to the R2 staging memory. Those tuples are then merged with any existing staged tuples and are executed when R2 is eventually evaluated.

Example 3: Rules R3 and R4 are added and share patterns A and B.

Rules R3 and R4 have three segments and R1 has two segments. Patterns A and B are shared by R1, R3, and R4, while pattern D is shared by R3 and R4.

Example 4: A single rule (R1) with a subnetwork and no pattern sharing.

Subnetworks are formed when a Not, Exists, or Accumulate node contains more than one element. In this example, the element B not( C ) forms the subnetwork. The element not( C ) is a single element that does not require a subnetwork and is therefore merged inside of the Not node. The subnetwork uses a dedicated segment. Rule R1 still has a path of two segments and the subnetwork forms another inner path. When the subnetwork is linked, it is also linked in the outer segment.

Example 5: Rule R1 with a subnetwork that is shared by rule R2.

The subnetwork nodes in a rule can be shared by another rule that does not have a subnetwork. This sharing causes the subnetwork segment to be split into two segments.

Constrained Not nodes and Accumulate nodes can never unlink a segment, and are always considered to have their bits turned on.

The Phreak evaluation algorithm is stack based instead of method-recursion based. Rule evaluation can be paused and resumed at any time when a StackEntry is used to represent the node currently being evaluated.

When a rule evaluation reaches a subnetwork, a StackEntry object is created for the outer path segment and the subnetwork segment. The subnetwork segment is evaluated first, and when the set reaches the end of the subnetwork path, the segment is merged into a staging list for the outer node that the segment feeds into. The previous StackEntry object is then resumed and can now process the results of the subnetwork. This process has the added benefit, especially for Accumulate nodes, that all work is completed in a batch, before propagating to the child node.

The same stack system is used for efficient backward chaining. When a rule evaluation reaches a query node, the evaluation is paused and the query is added to the stack. The query is then evaluated to produce a result set, which is saved in a memory location for the resumed StackEntry object to pick up and propagate to the child node. If the query itself called other queries, the process repeats, while the current query is paused and a new evaluation is set up for the current query node.

Drools contains a RuleBaseConfiguration.java object that you can use to configure exception handler settings, multithreaded execution, and sequential mode in the Drools engine.

For the rule base configuration options, see the Drools RuleBaseConfiguration.java page in GitHub.

The following rule base configuration options are available for the Drools engine:

Sequential mode is an advanced rule base configuration in the Drools engine, supported by Phreak, that enables the Drools engine to evaluate rules one time in the order that they are listed in the Drools engine agenda without regard to changes in the working memory. In sequential mode, the Drools engine ignores any insert, modify, or update statements in rules and executes rules in a single sequence. As a result, rule execution may be faster in sequential mode, but important updates may not be applied to your rules.

Sequential mode applies to only stateless KIE sessions because stateful KIE sessions inherently use data from previously invoked KIE sessions. If you use a stateless KIE session and you want the execution of rules to influence subsequent rules in the agenda, then do not enable sequential mode. Sequential mode is disabled by default in the Drools engine.

To enable sequential mode, use one of the following options:

To configure sequential mode to use a dynamic agenda, use one of the following options:

When you enable sequential mode, the Drools engine evaluates rules in the following way:

In sequential mode, the LeftInputAdapterNode node creates a Command object and adds it to a list in the working memory of the Drools engine. This Command object contains references to the LeftInputAdapterNode node and the propagated object. These references stop any left-input propagations at insertion time so that the right-input propagation never needs to attempt to join the left inputs. The references also avoid the need for the left-input memory.

All nodes have their memory turned off, including the left-input tuple memory, but excluding the right-input object memory. After all the assertions are finished and the right-input memory of all the objects is populated, the Drools engine iterates over the list of LeftInputAdatperNode Command objects. The objects propagate down the network, attempting to join the right-input objects, but they are not retained in the left input.

The agenda with a priority queue to schedule the tuples is replaced by an element for each rule. The sequence number of the RuleTerminalNode node indicates the element where to place the match. After all Command objects have finished, the elements are checked and existing matches are executed. To improve performance, the first and the last populated cell in the elements are retained.

When the network is constructed, each RuleTerminalNode node receives a sequence number based on its salience number and the order in which it was added to the network.

The right-input node memories are typically hash maps for fast object deletion. Because object deletions are not supported, Phreak uses an object list when the values of the object are not indexed. For a large number of objects, indexed hash maps provide a performance increase. If an object has only a few instances, Phreak uses an object list instead of an index.

In Drools, an event is a record of a significant change of state in the application domain at a point in time. Depending on how the domain is modeled, the change of state may be represented by a single event, multiple atomic events, or hierarchies of correlated events. From a complex event processing (CEP) perspective, an event is a type of fact or object that occurs at a specific point in time, and a business rule is a definition of how to react to the data from that fact or object. For example, in a stock broker application, a change in security prices, a change in ownership from seller to buyer, or a change in an account holder’s balance are all considered to be events because a change has occurred in the state of the application domain at a given time.

Events have the following key characteristics:

You can declare facts as events in your Java class or DRL rule file so that the Drools engine handles the facts as events during complex event processing. You can declare the facts as interval-based events or point-in-time events. Interval-based events have a duration time and persist in the working memory of the Drools engine until their duration time has lapsed. Point-in-time events have no duration and are essentially interval-based events with a duration of zero.

For the relevant fact type in your Java class or DRL rule file, enter the @role( event ) metadata tag and parameter. The @role metadata tag accepts the following two values:

For example, the following snippet declares that the StockPoint fact type in a stock broker application must be handled as an event:

If StockPoint is a fact type declared in the DRL rule file instead of in a pre-existing class, you can declare the event in-line in your application code:

The Drools engine uses the following metadata tags for events that are inserted into the working memory of the Drools engine. You can change the default metadata tag values in your Java class or DRL rule file as needed.

The Drools engine runs in either cloud mode or stream mode. In cloud mode, the Drools engine processes facts as facts with no temporal constraints, independent of time, and in no particular order. In stream mode, the Drools engine processes facts as events with strong temporal constraints, in real time or near real time. Stream mode uses synchronization to make event processing possible in Drools.

By default, the Drools engine does not re-evaluate all fact patterns for fact types each time a rule is triggered, but instead reacts only to modified properties that are constrained or bound inside a given pattern. For example, if a rule calls modify() as part of the rule actions but the action does not generate new data in the KIE base, the Drools engine does not automatically re-evaluate all fact patterns because no data was modified. This property reactivity behavior prevents unwanted recursions in the KIE base and results in more efficient rule evaluation. This behavior also means that you do not always need to use the no-loop rule attribute to avoid infinite recursion.

You can modify or disable this property reactivity behavior with the following KnowledgeBuilderConfiguration options, and then use a property-change setting in your Java class or DRL files to fine-tune property reactivity as needed:

Alternatively, you can update the drools.propertySpecific system property in the standalone.xml file of your Drools distribution:

The Drools engine supports the following property-change settings and listeners for fact classes or declared DRL fact types:

In stream mode, the Drools engine supports the following temporal operators for events that are inserted into the working memory of the Drools engine. You can use these operators to define the temporal reasoning behavior of the events that you declare in your Java class or DRL rule file. Temporal operators are not supported when the Drools engine is running in cloud mode.