# Drools Documentation

The Drools Team <https://www.drools.org/community/team.html>
version 7.19.0.Final

# Welcome

## 1. Introduction

### 1.1. Introduction

KIE (Knowledge Is Everything) is an umbrella project introduced to bring our related technologies together under one roof. It also acts as the core shared between our projects.

KIE contains the following different but related projects offering a complete portfolio of solutions for business automation and management:

1. Drools is a business-rule management system with a forward-chaining and backward-chaining inference-based rules engine, allowing fast and reliable evaluation of business rules and complex event processing. A rules engine is also a fundamental building block to create an expert system which, in artificial intelligence, is a computer system that emulates the decision-making ability of a human expert.

2. jBPM is a flexible Business Process Management suite allowing you to model your business goals by describing the steps that need to be executed to achieve those goals.

3. OptaPlanner is a constraint solver that optimizes use cases such as employee rostering, vehicle routing, task assignment and cloud optimization.

4. Business Central is is a full featured web application for the visual composition of custom business rules and processes.

5. UberFire is a web-based workbench framework inspired by Eclipse Rich Client Platform.

The 7.x series will follow a more agile approach with more regular and iterative releases. We plan to do some bigger changes than normal for a series of minor releases, and users need to be aware those are coming before adopting.

1. UI sections and links will become object oriented, rather than task oriented. https://en.wikipedia.org/wiki/Object-oriented_user_interface

2. Authoring/Library will become project oriented, rather than repository oriented. You’ll create, browse and open projects rather than repositories. The repository concept will be pushed lower, for instance it’ll be created automaticaly when you create the projcet.

3. The old form modeller will be removed and only the new one made available. Although old forms will continue to render.

4. The new designer will continue to mature with more nodes and improved UXD. Eventually it’ll become the default editor, but we will not remove the old one until there is feature parity in BPMN2 support.

5. Continued UXD improvements in lots of places.

6. We will introduce the AppFormer project, this will be a re-org and consolidation of existing projects and result in some artifact renames. UberFire will become AppFormer-Core, forms, data modeller and dashbuilder will come under AppFormer. Dashbuilder will most likely becalled Appformer-Insight.

The 8.x series will come towards the end of this year. We have ongoing parallel work to introduce concepts of workspaces with improved git support, that will have a built in workflow for forking and pull requests. This will be combined with horizontal scaling and improved high availability. These changes are important for usability and cloud scalability, but too much of a change for a minor release, hence the bump to 8.x

### 1.2. Getting Involved

We are often asked "How do I get involved". Luckily the answer is simple, just write some code and submit it :) There are no hoops you have to jump through or secret handshakes. We have a very minimal "overhead" that we do request to allow for scalable project development. Below we provide a general overview of the tools and "workflow" we request, along with some general advice.

If you contribute some good work, don’t forget to blog about it :)

#### 1.2.1. Sign up to jboss.org

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

#### 1.2.2. Sign the Contributor Agreement

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/

#### 1.2.3. Submitting issues via JIRA

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

#### 1.2.4. Fork GitHub

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

#### 1.2.5. Writing Tests

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 their 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

#### 1.2.6. Commit with Correct Conventions

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 original master and then push your commits back to your fork.

#### 1.2.7. Submit Pull Requests

With your code rebased from original master 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 their 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.

### 1.3. Installation and Setup (Core and IDE)

#### 1.3.1. Installing and using

Drools provides an Eclipse-based IDE (which is optional), but at its core only Java 1.5 (Java SE) is required.

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.

##### 1.3.1.1. Dependencies and JARs

Drools is broken down into a few modules, some are required during rule development/compiling, and some are required at runtime. In many cases, people will simply want to include all the dependencies at runtime, and this is fine. It allows you to have the most flexibility. However, some may prefer to have their "runtime" stripped down to the bare minimum, as they will be deploying rules in binary form - this is also possible. The core Drools engine can be quite compact, and only requires a few 100 kilobytes across 3 JAR files.

The following is a description of the important libraries that make up JBoss Drools

- knowledge-api.jar - this provides the interfaces and factories. It also helps clearly show what is intended as a user API and what is just an engine API.

- knowledge-internal-api.jar - this provides internal interfaces and factories.

- drools-core.jar - this is the core Drools engine, runtime component. Contains both the RETE engine and the LEAPS engine. This is the only runtime dependency if you are pre-compiling rules (and deploying via Package or RuleBase objects).

- drools-compiler.jar - this contains the compiler/builder components to take rule source, and build executable rule bases. This is often a runtime dependency of your application, but it need not be if you are pre-compiling your rules. This depends on drools-core.

- drools-jsr94.jar - this is the JSR-94 compliant implementation, this is essentially a layer over the drools-compiler component. Note that due to the nature of the JSR-94 specification, not all features are easily exposed via this interface. In some cases, it will be easier to go direct to the Drools API, but in some environments the JSR-94 is mandated.

- drools-decisiontables.jar - this is the decision tables 'compiler' component, which uses the drools-compiler component. This supports both excel and CSV input formats.

There are quite a few other dependencies which the above components require, most of which are for the drools-compiler, drools-jsr94 or drools-decisiontables module. Some key ones to note are "POI" which provides the spreadsheet parsing ability, and "antlr" which provides the parsing for the rule language itself.

| if you are using Drools in J2EE or servlet containers and you come across classpath issues with "JDT", then you can switch to the janino compiler. Set the system property "drools.compiler": For example: -Ddrools.compiler=JANINO.

For up to date info on dependencies in a release, consult the released POMs, which can be found on the Maven repository.

##### 1.3.1.2. Use with Maven, Gradle, Ivy, Buildr or Ant

The JARs are also available in the central Maven repository (and also in https://repository.jboss.org/nexus/index.html#nexus-search;gavorg.drools~[the JBoss Maven repository]).

If you use Maven, add KIE and Drools dependencies in your project’s pom.xml like this:

      <dependencyManagement>
        <dependencies>
          <dependency>
            <groupId>org.drools</groupId>
            <artifactId>drools-bom</artifactId>
            <type>pom</type>
            <version>...</version>
            <scope>import</scope>
          </dependency>
          ...
        </dependencies>
      </dependencyManagement>
      <dependencies>
        <dependency>
          <groupId>org.kie</groupId>
          <artifactId>kie-api</artifactId>
        </dependency>
        <dependency>
          <groupId>org.drools</groupId>
          <artifactId>drools-compiler</artifactId>
          <scope>runtime</scope>
        </dependency>
        ...
      <dependencies>

This is similar for Gradle, Ivy and Buildr. To identify the latest version, check the Maven repository.

If you’re still using Ant (without Ivy), copy all the JARs from the download zip’s binaries directory and manually verify that your classpath doesn’t contain duplicate JARs.

##### 1.3.1.3. Runtime

The "runtime" requirements mentioned here are if you are deploying rules as their binary form (either as KnowledgePackage objects, or KnowledgeBase objects etc). This is an optional feature that allows you to keep your runtime very light. You may use drools-compiler to produce rule packages "out of process", and then deploy them to a runtime system. This runtime system only requires drools-core.jar and knowledge-api for execution. This is an optional deployment pattern, and many people do not need to "trim" their application this much, but it is an ideal option for certain environments.

##### 1.3.1.4. Installing IDE (Rule Workbench)

The rule workbench (for Eclipse) requires that you have Eclipse 3.4 or greater, as well as Eclipse GEF 3.4 or greater. You can install it either by downloading the plug-in or using the update site.

Another option is to use the JBoss IDE, which comes with all the plug-in requirements pre packaged, as well as a choice of other tools separate to rules. You can choose just to install rules from the "bundle" that JBoss IDE ships with.

###### Installing GEF (a required dependency)

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.

###### Installing GEF from zip file

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.

###### Installing Drools plug-in from zip file

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.

https://www.drools.org/download/download.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:

https://www.drools.org/

###### Drools Runtimes

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.

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.

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.

#### 1.3.2. Building from source

##### 1.3.2.1. Getting the sources

The source code of each Maven artifact is available in the JBoss Maven repository as a source JAR. The same source JARs are also included in the download zips. However, if you want to build from source, it’s highly recommended to get our sources from our source control.

Drools and jBPM use Git for source control. The blessed git repositories are hosted on GitHub:

- https://github.com/kiegroup

Git allows you to fork our code, independently make personal changes on it, yet still merge in our latest changes regularly and optionally share your changes with us. To learn more about git, read the free book Git Pro.

##### 1.3.2.2. Building the sources

In essense, building from source is very easy, for example if you want to build the guvnor project:

    $ git clone git@github.com:kiegroup/guvnor.git
    ...
    $ cd guvnor
    $ mvn clean install -DskipTests -Dfull
    ...

However, there are a lot potential pitfalls, so if you’re serious about building from source and possibly contributing to the project, follow the instructions in the README file in droolsjbpm-build-bootstrap.

#### 1.3.3. Eclipse

##### 1.3.3.1. Importing Eclipse Projects

With the Eclipse project files generated they can now be imported into Eclipse. When starting Eclipse open the workspace in the root of your subversion checkout.

When calling `mvn install` all the project dependencies were downloaded and added to the local Maven repository. Eclipse cannot find those dependencies unless you tell it where that repository is. To do this setup an `M2_REPO` classpath variable.

# KIE

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

## 2. KIE

### 2.1. Overview

#### 2.1.1. Anatomy of Projects

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:

Figure 1. KIE Anatomy

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.

#### 2.1.2. Lifecycles

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:

- Author

  - Authoring of knowledge using a UI metaphor, such as: DRL, BPMN2, decision table, class models.

- Build

  - Builds the authored knowledge into deployable units.

  - For KIE this unit is a JAR.

- Test

  - Test KIE knowledge before it’s deployed to the application.

- Deploy

  - Deploys the unit to a location where applications may utilize (consume) them.

  - KIE uses Maven style repository.

- Utilize

  - The loading of a JAR to provide a KIE session (KieSession), for which the application can interact with.

  - KIE exposes the JAR at runtime via a KIE container (KieContainer).

  - KieSessions, for the runtime’s to interact with, are created from the KieContainer.

- Run

  - System interaction with the KieSession, via API.

- Work

  - User interaction with the KieSession, via command line or UI.

- Manage

  - Manage any KieSession or KieContainer.

#### 2.1.3. Installation environment options for Drools

With Drools, you can set up a development environment to develop business applications, a runtime environment to run those applications to support decisions, or both.

- Development environment: Typically consists of one Business Central installation and at least one KIE Server installation. You can use Business Central to design decisions and other artifacts, and you can use KIE Server to execute and test the artifacts that you created.

- Runtime environment: Consists of one or more KIE Server instances with or without Business Central. Business Central has an embedded Drools controller. If you install Business Central, use the Menu → Deploy → Execution servers page to create and maintain containers. If you want to automate KIE Server management without Business Central, you can use the headless Drools controller.

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 Drools development environments is high availability and enhanced collaboration, while the primary benefit of clustering Drools runtime environments is high availability and load balancing. High availability decreases the chance of a loss of data 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. Load balancing shares the computing load across the nodes of the cluster to improve the overall performance.

| Clustering of the runtime environment is currently supported on Red Hat JBoss EAP 7.2 only. Clustering of Business Central is currently a Technology Preview feature that is not yet intended for production use.

Additional resources

- Installation and Setup (Core and IDE)

- [_wb.advancedsettings]

#### 2.1.4. Decision-authoring assets in Drools

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.

Table 1. Decision-authoring assets supported in Drools
Asset | Highlights | Authoring tools | Documentation
--- | --- | --- | ---
Decision Model and Notation (DMN) models | Are decision models based on a notation standard defined by the Object Management Group (OMG) Use graphical decision requirements diagrams (DRDs) with one or more decision requirements graphs (DRGs) to trace business decision flows Use an XML schema that allows the DMN models to be shared between DMN-compliant platforms Support Friendly Enough Expression Language (FEEL) to define decision logic in DMN decision tables and other DMN boxed expressions Are optimal for creating comprehensive, illustrative, and stable decision flows | Business Central or other DMN-compliant editor | Decision Model and Notation (DMN)
Guided decision tables | Are tables of rules that you create in a UI-based table designer in Business Central Are a wizard-led alternative to spreadsheet decision tables Provide fields and options for acceptable input Support template keys and values for creating rule templates Support hit policies, real-time validation, and other additional features not supported in other assets Are optimal for creating rules in a controlled tabular format to minimize compilation errors | Business Central | [guided-decision-tables-con]
Spreadsheet decision tables | Are XLS or XLSX spreadsheet decision tables that you can upload into Business Central Support template keys and values for creating rule templates Are optimal for creating rules in decision tables already managed outside of Business Central Have strict syntax requirements for rules to be compiled properly when uploaded | Spreadsheet editor | [decision-tables-con_decision-tables]
Guided rules | Are individual rules that you create in a UI-based rule designer in Business Central Provide fields and options for acceptable input Are optimal for creating single rules in a controlled format to minimize compilation errors | Business Central | [guided-rules-con_kie-apis]
Guided rule templates | Are reusable rule structures that you create in a UI-based template designer in Business Central Provide fields and options for acceptable input Support template keys and values for creating rule templates (fundamental to the purpose of this asset) Are optimal for creating many rules with the same rule structure but with different defined field values | Business Central | [guided-rule-templates-con]
DRL rules | Are individual rules that you define directly in `.drl` text files Provide the most flexibility for defining rules and other technicalities of rule behavior Can be created in certain standalone environments and integrated with Drools Are optimal for creating rules that require advanced DRL options Have strict syntax requirements for rules to be compiled properly | Business Central or integrated development environment (IDE) | [drl-rules-con_decision-tables]
Predictive Model Markup Language (PMML) models | Are predictive data-analytic models based on a notation standard defined by the Data Mining Group (DMG) Use an XML schema that allows the PMML models to be shared between PMML-compliant platforms Support Regression, Scorecard, Tree, Mining, and other model types Can be included with a standalone Drools project or imported into a project in Business Central Are optimal for incorporating predictive data into decision services in Drools | PMML or XML editor | [pmml-con_pmml-models]

#### 2.1.5. Project storage and build options with 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:

Table 2. Project version control options (Git)
Versioning option | Description | Documentation
--- | --- | ---
Business Central Git VFS | Business Central contains a built-in Git Virtual File System (VFS) that stores all processes, rules, and other artifacts that you create in the authoring environment. Git is a distributed version control system that implements revisions as commit objects. When you commit your changes into a repository, a new commit object in the Git repository is created. When you create a project in Business Central, the project is added to the Git repository connected to Business Central. | NA
External Git repository | If you have Drools projects in Git repositories outside of Business Central, you can import them into Drools spaces and use Git hooks to synchronize the internal and external Git repositories. | NA

Table 3. Project management options (Maven)
Management option | Description | Documentation
--- | --- | ---
Business Central Maven repository | Business Central contains a built-in Maven repository that organizes and builds project assets that you create in the authoring environment. Maven is a distributed build-automation tool that uses repositories to store Java libraries, plug-ins, and other build artifacts. When building projects and archetypes, Maven dynamically retrieves Java libraries and Maven plug-ins from local or remote repositories to promote shared dependencies across projects.
| For a production environment, consider using an external Maven repository configured with Business Central.
| [_wb.artifactrepository]
External Maven repository | If you have Drools projects in an external Maven repository, such as Nexus or Artifactory, you can create a `settings.xml` file with connection details and add that file path to the `kie.maven.settings.custom` property in your project `standalone-full.xml` file. | Maven Settings Reference Settings.xml and Remote Repository Setup

Table 4. Project build options
Build option | Description | Documentation
--- | --- | ---
Business Central (KJAR) | Business Central builds Drools projects stored in either the built-in Maven repository or a configured external Maven repository. Projects in Business Central are packaged automatically as knowledge JAR (KJAR) files with all components needed for deployment when you build the projects. | [_wb.quickstartbuildanddeloy]
Standalone Maven project (KJAR) | If you have a standalone Drools Maven project outside of Business Central, you can edit the project `pom.xml` file to package your project as a KJAR file, and then add a `kmodule.xml` file with the KIE base and KIE session configurations needed to build the project. | Building with Maven
Embedded Java application (KJAR) | If you have an embedded Java application from which you want to build your Drools project, you can use a `KieModuleModel` instance to programatically create a `kmodule.xml` file with the KIE base and KIE session configurations, and then add all resources in your project to the KIE virtual file system `KieFileSystem` to build the project. | Defining a KieModule programmatically
CI/CD tool (KJAR) | If you use a tool for continuous integration and continuous delivery (CI/CD), you can configure the tool set to integrate with your Drools Git repositories to build a specified project. Ensure that your projects are packaged and built as KJAR files to ensure optimal deployment. | NA

#### 2.1.6. Project deployment options with Drools

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:

Table 5. Project deployment options
Deployment option | Description | Documentation
--- | --- | ---
Deployment to KIE Server | KIE Server is the server provided with Drools that runs the decision services, process applications, and other deployable assets from a packaged and deployed Drools project (KJAR file). These services are consumed at run time through an instantiated KIE container, or deployment unit. You can deploy and maintain deployment units in KIE Server using Business Central or using a headless Drools controller with its associated REST API (considered a managed KIE Server instance). You can also deploy and maintain deployment units using the KIE Server REST API or Java client API from a standalone Maven project, an embedded Java application, or other custom environment (considered an unmanaged KIE Server instance). | Deploying [_ch.kie.server]
Deployment to an embedded Java application | If you want to deploy Drools projects to your own Java virtual machine (JVM) environment, microservice, or application server, you can bundle the application resources in the project WAR files to create a deployment unit similar to a KIE container. You can also use the core KIE APIs (not KIE Server APIs) to configure a KIE scanner to periodically update KIE containers. | KIE Public API

#### 2.1.7. Asset execution options with Drools

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:

Table 6. Asset execution options
Execution option | Description | Documentation
--- | --- | ---
Execution in KIE Server | If you deployed Drools project assets to KIE Server, you can use the KIE Server REST API or Java client API to execute and interact with the deployed assets. You can also use Business Central or the headless Drools controller outside of Business Central to manage the configurations and KIE containers in the KIE Server instances associated with your deployed assets. | [_ch.kie.server]
Execution in an embedded Java application | If you deployed Drools project assets in your own Java virtual machine (JVM) environment, microservice, or application server, you can use custom APIs or application interactions with core KIE APIs (not KIE Server APIs) to execute assets in the embedded engine. | KIE Public API
Execution in a local environment for extended testing | As part of your development cycle, you can execute assets locally to ensure that the assets you have created in Drools function as intended. You can use local execution in addition to or instead of executing assets after deployment. | [decision-examples-IDE-proc_drools-examples]

| Smart Router (KIE Server router) Depending on your deployment and execution environment, you can use a Smart Router to aggregate multiple independent KIE Server instances as though they are a single server. Smart Router is a single endpoint that can receive calls from client applications to any of your services and route each call automatically to the KIE Server that runs the service. For more information about Smart Router, see KIE Server router.

#### 2.1.8. Example decision management architectures with Drools

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.

Drools on Wildfly with Business Central and KIE Server

  - Installation environment: Drools on Wildfly

  - Project storage and build environment: External Git repository for project versioning synchronized with the Business Central Git repository using Git hooks, and external Maven repository for project management and building configured with KIE Server

  - Asset-authoring tool: Business Central

  - Main asset types: Decision Model and Notation (DMN) models for decisions

  - Project deployment and execution environment: KIE Server

  - Scenario advantages:

    - Stable implementation of Drools in an on-premise development environment

    - Access to the repositories, assets, asset designers, and project build options in Business Central

    - Standardized asset-authoring approach using DMN for optimal integration and stability

    - Access to KIE Server functionality and KIE APIs for asset deployment and execution

Drools on Wildfly with an IDE and KIE Server

  - Installation environment: Drools on Wildfly

  - Project storage and build environment: External Git repository for project versioning (not synchronized with Business Central) and external Maven repository for project management and building configured with KIE Server

  - Asset-authoring tools: Integrated development environment (IDE), such as Eclipse, and a spreadsheet editor or a Decision Model and Notation (DMN) modeling tool for other decision formats

  - Main asset types: Drools Rule Language (DRL) rules, spreadsheet decision tables, and Decision Model and Notation (DMN) models for decisions

  - Project deployment and execution environment: KIE Server

  - Scenario advantages:

    - Flexible implementation of Drools in an on-premise development environment

    - Ability to define business assets using an external IDE and other asset-authoring tools of your choice

    - Access to KIE Server functionality and KIE APIs for asset deployment and execution

Drools with an IDE and an embedded Java application

  - Installation environment: Drools Drools engine embedded within a custom application

  - Project storage and build environment: External Git repository for project versioning (not synchronized with Business Central) and external Maven repository for project management and building configured with your embedded Java application (not configured with KIE Server)

  - Asset-authoring tools: Integrated development environment (IDE), such as Eclipse, and a spreadsheet editor or a Decision Model and Notation (DMN) modeling tool for other decision formats

  - Main asset types: Drools Rule Language (DRL) rules, spreadsheet decision tables, and Decision Model and Notation (DMN) models for decisions

  - Project deployment and execution environment: Embedded Java application, such as in a Java virtual machine (JVM) environment, microservice, or custom application server

  - Scenario advantages:

    - Custom implementation of Drools in an on-premise development environment with an embedded Java application

    - Ability to define business assets using an external IDE and other asset-authoring tools of your choice

    - Use of custom APIs to interact with core KIE APIs (not KIE Server APIs) and to execute assets in the embedded engine

### 2.2. Build, Deploy, Utilize and Run

#### 2.2.1. Introduction

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

Figure 2. Example project layout and Maven POM

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.

Figure 3. Example project layout and Maven POM

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 supports 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.

#### 2.2.2. Building

Figure 4. org.kie.api.core.builder

##### 2.2.2.1. Creating and building a Kie Project

A Kie Project has the structure of a normal Maven project with the only peculiarity of including a kmodule.xml file defining in a declaratively way the `KieBase`s and `KieSession`s that can be created from it. This file has to be placed in the resources/META-INF folder of the Maven project while all the other Kie artifacts, such as DRL or a Excel files, must be stored in the resources folder or in any other subfolder under it.

Since meaningful defaults have been provided for all configuration aspects, the simplest kmodule.xml file can contain just an empty kmodule tag like the following:

Example 1. An empty kmodule.xml file

    <?xml version="1.0" encoding="UTF-8"?>
    <kmodule xmlns="http://www.drools.org/xsd/kmodule"/>

In this way the kmodule will contain one single default `KieBase`. All Kie assets stored under the resources folder, or any of its subfolders, will be compiled and added to it. To trigger the building of these artifacts it is enough to create a `KieContainer` for them.

Figure 5. KieContainer

For this simple case it is enough to create a `KieContainer` that reads the files to be built from the classpath:

Example 2. Creating a KieContainer from the classpath

    KieServices kieServices = KieServices.Factory.get();
    KieContainer kContainer = kieServices.getKieClasspathContainer();

` KieServices` is the interface from where it possible to access all the Kie building and runtime facilities:

Figure 6. KieServices

In this way all the Java sources and the Kie resources are compiled and deployed into the KieContainer which makes its contents available for use at runtime.

##### 2.2.2.2. The kmodule.xml file

As explained in the former section, the kmodule.xml file is the place where it is possible to declaratively configure the `KieBase`(s) and `KieSession`(s) that can be created from a KIE project.

In particular a `KieBase` is a repository of all the application’s knowledge definitions. It will contain rules, processes, functions, and type models. The `KieBase` itself does not contain data; instead, sessions are created from the `KieBase` into which data can be inserted and from which process instances may be started. Creating the `KieBase` can be heavy, whereas session creation is very light, so it is recommended that `KieBase` be cached where possible to allow for repeated session creation. However end-users usually shouldn’t worry about it, because this caching mechanism is already automatically provided by the `KieContainer`.

Figure 7. KieBase

Conversely the `KieSession` stores and executes on the runtime data. It is created from the `KieBase` or more easily can be created directly from the `KieContainer` if it has been defined in the kmodule.xml file

Figure 8. KieSession

The kmodule.xml allows to define and configure one or more `KieBase`s and for each `KieBase` all the different `KieSession`s that can be created from it, as showed by the follwing example:

Example 3. A sample kmodule.xml file

    <kmodule xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
            xmlns="http://www.drools.org/xsd/kmodule">
      <configuration>
        <property key="drools.evaluator.supersetOf" value="org.mycompany.SupersetOfEvaluatorDefinition"/>
      </configuration>
      <kbase name="KBase1" default="true" eventProcessingMode="cloud" equalsBehavior="equality" declarativeAgenda="enabled" packages="org.domain.pkg1">
        <ksession name="KSession2_1" type="stateful" default="true"/>
        <ksession name="KSession2_2" type="stateless" default="false" beliefSystem="jtms"/>
      </kbase>
      <kbase name="KBase2" default="false" eventProcessingMode="stream" equalsBehavior="equality" declarativeAgenda="enabled" packages="org.domain.pkg2, org.domain.pkg3" includes="KBase1">
        <ksession name="KSession3_1" type="stateful" default="false" clockType="realtime">
          <fileLogger file="drools.log" threaded="true" interval="10"/>
          <workItemHandlers>
            <workItemHandler name="name" type="org.domain.WorkItemHandler"/>
          </workItemHandlers>
          <calendars>
            <calendar name="monday" type="org.domain.Monday"/>
          </calendars>
          <listeners>
            <ruleRuntimeEventListener type="org.domain.RuleRuntimeListener"/>
            <agendaEventListener type="org.domain.FirstAgendaListener"/>
            <agendaEventListener type="org.domain.SecondAgendaListener"/>
            <processEventListener type="org.domain.ProcessListener"/>
          </listeners>
        </ksession>
      </kbase>
    </kmodule>

Here the tag contains a list of key-value pairs that are the optional properties used to configure the `KieBase`s building process. For instance this sample kmodule.xml file defines an additional custom operator named `supersetOf` and implemented by the `org.mycompany.SupersetOfEvaluatorDefinition` class.

After this 2 `KieBase`s have been defined and it is possible to instance 2 different types of `KieSession`s from the first one, while only one from the second. A list of the attributes that can be defined on the kbase tag, together with their meaning and default values follows:

Table 7. kbase Attributes
Attribute name | Default value | Admitted values | Meaning
--- | --- | --- | ---
name | none | any | The name with which retrieve this KieBase from the KieContainer. This is the only mandatory attribute.
includes | none | any comma separated list | A comma separated list of other KieBases contained in this kmodule. The artifacts of all these KieBases will be also included in this one.
packages | all | any comma separated list | By default all the Drools artifacts under the resources folder, at any level, are included into the KieBase. This attribute allows to limit the artifacts that will be compiled in this KieBase to only the ones belonging to the list of packages.
default | false | true, false | Defines if this KieBase is the default one for this module, so it can be created from the KieContainer without passing any name to it. There can be at most one default KieBase in each module.
equalsBehavior | identity | identity, equality | Defines the behavior of Drools when a new fact is inserted into the Working Memory. With identity it always create a new FactHandle unless the same object isn’t already present in the Working Memory, while with equality only if the newly inserted object is not equal (according to its equal method) to an already existing fact.
eventProcessingMode | cloud | cloud, stream | When compiled in cloud mode the KieBase treats events as normal facts, while in stream mode allow temporal reasoning on them.
declarativeAgenda | disabled | disabled, enabled | Defines if the Declarative Agenda is enabled or not.

Similarly all attributes of the ksession tag (except of course the name) have meaningful default. They are listed and described in the following table:

Table 8. ksession Attributes
Attribute name | Default value | Admitted values | Meaning
--- | --- | --- | ---
name | none | any | Unique name of this KieSession. Used to fetch the KieSession from the KieContainer. This is the only mandatory attribute.
type | stateful | stateful, stateless | A stateful session allows to iteratively work with the Working Memory, while a stateless one is a one-off execution of a Working Memory with a provided data set.
default | false | true, false | Defines if this KieSession is the default one for this module, so it can be created from the KieContainer without passing any name to it. In each module there can be at most one default KieSession for each type.
clockType | realtime | realtime, pseudo | Defines if events timestamps are determined by the system clock or by a psuedo clock controlled by the application. This clock is specially useful for unit testing temporal rules.
beliefSystem | simple | simple, jtms, defeasible | Defines the type of belief system used by the KieSession.

As outlined in the former kmodule.xml sample, it is also possible to declaratively create on each `KieSession` a file (or a console) logger, one or more `WorkItemHandler`s and `Calendar`s plus some listeners that can be of 3 different types: ruleRuntimeEventListener, agendaEventListener and processEventListener

Having defined a kmodule.xml like the one in the former sample, it is now possible to simply retrieve the KieBases and KieSessions from the KieContainer using their names.

Example 4. Retriving KieBases and KieSessions from the KieContainer

    KieServices kieServices = KieServices.Factory.get();
    KieContainer kContainer = kieServices.getKieClasspathContainer();

    KieBase kBase1 = kContainer.getKieBase("KBase1");
    KieSession kieSession1 = kContainer.newKieSession("KSession2_1");
    StatelessKieSession kieSession2 = kContainer.newStatelessKieSession("KSession2_2");

It has to be noted that since KSession2_1 and KSession2_2 are of 2 different types (the first is stateful, while the second is stateless) it is necessary to invoke 2 different methods on the `KieContainer` according to their declared type. If the type of the `KieSession` requested to the `KieContainer` doesn’t correspond with the one declared in the kmodule.xml file the `KieContainer` will throw a `RuntimeException`. Also since a `KieBase` and a `KieSession` have been flagged as default is it possible to get them from the `KieContainer` without passing any name.

Example 5. Retriving default KieBases and KieSessions from the KieContainer

    KieContainer kContainer = ...

    KieBase kBase1 = kContainer.getKieBase(); // returns KBase1
    KieSession kieSession1 = kContainer.newKieSession(); // returns KSession2_1

Since a Kie project is also a Maven project the groupId, artifactId and version declared in the pom.xml file are used to generate a `ReleaseId` that uniquely identifies this project inside your application. This allows creation of a new KieContainer from the project by simply passing its `ReleaseId` to the `KieServices`.

Example 6. Creating a KieContainer of an existing project by ReleaseId

    KieServices kieServices = KieServices.Factory.get();
    ReleaseId releaseId = kieServices.newReleaseId( "org.acme", "myartifact", "1.0" );
    KieContainer kieContainer = kieServices.newKieContainer( releaseId );

##### 2.2.2.3. Building with Maven

The KIE plugin for Maven ensures that artifact resources are validated and pre-compiled, it is recommended that this is used at all times. To use the plugin simply add it to the build section of the Maven pom.xml and activate it by using packaging `kjar`.

Example 7. Adding the KIE plugin to a Maven pom.xml and activating it

      <packaging>kjar</packaging>
      ...
      <build>
        <plugins>
          <plugin>
            <groupId>org.kie</groupId>
            <artifactId>kie-maven-plugin</artifactId>
            <version>7.19.0.Final</version>
            <extensions>true</extensions>
          </plugin>
        </plugins>
      </build>

The plugin comes with support for all the Drools/jBPM knowledge resources. However, in case you are using specific KIE annotations in your Java classes, like for example `@kie.api.Position`, you will need to add compile time dependency on `kie-api` into your project. We recommend to use the provided scope for all the additional KIE dependencies. That way the kjar stays as lightweight as possible, and not dependant on any particular KIE version.

Building a KIE module without the Maven plugin will copy all the resources, as is, into the resulting JAR. When that JAR is loaded by the runtime, it will attempt to build all the resources then. If there are compilation issues it will return a null KieContainer. It also pushes the compilation overhead to the runtime. In general this is not recommended, and the Maven plugin should always be used.

##### 2.2.2.4. Defining a KieModule programmatically

It is also possible to define the `KieBase`s and `KieSession`s belonging to a KieModule programmatically instead of the declarative definition in the kmodule.xml file. The same programmatic API also allows in explicitly adding the file containing the Kie artifacts instead of automatically read them from the resources folder of your project. To do that it is necessary to create a `KieFileSystem`, a sort of virtual file system, and add all the resources contained in your project to it.

Figure 9. KieFileSystem

Like all other Kie core components you can obtain an instance of the `KieFileSystem` from the `KieServices`. The kmodule.xml configuration file must be added to the filesystem. This is a mandatory step. Kie also provides a convenient fluent API, implemented by the `KieModuleModel`, to programmatically create this file.

Figure 10. KieModuleModel

To do this in practice it is necessary to create a `KieModuleModel` from the `KieServices`, configure it with the desired `KieBase`s and `KieSession`s, convert it in XML and add the XML to the `KieFileSystem`. This process is shown by the following example:

Example 8. Creating a kmodule.xml programmatically and adding it to a KieFileSystem

    KieServices kieServices = KieServices.Factory.get();
    KieModuleModel kieModuleModel = kieServices.newKieModuleModel();

    KieBaseModel kieBaseModel1 = kieModuleModel.newKieBaseModel( "KBase1 ")
            .setDefault( true )
            .setEqualsBehavior( EqualityBehaviorOption.EQUALITY )
            .setEventProcessingMode( EventProcessingOption.STREAM );

    KieSessionModel ksessionModel1 = kieBaseModel1.newKieSessionModel( "KSession1" )
            .setDefault( true )
            .setType( KieSessionModel.KieSessionType.STATEFUL )
            .setClockType( ClockTypeOption.get("realtime") );

    KieFileSystem kfs = kieServices.newKieFileSystem();
    kfs.writeKModuleXML(kieModuleModel.toXML());

At this point it is also necessary to add to the `KieFileSystem`, through its fluent API, all others Kie artifacts composing your project. These artifacts have to be added in the same position of a corresponding usual Maven project.

Example 9. Adding Kie artifacts to a KieFileSystem

    KieFileSystem kfs = ...
    kfs.write( "src/main/resources/KBase1/ruleSet1.drl", stringContainingAValidDRL )
            .write( "src/main/resources/dtable.xls",
                    kieServices.getResources().newInputStreamResource( dtableFileStream ) );

This example shows that it is possible to add the Kie artifacts both as plain Strings and as `Resource`s. In the latter case the `Resource`s can be created by the `KieResources` factory, also provided by the `KieServices`. The `KieResources` provides many convenient factory methods to convert an `InputStream`, a `URL`, a `File`, or a `String` representing a path of your file system to a `Resource` that can be managed by the `KieFileSystem`.

Figure 11. KieResources

Normally the type of a `Resource` can be inferred from the extension of the name used to add it to the `KieFileSystem`. However it also possible to not follow the Kie conventions about file extensions and explicitly assign a specific `ResourceType` to a `Resource` as shown below:

Example 10. Creating and adding a Resource with an explicit type

    KieFileSystem kfs = ...
    kfs.write( "src/main/resources/myDrl.txt",
               kieServices.getResources().newInputStreamResource( drlStream )
                          .setResourceType(ResourceType.DRL) );

Add all the resources to the `KieFileSystem` and build it by passing the `KieFileSystem` to a `KieBuilder`

Figure 12. KieBuilder

When the contents of a `KieFileSystem` are successfully built, the resulting `KieModule` is automatically added to the `KieRepository`. The `KieRepository` is a singleton acting as a repository for all the available `KieModule`s.

Figure 13. KieRepository

After this it is possible to create through the `KieServices` a new `KieContainer` for that `KieModule` using its `ReleaseId`. However, since in this case the `KieFileSystem` doesn’t contain any pom.xml file (it is possible to add one using the `KieFileSystem.writePomXML` method), Kie cannot determine the `ReleaseId` of the `KieModule` and assign to it a default one. This default `ReleaseId` can be obtained from the `KieRepository` and used to identify the `KieModule` inside the `KieRepository` itself. The following example shows this whole process.

Example 11. Building the contents of a KieFileSystem and creating a KieContainer

    KieServices kieServices = KieServices.Factory.get();
    KieFileSystem kfs = ...
    kieServices.newKieBuilder( kfs ).buildAll();
    KieContainer kieContainer = kieServices.newKieContainer(kieServices.getRepository().getDefaultReleaseId());

At this point it is possible to get `KieBase`s and create new `KieSession`s from this `KieContainer` exactly in the same way as in the case of a `KieContainer` created directly from the classpath.

It is a best practice to check the compilation results. The `KieBuilder` reports compilation results of 3 different severities: ERROR, WARNING and INFO. An ERROR indicates that the compilation of the project failed and in the case no `KieModule` is produced and nothing is added to the `KieRepository`. WARNING and INFO results can be ignored, but are available for inspection.

Example 12. Checking that a compilation didn’t produce any error

    KieBuilder kieBuilder = kieServices.newKieBuilder( kfs ).buildAll();
    assertEquals( 0, kieBuilder.getResults().getMessages( Message.Level.ERROR ).size() );

##### 2.2.2.5. Changing the Default Build Result Severity

In some cases, it is possible to change the default severity of a type of build result. For instance, when a new rule with the same name of an existing rule is added to a package, the default behavior is to replace the old rule by the new rule and report it as an INFO. This is probably ideal for most use cases, but in some deployments the user might want to prevent the rule update and report it as an error.

Changing the default severity for a result type, configured like any other option in Drools, can be done by API calls, system properties or configuration files. As of this version, Drools supports configurable result severity for rule updates and function updates. To configure it using system properties or configuration files, the user has to use the following properties:

Example 13. Setting the severity using properties

    // sets the severity of rule updates
    drools.kbuilder.severity.duplicateRule = <INFO|WARNING|ERROR>
    // sets the severity of function updates
    drools.kbuilder.severity.duplicateFunction = <INFO|WARNING|ERROR>

##### 2.2.2.6. Building and running Drools in a fat jar

Many modules of Drools (e.g. drools-core, drools-compiler) have a file named `kie.conf` containing the names of the classes implementing the services provided by the corresponding module. When running Drools in a fat JAR, for example created by the Maven Shade Plugin, those various `kie.conf` files need to be merged, otherwise , the fat JAR will contain only 1 kie.conf from a single dependency, resulting into errors. You can merge resources in the Maven Shade Plugin using transformers, like this:

    <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
        <resource>META-INF/kie.conf</resource>
    </transformer>

For instance this is required when running Drools in a Vert.x application. In this case the Maven Shade Plugin can be configured as it follows:

    <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-shade-plugin</artifactId>
        <version>3.1.0</version>
        <executions>
            <execution>
                <phase>package</phase>
                <goals>
                    <goal>shade</goal>
                </goals>
                <configuration>
                    <transformers>
                        <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                            <manifestEntries>
                                <Main-Class>io.vertx.core.Launcher</Main-Class>
                                <Main-Verticle>${main.verticle}</Main-Verticle>
                            </manifestEntries>
                        </transformer>
                        <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                            <resource>META-INF/services/io.vertx.core.spi.VerticleFactory</resource>
                        </transformer>
                        <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
                            <resource>META-INF/kie.conf</resource>
                        </transformer>
                    </transformers>
                    <artifactSet>
                    </artifactSet>
                    <outputFile>${project.build.directory}/${project.artifactId}-${project.version}-fat.jar</outputFile>
                </configuration>
            </execution>
        </executions>
    </plugin>

#### 2.2.3. Deploying

##### 2.2.3.1. KieBase

The `KieBase` is a repository of all the application’s knowledge definitions. It will contain rules, processes, functions, and type models. The `KieBase` itself does not contain data; instead, sessions are created from the `KieBase` into which data can be inserted and from which process instances may be started. The `KieBase` can be obtained from the `KieContainer` containing the `KieModule` where the `KieBase` has been defined.

Figure 14. KieBase

Sometimes, for instance in a OSGi environment, the `KieBase` needs to resolve types that are not in the default class loader. In this case it will be necessary to create a `KieBaseConfiguration` with an additional class loader and pass it to `KieContainer` when creating a new `KieBase` from it.

Example 14. Creating a new KieBase with a custom ClassLoader

    KieServices kieServices = KieServices.Factory.get();
    KieBaseConfiguration kbaseConf = kieServices.newKieBaseConfiguration( null, MyType.class.getClassLoader() );
    KieBase kbase = kieContainer.newKieBase( kbaseConf );

##### 2.2.3.2. KieSessions and KieBase Modifications

KieSessions will be discussed in more detail in section "Running". The `KieBase` creates and returns `KieSession` objects, and it may optionally keep references to those. When `KieBase` modifications occur those modifications are applied against the data in the sessions. This reference is a weak reference and it is also optional, which is controlled by a boolean flag.

##### 2.2.3.3. KieScanner

The `KieScanner` allows continuous monitoring of your Maven repository to check whether a new release of a Kie project has been installed. A new release is deployed in the `KieContainer` wrapping that project. The use of the `KieScanner` requires kie-ci.jar to be on the classpath.

Figure 15. KieScanner

A `KieScanner` can be registered on a `KieContainer` as in the following example.

Example 15. Registering and starting a KieScanner on a KieContainer

    KieServices kieServices = KieServices.Factory.get();
    ReleaseId releaseId = kieServices.newReleaseId( "org.acme", "myartifact", "1.0-SNAPSHOT" );
    KieContainer kContainer = kieServices.newKieContainer( releaseId );
    KieScanner kScanner = kieServices.newKieScanner( kContainer );

    // Start the KieScanner polling the Maven repository every 10 seconds
    kScanner.start( 10000L );

In this example the `KieScanner` is configured to run with a fixed time interval, but it is also possible to run it on demand by invoking the `scanNow()` method on it. If the `KieScanner` finds, in the Maven repository, an updated version of the Kie project used by that `KieContainer` it automatically downloads the new version and triggers an incremental build of the new project. At this point, existing `KieBase`s and `KieSession`s under the control of `KieContainer` will get automatically upgraded with it - specifically, those `KieBase`s obtained with `getKieBase()` along with their related `KieSession`s, and any `KieSession` obtained directly with `KieContainer.newKieSession()` thus referencing the default `KieBase`. Additionally, from this moment on, all the new `KieBase`s and `KieSession`s created from that `KieContainer` will use the new project version. Please notice however any existing `KieBase` which was obtained via `newKieBase()` before the KieScanner upgrade, and any of its related `KieSession`s, will not get automatically upgraded; this is because `KieBase`s obtained via `newKieBase()` are not under the direct control of the `KieContainer`.

The `KieScanner` will only pickup changes to deployed jars if it is using a SNAPSHOT, version range, the LATEST, or the RELEASE setting. Fixed versions will not automatically update at runtime.

In case you don’t want to install a maven repository, it is also possible to have a `KieScanner` that works by simply fetching update from a folder of a plain file system. You can create such a `KieScanner` as simply as

    KieServices kieServices = KieServices.Factory.get();
    KieScanner kScanner = kieServices.newKieScanner( kContainer, "/myrepo/kjars" );

where "/myrepo/kjars" will be the folder where the `KieScanner` will look for kjar updates. The jar files placed in this folder have to follow the maven convention and then have to be a name in the form {artifactId}-{versionId}.jar

##### 2.2.3.4. Maven Versions and Dependencies

Maven supports a number of mechanisms to manage versioning and dependencies within applications. Modules can be published with specific version numbers, or they can use the SNAPSHOT suffix. Dependencies can specify version ranges to consume, or take avantage of SNAPSHOT mechanism.

StackOverflow provides a very good description for this, which is reproduced below.

http://stackoverflow.com/questions/30571/how-do-i-tell-maven-to-use-the-latest-version-of-a-dependency

| Since Maven 3.x metaversions RELEASE and LATEST are no longer supported for the sake of reproducible builds.

See the POM Syntax section of the Maven book for more details.

http://books.sonatype.com/mvnref-book/reference/pom-relationships-sect-pom-syntax.html

http://books.sonatype.com/mvnref-book/reference/pom-relationships-sect-project-dependencies.html

Here’s an example illustrating the various options. In the Maven repository, com.foo:my-foo has the following metadata:

    <metadata>
      <groupId>com.foo</groupId>
      <artifactId>my-foo</artifactId>
      <version>2.0.0</version>
      <versioning>
        <release>1.1.1</release>
        <versions>
          <version>1.0</version>
          <version>1.0.1</version>
          <version>1.1</version>
          <version>1.1.1</version>
          <version>2.0.0</version>
        </versions>
        <lastUpdated>20090722140000</lastUpdated>
      </versioning>
    </metadata>

If a dependency on that artifact is required, you have the following options (other version ranges can be specified of course, just showing the relevant ones here): Declare an exact version (will always resolve to 1.0.1):

    <version>[1.0.1]</version>

    Declare an explicit version (will always resolve to 1.0.1 unless a collision occurs, when Maven will select a matching version):

    <version>1.0.1</version>

    Declare a version range for all 1.x (will currently resolve to 1.1.1):

    <version>[1.0.0,2.0.0)</version>

    Declare an open-ended version range (will resolve to 2.0.0):

    <version>[1.0.0,)</version>

    Declare the version as LATEST (will resolve to 2.0.0):

    <version>LATEST</version>

    Declare the version as RELEASE (will resolve to 1.1.1):

    <version>RELEASE</version>

Note that by default your own deployments will update the "latest" entry in the Maven metadata, but to update the "release" entry, you need to activate the "release-profile" from the Maven super POM. You can do this with either "-Prelease-profile" or "-DperformRelease=true"

##### 2.2.3.5. Settings.xml and Remote Repository Setup

The maven settings.xml is used to configure Maven execution. Detailed instructions can be found at the Maven website:

http://maven.apache.org/settings.html

The settings.xml file can be located in 3 locations, the actual settings used is a merge of those 3 locations.

- The Maven install: `$M2_HOME/conf/settings.xml`

- A user’s install: `${user.home}/.m2/settings.xml`

- Folder location specified by the system property `kie.maven.settings.custom`

The settings.xml is used to specify the location of remote repositories. It is important that you activate the profile that specifies the remote repository, typically this can be done using "activeByDefault":

    <profiles>
      <profile>
        <id>profile-1</id>
        <activation>
          <activeByDefault>true</activeByDefault>
        </activation>
        ...
      </profile>
    </profiles>

Maven provides detailed documentation on using multiple remote repositories:

http://maven.apache.org/guides/mini/guide-multiple-repositories.html

#### 2.2.4. Running

##### 2.2.4.1. KieBase

The `KieBase` is a repository of all the application’s knowledge definitions. It will contain rules, processes, functions, and type models. The `KieBase` itself does not contain data; instead, sessions are created from the `KieBase` into which data can be inserted and from which process instances may be started. The `KieBase` can be obtained from the `KieContainer` containing the `KieModule` where the `KieBase` has been defined.

Example 16. Getting a KieBase from a KieContainer

    KieBase kBase = kContainer.getKieBase();

##### 2.2.4.2. KieSession

The `KieSession` stores and executes on the runtime data. It is created from the `KieBase`.

Figure 16. KieSession
Example 17. Create a KieSession from a KieBase

    KieSession ksession = kbase.newKieSession();

##### 2.2.4.3. KieRuntime

###### KieRuntime

The `KieRuntime` provides methods that are applicable to both rules and processes, such as setting globals and registering channels. ("Exit point" is an obsolete synonym for "channel".)

Figure 17. KieRuntime
Globals

Globals are named objects that are made visible to the Drools engine, but in a way that is fundamentally different from the one for facts: changes in the object backing a global do not trigger reevaluation of rules. Still, globals are useful for providing static information, as an object offering services that are used in the RHS of a rule, or as a means to return objects from the Drools engine. When you use a global on the LHS of a rule, make sure it is immutable, or, at least, don’t expect changes to have any effect on the behavior of your rules.

A global must be declared in a rules file, and then it needs to be backed up with a Java object.

    global java.util.List list

With the KIE base now aware of the global identifier and its type, it is now possible to call `ksession.setGlobal()` with the global’s name and an object, for any session, to associate the object with the global. Failure to declare the global type and identifier in DRL code will result in an exception being thrown from this call.

    List list = new ArrayList();
    ksession.setGlobal("list", list);

Make sure to set any global before it is used in the evaluation of a rule. Failure to do so results in a `NullPointerException`.

##### 2.2.4.4. Event Model

The event package provides means to be notified of Drools engine events, including rules firing, objects being asserted, etc. This allows separation of logging and auditing activities from the main part of your application (and the rules).

The `KieRuntimeEventManager` interface is implemented by the `KieRuntime` which provides two interfaces, `RuleRuntimeEventManager` and `ProcessEventManager`. We will only cover the `RuleRuntimeEventManager` here.

Figure 18. KieRuntimeEventManager

The `RuleRuntimeEventManager` allows for listeners to be added and removed, so that events for the working memory and the agenda can be listened to.

Figure 19. RuleRuntimeEventManager

The following code snippet shows how a simple agenda listener is declared and attached to a session. It will print matches after they have fired.

Example 18. Adding an AgendaEventListener

    ksession.addEventListener( new DefaultAgendaEventListener() {
        public void afterMatchFired(AfterMatchFiredEvent event) {
            super.afterMatchFired( event );
            System.out.println( event );
        }
    });

Drools also provides `DebugRuleRuntimeEventListener` and `DebugAgendaEventListener` which implement each method with a debug print statement. To print all Working Memory events, you add a listener like this:

Example 19. Adding a DebugRuleRuntimeEventListener

    ksession.addEventListener( new DebugRuleRuntimeEventListener() );

All emitted events implement the `KieRuntimeEvent` interface which can be used to retrieve the actual `KnowlegeRuntime` the event originated from.

Figure 20. KieRuntimeEvent

The events currently supported are:

- MatchCreatedEvent

- MatchCancelledEvent

- BeforeMatchFiredEvent

- AfterMatchFiredEvent

- AgendaGroupPushedEvent

- AgendaGroupPoppedEvent

- ObjectInsertEvent

- ObjectDeletedEvent

- ObjectUpdatedEvent

- ProcessCompletedEvent

- ProcessNodeLeftEvent

- ProcessNodeTriggeredEvent

- ProcessStartEvent

##### 2.2.4.5. KieRuntimeLogger

The KieRuntimeLogger uses the comprehensive event system in Drools to create an audit log that can be used to log the execution of an application for later inspection, using tools such as the Eclipse audit viewer.

Figure 21. KieLoggers
Example 20. FileLogger

    KieRuntimeLogger logger =
      KieServices.Factory.get().getLoggers().newFileLogger(ksession, "logdir/mylogfile");
    ...
    logger.close();

##### 2.2.4.6. Commands and the CommandExecutor

KIE has the concept of stateful or stateless sessions. Stateful sessions have already been covered, which use the standard KieRuntime, and can be worked with iteratively over time. Stateless is a one-off execution of a KieRuntime with a provided data set. It may return some results, with the session being disposed at the end, prohibiting further iterative interactions. You can think of stateless as treating an engine like a function call with optional return results.

The foundation for this is the `CommandExecutor` interface, which both the stateful and stateless interfaces extend. This returns an `ExecutionResults`:

Figure 22. CommandExecutor
Figure 23. ExecutionResults

The `CommandExecutor` allows for commands to be executed on those sessions, the only difference being that the StatelessKieSession executes `fireAllRules()` at the end before disposing the session. The commands can be created using the `CommandExecutor` .The Javadocs provide the full list of the allowed comands using the `CommandExecutor`.

setGlobal and getGlobal are two commands relevant to both Drools and jBPM.

Set Global calls setGlobal underneath. The optional boolean indicates whether the command should return the global’s value as part of the `ExecutionResults`. If true it uses the same name as the global name. A String can be used instead of the boolean, if an alternative name is desired.

Example 21. Set Global Command

    StatelessKieSession ksession = kbase.newStatelessKieSession();
    ExecutionResults bresults =
        ksession.execute( CommandFactory.newSetGlobal( "stilton", new Cheese( "stilton" ), true);
    Cheese stilton = bresults.getValue( "stilton" );

Allows an existing global to be returned. The second optional String argument allows for an alternative return name.

Example 22. Get Global Command

    StatelessKieSession ksession = kbase.newStatelessKieSession();
    ExecutionResults bresults =
        ksession.execute( CommandFactory.getGlobal( "stilton" );
    Cheese stilton = bresults.getValue( "stilton" );

All the above examples execute single commands. The `BatchExecution` represents a composite command, created from a list of commands. It will iterate over the list and execute each command in turn. This means you can insert some objects, start a process, call fireAllRules and execute a query, all in a single `execute(…​)` call, which is quite powerful.

The StatelessKieSession will execute `fireAllRules()` automatically at the end. However the keen-eyed reader probably has already noticed the `FireAllRules` command and wondered how that works with a StatelessKieSession. The `FireAllRules` command is allowed, and using it will disable the automatic execution at the end; think of using it as a sort of manual override function.

Any command, in the batch, that has an out identifier set will add its results to the returned `ExecutionResults` instance. Let’s look at a simple example to see how this works. The example presented includes command from the Drools and jBPM, for the sake of illustration. They are covered in more detail in the Drool and jBPM specific sections.

Example 23. BatchExecution Command

    StatelessKieSession ksession = kbase.newStatelessKieSession();

    List cmds = new ArrayList();
    cmds.add( CommandFactory.newInsertObject( new Cheese( "stilton", 1), "stilton") );
    cmds.add( CommandFactory.newStartProcess( "process cheeses" ) );
    cmds.add( CommandFactory.newQuery( "cheeses" ) );
    ExecutionResults bresults = ksession.execute( CommandFactory.newBatchExecution( cmds ) );
    Cheese stilton = ( Cheese ) bresults.getValue( "stilton" );
    QueryResults qresults = ( QueryResults ) bresults.getValue( "cheeses" );

In the above example multiple commands are executed, two of which populate the `ExecutionResults`. The query command defaults to use the same identifier as the query name, but it can also be mapped to a different identifier.

All commands support XML and jSON marshalling using XStream, as well as JAXB marshalling. This is covered in [_ch.commands].

##### 2.2.4.7. StatelessKieSession

The `StatelessKieSession` wraps the `KieSession`, instead of extending it. Its main focus is on the decision service type scenarios. It avoids the need to call `dispose()`. Stateless sessions do not support iterative insertions and the method call `fireAllRules()` from Java code; the act of calling `execute()` is a single-shot method that will internally instantiate a `KieSession`, add all the user data and execute user commands, call `fireAllRules()`, and then call `dispose()`. While the main way to work with this class is via the `BatchExecution` (a subinterface of `Command`) as supported by the `CommandExecutor` interface, two convenience methods are provided for when simple object insertion is all that’s required. The `CommandExecutor` and `BatchExecution` are talked about in detail in their own section.

Figure 24. StatelessKieSession

Our simple example shows a stateless session executing a given collection of Java objects using the convenience API. It will iterate the collection, inserting each element in turn.

Example 24. Simple StatelessKieSession execution with a Collection

    StatelessKieSession ksession = kbase.newStatelessKieSession();
    ksession.execute( collection );

If this was done as a single Command it would be as follows:

Example 25. Simple StatelessKieSession execution with InsertElements Command

    ksession.execute( CommandFactory.newInsertElements( collection ) );

If you wanted to insert the collection itself, and the collection’s individual elements, then `CommandFactory.newInsert(collection)` would do the job.

Methods of the `CommandFactory` create the supported commands, all of which can be marshalled using XStream and the `BatchExecutionHelper`. `BatchExecutionHelper` provides details on the XML format as well as how to use Drools Pipeline to automate the marshalling of `BatchExecution` and `ExecutionResults`.

`StatelessKieSession` supports globals, scoped in a number of ways. We cover the non-command way first, as commands are scoped to a specific execution call. Globals can be resolved in three ways.

- The StatelessKieSession method `getGlobals()` returns a Globals instance which provides access to the session’s globals. These are shared for all execution calls. Exercise caution regarding mutable globals because execution calls can be executing simultaneously in different threads.

  Example 26. Session scoped global

      StatelessKieSession ksession = kbase.newStatelessKieSession();
      // Set a global hbnSession, that can be used for DB interactions in the rules.
      ksession.setGlobal( "hbnSession", hibernateSession );
      // Execute while being able to resolve the "hbnSession" identifier.
      ksession.execute( collection );

- Using a delegate is another way of global resolution. Assigning a value to a global (with `setGlobal(String, Object)`) results in the value being stored in an internal collection mapping identifiers to values. Identifiers in this internal collection will have priority over any supplied delegate. Only if an identifier cannot be found in this internal collection, the delegate global (if any) will be used.

- The third way of resolving globals is to have execution scoped globals. Here, a `Command` to set a global is passed to the `CommandExecutor`.

The `CommandExecutor` interface also offers the ability to export data via "out" parameters. Inserted facts, globals and query results can all be returned.

Example 27. Out identifiers

    // Set up a list of commands
    List cmds = new ArrayList();
    cmds.add( CommandFactory.newSetGlobal( "list1", new ArrayList(), true ) );
    cmds.add( CommandFactory.newInsert( new Person( "jon", 102 ), "person" ) );
    cmds.add( CommandFactory.newQuery( "Get People" "getPeople" );

    // Execute the list
    ExecutionResults results =
      ksession.execute( CommandFactory.newBatchExecution( cmds ) );

    // Retrieve the ArrayList
    results.getValue( "list1" );
    // Retrieve the inserted Person fact
    results.getValue( "person" );
    // Retrieve the query as a QueryResults instance.
    results.getValue( "Get People" );

##### 2.2.4.8. Marshalling

The `KieMarshallers` are used to marshal and unmarshal KieSessions.

Figure 25. KieMarshallers

An instance of the `KieMarshallers` can be retrieved from the `KieServices`. A simple example is shown below:

Example 28. Simple Marshaller Example

    // ksession is the KieSession
    // kbase is the KieBase
    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    Marshaller marshaller = KieServices.Factory.get().getMarshallers().newMarshaller( kbase );
    marshaller.marshall( baos, ksession );
    baos.close();

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

Example 29. IdentityMarshallingStrategy

    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    KieMarshallers kMarshallers = KieServices.Factory.get().getMarshallers()
    ObjectMarshallingStrategy oms = kMarshallers.newIdentityMarshallingStrategy()
    Marshaller marshaller =
            kMarshallers.newMarshaller( kbase, new ObjectMarshallingStrategy[]{ oms } );
    marshaller.marshall( baos, ksession );
    baos.close();

Im most cases, a single strategy is insufficient. For added flexibility, the `ObjectMarshallingStrategyAcceptor` interface can be used. This Marshaller has a chain of strategies, and while reading or writing a user object it iterates the strategies asking if they accept responsibility for marshalling the user object. One of the provided implementations is `ClassFilterAcceptor`. This allows strings and wild cards to be used to match class names. The default is ".", so in the above example the Identity Marshalling Strategy is used which has a default "." acceptor.

Assuming that we want to serialize all classes except for one given package, where we will use identity lookup, we could do the following:

Example 30. IdentityMarshallingStrategy with Acceptor

    ByteArrayOutputStream baos = new ByteArrayOutputStream();
    KieMarshallers kMarshallers = KieServices.Factory.get().getMarshallers()
    ObjectMarshallingStrategyAcceptor identityAcceptor =
            kMarshallers.newClassFilterAcceptor( new String[] { "org.domain.pkg1.*" } );
    ObjectMarshallingStrategy identityStrategy =
            kMarshallers.newIdentityMarshallingStrategy( identityAcceptor );
    ObjectMarshallingStrategy sms = kMarshallers.newSerializeMarshallingStrategy();
    Marshaller marshaller =
            kMarshallers.newMarshaller( kbase,
                                        new ObjectMarshallingStrategy[]{ identityStrategy, sms } );
    marshaller.marshall( baos, ksession );
    baos.close();

Note that the acceptance checking order is in the natural order of the supplied elements.

##### 2.2.4.9. Persistence and Transactions

Longterm out of the box persistence with Java Persistence API (JPA) is possible with Drools. It is necessary to have some implementation of the Java Transaction API (JTA) installed. For development purposes the Bitronix Transaction Manager is suggested, as it’s simple to set up and works embedded, but for production use JBoss Transactions is recommended.

Example 31. Simple example using transactions

    KieServices kieServices = KieServices.Factory.get();
    Environment env = kieServices.newEnvironment();
    env.set( EnvironmentName.ENTITY_MANAGER_FACTORY,
             Persistence.createEntityManagerFactory( "emf-name" ) );
    env.set( EnvironmentName.TRANSACTION_MANAGER,
             TransactionManagerServices.getTransactionManager() );

    // KieSessionConfiguration may be null, and a default will be used
    KieSession ksession =
            kieServices.getStoreServices().newKieSession( kbase, null, env );
    int sessionId = ksession.getId();

    UserTransaction ut =
      (UserTransaction) new InitialContext().lookup( "java:comp/UserTransaction" );
    ut.begin();
    ksession.insert( data1 );
    ksession.insert( data2 );
    ksession.startProcess( "process1" );
    ut.commit();

To use a JPA, the Environment must be set with both the `EntityManagerFactory` and the `TransactionManager`. If rollback occurs the ksession state is also rolled back, hence it is possible to continue to use it after a rollback. To load a previously persisted KieSession you’ll need the id, as shown below:

Example 32. Loading a KieSession

    KieSession ksession =
            kieServices.getStoreServices().loadKieSession( sessionId, kbase, null, env );

To enable persistence several classes must be added to your persistence.xml, as in the example below:

Example 33. Configuring JPA

    <persistence-unit name="org.drools.persistence.jpa" transaction-type="JTA">
       <provider>org.hibernate.ejb.HibernatePersistence</provider>
       <jta-data-source>jdbc/BitronixJTADataSource</jta-data-source>
       <class>org.drools.persistence.info.SessionInfo</class>
       <class>org.drools.persistence.info.WorkItemInfo</class>
       <properties>
             <property name="hibernate.dialect" value="org.hibernate.dialect.H2Dialect"/>
             <property name="hibernate.max_fetch_depth" value="3"/>
             <property name="hibernate.hbm2ddl.auto" value="update" />
             <property name="hibernate.show_sql" value="true" />
             <property name="hibernate.transaction.manager_lookup_class"
                          value="org.hibernate.transaction.BTMTransactionManagerLookup" />
       </properties>
    </persistence-unit>

The jdbc JTA data source would have to be configured first. Bitronix provides a number of ways of doing this, and its documentation should be consulted for details. For a quick start, here is the programmatic approach:

Example 34. Configuring JTA DataSource

    PoolingDataSource ds = new PoolingDataSource();
    ds.setUniqueName( "jdbc/BitronixJTADataSource" );
    ds.setClassName( "org.h2.jdbcx.JdbcDataSource" );
    ds.setMaxPoolSize( 3 );
    ds.setAllowLocalTransactions( true );
    ds.getDriverProperties().put( "user", "sa" );
    ds.getDriverProperties().put( "password", "sasa" );
    ds.getDriverProperties().put( "URL", "jdbc:h2:mem:mydb" );
    ds.init();

Bitronix also provides a simple embedded JNDI service, ideal for testing. To use it, add a jndi.properties file to your META-INF folder and add the following line to it:

Example 35. JNDI properties

    java.naming.factory.initial=bitronix.tm.jndi.BitronixInitialContextFactory

#### 2.2.5. Installation and Deployment Cheat Sheets

Figure 26. Installation Overview
Figure 27. Deployment Overview

#### 2.2.6. Build, Deploy and Utilize Examples

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. Where as Run refers to the act of interacting with those runtimes.

##### 2.2.6.1. Default KieSession

- Project: default-kesession.

- Summary: Empty kmodule.xml KieModule on the classpath that includes all resources in a single default KieBase. The example shows the retrieval of the default KieSession from the classpath.

An empty kmodule.xml will produce a single KieBase that includes all files found under resources path, be it DRL, BPMN2, XLS etc. That single KieBase is the default and also includes a single default KieSession. Default means they can be created without knowing their names.

Example 36. Author - kmodule.xml

    <kmodule xmlns="http://www.drools.org/xsd/kmodule"> </kmodule>

Example 37. Build and Install - Maven

    mvn install

ks.getKieClasspathContainer() returns the KieContainer that contains the KieBases deployed onto the environment classpath. kContainer.newKieSession() creates the default KieSession. Notice that you no longer need to look up the KieBase, in order to create the KieSession. The KieSession knows which KieBase it’s associated with, and use that, which in this case is the default KieBase.

Example 38. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieContainer kContainer = ks.getKieClasspathContainer();

    KieSession kSession = kContainer.newKieSession();
    kSession.setGlobal("out", out);
    kSession.insert(new Message("Dave", "Hello, HAL. Do you read me, HAL?"));
    kSession.fireAllRules();

##### 2.2.6.2. Named KieSession

- Project: named-kiesession.

- Summary: kmodule.xml that has one named KieBase and one named KieSession. The examples shows the retrieval of the named KieSession from the classpath.

kmodule.xml will produce a single named KieBase, 'kbase1' that includes all files found under resources path, be it DRL, BPMN2, XLS etc. KieSession 'ksession1' is associated with that KieBase and can be created by name.

Example 39. Author - kmodule.xml

    <kmodule xmlns="http://www.drools.org/xsd/kmodule">
        <kbase name="kbase1">
            <ksession name="ksession1"/>
        </kbase>
    </kmodule>

Example 40. Build and Install - Maven

    mvn install

ks.getKieClasspathContainer() returns the KieContainer that contains the KieBases deployed onto the environment classpath. This time the KieSession uses the name 'ksession1'. You do not need to lookup the KieBase first, as it knows which KieBase 'ksession1' is assocaited with.

Example 41. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieContainer kContainer = ks.getKieClasspathContainer();

    KieSession kSession = kContainer.newKieSession("ksession1");
    kSession.setGlobal("out", out);
    kSession.insert(new Message("Dave", "Hello, HAL. Do you read me, HAL?"));
    kSession.fireAllRules();

##### 2.2.6.3. KieBase Inheritence

- Project: kiebase-inclusion.

- Summary: 'kmodule.xml' demonstrates that one KieBase can include the resources from another KieBase, from another KieModule. In this case it inherits the named KieBase from the 'name-kiesession' example. The included KieBase can be from the current KieModule or any other KieModule that is in the pom.xml dependency list.

kmodule.xml will produce a single named KieBase, 'kbase2' that includes all files found under resources path, be it DRL, BPMN2, XLS etc. Further it will include all the resources found from the KieBase 'kbase1', due to the use of the 'includes' attribute. KieSession 'ksession2' is associated with that KieBase and can be created by name.

Example 42. Author - kmodule.xml

    <kbase name="kbase2" includes="kbase1">
        <ksession name="ksession2"/>
    </kbase>

This example requires that the previous example, 'named-kiesession', is built and installed to the local Maven repository first. Once installed it can be included as a dependency, using the standard Maven <dependencies> element.

Example 43. Author - pom.xml

    <project xmlns="http://maven.apache.org/POM/4.0.0"
             xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
             xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
      <modelVersion>4.0.0</modelVersion>
      <parent>
        <groupId>org.drools</groupId>
        <artifactId>drools-examples-api</artifactId>
        <version>6.0.0/version>
      </parent>

      <artifactId>kiebase-inclusion</artifactId>
      <name>Drools API examples - KieBase Inclusion</name>

      <dependencies>
        <dependency>
          <groupId>org.drools</groupId>
          <artifactId>drools-compiler</artifactId>
        </dependency>
        <dependency>
          <groupId>org.drools</groupId>
          <artifactId>named-kiesession</artifactId>
          <version>6.0.0</version>
        </dependency>
      </dependencies>

    </project>

Once 'named-kiesession' is built and installed this example can be built and installed as normal. Again the act of installing, will force the unit tests to run, demonstrating the use case.

Example 44. Build and Install - Maven

    mvn install

ks.getKieClasspathContainer() returns the KieContainer that contains the KieBases deployed onto the environment classpath. This time the KieSession uses the name 'ksession2'. You do not need to lookup the KieBase first, as it knows which KieBase 'ksession1' is assocaited with. Notice two rules fire this time, showing that KieBase 'kbase2' has included the resources from the dependency KieBase 'kbase1'.

Example 45. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieContainer kContainer = ks.getKieClasspathContainer();
    KieSession kSession = kContainer.newKieSession("ksession2");
    kSession.setGlobal("out", out);

    kSession.insert(new Message("Dave", "Hello, HAL. Do you read me, HAL?"));
    kSession.fireAllRules();

    kSession.insert(new Message("Dave", "Open the pod bay doors, HAL."));
    kSession.fireAllRules();

##### 2.2.6.4. Multiple KieBases

- Project: 'multiple-kbases.

- Summary: Demonstrates that the 'kmodule.xml' can contain any number of KieBase or KieSession declarations. Introduces the 'packages' attribute to select the folders for the resources to be included in the KieBase.

kmodule.xml produces 6 different named KieBases. 'kbase1' includes all resources from the KieModule. The other KieBases include resources from other selected folders, via the 'packages' attribute. Note the use of wildcard '*', to select this package and all packages below it.

Example 46. Author - kmodule.xml

    <kmodule xmlns="http://www.drools.org/xsd/kmodule">

      <kbase name="kbase1">
        <ksession name="ksession1"/>
      </kbase>

      <kbase name="kbase2" packages="org.some.pkg">
        <ksession name="ksession2"/>
      </kbase>

      <kbase name="kbase3" includes="kbase2" packages="org.some.pkg2">
        <ksession name="ksession3"/>
      </kbase>

      <kbase name="kbase4" packages="org.some.pkg, org.other.pkg">
        <ksession name="ksession4"/>
      </kbase>

      <kbase name="kbase5" packages="org.*">
        <ksession name="ksession5"/>
      </kbase>

      <kbase name="kbase6" packages="org.some.*">
        <ksession name="ksession6"/>
      </kbase>
    </kmodule>

Example 47. Build and Install - Maven

    mvn install

Only part of the example is included below, as there is a test method per KieSession, but each one is a repetition of the other, with different list expectations.

Example 48. Utilize and Run - Java

    @Test
    public void testSimpleKieBase() {
        List<Integer> list = useKieSession("ksession1");
        // no packages imported means import everything
        assertEquals(4, list.size());
        assertTrue( list.containsAll( asList(0, 1, 2, 3) ) );
    }

    //.. other tests for ksession2 to ksession6 here

    private List<Integer> useKieSession(String name) {
        KieServices ks = KieServices.Factory.get();
        KieContainer kContainer = ks.getKieClasspathContainer();
        KieSession kSession = kContainer.newKieSession(name);

        List<Integer> list = new ArrayList<Integer>();
        kSession.setGlobal("list", list);
        kSession.insert(1);
        kSession.fireAllRules();

        return list;
    }

##### 2.2.6.5. KieContainer from KieRepository

- Project: kcontainer-from-repository

- Summary: The project does not contain a kmodule.xml, nor does the pom.xml have any dependencies for other KieModules. Instead the Java code demonstrates the loading of a dynamic KieModule from a Maven repository.

The pom.xml must include kie-ci as a depdency, to ensure Maven is available at runtime. As this uses Maven under the hood you can also use the standard Maven settings.xml file.

Example 49. Author - pom.xml

    <project xmlns="http://maven.apache.org/POM/4.0.0"
             xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
             xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
      <modelVersion>4.0.0</modelVersion>
      <parent>
        <groupId>org.drools</groupId>
        <artifactId>drools-examples-api</artifactId>
        <version>6.0.0</version>
      </parent>

      <artifactId>kiecontainer-from-kierepo</artifactId>
      <name>Drools API examples - KieContainer from KieRepo</name>

      <dependencies>
        <dependency>
          <groupId>org.kie</groupId>
          <artifactId>kie-ci</artifactId>
        </dependency>
      </dependencies>

    </project>

Example 50. Build and Install - Maven

    mvn install

In the previous examples the classpath KieContainer used. This example creates a dynamic KieContainer as specified by the ReleaseId. The ReleaseId uses Maven conventions for group id, artifact id and version. It also obeys LATEST and SNAPSHOT for versions.

Example 51. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();

    // Install example1 in the local Maven repo before to do this
    KieContainer kContainer = ks.newKieContainer(ks.newReleaseId("org.drools", "named-kiesession", "6.0.0-SNAPSHOT"));

    KieSession kSession = kContainer.newKieSession("ksession1");
    kSession.setGlobal("out", out);

    Object msg1 = createMessage(kContainer, "Dave", "Hello, HAL. Do you read me, HAL?");
    kSession.insert(msg1);
    kSession.fireAllRules();

##### 2.2.6.6. Default KieSession from File

- Project: default-kiesession-from-file

- Summary: Dynamic KieModules can also be loaded from any Resource location. The loaded KieModule provides default KieBase and KieSession definitions.

No kmodue.xml file exists. The project 'default-kiesession' must be built first, so that the resulting JAR, in the target folder, can be referenced as a File.

Example 52. Build and Install - Maven

    mvn install

Any KieModule can be loaded from a Resource location and added to the KieRepository. Once deployed in the KieRepository it can be resolved via its ReleaseId. Note neither Maven or kie-ci are needed here. It will not set up a transitive dependency parent classloader.

Example 53. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieRepository kr = ks.getRepository();

    KieModule kModule = kr.addKieModule(ks.getResources().newFileSystemResource(getFile("default-kiesession")));

    KieContainer kContainer = ks.newKieContainer(kModule.getReleaseId());

    KieSession kSession = kContainer.newKieSession();
    kSession.setGlobal("out", out);

    Object msg1 = createMessage(kContainer, "Dave", "Hello, HAL. Do you read me, HAL?");
    kSession.insert(msg1);
    kSession.fireAllRules();

##### 2.2.6.7. Named KieSession from File

- Project: named-kiesession-from-file

- Summary: Dynamic KieModules can also be loaded from any Resource location. The loaded KieModule provides named KieBase and KieSession definitions.

No kmodue.xml file exists. The project 'named-kiesession' must be built first, so that the resulting JAR, in the target folder, can be referenced as a File.

Example 54. Build and Install - Maven

    mvn install

Any KieModule can be loaded from a Resource location and added to the KieRepository. Once in the KieRepository it can be resolved via its ReleaseId. Note neither Maven or kie-ci are needed here. It will not setup a transitive dependency parent classloader.

Example 55. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieRepository kr = ks.getRepository();

    KieModule kModule = kr.addKieModule(ks.getResources().newFileSystemResource(getFile("named-kiesession")));

    KieContainer kContainer = ks.newKieContainer(kModule.getReleaseId());

    KieSession kSession = kContainer.newKieSession("ksession1");
    kSession.setGlobal("out", out);

    Object msg1 = createMessage(kContainer, "Dave", "Hello, HAL. Do you read me, HAL?");
    kSession.insert(msg1);
    kSession.fireAllRules();

##### 2.2.6.8. KieModule with Dependent KieModule

- Project: kie-module-form-multiple-files

- Summary: Programmatically provide the list of dependant KieModules, without using Maven to resolve anything.

No kmodue.xml file exists. The projects 'named-kiesession' and 'kiebase-include' must be built first, so that the resulting JARs, in the target folders, can be referenced as Files.

Example 56. Build and Install - Maven

    mvn install

Creates two resources. One is for the main KieModule 'exRes1' the other is for the dependency 'exRes2'. Even though kie-ci is not present and thus Maven is not available to resolve the dependencies, this shows how you can manually specify the dependent KieModules, for the vararg.

Example 57. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieRepository kr = ks.getRepository();

    Resource ex1Res = ks.getResources().newFileSystemResource(getFile("kiebase-inclusion"));
    Resource ex2Res = ks.getResources().newFileSystemResource(getFile("named-kiesession"));

    KieModule kModule = kr.addKieModule(ex1Res, ex2Res);
    KieContainer kContainer = ks.newKieContainer(kModule.getReleaseId());

    KieSession kSession = kContainer.newKieSession("ksession2");
    kSession.setGlobal("out", out);

    Object msg1 = createMessage(kContainer, "Dave", "Hello, HAL. Do you read me, HAL?");
    kSession.insert(msg1);
    kSession.fireAllRules();

    Object msg2 = createMessage(kContainer, "Dave", "Open the pod bay doors, HAL.");
    kSession.insert(msg2);
    kSession.fireAllRules();

##### 2.2.6.9. Programmaticaly build a Simple KieModule with Defaults

- Project: kiemoduelmodel-example

- Summary: Programmaticaly buid a KieModule from just a single file. The POM and models are all defaulted. This is the quickest out of the box approach, but should not be added to a Maven repository.

Example 58. Build and Install - Maven

    mvn install

This programmatically builds a KieModule. It populates the model that represents the ReleaseId and kmodule.xml, and it adds the relevant resources. A pom.xml is generated from the ReleaseId.

Example 59. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieRepository kr = ks.getRepository();
    KieFileSystem kfs = ks.newKieFileSystem();

    kfs.write("src/main/resources/org/kie/example5/HAL5.drl", getRule());

    KieBuilder kb = ks.newKieBuilder(kfs);

    kb.buildAll(); // kieModule is automatically deployed to KieRepository if successfully built.
    if (kb.getResults().hasMessages(Level.ERROR)) {
        throw new RuntimeException("Build Errors:\n" + kb.getResults().toString());
    }

    KieContainer kContainer = ks.newKieContainer(kr.getDefaultReleaseId());

    KieSession kSession = kContainer.newKieSession();
    kSession.setGlobal("out", out);

    kSession.insert(new Message("Dave", "Hello, HAL. Do you read me, HAL?"));
    kSession.fireAllRules();

##### 2.2.6.10. Programmaticaly build a KieModule using Meta Models

- Project: kiemoduelmodel-example

- Summary: Programmaticaly build a KieModule, by creating its kmodule.xml meta model resources.

Example 60. Build and Install - Maven

    mvn install

This programmatically builds a KieModule. It populates the model that represents the ReleaseId and kmodule.xml, as well as add the relevant resources. A pom.xml is generated from the ReleaseId.

Example 61. Utilize and Run - Java

    KieServices ks = KieServices.Factory.get();
    KieFileSystem kfs = ks.newKieFileSystem();

    Resource ex1Res = ks.getResources().newFileSystemResource(getFile("named-kiesession"));
    Resource ex2Res = ks.getResources().newFileSystemResource(getFile("kiebase-inclusion"));

    ReleaseId rid = ks.newReleaseId("org.drools", "kiemodulemodel-example", "6.0.0-SNAPSHOT");
    kfs.generateAndWritePomXML(rid);

    KieModuleModel kModuleModel = ks.newKieModuleModel();
    kModuleModel.newKieBaseModel("kiemodulemodel")
                .addInclude("kiebase1")
                .addInclude("kiebase2")
                .newKieSessionModel("ksession6");

    kfs.writeKModuleXML(kModuleModel.toXML());
    kfs.write("src/main/resources/kiemodulemodel/HAL6.drl", getRule());

    KieBuilder kb = ks.newKieBuilder(kfs);
    kb.setDependencies(ex1Res, ex2Res);
    kb.buildAll(); // kieModule is automatically deployed to KieRepository if successfully built.
    if (kb.getResults().hasMessages(Level.ERROR)) {
        throw new RuntimeException("Build Errors:\n" + kb.getResults().toString());
    }

    KieContainer kContainer = ks.newKieContainer(rid);

    KieSession kSession = kContainer.newKieSession("ksession6");
    kSession.setGlobal("out", out);

    Object msg1 = createMessage(kContainer, "Dave", "Hello, HAL. Do you read me, HAL?");
    kSession.insert(msg1);
    kSession.fireAllRules();

    Object msg2 = createMessage(kContainer, "Dave", "Open the pod bay doors, HAL.");
    kSession.insert(msg2);
    kSession.fireAllRules();

    Object msg3 = createMessage(kContainer, "Dave", "What's the problem?");
    kSession.insert(msg3);
    kSession.fireAllRules();

### 2.3. Security

#### 2.3.1. Security Manager

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

Many times, the authoring of these metaphores 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.

##### 2.3.1.1. How to define a KIE Policy

Rules and processes can run with very restrict permissions, but the engine itself needs to perform many complex operations in order to work. Examples are: it needs to create classloaders, read system properties, access the file system, etc.

Once a security manager is installed, though, it will apply restrictions to all the code executing in the JVM according to the defined policy. For that reason, KIE allows the user to define two different policy files: one for the engine itself and one for the assets deployed into and executed by the engine.

One easy way to setup the enviroment is to give the engine itself a very permissive policy, while providing a constrained policy for rules and processes.

Policy files follow the standard policy file syntax as described in the Java documentation. For more details, see:

https://docs.oracle.com/javase/6/docs/technotes/guides/security/PolicyFiles.html#FileSyntax

A permissive policy file for the engine can look like the following:

Example 62. A sample engine.policy file

    grant {
        permission java.security.AllPermission;
    }

An example security policy for rules could be:

Example 63. A sample rules.policy file

    grant {
        permission java.util.PropertyPermission "*", "read";
        permission java.lang.RuntimePermission "accessDeclaredMembers";
    }

Please note that depending on what the rules and processes are supposed to do, many more permissions might need to be granted, like accessing files in the filesystem, databases, etc.

In order to use these policy files, all that is necessary is to execute the application with these files as parameters to the JVM. Three parameters are required:

Table 9. Parameters
Parameter | Meaning
--- | ---
-Djava.security.manager | Enables the security manager
-Djava.security.policy=<jvm_policy_file> | Defines the global policy file to be applied to the whole application, including the engine
-Dkie.security.policy=<kie_policy_file> | Defines the policy file to be applied to rules and processes

For instance:

`java -Djava.security.manager -Djava.security.policy=global.policy -Dkie.security.policy=rules.policy foo.bar.MyApp`

| When executing the engine inside a container, use your container’s documentation to find out how to configure the Security Manager and how to define the global security policy. Define the kie security policy as described above and set the `kie.security.policy` system property in order to configure the engine to use it.

| Please note that unless a Security Manager is configured, the `kie.security.policy` will be ignored.

| A Security Manager has a high performance impact in the JVM. Applications with strict performance requirements are strongly discouraged of using a Security Manager. An alternative is the use of other security procedures like the auditing of rules/processes before testing and deployment to prevent malicious code from being deployed to the environment.

# Drools Runtime and Language

Drools is a powerful Hybrid Reasoning System.

## 3. Hybrid Reasoning

### 3.1. Artificial Intelligence

#### 3.1.1. A Little History

Over the last few decades artificial intelligence (AI) became an unpopular term, with the well-known "AI Winter". There were large boasts from scientists and engineers looking for funding, which never lived up to expectations, resulting in many failed projects. Thinking Machines Corporation and the 5th Generation Computer (5GP) project probably exemplify best the problems at the time.

Thinking Machines Corporation was one of the leading AI firms in 1990, it had sales of nearly $65 million. Here is a quote from its brochure:

“Some day we will build a thinking machine. It will be a truly intelligent machine. One that can see and hear and speak. A machine that will be proud of us.”

Yet 5 years later it filed for bankruptcy protection under Chapter 11. The site inc.com has a fascinating article titled "The Rise and Fall of Thinking Machines". The article covers the growth of the industry and how a cosy relationship with Thinking Machines and DARPA over-heated the market, to the point of collapse. It explains how and why commerce moved away from AI and towards more practical number-crunching super computers.

The 5th Generation Computer project was a USD 400 million project in Japan to build a next generation computer. Valves (or Tubes) was the first generation, transistors the second, integrated circuits the third and finally microprocessors was the fourth. The fifth was intended to be a machine capable of effective Artificial Intelligence. This project spurred an "arms" race with the UK and USA, that caused much of the AI bubble. The 5GP would provide massive multi-cpu parallel processing hardware along with powerful knowledge representation and reasoning software via Prolog ; a type of [term]_ expert system_ . By 1992 the project was considered a failure and cancelled. It was the largest and most visible commercial venture for Prolog, and many of the failures are pinned on the problems of trying to run a logic based programming language concurrently on multi CPU hardware with effective results. Some believe that the failure of the 5GP project tainted Prolog and relegated it to academia, see "Whatever Happened to Prolog" by John C. Dvorak.

However while research funding dried up and the term AI became less used, many green shoots were planted and continued more quietly under discipline specific names: cognitive systems , machine learning , intelligent systems ,[term]_ knowledge representation and reasoning_ . Offshoots of these then made their way into commercial systems, such as expert systems in the Business Rules Management System (BRMS) market.

Imperative , system based languages, languages such as C, C++, Java and C#/.Net have dominated the last 20 years, enabled by the practicality of the languages and ability to run with good performance on commodity hardware. However many believe there is a renaissance underway in the field of AI, spurred by advances in hardware capabilities and AI research. In 2005 Heather Havenstein authored "Spring comes to AI winter" which outlines a case for this resurgence. Norvig and Russel dedicate several pages to what factors allowed the industry to overcome its problems and the research that came about as a result:

> Recent years have seen a revolution in both the content and the methodology of work in artificial intelligence. It is now more common to build on existing theories than to propose brand-new ones, to base claims on rigorous theorems or hard experimental evidence rather than on intuition, and to show relevance to real-world applications rather than toy examples.

— Artificial Intelligence: A Modern Approach

Computer vision , neural networks , machine learning and knowledge representation and reasoning (KRR) have made great strides towards becoming practical in commercial environments. For example, vision-based systems can now fully map out and navigate their environments with strong recognition skills. As a result we now have self-driving cars about to enter the commercial market. Ontological research, based around description logic, has provided very rich semantics to represent our world. Algorithms such as the tableaux algorithm have made it possible to use those rich semantics effectively in large complex ontologies. Early KRR systems, like Prolog in 5GP, were dogged by the limited semantic capabilities and memory restrictions on the size of those ontologies.

#### 3.1.2. Knowledge Representation and Reasoning

In A Little History talks about AI as a broader subject and touches on Knowledge Representation and Reasoning (KRR) and also Expert Systems, I’ll come back to Expert Systems later.

KRR is about how we represent our knowledge in symbolic form, i.e. how we describe something. Reasoning is about how we go about the act of thinking using this knowledge. System based object-oriented languages, like C++, Java and C#, have data definitions called classes for describing the composition and behaviour of modeled entities. In Java we call exemplars of these described things beans or instances. However those classification systems are limited to ensure computational efficiency. Over the years researchers have developed increasingly sophisticated ways to represent our world. Many of you may already have heard of OWL (Web Ontology Language). There is always a gap between what can be theoretically represented and what can be used computationally in practically timely manner, which is why OWL has different sub-languages from Lite to Full. It is not believed that any reasoning system can support OWL Full. However, algorithmic advances continue to narrow that gap and improve the expressiveness available to reasoning engines.

There are also many approaches to how these systems go about thinking. You may have heard discussions comparing the merits of forward chaining, which is reactive and data driven, with backward chaining, which is passive and query driven. Many other types of reasoning techniques exist, each of which enlarges the scope of the problems we can tackle declaratively. To list just a few: imperfect reasoning (fuzzy logic, certainty factors), defeasible logic, belief systems, temporal reasoning and correlation. You don’t need to understand all these terms to understand and use Drools. They are just there to give an idea of the range of scope of research topics, which is actually far more extensive, and continues to grow as researchers push new boundaries.

KRR is often referred to as the core of Artificial Intelligence. Even when using biological approaches like neural networks, which model the brain and are more about pattern recognition than thinking, they still build on KRR theory. My first endeavours with Drools were engineering oriented, as I had no formal training or understanding of KRR. Learning KRR has allowed me to get a much wider theoretical background, and to better understand both what I’ve done and where I’m going, as it underpins nearly all of the theoretical side to our Drools R&D. It really is a vast and fascinating subject that will pay dividends for those who take the time to learn. I know it did and still does for me. Bracham and Levesque have written a seminal piece of work, called "Knowledge Representation and Reasoning" that is a must read for anyone wanting to build strong foundations. I would also recommend the Russel and Norvig book "Artificial Intelligence, a modern approach" which also covers KRR.

#### 3.1.3. Rules Engines and Production Rule Systems (PRS)

We’ve now covered a brief history of AI and learnt that the core of AI is formed around KRR. We’ve shown than KRR is a vast and fascinating subject which forms the bulk of the theory driving Drools R&D.

The Drools engine is the computer program that delivers KRR functionality to the developer. At a high level it has three components:

- Ontology

- Rules

- Data

As previously mentioned the ontology is the representation model we use for our "things". It could use records or Java classes or full-blown OWL based ontologies. The rules perform the reasoning, i.e., they facilitate "thinking". The distinction between rules and ontologies blurs a little with OWL based ontologies, whose richness is rule based.

The term "rules engine" is quite ambiguous in that it can be any system that uses rules, in any form, that can be applied to data to produce outcomes. This includes simple systems like form validation and dynamic expression engines. The book "How to Build a Business Rules Engine" (2004) by Malcolm Chisholm exemplifies this ambiguity. The book is actually about how to build and alter a database schema to hold validation rules. The book then shows how to generate Visual Basic code from those validation rules to validate data entry. While perfectly valid, this is very different to what we are talking about.

Drools started life as a specific type of rules engine called a Production Rule System (PRS) and was based around the Rete algorithm (usually pronounced as two syllables, e.g., REH-te or RAY-tay). The Rete algorithm, developed by Charles Forgy in 1974, forms the brain of a Production Rule System and is able to scale to a large number of rules and facts. A Production Rule is a two-part structure: the Drools engine matches facts and data against Production Rules - also called Productions or just Rules - to infer conclusions which result in actions.

    when
        <conditions>
    then
        <actions>;

The process of matching the new or existing facts against Production Rules is called pattern matching , which is performed by the inference engine . Actions execute in response to changes in data, like a database trigger; we say this is a data driven approach to reasoning. The actions themselves can change data, which in turn could match against other rules causing them to fire; this is referred to as forward chaining

Drools 5.x implements and extends the Rete algorithm. This extended Rete algorithm is named ReteOO , signifying that Drools has an enhanced and optimized implementation of the Rete algorithm for object oriented systems. Other Rete based engines also have marketing terms for their proprietary enhancements to Rete, like RetePlus and Rete III. The most common enhancements are covered in "Production Matching for Large Learning Systems" (1995) by Robert B. Doorenbos' thesis, which presents Rete/UL. Drools 6.x introduces a new lazy algorithm named PHREAK ; which is covered in more detail in the PHREAK algorithm section.

The Rules are stored in the Production Memory and the facts that the Inference Engine matches against are kept in the Working Memory. Facts are asserted into the Working Memory where they may then be modified or retracted. A system with a large number of rules and facts may result in many rules being true for the same fact assertion; these rules are said to be in conflict. The Agenda manages the execution order of these conflicting rules using a Conflict Resolution strategy.

Figure 28. High-level View of a Production Rule System

#### 3.1.4. Hybrid Reasoning Systems (HRS)

You may have read discussions comparing the merits of forward chaining (reactive and data driven) or backward chaining (passive query). Here is a quick explanation of these two main types of reasoning.

Forward chaining is "data-driven" and thus reactionary, with facts being asserted into working memory, which results in one or more rules being concurrently true and scheduled for execution by the Agenda. In short, we start with a fact, it propagates through the rules, and we end in a conclusion.

Figure 29. Forward Chaining

Backward chaining is "goal-driven", meaning that we start with a conclusion which the Drools engine tries to satisfy. If it can’t, then it searches for conclusions that it can satisfy. These are known as subgoals, that will help satisfy some unknown part of the current goal. It continues this process until either the initial conclusion is proven or there are no more subgoals. Prolog is an example of a Backward Chaining engine. Drools can also do backward chaining, which we refer to as derivation queries.

Figure 30. Backward Chaining

Historically you would have to make a choice between systems like OPS5 (forward) or Prolog (backward). Nowadays many modern systems provide both types of reasoning capabilities. There are also many other types of reasoning techniques, each of which enlarges the scope of the problems we can tackle declaratively. To list just a few: imperfect reasoning (fuzzy logic, certainty factors), defeasible logic, belief systems, temporal reasoning and correlation. Modern systems are merging these capabilities, and others not listed, to create hybrid reasoning systems (HRS).

While Drools started out as a PRS, 5.x introduced Prolog style backward chaining reasoning as well as some functional programming styles. For this reason we now prefer the term Hybrid Reasoning System when describing Drools.

Drools currently provides crisp reasoning, but imperfect reasoning is almost ready. Initially this will be imperfect reasoning with fuzzy logic; later we’ll add support for other types of uncertainty. Work is also under way to bring OWL based ontological reasoning, which will integrate with our traits system. We also continue to improve our functional programming capabilities.

#### 3.1.5. Expert Systems

You will often hear the terms expert systems used to refer to production rule systems or Prolog -like systems. While this is normally acceptable, it’s technically incorrect as these are frameworks to build expert systems with, rather than expert systems themselves. It becomes an expert system once there is an ontological model to represent the domain and there are facilities for knowledge acquisition and explanation.

Mycin is the most famous expert system, built during the 70s. It is still heavily covered in academic literature, such as the recommended book "Expert Systems" by Peter Jackson.

Figure 31. Early History of Expert Systems

#### 3.1.6. Recommended Reading

* General AI, KRR and Expert System Books*

For those wanting to get a strong theoretical background in KRR and expert systems, I’d strongly recommend the following books. "Artificial Intelligence: A Modern Approach" is a must have, for anyone’s bookshelf.

- Introduction to Expert Systems

  - Peter Jackson

- Expert Systems: Principles and Programming

  - Joseph C. Giarratano, Gary D. Riley

- Knowledge Representation and Reasoning

  - Ronald J. Brachman, Hector J. Levesque

- Artificial Intelligence : A Modern Approach.

  - Stuart Russell and Peter Norvig

Figure 32. Recommended Reading

* Papers*

Here are some recommended papers that cover interesting areas in rules engine research:

- Production Matching for Large Learning Systems: Rete/UL (1993)

  - Robert B. Doorenbos

- Advances In Rete Pattern Matching

  - Marshall Schor, Timothy P. Daly, Ho Soo Lee, Beth R. Tibbitts (AAAI 1986)

- Collection-Oriented Match

  - Anurag Acharya and Milind Tambe (1993)

- The Leaps Algorithm

  - Don Batery (1990)

- Gator: An Optimized Discrimination Network for Active Database Rule Condition Testing

  - Eric Hanson , Mohammed S. Hasan (1993)

* Drools Books*

There are currently three Drools books, all from Packt Publishing.

- JBoss Drools Business Rules

  - Paul Browne

- Drools JBoss Rules 5.0 Developers Guide

  - Michal Bali

- Drools Developer’s Cookbook

  - Lucas Amador

Figure 33. Recommended Reading

### 3.2. Rete Algorithm

The Rete algorithm was invented by Dr. Charles Forgy and documented in his PhD thesis in 1978-79. A simplified version of the paper was published in 1982 (http://citeseer.ist.psu.edu/context/505087/0). The Latin word "rete" means "net" or "network". The Rete algorithm can be broken into 2 parts: rule compilation and runtime execution.

The compilation algorithm describes how the Rules in the Production Memory are processed to generate an efficient discrimination network. In non-technical terms, a discrimination network is used to filter data as it propagates through the network. The nodes at the top of the network would have many matches, and as we go down the network, there would be fewer matches. At the very bottom of the network are the terminal nodes. In Dr. Forgy’s 1982 paper, he described 4 basic nodes: root, 1-input, 2-input and terminal.

Figure 34. Rete Nodes

The root node is where all objects enter the network. From there, it immediately goes to the ObjectTypeNode. The purpose of the ObjectTypeNode is to make sure the Drools engine doesn’t do more work than it needs to. For example, say we have 2 objects: Account and Order. If the Drools engine tried to evaluate every single node against every object, it would waste a lot of cycles. To make things efficient, the Drools engine should only pass the object to the nodes that match the object type. The easiest way to do this is to create an ObjectTypeNode and have all 1-input and 2-input nodes descend from it. This way, if an application asserts a new Account, it won’t propagate to the nodes for the Order object. In Drools when an object is asserted it retrieves a list of valid ObjectTypesNodes via a lookup in a HashMap from the object’s Class; if this list doesn’t exist it scans all the ObjectTypeNodes finding valid matches which it caches in the list. This enables Drools to match against any Class type that matches with an `instanceof` check.

Figure 35. ObjectTypeNodes

ObjectTypeNodes can propagate to AlphaNodes, LeftInputAdapterNodes and BetaNodes. AlphaNodes are used to evaluate literal conditions. Although the 1982 paper only covers equality conditions, many RETE implementations support other operations. For example, `Account.name == "Mr Trout"` is a literal condition. When a rule has multiple literal conditions for a single object type, they are linked together. This means that if an application asserts an Account object, it must first satisfy the first literal condition before it can proceed to the next AlphaNode. In Dr. Forgy’s paper, he refers to these as IntraElement conditions. The following diagram shows the AlphaNode combinations for Cheese( name == "cheddar", strength == "strong" ):

Figure 36. AlphaNodes

Drools extends Rete by optimizing the propagation from ObjectTypeNode to AlphaNode using hashing. Each time an AlphaNode is added to an ObjectTypeNode it adds the literal value as a key to the HashMap with the AlphaNode as the value. When a new instance enters the ObjectType node, rather than propagating to each AlphaNode, it can instead retrieve the correct AlphaNode from the HashMap, thereby avoiding unnecessary literal checks.

There are two two-input nodes, JoinNode and NotNode, and both are types of BetaNodes. BetaNodes are used to compare 2 objects, and their fields, to each other. The objects may be the same or different types. By convention we refer to the two inputs as left and right. The left input for a BetaNode is generally a list of objects; in Drools this is a Tuple. The right input is a single object. Two Nodes can be used to implement 'exists' checks. BetaNodes also have memory. The left input is called the Beta Memory and remembers all incoming tuples. The right input is called the Alpha Memory and remembers all incoming objects. Drools extends Rete by performing indexing on the BetaNodes. For instance, if we know that a BetaNode is performing a check on a String field, as each object enters we can do a hash lookup on that String value. This means when facts enter from the opposite side, instead of iterating over all the facts to find valid joins, we do a lookup returning potentially valid candidates. At any point a valid join is found the Tuple is joined with the Object; which is referred to as a partial match; and then propagated to the next node.

Figure 37. JoinNode

To enable the first Object, in the above case Cheese, to enter the network we use a LeftInputNodeAdapter - this takes an Object as an input and propagates a single Object Tuple.

Terminal nodes are used to indicate a single rule having matched all its conditions; at this point we say the rule has a full match. A rule with an 'or' conditional disjunctive connective results in subrule generation for each possible logical branch; thus one rule can have multiple terminal nodes.

Drools also performs node sharing. Many rules repeat the same patterns, and node sharing allows us to collapse those patterns so that they don’t have to be re-evaluated for every single instance. The following two rules share the first pattern, but not the last:

    rule
    when
        Cheese( $cheddar : name == "cheddar" )
        $person : Person( favouriteCheese == $cheddar )
    then
        System.out.println( $person.getName() + " likes cheddar" );
    end

    rule
    when
        Cheese( $cheddar : name == "cheddar" )
        $person : Person( favouriteCheese != $cheddar )
    then
        System.out.println( $person.getName() + " does not like cheddar" );
    end

As you can see below, the compiled Rete network shows that the alpha node is shared, but the beta nodes are not. Each beta node has its own TerminalNode. Had the second pattern been the same it would have also been shared.

Figure 38. Node Sharing

### 3.3. ReteOO Algorithm

The ReteOO was developed throughout the 3, 4 and 5 series releases. It takes the RETE algorithm and applies well known enhancements, all of which are covered by existing academic literature:

Node sharing

- Sharing is applied to both the alpha and beta network. The beta network sharing is always from the root pattern.

Alpha indexing

- Alpha Nodes with many children use a hash lookup mechanism to avoid testing each result.

Beta indexing

- Join, Not and Exist nodes index their memories using a hash. This reduces the join attempts for equal checks. Recently range indexing was added to Not and Exists.

Tree based graphs

- Join matches did not contain any references to their parent or children matches. Deletions would have to recalculate all join matches again, which involves recreating all those join match objects, to be able to find the parts of the network where the tuples should be deleted. This is called symmetrical propagation. A tree graph provides parent and children references, so a deletion is just a matter of following those references. This is asymmetrical propagation. The result is faster and makes less impact on the GC. It is also more robust because changes in values will not cause memory leaks if they happen without the Drools engine being notified.

Modify-in-place

- Traditional RETE implements a modify as a delete + insert. This causes all join tuples to be GC’d, many of which are recreated again as part of the insert. Modify-in-place instead propagates as a single pass; every node is inspected.

Property reactive

- Also called "new trigger condition". Allows more fine grained reactivity to updates. A Pattern can react to changes to specific properties and ignore others. This alleviates problems of recursion and also helps with performance.

Sub-networks

- Not, Exists and Accumulate can each have nested conditional elements, which forms sub networks.

Backward Chaining

- Prolog style derivation trees for backward chaining are supported. The implementation is stack based, so does not have method recursion issues for large graphs.

Lazy Truth Maintenance

- Truth maintenance has a runtime cost which is incurred whether TMS is used or not. Lazy TMS enables it only on first use; further it is activated per object type, so irrelevant object types do not incur the runtime cost.

Heap based agenda

- The agenda uses a binary heap queue to sort rule matches by salience, rather than any linear search or maintenance approach.

Dynamic Rules

- Rules can be added and removed at runtime, while the Drools engine is still populated with data.

### 3.4. PHREAK Algorithm

Drools 6 introduces a new algorithm, that attempts to address some of the core issues of RETE. The algorithm is not a rewrite from scratch: it incorporates all of the existing code and enhancements from ReteOO. While PHREAK is an evolution of the RETE algorithm, it is no longer classified as a RETE implementation. Once an animal evolves beyond a certain point and key characteristics are changed, the animal becomes classified as a new species; in the same way PHREAK cannot be considered RETE. There are two key RETE characteristics that strongly identify any derivative strains, regardless of optimizations:

- That it is an eager, data oriented algorithm.

- All work is done using the insert, update or delete actions, eagerly producing all partial matches for all rules.

PHREAK in contrast is characterised as a lazy, goal oriented algorithm, where partial matching is aggressively delayed.

This eagerness of RETE can lead to a lot of churn in large systems, and much wasted work, where "wasted work" is defined as matching efforts that do not result in a rule firing.

PHREAK was heavily inspired by a number of algorithms, including (but not limited to) LEAPS, RETE/UL and Collection-Oriented Match. PHREAK has all enhancements listed in the ReteOO section. In addition it adds the following set of enhancements, which are explained in more detail in the following paragraphs.

- Three layers of contextual memory; Node, Segment and Rule memories.

- Rule, segment and node based linking.

- Lazy (delayed) rule evaluation.

- Isolated rule evaluation.

- Set oriented propagations.

- Stack based evaluations, with pause and resume.

When the PHREAK engine is started all rules are said to be unlinked; no rule evaluation can happen while rules are unlinked. The insert, update and delete actions are queued before entering the beta network. A simple heuristic, based on the rule most likely to result in firings, is used to select the next rule for evaluation; this delays the evaluation and firing of the other rules. Only once a rule has all right inputs populated will the rule be considered linked in, although no work is yet done. Instead a goal is created, that represents the rule, and placed into a priority queue; which is ordered by salience. Each queue itself is associated with an AgendaGroup. Only the active AgendaGroup will inspect its queue, popping the goal for the rule with the highest salience and submitting it for evaluation. So the work done shifts from the insert, update, delete phase to the fireAllRules phase. Only the rule for which the goal was created is evaluated, other potential rule evaluations from those facts are delayed. While individual rules are evaluated, node sharing is still achieved through the process of segmentation, which is explained later.

Each successful join attempt in RETE produces a tuple (or token, or partial match) that will be propagated to the child nodes. For this reason it is characterised as a tuple oriented algorithm. For each child node that it reaches it will attempt to join with the other side of the node; again each successful join attempt will be propagated straight away. This creates a descent recursion effect, thrashing the network of nodes as it ripples up and down, left and right from the point of entry into the beta network to all the reachable leaf nodes.

PHREAK propagation is set oriented (or collection-oriented), instead of tuple oriented. For the rule being evaluated it will visit the first node and process all queued inserts, updates and deletes. The results are added to a set and the set is propagated to the child node. In the child node all queued inserts, updates and deletes are processed, adding the results to the same set. Once finished that set is propagated to the next child node, and so on until the terminal node is reached. This creates a single pass, pipeline type effect that is isolated to the current rule being evaluated. This creates a batch process effect which can provide performance advantages for certain rule constructs, such as sub-networks with accumulates. In the future it will lend itself to being able to exploit multi-core machines in a number of ways.

The Linking and Unlinking uses a layered bit mask system, based on a network segmentation. When the rule network is built segments are created for nodes that are shared by the same set of rules. A rule itself is made up from a path of segments, although if there is no sharing that will be a single segment. A bit-mask offset is assigned to each node in the segment. Also another bit mask (the layering) is assigned to each segment in the rule’s path. When there is at least one input (data propagation) the node’s bit is set to on. When each node has its bit set to on the segment’s bit is also set to on. Conversely if any node’s bit is set to off, the segment is then also set to off. If each segment in the rule’s path is set to on, the rule is said to be linked in and a goal is created to schedule the rule for evaluation. The same bit-mask technique is used to also track dirty node, segments and rules; this allows for a rule already linked in to be scheduled for evaluation if it’s considered dirty since it was last evaluated.

This ensures that no rule will ever evaluate partial matches, if it’s impossible for it to result in rule instances because one of the joins has no data. This is possible in RETE and it will merrily churn away producing partial match attempts for all nodes, even if the last join is empty.

While the incremental rule evaluation always starts from the root node, the dirty bit masks are used to allow nodes and segments that are not dirty to be skipped.

Using the existence of at at least one items of data per node is a fairly basic heuristic. Future work would attempt to delay the linking even further, using techniques such as arc consistency to determine whether or not matching will result in rule instance firings.

Where as RETE has just a singe unit of memory, the node memory, PHREAK has 3 levels of memory. This allows for much more contextual understanding during evaluation of a Rule.

Figure 39. PHREAK 3 Layered memory system

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

Figure 40. Example1: Single rule, no sharing

Example 2 demonstrates what happens when another rule is added that shares the pattern A. A is placed in its own segment, resulting in two segments per rule. Those two segments form a path for their respective rules. The first segment is shared by both paths. When A is linked the segment becomes linked. It then iterates each path the segment is shared by, setting the bit 1 to on. If B and C are later turned on, the second segment for path R1 is linked in; this causes bit 2 to be turned on for R1. With bit 1 and bit 2 set to on for R1, the rule is now linked and a goal created to schedule the rule for later evaluation and firing.

When a rule is evaluated it is the segments that allow the results of matching to be shared. Each segment has a staging memory to queue all inserts, updates and deletes for that segment. If R1 were to be evaluated it would process A and result in a set of tuples. The algorithm detects that there is a segmentation split and will create peered tuples for each insert, update and delete in the set and add them to R2’s staging memory. Those tuples will be merged with any existing staged tuples and wait for R2 to eventually be evaluated.

Figure 41. Example 2: Two rules, with sharing

Example 3 adds a third rule and demonstrates what happens when A and B are shared. Only the bits for the segments are shown this time. Demonstrating that R4 has 3 segments, R3 has 3 segments and R1 has 2 segments. A and B are shared by R1, R3 and R4. While D is shared by R3 and R4.

Figure 42. Example 3: Three rules, with sharing

Sub-networks are formed when a Not, Exists or Accumulate node contains more than one element. In Example 4 "B not( C )" forms the sub network, note that "not( C )" is a single element which does not require a sub network and is therefore merged inside of the Not node.

The sub network gets its own segment. R1 still has a path of two segments. The sub network forms another "inner" path. When the sub network is linked in, it will link in the outer segment.

Figure 43. Example 4 : Single rule, with sub-network and no sharing

Example 5 shows that the sub-network nodes can be shared by a rule that does not have a sub-network. This results in the sub-network segment being split into two.

Figure 44. Example 5: Two rules, one with a sub-network and sharing

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

All rule evaluations are incremental, and will not waste work recomputing matches already produced.

The evaluation algorithm is stack, instead of method recursion, based. Evaluation can be paused and resumed at any time, via the use of a StackEntry to represent the node currently being evaluated.

When a rule evaluation reaches a sub-network a StackEntry is created for the outer path segment and the sub-network segment. The sub-network segment is evaluated first; when the set reaches the end of the sub-network path it is merged into a staging list for the outer node it feeds into. The previous StackEntry is then resumed and can now process the results of the sub network. This has the added benefit that all work is processed in a batch, before propagating to the child node; this is much more efficient for Accumulate nodes.

The same stack system can be used for efficient backward chaining. When a rule evaluation reaches a query node it again pauses the current evaluation, by placing it on the stack. The query is then evaluated which produces a result set, which is saved in a memory location for the resumed StackEntry to pick up and propagate to the child node. If the query itself called other queries the process would repeat, with the current query being paused and a new evaluation setup for the current query node.

One final point on performance: One single rule in general will not evaluate any faster with PHREAK than it does with RETE. For a given rule and same data set, which using a root context object to enable and disable matching, both attempt the same number of matches, produce the same number of rule instances and take roughly the same amount of time, except for the use case with subnetworks and Accumulates.

PHREAK can however be considered more forgiving that RETE for poorly written rule bases and with a more graceful degradation of performance as the number of rules and complexity increases.

RETE will also churn away producing partial matches for rules that do not have data in all the joins, whereas PHREAK will avoid this.

So it’s not that PHREAK is faster than RETE; it just won’t slow down as much as your system grows :)

AgendaGroups did not help in RETE performance, as all rules were evaluated at all times, regardless of the group. The same is true for salience, which is why root context objects are often used to limit matching attempts. PHREAK only evaluates rules for the active AgendaGroup, and within that group will attempt to avoid evaluation of rules (via salience) that do not result in rule instance firings.

With PHREAK AgendaGroups and salience now become useful performance tools. The root context objects are no longer needed and are potentially counterproductive to performance, as they force the flushing and recreation of matches for rules.

## 4. User Guide

### 4.1. The Basics

#### 4.1.1. Stateless KIE session

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

To use this rule it is necessary to put it a Drools file, just a plain text file with .drl extension , short for "Drools Rule Language". Let’s call this file licenseApplication.drl, and store it in a Kie Project. A Kie Project has the structure of a normal Maven project with an additional file (kmodule.xml) defining the `KieBase`s and `KieSession`s that can be created. This file has to be placed in the resources/META-INF folder of the Maven project while all the other Drools artifacts, such as the licenseApplication.drl containing the former rule, must be stored in the resources folder or in any other subfolder under it.

Since meaningful defaults have been provided for all configuration aspects, the simplest kmodule.xml file can contain just an empty kmodule tag like the following:

    <?xml version="1.0" encoding="UTF-8"?>
    <kmodule xmlns="http://www.drools.org/xsd/kmodule"/>

At this point it is possible to create a `KieContainer` that reads the files to be built, from the classpath.

    KieServices kieServices = KieServices.Factory.get();
    KieContainer kContainer = kieServices.getKieClasspathContainer();

The above code snippet compiles all the DRL files found on the classpath and put the result of this compilation, a `KieModule`, in the `KieContainer`. If there are no errors, we are now ready to create our session from the `KieContainer` and execute against some data:

    StatelessKieSession kSession = kContainer.newStatelessKieSession();
    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 will 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.

    StatelessKieSession kSession = kContainer.newStatelessKieSession();
    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)`.

The `KieCommands` commands factory, obtainable from the `KieServices` like all other factories of the KIE API, is used to create commands, so that the following is equivalent to `execute(Iterable it)`:

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

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

    KieCommands kieCommands = kieServices.getCommands();
    List<Command> cmds = new ArrayList<Command>();
    cmds.add( kieCommands.newInsert( new Person( "Mr John Smith" ), "mrSmith", true, null ) );
    cmds.add( kieCommands.newInsert( new Person( "Mr John Doe" ), "mrDoe", true, null ) );
    BatchExecutionResults results = ksession.execute( kieCommands.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`.

#### 4.1.2. Stateful KIE session

Stateful Sessions are long 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 KieBase contains references to Stateful KIE sessions when they are created. Since Stateful KIE session is the most commonly used session type it is just named `KieSession` in the KIE API. `KieSession` also supports the `BatchExecutor` interface, like `StatelessKieSession`, 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 Drools 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 Drools 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 Drools 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
        delete( $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

As we did in the Stateless Session example, the above rules should be placed in a single DRL file and saved into the resouces folder of your Maven project or any of its subfolder. As before, we can then obtain a `KieSession` from the `KieContainer`. The only difference is that this time we create a Stateful Session, whereas before we created a Stateless Session.

    KieServices kieServices = KieServices.Factory.get();
    KieContainer kContainer = kieServices.getKieClasspathContainer();
    KieSession ksession = kContainer.newKieSession();

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 Drools 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 Drools 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.delete( kitchenFireHandle );
    ksession.delete( 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.

#### 4.1.3. Methods versus Rules

People often confuse methods and rules, and new rule users often 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 Drools 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.

#### 4.1.4. 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 are no field constraints:

    rule "Show Sprinklers" 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

### 4.2. Execution Control

#### 4.2.1. Agenda

The Agenda is a Rete feature. It maintains set of rules that are able to execute, its job is to schedule that execution in a deterministic order.

During actions on the `RuleRuntime`, rules may become fully matched and eligible for execution; a single Rule Runtime Action can result in multiple eligible rules. When a rule is fully matched a Rule Match is created, referencing the rule and the matched facts, and placed onto the Agenda. The Agenda controls the execution order of these Matches using a Conflict Resolution strategy.

The Drools engine cycles repeatedly through two phases:

1. Rule Runtime Actions. This is where most of the work takes place, either in the Consequence (the RHS itself) or the main Java application process. Once the Consequence has finished or the main Java application process calls `fireAllRules()` the Drools engine switches to the Agenda Evaluation phase.

2. Agenda Evaluation. This attempts to select a rule to fire. If no rule is found it exits, otherwise it fires the found rule, switching the phase back to Rule Runtime Actions.

Figure 45. Two Phase Execution

The process repeats until the agenda is clear, in which case control returns to the calling application. When Rule Runtime Actions are taking place, no rules are being fired.

#### 4.2.2. Rule Matches and Conflict Sets.

##### 4.2.2.1. Cashflow Example

So far the data and the matching process has been simple and small. To mix things up a bit a new example will be explored that handles cashflow calculations over date periods. The state of the Drools engine will be illustratively shown at key stages to help get a better understanding of what is actually going on under the hood. Three classes will be used, as shown below. This will help us grow our understanding of pattern matching and joins further. We will then use this to illustrate different techniques for execution control.

    public class CashFlow {
        private Date   date;
        private double amount;
        private int    type;
        long           accountNo;
        // getter and setter methods here
    }

    public class Account {
        private long   accountNo;
        private double balance;
        // getter and setter methods here
    }

    public AccountPeriod {
        private Date start;
        private Date end;
        // getter and setter methods here
    }

By now you already know how to create KieBases and how to instantiate facts to populate the `KieSession`, so tables will be used to show the state of the inserted data, as it makes things clearer for illustration purposes. The tables below show that a single fact was inserted for the `Account`. Also inserted are a series of debits and credits as `CashFlow` objects for that account, extending over two quarters.

Figure 46. CashFlows and Account

Two rules can be used to determine the debit and credit for that quarter and update the Account balance. The two rules below constrain the cashflows for an account for a given time period. Notice the "&&" which use short cut syntax to avoid repeating the field name twice.

    rule "increase balance for credits"
    when
      ap : AccountPeriod()
      acc : Account( $accountNo : accountNo )
      CashFlow( type == CREDIT,
                accountNo == $accountNo,
                date >= ap.start && <= ap.end,
                $amount : amount )
    then
      acc.balance  += $amount;
    end
    rule "decrease balance for debits"
    when
      ap : AccountPeriod()
      acc : Account( $accountNo : accountNo )
      CashFlow( type == DEBIT,
                accountNo == $accountNo,
                date >= ap.start && <= ap.end,
                $amount : amount )
    then
      acc.balance -= $amount;
    end

Earlier we showed how rules would equate to SQL, which can often help people with an SQL background to understand rules. The two rules above can be represented with two views and a trigger for each view, as below:

    select * from Account acc,
                  Cashflow cf,
                  AccountPeriod ap
    where acc.accountNo == cf.accountNo and
          cf.type == CREDIT and
          cf.date >= ap.start and
          cf.date <= ap.end
    select * from Account acc,
                  Cashflow cf,
                  AccountPeriod ap
    where acc.accountNo == cf.accountNo and
          cf.type == DEBIT and
          cf.date >= ap.start and
          cf.date <= ap.end
    trigger : acc.balance += cf.amount
    trigger : acc.balance -= cf.amount

If the `AccountPeriod` is set to the first quarter we constrain the rule "increase balance for credits" to fire on two rows of data and "decrease balance for debits" to act on one row of data.

Figure 47. AccountingPeriod, CashFlows and Account

The two cashflow tables above represent the matched data for the two rules. The data is matched during the insertion stage and, as you discovered in the previous chapter, does not fire straight away, but only after `fireAllRules()` is called. Meanwhile, the rule plus its matched data is placed on the Agenda and referred to as an RuIe Match or Rule Instance. The Agenda is a table of Rule Matches that are able to fire and have their consequences executed, as soon as fireAllRules() is called. Rule Matches on the Agenda are referred to as a conflict set and their execution is determine by a conflict resolution strategy. Notice that the order of execution so far is considered arbitrary.

Figure 48. CashFlows and Account

After all of the above activations are fired, the account has a balance of -25.

Figure 49. CashFlows and Account

If the `AccountPeriod` is updated to the second quarter, we have just a single matched row of data, and thus just a single Rule Match on the Agenda.

The firing of that Activation results in a balance of 25.

Figure 50. CashFlows and Account
Figure 51. CashFlows and Account

##### 4.2.2.2. Conflict Resolution

What if you don’t want the order of rule execution to be arbitrary? When there is one or more Rule Match on the Agenda they are said to be in conflict, and a conflict resolution strategy is used to determine the order of execution. The Drools strategy is very simple and based around a salience value, which assigns a priority to a rule. Each rule has a default value of 0, the higher the value the higher the priority.

As a general rule, it is a good idea not to count on rules firing in any particular order, and to author the rules without worrying about a "flow". However when a flow is needed a number of possibilities exist beyond salience: agenda groups, rule flow groups, activation groups and control/semaphore facts.

As of Drools 6.0 rule definition order in the source file is used to set priority after salience.

##### 4.2.2.3. Salience

To illustrate Salience we add a rule to print the account balance, where we want this rule to be executed after all the debits and credits have been applied for all accounts. We achieve this by assigning a negative salience to this rule so that it fires after all rules with the default salience 0.

    rule "Print balance for AccountPeriod"
            salience -50
        when
            ap : AccountPeriod()
            acc : Account()
        then
            System.out.println( acc.accountNo + " : " + acc.balance );
    end

The table below depicts the resulting Agenda. The three debit and credit rules are shown to be in arbitrary order, while the print rule is ranked last, to execute afterwards.

Figure 52. CashFlows and Account

##### 4.2.2.4. Agenda Groups

Agenda groups allow you to place rules into groups, and to place those groups onto a stack. The stack has push/pop bevaviour. Calling "setFocus" places the group onto the stack:

    ksession.getAgenda().getAgendaGroup( "Group A" ).setFocus();

The agenda always evaluates the top of the stack. When all the rules have fired for a group, it is popped from the stack and the next group is evaluated.

    rule "increase balance for credits"
      agenda-group "calculation"
    when
      ap : AccountPeriod()
      acc : Account( $accountNo : accountNo )
      CashFlow( type == CREDIT,
                accountNo == $accountNo,
                date >= ap.start && <= ap.end,
                $amount : amount )
    then
      acc.balance  += $amount;
    end
    rule "Print balance for AccountPeriod"
      agenda-group "report"
    when
      ap : AccountPeriod()
      acc : Account()
    then
      System.out.println( acc.accountNo +
                          " : " + acc.balance );
    end

First set the focus to the "report" group and then by placing the focus on "calculation" we ensure that group is evaluated first.

    Agenda agenda = ksession.getAgenda();
    agenda.getAgendaGroup( "report" ).setFocus();
    agenda.getAgendaGroup( "calculation" ).setFocus();
    ksession.fireAllRules();

##### 4.2.2.5. Rule Flow

Drools also features ruleflow-group attributes which allows workflow diagrams to declaratively specify when rules are allowed to fire. The screenshot below is taken from Eclipse using the Drools plugin. It has two ruleflow-group nodes which ensures that the calculation rules are executed before the reporting rules.

The use of the ruleflow-group attribute in a rule is shown below.

    rule "increase balance for credits"
      ruleflow-group "calculation"
    when
      ap : AccountPeriod()
      acc : Account( $accountNo : accountNo )
      CashFlow( type == CREDIT,
                accountNo == $accountNo,
                date >= ap.start && <= ap.end,
                $amount : amount )
    then
      acc.balance  += $amount;
    end
    rule "Print balance for AccountPeriod"
      ruleflow-group "report"
    when
      ap : AccountPeriod()
      acc : Account()
    then
      System.out.println( acc.accountNo +
                          " : " + acc.balance );
    end

### 4.3. Inference

#### 4.3.1. 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 business rules, which are easier to maintain.

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

### 4.4. Truth Maintenance with Logical Objects

#### 4.4.1. 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 53. Stated Insertion
Figure 54. Logical Insertion

##### 4.4.1.1. 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). When a 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.

Returning to the code to issue bus passes, these two rules can + logically insert the ChildBusPass and AdultBusPass facts, 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 a 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

##### 4.4.1.2. 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.

##### 4.4.1.3. Deleting stated or logically asserted facts from the working memory

By default when a fact is deleted from the working memory Drools attempts to remove it both from the set of stated facts and also from the Truth Maintenance System in case it has been logically asserted. However, using an overload of the delete method, it is also possible to remove it only from one of the 2. For instance invoking:

    ksession.delete( factHandle, FactHandle.State.LOGICAL );

the fact is removed only if it has been logically asserted, but not if it is a stated fact. In this case, if the fact has been stated its deletion fails silently and it is ignored.

### 4.5. Logging

One way to illuminate the black box that is the Drools 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>

| 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>

## 5. Running

Ths sections extends the KIE Running section, which should be read first, with specifics for the Drools runtime.

### 5.1. KieRuntime

#### 5.1.1. EntryPoint

The `EntryPoint` provides the methods around inserting, updating and deleting facts. The term "entry point" is related to the fact that we have multiple partitions in a Working Memory and you can choose which one you are inserting into. The use of multiple entry points is more common in event processing use cases, but they can be used by pure rule applications as well.

The `KieRuntime` interface provides the main interaction with the Drools engine. It is available in rule consequences and process actions. In this manual the focus is on the methods and interfaces related to rules, and the methods pertaining to processes will be ignored for now. But you’ll notice that the `KieRuntime` inherits methods from both the `WorkingMemory` and the `ProcessRuntime`, thereby providing a unified API to work with processes and rules. When working with rules, three interfaces form the `KieRuntime`: `EntryPoint`, `WorkingMemory` and the `KieRuntime` itself.

Figure 55. EntryPoint

##### 5.1.1.1. Insert

In order for a fact to be evaluated against the rules in a `KieBase`, it has to be inserted into the session. This is done by calling the method `insert(yourObject)`. When a fact is inserted into the session, some of its properties might be immediately evaluated (eager evaluation) and some might be deferred for later evaluation (lazy evaluation). The exact behaviour depends on the Drools engine algorithm being used.

| Expert systems typically use the term assert or assertion to refer to facts made available to the system. However, due to "assert" being a keyword in most languages, we have decided to use the `insert` keyword; In this manual, the two terms are used interchangeably.

When an Object is inserted it returns a `FactHandle`. This `FactHandle` is the token used to represent your inserted object within the `WorkingMemory`. It is also used for interactions with the `WorkingMemory` when you wish to delete or modify an object.

    Cheese stilton = new Cheese("stilton");
    FactHandle stiltonHandle = ksession.insert( stilton );

As mentioned in the KieBase section, a Working Memory may operate in two assertion modes: either equality or identity. Identity is the default.

Identity means that the Working Memory uses an `IdentityHashMap` to store all asserted objects. New instance assertions always result in the return of new `FactHandle`, but if an instance is asserted again then it returns the original fact handle, i.e., it ignores repeated insertions for the same object.

Equality means that the Working Memory uses a `HashMap` to store all asserted objects. An object instance assertion will only return a new `FactHandle` if the inserted object is not equal (according to its `equal()/hashcode()` methods) to an already existing fact.

##### 5.1.1.2. Delete

In order to remove a fact from the session, the method `delete()` is used. When a fact is deleted, any matches that are active and depend on that fact will be cancelled. Note that it is possible to have rules that depend on the nonexistence of a fact, in which case deleting a fact may cause a rule to activate. (See the `not` and `exists` keywords).

| Expert systems typically use the term retract or retraction to refer to the operation of removing facts from the Working Memory. Drools prefers the keyword `delete` for symmetry with the keyword `insert`; Drools also supports the keyword `retract`, but it was deprecated in favor of `delete`. In this manual, the two terms are used interchangeably.

Retraction may be done using the `FactHandle` that was returned by the insert call. On the right hand side of a rule the `delete` statement is used, which works with a simple object reference.

    Cheese stilton = new Cheese("stilton");
    FactHandle stiltonHandle = ksession.insert( stilton );
    ....
    ksession.delete( stiltonHandle );

##### 5.1.1.3. Update

The Drools engine must be notified of modified facts, so that they can be reprocessed. You must use the `update()` method to notify the `WorkingMemory` of changed objects for those objects that are not able to notify the `WorkingMemory` themselves. Notice that `update()` always takes the modified object as a second parameter, which allows you to specify new instances for immutable objects. On the right hand side of a rule the `modify` statement is recommended, as it makes the changes and notifies the Drools engine in a single statement. Alternatively, after changing a fact object’s field values through calls of setter methods you must invoke `update` immediately, event before changing another fact, or you will cause problems with the indexing within the Drools engine. The modify statement avoids this problem.

    Cheese stilton = new Cheese("stilton");
    FactHandle stiltonHandle = workingMemory.insert( stilton );
    ...
    stilton.setPrice( 100 );
    workingMemory.update( stiltonHandle, stilton );

#### 5.1.2. RuleRuntime

The RuleRuntime provides access to the Agenda, permits query executions, and lets you access named Entry Points.

Figure 56. RuleRuntime

##### 5.1.2.1. Query

Queries are used to retrieve fact sets based on patterns, as they are used in rules. Patterns may make use of optional parameters. Queries can be defined in the KIE base, from where they are called up to return the matching results. While iterating over the result collection, any identifier bound in the query can be used to access the corresponding fact or fact field by calling the `get` method with the binding variable’s name as its argument. If the binding refers to a fact object, its FactHandle can be retrieved by calling `getFactHandle`, again with the variable’s name as the parameter.

Figure 57. QueryResults
Figure 58. QueryResultsRow
Example 64. Simple Query Example

    QueryResults results =
        ksession.getQueryResults( "my query", new Object[] { "string" } );
    for ( QueryResultsRow row : results ) {
        System.out.println( row.get( "varName" ) );
    }

##### 5.1.2.2. Live Queries

Invoking queries and processing the results by iterating over the returned set is not a good way to monitor changes over time.

To alleviate this, Drools provides Live Queries, which have a listener attached instead of returning an iterable result set. These live queries stay open by creating a view and publishing change events for the contents of this view. To activate, you start your query with parameters and listen to changes in the resulting view. The `dispose` method terminates the query and discontinues this reactive scenario.

Example 65. Implementing ViewChangedEventListener

    final List updated = new ArrayList();
    final List removed = new ArrayList();
    final List added = new ArrayList();

    ViewChangedEventListener listener = new ViewChangedEventListener() {
     public void rowUpdated(Row row) {
      updated.add( row.get( "$price" ) );
     }

     public void rowRemoved(Row row) {
      removed.add( row.get( "$price" ) );
     }

     public void rowAdded(Row row) {
      added.add( row.get( "$price" ) );
     }
    };

    // Open the LiveQuery
    LiveQuery query = ksession.openLiveQuery( "cheeses",
                                              new Object[] { "cheddar", "stilton" },
                                              listener );
    ...
    ...
    query.dispose() // calling dispose to terminate the live query

A Drools blog article contains an example of Glazed Lists integration for live queries:

http://blog.athico.com/2010/07/glazed-lists-examples-for-drools-live.html

#### 5.1.3. StatefulRuleSession

The `StatefulRuleSession` is inherited by the `KieSession` and provides the rule related methods that are relevant from outside of the Drools engine.

Figure 59. StatefulRuleSession

##### 5.1.3.1. Agenda Filters

Figure 60. AgendaFilters

` AgendaFilter` objects are optional implementations of the filter interface which are used to allow or deny the firing of a match. What you filter on is entirely up to the implementation. Drools 4.0 used to supply some out of the box filters, which have not be exposed in drools 5.0 knowledge-api, but they are simple to implement and the Drools 4.0 code base can be referred to.

To use a filter specify it while calling `fireAllRules()`. The following example permits only rules ending in the string `"Test"`. All others will be filtered out.

    ksession.fireAllRules( new RuleNameEndsWithAgendaFilter( "Test" ) );

### 5.2. Agenda

The Agenda is a Rete feature. During actions on the `WorkingMemory`, rules may become fully matched and eligible for execution; a single Working Memory Action can result in multiple eligible rules. When a rule is fully matched a Match is created, referencing the rule and the matched facts, and placed onto the Agenda. The Agenda controls the execution order of these Matches using a Conflict Resolution strategy.

The Drools engine cycles repeatedly through two phases:

1. Working Memory Actions. This is where most of the work takes place, either in the Consequence (the RHS itself) or the main Java application process. Once the Consequence has finished or the main Java application process calls `fireAllRules()` the Drools engine switches to the Agenda Evaluation phase.

2. Agenda Evaluation. This attempts to select a rule to fire. If no rule is found it exits, otherwise it fires the found rule, switching the phase back to Working Memory Actions.

Figure 61. Two Phase Execution

The process repeats until the agenda is clear, in which case control returns to the calling application. When Working Memory Actions are taking place, no rules are being fired.

Figure 62. Agenda

#### 5.2.1. Conflict Resolution

Conflict resolution is required when there are multiple rules on the agenda. (The basics to this are covered in chapter "Quick Start".) As firing a rule may have side effects on the working memory, the Drools engine needs to know in what order the rules should fire (for instance, firing ruleA may cause ruleB to be removed from the agenda).

The default conflict resolution strategies employed by Drools are: Salience and LIFO (last in, first out).

The most visible one is salience (or priority), in which case a user can specify that a certain rule has a higher priority (by giving it a higher number) than other rules. In that case, the rule with higher salience will be preferred. LIFO priorities are based on the assigned Working Memory Action counter value, with all rules created during the same action receiving the same value. The execution order of a set of firings with the same priority value is arbitrary.

As a general rule, it is a good idea not to count on rules firing in any particular order, and to author the rules without worrying about a "flow". However when a flow is needed a number of possibilities exist, including but not limited to: agenda groups, rule flow groups, activation groups, control/semaphore facts. These are discussed in later sections.

Drools 4.0 supported custom conflict resolution strategies; while this capability still exists in Drools it has not yet been exposed to the end user via knowledge-api in Drools 5.0.

#### 5.2.2. AgendaGroup

Figure 63. AgendaGroup

Agenda groups are a way to partition rules (matches, actually) on the agenda. At any one time, only one group has "focus" which means that matches for rules in that group only will take effect. You can also have rules with "auto focus" which means that the focus is taken for its agenda group when that rule’s conditions are true.

Agenda groups are known as "modules" in CLIPS terminology. While it best to design rules that do not need control flow, this is not always possible. Agenda groups provide a handy way to create a "flow" between grouped rules. You can switch the group which has focus either from within the Drools engine, or via the API. If your rules have a clear need for multiple "phases" or "sequences" of processing, consider using agenda-groups for this purpose.

Each time `setFocus()` is called it pushes that Agenda Group onto a stack. When the focus group is empty it is popped from the stack and the focus group that is now on top evaluates. An Agenda Group can appear in multiple locations on the stack. The default Agenda Group is "MAIN", with all rules which do not specify an Agenda Group being in this group. It is also always the first group on the stack, given focus initially, by default.

    ksession.getAgenda().getAgendaGroup( "Group A" ).setFocus();

The `clear()` method can be used to cancel all the activations generated by the rules belonging to a given Agenda Group before one has had a chance to fire.

    ksession.getAgenda().getAgendaGroup( "Group A" ).clear();

Note that, due to the lazy nature of the phreak algorithm used by Drools, the activations are by default materialized only at firing time, but it is possible to anticipate the evaluation and then the activation of a given rule at the moment when a fact is inserted into the session by annotating it with `@Propagation(IMMEDIATE)` as explained in the Propagation modes section.

#### 5.2.3. ActivationGroup

Figure 64. ActivationGroup

An activation group is a set of rules bound together by the same "activation-group" rule attribute. In this group only one rule can fire, and after that rule has fired all the other rules are cancelled from the agenda. The `clear()` method can be called at any time, which cancels all of the activations before one has had a chance to fire.

    ksession.getAgenda().getActivationGroup( "Group B" ).clear();

#### 5.2.4. RuleFlowGroup

Figure 65. RuleFlowGroup

A rule flow group is a group of rules associated by the "ruleflow-group" rule attribute. These rules can only fire when the group is activated. The group itself can only become active when the elaboration of the ruleflow diagram reaches the node representing the group. Here too, the `clear()` method can be called at any time to cancels all matches still remaining on the Agenda.

    ksession.getAgenda().getRuleFlowGroup( "Group C" ).clear();

### 5.3. Event Model

The event package provides means to be notified of Drools engine events, including rules firing, objects being asserted, etc. This allows you, for instance, to separate logging and auditing activities from the main part of your application (and the rules).

The `WorkingMemoryEventManager` allows for listeners to be added and removed, so that events for the working memory and the agenda can be listened to.

Figure 66. WorkingMemoryEventManager

The following code snippet shows how a simple agenda listener is declared and attached to a session. It will print matches after they have fired.

Example 66. Adding an AgendaEventListener

    ksession.addEventListener( new DefaultAgendaEventListener() {
       public void afterMatchFired(AfterMatchFiredEvent event) {
           super.afterMatchFired( event );
           System.out.println( event );
       }
    });

Drools also provides `DebugRuleRuntimeEventListener` and `DebugAgendaEventListener` which implement each method with a debug print statement. To print all Working Memory events, you add a listener like this:

Example 67. Adding a DebugRuleRuntimeEventListener

    ksession.addEventListener( new DebugRuleRuntimeEventListener() );

The events currently supported are:

- MatchCreatedEvent

- MatchCancelledEvent

- BeforeMatchFiredEvent

- AfterMatchFiredEvent

- AgendaGroupPushedEvent

- AgendaGroupPoppedEvent

- ObjectInsertEvent

- ObjectDeletedEvent

- ObjectUpdatedEvent

- ProcessCompletedEvent

- ProcessNodeLeftEvent

- ProcessNodeTriggeredEvent

- ProcessStartEvent

### 5.4. StatelessKieSession

The `StatelessKieSession` wraps the `KieSession`, instead of extending it. Its main focus is on decision service type scenarios. It avoids the need to call `dispose()`. Stateless sessions do not support iterative insertions and the method call `fireAllRules()` from Java code; the act of calling `execute()` is a single-shot method that will internally instantiate a `KieSession`, add all the user data and execute user commands, call `fireAllRules()`, and then call `dispose()`. While the main way to work with this class is via the `BatchExecution` (a subinterface of `Command`) as supported by the `CommandExecutor` interface, two convenience methods are provided for when simple object insertion is all that’s required. The `CommandExecutor` and `BatchExecution` are talked about in detail in their own section.

Figure 67. StatelessKieSession

Our simple example shows a stateless session executing a given collection of Java objects using the convenience API. It will iterate the collection, inserting each element in turn.

Example 68. Simple StatelessKieSession execution with a Collection

    StatelessKieSession ksession = kbase.newStatelessKieSession();
    ksession.execute( collection );

If this was done as a single Command it would be as follows:

Example 69. Simple StatelessKieSession execution with InsertElements Command

    ksession.execute( CommandFactory.newInsertElements( collection ) );

If you wanted to insert the collection itself, and the collection’s individual elements, then `CommandFactory.newInsert(collection)` would do the job.

Methods of the `CommandFactory` create the supported commands, all of which can be marshalled using XStream and the `BatchExecutionHelper`. `BatchExecutionHelper` provides details on the XML format as well as how to use Drools Pipeline to automate the marshalling of `BatchExecution` and `ExecutionResults`.

`StatelessKieSession` supports globals, scoped in a number of ways. I’ll cover the non-command way first, as commands are scoped to a specific execution call. Globals can be resolved in three ways.

- The StatelessKieSession method `getGlobals()` returns a Globals instance which provides access to the session’s globals. These are shared for all execution calls. Exercise caution regarding mutable globals because execution calls can be executing simultaneously in different threads.

  Example 70. Session scoped global

      StatelessKieSession ksession = kbase.newStatelessKieSession();
      // Set a global hbnSession, that can be used for DB interactions in the rules.
      ksession.setGlobal( "hbnSession", hibernateSession );
      // Execute while being able to resolve the "hbnSession" identifier.
      ksession.execute( collection );

- Using a delegate is another way of global resolution. Assigning a value to a global (with `setGlobal(String, Object)`) results in the value being stored in an internal collection mapping identifiers to values. Identifiers in this internal collection will have priority over any supplied delegate. Only if an identifier cannot be found in this internal collection, the delegate global (if any) will be used.

- The third way of resolving globals is to have execution scoped globals. Here, a `Command` to set a global is passed to the `CommandExecutor`.

The `CommandExecutor` interface also offers the ability to export data via "out" parameters. Inserted facts, globals and query results can all be returned.

Example 71. Out identifiers

    // Set up a list of commands
    List cmds = new ArrayList();
    cmds.add( CommandFactory.newSetGlobal( "list1", new ArrayList(), true ) );
    cmds.add( CommandFactory.newInsert( new Person( "jon", 102 ), "person" ) );
    cmds.add( CommandFactory.newQuery( "Get People" "getPeople" );

    // Execute the list
    ExecutionResults results =
      ksession.execute( CommandFactory.newBatchExecution( cmds ) );

    // Retrieve the ArrayList
    results.getValue( "list1" );
    // Retrieve the inserted Person fact
    results.getValue( "person" );
    // Retrieve the query as a QueryResults instance.
    results.getValue( "Get People" );

#### 5.4.1. Sequential Mode

With Rete you have a stateful session where objects can be asserted and modified over time, and where rules can also be added and removed. Now what happens if we assume a stateless session, where after the initial data set no more data can be asserted or modified and rules cannot be added or removed? Certainly it won’t be necessary to re-evaluate rules, and the Drools engine will be able to operate in a simplified way.

1. Order the Rules by salience and position in the ruleset (by setting a sequence attribute on the rule terminal node).

2. Create an elements, one element for each possible rule match; element position indicates firing order.

3. Turn off all node memories, except the right-input Object memory.

4. Disconnect the Left Input Adapter Node propagation, and let the Object plus the Node be referenced in a Command object, which is added to a list on the Working Memory for later execution.

5. Assert all objects, and, when all assertions are finished and thus right-input node memories are populated, check the Command list and execute each in turn.

6. All resulting Matches should be placed in the elements, based upon the determined sequence number of the Rule. Record the first and last populated elements, to reduce the iteration range.

7. Iterate the elements of Matches, executing populated element in turn.

8. If we have a maximum number of allowed rule executions, we can exit our network evaluations early to fire all the rules in the elements.

The `LeftInputAdapterNode` no longer creates a Tuple, adding the Object, and then propagate the Tuple – instead a Command object is created and added to a list in the Working Memory. This Command object holds a reference to the `LeftInputAdapterNode` and the propagated object. This stops any left-input propagations at insertion time, so that we know that a right-input propagation will never need to attempt a join with the left-inputs (removing the need for left-input memory). All nodes have their memory turned off, including the left-input Tuple memory but excluding the right-input object memory, which means that the only node remembering an insertion propagation is the right-input object memory. Once all the assertions are finished and all right-input memories populated, we can then iterate the list of `LeftInputAdatperNode` Command objects calling each in turn. They will propagate down the network attempting to join with the right-input objects, but they won’t be remembered in the left input as we know there will be no further object assertions and thus propagations into the right-input memory.

There is no longer an Agenda, with a priority queue to schedule the Tuples; instead, there is simply an elements for the number of rules. The sequence number of the `RuleTerminalNode` indicates the element within the elements where to place the Match. Once all Command objects have finished we can iterate our elements, checking each element in turn, and firing the Matches if they exist. To improve performance, we remember the first and the last populated cell in the elements. The network is constructed, with each `RuleTerminalNode` being given a sequence number based on a salience number and its order of being added to the network.

Typically the right-input node memories are Hash Maps, for fast object deletion; here, as we know there will be no object deletions, we can use a list when the values of the object are not indexed. For larger numbers of objects indexed Hash Maps provide a performance increase; if we know an object type has only a few instances, indexing is probably not advantageous, and a list can be used.

Sequential mode can only be used with a Stateless Session and is off by default. To turn it on, either call `RuleBaseConfiguration.setSequential(true)`, or set the rulebase configuration property `drools.sequential` to true. Sequential mode can fall back to a dynamic agenda by calling `setSequentialAgenda` with `SequentialAgenda.DYNAMIC`. You may also set the "drools.sequential.agenda" property to "sequential" or "dynamic".

### 5.5. Rule Execution Modes

Drools provides two modes for rule execution - passive and active.

As a general guideline, Passive Mode is most suitable for Drools engine applications which need to explicitly control when the Drools engine shall evaluate and fire the rules, or for CEP applications making use of the Pseudo Clock. Active Mode is most effective for Drools engine applications which delegate control of when rules are evaluated and fired to the Drools engine, or for typical CEP application making use of the Real Time Clock.

#### 5.5.1. Passive Mode

With Passive mode not only is the user responsible for working memory operations, such as `insert()`, but also for when the rules are to evaluate the data and fire the resulting rule instantiations - using `fireAllRules()` .

An example outline of Drools code for a CEP application making use of Passive Mode:

    KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();
    config.setOption( ClockTypeOption.get("pseudo") );
    KieSession session = kbase.newKieSession( conf, null );
    SessionPseudoClock clock = session.getSessionClock();

    session.insert( tick1 );
    session.fireAllRules();

    clock.advanceTime(1, TimeUnit.SECONDS);
    session.insert( tick2 );
    session.fireAllRules();

    clock.advanceTime(1, TimeUnit.SECONDS);
    session.insert( tick3 );
    session.fireAllRules();

    session.dispose();

#### 5.5.2. Active Mode

Drools offers a `fireUntilHalt()` method, that starts the Drools engine in Active Mode, which is asynchronous in behavior, where rules will be continually evaluated and fired, until a `halt()` call is made.

This is specially useful for CEP scenarios that require what is commonly known as "active queries".

Please note calling `fireUntilHalt()` blocks the current thread, while the Drools engine will start and continue running asynchronously until the `halt()` is called on the KieSession. It is suggested therefore to call `fireUntilHalt()` from a dedicated thread, so the current thread does not get blocked indefinitely; this also enable the current thread to call `halt()` at a later stage, ref. examples below.

An example outline of Drools code for a CEP application making use of Active Mode:

    KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();
    config.setOption( ClockTypeOption.get("realtime") );
    KieSession session = kbase.newKieSession( conf, null );

    new Thread( new Runnable() {
      @Override
      public void run() {
          session.fireUntilHalt();
      }
    } ).start();

    session.insert( tick1 );

    ... Thread.sleep( 1000L ); ...

    session.insert( tick2 );

    ... Thread.sleep( 1000L ); ...

    session.insert( tick3 );

    session.halt();
    session.dispose();

| Generally, it is not recommended mixing `fireAllRules()` and `fireUntilHalt()`, especially from different threads. However the Drools engine is able to handle such situations safely, thanks to the internal state machine. If `fireAllRules()` is running and a call `fireUntilHalt()` is made, the Drools engine will wait until the `fireAllRules()` is finished and then start `fireUntilHalt()`. However if `fireUntilHalt()` is running and `fireAllRules()` is called, the later is ignored and will just return directly. For more details about thread-safety and the internal state machine, reference section "Improved multi-threading behaviour".

##### 5.5.2.1. Performing KieSession operations atomically when in Active Mode

When in Active Mode, the Drools engine is in control of when the rule shall be evaluated and fired; therefore it is important that operations on the KieSession are performed in a thread-safe manner. Additionally, from a client-side perspective, there might be the need for more than one operations to be called on the KieSession in between rule evaluations, but for engine to consider these as an atomic operation: for example, inserting more than one Fact at a given time, but for the Drools engine to await until all the inserts are done, before evaluating the rules again.

Drools offers a `submit()` method to group and perform operations on the KieSession as a thread-safe atomic action, while in Active Mode.

An example outline of Drools code to perform KieSession operations atomically when in Active Mode:

    KieSession session = ...;

    new Thread( new Runnable() {
      @Override
      public void run() {
          session.fireUntilHalt();
      }
    } ).start();

    final FactHandle fh = session.insert( fact_a );

    ... Thread.sleep( 1000L ); ...

    session.submit( new KieSession.AtomicAction() {
      @Override
      public void execute( KieSession kieSession ) {
        fact_a.setField("value");
        kieSession.update( fh, fact_a );
        kieSession.insert( fact_1 );
        kieSession.insert( fact_2 );
        kieSession.insert( fact_3 );
      }
    } );

    ... Thread.sleep( 1000L ); ...

    session.insert( fact_z );

    session.halt();
    session.dispose();

As a reminder example, the fact handle could also be retrieved from the KieSession:

    ...
    session.insert( fact_a );

    ... Thread.sleep( 1000L ); ...

    session.submit( new KieSession.AtomicAction() {
      @Override
      public void execute( KieSession kieSession ) {
        final FactHandle fh = kieSession.getFactHandle( fact_a );
        fact_a.setField("value");
        kieSession.update( fh, fact_a );
        kieSession.insert( fact_1 );
        kieSession.insert( fact_2 );
        kieSession.insert( fact_3 );
      }
    } );

    ...

### 5.6. Propagation modes

The introduction of PHREAK as default algorithm for the Drools engine made the rules' evaluation lazy. This new Drools lazy behavior allowed a relevant performance boost but, in some very specific cases, breaks the semantic of a few Drools features.

More precisely in some circumstances it is necessary to propagate the insertion of new fact into th session immediately. For instance Drools allows a query to be executed in pull only (or passive) mode by prepending a '?' symbol to its invocation as in the following example:

Example 72. A passive query

    query Q (Integer i)
        String( this == i.toString() )
    end
    rule R when
        $i : Integer()
        ?Q( $i; )
    then
        System.out.println( $i );
    end

In this case, since the query is passive, it shouldn’t react to the insertion of a String matching the join condition in the query itself. In other words this sequence of commands

    KieSession ksession = ...
    ksession.insert(1);
    ksession.insert("1");
    ksession.fireAllRules();

shouldn’t cause the rule R to fire because the String satisfying the query condition has been inserted after the Integer and the passive query shouldn’t react to this insertion. Conversely the rule should fire if the insertion sequence is inverted because the insertion of the Integer, when the passive query can be satisfied by the presence of an already existing String, will trigger it.

Unfortunately the lazy nature of PHREAK doesn’t allow the Drools engine to make any distinction regarding the insertion sequence of the two facts, so the rule will fire in both cases. In circumstances like this it is necessary to evaluate the rule eagerly as done by the old RETEOO-based engine.

In other cases it is required that the propagation is eager, meaning that it is not immedate, but anyway has to happen before the Drools engine/agenda starts scheduled evaluations. For instance this is necessary when a rule has the no-loop or the lock-on-active attribute and in fact when this happens this propagation mode is automatically enforced by the Drools engine.

To cover these use cases, and in all other situations where an immediate or eager rule evaluation is required, it is possible to declaratively specify so by annotating the rule itself with @Propagation(Propagation.Type), where Propagation.Type is an enumeration with 3 possible values:

- IMMEDIATE means that the propagation is performed immediately.

- EAGER means that the propagation is performed lazily but eagerly evaluated before scheduled evaluations.

- LAZY means that the propagation is totally lazy and this is default PHREAK behaviour

This means that the following drl:

Example 73. A data-driven rule using a passive query

    query Q (Integer i)
        String( this == i.toString() )
    end
    rule R @Propagation(IMMEDIATE) when
        $i : Integer()
        ?Q( $i; )
    then
        System.out.println( $i );
    end

will make the rule R to fire if and only if the Integer is inserted after the String, thus behaving in accordance with the semantic of the passive query.

### 5.7. Commands and the CommandExecutor

The `CommandFactory` allows for commands to be executed on those sessions, the only difference being that the Stateless KIE session executes `fireAllRules()` at the end before disposing the session. The currently supported commands are:

- FireAllRules

- GetGlobal

- SetGlobal

- InsertObject

- InsertElements

- Query

- StartProcess

- BatchExecution

` InsertObject` will insert a single object, with an optional "out" identifier. `InsertElements` will iterate an Iterable, inserting each of the elements. What this means is that a Stateless KIE session is no longer limited to just inserting objects, it can now start processes or execute queries, and do this in any order.

Example 74. Insert Command

    StatelessKieSession ksession = kbase.newStatelessKieSession();
    ExecutionResults bresults =
      ksession.execute( CommandFactory.newInsert( new Cheese( "stilton" ), "stilton_id" ) );
    Stilton stilton = bresults.getValue( "stilton_id" );

The execute method always returns an `ExecutionResults` instance, which allows access to any command results if they specify an out identifier such as the "stilton_id" above.

Example 75. InsertElements Command

    StatelessKieSession ksession = kbase.newStatelessKieSession();
    Command cmd = CommandFactory.newInsertElements( Arrays.asList( Object[] {
                      new Cheese( "stilton" ),
                      new Cheese( "brie" ),
                      new Cheese( "cheddar" ),
                  });
    ExecutionResults bresults = ksession.execute( cmd );

The execute method only allows for a single command. That’s where `BatchExecution` comes in, which represents a composite command, created from a list of commands. Now, execute will iterate over the list and execute each command in turn. This means you can insert some objects, start a process, call fireAllRules and execute a query, all in a single `execute(…​)` call, which is quite powerful.

As mentioned previosly, the StatelessKieSession will execute `fireAllRules()` automatically at the end. However the keen-eyed reader probably has already noticed the `FireAllRules` command and wondered how that works with a StatelessKieSession. The `FireAllRules` command is allowed, and using it will disable the automatic execution at the end; think of using it as a sort of manual override function.

A custom XStream marshaller can be used with the Drools Pipeline to achieve XML scripting, which is perfect for services. Here are two simple XML samples, one for the BatchExecution and one for the `ExecutionResults`.

Example 76. Simple BatchExecution XML

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

Example 77. Simple ExecutionResults XML

    <execution-results>
       <result identifier='outStilton'>
          <org.drools.compiler.Cheese>
             <type>stilton</type>
             <oldPrice>25</oldPrice>
             <price>30</price>
          </org.drools.compiler.Cheese>
       </result>
    </execution-results>

Spring and Camel, covered in the integrations book, facilitate declarative services.

Example 78. BatchExecution Marshalled to XML

    <batch-execution>
      <insert out-identifier="stilton">
        <org.drools.compiler.Cheese>
          <type>stilton</type>
          <price>1</price>
          <oldPrice>0</oldPrice>
        </org.drools.compiler.Cheese>
      </insert>
      <query out-identifier='cheeses2' name='cheesesWithParams'>
        <string>stilton</string>
        <string>cheddar</string>
      </query>
    </batch-execution>

The `CommandExecutor` returns an `ExecutionResults`, and this is handled by the pipeline code snippet as well. A similar output for the <batch-execution> XML sample above would be:

Example 79. ExecutionResults Marshalled to XML

    <execution-results>
      <result identifier="stilton">
        <org.drools.compiler.Cheese>
          <type>stilton</type>
          <price>2</price>
        </org.drools.compiler.Cheese>
      </result>
      <result identifier='cheeses2'>
        <query-results>
          <identifiers>
            <identifier>cheese</identifier>
          </identifiers>
          <row>
            <org.drools.compiler.Cheese>
              <type>cheddar</type>
              <price>2</price>
              <oldPrice>0</oldPrice>
            </org.drools.compiler.Cheese>
          </row>
          <row>
            <org.drools.compiler.Cheese>
              <type>cheddar</type>
              <price>1</price>
              <oldPrice>0</oldPrice>
            </org.drools.compiler.Cheese>
          </row>
        </query-results>
      </result>
    </execution-results>

The `BatchExecutionHelper` provides a configured XStream instance to support the marshalling of Batch Executions, where the resulting XML can be used as a message format, as shown above. Configured converters only exist for the commands supported via the Command Factory. The user may add other converters for their user objects. This is very useful for scripting stateless or stateful KIE sessions, especially when services are involved.

There is currently no XML schema to support schema validation. The basic format is outlined here, and the drools-pipeline module has an illustrative unit test in the `XStreamBatchExecutionTest` unit test. The root element is <batch-execution> and it can contain zero or more commands elements.

Example 80. Root XML element

    <batch-execution>
    ...
    </batch-execution>

This contains a list of elements that represent commands, the supported commands is limited to those Commands provided by the Command Factory. The most basic of these is the <insert> element, which inserts objects. The contents of the insert element is the user object, as dictated by XStream.

Example 81. Insert

    <batch-execution>
       <insert>
          ...<!-- any user object -->
       </insert>
    </batch-execution>

The insert element features an "out-identifier" attribute, demanding that the inserted object will also be returned as part of the result payload.

Example 82. Insert with Out Identifier Command

    <batch-execution>
       <insert out-identifier='userVar'>
          ...
       </insert>
    </batch-execution>

It’s also possible to insert a collection of objects using the <insert-elements> element. This command does not support an out-identifier. The `org.domain.UserClass` is just an illustrative user object that XStream would serialize.

Example 83. Insert Elements command

    <batch-execution>
       <insert-elements>
          <org.domain.UserClass>
             ...
          </org.domain.UserClass>
          <org.domain.UserClass>
             ...
          </org.domain.UserClass>
          <org.domain.UserClass>
             ...
          </org.domain.UserClass>
       </insert-elements>
    </batch-execution>

While the `out` attribute is useful in returning specific instances as a result payload, we often wish to run actual queries. Both parameter and parameterless queries are supported. The `name` attribute is the name of the query to be called, and the `out-identifier` is the identifier to be used for the query results in the `<execution-results>` payload.

Example 84. Query Command

    <batch-execution>
       <query out-identifier='cheeses' name='cheeses'/>
       <query out-identifier='cheeses2' name='cheesesWithParams'>
          <string>stilton</string>
          <string>cheddar</string>
       </query>
    </batch-execution>

### 5.8. KieSessions pool

In high volume use cases `KieSession`s get created and disposed with a very high frequency. In general this operation is not extremely time consuming, but when repeated millions of times can become a bottleneck and also requires a huge GC effort. It is possible to greatly alleviate this problem by using a pool of `KieSession`s. To obtain such a pool from a `KieContainer` is enough to invoke on it the method

    KieContainerSessionsPool KieContainer.newKieSessionsPool(int initialSize)

where `initialSize` is the number of the `KieSession`s that will be initially created in the pool. However, if required by the running application, the number of `KieSession`s in the pool will dynamically grow beyond that value. At this point you can create `KieSession`s from that pool as you would normally do from a `KieContainer`:

    KieContainerSessionsPool pool = kContainer.newKieSessionsPool(10);
    KieSession kSession = pool.newKieSession();

Now you can use the KieSession as per normal, and when you call `dispose()` on it, instead of being destroyed, it just gets resetted and pushed back into the pool. Note that using this pool will also affect the case when you have one or more `StatelessKieSession`s and you keep reusing them with multiple call to the `execute()` method. In fact a `StatelessKieSession` created directly from a `KieContainer` will keep to internally create a new `KieSession` for each `execute()` invocation. Conversely if you create the `StatelessKieSession` from the pool it will internally uses the `KieSession`s provided by the pool itself. In other words even if you asked a `StatelessKieSession` to the pool, what is actually pooled are the `KieSession`s that are wrapped by it.

Once you’re done with the pool it is required that you call the `shutdown()` method on it to avoid memory leaks. Alternatively calling `dispose()` on the whole `KieContainer` will also automatically shutdown all the pools eventually created from it.

### 5.9. Rule units

You can use rule units to partition a rule set into smaller units, bind different data sources to those units, and then execute the individual unit. A rule unit consists of data sources, global variables, and rules.

You can define a rule unit by implementing the `RuleUnit` interface as shown in the following example:

Example rule unit class

    package org.mypackage.myunit;

    public static class AdultUnit implements RuleUnit {
        private int adultAge;
        private DataSource<Person> persons;

        public AdultUnit( ) { }

        public AdultUnit( DataSource<Person> persons, int age ) {
            this.persons = persons;
            this.age = age;
        }

        // A DataSource of Persons in this rule unit
        public DataSource<Person> getPersons() {
            return persons;
        }

        // A global variable valid in this rule unit
        public int getAdultAge() {
            return adultAge;
        }

        // --- life cycle methods

        @Override
        public void onStart() {
            System.out.println("AdultUnit started.");
        }

        @Override
        public void onEnd() {
            System.out.println("AdultUnit ended.");
        }
    }

In this example, `persons` is a source of facts of type `Person`. It represents that part of the working memory, which is related to that specific entry-point used when the rule unit is evaluated. The `adultAge` global variable is accessible from all the rules belonging to this rule unit. The last two methods are part of the rule unit life cycle and are invoked by the Drools engine.

The lifecycle of a rule unit can be monitored overriding the following methods:

Table 10. Rule unit life cycle methods
Method | Invoked when
--- | ---
onStart() | the Drools engine starts evaluating the unit
onEnd() | the evaluation of this unit terminates
onSuspend() | the execution of unit is suspended (only for runUntilHalt)
onResume() | the execution of unit is resumed (only for runUntilHalt)
onYield(RuleUnit other) | the consequence of rule in a given rule unit triggers the execution of a different unit

All of these methods have an empty default implementation inside the `RuleUnit` interface, so their implementation is optional. At this point, it is possible to add one or more rules to this rule unit. By default all the rules in a DRL file are automatically associated to a rule unit following a naming convention of the name of the DRL file itself. If the DRL file is in the same package and has the same name as a class implementing the `RuleUnit` interface, then all of the rules in that DRL file will implicitly belong to that unit.

For example, all the rules in the DRL file named `AdultUnit.drl` in the package `org.mypackage.myunit` are automatically part of the rule unit `org.mypackage.myunit.AdultUnit`.

You can avoid using this naming convention and explicitly declare the unit to which the rules in a DRL file belong by using the new `unit` keyword. The unit declaration must always immediately follow the package declaration and contain the name of the class in that package of which the rules in the DRL file are part.

Example rule belonging to the rule unit

    package org.mypackage.myunit
    unit AdultUnit

    rule Adult when
        $p : Person(age >= adultAge) from persons
    then
        System.out.println($p.getName() + " is adult and greater than " + adultAge);
    end

| You cannot mix rules with and without a rule unit in the same KIE base.

Additionally, you can rewrite the same pattern in a more convenient way using the oopath notation introduced in drools 6, as shown in the following example:

Example rule belonging to a rule unit using the oopath notation

    package org.mypackage.myunit
    unit AdultUnit

    rule Adult when
        $p : /persons[age >= adultAge]
    then
        System.out.println($p.getName() + " is adult and greater than " + adultAge);
    end

In this example, the persons matched by the left-hand side of the rule is retrieved from the `DataSource` contained in the rule unit class with the same name. The `adultAge` variable is used in both the left-hand side and right-hand side of the rule in the same way that a global variable is defined at the DRL level. The `persons` DataSource acts as a specific entry point, feeding the working memory.

The easiest way to create a `DataSource` is using a fixed set of data, as shown in the following example:

    DataSource<Person> persons = DataSource.create( new Person( "Mario", 42 ),
                                                    new Person( "Marilena", 44 ),
                                                    new Person( "Sofia", 4 ) );

To execute one or more rule units defined in a given KIE base, create a new `RuleUnitExecutor` and bind it to the KIE base:

    KieBase kbase = kieContainer.getKieBase();
    RuleUnitExecutor executor = RuleUnitExecutor.create().bind( kbase );

At this point, create the `AdultUnit` by passing the persons `DataSource` to it and running it on the `RuleUnitExecutor`:

    RuleUnit adultUnit = new AdultUnit(persons, 18);
    executor.run( adultUnit );

This produces the following output:

    org.mypackage.myunit.AdultUnit started.
    Marilena is adult and greater than 18
    Mario is adult and greater than 18
    org.mypackage.myunit.AdultUnit ended.

Instead of explicitly creating the rule unit instance, you can pass to the executor the rule unit class that you want to run and let the executor create an instance of it. You can then set the `DataSource` and other variables before running it. In order to do so, ensure that you have previously registered those variables on the executor so that the following code produces exactly the same result as the former example:

    executor.bindVariable( "persons", persons );
            .bindVariable( "adultAge", 18 );
    executor.run( AdultUnit.class );

The name passed to the `RuleUnitExecutor.bindVariable()` method is used at run time to bind the variable to the field of the rule unit class with the same name. For example, in the previous example the `RuleUnitExecutor` inserts into the new rule unit the data source formerly bound to the `"persons"` name and the value `18` bound to the String `"adultAge"` to the fields with the corresponding names inside the `AdultUnit` class.

You can override this default and explicitly define a logical binding name for each field of the rule unit class using the `@UnitVar` annotation. For example, the field binding in the following class can be redefined with alternative names:

    package org.mypackage.myunit;

    public static class AdultUnit implements RuleUnit {
        @UnitVar("minAge")
        private int adultAge = 18;

        @UnitVar("data")
        private DataSource<Person> persons;
    }

You can then bind the variables to the executor using those alternative names and run the unit:

    executor.bindVariable( "data", persons );
            .bindVariable( "minAge", 18 );
    executor.run( AdultUnit.class );

You can execute a rule unit in passive mode as shown in the previous example (equivalent to invoking `fireAllRules` on an entire KIE session) or in active mode using the `runUntilHalt` (equivalent to the KIE session `fireUntilHalt`).

As for the `fireUntilHalt`, the `runUntilHalt` is blocking and therefore has to be issued on a separated thread:

    new Thread( () -> executor.runUntilHalt( adultUnit ) ).start();

#### 5.9.1. Data sources

A `DataSource` is a source of the data processed by a given rule unit. A rule unit can have zero or more data sources and to each DataSource declared inside a rule unit corresponds a different entry-point into the rule unit executor. A DataSource can be shared by different units, but in this case there will be many different entry-points, one for each unit, through which the same objects will be inserted.

In other terms the `DataSource` represents the entry-point of the rule unit, so it is possible to insert a new fact into it:

    Person mario = new Person( "Mario", 42 );
    FactHandle marioFh = persons.insert( mario );

Modify the fact, optionally specifying the set of properties that have been modified in order to leverage property reactivity:

    mario.setAge( 43 );
    persons.update( marioFh, mario, "age" );

or delete it

    persons.delete( marioFh );

#### 5.9.2. Imperatively running and declaratively guarding a RuleUnit

As anticipated, you can define multiple rule units in the same KIE base and these units can work in a coordinated way by invoking or guarding the execution of each other. To demonstrate this let’s suppose having the following 2 drl files each of them containing a rule belonging to a distinct rule unit.

    package org.mypackage.myunit
    unit AdultUnit

    rule Adult when
        Person(age >= 18, $name : name) from persons
    then
        System.out.println($name + " is adult");
    end

    package org.mypackage.myunit
    unit NotAdultUnit

    rule NotAdult when
        $p : Person(age < 18, $name : name) from persons
    then
        System.out.println($name + " is NOT adult");
        modify($p) { setAge(18); }
        drools.run( AdultUnit.class );
    end

Also suppose to have a `RuleUnitExecutor` created from the `KieBase` built out of these rules and a `DataSource` of Persons bound to it.

    RuleUnitExecutor executor = RuleUnitExecutor.create().bind( kbase );
    DataSource<Person> persons = executor.newDataSource( "persons",
                                                         new Person( "Mario", 42 ),
                                                         new Person( "Marilena", 44 ),
                                                         new Person( "Sofia", 4 ) );

Note that in this case we are creating the `DataSource` directly out of the `RuleUnitExecutor` and binding it to the "persons" variable in a single statement.

At this point trying to execute the NotAdultUnit unit we obtain the following output:

    Sofia is NOT adult
    Mario is adult
    Marilena is adult
    Sofia is adult

In fact the NotAdult rule finds a match when evaluating the person "Sofia" who has an age lower than 18. Then it modifies her age to 18 and with the statement `drools.run( AdultUnit.class )` triggers the execution of the other unit which has a rule that now can fire for all the 3 persons in the `DataSource`. This means that the `drools.run()` statement inside a consequence is the way to imperatively interrupt the execution of a rule unit and cede the control to a different rule unit.

Conversely the `drools.guard()` statement allows to declaratively schedule the execution of another rule unit when the condition in the LHS of the rule containing that statement is met. More precisely, using this mechanism a rule in a given rule unit acts as a guard for a different unit. This means that, when the Drools engine produces at least one match for the LHS of the guarding rule, the guarded RuleUnit is considered active. Of course a RuleUnit can have more than one guarding rule.

Let’s see how this works with another practical example. Suppose of having a simple `BoxOffice` class

    public class BoxOffice {
        private boolean open;

        public BoxOffice( boolean open ) {
            this.open = open;
        }

        public boolean isOpen() {
            return open;
        }

        public void setOpen( boolean open ) {
            this.open = open;
        }
    }

and a `BoxOfficeUnit` with a data source of box offices.

    public class BoxOfficeUnit implements RuleUnit {
        private DataSource<BoxOffice> boxOffices;

        public DataSource<BoxOffice> getBoxOffices() {
            return boxOffices;
        }
    }

We introduce now the requirement to keep selling tickets for the event as long as there is at least one opened box office. To achieve this let’s define a second unit with a `DataSource` of person and a second one of tickets.

    public class TicketIssuerUnit implements RuleUnit {
        private DataSource<Person> persons;
        private DataSource<AdultTicket> tickets;

        private List<String> results;

        public TicketIssuerUnit() { }

        public TicketIssuerUnit( DataSource<Person> persons, DataSource<AdultTicket> tickets ) {
            this.persons = persons;
            this.tickets = tickets;
        }

        public DataSource<Person> getPersons() {
            return persons;
        }

        public DataSource<AdultTicket> getTickets() {
            return tickets;
        }

        public List<String> getResults() {
            return results;
        }
    }

Then we can define a first rule in the BoxOfficeUnit that guards for this second unit.

    package org.mypackage.myunit;
    unit BoxOfficeUnit;

    rule BoxOfficeIsOpen when
        $box: /boxOffices[ open ]
    then
        drools.guard( TicketIssuerUnit.class );
    end

In this way we achieved what we have anticipated: by running the BoxOfficeUnit at some point it will also evaluates the rules in the TicketIssuerUnit defined as

    package org.mypackage.myunit;
    unit TicketIssuerUnit;

    rule IssueAdultTicket when
        $p: /persons[ age >= 18 ]
    then
        tickets.insert(new AdultTicket($p));
    end
    rule RegisterAdultTicket when
        $t: /tickets
    then
        results.add( $t.getPerson().getName() );
    end

that is guarded by the BoxOfficeIsOpen rule, until there will exist at least a set of facts satisfying the LHS patterns of that rule. In other terms the existence of at least one open box office will keep the guarding rule and in turn its guarded unit active as it is evident in the following use case.

    DataSource<Person> persons = executor.newDataSource( "persons" );
    DataSource<BoxOffice> boxOffices = executor.newDataSource( "boxOffices" );
    DataSource<AdultTicket> tickets = executor.newDataSource( "tickets" );

    List<String> list = new ArrayList<>();
    executor.bindVariable( "results", list );

    // two open box offices
    BoxOffice office1 = new BoxOffice(true);
    FactHandle officeFH1 = boxOffices.insert( office1 );
    BoxOffice office2 = new BoxOffice(true);
    FactHandle officeFH2 = boxOffices.insert( office2 );

    persons.insert(new Person("Mario", 40));
    // fire BoxOfficeIsOpen -> run TicketIssuerUnit -> fire RegisterAdultTicket
    executor.run(BoxOfficeUnit.class);

    assertEquals( 1, list.size() );
    assertEquals( "Mario", list.get(0) );
    list.clear();

    persons.insert(new Person("Matteo", 30));
    executor.run(BoxOfficeUnit.class); // fire RegisterAdultTicket

    assertEquals( 1, list.size() );
    assertEquals( "Matteo", list.get(0) );
    list.clear();

    // close one box office, the other is still open
    office1.setOpen(false);
    boxOffices.update(officeFH1, office1);
    persons.insert(new Person("Mark", 35));
    executor.run(BoxOfficeUnit.class);

    assertEquals( 1, list.size() );
    assertEquals( "Mark", list.get(0) );
    list.clear();

    // all box offices, are now closed
    office2.setOpen(false);
    boxOffices.update(officeFH2, office2); // guarding rule no longer true
    persons.insert(new Person("Edson", 35));
    executor.run(BoxOfficeUnit.class); // no fire

    assertEquals( 0, list.size() );

#### 5.9.3. RuleUnit identity

Since a rule can guard multiple rule units and at the same time a unit can be guarded and then activated by multiple rules, it is necessary to clearly define what is the identity of a given unit. By the default the identity of a unit is simply the rule unit class. This is encoded in the `getUnitIdentity()` default method of the `RuleUnit` interface

    default Identity getUnitIdentity() {
        return new Identity( getClass() );
    }

and implies that each unit is threated as a singleton by the `RuleUnitExecutor`. To demonstrate this let’s suppose of having a simple `RuleUnit` class with only a `DataSource` accepting any kind of object

    public class Unit0 implements RuleUnit {
        private DataSource<Object> input;

        public DataSource<Object> getInput() {
            return input;
        }
    }

together with a rule belonging to this unit that guards another unit using 2 different conditions.

    package org.mypackage.myunit
    unit Unit0

    rule GuardAgeCheck when
        $i: /input#Integer
        $s: /input#String
    then
        drools.guard( new AgeCheckUnit($i) );
        drools.guard( new AgeCheckUnit($s.length()) );
    end

This second `RuleUnit` is intended to check the age of a set of persons. Then it has a `DataSource` of the persons to check, a minAge variable against which doing this check and a list were accumulating the results

    public class AgeCheckUnit implements RuleUnit {
        private final int minAge;
        private DataSource<Person> persons;
        private List<String> results;

        public AgeCheckUnit( int minAge ) {
            this.minAge = minAge;
        }

        public DataSource<Person> getPersons() {
            return persons;
        }

        public int getMinAge() {
            return minAge;
        }

        public List<String> getResults() {
            return results;
        }
    }

while the corresponding rule actually performing the check of the persons in the `DataSource` is the following:

    package org.mypackage.myunit
    unit AgeCheckUnit

    rule CheckAge when
        $p : /persons{ age > minAge }
    then
        results.add($p.getName() + ">" + minAge);
    end

At this point we can create a `RuleUnitExecutor`, bind it to the KIE base containing these 2 units and also create the 2 `DataSource`s to feed the same units.

    RuleUnitExecutor executor = RuleUnitExecutor.create().bind( kbase );

    DataSource<Object> input = executor.newDataSource( "input" );
    DataSource<Person> persons = executor.newDataSource( "persons",
                                                         new Person( "Mario", 42 ),
                                                         new Person( "Sofia", 4 ) );

    List<String> results = new ArrayList<>();
    executor.bindVariable( "results", results );

We are now ready to insert some objects into the input data source and execute the Unit0.

    ds.insert("test");
    ds.insert(3);
    ds.insert(4);
    executor.run(Unit0.class);

As outcome of this execution the results list will contain the following:

    [Sofia>3, Mario>3]

As anticipated the rule unit named AgeCheckUnit is seen as a singleton and then executed only once, this time with `minAge` equals to 3 (but this is not deterministic). Both the String "test" and the Integer 4 inserted into the input data source could also trigger a second execution with `minAge` set to 4, but this is not happening because another unit with the same identity has been already evaluated. To fix this problem it is enough to override the `getUnitIdentity()` method in the `AgeCheckUnit` class to also include the variable minAge in its identity.

    public class AgeCheckUnit implements RuleUnit {

        ...

        @Override
        public Identity getUnitIdentity() {
            return new Identity(getClass(), minAge);
        }
    }

Having done so, the units with minAge 3 and 4 are considered two different units and then both evaluated, so trying to rerun the former example the result list will now contain

    [Mario>4, Sofia>3, Mario>3]

## 6. Rule Language Reference

### 6.1. Overview

Drools has a "native" rule language. This format is very light in terms of punctuation, and supports natural and domain specific languages via "expanders" that allow the language to morph to your problem domain. This chapter is mostly concerted with this native rule format. The diagrams used to present the syntax are known as "railroad" diagrams, and they are basically flow charts for the language terms. The technically very keen may also refer to DRL.g which is the Antlr3 grammar for the rule language. If you use Business Central, a lot of the rule structure is done for you with content assistance, for example, type "ru" and press ctrl+space, and it will build the rule structure for you.

#### 6.1.1. A rule file

A rule file is typically a file with a .drl extension. In a DRL file you can have multiple rules, queries and functions, as well as some resource declarations like imports, globals and attributes that are assigned and used by your rules and queries. However, you are also able to spread your rules across multiple rule files (in that case, the extension .rule is suggested, but not required) - spreading rules across files can help with managing large numbers of rules. A DRL file is simply a text file.

The overall structure of a rule file is:

Example 85. Rules file

    package package-name

    imports

    globals

    functions

    queries

    rules

The order in which the elements are declared is not important, except for the package name that, if declared, must be the first element in the rules file. All elements are optional, so you will use only those you need. We will discuss each of them in the following sections.

#### 6.1.2. What makes a rule

For the impatient, just as an early view, a rule has the following rough structure:

    rule "name"
        attributes
        when
            LHS
        then
            RHS
    end

It’s really that simple. Mostly punctuation is not needed, even the double quotes for "name" are optional, as are newlines. Attributes are simple (always optional) hints to how the rule should behave. LHS is the conditional parts of the rule, which follows a certain syntax which is covered below. RHS is basically a block that allows dialect specific semantic code to be executed.

It is important to note that white space is not important, except in the case of domain specific languages, where lines are processed one by one and spaces may be significant to the domain language.

### 6.2. Keywords

Drools 5 introduces the concept of hard and soft keywords.

Hard keywords are reserved, you cannot use any hard keyword when naming your domain objects, properties, methods, functions and other elements that are used in the rule text.

Here is the list of hard keywords that must be avoided as identifiers when writing rules:

- `true`

- `false`

- `null`

Soft keywords are just recognized in their context, enabling you to use these words in any other place if you wish, although, it is still recommended to avoid them, to avoid confusions, if possible. Here is a list of the soft keywords:

- `lock-on-active`

- `date-effective`

- `date-expires`

- `no-loop`

- `auto-focus`

- `activation-group`

- `agenda-group`

- `ruleflow-group`

- `entry-point`

- `duration`

- `package`

- `import`

- `dialect`

- `salience`

- `enabled`

- `attributes`

- `rule`

- `extend`

- when

- then

- `template`

- `query`

- `declare`

- `function`

- `global`

- `eval`

- `not`

- `in`

- `or`

- `and`

- `exists`

- `forall`

- accumulate

- collect

- from

- `action`

- `reverse`

- `result`

- `end`

- over

- `init`

Of course, you can have these (hard and soft) words as part of a method name in camel case, like notSomething() or accumulateSomething() - there are no issues with that scenario.

Although the 3 hard keywords above are unlikely to be used in your existing domain models, if you absolutely need to use them as identifiers instead of keywords, the DRL language provides the ability to escape hard keywords on rule text. To escape a word, simply enclose it in grave accents, like this:

    Holiday( `true` == "yes" ) // please note that Drools will resolve that reference to the method Holiday.isTrue()

### 6.3. Comments

Comments are sections of text that are ignored by the Drools engine. They are stripped out when they are encountered, except inside semantic code blocks, like the RHS of a rule.

#### 6.3.1. Single line comment

To create single line comments, you can use '//'. The parser will ignore anything in the line after the comment symbol. Example:

    rule "Testing Comments"
    when
        // this is a single line comment
        eval( true ) // this is a comment in the same line of a pattern
    then
        // this is a comment inside a semantic code block
    end

| '#' for comments has been removed.

#### 6.3.2. Multi-line comment

Figure 68. Multi-line comment

Multi-line comments are used to comment blocks of text, both in and outside semantic code blocks. Example:

    rule "Test Multi-line Comments"
    when
        /* this is a multi-line comment
           in the left hand side of a rule */
        eval( true )
    then
        /* and this is a multi-line comment
           in the right hand side of a rule */
    end

### 6.4. Error Messages

Drools 5 introduces standardized error messages. This standardization aims to help users to find and resolve problems in a easier and faster way. In this section you will learn how to identify and interpret those error messages, and you will also receive some tips on how to solve the problems associated with them.

#### 6.4.1. Message format

The standardization includes the error message format and to better explain this format, let’s use the following example:

Figure 69. Error Message Format

1st Block: This area identifies the error code.

2nd Block: Line and column information.

3rd Block: Some text describing the problem.

4th Block: This is the first context. Usually indicates the rule, function, template or query where the error occurred. This block is not mandatory.

5th Block: Identifies the pattern where the error occurred. This block is not mandatory.

#### 6.4.2. Error Messages Description

##### 6.4.2.1. 101: No viable alternative

Indicates the most common errors, where the parser came to a decision point but couldn’t identify an alternative. Here are some examples:

    1: rule one
    2:   when
    3:     exists Foo()
    4:     exits Bar()  // "exits"
    5:   then
    6: end

The above example generates this message:

- [ERR 101] Line 4:4 no viable alternative at input 'exits' in rule one

At first glance this seems to be valid syntax, but it is not (exits != exists). Let’s take a look at next example:

    1: package org.drools.examples;
    2: rule
    3:   when
    4:     Object()
    5:   then
    6:     System.out.println("A RHS");
    7: end

Now the above code generates this message:

- [ERR 101] Line 3:2 no viable alternative at input 'WHEN'

This message means that the parser encountered the token WHEN, actually a hard keyword, but it’s in the wrong place since the rule name is missing.

The error "no viable alternative" also occurs when you make a simple lexical mistake. Here is a sample of a lexical problem:

    1: rule simple_rule
    2:   when
    3:     Student( name == "Andy )
    4:   then
    5: end

The above code misses to close the quotes and because of this the parser generates this error message:

- [ERR 101] Line 0:-1 no viable alternative at input '<eof>' in rule simple_rule in pattern Student

| Usually the Line and Column information are accurate, but in some cases (like unclosed quotes), the parser generates a 0:-1 position. In this case you should check whether you didn’t forget to close quotes, apostrophes or parentheses.

##### 6.4.2.2. 102: Mismatched input

This error indicates that the parser was looking for a particular symbol that it didn’t ﬁnd at the current input position. Here are some samples:

    1: rule simple_rule
    2:   when
    3:     foo3 : Bar(

The above example generates this message:

- [ERR 102] Line 0:-1 mismatched input '<eof>' expecting ')' in rule simple_rule in pattern Bar

To fix this problem, it is necessary to complete the rule statement.

| Usually when you get a 0:-1 position, it means that parser reached the end of source.

The following code generates more than one error message:

    1: package org.drools.examples;
    2:
    3: rule "Avoid NPE on wrong syntax"
    4:   when
    5:     not( Cheese( ( type == "stilton", price == 10 ) || ( type == "brie", price == 15 ) ) from $cheeseList )
    6:   then
    7:     System.out.println("OK");
    8: end

These are the errors associated with this source:

- [ERR 102] Line 5:36 mismatched input ',' expecting ')' in rule "Avoid NPE on wrong syntax" in pattern Cheese

- [ERR 101] Line 5:57 no viable alternative at input 'type' in rule "Avoid NPE on wrong syntax"

- [ERR 102] Line 5:106 mismatched input ')' expecting 'then' in rule "Avoid NPE on wrong syntax"

Note that the second problem is related to the first. To fix it, just replace the commas (',') by AND operator ('&&').

| In some situations you can get more than one error message. Try to fix one by one, starting at the first one. Some error messages are generated merely as consequences of other errors.

##### 6.4.2.3. 103: Failed predicate

A validating semantic predicate evaluated to false. Usually these semantic predicates are used to identify soft keywords. This sample shows exactly this situation:

     1: package nesting;
     2: dialect "mvel"
     3:
     4: import org.drools.compiler.Person
     5: import org.drools.compiler.Address
     6:
     7: fdsfdsfds
     8:
     9: rule "test something"
    10:   when
    11:     p: Person( name=="Michael" )
    12:   then
    13:     p.name = "other";
    14:     System.out.println(p.name);
    15: end

With this sample, we get this error message:

- [ERR 103] Line 7:0 rule 'rule_key' failed predicate: {(validateIdentifierKey(DroolsSoftKeywords.RULE))}? in rule

The fdsfdsfds text is invalid and the parser couldn’t identify it as the soft keyword `rule`.

| This error is very similar to 102: Mismatched input, but usually involves soft keywords.

##### 6.4.2.4. 104: Trailing semi-colon not allowed

This error is associated with the `eval` clause, where its expression may not be terminated with a semicolon. Check this example:

    1: rule simple_rule
    2:   when
    3:     eval(abc();)
    4:   then
    5: end

Due to the trailing semicolon within eval, we get this error message:

- [ERR 104] Line 3:4 trailing semi-colon not allowed in rule simple_rule

This problem is simple to fix: just remove the semi-colon.

##### 6.4.2.5. 105: Early Exit

The recognizer came to a subrule in the grammar that must match an alternative at least once, but the subrule did not match anything. Simply put: the parser has entered a branch from where there is no way out. This example illustrates it:

    1: template test_error
    2:   aa s  11;
    3: end

This is the message associated to the above sample:

- [ERR 105] Line 2:2 required (…​)+ loop did not match anything at input 'aa' in template test_error

To fix this problem it is necessary to remove the numeric value as it is neither a valid data type which might begin a new template slot nor a possible start for any other rule file construct.

#### 6.4.3. Other Messages

Any other message means that something bad has happened, so please contact the development team.

### 6.5. Package

A package is a collection of rules and other related constructs, such as imports and globals. The package members are typically related to each other - perhaps HR rules, for instance. A package represents a namespace, which ideally is kept unique for a given grouping of rules. The package name itself is the namespace, and is not related to files or folders in any way.

It is possible to assemble rules from multiple rule sources, and have one top level package configuration that all the rules are kept under (when the rules are assembled). Although, it is not possible to merge into the same package resources declared under different names. A single Rulebase may, however, contain multiple packages built on it. A common structure is to have all the rules for a package in the same file as the package declaration (so that is it entirely self-contained).

The following railroad diagram shows all the components that may make up a package. Note that a package must have a namespace and be declared using standard Java conventions for package names; i.e., no spaces, unlike rule names which allow spaces. In terms of the order of elements, they can appear in any order in the rule file, with the exception of the `package` statement, which must be at the top of the file. In all cases, the semicolons are optional.

Figure 70. package

Notice that any rule attribute (as described the section Rule Attributes) may also be written at package level, superseding the attribute’s default value. The modified default may still be replaced by an attribute setting within a rule.

#### 6.5.1. import

Figure 71. import

Import statements work like import statements in Java. You need to specify the fully qualified paths and type names for any objects you want to use in the rules. Drools automatically imports classes from the Java package of the same name, and also from the package `java.lang`.

#### 6.5.2. global

Figure 72. global

With `global` you define global variables. They are used to make application objects available to the rules. Typically, they are used to provide data or services that the rules use, especially application services used in rule consequences, and to return data from the rules, like logs or values added in rule consequences, or for the rules to interact with the application, doing callbacks. Globals are not inserted into the Working Memory, and therefore a global should never be used to establish conditions in rules except when it has a constant immutable value. The Drools engine cannot be notified about value changes of globals and does not track their changes. Incorrect use of globals in constraints may yield surprising results - surprising in a bad way.

If multiple packages declare globals with the same identifier they must be of the same type and all of them will reference the same global value.

In order to use globals you must:

1. Declare your global variable in your rules file and use it in rules. For example:

       global java.util.List myGlobalList;

       rule "Using a global"
       when
           eval( true )
       then
           myGlobalList.add( "Hello World" );
       end

2. Set the global value on your working memory. It is a best practice to set all global values before asserting any fact to the working memory. Example:

       List list = new ArrayList();
       KieSession kieSession = kiebase.newKieSession();
       kieSession.setGlobal( "myGlobalList", list );

Note that these are just named instances of objects that you pass in from your application to the working memory. This means you can pass in any object you want: you could pass in a service locator, or perhaps a service itself. With the new `from` element it is now common to pass a Hibernate session as a global, to allow `from` to pull data from a named Hibernate query.

One example may be an instance of a Email service. In your integration code that is calling the Drools engine, you obtain your emailService object, and then set it in the working memory. In the DRL, you declare that you have a global of type EmailService, and give it the name "email". Then in your rule consequences, you can use things like email.sendSMS(number, message).

Globals are not designed to share data between rules and they should never be used for that purpose. Rules always reason and react to the working memory state, so if you want to pass data from rule to rule, assert the data as facts into the working memory.

Care must be taken when changing data held by globals because the Drools engine is not aware of those changes, hence cannot react to them.

### 6.6. Function

Figure 73. function

Functions are a way to put semantic code in your rule source file, as opposed to in normal Java classes. They can’t do anything more than what you can do with helper classes. (In fact, the compiler generates the helper class for you behind the scenes.) The main advantage of using functions in a rule is that you can keep the logic all in one place, and you can change the functions as needed (which can be a good or a bad thing). Functions are most useful for invoking actions on the consequence (`then`) part of a rule, especially if that particular action is used over and over again, perhaps with only differing parameters for each rule.

A typical function declaration looks like:

    function String hello(String name) {
        return "Hello "+name+"!";
    }

Note that the `function` keyword is used, even though its not really part of Java. Parameters to the function are defined as for a method, and you don’t have to have parameters if they are not needed. The return type is defined just like in a regular method.

Alternatively, you could use a static method in a helper class, e.g., `Foo.hello()`. Drools supports the use of function imports, so all you would need to do is:

    import function my.package.Foo.hello

Irrespective of the way the function is defined or imported, you use a function by calling it by its name, in the consequence or inside a semantic code block. Example:

    rule "using a static function"
    when
        eval( true )
    then
        System.out.println( hello( "Bob" ) );
    end

### 6.7. Type Declaration

Figure 74. meta_data
Figure 75. type_declaration

Type declarations have two main goals in the Drools engine: to allow the declaration of new types, and to allow the declaration of metadata for types.

- Declaring new types: Drools works out of the box with plain Java objects as facts. Sometimes, however, users may want to define the model directly to the Drools engine, without worrying about creating models in a lower level language like Java. At other times, there is a domain model already built, but eventually the user wants or needs to complement this model with additional entities that are used mainly during the reasoning process.

- Declaring metadata: facts may have meta information associated to them. Examples of meta information include any kind of data that is not represented by the fact attributes and is consistent among all instances of that fact type. This meta information may be queried at runtime by the Drools engine and used in the reasoning process.

#### 6.7.1. Declaring New Types

To declare a new type, all you need to do is use the keyword `declare`, followed by the list of fields, and the keyword `end`. A new fact must have a list of fields, otherwise the Drools engine will look for an existing fact class in the classpath and raise an error if not found.

Example 86. Declaring a new fact type: Address

    declare Address
       number : int
       streetName : String
       city : String
    end

The previous example declares a new fact type called `Address`. This fact type will have three attributes: `number`, `streetName` and `city`. Each attribute has a type that can be any valid Java type, including any other class created by the user or even other fact types previously declared.

For instance, we may want to declare another fact type `Person`:

Example 87. declaring a new fact type: Person

    declare Person
        name : String
        dateOfBirth : java.util.Date
        address : Address
    end

As we can see on the previous example, `dateOfBirth` is of type `java.util.Date`, from the Java API, while `address` is of the previously defined fact type Address.

You may avoid having to write the fully qualified name of a class every time you write it by using the `import` clause, as previously discussed.

Example 88. Avoiding the need to use fully qualified class names by using import

    import java.util.Date

    declare Person
        name : String
        dateOfBirth : Date
        address : Address
    end

When you declare a new fact type, Drools will, at compile time, generate bytecode that implements a Java class representing the fact type. The generated Java class will be a one-to-one Java Bean mapping of the type definition. So, for the previous example, the generated Java class would be:

Example 89. generated Java class for the previous Person fact typedeclaration

    public class Person implements Serializable {
        private String name;
        private java.util.Date dateOfBirth;
        private Address address;

        // empty constructor
        public Person() {...}

        // constructor with all fields
        public Person( String name, Date dateOfBirth, Address address ) {...}

        // if keys are defined, constructor with keys
        public Person( ...keys... ) {...}

        // getters and setters
        // equals/hashCode
        // toString
    }

Since the generated class is a simple Java class, it can be used transparently in the rules, like any other fact.

Example 90. Using the declared types in rules

    rule "Using a declared Type"
    when<
        $p : Person( name == "Bob" )
    then
        // Insert Mark, who is Bob's mate.
        Person mark = new Person();
        mark.setName( "Mark" );
        insert( mark );
    end

##### 6.7.1.1. Declaring enumerative types

DRL also supports the declaration of enumerative types. Such type declarations require the additional keyword enum, followed by a comma separated list of admissible values terminated by a semicolon.

    rule "Using a declared Type"
    when
        $p : Person( name == "Bob" )
    then
        // Insert Mark, who is Bob's mate.
        Person mark = new Person();
        mark.setName( "Mark" );
        insert( mark );
    end

The compiler will generate a valid Java enum, with static methods valueOf() and values(), as well as instance methods ordinal(), compareTo() and name().

Complex enums are also partially supported, declaring the internal fields similarly to a regular type declaration. Notice that as of version 6.x, enum fields do NOT support other declared types or enums

    declare enum DaysOfWeek
       SUN("Sunday"),MON("Monday"),TUE("Tuesday"),WED("Wednesday"),THU("Thursday"),FRI("Friday"),SAT("Saturday");

       fullName : String
    end

Enumeratives can then be used in rules

Example 91. Using declarative enumerations in rules

    rule "Using a declared Enum"
    when
       $p : Employee( dayOff == DaysOfWeek.MONDAY )
    then
       ...
    end

#### 6.7.2. Declaring Metadata

Metadata may be assigned to several different constructions in Drools: fact types, fact attributes and rules. Drools uses the at sign ('@') to introduce metadata, and it always uses the form:

    @metadata_key( metadata_value )

The parenthesized metadata_value is optional.

For instance, if you want to declare a metadata attribute like `author`, whose value is Bob, you could simply write:

Example 92. Declaring a metadata attribute

    @author( Bob )

Drools allows the declaration of any arbitrary metadata attribute, but some will have special meaning to the Drools engine, while others are simply available for querying at runtime. Drools allows the declaration of metadata both for fact types and for fact attributes. Any metadata that is declared before the attributes of a fact type are assigned to the fact type, while metadata declared after an attribute are assigned to that particular attribute.

Example 93. Declaring metadata attributes for fact types and attributes

    import java.util.Date

    declare Person
        @author( Bob )
        @dateOfCreation( 01-Feb-2009 )

        name : String @key @maxLength( 30 )
        dateOfBirth : Date
        address : Address
    end

In the previous example, there are two metadata items declared for the fact type (`@author` and `@dateOfCreation`) and two more defined for the name attribute (`@key` and `@maxLength`). Please note that the `@key` metadata has no required value, and so the parentheses and the value were omitted.:

##### 6.7.2.1. Predefined class level annotations

Some annotations have predefined semantics that are interpreted by the Drools engine. The following is a list of some of these predefined annotations and their meaning.

###### @role( <fact | event> )

The @role annotation defines how the Drools engine should handle instances of that type: either as regular facts or as events. It accepts two possible values:

- fact : this is the default, declares that the type is to be handled as a regular fact.

- event : declares that the type is to be handled as an event.

The following example declares that the fact type StockTick in a stock broker application is to be handled as an event.

Example 94. declaring a fact type as an event

    import some.package.StockTick

    declare StockTick
        @role( event )
    end

The same applies to facts declared inline. If StockTick was a fact type declared in the DRL itself, instead of a previously existing class, the code would be:

Example 95. declaring a fact type and assigning it the event role

    declare StockTick
        @role( event )

        datetime : java.util.Date
        symbol : String
        price : double
    end

###### @typesafe( <boolean> )

By default all type declarations are compiled with type safety enabled; @typesafe( false ) provides a means to override this behaviour by permitting a fall-back, to type unsafe evaluation where all constraints are generated as MVEL constraints and executed dynamically. This can be important when dealing with collections that do not have any generics or mixed type collections.

###### @timestamp( <attribute name> )

Every event has an associated timestamp assigned to it. By default, the timestamp for a given event is read from the Session Clock and assigned to the event at the time the event is inserted into the working memory. Although, sometimes, the event has the timestamp as one of its own attributes. In this case, the user may tell the Drools engine to use the timestamp from the event’s attribute instead of reading it from the Session Clock.

    @timestamp( <attributeName> )

To tell the Drools engine what attribute to use as the source of the event’s timestamp, just list the attribute name as a parameter to the @timestamp tag.

Example 96. declaring the VoiceCall timestamp attribute

    declare VoiceCall
        @role( event )
        @timestamp( callDateTime )
    end

###### @duration( <attribute name> )

Drools supports both event semantics: point-in-time events and interval-based events. A point-in-time event is represented as an interval-based event whose duration is zero. By default, all events have duration zero. The user may attribute a different duration for an event by declaring which attribute in the event type contains the duration of the event.

    @duration( <attributeName> )

So, for our VoiceCall fact type, the declaration would be:

Example 97. declaring the VoiceCall duration attribute

    declare VoiceCall
        @role( event )
        @timestamp( callDateTime )
        @duration( callDuration )
    end

###### @expires( <time interval> )

| This tag is only considered when running the Drools engine in STREAM mode. Also, additional discussion on the effects of using this tag is made on the Memory Management section. It is included here for completeness.

Events may be automatically expired after some time in the working memory. Typically this happens when, based on the existing rules in the KIE base, the event can no longer match and activate any rules. Although, it is possible to explicitly define when an event should expire.

    @expires( <timeOffset> )

The value of timeOffset is a temporal interval in the form:

    [#d][#h][#m][#s][#[ms]]

Where [ ] means an optional parameter and \# means a numeric value.

So, to declare that the VoiceCall facts should be expired after 1 hour and 35 minutes after they are inserted into the working memory, the user would write:

Example 98. declaring the expiration offset for the VoiceCall events

    declare VoiceCall
        @role( event )
        @timestamp( callDateTime )
        @duration( callDuration )
        @expires( 1h35m )
    end

The @expires policy will take precedence and override the implicit expiration offset calculated from temporal constraints and sliding windows in the KIE base.

###### @propertyChangeSupport

Facts that implement support for property changes as defined in the Javabean(tm) spec, now can be annotated so that the Drools 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.

Example 99. @propertyChangeSupport

    declare Person
        @propertyChangeSupport
    end

###### @propertyReactive

Make this type property reactive. See Fine grained property change listeners section for details.

###### @serialVersionUID

To improve the compatibility of serialized KieSession, it has been introduced the possibility to specify the serialVersionUID on the classes generated from the declared types through an annotation like the following:

    declare MyClass
      @serialVersionUID( 42 )
      name : String
    end

##### 6.7.2.2. Predefined attribute level annotations

As noted before, Drools also supports annotations in type attributes. Here is a list of predefined attribute annotations.

###### @key

Declaring an attribute as a key attribute has 2 major effects on generated types:

1. The attribute will be used as a key identifier for the type, and as so, the generated class will implement the equals() and hashCode() methods taking the attribute into account when comparing instances of this type.

2. Drools will generate a constructor using all the key attributes as parameters.

For instance:

Example 100. example of @key declarations for a type

    declare Person
        firstName : String @key
        lastName : String @key
        age : int
    end

For the previous example, Drools will generate equals() and hashCode() methods that will check the firstName and lastName attributes to determine if two instances of Person are equal to each other, but will not check the age attribute. It will also generate a constructor taking firstName and lastName as parameters, allowing one to create instances with a code like this:

Example 101. creating an instance using the key constructor

    Person person = new Person( "John", "Doe" );

###### @position

Patterns support positional arguments on type declarations.

Positional arguments are ones where you don’t need to specify the field name, as the position maps to a known named field. i.e. Person( name == "mark" ) can be rewritten as Person( "mark"; ). The semicolon ';' is important so that the Drools engine knows that everything before it is a positional argument. Otherwise we might assume it was a boolean expression, which is how it could be interpreted after the semicolon. You can mix positional and named arguments on a pattern by using the semicolon ';' to separate them. Any variables used in a positional that have not yet been bound will be bound to the field that maps to that position.

    declare Cheese
        name : String
        shop : String
        price : int
    end

The default order is the declared order, but this can be overridden using @position

    declare Cheese
        name : String @position(1)
        shop : String @position(2)
        price : int @position(0)
    end

The @Position annotation, in the org.drools.definition.type package, can be used to annotate original pojos on the classpath. Currently only fields on classes can be annotated. Inheritance of classes is supported, but not interfaces of methods yet.

Example patterns, with two constraints and a binding. Remember semicolon ';' is used to differentiate the positional section from the named argument section. Variables and literals and expressions using just literals are supported in positional arguments, but not variables.

    Cheese( "stilton", "Cheese Shop", p; )
    Cheese( "stilton", "Cheese Shop"; p : price )
    Cheese( "stilton"; shop == "Cheese Shop", p : price )
    Cheese( name == "stilton"; shop == "Cheese Shop", p : price )

@Position is inherited when beans extend each other; while not recommended, two fields may have the same @position value, and not all consecutive values need be declared. If a @position is repeated, the conflict is solved using inheritance (fields in the superclass have the precedence) and the declaration order. If a @position value is missing, the first field without an explicit @position (if any) is selected to fill the gap. As always, conflicts are resolved by inheritance and declaration order.

    declare Cheese
        name : String
        shop : String @position(2)
        price : int @position(0)
    end

    declare SeasonedCheese extends Cheese
        year : Date @position(0)
        origin : String @position(6)
        country : String
    end

In the example, the field order would be : price (@position 0 in the superclass), year (@position 0 in the subclass), name (first field with no @position), shop (@position 2), country (second field without @position), origin.

#### 6.7.3. Declaring Metadata for Existing Types

Drools allows the declaration of metadata attributes for existing types in the same way as when declaring metadata attributes for new fact types. The only difference is that there are no fields in that declaration.

For instance, if there is a class org.drools.examples.Person, and one wants to declare metadata for it, it’s possible to write the following code:

Example 102. Declaring metadata for an existing type

    import org.drools.examples.Person

    declare Person
        @author( Bob )
        @dateOfCreation( 01-Feb-2009 )
    end

Instead of using the import, it is also possible to reference the class by its fully qualified name, but since the class will also be referenced in the rules, it is usually shorter to add the import and use the short class name everywhere.

Example 103. Declaring metadata using the fully qualified class name

    declare org.drools.examples.Person
        @author( Bob )
        @dateOfCreation( 01-Feb-2009 )
    end

#### 6.7.4. Parametrized constructors for declared types

Generate constructors with parameters for declared types.

Example: for a declared type like the following:

    declare Person
        firstName : String @key
        lastName : String @key
        age : int
    end

The compiler will implicitly generate 3 constructors: one without parameters, one with the @key fields, and one with all fields.

    Person() // parameterless constructor
    Person( String firstName, String lastName )
    Person( String firstName, String lastName, int age )

#### 6.7.5. Non Typesafe Classes

@typesafe( <boolean>) has been added to type declarations. By default all type declarations are compiled with type safety enabled; @typesafe( false ) provides a means to override this behaviour by permitting a fall-back, to type unsafe evaluation where all constraints are generated as MVEL constraints and executed dynamically. This can be important when dealing with collections that do not have any generics or mixed type collections.

#### 6.7.6. Accessing Declared Types from the Application Code

Declared types are usually used inside rules files, while Java models are used when sharing the model between rules and applications. Although, sometimes, the application may need to access and handle facts from the declared types, especially when the application is wrapping the Drools engine and providing higher level, domain specific user interfaces for rules management.

In such cases, the generated classes can be handled as usual with the Java Reflection API, but, as we know, that usually requires a lot of work for small results. Therefore, Drools provides a simplified API for the most common fact handling the application may want to do.

The first important thing to realize is that a declared fact will belong to the package where it was declared. So, for instance, in the example below, `Person` will belong to the `org.drools.examples` package, and so the fully qualified name of the generated class will be `org.drools.examples.Person`.

Example 104. Declaring a type in the org.drools.examples package

    package org.drools.examples

    import java.util.Date

    declare Person
        name : String
        dateOfBirth : Date
        address : Address
    end

Declared types, as discussed previously, are generated at KIE base compilation time, i.e., the application will only have access to them at application run time. Therefore, these classes are not available for direct reference from the application.

Drools then provides an interface through which users can handle declared types from the application code: `org.drools.definition.type.FactType`. Through this interface, the user can instantiate, read and write fields in the declared fact types.

Example 105. Handling declared fact types through the API

    // get a reference to a KIE base with a declared type:
    KieBase kbase = ...

    // get the declared FactType
    FactType personType = kbase.getFactType( "org.drools.examples",
                                             "Person" );

    // handle the type as necessary:
    // create instances:
    Object bob = personType.newInstance();

    // set attributes values
    personType.set( bob,
                    "name",
                    "Bob" );
    personType.set( bob,
                    "age",
                    42 );

    // insert fact into a session
    KieSession ksession = ...
    ksession.insert( bob );
    ksession.fireAllRules();

    // read attributes
    String name = personType.get( bob, "name" );
    int age = personType.get( bob, "age" );

The API also includes other helpful methods, like setting all the attributes at once, reading values from a Map, or reading all attributes at once, into a Map.

Although the API is similar to Java reflection (yet much simpler to use), it does not use reflection underneath, relying on much more performant accessors implemented with generated bytecode.

#### 6.7.7. Type Declaration 'extends'

Type declarations now support 'extends' keyword for inheritance

In order to extend a type declared in Java by a DRL declared subtype, repeat the supertype in a declare statement without any fields.

    import org.people.Person

    declare Person end

    declare Student extends Person
        school : String
    end

    declare LongTermStudent extends Student
        years : int
        course : String
    end

### 6.8. Rule

Figure 76. rule

A rule specifies that when a particular set of conditions occur, specified in the Left Hand Side (LHS), then do what queryis specified as a list of actions in the Right Hand Side (RHS). A common question from users is "Why use when instead of if?" "When" was chosen over "if" because "if" is normally part of a procedural execution flow, where, at a specific point in time, a condition is to be checked.

In contrast, "when" indicates that the condition evaluation is not tied to a specific evaluation sequence or point in time, but that it happens continually, at any time during the life time of the Drools engine; whenever the condition is met, the actions are executed.

A rule must have a name, unique within its rule package. If you define a rule twice in the same DRL it produces an error while loading. If you add a DRL that includes a rule name already in the package, it replaces the previous rule. If a rule name is to have spaces, then it will need to be enclosed in double quotes (it is best to always use double quotes).

Attributes - described below - are optional. They are best written one per line.

The LHS of the rule follows the `when` keyword (ideally on a new line), similarly the RHS follows the `then` keyword (again, ideally on a newline). The rule is terminated by the keyword `end`. Rules cannot be nested.

Example 106. Rule Syntax Overview

    rule "<name>"
        <attribute>*
    when
        <conditional element>*
    then
        <action>*
    end

Example 107. A simple rule

    rule "Approve if not rejected"
      salience -100
      agenda-group "approval"
        when
            not Rejection()
            p : Policy(approved == false, policyState:status)
            exists Driver(age > 25)
            Process(status == policyState)
        then
            log("APPROVED: due to no objections.");
            p.setApproved(true);
    end

#### 6.8.1. Rule Attributes

Rule attributes provide a declarative way to influence the behavior of the rule. Some are quite simple, while others are part of complex subsystems such as ruleflow. To get the most from Drools you should make sure you have a proper understanding of each attribute.

Figure 77. rule attributes

`no-loop`

  default value: `false`

  type: Boolean

  When a rule’s consequence modifies a fact it may cause the rule to activate again, causing an infinite loop. Setting no-loop to true will skip the creation of another Activation for the rule with the current set of facts.

`ruleflow-group`

  default value: N/A

  type: String

  Ruleflow is a Drools feature that lets you exercise control over the firing of rules. Rules that are assembled by the same ruleflow-group identifier fire only when their group is active.

`lock-on-active`

  default value: `false`

  type: Boolean

  Whenever a ruleflow-group becomes active or an agenda-group receives the focus, any rule within that group that has lock-on-active set to true will not be activated any more; irrespective of the origin of the update, the activation of a matching rule is discarded. This is a stronger version of `no-loop`, because the change could now be caused not only by the rule itself. It’s ideal for calculation rules where you have a number of rules that modify a fact and you don’t want any rule re-matching and firing again. Only when the ruleflow-group is no longer active or the agenda-group loses the focus those rules with lock-on-active set to true become eligible again for their activations to be placed onto the agenda.

`salience`

  default value: `0`

  type: integer

  Each rule has an integer salience attribute which defaults to zero and can be negative or positive. Salience is a form of priority where rules with higher salience values are given higher priority when ordered in the Activation queue.

  Drools also supports dynamic salience where you can use an expression involving bound variables.

Example 108. Dynamic Salience

    rule "Fire in rank order 1,2,.."
            salience( -$rank )
        when
            Element( $rank : rank,... )
        then
            ...
    end

`agenda-group`

  default value: MAIN

  type: String

  Agenda groups allow the user to partition the Agenda providing more execution control. Only rules in the agenda group that has acquired the focus are allowed to fire.

`auto-focus`

  default value: `false`

  type: Boolean

  When a rule is activated where the `auto-focus` value is true and the rule’s agenda group does not have focus yet, then it is given focus, allowing the rule to potentially fire.

`activation-group`

  default value: N/A

  type: String

  Rules that belong to the same activation-group, identified by this attribute’s string value, will only fire exclusively. More precisely, the first rule in an activation-group to fire will cancel all pending activations of all rules in the group, i.e., stop them from firing.

  Note: This used to be called Xor group, but technically it’s not quite an Xor. You may still hear people mention Xor group; just swap that term in your mind with activation-group.

`dialect`

  default value: as specified by the package

  type: String

  possible values: "java" or "mvel"

  The dialect species the language to be used for any code expressions in the LHS or the RHS code block. Currently two dialects are available, Java and MVEL. While the dialect can be specified at the package level, this attribute allows the package definition to be overridden for a rule.

`date-effective`

  default value: N/A

  type: String, containing a date and time definition

  A rule can only activate if the current date and time is after date-effective attribute.

`date-expires`

  default value: N/A

  type: String, containing a date and time definition

  A rule cannot activate if the current date and time is after the date-expires attribute.

`duration`

  default value: no default value

  type: long

  The duration dictates that the rule will fire after a specified duration, if it is still true.

Example 109. Some attribute examples

    rule "my rule"
      salience 42
      agenda-group "number 1"
        when ...

#### 6.8.2. Timers and Calendars

Rules now support both interval and cron based timers, which replace the now deprecated duration attribute.

Example 110. Sample timer attribute uses

    timer ( int: <initial delay> <repeat interval>? )
    timer ( int: 30s )
    timer ( int: 30s 5m )

    timer ( cron: <cron expression> )
    timer ( cron:* 0/15 * * * ? )

Interval (indicated by "int:") timers follow the semantics of java.util.Timer objects, with an initial delay and an optional repeat interval. Cron (indicated by "cron:") timers follow standard Unix cron expressions:

Example 111. A Cron Example

    rule "Send SMS every 15 minutes"
        timer (cron:* 0/15 * * * ?)
    when
        $a : Alarm( on == true )
    then
        channels[ "sms" ].insert( new Sms( $a.mobileNumber, "The alarm is still on" );
    end

A rule controlled by a timer becomes active when it matches, and once for each individual match. Its consequence is executed repeatedly, according to the timer’s settings. This stops as soon as the condition doesn’t match any more.

Consequences are executed even after control returns from a call to fireUntilHalt. Moreover, the Drools engine remains reactive to any changes made to the Working Memory. For instance, removing a fact that was involved in triggering the timer rule’s execution causes the repeated execution to terminate, or inserting a fact so that some rule matches will cause that rule to fire. But the Drools engine is not continually active, only after a rule fires, for whatever reason. Thus, reactions to an insertion done asynchronously will not happen until the next execution of a timer-controlled rule. Disposing a session puts an end to all timer activity.

Conversely when the Drools engine runs in passive mode (i.e.: using fireAllRules instead of fireUntilHalt) by default it doesn’t fire consequences of timed rules unless fireAllRules isn’t invoked again. However it is possible to change this default behavior by configuring the KieSession with a `TimedRuleExecutionOption` as shown in the following example.

Example 112. Configuring a KieSession to automatically execute timed rules

    KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();
    ksconf.setOption( TimedRuleExecutionOption.YES );
    KSession ksession = kbase.newKieSession(ksconf, null);

It is also possible to have a finer grained control on the timed rules that have to be automatically executed. To do this it is necessary to set a `FILTERED` `TimedRuleExecutionOption` that allows to define a callback to filter those rules, as done in the next example.

Example 113. Configuring a filter to choose which timed rules should be automatically executed

    KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();
    conf.setOption( new TimedRuleExecutionOption.FILTERED(new TimedRuleExecutionFilter() {
        public boolean accept(Rule[] rules) {
            return rules[0].getName().equals("MyRule");
        }
    }) );

For what regards interval timers it is also possible to define both the delay and interval as an expression instead of a fixed value. To do that it is necessary to use an expression timer (indicated by "expr:") as in the following example:

Example 114. An Expression Timer Example

    declare Bean
        delay   : String = "30s"
        period  : long = 60000
    end

    rule "Expression timer"
        timer( expr: $d, $p )
    when
        Bean( $d : delay, $p : period )
    then
    end

The expressions, `$d` and `$p` in this case, can use any variable defined in the pattern matching part of the rule and can be any String that can be parsed in a time duration or any numeric value that will be internally converted in a long representing a duration expressed in milliseconds.

Both interval and expression timers can have 3 optional parameters named "start", "end" and "repeat-limit". When one or more of these parameters are used the first part of the timer definition must be followed by a semicolon ';' and the parameters have to be separated by a comma ',' as in the following example:

Example 115. An Interval Timer with a start and an end

    timer (int: 30s 10s; start=3-JAN-2010, end=5-JAN-2010)

The value for start and end parameters can be a Date, a String representing a Date or a long, or more in general any Number, that will be transformed in a Java Date applying the following conversion:

    new Date( ((Number) n).longValue() )

Conversely the repeat-limit can be only an integer and it defines the maximum number of repetitions allowed by the timer. If both the end and the repeat-limit parameters are set the timer will stop when the first of the two will be matched.

The using of the start parameter implies the definition of a phase for the timer, where the beginning of the phase is given by the start itself plus the eventual delay. In other words in this case the timed rule will then be scheduled at times:

    start + delay + n*period

for up to repeat-limit times and no later than the end timestamp (whichever first). For instance the rule having the following interval timer

    timer ( int: 30s 1m; start="3-JAN-2010" )

will be scheduled at the 30th second of every minute after the midnight of the 3-JAN-2010. This also means that if for example you turn the system on at midnight of the 3-FEB-2010 it won’t be scheduled immediately but will preserve the phase defined by the timer and so it will be scheduled for the first time 30 seconds after the midnight.

If for some reason the system is paused (e.g. the session is serialized and then deserialized after a while) the rule will be scheduled only once to recover from missing activations (regardless of how many activations we missed) and subsequently it will be scheduled again in phase with the timer.

Calendars are used to control when rules can fire. The Calendar API is modelled on Quartz:

Example 116. Adapting a Quartz Calendar

    Calendar weekDayCal = QuartzHelper.quartzCalendarAdapter(org.quartz.Calendar quartzCal)

Calendars are registered with the `KieSession`:

Example 117. Registering a Calendar

    ksession.getCalendars().set( "weekday", weekDayCal );

They can be used in conjunction with normal rules and rules including timers. The rule attribute "calendars" may contain one or more comma-separated calendar names written as string literals.

Example 118. Using Calendars and Timers together

    rule "weekdays are high priority"
       calendars "weekday"
       timer (int:0 1h)
    when
        Alarm()
    then
        send( "priority high - we have an alarm" );
    end

    rule "weekend are low priority"
       calendars "weekend"
       timer (int:0 4h)
    when
        Alarm()
    then
        send( "priority low - we have an alarm" );
    end

#### 6.8.3. Left Hand Side (when) syntax

##### 6.8.3.1. What is the Left Hand Side?

The Left Hand Side (LHS) is a common name for the conditional part of the rule. It consists of zero or more Conditional Elements. If the LHS is empty, it will be considered as a condition element that is always true and it will be activated once, when a new WorkingMemory session is created.

Figure 78. Left Hand Side
Example 119. Rule without a Conditional Element

    rule "no CEs"
    when
        // empty
    then
        ... // actions (executed once)
    end

    // The above rule is internally rewritten as:

    rule "eval(true)"
    when
        eval( true )
    then
        ... // actions (executed once)
    end

Conditional elements work on one or more patterns (which are described below). The most common conditional element is " `and"`. Therefore it is implicit when you have multiple patterns in the LHS of a rule that are not connected in any way:

Example 120. Implicit and

    rule "2 unconnected patterns"
    when
        Pattern1()
        Pattern2()
    then
        ... // actions
    end

    // The above rule is internally rewritten as:

    rule "2 and connected patterns"
    when
        Pattern1()
        and Pattern2()
    then
        ... // actions
    end

| An “and” cannot have a leading declaration binding (unlike for example `or`). This is obvious, since a declaration can only reference a single fact at a time, and when the “and” is satisfied it matches both facts - so which fact would the declaration bind to?
    // Compile error
    $person : (Person( name == "Romeo" ) and Person( name == "Juliet"))

##### 6.8.3.2. Pattern (conditional element)

###### What is a pattern?

A pattern element is the most important Conditional Element. It can potentially match on each fact that is inserted in the working memory.

A pattern contains of zero or more constraints and has an optional pattern binding. The railroad diagram below shows the syntax for this.

Figure 79. Pattern

In its simplest form, with no constraints, a pattern matches against a fact of the given type. In the following case the type is `Cheese`, which means that the pattern will match against all `Person` objects in the Working Memory:

    Person()

The type need not be the actual class of some fact object. Patterns may refer to superclasses or even interfaces, thereby potentially matching facts from many different classes.

    Object() // matches all objects in the working memory

Inside of the pattern parenthesis is where all the action happens: it defines the constraints for that pattern. For example, with a age related constraint:

    Person( age == 100 )

| For backwards compatibility reasons it’s allowed to suffix patterns with the `;` character. But it is not recommended to do that.

###### Pattern binding

For referring to the matched object, use a pattern binding variable such as `$p`.

Example 121. Pattern with a binding variable

    rule ...
    when
        $p : Person()
    then
        System.out.println( "Person " + $p );
    end

The prefixed dollar symbol (`$`) is just a convention; it can be useful in complex rules where it helps to easily differentiate between variables and fields, but it is not mandatory.

##### 6.8.3.3. Constraint (part of a pattern)

###### What is a constraint?

A constraint is an expression that returns `true` or `false`. This example has a constraint that states 5 is smaller than 6:

    Person( 5 < 6 )  // just an example, as constraints like this would be useless in a real pattern

In essence, it’s a Java expression with some enhancements (such as property access) and a few differences (such as `equals()` semantics for `==`). Let’s take a deeper look.

###### Property access on Java Beans (POJO’s)

Any bean property can be used directly. A bean property is exposed using a standard Java bean getter: a method `getMyProperty()` (or `isMyProperty()` for a primitive boolean) which takes no arguments and return something. For example: the age property is written as `age` in DRL instead of the getter `getAge()`:

    Person( age == 50 )

    // this is the same as:
    Person( getAge() == 50 )

Drools uses the standard JDK `Introspector` class to do this mapping, so it follows the standard Java bean specification.

| We recommend using property access (`age`) over using getters explicitly (`getAge()`) because of performance enhancements through field indexing.

| Property accessors must not change the state of the object in a way that may effect the rules. Remember that the Drools engine effectively caches the results of its matching in between invocations to make it faster.
    public int getAge() {
        age++; // Do NOT do this
        return age;
    }
    public int getAge() {
        Date now = DateUtil.now(); // Do NOT do this
        return DateUtil.differenceInYears(now, birthday);
    }
To solve this latter case, insert a fact that wraps the current date into working memory and update that fact between `fireAllRules` as needed.

| The following fallback applies: if the getter of a property cannot be found, the compiler will resort to using the property name as a method name and without arguments:
    Person( age == 50 )

    // If Person.getAge() does not exists, this falls back to:
    Person( age() == 50 )

Nested property access is also supported:

    Person( address.houseNumber == 50 )

    // this is the same as:
    Person( getAddress().getHouseNumber() == 50 )

Nested properties are also indexed.

| In a stateful session, care should be taken when using nested accessors as the Working Memory is not aware of any of the nested values, and does not know when they change. Either consider them immutable while any of their parent references are inserted into the Working Memory. Or, instead, if you wish to modify a nested value you should mark all of the outer facts as updated. In the above example, when the `houseNumber` changes, any `Person` with that `Address` must be marked as updated.

###### Java expression

You can use any Java expression that returns a `boolean` as a constraint inside the parentheses of a pattern. Java expressions can be mixed with other expression enhancements, such as property access:

    Person( age == 50 )

It is possible to change the evaluation priority by using parentheses, as in any logic or mathematical expression:

    Person( age > 100 && ( age % 10 == 0 ) )

It is possible to reuse Java methods:

    Person( Math.round( weight / ( height * height ) ) < 25.0 )

| As for property accessors, methods must not change the state of the object in a way that may affect the rules. Any method executed on a fact in the LHS should be a read only method.
    Person( incrementAndGetAge() == 10 ) // Do NOT do this

| The state of a fact should not change between rule invocations (unless those facts are marked as updated to the working memory on every change):
    Person( System.currentTimeMillis() % 1000 == 0 ) // Do NOT do this

Normal Java operator precedence applies, see the operator precedence list below.

| All operators have normal Java semantics except for `==` and `!=`. The `==` operator has null-safe `equals()` semantics:
    // Similar to: java.util.Objects.equals(person.getFirstName(), "John")
    // so (because "John" is not null) similar to:
    // "John".equals(person.getFirstName())
    Person( firstName == "John" )
The `!=` operator has null-safe `!equals()` semantics:
    // Similar to: !java.util.Objects.equals(person.getFirstName(), "John")
    Person( firstName != "John" )

Type coercion is always attempted if the field and the value are of different types; exceptions will be thrown if a bad coercion is attempted. For instance, if "ten" is provided as a string in a numeric evaluator, an exception is thrown, whereas "10" would coerce to a numeric 10. Coercion is always in favor of the field type and not the value type:

    Person( age == "10" ) // "10" is coerced to 10

###### Comma separated AND

The comma character (‘`,`’) is used to separate constraint groups. It has implicit AND connective semantics.

    // Person is at least 50 and weighs at least 80 kg
    Person( age > 50, weight > 80 )

    // Person is at least 50, weighs at least 80 kg and is taller than 2 meter.
    Person( age > 50, weight > 80, height > 2 )

| Although the `&&` and `,` operators have the same semantics, they are resolved with different priorities: The `&&` operator precedes the `||` operator. Both the `&&` and `||` operator precede the `,` operator. See the operator precedence list below. The comma operator should be preferred at the top level constraint, as it makes constraints easier to read and the Drools engine will often be able to optimize them better.

The comma (`,`) operator cannot be embedded in a composite constraint expression, such as parentheses:

    Person( ( age > 50, weight > 80 ) || height > 2 ) // Do NOT do this: compile error

    // Use this instead
    Person( ( age > 50 && weight > 80 ) || height > 2 )

###### Binding variables

A property can be bound to a variable:

    // 2 persons of the same age
    Person( $firstAge : age ) // binding
    Person( age == $firstAge ) // constraint expression

The prefixed dollar symbol (`$`) is just a convention; it can be useful in complex rules where it helps to easily differentiate between variables and fields.

| For backwards compatibility reasons, It’s allowed (but not recommended) to mix a constraint binding and constraint expressions as such:
    // Not recommended
    Person( $age : age * 2 < 100 )
    // Recommended (separates bindings and constraint expressions)
    Person( age * 2 < 100, $age : age )

Bound variable restrictions using the operator `==` provide for very fast execution as it use hash indexing to improve performance.

###### Unification

Drools does not allow bindings to the same declaration. However this is an important aspect to derivation query unification. While positional arguments are always processed with unification a special unification symbol, ':=', was introduced for named arguments named arguments. The following "unifies" the age argument across two people.

    Person( $age := age )
    Person( $age := age)

In essence unification will declare a binding for the first occurrence and constrain to the same value of the bound field for sequence occurrences.

###### Grouped accessors for nested objects

Often it happens that it is necessary to access multiple properties of a nested object as in the following example

    Person( name == "mark", address.city == "london", address.country == "uk" )

These accessors to nested objects can be grouped with a '.(…​)' syntax providing more readable rules as in

    Person( name == "mark", address.( city == "london", country == "uk") )

Note the '.' prefix, this is necessary to differentiate the nested object constraints from a method call.

###### Inline casts and coercion

When dealing with nested objects, it also quite common the need to cast to a subtype. It is possible to do that via the # symbol as in:

    Person( name == "mark", address#LongAddress.country == "uk" )

This example casts Address to LongAddress, making its getters available. If the cast is not possible (instanceof returns false), the evaluation will be considered false. Also fully qualified names are supported:

    Person( name == "mark", address#org.domain.LongAddress.country == "uk" )

It is possible to use multiple inline casts in the same expression:

    Person( name == "mark", address#LongAddress.country#DetailedCountry.population > 10000000 )

moreover, since we also support the instanceof operator, if that is used we will infer its results for further uses of that field, within that pattern:

    Person( name == "mark", address instanceof LongAddress, address.country == "uk" )

###### Special literal support

Besides normal Java literals (including Java 5 enums), this literal is also supported:

###### Date literal

The date format `dd-MMM-yyyy` is supported by default. You can customize this by providing an alternative date format mask as the system property named `drools.dateformat`. This system property can also contain a time format mask part (e.g.`drools.dateformat="dd-MMM-yyyy HH:mm"`). Another possibility to customize the date format is to change the language locale with `drools.defaultlanguage` and `drools.defaultcountry` system properties (e.g. locale of Thailand set as `drools.defaultlanguage=th` and `drools.defaultcountry=TH`).

Example 122. Date Literal Restriction

    Cheese( bestBefore < "27-Oct-2009" )

###### List and Map access

It’s possible to directly access a `List` value by index:

    // Same as childList(0).getAge() == 18
    Person( childList[0].age == 18 )

It’s also possible to directly access a `Map` value by key:

    // Same as credentialMap.get("jsmith").isValid()
    Person( credentialMap["jsmith"].valid )

###### Abbreviated combined relation condition

This allows you to place more than one restriction on a field using the restriction connectives `&&` or `||`. Grouping via parentheses is permitted, resulting in a recursive syntax pattern.

Figure 80. Abbreviated combined relation condition
Figure 81. Abbreviated combined relation condition withparentheses

    // Simple abbreviated combined relation condition using a single &&
    Person( age > 30 && < 40 )

    // Complex abbreviated combined relation using groupings
    Person( age ( (> 30 && < 40) ||
                  (> 20 && < 25) ) )

    // Mixing abbreviated combined relation with constraint connectives
    Person( age > 30 && < 40 || location == "london" )

###### Special DRL operators

Figure 82. Operators

Coercion to the correct value for the evaluator and the field will be attempted.

###### The operators `< ⇐ > >=`

These operators can be used on properties with natural ordering. For example, for Date fields, `<` means before, for `String` fields, it means alphabetically lower.

    Person( firstName < $otherFirstName )

    Person( birthDate < $otherBirthDate )

Only applies on `Comparable` properties.

###### Null-safe dereferencing operator

The !. operator allows to derefencing in a null-safe way. More in details the matching algorithm requires the value to the left of the !. operator to be not null in order to give a positive result for pattern matching itself. In other words the pattern:

    Person( $streetName : address!.street )

will be internally translated in:

    Person( address != null, $streetName : address.street )

###### The operator `matches`

Matches a field against any valid Java Regular Expression. Typically that regexp is a string literal, but variables that resolve to a valid regexp are also allowed.

Example 123. Regular Expression Constraint

    Cheese( type matches "(Buffalo)?\\S*Mozzarella" )

| Like in Java, regular expressions written as string literals need to escape '\\'.

Only applies on `String` properties. Using `matches` against a `null` value always evaluates to false.

###### The operator `not matches`

The operator returns true if the String does not match the regular expression. The same rules apply as for the `matches` operator. Example:

Example 124. Regular Expression Constraint

    Cheese( type not matches "(Buffalo)?\\S*Mozzarella" )

Only applies on `String` properties. Using `not matches` against a `null` value always evaluates to true.

###### The operator `contains`

The operator `contains` is used to check whether a field that is a Collection or elements contains the specified value.

Example 125. Contains with Collections

    CheeseCounter( cheeses contains "stilton" ) // contains with a String literal
    CheeseCounter( cheeses contains $var ) // contains with a variable

Only applies on `Collection` properties.

The operator `contains` can also be used in place of `String.contains()` constraints checks.

Example 126. Contains with String literals

    Cheese( name contains "tilto" )
    Person( fullName contains "Jr" )
    String( this contains "foo" )

###### The operator `not contains`

The operator `not contains` is used to check whether a field that is a Collection or elements does not contain the specified value.

Example 127. Literal Constraint with Collections

    CheeseCounter( cheeses not contains "cheddar" ) // not contains with a String literal
    CheeseCounter( cheeses not contains $var ) // not contains with a variable

Only applies on `Collection` properties.

| For backward compatibility, the `excludes` operator is supported as a synonym for `not contains`.

The operator `not contains` can also be used in place of the logical negation of `String.contains()` for constraints checks - i.e.: `! String.contains()`

Example 128. Contains with String literals

    Cheese( name not contains "tilto" )
    Person( fullName not contains "Jr" )
    String( this not contains "foo" )

###### The operator `memberOf`

The operator `memberOf` is used to check whether a field is a member of a collection or elements; that collection must be a variable.

Example 129. Literal Constraint with Collections

    CheeseCounter( cheese memberOf $matureCheeses )

###### The operator `not memberOf`

The operator `not memberOf` is used to check whether a field is not a member of a collection or elements; that collection must be a variable.

Example 130. Literal Constraint with Collections

    CheeseCounter( cheese not memberOf $matureCheeses )

###### The operator `soundslike`

This operator is similar to `matches`, but it checks whether a word has almost the same sound (using English pronunciation) as the given value. This is based on the Soundex algorithm (see `http://en.wikipedia.org/wiki/Soundex`).

Example 131. Test with soundslike

    // match cheese "fubar" or "foobar"
    Cheese( name soundslike 'foobar' )

###### The operator `str`

This operator `str` is used to check whether a field that is a `String` starts with or ends with a certain value. It can also be used to check the length of the String.

    Message( routingValue str[startsWith] "R1" )

    Message( routingValue str[endsWith] "R2" )

    Message( routingValue str[length] 17 )

###### The operators `in` and `notin` (compound value restriction)

The compound value restriction is used where there is more than one possible value to match. Currently only the `in` and `not in` evaluators support this. The second operand of this operator must be a comma-separated list of values, enclosed in parentheses. Values may be given as variables, literals, return values or qualified identifiers. Both evaluators are actually syntactic sugar, internally rewritten as a list of multiple restrictions using the operators `!=` and `==`.

Figure 83. compoundValueRestriction
Example 132. Compound Restriction using "in"

    Person( $cheese : favouriteCheese )
    Cheese( type in ( "stilton", "cheddar", $cheese ) )

###### Inline eval operator (deprecated)

Figure 84. Inline Eval Expression

An inline eval constraint can use any valid dialect expression as long as it results to a primitive boolean. The expression must be constant over time. Any previously bound variable, from the current or previous pattern, can be used; autovivification is also used to auto-create field binding variables. When an identifier is found that is not a current variable, the builder looks to see if the identifier is a field on the current object type, if it is, the field binding is auto-created as a variable of the same name. This is called autovivification of field variables inside of inline eval’s.

This example will find all male-female pairs where the male is 2 years older than the female; the variable `age` is auto-created in the second pattern by the autovivification process.

Example 133. Return Value operator

    Person( girlAge : age, sex = "F" )
    Person( eval( age == girlAge + 2 ), sex = 'M' ) // eval() is actually obsolete in this example

| Inline eval’s are effectively obsolete as their inner syntax is now directly supported. It’s recommended not to use them. Simply write the expression without wrapping eval() around it.

###### Operator precedence

The operators are evaluated in this precedence:

Table 11. Operator precedence
Operator type | Operators | Notes
--- | --- | ---
(nested / null safe) property access | `.``!.` | Not normal Java semantics
List/Map access | `[ ]` | Not normal Java semantics
constraint binding | `:` | Not normal Java semantics
multiplicative | `\*``/``%` |
additive | `\+``-` |
shift | `<<``>>``>>>` |
relational | `<``>``⇐``>=``instanceof` |
equality | `==``!=` | Does not use normal Java (not) same semantics: uses (not) equals semantics instead.
non-short circuiting AND | `&` |
non-short circuiting exclusive OR | `^` |
non-short circuiting inclusive OR | `|` |
logical AND | `&&` |
logical OR | `||` |
ternary | `? :` |
Comma separated AND | `,` | Not normal Java semantics

##### 6.8.3.4. Positional Arguments

Patterns now support positional arguments on type declarations.

Positional arguments are ones where you don’t need to specify the field name, as the position maps to a known named field. i.e. Person( name == "mark" ) can be rewritten as Person( "mark"; ). The semicolon ';' is important so that the Drools engine knows that everything before it is a positional argument. Otherwise we might assume it was a boolean expression, which is how it could be interpreted after the semicolon. You can mix positional and named arguments on a pattern by using the semicolon ';' to separate them. Any variables used in a positional that have not yet been bound will be bound to the field that maps to that position.

    declare Cheese
        name : String
        shop : String
        price : int
    end

Example patterns, with two constraints and a binding. Remember semicolon ';' is used to differentiate the positional section from the named argument section. Variables and literals and expressions using just literals are supported in positional arguments, but not variables. Positional arguments are always resolved using unification.

    Cheese( "stilton", "Cheese Shop", p; )
    Cheese( "stilton", "Cheese Shop"; p : price )
    Cheese( "stilton"; shop == "Cheese Shop", p : price )
    Cheese( name == "stilton"; shop == "Cheese Shop", p : price )

Positional arguments that are given a previously declared binding will constrain against that using unification; these are referred to as input arguments. If the binding does not yet exist, it will create the declaration binding it to the field represented by the position argument; these are referred to as output arguments.

##### 6.8.3.5. Fine grained property change listeners

When you call modify() (see the modify statement section) on a given object it will trigger a revaluation of all patterns of the matching object type in the KIE base. This can can lead to unwanted and useless evaluations and in the worst cases to infinite recursions. The only workaround to avoid it was to split up your objects into smaller ones having a 1 to 1 relationship with the original object.

This has been introduced to provide an easier and more consistent way to overcome this problem. In fact it allows the pattern matching to only react to modification of properties actually constrained or bound inside of a given pattern. That will help with performance and recursion and avoid artificial object splitting.

This feature is enabled by default, but in case you need or want to dectivate it on a specific bean you can annotate it with @classReactive. This annotation works both on DRL type declarations:

    declare Person
    @classReactive
        firstName : String
        lastName : String
    end

and on Java classes:

    @ClassReactive
        public static class Person {
        private String firstName;
        private String lastName;
    }

By using this feature, for instance, if you have a rule like the following:

    rule "Every person named Mario is a male" when
        $person : Person( firstName == "Mario" )
    then
        modify ( $person )  { setMale( true ) }
    end

you won’t have to add the no-loop attribute to it in order to avoid an infinite recursion because the Drools engine recognizes that the pattern matching is done on the 'firstName' property while the RHS of the rule modifies the 'male' one. Note that this feature does not work for update(), and this is one of the reasons why we promote modify() since it encapsulates the field changes within the statement. Moreover, on Java classes, you can also annotate any method to say that its invocation actually modifies other properties. For instance in the former Person class you could have a method like:

    @Modifies( { "firstName", "lastName" } )
    public void setName(String name) {
        String[] names = name.split("\\s");
        this.firstName = names[0];
        this.lastName = names[1];
    }

That means that if a rule has a RHS like the following:

    modify($person) { setName("Mario Fusco") }

it will correctly recognize that the values of both properties 'firstName' and 'lastName' could have potentially been modified and act accordingly, not missing of reevaluating the patterns constrained on them. At the moment the usage of @Modifies is not allowed on fields but only on methods. This is coherent with the most common scenario where the @Modifies will be used for methods that are not related with a class field as in the Person.setName() in the former example. Also note that @Modifies is not transitive, meaning that if another method internally invokes the Person.setName() one it won’t be enough to annotate it with @Modifies( { "name" } ), but it is necessary to use @Modifies( { "firstName", "lastName" } ) even on it. Very likely @Modifies transitivity will be implemented in the next release.

For what regards nested accessors, the Drools engine will be notified only for top level fields. In other words a pattern matching like:

    Person ( address.city.name == "London )

will be revaluated only for modification of the 'address' property of a Person object. In the same way the constraints analysis is currently strictly limited to what there is inside a pattern. Another example could help to clarify this. An LHS like the following:

    $p : Person( )
    Car( owner = $p.name )

will not listen on modifications of the person’s name, while this one will do:

    Person( $name : name )
    Car( owner = $name )

To overcome this problem it is possible to annotate a pattern with @watch as it follows:

    $p : Person( ) @watch ( name )
    Car( owner = $p.name )

Indeed, annotating a pattern with @watch allows you to modify the inferred set of properties for which that pattern will react. Note that the properties named in the @watch annotation are actually added to the ones automatically inferred, but it is also possible to explicitly exclude one or more of them prepending their name with a ! and to make the pattern to listen for all or none of the properties of the type used in the pattern respectively with the wildcrds * and !*. So, for example, you can annotate a pattern in the LHS of a rule like:

    // listens for changes on both firstName (inferred) and lastName
    Person( firstName == $expectedFirstName ) @watch( lastName )

    // listens for all the properties of the Person bean
    Person( firstName == $expectedFirstName ) @watch( * )

    // listens for changes on lastName and explicitly exclude firstName
    Person( firstName == $expectedFirstName ) @watch( lastName, !firstName )

    // listens for changes on all the properties except the age one
    Person( firstName == $expectedFirstName ) @watch( *, !age )

Since it doesn’t make sense to use this annotation on a pattern using a type annotated with @ClassReactive the rule compiler will raise a compilation error if you try to do so. Also the duplicated usage of the same property in @watch (for example like in: @watch( firstName, ! firstName ) ) will end up in a compilation error. In a next release we will make the automatic detection of the properties to be listened smarter by doing analysis even outside of the pattern.

It is also possible to enable this feature only on specific types of your model or to completely disallow it by using on option of the KnowledgeBuilderConfiguration. In particular this new PropertySpecificOption can have one of the following 3 values:

    - DISABLED => the feature is turned off and all the other related annotations are just ignored
    - ALLOWED => types are not property reactive unless they are not annotated with @PropertyReactive (which is the dual of @ClassReactive)
    - ALWAYS => all types are property reactive. This is the default behavior

So, for example, to have a KnowledgeBuilder for which property reactivity is disabled by default:

    KnowledgeBuilderConfiguration config = KnowledgeBuilderFactory.newKnowledgeBuilderConfiguration();
    config.setOption(PropertySpecificOption.ALLOWED);
    KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder(config);

In this last case it will be possible to reenable the property reactivity feature on a specific type by annotating it with @PropertyReactive.

It is important to notice that property reactivity is automatically available only for modifications performed inside the consequence of a rule. Conversely a programmatic update is unaware of the object’s properties that have been changed, so it is unable of using this feature.

To workaround this limitation it is possible to optionally specify in an update statement the names of the properties that have been changed in the modified object as in the following example:

    Person me = new Person("me", 40);
    FactHandle meHandle = ksession.insert( me );

    me.setAge(41);
    me.setAddress("California Avenue");
    ksession.update( meHandle, me, "age", "address" );

##### 6.8.3.6. Basic conditional elements

###### Conditional Element `and`

The Conditional Element `"and"` is used to group other Conditional Elements into a logical conjunction. Drools supports both prefix `and` and infix `and`.

Figure 85. infixAnd

Traditional infix `and` is supported:

    //infixAnd
    Cheese( cheeseType : type ) and Person( favouriteCheese == cheeseType )

Explicit grouping with parentheses is also supported:

    //infixAnd with grouping
    ( Cheese( cheeseType : type ) and
      ( Person( favouriteCheese == cheeseType ) or
        Person( favouriteCheese == cheeseType ) )

| The symbol `&&` (as an alternative to `and`) is deprecated. But it is still supported in the syntax for backwards compatibility.

Figure 86. prefixAnd

Prefix `and` is also supported:

    (and Cheese( cheeseType : type )
         Person( favouriteCheese == cheeseType ) )

The root element of the LHS is an implicit prefix `and` and doesn’t need to be specified:

Example 134. implicit root prefixAnd

    when
        Cheese( cheeseType : type )
        Person( favouriteCheese == cheeseType )
    then
        ...

###### Conditional Element `or`

The Conditional Element `or` is used to group other Conditional Elements into a logical disjunction. Drools supports both prefix `or` and infix `or`.

Figure 87. infixOr

Traditional infix `or` is supported:

    //infixOr
    Cheese( cheeseType : type ) or Person( favouriteCheese == cheeseType )

Explicit grouping with parentheses is also supported:

    //infixOr with grouping
    ( Cheese( cheeseType : type ) or
      ( Person( favouriteCheese == cheeseType ) and
        Person( favouriteCheese == cheeseType ) )

| The symbol `||` (as an alternative to `or`) is deprecated. But it is still supported in the syntax for backwards compatibility.

Figure 88. prefixOr

Prefix `or` is also supported:

    (or Person( sex == "f", age > 60 )
        Person( sex == "m", age > 65 )
    )

| The behavior of the Conditional Element `or` is different from the connective `||` for constraints and restrictions in field constraints. The Drools engine actually has no understanding of the Conditional Element `or`. Instead, via a number of different logic transformations, a rule with `or` is rewritten as a number of subrules. This process ultimately results in a rule that has a single `or` as the root node and one subrule for each of its CEs. Each subrule can activate and fire like any normal rule; there is no special behavior or interaction between these subrules. - This can be most confusing to new rule authors.

The Conditional Element `or` also allows for optional pattern binding. This means that each resulting subrule will bind its pattern to the pattern binding. Each pattern must be bound separately, using eponymous variables:

    pensioner : ( Person( sex == "f", age > 60 ) or Person( sex == "m", age > 65 ) )

    (or pensioner : Person( sex == "f", age > 60 )
        pensioner : Person( sex == "m", age > 65 ) )

Since the conditional element `or` results in multiple subrule generation, one for each possible logically outcome, the example above would result in the internal generation of two rules. These two rules work independently within the Working Memory, which means both can match, activate and fire - there is no shortcutting.

The best way to think of the conditional element `or` is as a shortcut for generating two or more similar rules. When you think of it that way, it’s clear that for a single rule there could be multiple activations if two or more terms of the disjunction are true.

###### Conditional Element `not`

Figure 89. not

The CE `not` is first order logic’s non-existential quantifier and checks for the non-existence of something in the Working Memory. Think of "not" as meaning "there must be none of…​".

The keyword `not` may be followed by parentheses around the CEs that it applies to. In the simplest case of a single pattern (like below) you may optionally omit the parentheses.

Example 135. No Busses

    not Bus()

Example 136. No red Busses

    // Brackets are optional:
    not Bus(color == "red")
    // Brackets are optional:
    not ( Bus(color == "red", number == 42) )
    // "not" with nested infix and - two patterns,
    // brackets are requires:
    not ( Bus(color == "red") and
          Bus(color == "blue") )

###### Conditional Element `exists`

Figure 90. exists

The CE `exists` is first order logic’s existential quantifier and checks for the existence of something in the Working Memory. Think of "exists" as meaning "there is at least one..". It is different from just having the pattern on its own, which is more like saying "for each one of…​". If you use `exists` with a pattern, the rule will only activate at most once, regardless of how much data there is in working memory that matches the condition inside of the `exists` pattern. Since only the existence matters, no bindings will be established.

The keyword `exists` must be followed by parentheses around the CEs that it applies to. In the simplest case of a single pattern (like below) you may omit the parentheses.

Example 137. At least one Bus

    exists Bus()

Example 138. At least one red Bus

    exists Bus(color == "red")
    // brackets are optional:
    exists ( Bus(color == "red", number == 42) )
    // "exists" with nested infix and,
    // brackets are required:
    exists ( Bus(color == "red") and
             Bus(color == "blue") )

##### 6.8.3.7. Advanced conditional elements

###### Conditional Element `forall`

Figure 91. forall

The Conditional Element `forall` completes the First Order Logic support in Drools. The Conditional Element `forall` evaluates to true when all facts that match the first pattern match all the remaining patterns. Example:

    rule "All English buses are red"
    when
        forall( $bus : Bus( type == 'english')
                       Bus( this == $bus, color = 'red' ) )
    then
        // all English buses are red
    end

In the above rule, we "select" all Bus objects whose type is "english". Then, for each fact that matches this pattern we evaluate the following patterns and if they match, the forall CE will evaluate to true.

To state that all facts of a given type in the working memory must match a set of constraints, `forall` can be written with a single pattern for simplicity. Example:

Example 139. Single Pattern Forall

    rule "All Buses are Red"
    when
        forall( Bus( color == 'red' ) )
    then
        // all Bus facts are red
    end

Another example shows multiple patterns inside the `forall`:

Example 140. Multi-Pattern Forall

    rule "all employees have health and dental care programs"
    when
        forall( $emp : Employee()
                HealthCare( employee == $emp )
                DentalCare( employee == $emp )
              )
    then
        // all employees have health and dental care
    end

Forall can be nested inside other CEs. For instance, `forall` can be used inside a `not` CE. Note that only single patterns have optional parentheses, so that with a nested `forall` parentheses must be used:

Example 141. Combining Forall with Not CE

    rule "not all employees have health and dental care"
    when
        not ( forall( $emp : Employee()
                      HealthCare( employee == $emp )
                      DentalCare( employee == $emp ) )
            )
    then
        // not all employees have health and dental care
    end

As a side note, `forall( p1 p2 p3…​)` is equivalent to writing:

    not(p1 and not(and p2 p3...))

Also, it is important to note that `forall` is a scope delimiter. Therefore, it can use any previously bound variable, but no variable bound inside it will be available for use outside of it.

###### Conditional Element `from`

Figure 92. from

The Conditional Element `from` enables users to specify an arbitrary source for data to be matched by LHS patterns. This allows the Drools engine to reason over data not in the Working Memory. The data source could be a sub-field on a bound variable or the results of a method call. It is a powerful construction that allows out of the box integration with other application components and frameworks. One common example is the integration with data retrieved on-demand from databases using hibernate named queries.

The expression used to define the object source is any expression that follows regular MVEL syntax. Therefore, it allows you to easily use object property navigation, execute method calls and access maps and collections elements.

Here is a simple example of reasoning and binding on another pattern sub-field:

    rule "validate zipcode"
    when
        Person( $personAddress : address )
        Address( zipcode == "23920W") from $personAddress
    then
        // zip code is ok
    end

With all the flexibility from the new expressiveness in the Drools engine you can slice and dice this problem many ways. This is the same but shows how you can use a graph notation with the 'from':

    rule "validate zipcode"
    when
        $p : Person( )
        $a : Address( zipcode == "23920W") from $p.address
    then
        // zip code is ok
    end

Previous examples were evaluations using a single pattern. The CE `from` also support object sources that return a collection of objects. In that case, `from` will iterate over all objects in the collection and try to match each of them individually. For instance, if we want a rule that applies 10% discount to each item in an order, we could do:

    rule "apply 10% discount to all items over US$ 100,00 in an order"
    when
        $order : Order()
        $item  : OrderItem( value > 100 ) from $order.items
    then
        // apply discount to $item
    end

The above example will cause the rule to fire once for each item whose value is greater than 100 for each given order.

You must take caution, however, when using `from`, especially in conjunction with the `lock-on-active` rule attribute as it may produce unexpected results. Consider the example provided earlier, but now slightly modified as follows:

    rule "Assign people in North Carolina (NC) to sales region 1"
    ruleflow-group "test"
    lock-on-active true
    when
        $p : Person( )
        $a : Address( state == "NC") from $p.address
    then
        modify ($p) {} // Assign person to sales region 1 in a modify block
    end

    rule "Apply a discount to people in the city of Raleigh"
    ruleflow-group "test"
    lock-on-active true
    when
        $p : Person( )
        $a : Address( city == "Raleigh") from $p.address
    then
        modify ($p) {} // Apply discount to person in a modify block
    end

In the above example, persons in Raleigh, NC should be assigned to sales region 1 and receive a discount; i.e., you would expect both rules to activate and fire. Instead you will find that only the second rule fires.

If you were to turn on the audit log, you would also see that when the second rule fires, it deactivates the first rule. Since the rule attribute `lock-on-active` prevents a rule from creating new activations when a set of facts change, the first rule fails to reactivate. Though the set of facts have not changed, the use of `from` returns a new fact for all intents and purposes each time it is evaluated.

First, it’s important to review why you would use the above pattern. You may have many rules across different rule-flow groups. When rules modify working memory and other rules downstream of your RuleFlow (in different rule-flow groups) need to be reevaluated, the use of `modify` is critical. You don’t, however, want other rules in the same rule-flow group to place activations on one another recursively. In this case, the `no-loop` attribute is ineffective, as it would only prevent a rule from activating itself recursively. Hence, you resort to `lock-on-active`.

There are several ways to address this issue:

- Avoid the use of `from` when you can assert all facts into working memory or use nested object references in your constraint expressions (shown below).

- Place the variable assigned used in the modify block as the last sentence in your condition (LHS).

- Avoid the use of `lock-on-active` when you can explicitly manage how rules within the same rule-flow group place activations on one another (explained below).

The preferred solution is to minimize use of `from` when you can assert all your facts into working memory directly. In the example above, both the Person and Address instance can be asserted into working memory. In this case, because the graph is fairly simple, an even easier solution is to modify your rules as follows:

    rule "Assign people in North Carolina (NC) to sales region 1"
    ruleflow-group "test"
    lock-on-active true
    when
        $p : Person(address.state == "NC" )
    then
        modify ($p) {} // Assign person to sales region 1 in a modify block
    end

    rule "Apply a discount to people in the city of Raleigh"
    ruleflow-group "test"
    lock-on-active true
    when
        $p : Person(address.city == "Raleigh" )
    then
        modify ($p) {} //Apply discount to person in a modify block
    end

Now, you will find that both rules fire as expected. However, it is not always possible to access nested facts as above. Consider an example where a Person holds one or more Addresses and you wish to use an existential quantifier to match people with at least one address that meets certain conditions. In this case, you would have to resort to the use of `from` to reason over the collection.

There are several ways to use `from` to achieve this and not all of them exhibit an issue with the use of `lock-on-active`. For example, the following use of `from` causes both rules to fire as expected:

    rule "Assign people in North Carolina (NC) to sales region 1"
    ruleflow-group "test"
    lock-on-active true
    when
        $p : Person($addresses : addresses)
        exists (Address(state == "NC") from $addresses)
    then
        modify ($p) {} // Assign person to sales region 1 in a modify block
    end

    rule "Apply a discount to people in the city of Raleigh"
    ruleflow-group "test"
    lock-on-active true
    when
        $p : Person($addresses : addresses)
        exists (Address(city == "Raleigh") from $addresses)
    then
        modify ($p) {} // Apply discount to person in a modify block
    end

However, the following slightly different approach does exhibit the problem:

    rule "Assign people in North Carolina (NC) to sales region 1"
    ruleflow-group "test"
    lock-on-active true
    when
        $assessment : Assessment()
        $p : Person()
        $addresses : List() from $p.addresses
        exists (Address( state == "NC") from $addresses)
    then
        modify ($assessment) {} // Modify assessment in a modify block
    end

    rule "Apply a discount to people in the city of Raleigh"
    ruleflow-group "test"
    lock-on-active true
    when
        $assessment : Assessment()
        $p : Person()
        $addresses : List() from $p.addresses
        exists (Address( city == "Raleigh") from $addresses)
    then
        modify ($assessment) {} // Modify assessment in a modify block
    end

In the above example, the $addresses variable is returned from the use of `from`. The example also introduces a new object, assessment, to highlight one possible solution in this case. If the $assessment variable assigned in the condition (LHS) is moved to the last condition in each rule, both rules fire as expected.

Though the above examples demonstrate how to combine the use of `from` with `lock-on-active` where no loss of rule activations occurs, they carry the drawback of placing a dependency on the order of conditions on the LHS. In addition, the solutions present greater complexity for the rule author in terms of keeping track of which conditions may create issues.

A better alternative is to assert more facts into working memory. In this case, a person’s addresses may be asserted into working memory and the use of `from` would not be necessary.

There are cases, however, where asserting all data into working memory is not practical and we need to find other solutions. Another option is to reevaluate the need for `lock-on-active`. An alternative to `lock-on-active` is to directly manage how rules within the same rule-flow group activate one another by including conditions in each rule that prevent rules from activating each other recursively when working memory is modified. For example, in the case above where a discount is applied to citizens of Raleigh, a condition may be added to the rule that checks whether the discount has already been applied. If so, the rule does not activate.

| The pattern containing a from clause cannot be followed by another pattern starting with a parenthesis as in the following example
    rule R when
      $l : List( )
      String() from $l
      (String() or Number())
    then end
This is because in that case the DRL parser reads the from expression as "from $l (String() or Number())" and it is impossible to disambiguate this expression from a function call. The straightforward fix to this is wrapping also the from clause in parenthesis as it follows:
    rule R when
      $l : List( )
      (String() from $l)
      (String() or Number())
    then end

###### Conditional Element `collect`

Figure 93. collect

The Conditional Element `collect` allows rules to reason over a collection of objects obtained from the given source or from the working memory. In First Oder Logic terms this is the cardinality quantifier. A simple example:

    import java.util.ArrayList

    rule "Raise priority if system has more than 3 pending alarms"
    when
        $system : System()
        $alarms : ArrayList( size >= 3 )
                  from collect( Alarm( system == $system, status == 'pending' ) )
    then
        // Raise priority, because system $system has
        // 3 or more alarms pending. The pending alarms
        // are $alarms.
    end

In the above example, the rule will look for all pending alarms in the working memory for each given system and group them in ArrayLists. If 3 or more alarms are found for a given system, the rule will fire.

The result pattern of `collect` can be any concrete class that implements the `java.util.Collection` interface and provides a default no-arg public constructor. This means that you can use Java collections like ArrayList, LinkedList, HashSet, etc., or your own class, as long as it implements the `java.util.Collection` interface and provide a default no-arg public constructor.

Both source and result patterns can be constrained as any other pattern.

Variables bound before the `collect` CE are in the scope of both source and result patterns and therefore you can use them to constrain both your source and result patterns. But note that `collect` is a scope delimiter for bindings, so that any binding made inside of it is not available for use outside of it.

Collect accepts nested `from` CEs. The following example is a valid use of "collect":

    import java.util.LinkedList;

    rule "Send a message to all mothers"
    when
        $town : Town( name == 'Paris' )
        $mothers : LinkedList()
                   from collect( Person( gender == 'F', children > 0 )
                                 from $town.getPeople()
                               )
    then
        // send a message to all mothers
    end

###### Conditional Element `accumulate`

Figure 94. accumulate

The Conditional Element `accumulate` is a more flexible and powerful form of `collect`, in the sense that it can be used to do what `collect` does and also achieve results that the CE `collect` is not capable of achieving. Accumulate allows a rule to iterate over a collection of objects, executing custom actions for each of the elements, and at the end, it returns a result object.

Accumulate supports both the use of pre-defined accumulate functions, or the use of inline custom code. Inline custom code should be avoided though, as it is harder for rule authors to maintain, and frequently leads to code duplication. Accumulate functions are easier to test and reuse.

The Accumulate CE also supports multiple different syntaxes. The preferred syntax is the top level accumulate, as noted bellow, but all other syntaxes are supported for backward compatibility.

###### Accumulate CE (preferred syntax)

The top level accumulate syntax is the most compact and flexible syntax. The simplified syntax is as follows:

    accumulate( <source pattern>; <functions> [;<constraints>] )

For instance, a rule to calculate the minimum, maximum and average temperature reading for a given sensor and that raises an alarm if the minimum temperature is under 20C degrees and the average is over 70C degrees could be written in the following way, using Accumulate:

| The DRL language defines “`acc`” as a synonym of “`accumulate`”. The author might prefer to use “`acc`” as a less verbose keyword or the full keyword “`accumulate`” for legibility.

    rule "Raise alarm"
    when
        $s : Sensor()
        accumulate( Reading( sensor == $s, $temp : temperature );
                    $min : min( $temp ),
                    $max : max( $temp ),
                    $avg : average( $temp );
                    $min < 20, $avg > 70 )
    then
        // raise the alarm
    end

In the above example, min, max and average are Accumulate Functions and will calculate the minimum, maximum and average temperature values over all the readings for each sensor.

Drools ships with several built-in accumulate functions, including:

- average

- min

- max

- count

- sum

- variance

- standardDeviation

- collectList

- collectSet

These common functions accept any expression as input. For instance, if someone wants to calculate the average profit on all items of an order, a rule could be written using the average function:

    rule "Average profit"
    when
        $order : Order()
        accumulate( OrderItem( order == $order, $cost : cost, $price : price );
                    $avgProfit : average( 1 - $cost / $price ) )
    then
        // average profit for $order is $avgProfit
    end

Accumulate Functions are all pluggable. That means that if needed, custom, domain specific functions can easily be added to the Drools engine and rules can start to use them without any restrictions. To implement a new Accumulate Function all one needs to do is to create a Java class that implements the `org.kie.api.runtime.rule.AccumulateFunction` interface. As an example of an Accumulate Function implementation, the following is the implementation of the `average` function:

    /**
     * An implementation of an accumulator capable of calculating average values
     */
    public class AverageAccumulateFunction implements org.kie.api.runtime.rule.AccumulateFunction<AverageAccumulateFunction.AverageData> {

        public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {

        }

        public void writeExternal(ObjectOutput out) throws IOException {

        }

        public static class AverageData implements Externalizable {
            public int    count = 0;
            public double total = 0;

            public AverageData() {}

            public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
                count   = in.readInt();
                total   = in.readDouble();
            }

            public void writeExternal(ObjectOutput out) throws IOException {
                out.writeInt(count);
                out.writeDouble(total);
            }

        }

        /* (non-Javadoc)
         * @see org.kie.api.runtime.rule.AccumulateFunction#createContext()
         */
        public AverageData createContext() {
            return new AverageData();
        }

        /* (non-Javadoc)
         * @see org.kie.api.runtime.rule.AccumulateFunction#init(java.io.Serializable)
         */
        public void init(AverageData context) {
            context.count = 0;
            context.total = 0;
        }

        /* (non-Javadoc)
         * @see org.kie.api.runtime.rule.AccumulateFunction#accumulate(java.io.Serializable, java.lang.Object)
         */
        public void accumulate(AverageData context,
                               Object value) {
            context.count++;
            context.total += ((Number) value).doubleValue();
        }

        /* (non-Javadoc)
         * @see org.kie.api.runtime.rule.AccumulateFunction#reverse(java.io.Serializable, java.lang.Object)
         */
        public void reverse(AverageData context, Object value) {
            context.count--;
            context.total -= ((Number) value).doubleValue();
        }

        /* (non-Javadoc)
         * @see org.kie.api.runtime.rule.AccumulateFunction#getResult(java.io.Serializable)
         */
        public Object getResult(AverageData context) {
            return new Double( context.count == 0 ? 0 : context.total / context.count );
        }

        /* (non-Javadoc)
         * @see org.kie.api.runtime.rule.AccumulateFunction#supportsReverse()
         */
        public boolean supportsReverse() {
            return true;
        }

        /* (non-Javadoc)
         * @see org.kie.api.runtime.rule.AccumulateFunction#getResultType()
         */
        public Class< ? > getResultType() {
            return Number.class;
        }

    }

The code for the function is very simple, as we could expect, as all the "dirty" integration work is done by the Drools engine. Finally, to use the function in the rules, the author can import it using the "import accumulate" statement:

    import accumulate <class_name> <function_name>

For instance, if one implements the class `some.package.VarianceFunction` function that implements the `variance` function and wants to use it in the rules, he would do the following:

Example 142. Example of importing and using the custom “`variance`” accumulate function

    import accumulate some.package.VarianceFunction variance

    rule "Calculate Variance"
    when
        accumulate( Test( $s : score ), $v : variance( $s ) )
    then
        // the variance of the test scores is $v
    end

| The built in functions (sum, average, etc) are imported automatically by the Drools engine. Only user-defined custom accumulate functions need to be explicitly imported.

| For backward compatibility, Drools still supports the configuration of accumulate functions through configuration files and system properties, but this is a deprecated method. In order to configure the variance function from the previous example using the configuration file or system property, the user would set a property like this:
    drools.accumulate.function.variance = some.package.VarianceFunction
Please note that "`drools.accumulate.function.`" is a prefix that must always be used, "`variance`" is how the function will be used in the drl files, and "`some.package.VarianceFunction`" is the fully qualified name of the class that implements the function behavior.

###### Alternate Syntax: single function with return type

The accumulate syntax evolved over time with the goal of becoming more compact and expressive. Nevertheless, Drools still supports previous syntaxes for backward compatibility purposes.

In case the rule is using a single accumulate function on a given accumulate, the author may add a pattern for the result object and use the "from" keyword to link it to the accumulate result. Example: a rule to apply a 10% discount on orders over $100 could be written in the following way:

    rule "Apply 10% discount to orders over US$ 100,00"
    when
        $order : Order()
        $total : Number( doubleValue > 100 )
                 from accumulate( OrderItem( order == $order, $value : value ),
                                  sum( $value ) )
    then
        // apply discount to $order
    end

In the above example, the accumulate element is using only one function (sum), and so, the rules author opted to explicitly write a pattern for the result type of the accumulate function (Number) and write the constraints inside it. There are no problems in using this syntax over the compact syntax presented before, except that is is a bit more verbose. Also note that it is not allowed to use both the return type and the functions binding in the same accumulate statement.

Compile-time checks are performed in order to ensure the pattern used with the "`from`" keyword is assignable from the result of the accumulate function used.

| With this syntax, the "`from`" binds to the single result returned by the accumulate function, and it does not iterate.

In the above example, "`$total`" is bound to the result returned by the accumulate sum() function.

As another example however, if the result of the accumulate function is a collection, "`from`" still binds to the single result and it does not iterate:

    rule "Person names"
    when
      $x : Object() from accumulate(MyPerson( $val : name );
                                    collectList( $val ) )
    then
      // $x is a List
    end

The binded "`$x : Object()`" is the List itself, returned by the collectList accumulate function used.

This is an important distinction to highlight, as the "`from`" keyword can also be used separately of accumulate, to iterate over the elements of a collection:

    rule "Iterate the numbers"
    when
        $xs : List()
        $x : Integer() from $xs
    then
      // $x matches and binds to each Integer in the collection
    end

While this syntax is still supported for backward compatibility purposes, for this and other reasons we encourage rule authors to make use instead of the Accumulate CE preferred syntax (described in the previous chapter), so to avoid any potential pitfalls, as described by these examples.

###### Accumulate with inline custom code

| The use of accumulate with inline custom code is not a good practice for several reasons, including difficulties on maintaining and testing rules that use them, as well as the inability of reusing that code. Implementing your own accumulate functions is very simple and straightforward, they are easy to unit test and to use. This form of accumulate is supported for backward compatibility only.

Another possible syntax for the accumulate is to define inline custom code, instead of using accumulate functions. As noted on the previous warned, this is discouraged though for the stated reasons.

The general syntax of the `accumulate` CE with inline custom code is:

    <result pattern> from accumulate( <source pattern>,
                                      init( <init code> ),
                                      action( <action code> ),
                                      reverse( <reverse code> ),
                                      result( <result expression> ) )

The meaning of each of the elements is the following:

- <source pattern>: the source pattern is a regular pattern that the Drools engine will try to match against each of the source objects.

- <init code>: this is a semantic block of code in the selected dialect that will be executed once for each tuple, before iterating over the source objects.

- <action code>: this is a semantic block of code in the selected dialect that will be executed for each of the source objects.

- <reverse code>: this is an optional semantic block of code in the selected dialect that if present will be executed for each source object that no longer matches the source pattern. The objective of this code block is to undo any calculation done in the <action code> block, so that the Drools engine can do decremental calculation when a source object is modified or deleted, hugely improving performance of these operations.

- <result expression>: this is a semantic expression in the selected dialect that is executed after all source objects are iterated.

- <result pattern>: this is a regular pattern that the Drools engine tries to match against the object returned from the <result expression>. If it matches, the `accumulate` conditional element evaluates to true and the Drools engine proceeds with the evaluation of the next CE in the rule. If it does not matches, the `accumulate` CE evaluates to false and the Drools engine stops evaluating CEs for that rule.

It is easier to understand if we look at an example:

    rule "Apply 10% discount to orders over US$ 100,00"
    when
        $order : Order()
        $total : Number( doubleValue > 100 )
                 from accumulate( OrderItem( order == $order, $value : value ),
                                  init( double total = 0; ),
                                  action( total += $value; ),
                                  reverse( total -= $value; ),
                                  result( total ) )
    then
        // apply discount to $order
    end

In the above example, for each `Order` in the Working Memory, the Drools engine will execute the init code initializing the total variable to zero. Then it will iterate over all `OrderItem` objects for that order, executing the action for each one (in the example, it will sum the value of all items into the total variable). After iterating over all `OrderItem` objects, it will return the value corresponding to the result expression (in the above example, the value of variable `total`). Finally, the Drools engine will try to match the result with the `Number` pattern, and if the double value is greater than 100, the rule will fire.

The example used Java as the semantic dialect, and as such, note that the usage of the semicolon as statement delimiter is mandatory in the init, action and reverse code blocks. The result is an expression and, as such, it does not admit ';'. If the user uses any other dialect, he must comply to that dialect’s specific syntax.

As mentioned before, the reverse code is optional, but it is strongly recommended that the user writes it in order to benefit from the improved performance on update and delete.

The `accumulate` CE can be used to execute any action on source objects. The following example instantiates and populates a custom object:

    rule "Accumulate using custom objects"
    when
        $person   : Person( $likes : likes )
        $cheesery : Cheesery( totalAmount > 100 )
                    from accumulate( $cheese : Cheese( type == $likes ),
                                     init( Cheesery cheesery = new Cheesery(); ),
                                     action( cheesery.addCheese( $cheese ); ),
                                     reverse( cheesery.removeCheese( $cheese ); ),
                                     result( cheesery ) );
    then
        // do something
    end

##### 6.8.3.8. Conditional Element `eval`

Figure 95. eval

The conditional element `eval` is essentially a catch-all which allows any semantic code (that returns a primitive boolean) to be executed. This code can refer to variables that were bound in the LHS of the rule, and functions in the rule package. Overuse of eval reduces the declarativeness of your rules and can result in a poorly performing engine. While `eval` can be used anywhere in the patterns, the best practice is to add it as the last conditional element in the LHS of a rule.

Evals cannot be indexed and thus are not as efficient as Field Constraints. However this makes them ideal for being used when functions return values that change over time, which is not allowed within Field Constraints.

For folks who are familiar with Drools 2.x lineage, the old Drools parameter and condition tags are equivalent to binding a variable to an appropriate type, and then using it in an eval node.

    p1 : Parameter()
    p2 : Parameter()
    eval( p1.getList().containsKey( p2.getItem() ) )

    p1 : Parameter()
    p2 : Parameter()
    // call function isValid in the LHS
    eval( isValid( p1, p2 ) )

##### 6.8.3.9. Railroad diagrams

#### 6.8.4. The Right Hand Side (then)

##### 6.8.4.1. Usage

The Right Hand Side (RHS) is a common name for the consequence or action part of the rule; this part should contain a list of actions to be executed. It is bad practice to use imperative or conditional code in the RHS of a rule; as a rule should be atomic in nature - "when this, then do this", not "when this, maybe do this". The RHS part of a rule should also be kept small, thus keeping it declarative and readable. If you find you need imperative and/or conditional code in the RHS, then maybe you should be breaking that rule down into multiple rules. The main purpose of the RHS is to insert, delete or modify working memory data. To assist with that there are a few convenience methods you can use to modify working memory; without having to first reference a working memory instance.

`update(`object, handle`);` will tell the Drools engine that an object has changed (one that has been bound to something on the LHS) and rules may need to be reconsidered.

`update(`object`);` can also be used; here the Knowledge Helper will look up the facthandle for you, via an identity check, for the passed object. (Note that if you provide Property Change Listeners to your Java beans that you are inserting into the Drools engine, you can avoid the need to call `update()` when the object changes.). After a fact’s field values have changed you must call update before changing another fact, or you will cause problems with the indexing within the Drools engine. The modify keyword avoids this problem.

`insert(new` Something`());` will place a new object of your creation into the Working Memory.

`insertLogical(new` Something`());` is similar to insert, but the object will be automatically deleted when there are no more facts to support the truth of the currently firing rule.

`delete(`handle`);` removes an object from Working Memory.

These convenience methods are basically macros that provide short cuts to the `KnowledgeHelper` instance that lets you access your Working Memory from rules files. The predefined variable `drools` of type `KnowledgeHelper` lets you call several other useful methods. (Refer to the `KnowledgeHelper` interface documentation for more advanced operations).

- The call `drools.halt()` terminates rule execution immediately. This is required for returning control to the point whence the current session was put to work with `fireUntilHalt()`.

- Methods `insert(Object o)`, `update(Object o)` and `delete(Object o)` can be called on `drools` as well, but due to their frequent use they can be called without the object reference.

- `drools.getWorkingMemory()` returns the `WorkingMemory` object.

- `drools.setFocus( String s)` sets the focus to the specified agenda group.

- `drools.getRule().getName()`, called from a rule’s RHS, returns the name of the rule.

- `drools.getTuple()` returns the Tuple that matches the currently executing rule, and `drools.getActivation()` delivers the corresponding Activation. (These calls are useful for logging and debugging purposes.)

The full Knowledge Runtime API is exposed through another predefined variable, `kcontext`, of type `KieContext`. Its method `getKieRuntime()` delivers an object of type `KieRuntime`, which, in turn, provides access to a wealth of methods, many of which are quite useful for coding RHS logic.

- The call `kcontext.getKieRuntime().halt()` terminates rule execution immediately.

- The accessor `getAgenda()` returns a reference to this session’s `Agenda`, which in turn provides access to the various rule groups: activation groups, agenda groups, and rule flow groups. A fairly common paradigm is the activation of some agenda group, which could be done with the lengthy call:

      // give focus to the agenda group CleanUp
      kcontext.getKieRuntime().getAgenda().getAgendaGroup( "CleanUp" ).setFocus();

  (You can achieve the same using `drools.setFocus( "CleanUp" )`.)

- To run a query, you call `getQueryResults(String query)`, whereupon you may process the results, as explained in section Query. Using `kcontext.getKieRuntime().getQueryResults()` or using `drools.getKieRuntime().getQueryResults()` is the proper method of running a query from a rule’s RHS, and the only supported way.

- A set of methods dealing with event management lets you, among other things, add and remove event listeners for the Working Memory and the Agenda.

- Method `getKieBase()` returns the `KieBase` object, the backbone of all the Knowledge in your system, and the originator of the current session.

- You can manage globals with `setGlobal(…​)`, `getGlobal(…​)` and `getGlobals()`.

- Method `getEnvironment()` returns the runtime’s `Environment` which works much like what you know as your operating system’s environment.

##### 6.8.4.2. The `modify` Statement

This language extension provides a structured approach to fact updates. It combines the update operation with a number of setter calls to change the object’s fields. This is the syntax schema for the `modify` statement:

    modify ( <fact-expression> ) {
        <expression> [ , <expression> ]*
    }

The parenthesized <fact-expression> must yield a fact object reference. The expression list in the block should consist of setter calls for the given object, to be written without the usual object reference, which is automatically prepended by the compiler.

The example illustrates a simple fact modification.

Example 143. A modify statement

    rule "modify stilton"
    when
        $stilton : Cheese(type == "stilton")
    then
        modify( $stilton ){
            setPrice( 20 ),
            setAge( "overripe" )
        }
    end

The advantages in using the modify statment are particularly clear when used in conjuction with fine grained property change listeners. See the corresponding section for more details.

#### 6.8.5. Conditional named consequences

Sometimes the constraint of having one single consequence for each rule can be somewhat limiting and leads to verbose and difficult to be maintained repetitions like in the following example:

    rule "Give 10% discount to customers older than 60"
    when
        $customer : Customer( age > 60 )
    then
        modify($customer) { setDiscount( 0.1 ) };
    end

    rule "Give free parking to customers older than 60"
    when
        $customer : Customer( age > 60 )
        $car : Car ( owner == $customer )
    then
        modify($car) { setFreeParking( true ) };
    end

It is already possible to partially overcome this problem by making the second rule extending the first one like in:

    rule "Give 10% discount to customers older than 60"
    when
        $customer : Customer( age > 60 )
    then
        modify($customer) { setDiscount( 0.1 ) };
    end

    rule "Give free parking to customers older than 60"
        extends "Give 10% discount to customers older than 60"
    when
        $car : Car ( owner == $customer )
    then
        modify($car) { setFreeParking( true ) };
    end

Anyway this feature makes it possible to define more labelled consequences other than the default one in a single rule, so, for example, the 2 former rules can be compacted in only one like it follows:

    rule "Give 10% discount and free parking to customers older than 60"
    when
        $customer : Customer( age > 60 )
        do[giveDiscount]
        $car : Car ( owner == $customer )
    then
        modify($car) { setFreeParking( true ) };
    then[giveDiscount]
        modify($customer) { setDiscount( 0.1 ) };
    end

This last rule has 2 consequences, the usual default one, plus another one named "giveDiscount" that is activated, using the keyword do, as soon as a customer older than 60 is found in the KIE base, regardless of the fact that he owns a car or not. The activation of a named consequence can be also guarded by an additional condition like in this further example:

    rule "Give free parking to customers older than 60 and 10% discount to golden ones among them"
    when
        $customer : Customer( age > 60 )
        if ( type == "Golden" ) do[giveDiscount]
        $car : Car ( owner == $customer )
    then
        modify($car) { setFreeParking( true ) };
    then[giveDiscount]
        modify($customer) { setDiscount( 0.1 ) };
    end

The condition in the if statement is always evaluated on the pattern immediately preceding it. In the end this last, a bit more complicated, example shows how it is possible to switch over different conditions using a nested if/else statement:

    rule "Give free parking and 10% discount to over 60 Golden customer and 5% to Silver ones"
    when
        $customer : Customer( age > 60 )
        if ( type == "Golden" ) do[giveDiscount10]
        else if ( type == "Silver" ) break[giveDiscount5]
        $car : Car ( owner == $customer )
    then
        modify($car) { setFreeParking( true ) };
    then[giveDiscount10]
        modify($customer) { setDiscount( 0.1 ) };
    then[giveDiscount5]
        modify($customer) { setDiscount( 0.05 ) };
    end

Here the purpose is to give a 10% discount AND a free parking to Golden customers over 60, but only a 5% discount (without free parking) to the Silver ones. This result is achieved by activating the consequence named "giveDiscount5" using the keyword break instead of do. In fact do just schedules a consequence in the agenda, allowing the remaining part of the LHS to continue of being evaluated as per normal, while break also blocks any further pattern matching evaluation. Note, of course, that the activation of a named consequence not guarded by any condition with break doesn’t make sense (and generates a compile time error) since otherwise the LHS part following it would be never reachable.

#### 6.8.6. A Note on Auto-boxing and Primitive Types

Drools attempts to preserve numbers in their primitive or object wrapper form, so a variable bound to an int primitive when used in a code block or expression will no longer need manual unboxing; unlike Drools 3.0 where all primitives were autoboxed, requiring manual unboxing. A variable bound to an object wrapper will remain as an object; the existing JDK 1.5 and JDK 5 rules to handle auto-boxing and unboxing apply in this case. When evaluating field constraints, the system attempts to coerce one of the values into a comparable format; so a primitive is comparable to an object wrapper.

### 6.9. Query

Figure 96. query

A query is a simple way to search the working memory for facts that match the stated conditions. Therefore, it contains only the structure of the LHS of a rule, so that you specify neither "when" nor "then". A query has an optional set of parameters, each of which can be optionally typed. If the type is not given, the type Object is assumed. The Drools engine will attempt to coerce the values as needed. Query names are global to the KieBase; so do not add queries of the same name to different packages for the same RuleBase.

To return the results use `ksession.getQueryResults("name")`, where "name" is the query’s name. This returns a list of query results, which allow you to retrieve the objects that matched the query.

The first example presents a simple query for all the people over the age of 30. The second one, using parameters, combines the age limit with a location.

Example 144. Query People over the age of 30

    query "people over the age of 30"
        person : Person( age > 30 )
    end

Example 145. Query People over the age of x, and who live in y

    query "people over the age of x"  (int x, String y)
        person : Person( age > x, location == y )
    end

We iterate over the returned QueryResults using a standard "for" loop. Each element is a QueryResultsRow which we can use to access each of the columns in the tuple. These columns can be accessed by bound declaration name or index position.

Example 146. Query People over the age of 30

    QueryResults results = ksession.getQueryResults( "people over the age of 30" );
    System.out.println( "we have " + results.size() + " people over the age  of 30" );

    System.out.println( "These people are are over 30:" );

    for ( QueryResultsRow row : results ) {
        Person person = ( Person ) row.get( "person" );
        System.out.println( person.getName() + "\n" );
    }

Support for positional syntax has been added for more compact code. By default the declared type order in the type declaration matches the argument position. But it possible to override these using the @position annotation. This allows patterns to be used with positional arguments, instead of the more verbose named arguments.

    declare Cheese
        name : String @position(1)
        shop : String @position(2)
        price : int @position(0)
    end

The @Position annotation, in the org.drools.definition.type package, can be used to annotate original pojos on the classpath. Currently only fields on classes can be annotated. Inheritance of classes is supported, but not interfaces or methods. The isContainedIn query below demonstrates the use of positional arguments in a pattern; `Location(x, y;)` instead of `Location( thing == x, location == y).`

Queries can now call other queries, this combined with optional query arguments provides derivation query style backward chaining. Positional and named syntax is supported for arguments. It is also possible to mix both positional and named, but positional must come first, separated by a semi colon. Literal expressions can be passed as query arguments, but at this stage you cannot mix expressions with variables. Here is an example of a query that calls another query. Note that 'z' here will always be an 'out' variable. The '?' symbol means the query is pull only, once the results are returned you will not receive further results as the underlying data changes.

    declare Location
        thing : String
        location : String
    end

    query isContainedIn( String x, String y )
        Location(x, y;)
        or
        ( Location(z, y;) and ?isContainedIn(x, z;) )
    end

As previously mentioned you can use live "open" queries to reactively receive changes over time from the query results, as the underlying data it queries against changes. Notice the "look" rule calls the query without using '?'.

    query isContainedIn( String x, String y )
        Location(x, y;)
        or
        ( Location(z, y;) and isContainedIn(x, z;) )
    end

    rule look when
        Person( $l : likes )
        isContainedIn( $l, 'office'; )
    then
       insertLogical( $l 'is in the office' );
    end

Drools supports unification for derivation queries, in short this means that arguments are optional. It is possible to call queries from Java leaving arguments unspecified using the static field org.drools.core.runtime.rule.Variable.v - note you must use 'v' and not an alternative instance of Variable. These are referred to as 'out' arguments. Note that the query itself does not declare at compile time whether an argument is in or an out, this can be defined purely at runtime on each use. The following example will return all objects contained in the office.

    results = ksession.getQueryResults( "isContainedIn", new Object[] {  Variable.v, "office" } );
    l = new ArrayList<List<String>>();
    for ( QueryResultsRow r : results ) {
        l.add( Arrays.asList( new String[] { (String) r.get( "x" ), (String) r.get( "y" ) } ) );
    }

The algorithm uses stacks to handle recursion, so the method stack will not blow up.

It is also possible to use as input argument for a query both the field of a fact as in:

    query contains(String $s, String $c)
        $s := String( this.contains( $c ) )
    end

    rule PersonNamesWithA when
        $p : Person()
        contains( $p.name, "a"; )
    then
    end

and more in general any kind of valid expression like in:

    query checkLength(String $s, int $l)
        $s := String( length == $l )
    end

    rule CheckPersonNameLength when
        $i : Integer()
        $p : Person()
        checkLength( $p.name, 1 + $i + $p.age; )
    then
    end

The following is not yet supported:

- List and Map unification

- Expression unification - pred( X, X + 1, X * Y / 7 )

### 6.10. Domain Specific Languages

Domain Specific Languages (or DSLs) are a way of creating a rule language that is dedicated to your problem domain. A set of DSL definitions consists of transformations from DSL "sentences" to DRL constructs, which lets you use of all the underlying rule language and engine features. Given a DSL, you write rules in DSL rule (or DSLR) files, which will be translated into DRL files.

DSL and DSLR files are plain text files, and you can use any text editor to create and modify them. But there are also DSL and DSLR editors, both in the IDE as well as in the web based BRMS, and you can use those as well, although they may not provide you with the full DSL functionality.

#### 6.10.1. When to Use a DSL

DSLs can serve as a layer of separation between rule authoring (and rule authors) and the technical intricacies resulting from the modelling of domain object and the Drools engine’s native language and methods. If your rules need to be read and validated by domain experts (such as business analysts, for instance) who are not programmers, you should consider using a DSL; it hides implementation details and focuses on the rule logic proper. DSL sentences can also act as "templates" for conditional elements and consequence actions that are used repeatedly in your rules, possibly with minor variations. You may define DSL sentences as being mapped to these repeated phrases, with parameters providing a means for accommodating those variations.

DSLs have no impact on the Drools engine at runtime, they are just a compile time feature, requiring a special parser and transformer.

#### 6.10.2. DSL Basics

The Drools DSL mechanism allows you to customise conditional expressions and consequence actions. A global substitution mechanism ("keyword") is also available.

Example 147. Example DSL mapping

    [when]Something is {colour}=Something(colour=="{colour}")

In the preceding example, `[when]` indicates the scope of the expression, i.e., whether it is valid for the LHS or the RHS of a rule. The part after the bracketed keyword is the expression that you use in the rule; typically a natural language expression, but it doesn’t have to be. The part to the right of the equal sign ("=") is the mapping of the expression into the rule language. The form of this string depends on its destination, RHS or LHS. If it is for the LHS, then it ought to be a term according to the regular LHS syntax; if it is for the RHS then it might be a Java statement.

Whenever the DSL parser matches a line from the rule file written in the DSL with an expression in the DSL definition, it performs three steps of string manipulation. First, it extracts the string values appearing where the expression contains variable names in braces (here: `{colour}`). Then, the values obtained from these captures are then interpolated wherever that name, again enclosed in braces, occurs on the right hand side of the mapping. Finally, the interpolated string replaces whatever was matched by the entire expression in the line of the DSL rule file.

Note that the expressions (i.e., the strings on the left hand side of the equal sign) are used as regular expressions in a pattern matching operation against a line of the DSL rule file, matching all or part of a line. This means you can use (for instance) a '?' to indicate that the preceding character is optional. One good reason to use this is to overcome variations in natural language phrases of your DSL. But, given that these expressions are regular expression patterns, this also means that all "magic" characters of Java’s pattern syntax have to be escaped with a preceding backslash ('\').

It is important to note that the compiler transforms DSL rule files line by line. In the above example, all the text after "Something is " to the end of the line is captured as the replacement value for "{colour}", and this is used for interpolating the target string. This may not be exactly what you want. For instance, when you intend to merge different DSL expressions to generate a composite DRL pattern, you need to transform a DSLR line in several independent operations. The best way to achieve this is to ensure that the captures are surrounded by characteristic text - words or even single characters. As a result, the matching operation done by the parser plucks out a substring from somewhere within the line. In the example below, quotes are used as distinctive characters. Note that the characters that surround the capture are not included during interpolation, just the contents between them.

As a rule of thumb, use quotes for textual data that a rule editor may want to enter. You can also enclose the capture with words to ensure that the text is correctly matched. Both is illustrated by the following example. Note that a single line such as `Something is "green" and another solid thing` is now correctly expanded.

Example 148. Example with quotes

    [when]something is "{colour}"=Something(colour=="{colour}")
    [when]another {state} thing=OtherThing(state=="{state})"

It is a good idea to avoid punctuation (other than quotes or apostrophes) in your DSL expressions as much as possible. The main reason is that punctuation is easy to forget for rule authors using your DSL. Another reason is that parentheses, the period and the question mark are magic characters, requiring escaping in the DSL definition.

In a DSL mapping, the braces "{" and "}" should only be used to enclose a variable definition or reference, resulting in a capture. If they should occur literally, either in the expression or within the replacement text on the right hand side, they must be escaped with a preceding backslash ("\"):

    [then]do something= if (foo) \{ doSomething(); \}

| If braces "{" and "}" should appear in the replacement string of a DSL definition, escape them with a backslash ('\').

Example 149. Examples of DSL mapping entries

    # This is a comment to be ignored.
    [when]There is a person with name of "{name}"=Person(name=="{name}")
    [when]Person is at least {age} years old and lives in "{location}"=
          Person(age >= {age}, location=="{location}")
    [then]Log "{message}"=System.out.println("{message}");
    [when]And = and

Given the above DSL examples, the following examples show the expansion of various DSLR snippets:

Example 150. Examples of DSL expansions

    There is a person with name of "Kitty"
       ==> Person(name="Kitty")
    Person is at least 42 years old and lives in "Atlanta"
       ==> Person(age >= 42, location="Atlanta")
    Log "boo"
       ==> System.out.println("boo");
    There is a person with name of "Bob" And Person is at least 30 years old and lives in "Utah"
       ==> Person(name="Bob") and Person(age >= 30, location="Utah")

| Don’t forget that if you are capturing plain text from a DSL rule line and want to use it as a string literal in the expansion, you must provide the quotes on the right hand side of the mapping.

You can chain DSL expressions together on one line, as long as it is clear to the parser where one ends and the next one begins and where the text representing a parameter ends. (Otherwise you risk getting all the text until the end of the line as a parameter value.) The DSL expressions are tried, one after the other, according to their order in the DSL definition file. After any match, all remaining DSL expressions are investigated, too.

The resulting DRL text may consist of more than one line. Line ends are in the replacement text are written as `\n`.

#### 6.10.3. Adding Constraints to Facts

A common requirement when writing rule conditions is to be able to add an arbitrary combination of constraints to a pattern. Given that a fact type may have many fields, having to provide an individual DSL statement for each combination would be plain folly.

The DSL facility allows you to add constraints to a pattern by a simple convention: if your DSL expression starts with a hyphen (minus character, "-") it is assumed to be a field constraint and, consequently, is is added to the last pattern line preceding it.

For an example, lets take look at class `Cheese`, with the following fields: type, price, age and country. We can express some LHS condition in normal DRL like the following

    Cheese(age < 5, price == 20, type=="stilton", country=="ch")

The DSL definitions given below result in three DSL phrases which may be used to create any combination of constraint involving these fields.

    [when]There is a Cheese with=Cheese()
    [when]- age is less than {age}=age<{age}
    [when]- type is '{type}'=type=='{type}'
    [when]- country equal to '{country}'=country=='{country}'

You can then write rules with conditions like the following:

    There is a Cheese with
            - age is less than 42
            - type is 'stilton'

     The parser will pick up a line beginning with "-" and add it as a constraint to  the preceding pattern, inserting a comma when it is required.
    For the preceding example, the resulting DRL is:

    Cheese(age<42, type=='stilton')

Combining all numeric fields with all relational operators (according to the DSL expression "age is less than…​" in the preceding example) produces an unwieldy amount of DSL entries. But you can define DSL phrases for the various operators and even a generic expression that handles any field constraint, as shown below. (Notice that the expression definition contains a regular expression in addition to the variable name.)

    [when][]is less than or equal to=<=
    [when][]is less than=<
    [when][]is greater than or equal to=>=
    [when][]is greater than=>
    [when][]is equal to===
    [when][]equals===
    [when][]There is a Cheese with=Cheese()
    [when][]- {field:\w*} {operator} {value:\d*}={field} {operator} {value}

Given these DSL definitions, you can write rules with conditions such as:

    There is a Cheese with
       - age is less than 42
       - rating is greater than 50
       - type equals 'stilton'

In this specific case, a phrase such as "is less than" is replaced by `<`, and then the line matches the last DSL entry. This removes the hyphen, but the final result is still added as a constraint to the preceding pattern. After processing all of the lines, the resulting DRL text is:

    Cheese(age<42, rating > 50, type=='stilton')

| The order of the entries in the DSL is important if separate DSL expressions are intended to match the same line, one after the other.

#### 6.10.4. Developing a DSL

A good way to get started is to write representative samples of the rules your application requires, and to test them as you develop. This will provide you with a stable framework of conditional elements and their constraints. Rules, both in DRL and in DSLR, refer to entities according to the data model representing the application data that should be subject to the reasoning process defined in rules. Notice that writing rules is generally easier if most of the data model’s types are facts.

Given an initial set of rules, it should be possible to identify recurring or similar code snippets and to mark variable parts as parameters. This provides reliable leads as to what might be a handy DSL entry. Also, make sure you have a full grasp of the jargon the domain experts are using, and base your DSL phrases on this vocabulary.

You may postpone implementation decisions concerning conditions and actions during this first design phase by leaving certain conditional elements and actions in their DRL form by prefixing a line with a greater sign (">"). (This is also handy for inserting debugging statements.)

During the next development phase, you should find that the DSL configuration stabilizes pretty quickly. New rules can be written by reusing the existing DSL definitions, or by adding a parameter to an existing condition or consequence entry.

Try to keep the number of DSL entries small. Using parameters lets you apply the same DSL sentence for similar rule patterns or constraints. But do not exaggerate: authors using the DSL should still be able to identify DSL phrases by some fixed text.

#### 6.10.5. DSL and DSLR Reference

A DSL file is a text file in a line-oriented format. Its entries are used for transforming a DSLR file into a file according to DRL syntax.

- A line starting with "" or "//" (with or without preceding white space) is treated as a comment. A comment line starting with "/" is scanned for words requesting a debug option, see below.

- Any line starting with an opening bracket ("[") is assumed to be the first line of a DSL entry definition.

- Any other line is appended to the preceding DSL entry definition, with the line end replaced by a space.

A DSL entry consists of the following four parts:

- A scope definition, written as one of the keywords "when" or "condition", "then" or "consequence", "*" and "keyword", enclosed in brackets ("[" and "]"). This indicates whether the DSL entry is valid for the condition or the consequence of a rule, or both. A scope indication of "keyword" means that the entry has global significance, i.e., it is recognized anywhere in a DSLR file.

- A type definition, written as a Java class name, enclosed in brackets. This part is optional unless the next part begins with an opening bracket. An empty pair of brackets is valid, too.

- A DSL expression consists of a (Java) regular expression, with any number of embedded variable definitions, terminated by an equal sign ("="). A variable definition is enclosed in braces ("{" and "}"). It consists of a variable name and two optional attachments, separated by colons (":"). If there is one attachment, it is a regular expression for matching text that is to be assigned to the variable; if there are two attachments, the first one is a hint for the GUI editor and the second one the regular expression.

  Note that all characters that are "magic" in regular expressions must be escaped with a preceding backslash ("\") if they should occur literally within the expression.

- The remaining part of the line after the delimiting equal sign is the replacement text for any DSLR text matching the regular expression. It may contain variable references, i.e., a variable name enclosed in braces. Optionally, the variable name may be followed by an exclamation mark ("!") and a transformation function, see below.

  Note that braces ("{" and "}") must be escaped with a preceding backslash ("\") if they should occur literally within the replacement string.

Debugging of DSL expansion can be turned on, selectively, by using a comment line starting with "#/" which may contain one or more words from the table presented below. The resulting output is written to standard output.

Table 12. Debug options for DSL expansion
Word | Description
--- | ---
result | Prints the resulting DRL text, with line numbers.
steps | Prints each expansion step of condition and consequence lines.
keyword | Dumps the internal representation of all DSL entries with scope "keyword".
when | Dumps the internal representation of all DSL entries with scope "when" or "*".
then | Dumps the internal representation of all DSL entries with scope "then" or "*".
usage | Displays a usage statistic of all DSL entries.

Below are some sample DSL definitions, with comments describing the language features they illustrate.

    # Comment: DSL examples

    #/ debug: display result and usage

    # keyword definition: replaces "regula" by "rule"
    [keyword][]regula=rule

    # conditional element: "T" or "t", "a" or "an", convert matched word
    [when][][Tt]here is an? {entity:\w+}=
            ${entity!lc}: {entity!ucfirst} ()

    # consequence statement: convert matched word, literal braces
    [then][]update {entity:\w+}=modify( ${entity!lc} )\{ \}

The transformation of a DSLR file proceeds as follows:

1. The text is read into memory.

2. Each of the "keyword" entries is applied to the entire text. First, the regular expression from the keyword definition is modified by replacing white space sequences with a pattern matching any number of white space characters, and by replacing variable definitions with a capture made from the regular expression provided with the definition, or with the default (".*?"). Then, the DSLR text is searched exhaustively for occurrences of strings matching the modified regular expression. Substrings of a matching string corresponding to variable captures are extracted and replace variable references in the corresponding replacement text, and this text replaces the matching string in the DSLR text.

3. Sections of the DSLR text between "when" and "then", and "then" and "end", respectively, are located and processed in a uniform manner, line by line, as described below.

   For a line, each DSL entry pertaining to the line’s section is taken in turn, in the order it appears in the DSL file. Its regular expression part is modified: white space is replaced by a pattern matching any number of white space characters; variable definitions with a regular expression are replaced by a capture with this regular expression, its default being ".*?". If the resulting regular expression matches all or part of the line, the matched part is replaced by the suitably modified replacement text.

   Modification of the replacement text is done by replacing variable references with the text corresponding to the regular expression capture. This text may be modified according to the string transformation function given in the variable reference; see below for details.

   If there is a variable reference naming a variable that is not defined in the same entry, the expander substitutes a value bound to a variable of that name, provided it was defined in one of the preceding lines of the current rule.

4. If a DSLR line in a condition is written with a leading hyphen, the expanded result is inserted into the last line, which should contain a pattern CE, i.e., a type name followed by a pair of parentheses. if this pair is empty, the expanded line (which should contain a valid constraint) is simply inserted, otherwise a comma (",") is inserted beforehand.

   If a DSLR line in a consequence is written with a leading hyphen, the expanded result is inserted into the last line, which should contain a "modify" statement, ending in a pair of braces ("{" and "}"). If this pair is empty, the expanded line (which should contain a valid method call) is simply inserted, otherwise a comma (",") is inserted beforehand.

| It is currently not possible to use a line with a leading hyphen to insert text into other conditional element forms (e.g., "accumulate") or it may only work for the first insertion (e.g., "eval").

All string transformation functions are described in the following table.

Table 13. String transformation functions
Name | Description
--- | ---
uc | Converts all letters to upper case.
lc | Converts all letters to lower case.
ucfirst | Converts the first letter to upper case, and all other letters to lower case.
num | Extracts all digits and "-" from the string. If the last two digits in the original string are preceded by "." or ",", a decimal period is inserted in the corresponding position.
a?b/c | Compares the string with string a, and if they are equal, replaces it with b, otherwise with c. But c can be another triplet a, b, c, so that the entire structure is, in fact, a translation table.

The following DSL examples show how to use string transformation functions.

    # definitions for conditions
    [when][]There is an? {entity}=${entity!lc}: {entity!ucfirst}()
    [when][]- with an? {attr} greater than {amount}={attr} <= {amount!num}
    [when][]- with a {what} {attr}={attr} {what!positive?>0/negative?%lt;0/zero?==0/ERROR}

A file containing a DSL definition has to be put under the resources folder or any of its subfolders like any other drools artifact. It must have the extension `.dsl`, or alternatively be marked with type `ResourceType.DSL`. when programmatically added to a `KieFileSystem`. For a file using DSL definition, the extension `.dslr` should be used, while it can be added to a `KieFileSystem` with type `ResourceType.DSLR`.

For parsing and expanding a DSLR file the DSL configuration is read and supplied to the parser. Thus, the parser can "recognize" the DSL expressions and transform them into native rule language expressions.

## 7. Complex Event Processing

### 7.1. Complex Event Processing

There is no broadly accepted definition on the term Complex Event Processing. The term Event by itself is frequently overloaded and used to refer to several different things, depending on the context it is used. Defining terms is not the goal of this guide and as so, lets adopt a loose definition that, although not formal, will allow us to proceed with a common understanding.

So, in the scope of this guide:

| Event, is a record of a significant change of state in the application domain at a given point in time.

For instance, on a Stock Broker application, when a sale operation is executed, it causes a change of state in the domain. This change of state can be observed on several entities in the domain, like the price of the securities that changed to match the value of the operation, the ownership of the traded assets that changed from the seller to the buyer, the balance of the accounts from both seller and buyer that are credited and debited, etc. Depending on how the domain is modelled, this change of state may be represented by a single event, multiple atomic events or even hierarchies of correlated events. In any case, in the context of this guide, Event is the record of the change of a particular piece of data in the domain.

Events are processed by computer systems since they were invented, and throughout the history, systems responsible for that were given different names and different methodologies were employed. It wasn’t until the 90’s though, that a more focused work started on EDA (Event Driven Architecture) with a more formal definition on the requirements and goals for event processing. Old messaging systems started to change to address such requirements and new systems started to be developed with the single purpose of event processing. Two trends were born under the names of Event Stream Processing and Complex Event Processing.

In the very beginnings, Event Stream Processing was focused on the capabilities of processing streams of events in (near) real time, while the main focus of Complex Event Processing was on the correlation and composition of atomic events into complex (compound) events. An important (maybe the most important) milestone was the publishing of Dr. David Luckham’s book "The Power of Events" in 2002. In the book, Dr Luckham introduces the concept of Complex Event Processing and how it can be used to enhance systems that deal with events. Over the years, both trends converged to a common understanding and today these systems are all referred to as CEP systems.

This is a very simplistic explanation to a really complex and fertile field of research, but sets a high level and common understanding of the concepts that this guide will introduce.

The current understanding of what Complex Event Processing is may be briefly described as the following quote from Wikipedia:

| "Complex Event Processing, or CEP, is primarily an event processing concept that deals with the task of processing multiple events with the goal of identifying the meaningful events within the event cloud. CEP employs techniques such as detection of complex patterns of many events, event correlation and abstraction, event hierarchies, and relationships between events such as causality, membership, and timing, and event-driven processes." — http://en.wikipedia.org/wiki/Complex_event_processing

In other words, CEP is about detecting and selecting the interesting events (and only them) from an event cloud, finding their relationships and inferring new data from them and their relationships.

| For the remaining of this guide, we will use the terms Complex Event Processing and CEP as a broad reference for any of the related technologies and techniques, including but not limited to, CEP, Complex Event Processing, ESP, Event Stream Processing and Event Processing in general.

### 7.2. Drools Fusion

Event Processing use cases, in general, share several requirements and goals with Business Rules use cases. These overlaps happen both on the business side and on the technical side.

On the Business side:

- Business rules are frequently defined based on the occurrence of scenarios triggered by events. Examples could be:

  - On an algorithmic trading application: take an action if the security price increases X% compared to the day opening price, where the price increases are usually denoted by events on a Stock Trade application.

  - On a monitoring application: take an action if the temperature on the server room increases X degrees in Y minutes, where sensor readings are usually denoted by events.

- Both business rules and event processing queries change frequently and require immediate response for the business to adapt itself to new market conditions, new regulations and new enterprise policies.

From a technical perspective:

- Both require seamless integration with the enterprise infrastructure and applications, specially on autonomous governance, including, but not limited to, lifecycle management, auditing, security, etc.

- Both have functional requirements like pattern matching and non-functional requirements like response time and query/rule explanation.

Even sharing requirements and goals, historically, both fields were born appart and although the industry evolved and one can find good products on the market, they either focus on event processing or on business rules management. That is due not only because of historical reasons but also because, even overlapping in part, use cases do have some different requirements.

| Drools was also born as a rules engine several years ago, but following the vision of becoming a single platform for behavioral modelling, it soon realized that it could only achieve this goal by crediting the same importance to the three complementary business modelling techniques: Business Rules Management Business Processes Management Complex Event Processing

In this context, Drools Fusion is the module responsible for adding event processing capabilities into the platform.

Supporting Complex Event Processing, though, is much more than simply understanding what an event is. CEP scenarios share several common and distinguishing characteristics:

- Usually required to process huge volumes of events, but only a small percentage of the events are of real interest.

- Events are usually immutable, since they are a record of state change.

- Usually the rules and queries on events must run in reactive modes, i.e., react to the detection of event patterns.

- Usually there are strong temporal relationships between related events.

- Individual events are usually not important. The system is concerned about patterns of related events and their relationships.

- Usually, the system is required to perform composition and aggregation of events.

Based on this general common characteristics, Drools Fusion defined a set of goals to be achieved in order to support Complex Event Processing appropriately:

- Support Events, with their proper semantics, as first class citizens.

- Allow detection, correlation, aggregation and composition of events.

- Support processing of Streams of events.

- Support temporal constraints in order to model the temporal relationships between events.

- Support sliding windows of interesting events.

- Support a session scoped unified clock.

- Support the required volumes of events for CEP use cases.

- Support to (re)active rules.

- Support adapters for event input into the Drools engine (pipeline).

The above list of goals are based on the requirements not covered by Drools Expert itself, since in a unified platform, all features of one module are leveraged by the other modules. This way, Drools Fusion is born with enterprise grade features like Pattern Matching, that is paramount to a CEP product, but that is already provided by Drools Expert. In the same way, all features provided by Drools Fusion are leveraged by Drools Flow (and vice-versa) making process management aware of event processing and vice-versa.

For the remaining of this guide, we will go through each of the features Drools Fusion adds to the platform. All these features are available to support different use cases in the CEP world, and the user is free to select and use the ones that will help him model his business use case.

### 7.3. Event Semantics

An event is a fact that present a few distinguishing characteristics:

- Usually immutables: since, by the previously discussed definition, events are a record of a state change in the application domain, i.e., a record of something that already happened, and the past can not be "changed", events are immutables. This constraint is an important requirement for the development of several optimizations and for the specification of the event lifecycle. This does not mean that the Java object representing the object must be immutable. Quite the contrary, the Drools engine does not enforce immutability of the object model, because one of the most common use cases for rules is event data enrichment.

  | As a best practice, the application is allowed to populate un-populated event attributes (to enrich the event with inferred data), but already populated attributes should never be changed.

- Strong temporal constraints: rules involving events usually require the correlation of multiple events, specially temporal correlations where events are said to happen at some point in time relative to other events.

- Managed lifecycle: due to their immutable nature and the temporal constraints, events usually will only match other events and facts during a limited window of time, making it possible for the Drools engine to manage the lifecycle of the events automatically. In other words, one an event is inserted into the working memory, it is possible for the Drools engine to find out when an event can no longer match other facts and automatically delete it, releasing its associated resources.

- Use of sliding windows: since all events have timestamps associated to them, it is possible to define and use sliding windows over them, allowing the creation of rules on aggregations of values over a period of time. Example: average of an event value over 60 minutes.

Drools supports the declaration and usage of events with both semantics: point-in-time events and interval-based events.

| A simplistic way to understand the unitification of the semantics is to consider a point-in-time event as an interval-based event whose duration is zero.

### 7.4. Event Processing Modes

Rules engines in general have a well known way of processing data and rules and provide the application with the results. Also, there is not many requirements on how facts should be presented to the rules engine, specially because in general, the processing itself is time independent. That is a good assumption for most scenarios, but not for all of them. When the requirements include the processing of real time or near real time events, time becomes and important variable of the reasoning process.

The following sections will explain the impact of time on rules reasoning and the two modes provided by Drools for the reasoning process.

#### 7.4.1. Cloud Mode

The CLOUD processing mode is the default processing mode. Users of rules engines are familiar with this mode because it behaves in exactly the same way as any pure forward chaining rules engine, including previous versions of Drools.

When running in CLOUD mode, the Drools engine sees all facts in the working memory, does not matter if they are regular facts or events, as a whole. There is no notion of flow of time, although events have a timestamp as usual. In other words, although the Drools engine knows that a given event was created, for instance, on January 1st 2009, at 09:35:40.767, it is not possible for the Drools engine to determine how "old" the event is, because there is no concept of "now".

In this mode, the Drools engine will apply its usual many-to-many pattern matching algorithm, using the rules constraints to find the matching tuples, activate and fire rules as usual.

This mode does not impose any kind of additional requirements on facts. So for instance:

- There is no notion of time. No requirements clock synchronization.

- There is no requirement on event ordering. The Drools engine looks at the events as an unordered cloud against which the Drools engine tries to match rules.

On the other hand, since there is no requirements, some benefits are not available either. For instance, in CLOUD mode, it is not possible to use sliding windows, because sliding windows are based on the concept of "now" and there is no concept of "now" in CLOUD mode.

Since there is no ordering requirement on events, it is not possible for the Drools engine to determine when events can no longer match and as so, there is no automatic life-cycle management for events. I.e., the application must explicitly delete events when they are no longer necessary, in the same way the application does with regular facts.

Cloud mode is the default execution mode for Drools, but in any case, as any other configuration in Drools, it is possible to change this behavior either by setting a system property, using configuration property files or using the API. The corresponding property is:

    KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();
    config.setOption( EventProcessingOption.CLOUD );

The equivalent property is:

    drools.eventProcessingMode = cloud

#### 7.4.2. Stream Mode

The STREAM processing mode is the mode of choice when the application needs to process streams of events. It adds a few common requirements to the regular processing, but enables a whole lot of features that make stream event processing a lot simpler.

The main requirements to use STREAM mode are:

- Events in each stream must be time-ordered. I.e., inside a given stream, events that happened first must be inserted first into the Drools engine.

- The Drools engine will force synchronization between streams through the use of the session clock, so, although the application does not need to enforce time ordering between streams, the use of non-time-synchronized streams may result in some unexpected results.

Given that the above requirements are met, the application may enable the STREAM mode using the following API:

    KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();
    config.setOption( EventProcessingOption.STREAM );

Or, the equivalent property:

    drools.eventProcessingMode = stream

When using the STREAM, the Drools engine knows the concept of flow of time and the concept of "now", i.e., the Drools engine understands how old events are based on the current timestamp read from the Session Clock. This characteristic allows the Drools engine to provide the following additional features to the application:

- Sliding Window support

- Automatic Event Lifecycle Management

- Automatic Rule Delaying when using Negative Patterns

All these features are explained in the following sections.

##### 7.4.2.1. Role of Session Clock in Stream mode

When running the Drools engine in CLOUD mode, the session clock is used only to time stamp the arriving events that don’t have a previously defined timestamp attribute. Although, in STREAM mode, the Session Clock assumes an even more important role.

In STREAM mode, the session clock is responsible for keeping the current timestamp, and based on it, the Drools engine does all the temporal calculations on event’s aging, synchronizes streams from multiple sources, schedules future tasks and so on.

Check the documentation on the Session Clock section to know how to configure and use different session clock implementations.

##### 7.4.2.2. Negative Patterns in Stream Mode

Negative patterns behave different in STREAM mode when compared to CLOUD mode. In CLOUD mode, the Drools engine assumes that all facts and events are known in advance (there is no concept of flow of time) and so, negative patterns are evaluated immediately.

When running in STREAM mode, negative patterns with temporal constraints may require the Drools engine to wait for a time period before activating a rule. The time period is automatically calculated by the Drools engine in a way that the user does not need to use any tricks to achieve the desired result.

For instance:

Example 151. a rule that activates immediately upon matching

    rule "Sound the alarm"
    when
        $f : FireDetected( )
        not( SprinklerActivated( ) )
    then
        // sound the alarm
    end

The above rule has no temporal constraints that would require delaying the rule, and so, the rule activates immediately. The following rule on the other hand, must wait for 10 seconds before activating, since it may take up to 10 seconds for the sprinklers to activate:

Example 152. a rule that automatically delays activation due to temporalconstraints

    rule "Sound the alarm"
    when
        $f : FireDetected( )
        not( SprinklerActivated( this after[0s,10s] $f ) )
    then
        // sound the alarm
    end

This behaviour allows the Drools engine to keep consistency when dealing with negative patterns and temporal constraints at the same time. The above would be the same as writing the rule as below, but does not burden the user to calculate and explicitly write the appropriate duration parameter:

Example 153. same rule with explicit duration parameter

    rule "Sound the alarm"
        duration( 10s )
    when
        $f : FireDetected( )
        not( SprinklerActivated( this after[0s,10s] $f ) )
    then
        // sound the alarm
    end

The following rule expects every 10 seconds at least one “Heartbeat” event, if not the rule fires. The special case in this rule is that we use the same type of the object in the first pattern and in the negative pattern. The negative pattern has the temporal constraint to wait between 0 to 10 seconds before firing and it excludes the Heartbeat bound to $h. Excluding the bound Heartbeat is important since the temporal constraint [0s, …​] does not exclude by itself the bound event $h from being matched again, thus preventing the rule to fire.

Example 154. excluding bound events in negative patterns

    rule "Sound the alarm"
    when
        $h: Heartbeat( ) from entry-point "MonitoringStream"
        not( Heartbeat( this != $h, this after[0s,10s] $h ) from entry-point "MonitoringStream" )
    then
        // Sound the alarm
    end

### 7.5. Session Clock

Reasoning over time requires a reference clock. Just to mention one example, if a rule reasons over the average price of a given stock over the last 60 minutes, how the Drools engine knows what stock price changes happened over the last 60 minutes in order to calculate the average? The obvious response is: by comparing the timestamp of the events with the "current time". How the Drools engine knows what time is now? Again, obviously, by querying the Session Clock.

The session clock implements a strategy pattern, allowing different types of clocks to be plugged and used by the Drools engine. This is very important because the Drools engine may be running in an elements of different scenarios that may require different clock implementations. Just to mention a few:

- Rules testing: testing always requires a controlled environment, and when the tests include rules with temporal constraints, it is necessary to not only control the input rules and facts, but also the flow of time.

- Regular execution: usually, when running rules in production, the application will require a real time clock that allows the Drools engine to react immediately to the time progression.

- Special environments: specific environments may have specific requirements on time control. Cluster environments may require clock synchronization through heart beats, or JEE environments may require the use of an AppServer provided clock, etc.

- Rules replay or simulation: to replay scenarios or simulate scenarios it is necessary that the application also controls the flow of time.

#### 7.5.1. Available Clock Implementations

Drools 5 provides 2 clock implementations out of the box. The default real time clock, based on the system clock, and an optional pseudo clock, controlled by the application.

##### 7.5.1.1. Real Time Clock

By default, Drools uses a real time clock implementation that internally uses the system clock to determine the current timestamp.

To explicitly configure the Drools engine to use the real time clock, just set the session configuration parameter to real time:

    KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();
    config.setOption( ClockTypeOption.get("realtime") );

##### 7.5.1.2. Pseudo Clock

Drools also offers out of the box an implementation of a clock that is controlled by the application that is called Pseudo Clock. This clock is specially useful for unit testing temporal rules since it can be controlled by the application and so the results become deterministic.

To configure the pseudo session clock, do:

    KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();
    config.setOption( ClockTypeOption.get("pseudo") );

As an example of how to control the pseudo session clock:

    KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();
    conf.setOption( ClockTypeOption.get( "pseudo" ) );
    KieSession session = kbase.newKieSession( conf, null );

    SessionPseudoClock clock = session.getSessionClock();

    // then, while inserting facts, advance the clock as necessary:
    FactHandle handle1 = session.insert( tick1 );
    clock.advanceTime( 10, TimeUnit.SECONDS );
    FactHandle handle2 = session.insert( tick2 );
    clock.advanceTime( 30, TimeUnit.SECONDS );
    FactHandle handle3 = session.insert( tick3 );

### 7.6. Sliding Windows

Sliding Windows are a way to scope the events of interest by defining a window that is constantly moving. The two most common types of sliding window implementations are time based windows and length based windows.

The next sections will detail each of them.

| Sliding Windows are only available when running the Drools engine in STREAM mode. Check the Event Processing Mode section for details on how the STREAM mode works.

| Sliding windows start to match immediately and defining a sliding window does not imply that the rule has to wait for the sliding window to be "full" in order to match. For instance, a rule that calculates the average of an event property on a window:length(10) will start calculating the average immediately, and it will start at 0 (zero) for no-events, and will update the average as events arrive one by one.

#### 7.6.1. Sliding Time Windows

Sliding Time Windows allow the user to write rules that will only match events occurring in the last X time units.

For instance, if the user wants to consider only the Stock Ticks that happened in the last 2 minutes, the pattern would look like this:

    StockTick() over window:time( 2m )

Drools uses the "over" keyword to associate windows to patterns.

On a more elaborate example, if the user wants to sound an alarm in case the average temperature over the last 10 minutes read from a sensor is above the threshold value, the rule would look like:

Example 155. aggregating values over time windows

    rule "Sound the alarm in case temperature rises above threshold"
    when
        TemperatureThreshold( $max : max )
        Number( doubleValue > $max ) from accumulate(
            SensorReading( $temp : temperature ) over window:time( 10m ),
            average( $temp ) )
    then
        // sound the alarm
    end

The Drools engine will automatically disregard any SensorReading older than 10 minutes and keep the calculated average consistent.

| Please note that time based windows are considered when calculating the interval an event remains in the working memory before being expired, but an event falling off a sliding window does not mean by itself that the event will be discarded from the working memory, as there might be other rules that depend on that event. The Drools engine will discard events only when no other rules depend on that event and the expiration policy for that event type is fulfilled.

#### 7.6.2. Sliding Length Windows

Sliding Length Windows work the same way as Time Windows, but consider events based on order of their insertion into the session instead of flow of time.

For instance, if the user wants to consider only the last 10 RHT Stock Ticks, independent of how old they are, the pattern would look like this:

    StockTick( company == "RHT" ) over window:length( 10 )

As you can see, the pattern is similar to the one presented in the previous section, but instead of using window:time to define the sliding window, it uses window:length.

Using a similar example to the one in the previous section, if the user wants to sound an alarm in case the average temperature over the last 100 readings from a sensor is above the threshold value, the rule would look like:

Example 156. aggregating values over length windows

    rule "Sound the alarm in case temperature rises above threshold"
    when
        TemperatureThreshold( $max : max )
        Number( doubleValue > $max ) from accumulate(
            SensorReading( $temp : temperature ) over window:length( 100 ),
            average( $temp ) )
    then
        // sound the alarm
    end

The Drools engine will consider only the last 100 readings to calculate the average temperature.

| Please note that falling off a length based window is not criteria for event expiration in the session. The Drools engine disregards events that fall off a window when calculating that window, but does not remove the event from the session based on that condition alone as there might be other rules that depend on that event.

| Please note that length based windows do not define temporal constraints for event expiration from the session, and the Drools engine will not consider them. If events have no other rules defining temporal constraints and no explicit expiration policy, the Drools engine will keep them in the session indefinitely.

When using a sliding window, alpha constraints are evaluated before the window is considered, but beta (join) constraints are evaluated afterwards. This usually doesn’t make a difference when time windows are concerned, but it’s important when using a length window. For example this pattern:

    StockTick( company == "RHT" ) over window:length( 10 )

defines a window of (at most) 10 StockTicks all having company equal to "RHT", while the following one:

    $s : String()
    StockTick( company == $s ) over window:length( 10 )

first creates a window of (at most) 10 StockTicks regardless of the value of their company attribute and then filters among them only the ones having the company equal to the String selected from the working memory.

#### 7.6.3. Window Declaration

The Drools engine also supports the declaration of Windows. This promotes a clear separation between what are the filters applied to the window and what are the constraints applied to the result of window. It also allows easy reuse of windows among multiple rules.

Another benefit is a new implementation of the basic window support in the Drools engine, increasing the overall performance of the rules that use sliding windows.

The simplified EBNF to declare a window is:

    windowDeclaration := DECLARE WINDOW ID annotation* lhsPatternBind END

For example a window containing only the last 10 stock ticks from a given source can be defined like:

    declare window Ticks
        StockTick( source == "NYSE" )
            over window:length( 10 )
            from entry-point STStream
    end

Rules can then use this declared window by using it as a source for a FROM as in:

    rule "RHT ticks in the window"
        when
            accumulate( StockTick( company == "RHT" ) from window Ticks,
                        $cnt : count(1) )
        then
            // there has been $cnt RHT ticks over the last 10 ticks
    end

Note that this example also demonstrates how the window declaration allows to separate the constraints applied to the window (only the StockTicks having "NYSE" as source are among the 10 events included into window) and the constraints applied to the window result (among the last 10 events having "NYSE" as source only the ones with company equal to "RHT" are selected).

### 7.7. Streams Support

Most CEP use cases have to deal with streams of events. The streams can be provided to the application in various forms, from JMS queues to flat text files, from database tables to raw sockets or even through web service calls. In any case, the streams share a common set of characteristics:

- events in the stream are ordered by a timestamp. The timestamp may have different semantics for different streams but they are always ordered internally.

- volumes of events are usually high.

- atomic events are rarely useful by themselves. Usually meaning is extracted from the correlation between multiple events from the stream and also from other sources.

- streams may be homogeneous, i.e. contain a single type of events, or heterogeneous, i.e. contain multiple types of events.

Drools generalized the concept of a stream as an "entry point" into the Drools engine. An entry point is for drools a gate from which facts come. The facts may be regular facts or special facts like events.

In Drools, facts from one entry point (stream) may join with facts from any other entry point or event with facts from the working memory. Although, they never mix, i.e., they never lose the reference to the entry point through which they entered the Drools engine. This is important because one may have the same type of facts coming into the Drools engine through several entry points, but one fact that is inserted into the Drools engine through entry point A will never match a pattern from a entry point B, for example.

#### 7.7.1. Declaring and Using Entry Points

Entry points are declared implicitly in Drools by directly making use of them in rules. I.e. referencing an entry point in a rule will make the Drools engine, at compile time, to identify and create the proper internal structures to support that entry point.

So, for instance, lets imagine a banking application, where transactions are fed into the system coming from streams. One of the streams contains all the transactions executed in ATM machines. So, if one of the rules says: a withdraw is authorized if and only if the account balance is over the requested withdraw amount, the rule would look like:

Example 157. Example of Stream Usage

    rule "authorize withdraw"
    when
        WithdrawRequest( $ai : accountId, $am : amount ) from entry-point "ATM Stream"
        CheckingAccount( accountId == $ai, balance > $am )
    then
        // authorize withdraw
    end

In the previous example, the Drools engine compiler will identify that the pattern is tied to the entry point "ATM Stream" and will both create all the necessary structures for the rulebase to support the "ATM Stream" and will only match WithdrawRequests coming from the "ATM Stream". In the previous example, the rule is also joining the event from the stream with a fact from the main working memory (CheckingAccount).

Now, lets imagine a second rule that states that a fee of $2 must be applied to any account for which a withdraw request is placed at a bank branch:

Example 158. Using a different Stream

    rule "apply fee on withdraws on branches"
    when
        WithdrawRequest( $ai : accountId, processed == true ) from entry-point "Branch Stream"
        CheckingAccount( accountId == $ai )
    then
        // apply a $2 fee on the account
    end

The previous rule will match events of the exact same type as the first rule (WithdrawRequest), but from two different streams, so an event inserted into "ATM Stream" will never be evaluated against the pattern on the second rule, because the rule states that it is only interested in patterns coming from the "Branch Stream".

So, entry points, besides being a proper abstraction for streams, are also a way to scope facts in the working memory, and a valuable tool for reducing cross products explosions. But that is a subject for another time.

Inserting events into an entry point is equally simple. Instead of inserting events directly into the working memory, insert them into the entry point as shown in the example below:

Example 159. Inserting facts into an entry point

    // create your rulebase and your session as usual
    KieSession session = ...

    // get a reference to the entry point
    EntryPoint atmStream = session.getEntryPoint( "ATM Stream" );

    // and start inserting your facts into the entry point
    atmStream.insert( aWithdrawRequest );

The previous example shows how to manually insert facts into a given entry point. Although, usually, the application will use one of the many adapters to plug a stream end point, like a JMS queue, directly into the Drools engine entry point, without coding the inserts manually. The Drools pipeline API has several adapters and helpers to do that as well as examples on how to do it.

### 7.8. Memory Management for Events

| The automatic memory management for events is only performed when running the Drools engine in STREAM mode. Check the Event Processing Mode section for details on how the STREAM mode works.

One of the benefits of running the Drools engine in STREAM mode is that the Drools engine can detect when an event can no longer match any rule due to its temporal constraints. When that happens, the Drools engine can safely delete the event from the session without side effects and release any resources used by that event.

There are basically 2 ways for the Drools engine to calculate the matching window for a given event:

- explicitly, using the expiration policy

- implicitly, analyzing the temporal constraints on events

#### 7.8.1. Explicit expiration offset

The first way of allowing the Drools engine to calculate the window of interest for a given event type is by explicitly setting it. To do that, just use the declare statement and define an expiration for the fact type:

Example 160. explicitly defining an expiration offset of 30 minutes forStockTick events

    declare StockTick
        @expires( 30m )
    end

The above example declares an expiration offset of 30 minutes for StockTick events. After that time, assuming no rule still needs the event, the Drools engine will expire and remove the event from the session automatically.

| An explicit expiration policy for a given event type overrides any inferred expiration offset for that same type.

#### 7.8.2. Inferred expiration offset

Another way for the Drools engine to calculate the expiration offset for a given event is implicitly, by analyzing the temporal constraints in the rules. For instance, given the following rule:

Example 161. example rule with temporal constraints

    rule "correlate orders"
    when
        $bo : BuyOrderEvent( $id : id )
        $ae : AckEvent( id == $id, this after[0,10s] $bo )
    then
        // do something
    end

Analyzing the above rule, the Drools engine automatically calculates that whenever a BuyOrderEvent matches, it needs to store it for up to 10 seconds to wait for matching AckEvent’s. So, the implicit expiration offset for BuyOrderEvent will be 10 seconds. AckEvent, on the other hand, can only match existing BuyOrderEvent’s, and so its expiration offset will be zero seconds.

The Drools engine will make this analysis for the whole rulebase and find the offset for every event type.

| An explicit expiration policy for a given event type overrides any inferred expiration offset for that same type.

### 7.9. Temporal Reasoning

Temporal reasoning is another requirement of any CEP system. As discussed previously, one of the distinguishing characteristics of events is their strong temporal relationships.

Temporal reasoning is an extensive field of research, from its roots on Temporal Modal Logic to its more practical applications in business systems. There are hundreds of papers and thesis written and approaches are described for several applications. Drools once more takes a pragmatic and simple approach based on several sources, but specially worth noting the following papers:

- [ALLEN81] Allen, J.F.. An Interval-based Representation of Temporal Knowledge. 1981.

- [ALLEN83] Allen, J.F.. Maintaining knowledge about temporal intervals. 1983.

- [BENNE00] Bennet, Brandon and Galton, Antony P.. A Unifying Semantics for Time and Events. 2005.

- [YONEK05] Yoneki, Eiko and Bacon, Jean. Unified Semantics for Event Correlation Over Time and Space in Hybrid Network Environments. 2005.

Drools implements the Interval-based Time Event Semantics described by Allen, and represents Point-in-Time Events as Interval-based evens with duration 0 (zero).

| For all temporal operator intervals, the "" (star) symbol is used to indicate positive infinity and the "-" (minus star) is used to indicate negative infinity.

#### 7.9.1. Temporal Operators

Drools implements all 13 operators defined by Allen and also their logical complement (negation). This section details each of the operators and their parameters.

##### 7.9.1.1. After

The after evaluator correlates two events and matches when the temporal distance from the current event to the event being correlated belongs to the distance range declared for the operator.

Lets look at an example:

    $eventA : EventA( this after[ 3m30s, 4m ] $eventB )

The previous pattern will match if and only if the temporal distance between the time when $eventB finished and the time when $eventA started is between ( 3 minutes and 30 seconds ) and ( 4 minutes ). In other words:

     3m30s <= $eventA.startTimestamp - $eventB.endTimeStamp <= 4m

The temporal distance interval for the after operator is optional:

- If two values are defined (like in the example below), the interval starts on the first value and finishes on the second.

- If only one value is defined, the interval starts on the value and finishes on the positive infinity.

- If no value is defined, it is assumed that the initial value is 1ms and the final value is the positive infinity.

| It is possible to define negative distances for this operator. Example:
    $eventA : EventA( this after[ -3m30s, -2m ] $eventB )

| If the first value is greater than the second value, the Drools engine automatically reverses them, as there is no reason to have the first value greater than the second value. Example: the following two patterns are considered to have the same semantics:
    $eventA : EventA( this after[ -3m30s, -2m ] $eventB )
    $eventA : EventA( this after[ -2m, -3m30s ] $eventB )

| The after, before and coincides operators can be used to define constraints between events, java.util.Date attributes, and long attributes (interpreted as timestamps since epoch) in any combination. Example:
    EventA( this after $someDate )

##### 7.9.1.2. Before

The before evaluator correlates two events and matches when the temporal distance from the event being correlated to the current correlated belongs to the distance range declared for the operator.

Lets look at an example:

    $eventA : EventA( this before[ 3m30s, 4m ] $eventB )

The previous pattern will match if and only if the temporal distance between the time when $eventA finished and the time when $eventB started is between ( 3 minutes and 30 seconds ) and ( 4 minutes ). In other words:

     3m30s <= $eventB.startTimestamp - $eventA.endTimeStamp <= 4m

The temporal distance interval for the before operator is optional:

- If two values are defined (like in the example below), the interval starts on the first value and finishes on the second.

- If only one value is defined, then the interval starts on the value and finishes on the positive infinity.

- If no value is defined, it is assumed that the initial value is 1ms and the final value is the positive infinity.

| It is possible to define negative distances for this operator. Example:
    $eventA : EventA( this before[ -3m30s, -2m ] $eventB )

| If the first value is greater than the second value, the Drools engine automatically reverses them, as there is no reason to have the first value greater than the second value. Example: the following two patterns are considered to have the same semantics:
    $eventA : EventA( this before[ -3m30s, -2m ] $eventB )
    $eventA : EventA( this before[ -2m, -3m30s ] $eventB )

| The after, before and coincides operators can be used to define constraints between events, java.util.Date attributes, and long attributes (interpreted as timestamps since epoch) in any combination. Example:
    EventA( this after $someDate )

##### 7.9.1.3. Coincides

The coincides evaluator correlates two events and matches when both happen at the same time. Optionally, the evaluator accept thresholds for the distance between events' start and finish timestamps.

Lets look at an example:

    $eventA : EventA( this coincides $eventB )

The previous pattern will match if and only if the start timestamps of both $eventA and $eventB are the same AND the end timestamp of both $eventA and $eventB also are the same.

Optionally, this operator accepts one or two parameters. These parameters are the thresholds for the distance between matching timestamps.

- If only one parameter is given, it is used for both start and end timestamps.

- If two parameters are given, then the first is used as a threshold for the start timestamp and the second one is used as a threshold for the end timestamp.

In other words:

    $eventA : EventA( this coincides[15s, 10s] $eventB )

Above pattern will match if and only if:

    abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 15s &&
    abs( $eventA.endTimestamp - $eventB.endTimestamp ) <= 10s

| It makes no sense to use negative interval values for the parameters and the Drools engine will raise an error if that happens.

| The after, before and coincides operators can be used to define constraints between events, java.util.Date attributes, and long attributes (interpreted as timestamps since epoch) in any combination. Example:
    EventA( this after $someDate )

##### 7.9.1.4. During

The during evaluator correlates two events and matches when the current event happens during the occurrence of the event being correlated.

Lets look at an example:

    $eventA : EventA( this during $eventB )

The previous pattern will match if and only if the $eventA starts after $eventB starts and finishes before $eventB finishes.

In other words:

    $eventB.startTimestamp < $eventA.startTimestamp <= $eventA.endTimestamp < $eventB.endTimestamp

The during operator accepts 1, 2 or 4 optional parameters as follow:

- If one value is defined, this will be the maximum distance between the start timestamp of both event and the maximum distance between the end timestamp of both events in order to operator match. Example:

      $eventA : EventA( this during[ 5s ] $eventB )

  Will match if and only if:

      0 < $eventA.startTimestamp - $eventB.startTimestamp <= 5s &&
      0 < $eventB.endTimestamp - $eventA.endTimestamp <= 5s

- If two values are defined, the first value will be the minimum distance between the timestamps of both events, while the second value will be the maximum distance between the timestamps of both events. Example:

      $eventA : EventA( this during[ 5s, 10s ] $eventB )

  Will match if and only if:

      5s <= $eventA.startTimestamp - $eventB.startTimestamp <= 10s &&
      5s <= $eventB.endTimestamp - $eventA.endTimestamp <= 10s

- If four values are defined, the first two values will be the minimum and maximum distances between the start timestamp of both events, while the last two values will be the minimum and maximum distances between the end timestamp of both events. Example:

      $eventA : EventA( this during[ 2s, 6s, 4s, 10s ] $eventB )

  Will match if and only if:

      2s <= $eventA.startTimestamp - $eventB.startTimestamp <= 6s &&
      4s <= $eventB.endTimestamp - $eventA.endTimestamp <= 10s

##### 7.9.1.5. Finishes

The finishes evaluator correlates two events and matches when the current event’s start timestamp happens after the correlated event’s start timestamp, but both end timestamps occur at the same time.

Lets look at an example:

    $eventA : EventA( this finishes $eventB )

The previous pattern will match if and only if the $eventA starts after $eventB starts and finishes at the same time $eventB finishes.

In other words:

    $eventB.startTimestamp < $eventA.startTimestamp &&
    $eventA.endTimestamp == $eventB.endTimestamp

The finishes evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of both events in order for the operator to match. Example:

    $eventA : EventA( this finishes[ 5s ] $eventB )

Will match if and only if:

    $eventB.startTimestamp < $eventA.startTimestamp &&
    abs( $eventA.endTimestamp - $eventB.endTimestamp ) <= 5s

| It makes no sense to use a negative interval value for the parameter and the Drools engine will raise an exception if that happens.

##### 7.9.1.6. Finished By

The finishedby evaluator correlates two events and matches when the current event start timestamp happens before the correlated event start timestamp, but both end timestamps occur at the same time. This is the symmetrical opposite of finishes evaluator.

Lets look at an example:

    $eventA : EventA( this finishedby $eventB )

The previous pattern will match if and only if the $eventA starts before $eventB starts and finishes at the same time $eventB finishes.

In other words:

    $eventA.startTimestamp < $eventB.startTimestamp &&
    $eventA.endTimestamp == $eventB.endTimestamp

The finishedby evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of both events in order for the operator to match. Example:

    $eventA : EventA( this finishedby[ 5s ] $eventB )

Will match if and only if:

    $eventA.startTimestamp < $eventB.startTimestamp &&
    abs( $eventA.endTimestamp - $eventB.endTimestamp ) <= 5s

| It makes no sense to use a negative interval value for the parameter and the Drools engine will raise an exception if that happens.

##### 7.9.1.7. Includes

The includes evaluator correlates two events and matches when the event being correlated happens during the current event. It is the symmetrical opposite of during evaluator.

Lets look at an example:

    $eventA : EventA( this includes $eventB )

The previous pattern will match if and only if the $eventB starts after $eventA starts and finishes before $eventA finishes.

In other words:

    $eventA.startTimestamp < $eventB.startTimestamp <= $eventB.endTimestamp < $eventA.endTimestamp

The includes operator accepts 1, 2 or 4 optional parameters as follow:

- If one value is defined, this will be the maximum distance between the start timestamp of both event and the maximum distance between the end timestamp of both events in order to operator match. Example:

      $eventA : EventA( this includes[ 5s ] $eventB )

  Will match if and only if:

      0 < $eventB.startTimestamp - $eventA.startTimestamp <= 5s &&
      0 < $eventA.endTimestamp - $eventB.endTimestamp <= 5s

- If two values are defined, the first value will be the minimum distance between the timestamps of both events, while the second value will be the maximum distance between the timestamps of both events. Example:

      $eventA : EventA( this includes[ 5s, 10s ] $eventB )

  Will match if and only if:

      5s <= $eventB.startTimestamp - $eventA.startTimestamp <= 10s &&
      5s <= $eventA.endTimestamp - $eventB.endTimestamp <= 10s

- If four values are defined, the first two values will be the minimum and maximum distances between the start timestamp of both events, while the last two values will be the minimum and maximum distances between the end timestamp of both events. Example:

      $eventA : EventA( this includes[ 2s, 6s, 4s, 10s ] $eventB )

  Will match if and only if:

      2s <= $eventB.startTimestamp - $eventA.startTimestamp <= 6s &&
      4s <= $eventA.endTimestamp - $eventB.endTimestamp <= 10s

##### 7.9.1.8. Meets

The meets evaluator correlates two events and matches when the current event’s end timestamp happens at the same time as the correlated event’s start timestamp.

Lets look at an example:

    $eventA : EventA( this meets $eventB )

The previous pattern will match if and only if the $eventA finishes at the same time $eventB starts.

In other words:

    abs( $eventB.startTimestamp - $eventA.endTimestamp ) == 0

The meets evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of current event and the start timestamp of the correlated event in order for the operator to match. Example:

    $eventA : EventA( this meets[ 5s ] $eventB )

Will match if and only if:

    abs( $eventB.startTimestamp - $eventA.endTimestamp) <= 5s

| It makes no sense to use a negative interval value for the parameter and the Drools engine will raise an exception if that happens.

##### 7.9.1.9. Met By

The metby evaluator correlates two events and matches when the current event’s start timestamp happens at the same time as the correlated event’s end timestamp.

Lets look at an example:

    $eventA : EventA( this metby $eventB )

The previous pattern will match if and only if the $eventA starts at the same time $eventB finishes.

In other words:

    abs( $eventA.startTimestamp - $eventB.endTimestamp ) == 0

The metby evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of the correlated event and the start timestamp of the current event in order for the operator to match. Example:

    $eventA : EventA( this metby[ 5s ] $eventB )

Will match if and only if:

    abs( $eventA.startTimestamp - $eventB.endTimestamp) <= 5s

| It makes no sense to use a negative interval value for the parameter and the Drools engine will raise an exception if that happens.

##### 7.9.1.10. Overlaps

The overlaps evaluator correlates two events and matches when the current event starts before the correlated event starts and finishes after the correlated event starts, but before the correlated event finishes. In other words, both events have an overlapping period.

Lets look at an example:

    $eventA : EventA( this overlaps $eventB )

The previous pattern will match if and only if:

    $eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp

The overlaps operator accepts 1 or 2 optional parameters as follow:

- If one parameter is defined, this will be the maximum distance between the start timestamp of the correlated event and the end timestamp of the current event. Example:

      $eventA : EventA( this overlaps[ 5s ] $eventB )

  Will match if and only if:

      $eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp &&
      0 <= $eventA.endTimestamp - $eventB.startTimestamp <= 5s

- If two values are defined, the first value will be the minimum distance and the second value will be the maximum distance between the start timestamp of the correlated event and the end timestamp of the current event. Example:

      $eventA : EventA( this overlaps[ 5s, 10s ] $eventB )

  Will match if and only if:

      $eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp &&
      5s <= $eventA.endTimestamp - $eventB.startTimestamp <= 10s

##### 7.9.1.11. Overlapped By

The overlappedby evaluator correlates two events and matches when the correlated event starts before the current event starts and finishes after the current event starts, but before the current event finishes. In other words, both events have an overlapping period.

Lets look at an example:

    $eventA : EventA( this overlappedby $eventB )

The previous pattern will match if and only if:

    $eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp

The overlappedby operator accepts 1 or 2 optional parameters as follow:

- If one parameter is defined, this will be the maximum distance between the start timestamp of the current event and the end timestamp of the correlated event. Example:

      $eventA : EventA( this overlappedby[ 5s ] $eventB )

  Will match if and only if:

      $eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp &&
      0 <= $eventB.endTimestamp - $eventA.startTimestamp <= 5s

- If two values are defined, the first value will be the minimum distance and the second value will be the maximum distance between the start timestamp of the current event and the end timestamp of the correlated event. Example:

      $eventA : EventA( this overlappedby[ 5s, 10s ] $eventB )

  Will match if and only if:

      $eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp &&
      5s <= $eventB.endTimestamp - $eventA.startTimestamp <= 10s

##### 7.9.1.12. Starts

The starts evaluator correlates two events and matches when the current event’s end timestamp happens before the correlated event’s end timestamp, but both start timestamps occur at the same time.

Lets look at an example:

    $eventA : EventA( this starts $eventB )

The previous pattern will match if and only if the $eventA finishes before $eventB finishes and starts at the same time $eventB starts.

In other words:

    $eventA.startTimestamp == $eventB.startTimestamp &&
    $eventA.endTimestamp < $eventB.endTimestamp

The starts evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the start timestamp of both events in order for the operator to match. Example:

    $eventA : EventA( this starts[ 5s ] $eventB )

Will match if and only if:

    abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 5s &&
    $eventA.endTimestamp < $eventB.endTimestamp

| It makes no sense to use a negative interval value for the parameter and the Drools engine will raise an exception if that happens.

##### 7.9.1.13. Started By

The startedby evaluator correlates two events and matches when the correlating event’s end timestamp happens before the current event’s end timestamp, but both start timestamps occur at the same time. Lets look at an example:

    $eventA : EventA( this startedby $eventB )

The previous pattern will match if and only if the $eventB finishes before $eventA finishes and starts at the same time $eventB starts.

In other words:

    $eventA.startTimestamp == $eventB.startTimestamp &&
    $eventA.endTimestamp > $eventB.endTimestamp

The startedby evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the start timestamp of both events in order for the operator to match. Example:

    $eventA : EventA( this starts[ 5s ] $eventB )

Will match if and only if:

    abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 5s &&
    $eventA.endTimestamp > $eventB.endTimestamp

| It makes no sense to use a negative interval value for the parameter and the Drools engine will raise an exception if that happens.

## 8. Decision Model and Notation (DMN)

### 8.1. Decision Model and Notation (DMN)

Decision Model and Notation (DMN) is a standard established by the Object Management Group (OMG) for describing and modeling operational decisions. DMN defines an XML schema that enables DMN models to be shared between DMN-compliant platforms and across organizations so that business analysts and business rules developers can collaborate in designing and implementing DMN decision services. The DMN standard is similar to and can be used together with the Business Process Model and Notation (BPMN) standard for designing and modeling business processes.

For more information about the background and applications of DMN, see the OMG Decision Model and Notation specification.

#### 8.1.1. DMN conformance levels

The DMN specification defines three incremental levels of conformance in a software implementation. A product that claims compliance at one level must also be compliant with any preceding levels. For example, a conformance level 3 implementation must also include the supported components in conformance levels 1 and 2. For the formal definitions of each conformance level, see the OMG Decision Model and Notation specification.

The following are summaries of the three DMN conformance levels:

Conformance level 1

  A DMN conformance level 1 implementation supports decision requirement diagrams (DRDs), decision logic, and decision tables, but decision models are not executable. Any language can be used to define the expressions, including natural, unstructured languages.

Conformance level 2

  A DMN conformance level 2 implementation includes the requirements in conformance level 1, and supports Simplified Friendly Enough Expression Language (S-FEEL) expressions and fully executable decision models.

Conformance level 3

  A DMN conformance level 3 implementation includes the requirements in conformance levels 1 and 2, and supports Friendly Enough Expression Language (FEEL) expressions, the full set of boxed expressions, and fully executable decision models.

Drools provides design and runtime support for DMN 1.2 models at conformance level 3. You can design your DMN models directly in Business Central or import existing DMN models into your Drools projects for deployment and execution.

#### 8.1.2. DMN decision requirements diagram (DRD) components

A decision requirements diagram (DRD) is a visual representation of your DMN model. This diagram consists of one or more decision requirements graphs (DRGs) that represent a particular domain of an overall DRD. The DRGs trace business decisions using decision nodes, business knowledge models, sources of business knowledge, input data, and decision services.

The following table summarizes the components in a DRD:

Table 14. DRD components
Component | Description | Notation
--- | --- | ---
Elements | Decision | Node where one or more input elements determine an output based on defined decision logic. |
Business knowledge model | Reusable function with one or more decision elements. Decisions that have the same logic but depend on different sub-input data or sub-decisions use business knowledge models to determine which procedure to follow. |
Knowledge source | External authorities, documents, committees, or policies that regulate a decision or business knowledge model. Knowledge sources are references to real-world factors rather than executable business rules. |
Input data | Information used in a decision node or a business knowledge model. Input data usually includes business-level concepts or objects relevant to the business, such as loan applicant data used in a lending strategy. |
Decision service | Top-level decision containing a set of reusable decisions published as a service for invocation. A decision service can be invoked from an external application or a BPMN business process. |
Requirement connectors | Information requirement | Connection from an input data node or decision node to another decision node that requires the information. |
Knowledge requirement | Connection from a business knowledge model to a decision node or to another business knowledge model that invokes the decision logic. |
Authority requirement | Connection from an input data node or a decision node to a dependent knowledge source or from a knowledge source to a decision node, business knowledge model, or another knowledge source. |
Artifacts | Text annotation | Explanatory note associated with an input data node, decision node, business knowledge model, or knowledge source. |
Association | Connection from an input data node, decision node, business knowledge model, or knowledge source to a text annotation. |

The following table summarizes the permitted connectors between DRD elements:

Table 15. DRD connector rules
Starts from | Connects to | Connection type | Example
--- | --- | --- | ---
Decision | Decision | Information requirement |
Business knowledge model | Decision | Knowledge requirement |
Business knowledge model |
Decision service | Decision | Knowledge requirement |
Business knowledge model |
Input data | Decision | Information requirement |
Knowledge source | Authority requirement |
Knowledge source | Decision | Authority requirement |
Business knowledge model |
Knowledge source |
Decision | Text annotation | Association |
Business knowledge model |
Knowledge source |
Input data |

The following example DRD illustrates some of these DMN components in practice:

Figure 97. Example DRD: Loan prequalification

The following example DRD illustrates DMN components that are part of a reusable decision service:

Figure 98. Example DRD: Phone call handling as a decision service

In a DMN decision service node, the decision nodes in the bottom segment incorporate input data from outside of the decision service to arrive at a final decision in the top segment of the decision service node. The resulting top-level decisions from the decision service are then implemented in any subsequent decisions or business knowledge requirements of the DMN model. You can reuse DMN decision services in other DMN models to apply the same decision logic with different input data and different outgoing connections.

#### 8.1.3. Rule expressions in FEEL

Friendly Enough Expression Language (FEEL) is an expression language defined by the Object Management Group (OMG) DMN specification. FEEL expressions define the logic of a decision in a DMN model. FEEL is designed to facilitate both decision modeling and execution by assigning semantics to the decision model constructs. FEEL expressions in decision requirements diagrams (DRDs) occupy table cells in boxed expressions for decision nodes and business knowledge models.

For more information about FEEL in DMN, see the OMG Decision Model and Notation specification.

##### 8.1.3.1. Variable and function names in FEEL

Unlike many traditional expression languages, Friendly Enough Expression Language (FEEL) supports spaces and a few special characters as part of variable and function names. A FEEL name must start with a `letter`, `?`, or `_` element. The unicode letter characters are also allowed. Variable names cannot start with a language keyword, such as `and`, `true`, or `every`. The remaining characters in a variable name can be any of the starting characters, as well as `digits`, white spaces, and special characters such as `+`, `-`, `/`, `*`, `'`, and `.`.

For example, the following names are all valid FEEL names:

- Age

- Birth Date

- Flight 234 pre-check procedure

Several limitations apply to variable and function names in FEEL:

Ambiguity

  The use of spaces, keywords, and other special characters as part of names can make FEEL ambiguous. The ambiguities are resolved in the context of the expression, matching names from left to right. The parser resolves the variable name as the longest name matched in scope. You can use `( )` to disambiguate names if necessary.

Spaces in names

  The DMN specification limits the use of spaces in FEEL names. According to the DMN specification, names can contain multiple spaces but not two consecutive spaces.

  In order to make the language easier to use and avoid common errors due to spaces, Drools removes the limitation on the use of consecutive spaces. Drools supports variable names with any number of consecutive spaces, but normalizes them into a single space. For example, the two variable references `First Name` and `First Name` are both acceptable in Drools.

  Drools also normalizes the use of other white spaces, like the non-breakable white space that is common in web pages, tabs, and line breaks. From a Drools FEEL engine perspective, all of these characters are normalized into a single white space before processing.

The keyword `in`

  The keyword `in` is the only keyword in the language that cannot be used as part of a variable name. Although the specifications allow the use of keywords in the middle of variable names, the use of `in` in variable names conflicts with the grammar definition of `for`, `every` and `some` expression constructs.

##### 8.1.3.2. Data types in FEEL

Friendly Enough Expression Language (FEEL) supports the following data types:

- Numbers

- Strings

- Boolean values

- Dates

- Time

- Date and time

- Days and time duration

- Years and months duration

- Functions

- Contexts

- Ranges (or intervals)

- Lists

| The DMN specification currently does not provide an explicit way of declaring a variable as a `function`, `context`, `range`, or `list`, but Drools extends the DMN built-in types to support variables of these types.

The following are descriptions of each data type:

Numbers

  Numbers in FEEL are based on the IEEE 754-2008 Decimal 128 format, with 34 digits of precision. Internally, numbers are represented in Java as `BigDecimals` with `MathContext DECIMAL128`. FEEL supports only one number data type, so the same type is used to represent both integers and floating point numbers.

  FEEL numbers use a dot (`.`) as a decimal separator. FEEL does not support `-INF`, `+INF`, or `NaN`. FEEL uses `null` to represent invalid numbers.

  Drools extends the DMN specification and supports additional number notations:

  - Scientific: You can use scientific notation with the suffix `e<exp>` or `E<exp>`. For example, `1.2e3` is the same as writing the expression `1.2*10**3`, but is a literal instead of an expression.

  - Hexadecimal: You can use hexadecimal numbers with the prefix `0x`. For example, `0xff` is the same as the decimal number `255`. Both uppercase and lowercase letters are supported. For example, `0XFF` is the same as `0xff`.

  - Type suffixes: You can use the type suffixes `f`, `F`, `d`, `D`, `l`, and `L`. These suffixes are ignored.

Strings

  Strings in FEEL are any sequence of characters delimited by double quotation marks.

  Example:

      "John Doe"

Boolean values

  FEEL uses three-valued boolean logic, so a boolean logic expression may have values `true`, `false`, or `null`.

Dates

  Date literals are not supported in FEEL, but you can use the built-in `date()` function to construct date values. Date strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document. The format is `"YYYY-MM-DD"` where `YYYY` is the year with four digits, `MM` is the number of the month with two digits, and `DD` is the number of the day.

  Example:

      date( "2017-06-23" )

  Date objects have time equal to `"00:00:00"`, which is midnight. The dates are considered to be local, without a timezone.

Time

  Time literals are not supported in FEEL, but you can use the built-in `time()` function to construct time values. Time strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document. The format is `"hh:mm:ss[.uuu][(+-)hh:mm]"` where `hh` is the hour of the day (from `00` to `23`), `mm` is the minutes in the hour, and `ss` is the number of seconds in the minute. Optionally, the string may define the number of milliseconds (`uuu`) within the second and contain a positive (`+`) or negative (`-`) offset from UTC time to define its timezone. Instead of using an offset, you can use the letter `z` to represent the UTC time, which is the same as an offset of `-00:00`. If no offset is defined, the time is considered to be local.

  Examples:

      time( "04:25:12" )
      time( "14:10:00+02:00" )
      time( "22:35:40.345-05:00" )
      time( "15:00:30z" )

  Time values that define an offset or a timezone cannot be compared to local times that do not define an offset or a timezone.

Date and time

  Date and time literals are not supported in FEEL, but you can use the built-in `date and time()` function to construct date and time values. Date and time strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document. The format is `"<date>T<time>"`, where `<date>` and `<time>` follow the prescribed XML schema formatting, conjoined by `T`.

  Examples:

      date and time( "2017-10-22T23:59:00" )
      date and time( "2017-06-13T14:10:00+02:00" )
      date and time( "2017-02-05T22:35:40.345-05:00" )
      date and time( "2017-06-13T15:00:30z" )

  Date and time values that define an offset or a timezone cannot be compared to local date and time values that do not define an offset or a timezone.

  | If your implementation of the DMN specification does not support spaces in the XML schema, use the keyword `dateTime` as a synonym of `date and time`.

Days and time duration

  Days and time duration literals are not supported in FEEL, but you can use the built-in `duration()` function to construct days and time duration values. Days and time duration strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document, but are restricted to only days, hours, minutes and seconds. Months and years are not supported.

  Examples:

      duration( "P1DT23H12M30S" )
      duration( "P23D" )
      duration( "PT12H" )
      duration( "PT35M" )

  | If your implementation of the DMN specification does not support spaces in the XML schema, use the keyword `dayTimeDuration` as a synonym of `days and time duration`.

Years and months duration

  Years and months duration literals are not supported in FEEL, but you can use the built-in `duration()` function to construct days and time duration values. Years and months duration strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document, but are restricted to only years and months. Days, hours, minutes, or seconds are not supported.

  Examples:

      duration( "P3Y5M" )
      duration( "P2Y" )
      duration( "P10M" )
      duration( "P25M" )

  | If your implementation of the DMN specification does not support spaces in the XML schema, use the keyword `yearMonthDuration` as a synonym of `years and months duration`.

Functions

  FEEL has `function` literals (or anonymous functions) that you can use to create functions. The DMN specification currently does not provide an explicit way of declaring a variable as a `function`, but Drools extends the DMN built-in types to support variables of functions.

  Example:

      function(a, b) a + b

  In this example, the FEEL expression creates a function that adds the parameters `a` and `b` and returns the result.

Contexts

  FEEL has `context` literals that you can use to create contexts. A `context` in FEEL is a list of key and value pairs, similar to maps in languages like Java. The DMN specification currently does not provide an explicit way of declaring a variable as a `context`, but Drools extends the DMN built-in types to support variables of contexts.

  Example:

      { x : 5, y : 3 }

  In this example, the expression creates a context with two entries, `x` and `y`, representing a coordinate in a chart.

  In DMN 1.2, another way to create contexts is to create an item definition that contains the list of keys as attributes, and then declare the variable as having that item definition type.

  The Drools DMN API supports DMN `ItemDefinition` structural types in a `DMNContext` represented in two ways:

  - User-defined Java type: Must be a valid JavaBeans object defining properties and getters for each of the components in the DMN `ItemDefinition`. If necessary, you can also use the `@FEELProperty` annotation for those getters representing a component name which would result in an invalid Java identifier.

  - `java.util.Map` interface: The map needs to define the appropriate entries, with the keys corresponding to the component name in the DMN `ItemDefinition`.

Ranges (or intervals)

  FEEL has `range` literals that you can use to create ranges or intervals. A `range` in FEEL is a value that defines a lower and an upper bound, where either can be open or closed. The DMN specification currently does not provide an explicit way of declaring a variable as a `range`, but Drools extends the DMN built-in types to support variables of ranges.

  The syntax of a range is defined in the following formats:

      range          := interval_start endpoint '..' endpoint interval_end
      interval_start := open_start | closed_start
      open_start     := '(' | ']'
      closed_start   := '['
      interval_end   := open_end | closed_end
      open_end       := ')' | '['
      closed_end     := ']'
      endpoint       := expression

  The expression for the endpoint must return a comparable value, and the lower bound endpoint must be lower than the upper bound endpoint.

  For example, the following literal expression defines an interval between `1` and `10`, including the boundaries (a closed interval on both endpoints):

      [ 1 .. 10 ]

  The following literal expression defines an interval between 1 hour and 12 hours, including the lower boundary (a closed interval), but excluding the upper boundary (an open interval):

      [ duration("PT1H") .. duration("PT12H") ]

  You can use ranges in decision tables to test for ranges of values, or use ranges in simple literal expressions. For example, the following literal expression returns `true` if the value of a variable `x` is between `0` and `100`:

      x in [ 1 .. 100 ]

Lists

  FEEL has `list` literals that you can use to create lists of items. A `list` in FEEL is represented by a comma-separated list of values enclosed in square brackets. The DMN specification currently does not provide an explicit way of declaring a variable as a `list`, but Drools extends the DMN built-in types to support variables of lists.

  Example:

      [ 2, 3, 4, 5 ]

  All lists in FEEL contain elements of the same type and are immutable. Elements in a list can be accessed by index, where the first element is `1`. Negative indexes can access elements starting from the end of the list so that `-1` is the last element.

  For example, the following expression returns the second element of a list `x`:

      x[2]

  The following expression returns the second-to-last element of a list `x`:

      x[-2]

#### 8.1.4. DMN decision logic in boxed expressions

Boxed expressions in DMN are tables that you use to define the underlying logic of decision nodes and business knowledge models in a decision requirements diagram (DRD) or decision requirements graph (DRG). Some boxed expressions can contain other boxed expressions, but the top-level boxed expression corresponds to the decision logic of a single DRD artifact. While DRDs with one or more DRGs represent the flow of a DMN decision model, boxed expressions define the actual decision logic of individual nodes. DRDs and boxed expressions together form a complete and functional DMN decision model.

The following are the types of DMN boxed expressions:

- Decision tables

- Literal expressions

- Contexts

- Relations

- Functions

- Invocations

- Lists

| Drools does not provide boxed list expressions in Business Central, but supports a FEEL `list` data type that you can use in boxed literal expressions. For more information about the `list` data type and other FEEL data types in Drools, see Data types in FEEL.

All Friendly Enough Expression Language (FEEL) expressions that you use in your boxed expressions must conform to the FEEL syntax requirements in the OMG Decision Model and Notation specification.

##### 8.1.4.1. DMN decision tables

A decision table in DMN is a visual representation of one or more business rules in a tabular format. You use decision tables to define rules for a decision node that applies those rules at a given point in the decision model. Each rule consists of a single row in the table, and includes columns that define the conditions (input) and outcome (output) for that particular row. The definition of each row is precise enough to derive the outcome using the values of the conditions. Input and output values can be FEEL expressions or defined data type values.

For example, the following decision table determines credit score ratings based on a defined range of a loan applicant’s credit score:

Figure 99. Decision table for credit score rating

The following decision table determines the next step in a lending strategy for applicants depending on applicant loan eligibility and the bureau call type:

Figure 100. Decision table for lending strategy

The following decision table determines applicant qualification for a loan as the concluding decision node in a loan prequalification decision model:

Figure 101. Decision table for loan prequalification

Decision tables are a popular way of modeling rules and decision logic, and are used in many methodologies (such as DMN) and implementation frameworks (such as Drools).

| Drools supports both DMN decision tables and Drools-native decision tables, but they are different types of assets with different syntax requirements and are not interchangeable. For more information about Drools-native decision tables in Drools, see [decision-tables-con_decision-tables].

###### Hit policies in DMN decision tables

Hit policies determine how to reach an outcome when multiple rules in a decision table match the provided input values. For example, if one rule in a decision table applies a sales discount to military personnel and another rule applies a discount to students, then when a customer is both a student and in the military, the decision table hit policy must indicate whether to apply one discount or the other (Unique, First) or both discounts (Collect Sum). You specify the single character of the hit policy (U, F, C+) in the upper-left corner of the decision table.

The following are supported DMN decision table hit policies:

- Unique (U): Permits only one rule to match. Any overlap raises an error.

- Any (A): Permits multiple rules to match, but they must all have the same output. If multiple matching rules do not have the same output, an error is raised.

- Priority (P): Permits multiple rules to match, with different outputs. The output that comes first in the output values list is selected.

- First (F): Uses the first match in rule order.

- Collect (C+, C>, C<, C#): Aggregates output from multiple rules based on an aggregation function.

  - Collect ( C ): Aggregates values in an arbitrary list.

  - Collect Sum (C+): Outputs the sum of all collected values. Values must be numeric.

  - Collect Min (C<): Outputs the minimum value among the matches. The resulting values must be comparable, such as numbers, dates, or text (lexicographic order).

  - Collect Max (C>): Outputs the maximum value among the matches. The resulting values must be comparable, such as numbers, dates or text (lexicographic order).

  - Collect Count (C#): Outputs the number of matching rules.

##### 8.1.4.2. Boxed literal expressions

A boxed literal expression in DMN is a literal FEEL expression as text in a table cell, typically with a labeled column and an assigned data type. You use boxed literal expressions to define simple or complex node logic or decision data directly in FEEL for a particular node in a decision. Literal FEEL expressions must conform to FEEL syntax requirements in the OMG Decision Model and Notation specification.

For example, the following boxed literal expression defines the minimum acceptable PITI calculation (principal, interest, taxes, and insurance) in a lending decision, where `acceptable rate` is a variable defined in the DMN model:

Figure 102. Boxed literal expression for minimum PITI value

The following boxed literal expression sorts a list of possible dating candidates (soul mates) in an online dating application based on their score on criteria such as age, location, and interests:

Figure 103. Boxed literal expression for matching online dating candidates

##### 8.1.4.3. Boxed context expressions

A boxed context expression in DMN is a set of variable names and values with a result value. Each name-value pair is a context entry. You use context expressions to represent data definitions in decision logic and set a value for a desired decision element within the DMN decision model. A value in a boxed context expression can be a data type value or FEEL expression, or can contain a nested sub-expression of any type, such as a decision table, a literal expression, or another context expression.

For example, the following boxed context expression defines the factors for sorting delayed passengers in a flight-rebooking decision model, based on defined data types (`tPassengerTable`, `tFlightNumberList`):

Figure 104. Boxed context expression for flight passenger waiting list

The following boxed context expression defines the factors that determine whether a loan applicant can meet minimum mortgage payments based on principal, interest, taxes, and insurance (PITI), represented as a front-end ratio calculation with a sub-context expression:

Figure 105. Boxed context expression for front-end client PITI ratio

##### 8.1.4.4. Boxed relation expressions

A boxed relation expression in DMN is a traditional data table with information about given entities, listed as rows. You use boxed relation tables to define decision data for relevant entities in a decision at a particular node. Boxed relation expressions are similar to context expressions in that they set variable names and values, but relation expressions contain no result value and list all variable values based on a single defined variable in each column.

For example, the following boxed relation expression provides information about employees in an employee rostering decision:

Figure 106. Boxed relation expression with employee information

##### 8.1.4.5. Boxed function expressions

A boxed function expression in DMN is a parameterized boxed expression containing a literal FEEL expression, a nested context expression of an external JAVA or PMML function, or a nested boxed expression of any type. By default, all business knowledge models are defined as boxed function expressions. You use boxed function expressions to call functions on your decision logic and to define all business knowledge models.

For example, the following boxed function expression determines airline flight capacity in a flight-rebooking decision model:

Figure 107. Boxed function expression for flight capacity

The following boxed function expression contains a basic Java function as a context expression for determining absolute value in a decision model calculation:

Figure 108. Boxed function expression for absolute value

The following boxed function expression determines a monthly mortgage installment as a business knowledge model in a lending decision, with the function value defined as a nested context expression:

Figure 109. Boxed function expression for installment calculation in business knowledge model

##### 8.1.4.6. Boxed invocation expressions

A boxed invocation expression in DMN is a boxed expression that invokes a business knowledge model. A boxed invocation expression contains the name of the business knowledge model to be invoked and a list of parameter bindings. Each binding is represented by two boxed expressions on a row: The box on the left contains the name of a parameter and the box on the right contains the binding expression whose value is assigned to the parameter to evaluate the invoked business knowledge model. You use boxed invocations to invoke at a particular decision node a business knowledge model defined in the decision model.

For example, the following boxed invocation expression invokes a `reassign next passenger` business knowledge model as the concluding decision node in a flight-rebooking decision model:

Figure 110. Boxed invocation expression to reassign flight passengers

The following boxed invocation expression invokes an `InstallmentCalculation` business knowledge model to calculate a monthly installment amount for a loan before proceeding to affordability decisions:

Figure 111. Boxed invocation expression for required monthly installment

#### 8.1.5. DMN model example

The following is a real-world DMN model example that demonstrates how you can use decision modeling to reach a decision based on input data, circumstances, and company guidelines. In this scenario, a flight from San Diego to New York is canceled, requiring the affected airline to find alternate arrangements for its inconvenienced passengers.

First, the airline collects the information necessary to determine how best to get the travelers to their destinations:

Input data

  - List of flights

  - List of passengers

Decisions

  - Prioritize the passengers who will get seats on a new flight

  - Determine which flights those passengers will be offered

Business knowledge models

  - The company process for determining passenger priority

  - Any flights that have space available

  - Company rules for determining how best to reassign inconvenienced passengers

The airline then uses the DMN standard to model its decision process in the following decision requirements diagram (DRD) for determining the best rebooking solution:

Figure 112. DRD for flight rebooking

Similar to flowcharts, DRDs use shapes to represent the different elements in a process. Ovals contain the two necessary input data, rectangles contain the decision points in the model, and rectangles with clipped corners (business knowledge models) contain reusable logic that can be repeatedly invoked.

The DRD draws logic for each element from boxed expressions that provide variable definitions using FEEL expressions or data type values.

Some boxed expressions are basic, such as the following decision for establishing a prioritized waiting list:

Figure 113. Boxed context expression example for prioritized wait list

Some boxed expressions are more complex with greater detail and calculation, such as the following business knowledge model for reassigning the next delayed passenger:

Figure 114. Boxed function expression for passenger reassignment

The following is the DMN source file for this decision model:

    <definitions xmlns="http://www.omg.org/spec/DMN/20151101/dmn.xsd" xmlns:kie="https://www.drools.org/kie-dmn" xmlns:feel="http://www.omg.org/spec/FEEL/20140401" id="_0019_flight_rebooking" name="0019-flight-rebooking" namespace="https://www.drools.org/kie-dmn">
      <itemDefinition id="_tFlight" name="tFlight">
        <itemComponent id="_tFlight_Flight" name="Flight Number">
          <typeRef>feel:string</typeRef>
        </itemComponent>
        <itemComponent id="_tFlight_From" name="From">
          <typeRef>feel:string</typeRef>
        </itemComponent>
        <itemComponent id="_tFlight_To" name="To">
          <typeRef>feel:string</typeRef>
        </itemComponent>
        <itemComponent id="_tFlight_Dep" name="Departure">
          <typeRef>feel:dateTime</typeRef>
        </itemComponent>
        <itemComponent id="_tFlight_Arr" name="Arrival">
          <typeRef>feel:dateTime</typeRef>
        </itemComponent>
        <itemComponent id="_tFlight_Capacity" name="Capacity">
          <typeRef>feel:number</typeRef>
        </itemComponent>
        <itemComponent id="_tFlight_Status" name="Status">
          <typeRef>feel:string</typeRef>
        </itemComponent>
      </itemDefinition>
      <itemDefinition id="_tFlightTable" isCollection="true" name="tFlightTable">
        <typeRef>kie:tFlight</typeRef>
      </itemDefinition>
      <itemDefinition id="_tPassenger" name="tPassenger">
        <itemComponent id="_tPassenger_Name" name="Name">
          <typeRef>feel:string</typeRef>
        </itemComponent>
        <itemComponent id="_tPassenger_Status" name="Status">
          <typeRef>feel:string</typeRef>
        </itemComponent>
        <itemComponent id="_tPassenger_Miles" name="Miles">
          <typeRef>feel:number</typeRef>
        </itemComponent>
        <itemComponent id="_tPassenger_Flight" name="Flight Number">
          <typeRef>feel:string</typeRef>
        </itemComponent>
      </itemDefinition>
      <itemDefinition id="_tPassengerTable" isCollection="true" name="tPassengerTable">
        <typeRef>kie:tPassenger</typeRef>
      </itemDefinition>
      <itemDefinition id="_tFlightNumberList" isCollection="true" name="tFlightNumberList">
        <typeRef>feel:string</typeRef>
      </itemDefinition>
      <inputData id="i_Flight_List" name="Flight List">
        <variable name="Flight List" typeRef="kie:tFlightTable"/>
      </inputData>
      <inputData id="i_Passenger_List" name="Passenger List">
        <variable name="Passenger List" typeRef="kie:tPassengerTable"/>
      </inputData>
      <decision name="Prioritized Waiting List" id="d_PrioritizedWaitingList">
        <variable name="Prioritized Waiting List" typeRef="kie:tPassengerTable"/>
        <informationRequirement>
          <requiredInput href="#i_Passenger_List"/>
        </informationRequirement>
        <informationRequirement>
          <requiredInput href="#i_Flight_List"/>
        </informationRequirement>
        <knowledgeRequirement>
          <requiredKnowledge href="#b_PassengerPriority"/>
        </knowledgeRequirement>
        <context>
          <contextEntry>
            <variable name="Cancelled Flights" typeRef="kie:tFlightNumberList"/>
            <literalExpression>
              <text>Flight List[ Status = "cancelled" ].Flight Number</text>
            </literalExpression>
          </contextEntry>
          <contextEntry>
            <variable name="Waiting List" typeRef="kie:tPassengerTable"/>
            <literalExpression>
              <text>Passenger List[ list contains( Cancelled Flights, Flight Number ) ]</text>
            </literalExpression>
          </contextEntry>
          <contextEntry>
            <literalExpression>
              <text>sort( Waiting List, passenger priority )</text>
            </literalExpression>
          </contextEntry>
        </context>
      </decision>
      <decision name="Rebooked Passengers" id="d_RebookedPassengers">
        <variable name="Rebooked Passengers" typeRef="kie:tPassengerTable"/>
        <informationRequirement>
          <requiredDecision href="#d_PrioritizedWaitingList"/>
        </informationRequirement>
        <informationRequirement>
          <requiredInput href="#i_Flight_List"/>
        </informationRequirement>
        <knowledgeRequirement>
          <requiredKnowledge href="#b_ReassignNextPassenger"/>
        </knowledgeRequirement>
        <invocation>
          <literalExpression>
            <text>reassign next passenger</text>
          </literalExpression>
          <binding>
            <parameter name="Waiting List"/>
            <literalExpression>
              <text>Prioritized Waiting List</text>
            </literalExpression>
          </binding>
          <binding>
            <parameter name="Reassigned Passengers List"/>
            <literalExpression>
              <text>[]</text>
            </literalExpression>
          </binding>
          <binding>
            <parameter name="Flights"/>
            <literalExpression>
              <text>Flight List</text>
            </literalExpression>
          </binding>
        </invocation>
      </decision>
      <businessKnowledgeModel id="b_PassengerPriority" name="passenger priority">
        <encapsulatedLogic>
          <formalParameter name="Passenger1" typeRef="kie:tPassenger"/>
          <formalParameter name="Passenger2" typeRef="kie:tPassenger"/>
          <decisionTable hitPolicy="UNIQUE">
            <input id="b_Passenger_Priority_dt_i_P1_Status" label="Passenger1.Status">
              <inputExpression typeRef="feel:string">
                <text>Passenger1.Status</text>
              </inputExpression>
              <inputValues>
                <text>"gold", "silver", "bronze"</text>
              </inputValues>
            </input>
            <input id="b_Passenger_Priority_dt_i_P2_Status" label="Passenger2.Status">
              <inputExpression typeRef="feel:string">
                <text>Passenger2.Status</text>
              </inputExpression>
              <inputValues>
                <text>"gold", "silver", "bronze"</text>
              </inputValues>
            </input>
            <input id="b_Passenger_Priority_dt_i_P1_Miles" label="Passenger1.Miles">
              <inputExpression typeRef="feel:string">
                <text>Passenger1.Miles</text>
              </inputExpression>
            </input>
            <output id="b_Status_Priority_dt_o" label="Passenger1 has priority">
              <outputValues>
                <text>true, false</text>
              </outputValues>
              <defaultOutputEntry>
                <text>false</text>
              </defaultOutputEntry>
            </output>
            <rule id="b_Passenger_Priority_dt_r1">
              <inputEntry id="b_Passenger_Priority_dt_r1_i1">
                <text>"gold"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r1_i2">
                <text>"gold"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r1_i3">
                <text>>= Passenger2.Miles</text>
              </inputEntry>
              <outputEntry id="b_Passenger_Priority_dt_r1_o1">
                <text>true</text>
              </outputEntry>
            </rule>
            <rule id="b_Passenger_Priority_dt_r2">
              <inputEntry id="b_Passenger_Priority_dt_r2_i1">
                <text>"gold"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r2_i2">
                <text>"silver","bronze"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r2_i3">
                <text>-</text>
              </inputEntry>
              <outputEntry id="b_Passenger_Priority_dt_r2_o1">
                <text>true</text>
              </outputEntry>
            </rule>
            <rule id="b_Passenger_Priority_dt_r3">
              <inputEntry id="b_Passenger_Priority_dt_r3_i1">
                <text>"silver"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r3_i2">
                <text>"silver"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r3_i3">
                <text>>= Passenger2.Miles</text>
              </inputEntry>
              <outputEntry id="b_Passenger_Priority_dt_r3_o1">
                <text>true</text>
              </outputEntry>
            </rule>
            <rule id="b_Passenger_Priority_dt_r4">
              <inputEntry id="b_Passenger_Priority_dt_r4_i1">
                <text>"silver"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r4_i2">
                <text>"bronze"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r4_i3">
                <text>-</text>
              </inputEntry>
              <outputEntry id="b_Passenger_Priority_dt_r4_o1">
                <text>true</text>
              </outputEntry>
            </rule>
            <rule id="b_Passenger_Priority_dt_r5">
              <inputEntry id="b_Passenger_Priority_dt_r5_i1">
                <text>"bronze"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r5_i2">
                <text>"bronze"</text>
              </inputEntry>
              <inputEntry id="b_Passenger_Priority_dt_r5_i3">
                <text>>= Passenger2.Miles</text>
              </inputEntry>
              <outputEntry id="b_Passenger_Priority_dt_r5_o1">
                <text>true</text>
              </outputEntry>
            </rule>
          </decisionTable>
        </encapsulatedLogic>
        <variable name="passenger priority" typeRef="feel:boolean"/>
      </businessKnowledgeModel>
      <businessKnowledgeModel id="b_ReassignNextPassenger" name="reassign next passenger">
        <encapsulatedLogic>
          <formalParameter name="Waiting List" typeRef="kie:tPassengerTable"/>
          <formalParameter name="Reassigned Passengers List" typeRef="kie:tPassengerTable"/>
          <formalParameter name="Flights" typeRef="kie:tFlightTable"/>
          <context>
            <contextEntry>
              <variable name="Next Passenger" typeRef="kie:tPassenger"/>
              <literalExpression>
                <text>Waiting List[1]</text>
              </literalExpression>
            </contextEntry>
            <contextEntry>
              <variable name="Original Flight" typeRef="kie:tFlight"/>
              <literalExpression>
                <text>Flights[ Flight Number = Next Passenger.Flight Number ][1]</text>
              </literalExpression>
            </contextEntry>
            <contextEntry>
              <variable name="Best Alternate Flight" typeRef="kie:tFlight"/>
              <literalExpression>
                <text>Flights[ From = Original Flight.From and To = Original Flight.To and Departure > Original Flight.Departure and Status = "scheduled" and has capacity( item, Reassigned Passengers List ) ][1]</text>
              </literalExpression>
            </contextEntry>
            <contextEntry>
              <variable name="Reassigned Passenger" typeRef="kie:tPassenger"/>
              <context>
                <contextEntry>
                  <variable name="Name" typeRef="feel:string"/>
                  <literalExpression>
                    <text>Next Passenger.Name</text>
                  </literalExpression>
                </contextEntry>
                <contextEntry>
                  <variable name="Status" typeRef="feel:string"/>
                  <literalExpression>
                    <text>Next Passenger.Status</text>
                  </literalExpression>
                </contextEntry>
                <contextEntry>
                  <variable name="Miles" typeRef="feel:number"/>
                  <literalExpression>
                    <text>Next Passenger.Miles</text>
                  </literalExpression>
                </contextEntry>
                <contextEntry>
                  <variable name="Flight Number" typeRef="feel:string"/>
                  <literalExpression>
                    <text>Best Alternate Flight.Flight Number</text>
                  </literalExpression>
                </contextEntry>
              </context>
            </contextEntry>
            <contextEntry>
              <variable name="Remaining Waiting List" typeRef="kie:tPassengerTable"/>
              <literalExpression>
                <text>remove( Waiting List, 1 )</text>
              </literalExpression>
            </contextEntry>
            <contextEntry>
              <variable name="Updated Reassigned Passengers List" typeRef="kie:tPassengerTable"/>
              <literalExpression>
                <text>append( Reassigned Passengers List, Reassigned Passenger )</text>
              </literalExpression>
            </contextEntry>
            <contextEntry>
              <literalExpression>
                <text>if count( Remaining Waiting List ) > 0 then reassign next passenger( Remaining Waiting List, Updated Reassigned Passengers List, Flights ) else Updated Reassigned Passengers List</text>
              </literalExpression>
            </contextEntry>
          </context>
        </encapsulatedLogic>
        <variable name="reassign next passenger" typeRef="kie:tPassengerTable"/>
        <knowledgeRequirement>
          <requiredKnowledge href="#b_HasCapacity"/>
        </knowledgeRequirement>
      </businessKnowledgeModel>
      <businessKnowledgeModel id="b_HasCapacity" name="has capacity">
        <encapsulatedLogic>
          <formalParameter name="flight" typeRef="kie:tFlight"/>
          <formalParameter name="rebooked list" typeRef="kie:tPassengerTable"/>
          <literalExpression>
            <text>flight.Capacity > count( rebooked list[ Flight Number = flight.Flight Number ] )</text>
          </literalExpression>
        </encapsulatedLogic>
        <variable name="has capacity" typeRef="feel:boolean"/>
      </businessKnowledgeModel>
    </definitions>

### 8.2. DMN support in Drools

Drools provides design and runtime support for DMN 1.2 models at conformance level 3. You can integrate DMN models with your Drools decision services in several ways:

- Design your DMN models directly in Business Central using the DMN designer

- Import DMN files into your project in Business Central (Menu → Design → Projects → Import Asset)

- Package DMN files as part of your project knowledge JAR (KJAR) file without Business Central

In addition to all DMN conformance level 3 requirements, Drools also includes enhancements and fixes to FEEL and DMN model components to optimize the experience of implementing DMN decision services with Drools. From a platform perspective, DMN models are like any other business asset in Drools, such as DRL files or spreadsheet decision tables, that you can include in your Drools project and deploy to KIE Server in order to start your DMN decision services.

For more information about including external DMN files with your Drools project packaging and deployment method, see Build, Deploy, Utilize and Run.

#### 8.2.1. FEEL enhancements in Drools

Drools includes the following enhancements and other changes to FEEL in the current DMN implementation:

- Space Sensitivity: This DMN implementation of the FEEL language is space insensitive. The goal is to avoid non-deterministic behavior based on the context and differences in behavior based on invisible characters, such as white spaces. This means that for this implementation, a variable named `first name` with one space is exactly the same as `first name` with two spaces in it.

- List functions `or()` and `and()` : The specification defines two list functions named `or()` and `and()`. However, according to the FEEL grammar, these are not valid function names, as `and` and `or` are reserved keywords. This implementation renames these functions to `any()` and `all()` respectively, in anticipation for DMN 1.2.

- Keyword `in` cannot be used in variable names: The specification defines that any keyword can be reused as part of a variable name, but the ambiguities caused with the `for …​ in …​ return` loop prevent the reuse of the `in` keyword. All other keywords are supported as part of variable names.

- Keywords are not supported in attributes of anonymous types: FEEL is not a strongly typed language and the parser must resolve ambiguity in name parts of an attribute of an anonymous type. The parser supports reusable keywords as part of a variable name defined in the scope, but the parser does not support keywords in attributes of an anonymous type. For example, `for item in Order.items return Federal Tax for Item( item )` is a valid and supported FEEL expression, where a function named `Federal Tax for Item(…​)` can be defined and invoked correctly in the scope. However, the expression `for i in [ {x and y : true, n : 1}, {x and y : false, n: 2} ] return i.x and y` is not supported because anonymous types are defined in the iteration context of the `for` expression and the parser cannot resolve the ambiguity.

- Support for date and time literals on ranges: According to the grammar rules #8, #18, #19, #34 and #62, `date and time` literals are supported in ranges (pages 110-111). Chapter 10.3.2.7 on page 114, on the other hand, contradicts the grammar and says they are not supported. This implementation chose to follow the grammar and support `date and time` literals on ranges, as well as extend the specification to support any arbitrary expression (see extensions below).

- Invalid time syntax: Chapter 10.3.2.3.4 on page 112 and bullet point about `time` on page 131 both state that the `time` string lexical representation follows the XML Schema Datatypes specification as well as ISO 8601. According to the XML Schema specification (https://www.w3.org/TR/xmlschema-2/#time), the lexical representation of a time follows the pattern `hh:mm:ss.sss` without any leading character. The DMN specification uses a leading "T" in several examples, that we understand is a typo and not in accordance with the standard.

- Support for scientific and hexadecimal notations: This implementation supports scientific and hexadecimal notation for numbers. For example, `1.2e5` (scientific notation), `0xD5` (hexadecimal notation).

- Support for expressions as end points in ranges: This implementation supports expressions as endpoints for ranges. For example, `[date("2016-11-24")..date("2016-11-27")]`

- Support for additional types: The specification only defines the following as basic types of the language:

  - number

  - string

  - boolean

  - days and time duration

  - years and month duration

  - time

  - date and time

    For completeness and orthogonality, this implementation also supports the following types:

  - context

  - list

  - range

  - function

  - unary test

- Support for unary tests: For completeness and orthogonality, unary tests are supported as first class citizens in the language. They are functions with an implicit single parameter and can be invoked in the same way as functions. For example,

  UnaryTestAsFunction.feel

        {
            is minor : < 18,
            Bob is minor : is minor( bob.age )
        }

- Support for additional built-in functions: The following additional functions are supported:

  - `now()` : Returns the current local date and time.

  - `today()` : Returns the current local date.

  - `decision table()` : Returns a decision table function, although the specification mentions a decision table. The function on page 114 is not implementable as defined.

  - `string( mask, p…​ )` : Returns a string formatted as per the mask. See Java String.format() for details on the mask syntax. For example, `string( "%4.2f", 7.1298 )` returns the string `"7.12"`.

- Support for additional date and time arithmetics: Subtracting two dates returns a day and time duration with the number of days between the two dates, ignoring daylight savings. For example,

  DateArithmetic.feel

      date( "2017-05-12" ) - date( "2017-04-25" ) = duration( "P17D" )

#### 8.2.2. DMN model enhancements in Drools

Drools includes the following enhancements to DMN model support in the current DMN implementation:

- Support for types with spaces on names: The DMN XML schema defines type refs such as QNames. The QNames do not allow spaces. Therefore, it is not possible to use types like FEEL `date and time`, `days and time duration` or `years and months duration`. This implementation does parse such typerefs as strings and allows type names with spaces. However, in order to comply with the XML schema, it also adds the following aliases to such types that can be used instead:

  - `date and time` = `dateTime`

  - `days and time duration` = `duration` or `dayTimeDuration`

  - `years and months duration` = `duration` or `yearMonthDuration`

    Note that, for the "duration" types, the user can simply use `duration` and the Drools engine will infer the proper duration, either `days and time duration` or `years and months duration`.

- Lists support heterogeneous element types: Currently this implementation supports lists with heterogeneous element types. This is an experimental extension and does limit the functionality of some functions and filters. This decision will be re-evaluated in the future.

- TypeRef link between Decision Tables and Item Definitions: On decision tables/input clause, if no values list is defined, the Drools engine automatically checks the type reference and applies the allowed values check if it is defined.

#### 8.2.3. Configurable DMN properties in Drools

Drools provides the following DMN properties that you can configure when you execute your DMN models on KIE Server or on your client application:

org.kie.dmn.strictConformance

  When enabled, this property disables by default any extensions or profiles provided beyond the DMN standard, such as some helper functions or enhanced features of DMN 1.2 backported into DMN 1.1. You can use this property to configure the Drools engine to support only pure DMN features, such as when running the DMN Technology Compatibility Kit (TCK).

  Default value: `false`

      -Dorg.kie.dmn.strictConformance=true

org.kie.dmn.runtime.typecheck

  When enabled, this property enables verification of actual values conforming to their declared types in the DMN model, as input or output of DRD elements. You can use this property to verify whether data supplied to the DMN model or produced by the DMN model is compliant with what is specified in the model.

  Default value: `false`

      -Dorg.kie.dmn.runtime.typecheck=true

org.kie.dmn.decisionservice.coercesingleton

  By default, this property makes the result of a decision service defining a single output decision be the single value of the output decision value. When disabled, this property makes the result of a decision service defining a single output decision be a `context` with the single entry for that decision. You can use this property to adjust your decision service outputs according to your project requirements.

  Default value: `true`

      -Dorg.kie.dmn.decisionservice.coercesingleton=false

org.kie.dmn.profiles.$PROFILE_NAME

  When valorized with a Java fully qualified name, this property loads a DMN profile onto the Drools engine at start time. You can use this property to implement a predefined DMN profile with supported features different from or beyond the DMN standard. For example, if you are creating DMN models using the Signavio DMN modeller, use this property to implement features from the Signavio DMN profile into your DMN decision service.

      -Dorg.kie.dmn.profiles.signavio=org.kie.dmn.signavio.KieDMNSignavioProfile

org.kie.dmn.compiler.execmodel

  When enabled, this property enables DMN decision table logic to be compiled into executable rule models during run time. You can use this property to evaluate DMN decision table logic more efficiently. This property is helpful when the executable model compilation was not originally performed during project compile time. Enabling this property may result in added compile time during the first evaluation by the Drools engine, but subsequent compilations are more efficient.

  Default value: `false`

      -Dorg.kie.dmn.compiler.execmodel=true

### 8.3. Creating and editing DMN models in Business Central

You can use the DMN designer in Business Central to design DMN decision requirements diagrams (DRDs) and define decision logic for a complete and functional DMN decision model. Drools provides design and runtime support for DMN 1.2 models at conformance level 3, and includes enhancements and fixes to FEEL and DMN model components to optimize the experience of implementing DMN decision services with Drools.

Procedure

1. In Business Central, go to Menu → Design → Projects and click the project name.

2. Create or import a DMN file in your Business Central project.

   To create a DMN file, click Add Asset → DMN, enter an informative DMN model name, select the appropriate Package, and click Ok.

   To import an existing DMN file, click Import Asset, enter the DMN model name, select the appropriate Package, select the DMN file to upload, and click Ok.

   The new DMN file is now listed in the DMN panel of the Project Explorer, and the DMN decision requirements diagram (DRD) canvas appears.

   | If you imported a DMN file that does not contain layout information, the imported decision requirements diagram (DRD) is formatted automatically in the DMN designer. Click Save in the DMN designer to save the DRD layout.

3. Begin adding components to your new or imported DMN decision requirements diagram (DRD) by clicking and dragging one of the DMN nodes from the left toolbar.

   The following DRD components are available:

   - Decision: Use this node for a DMN decision, where one or more input elements determine an output based on defined decision logic.

   - Business knowledge model: Use this node for reusable functions with one or more decision elements. Decisions that have the same logic but depend on different sub-input data or sub-decisions use business knowledge models to determine which procedure to follow.

   - Knowledge source: Use this node for external authorities, documents, committees, or policies that regulate a decision or business knowledge model. Knowledge sources are references to real-world factors rather than executable business rules.

   - Input data: Use this node for information used in a decision node or a business knowledge model. Input data usually includes business-level concepts or objects relevant to the business, such as loan applicant data used in a lending strategy.

   - Text annotation: Use this node for explanatory notes associated with an input data node, decision node, business knowledge model, or knowledge source.

   - Decision service: Use this node to enclose a set of reusable decisions implemented as a decision service for invocation. A decision service can be used in other DMN models and can be invoked from an external application or a BPMN business process.

4. In the DMN designer canvas, double-click the new DRD node to enter an informative node name.

5. If the node is a decision or business knowledge model, select the node to display the node options and click the Edit icon to open the DMN boxed expression designer to define the decision logic for the node:

   Figure 115. Opening a new decision node boxed expression
   Figure 116. Opening a new business knowledge model boxed expression

   By default, all business knowledge models are defined as boxed function expressions containing a literal FEEL expression, a nested context expression of an external JAVA or PMML function, or a nested boxed expression of any type.

   For decision nodes, you click the undefined table to select the type of boxed expression you want to use, such as a boxed literal expression, boxed context expression, decision table, or other DMN boxed expression.

   For business knowledge models, you click the top-left function cell to select the function type, or right-click the function value cell, select Clear, and select a boxed expression of another type.

6. In the selected boxed expression designer for either a decision node (any expression type) or business knowledge model (function expression), click the applicable table cells to define the table name, variable data types, variable names and values, function parameters and bindings, or FEEL expressions to include in the decision logic.

   You can right-click cells for additional actions where applicable, such as inserting or removing table rows and columns or clearing table contents.

   The following is an example decision table for a decision node that determines credit score ratings based on a defined range of a loan applicant’s credit score:

   Figure 117. Decision node decision table for credit score rating

   The following is an example boxed function expression for a business knowledge model that calculates mortgage payments based on principal, interest, taxes, and insurance (PITI) as a literal expression:

   Figure 118. Business knowledge model function for PITI calculation

7. After you define the decision logic for the selected node, click Back to "<NODE_NAME>" to return to the DRD view.

8. For the selected DRD node, use the available connection options to create and connect to the next node in the DRD, or click and drag a new node onto the DRD canvas from the left toolbar.

   The node type determines which connection options are supported. For example, an Input data node can connect to a decision node, knowledge source, or text annotation using the applicable connection type, whereas a Knowledge source node can connect to any DRD element. A Decision node can connect only to another decision or a text annotation.

   The following connection types are available, depending on the node type:

   - Information requirement: Use this connection from an input data node or decision node to another decision node that requires the information.

   - Knowledge requirement: Use this connection from a business knowledge model to a decision node or to another business knowledge model that invokes the decision logic.

   - Authority requirement: Use this connection from an input data node or a decision node to a dependent knowledge source or from a knowledge source to a decision node, business knowledge model, or another knowledge source.

   - Association: Use this connection from an input data node, decision node, business knowledge model, or knowledge source to a text annotation.

   Figure 119. Connecting credit score input to credit score rating decision

9. Continue adding and defining the remaining DRD components of your decision model. Periodically click Save in the DMN designer to save your work.

10. After you add and define all components of the DRD, click Save to save and validate the completed DRD.

    The following is an example DRD for a loan prequalification decision model:

    Figure 120. Completed DRD for loan prequalification

    The following is an example DRD for a phone call handling decision model using a reusable decision service:

    Figure 121. Completed DRD for phone call handling with a decision service

    In a DMN decision service node, the decision nodes in the bottom segment incorporate input data from outside of the decision service to arrive at a final decision in the top segment of the decision service node. The resulting top-level decisions from the decision service are then implemented in any subsequent decisions or business knowledge requirements of the DMN model. You can reuse DMN decision services in other DMN models to apply the same decision logic with different input data and different outgoing connections.

#### 8.3.1. Defining DMN decision logic in boxed expressions in Business Central

Boxed expressions in DMN are tables that you use to define the underlying logic of decision nodes and business knowledge models in a decision requirements diagram (DRD) or decision requirements graph (DRG). Some boxed expressions can contain other boxed expressions, but the top-level boxed expression corresponds to the decision logic of a single DRD artifact. While DRDs with one or more DRGs represent the flow of a DMN decision model, boxed expressions define the actual decision logic of individual nodes. DRDs and boxed expressions together form a complete and functional DMN decision model.

You can use the DMN designer in Business Central to define decision logic for your DRD components using built-in boxed expressions.

Prerequisites

- You have created or imported a DMN file in Business Central.

Procedure

1. In Business Central, go to Menu → Design → Projects, click the project name, and select the DMN file you want to modify.

2. In the DMN designer canvas, select a decision node or business knowledge model that you want to define and click the Edit icon to open the DMN boxed expression designer:

   Figure 122. Opening a new decision node boxed expression
   Figure 123. Opening a new business knowledge model boxed expression

   By default, all business knowledge models are defined as boxed function expressions containing a literal FEEL expression, a nested context expression of an external JAVA or PMML function, or a nested boxed expression of any type.

   For decision nodes, you click the undefined table to select the type of boxed expression you want to use, such as a boxed literal expression, boxed context expression, decision table, or other DMN boxed expression.

   For business knowledge models, you click the top-left function cell to select the function type, or right-click the function value cell, select Clear, and select a boxed expression of another type.

3. For this example, use a decision node and select Decision Table as the boxed expression type.

   A decision table in DMN is a visual representation of one or more rules in a tabular format. Each rule consists of a single row in the table, and includes columns that define the conditions (input) and outcome (output) for that particular row.

4. Click the input column header to define the name and data type for the input condition. For example, name the input column Credit Score.FICO with a `number` data type. This column specifies numeric credit score values or ranges of loan applicants.

5. Click the output column header to define the name and data type for the output values. For example, name the output column Credit Score Rating and next to the Data Type option, click Manage to go to the Data Types page where you can create a custom data type with score ratings as constraints.

6. On the Data Types page, click Add and create a Credit_Score_Rating data type as a `string`.

7. Click Constraints, select Enumeration from the drop-down options, and add the following constraints:

   - `"Excellent"`

   - `"Good"`

   - `"Fair"`

   - `"Poor"`

   - `"Bad"`

   For information about constraint types and syntax requirements for the specified data type, see the Decision Model and Notation specification.

8. Click Ok to save the constraints and click Save to save the data type.

9. Return to the Credit Score Rating decision table, click the Credit Score Rating column header, and set the data type to this new custom data type.

10. Use the Credit Score.FICO input column to define credit score values or ranges of values, and use the Credit Score Rating column to specify one of the corresponding ratings you defined in the Credit_Score_Rating data type.

    Right-click any value cell to insert or delete rows (rules) or columns (clauses).

    Figure 124. Decision node decision table for credit score rating

11. After you define all rules, click the top-left corner of the decision table to define the rule Hit Policy and Builtin Aggregator (for COLLECT hit policy only).

    The hit policy determines how to reach an outcome when multiple rules in a decision table match the provided input values. The built-in aggregator determines how to aggregate rule values when you use the COLLECT hit policy.

    The following example is a more complex decision table that determines applicant qualification for a loan as the concluding decision node in the same loan prequalification decision model:

    Figure 125. Decision table for loan prequalification

For boxed expression types other than decision tables, you follow these guidelines similarly to navigate the boxed expression tables and define variables and parameters for decision logic, but according to the requirements of the boxed expression type. Some boxed expressions, such as boxed literal expressions, can be single-column tables, while other boxed expressions, such as function, context, and invocation expressions, can be multi-column tables with nested boxed expressions of other types.

For example, the following boxed context expression defines the parameters that determine whether a loan applicant can meet minimum mortgage payments based on principal, interest, taxes, and insurance (PITI), represented as a front-end ratio calculation with a sub-context expression:

Figure 126. Boxed context expression for front-end client PITI ratio

The following boxed function expression determines a monthly mortgage installment as a business knowledge model in a lending decision, with the function value defined as a nested context expression:

Figure 127. Boxed function expression for installment calculation in business knowledge model

For more information and examples of each boxed expression type, see DMN decision logic in boxed expressions.

#### 8.3.2. Creating custom data types for DMN boxed expressions in Business Central

In DMN boxed expressions in Business Central, data types determine the structure of the data that you use within an associated table, column, or field in the boxed expression. You can use default DMN data types (such as String, Number, Boolean) or you can create custom data types to specify additional fields and constraints that you want to implement for the boxed expression values.

Custom data types that you create for a boxed expression can be simple or structured:

- Simple data types have only a name and a type assignment. Example: `Age (number)`.

- Structured data types contain multiple fields associated with a parent data type. Example: A single type `Person` containing the fields `Name (string)`, `Age (number)`, `Email (string)`.

Prerequisites

- You have created or imported a DMN file in Business Central.

Procedure

1. In Business Central, go to Menu → Design → Projects, click the project name, and select the DMN file you want to modify.

2. In the DMN designer canvas, select a decision node or business knowledge model for which you want to define the data types and click the Edit icon to open the DMN boxed expression designer.

3. If the boxed expression is for a decision node that is not yet defined, click the undefined table to select the type of boxed expression you want to use, such as a boxed literal expression, boxed context expression, decision table, or other DMN boxed expression.

4. Click the cell for the table header, column header, or parameter field (depending on the boxed expression type) for which you want to define the data type and click Manage to go to the Data Types page where you can create a custom data type.

   You can also set and manage custom output data types for a specified decision node or business knowledge model node by selecting the Diagram properties icon in the upper-right corner of the DMN designer:

   The data type that you define for a specified cell in a boxed expression determines the structure of the data that you use within that associated table, column, or field in the boxed expression.

   In this example, an output column Credit Score Rating for a DMN decision table defines a set of custom credit score ratings based on an applicant’s credit score.

5. On the Data Types page, click Add and create a Credit_Score_Rating data type as a `string`.

   If the data type requires a list of items, enable the List setting.

6. Click Constraints, select Enumeration from the drop-down options, and add the following constraints:

   - `"Excellent"`

   - `"Good"`

   - `"Fair"`

   - `"Poor"`

   - `"Bad"`

   For information about constraint types and syntax requirements for the specified data type, see the Decision Model and Notation specification.

7. Click Ok to save the constraints and click Save to save the data type.

8. Return to the Credit Score Rating decision table, click the Credit Score Rating column header, set the data type to this new custom data type, and define the rule values for that column with the rating constraints that you specified.

   Figure 128. Decision table for credit score rating

   In the DMN decision model for this scenario, the Credit Score Rating decision flows into the following Loan Prequalification decision that also requires custom data types:

9. Continuing with this example, return to the Data Types window, click Add, and create a Loan_Qualification data type as a `Structure` with no constraints.

10. Next to the Loan_Qualification data type, select the settings icon (three vertical dots) and select Insert nested field to insert sub-fields within this parent data type.

    You can use these sub-fields in association with the parent structured data type in boxed expressions, such as nested column headers in decision tables or nested table parameters in context or function expressions.

11. For this example, under the structured Loan_Qualification data type, add a Qualification field with `"Qualified"` and `"Not Qualified"` enumeration constraints, and a Reason field with no constraints. Add also a simple Back_End_Ratio and a Front_End_Ratio data type, both with `"Sufficient"` and `"Insufficient"` enumeration constraints.

    Click Save for each data type that you create.

12. Return to the decision table and, for each column, click the column header cell, set the data type to the new corresponding custom data type, and define the rule values as needed for the column with the constraints that you specified, if applicable.

    Figure 129. Decision table for loan prequalification

For boxed expression types other than decision tables, you follow these guidelines similarly to navigate the boxed expression tables and define custom data types as needed.

For example, the following boxed function expression uses custom `tCandidate` and `tProfile` structured data types to associate data for online dating compatibility:

Figure 130. Boxed function expression for online dating compatibility
Figure 131. Custom data type definitions for online dating compatibility
Figure 132. Parameter definitions with custom data types for online dating compatibility

#### 8.3.3. DMN designer navigation and properties in Business Central

The DMN designer provides the following additional features to help you navigate through the components and properties of decision requirements diagrams (DRDs).

DMN file and diagram views

  In the upper-left corner of the DMN designer, select the Project Explorer view to navigate between all DMN and other files or select the Decision Navigator view to navigate between the nodes and boxed expressions of a selected DRD:

  Figure 133. Project Explorer view
  Figure 134. Decision Navigator view

  In the upper-right corner of the DMN designer, select the Explore diagram icon to view an elevated preview of the selected DRD and to navigate between the nodes of the selected DRD:

  Figure 135. Explore diagram view
DRD properties and design

  In the upper-right corner of the DMN designer, select the Diagram properties icon to modify the identifying information, data types, and appearance of a selected DRD, DRD node, or boxed expression cell:

  Figure 136. DRD node properties

  To view the properties of the entire DRD, click the DRD canvas background instead of a specific node.

### 8.4. DMN model execution

You can create or import DMN files in your Drools project using Business Central or package the DMN files as part of your project knowledge JAR (KJAR) file without Business Central. After you implement your DMN files in your Drools project, you can execute the DMN decision service by deploying the KIE container that contains it to KIE Server for remote access or by manipulating the KIE container directly as a dependency of the calling application. Other options for creating and deploying DMN knowledge packages are also available, and most are similar for all types of knowledge assets, such as DRL files or process definitions.

For information about including external DMN assets with your project packaging and deployment method, see Build, Deploy, Utilize and Run.

#### 8.4.1. Embedding a DMN call directly in a Java application

A KIE container is local when the knowledge assets are either embedded directly into the calling program or are physically pulled in using Maven dependencies for the KJAR. You typically embed knowledge assets directly into a project if there is a tight relationship between the version of the code and the version of the DMN definition. Any changes to the decision take effect after you have intentionally updated and redeployed the application. A benefit of this approach is that proper operation does not rely on any external dependencies to the run time, which can be a limitation of locked-down environments.

Using Maven dependencies enables further flexibility because the specific version of the decision can dynamically change, (for example, by using a system property), and it can be periodically scanned for updates and automatically updated. This introduces an external dependency on the deploy time of the service, but executes the decision locally, reducing reliance on an external service being available during run time.

Prerequisites

- A KIE container is deployed in KIE Server in the form of a KJAR that includes the DMN model, ideally compiled as an executable model for more efficient execution:

      mvn clean install -DgenerateDMNModel=yes

  For more information about project packaging and deployment and executable models, see Build, Deploy, Utilize and Run.

Procedure

1. In your client application, add the following dependencies to the relevant classpath of your Java project:

       // Required for the DMN runtime API
       <dependency>
         <groupId>org.kie</groupId>
         <artifactId>kie-dmn-core</artifactId>
         <version>${drools.version}</version>
       </dependency>

       // Required if not using classpath KIE container
       <dependency>
         <groupId>org.kie</groupId>
         <artifactId>kie-ci</artifactId>
         <version>${drools.version}</version>
       </dependency>

   The `<version>` is the Maven artifact version for Drools currently used in your project (for example, 7.18.0.Final-redhat-00003).

2. Create a KIE container from `classpath` or `ReleaseId`:

       KieServices kieServices = KieServices.Factory.get();

       ReleaseId releaseId = kieServices.newReleaseId( "org.acme", "my-kjar", "1.0.0" );
       KieContainer kieContainer = kieServices.newKieContainer( releaseId );

   Alternative option:

       KieServices kieServices = KieServices.Factory.get();

       KieContainer kieContainer = kieServices.getKieClasspathContainer();

3. Obtain `DMNRuntime` from the KIE container and a reference to the DMN model to be evaluated, by using the model `namespace` and `modelName`:

       DMNRuntime dmnRuntime = KieRuntimeFactory.of(kieContainer.getKieBase()).get(DMNRuntime.class);

       String namespace = "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a";
       String modelName = "dmn-movieticket-ageclassification";

       DMNModel dmnModel = dmnRuntime.getModel(namespace, modelName);

4. Execute the decision services for the desired model:

       DMNContext dmnContext = dmnRuntime.newContext();  (1)

       for (Integer age : Arrays.asList(1,12,13,64,65,66)) {
           dmnContext.set("Age", age);  (2)
           DMNResult dmnResult =
               dmnRuntime.evaluateAll(dmnModel, dmnContext);  (3)

           for (DMNDecisionResult dr : dmnResult.getDecisionResults()) {  (4)
               log.info("Age: " + age + ", " +
                        "Decision: '" + dr.getDecisionName() + "', " +
                        "Result: " + dr.getResult());
         }
       }

   1 | Instantiate a new DMN Context to be the input for the model evaluation. Note that this example is looping through the Age Classification decision multiple times.
   2 | Assign input variables for the input DMN context.
   3 | Evaluate all DMN decisions defined in the DMN model.
   4 | Each evaluation may result in one or more results, creating the loop.

   This example prints the following output:

       Age 1 Decision 'AgeClassification' : Child
       Age 12 Decision 'AgeClassification' : Child
       Age 13 Decision 'AgeClassification' : Adult
       Age 64 Decision 'AgeClassification' : Adult
       Age 65 Decision 'AgeClassification' : Senior
       Age 66 Decision 'AgeClassification' : Senior

   If the DMN model was not previously compiled as an executable model for more efficient execution, you can enable the following property when you execute your DMN models:

       -Dorg.kie.dmn.compiler.execmodel=true

#### 8.4.2. Executing a DMN service using the KIE Server Java client API

The KIE Server Java client API provides a lightweight approach to invoking a remote DMN service either through the REST or JMS interfaces of KIE Server. This approach reduces the number of runtime dependencies necessary to interact with a KIE base. Decoupling the calling code from the decision definition also increases flexibility by enabling them to iterate independently at the appropriate pace.

For more information about the KIE Server Java client API, see [kie-server-java-api-con_kie-apis].

Prerequisites

- KIE Server is installed and configured, including a known user name and credentials for a user with the `kie-server` role. For installation options, see Installation and Setup (Core and IDE).

- A KIE container is deployed in KIE Server in the form of a KJAR that includes the DMN model, ideally compiled as an executable model for more efficient execution:

      mvn clean install -DgenerateDMNModel=yes

  For more information about project packaging and deployment and executable models, see Build, Deploy, Utilize and Run.

- You have the container ID of the KIE container containing the DMN model. If more than one model is present, you must also know the model namespace and model name of the relevant model.

Procedure

1. In your client application, add the following dependencies to the relevant classpath of your Java project:

       <dependency>
         <groupId>org.kie.server</groupId>
         <artifactId>kie-server-client</artifactId>
         <version>${drools.version}</version>
       </dependency>

   The `<version>` is the Maven artifact version for Drools currently used in your project (for example, 7.18.0.Final-redhat-00003).

2. Instantiate a `KieServicesClient` instance with the appropriate connection information.

   Example:

       KieServicesConfiguration conf =
           KieServicesFactory.newRestConfiguration(URL, USER, PASSWORD); (1)

       conf.setMarshallingFormat(MarshallingFormat.JSON);  (2)

       KieServicesClient kieServicesClient = KieServicesFactory.newKieServicesClient(conf);

   1 | The connection information: Example URL: `http://localhost:8080/kie-server/services/rest/server` The credentials should reference a user with the `kie-server` role.
   2 | The Marshalling format is an instance of `org.kie.server.api.marshalling.MarshallingFormat`. It controls whether the messages will be JSON or XML. Options for Marshalling format are JSON, JAXB, or XSTREAM.

3. Obtain a `DMNServicesClient` from the KIE server Java client connected to the related KIE Server by invoking the method `getServicesClient()` on the KIE server Java client instance:

       DMNServicesClient dmnClient = kieServicesClient.getServicesClient(DMNServicesClient.class );

   The `dmnClient` can now execute decision services on KIE Server.

4. Execute the decision services for the desired model.

   Example:

       for (Integer age : Arrays.asList(1,12,13,64,65,66)) {
           DMNContext dmnContext = dmnClient.newContext(); (1)
           dmnContext.set("Age", age);  (2)
           ServiceResponse<DMNResult> serverResp =   (3)
               dmnClient.evaluateAll($kieContainerId,
                                     $modelNamespace,
                                     $modelName,
                                     dmnContext);

           DMNResult dmnResult = serverResp.getResult();  (4)
           for (DMNDecisionResult dr : dmnResult.getDecisionResults()) {
               log.info("Age: " + age + ", " +
                        "Decision: '" + dr.getDecisionName() + "', " +
                        "Result: " + dr.getResult());
           }
       }

   1 | Instantiate a new DMN Context to be the input for the model evaluation. Note that this example is looping through the Age Classification decision multiple times.
   2 | Assign input variables for the input DMN Context.
   3 | Evaluate all the DMN Decisions defined in the DMN model: `$kieContainerId` is the ID of the container where the KJAR containing the DMN model is deployed `$modelNamespace` is the namespace for the model. `$modelName` is the name for the model.
   4 | The DMN Result object is available from the server response.

   At this point, the `dmnResult` contains all the decision results from the evaluated DMN model.

   You can also execute only a specific DMN decision in the model by using alternative methods of the `DMNServicesClient`.

   | If the KIE container only contains one DMN model, you can omit `$modelNamespace` and `$modelName` because the KIE Server API selects it by default.

#### 8.4.3. Executing a DMN service using the KIE Server REST API

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.

For more information about the KIE Server REST API, see [kie-server-rest-api-con_kie-apis].

Prerequisites

- KIE Server is installed and configured, including a known user name and credentials for a user with the `kie-server` role. For installation options, see Installation and Setup (Core and IDE).

- A KIE container is deployed in KIE Server in the form of a KJAR that includes the DMN model, ideally compiled as an executable model for more efficient execution:

      mvn clean install -DgenerateDMNModel=yes

  For more information about project packaging and deployment and executable models, see Build, Deploy, Utilize and Run.

- You have the container ID of the KIE container containing the DMN model. If more than one model is present, you must also know the model namespace and model name of the relevant model.

Procedure

1. Determine the base URL for accessing the KIE Server REST API endpoints. This requires knowing the following values (with the default local deployment values as an example):

   - Host (`localhost`)

   - Port (`8080`)

   - Root context (`kie-server`)

   - Base REST path (`services/rest/`)

   Example base URL in local deployment:

   `http://localhost:8080/kie-server/services/rest/`

2. Determine user authentication requirements.

   When users are defined directly in the KIE Server configuration, HTTP Basic authentication is used and requires the user name and password. Successful requests require that the user have the `kie-server` role.

   The following example demonstrates how to add credentials to a curl request:

       curl -u username:password <request>

   If KIE Server is configured with Red Hat Single Sign-On, the request must include a bearer token:

       curl -H "Authorization: bearer $TOKEN" <request>

3. Specify the format of the request and response. The REST API endpoints work with both JSON and XML formats and are set using request headers:

   JSON

       curl -H "accept: application/json" -H "content-type: application/json"

   XML

       curl -H "accept: application/xml" -H "content-type: application/xml"

4. (Optional) Query the container for a list of deployed decision models:

   [GET] `server/containers/{containerId}/dmn`

   Example curl request:

       curl -u krisv:krisv -H "accept: application/xml" -X GET "http://localhost:8080/kie-server/services/rest/server/containers/MovieDMNContainer/dmn"

   Sample XML output:

       <?xml version="1.0" encoding="UTF-8" standalone="yes"?>
       <response type="SUCCESS" msg="OK models successfully retrieved from container 'MovieDMNContainer'">
           <dmn-model-info-list>
               <model>
                   <model-namespace>http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a</model-namespace>
                   <model-name>dmn-movieticket-ageclassification</model-name>
                   <model-id>_99</model-id>
                   <decisions>
                       <dmn-decision-info>
                           <decision-id>_3</decision-id>
                           <decision-name>AgeClassification</decision-name>
                       </dmn-decision-info>
                   </decisions>
               </model>
           </dmn-model-info-list>
       </response>

   Sample JSON output:

       {
         "type" : "SUCCESS",
         "msg" : "OK models successfully retrieved from container 'MovieDMNContainer'",
         "result" : {
           "dmn-model-info-list" : {
             "models" : [ {
               "model-namespace" : "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a",
               "model-name" : "dmn-movieticket-ageclassification",
               "model-id" : "_99",
               "decisions" : [ {
                 "decision-id" : "_3",
                 "decision-name" : "AgeClassification"
               } ]
             } ]
           }
         }
       }

5. Execute the model:

   [POST] `server/containers/{containerId}/dmn`

   Example curl request:

       curl -u krisv:krisv -H "accept: application/json" -H "content-type: application/json" -X POST "http://localhost:8080/kie-server/services/rest/server/containers/MovieDMNContainer/dmn" -d "{ \"model-namespace\" : \"http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a\", \"model-name\" : \"dmn-movieticket-ageclassification\", \"decision-name\" : [ ], \"decision-id\" : [ ], \"dmn-context\" : {\"Age\" : 66}}"

   Example JSON request:

       {
         "model-namespace" : "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a",
         "model-name" : "dmn-movieticket-ageclassification",
         "decision-name" : [ ],
         "decision-id" : [ ],
         "dmn-context" : {"Age" : 66}
       }

   Example XML request (JAXB format):

       <?xml version="1.0" encoding="UTF-8"?>
       <dmn-evaluation-context>
           <model-namespace>http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a</model-namespace>
           <model-name>dmn-movieticket-ageclassification</model-name>
           <dmn-context xsi:type="jaxbListWrapper" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
               <type>MAP</type>
               <element xsi:type="jaxbStringObjectPair" key="Age">
                   <value xsi:type="xs:int" xmlns:xs="http://www.w3.org/2001/XMLSchema">66</value>
               </element>
           </dmn-context>
       </dmn-evaluation-context>

   | Regardless of the request format, the request requires the following elements: Model namespace Model name Context object containing input values

   Example JSON response:

       {
         "type" : "SUCCESS",
         "msg" : "OK from container 'MovieDMNContainer'",
         "result" : {
           "dmn-evaluation-result" : {
             "messages" : [ ],
             "model-namespace" : "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a",
             "model-name" : "dmn-movieticket-ageclassification",
             "decision-name" : [ ],
             "dmn-context" : {
               "Age" : 66,
               "AgeClassification" : "Senior"
             },
             "decision-results" : {
               "_3" : {
                 "messages" : [ ],
                 "decision-id" : "_3",
                 "decision-name" : "AgeClassification",
                 "result" : "Senior",
                 "status" : "SUCCEEDED"
               }
             }
           }
         }
       }

   Example XML (JAXB format) response:

       <?xml version="1.0" encoding="UTF-8" standalone="yes"?>
       <response type="SUCCESS" msg="OK from container 'MovieDMNContainer'">
             <dmn-evaluation-result>
                   <model-namespace>http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a</model-namespace>
                   <model-name>dmn-movieticket-ageclassification</model-name>
                   <dmn-context xsi:type="jaxbListWrapper" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
                         <type>MAP</type>
                         <element xsi:type="jaxbStringObjectPair" key="Age">
                               <value xsi:type="xs:int" xmlns:xs="http://www.w3.org/2001/XMLSchema">66</value>
                         </element>
                         <element xsi:type="jaxbStringObjectPair" key="AgeClassification">
                               <value xsi:type="xs:string" xmlns:xs="http://www.w3.org/2001/XMLSchema">Senior</value>
                         </element>
                   </dmn-context>
                   <messages/>
                   <decisionResults>
                         <entry>
                               <key>_3</key>
                               <value>
                                     <decision-id>_3</decision-id>
                                     <decision-name>AgeClassification</decision-name>
                                     <result xsi:type="xs:string" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Senior</result>
                                     <messages/>
                                     <status>SUCCEEDED</status>
                               </value>
                         </entry>
                   </decisionResults>
             </dmn-evaluation-result>
       </response>