Developing decision services in Red Hat Decision Manager

Red Hat Decision Manager 7.11

Abstract

This document describes how to develop decision services with Red Hat Decision Manager using Decision Model and Notation (DMN) models, Drools Rule Language (DRL) files, guided decision tables, and other decision-authoring assets.

Preface

As a developer of business decisions, you can use Red Hat Decision Manager to develop decision services using Decision Model and Notation (DMN) models, Drools Rule Language (DRL) rules, guided decision tables, and other rule-authoring assets.

Making open source more inclusive

Red Hat is committed to replacing problematic language in our code, documentation, and web properties. We are beginning with these four terms: master, slave, blacklist, and whitelist. Because of the enormity of this endeavor, these changes will be implemented gradually over several upcoming releases. For more details, see our CTO Chris Wright’s message.

Part I. Designing a decision service using DMN models

As a business analyst or business rules developer, you can use Decision Model and Notation (DMN) to model a decision service graphically. The decision requirements of a DMN decision model are determined by a decision requirements graph (DRG) that is depicted in one or more decision requirements diagrams (DRDs). A DRD can represent part or all of the overall DRG for the DMN model. DRDs trace business decisions from start to finish, with each decision node using logic defined in DMN boxed expressions such as decision tables.

Red Hat Decision Manager provides design and runtime support for DMN 1.2 models at conformance level 3, and runtime-only support for DMN 1.1 and 1.3 models at conformance level 3. You can design your DMN models directly in Business Central or with the Red Hat Decision Manager DMN modeler in VSCode, or import existing DMN models into your Red Hat Decision Manager projects for deployment and execution. Any DMN 1.1 and 1.3 models (do not contain DMN 1.3 features) that you import into Business Central, open in the DMN designer, and save are converted to DMN 1.2 models.

For more information about DMN, see the Object Management Group (OMG) Decision Model and Notation specification.

For a step-by-step tutorial with an example DMN decision service, see Getting started with decision services.

Chapter 1. Decision-authoring assets in Red Hat Decision Manager

Red Hat Decision Manager 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 Red Hat Decision Manager projects to help you decide or confirm the best method for defining decisions in your decision service.

Table 1.1. Decision-authoring assets supported in Red Hat Decision Manager

AssetHighlightsAuthoring toolsDocumentation

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) that represent part or all of the overall decision requirements graph (DRG) 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

Designing a decision service using DMN models

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

Designing a decision service using guided decision tables

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

Designing a decision service using spreadsheet 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

Designing a decision service using guided rules

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

Designing a decision service using guided rule templates

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 Red Hat Decision Manager
  • 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)

Designing a decision service using DRL rules

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 Red Hat Decision Manager project or imported into a project in Business Central
  • Are optimal for incorporating predictive data into decision services in Red Hat Decision Manager

PMML or XML editor

Designing a decision service using PMML models

Chapter 2. Red Hat Decision Manager BPMN and DMN modelers

Red Hat Decision Manager provides the following extensions or applications that you can use to design Business Process Model and Notation (BPMN) process models and Decision Model and Notation (DMN) decision models using graphical modelers.

  • Business Central: Enables you to view and design BPMN models, DMN models, and test scenario files in a related embedded designer.

    To use Business Central, you can set up a development environment containing a Business Central to design business rules and processes, and a KIE Server to execute and test the created business rules and processes.

  • Red Hat Decision Manager VSCode extension: Enables you to view and design BPMN models, DMN models, and test scenario files in Visual Studio Code (VSCode). The VSCode extension requires VSCode 1.46.0 or later.

    To install the Red Hat Decision Manager VSCode extension, select the Extensions menu option in VSCode and search for and install the Red Hat Business Automation Bundle extension.

  • Standalone BPMN and DMN editors: Enable you to view and design BPMN and DMN models embedded in your web applications. To download the necessary files, you can either use the NPM artifacts from the NPM registry or download the JavaScript files directly for the DMN standalone editor library at https://<YOUR_PAGE>/dmn/index.js and for the BPMN standalone editor library at https://<YOUR_PAGE>/bpmn/index.js.

2.1. Installing the Red Hat Decision Manager VSCode extension bundle

Red Hat Decision Manager provides a Red Hat Business Automation Bundle VSCode extension that enables you to design Decision Model and Notation (DMN) decision models, Business Process Model and Notation (BPMN) 2.0 business processes, and test scenarios directly in VSCode. VSCode is the preferred integrated development environment (IDE) for developing new business applications. Red Hat Decision Manager also provides individual DMN Editor and BPMN Editor VSCode extensions for DMN or BPMN support only, if needed.

Important

The editors in the VSCode are partially compatible with the editors in the Business Central, and several Business Central features are not supported in the VSCode.

Prerequisites

  • The latest stable version of VSCode is installed.

Procedure

  1. In your VSCode IDE, select the Extensions menu option and search for Red Hat Business Automation Bundle for DMN, BPMN, and test scenario file support.

    For DMN or BPMN file support only, you can also search for the individual DMN Editor or BPMN Editor extensions.

  2. When the Red Hat Business Automation Bundle extension appears in VSCode, select it and click Install.
  3. For optimal VSCode editor behavior, after the extension installation is complete, reload or close and re-launch your instance of VSCode.

After you install the VSCode extension bundle, any .dmn, .bpmn, or .bpmn2 files that you open or create in VSCode are automatically displayed as graphical models. Additionally, any .scesim files that you open or create are automatically displayed as tabular test scenario models for testing the functionality of your business decisions.

If the DMN, BPMN, or test scenario modelers open only the XML source of a DMN, BPMN, or test scenario file and displays an error message, review the reported errors and the model file to ensure that all elements are correctly defined.

Note

For new DMN or BPMN models, you can also enter dmn.new or bpmn.new in a web browser to design your DMN or BPMN model in the online modeler. When you finish creating your model, you can click Download in the online modeler page to import your DMN or BPMN file into your Red Hat Decision Manager project in VSCode.

2.2. Configuring the Red Hat Decision Manager standalone editors

Red Hat Decision Manager provides standalone editors that are distributed in a self-contained library providing an all-in-one JavaScript file for each editor. The JavaScript file uses a comprehensive API to set and control the editor.

You can install the standalone editors in three ways:

  • Download each JavaScript file manually
  • Use the NPM package

Procedure

  1. Install the standalone editors using one of the following methods:

    Download each JavaScript file manually: For this method, follow these steps:

    1. Download the JavaScript files.
    2. Add the downloaded Javascript files to your hosted application.
    3. Add the following <script> tag to your HTML page:

      Script tag for your HTML page for the DMN editor

      <script src="https://<YOUR_PAGE>/dmn/index.js"></script>

      Script tag for your HTML page for the BPMN editor

      <script src="https://<YOUR_PAGE>/bpmn/index.js"></script>

    Use the NPM package: For this method, follow these steps:

    1. Add the NPM package to your package.json file:

      Adding the NPM package

      npm install @redhat/kogito-tooling-kie-editors-standalone

    2. Import each editor library to your TypeScript file:

      Importing each editor

      import * as DmnEditor from "@redhat/kogito-tooling-kie-editors-standalone/dist/dmn"
      import * as BpmnEditor from "@redhat/kogito-tooling-kie-editors-standalone/dist/bpmn"

  2. After you install the standalone editors, open the required editor by using the provided editor API, as shown in the following example for opening a DMN editor. The API is the same for each editor.

    Opening the DMN standalone editor

    const editor = DmnEditor.open({
      container: document.getElementById("dmn-editor-container"),
      initialContent: Promise.resolve(""),
      readOnly: false,
      origin: "",
      resources: new Map([
        [
          "MyIncludedModel.dmn",
          {
            contentType: "text",
            content: Promise.resolve("")
          }
        ]
      ])
    });

    Use the following parameters with the editor API:

    Table 2.1. Example parameters

    ParameterDescription

    container

    HTML element in which the editor is appended.

    initialContent

    Promise to a DMN model content. This parameter can be empty, as shown in the following examples:

    • Promise.resolve("")
    • Promise.resolve("<DIAGRAM_CONTENT_DIRECTLY_HERE>")
    • fetch("MyDmnModel.dmn").then(content ⇒ content.text())

    readOnly (Optional)

    Enables you to allow changes in the editor. Set to false (default) to allow content editing and true for read-only mode in editor.

    origin (Optional)

    Origin of the repository. The default value is window.location.origin.

    resources (Optional)

    Map of resources for the editor. For example, this parameter is used to provide included models for the DMN editor or work item definitions for the BPMN editor. Each entry in the map contains a resource name and an object that consists of content-type (text or binary) and content (similar to the initialContent parameter).

    The returned object contains the methods that are required to manipulate the editor.

    Table 2.2. Returned object methods

    MethodDescription

    getContent(): Promise<string>

    Returns a promise containing the editor content.

    setContent(content: string): void

    Sets the content of the editor.

    getPreview(): Promise<string>

    Returns a promise containing an SVG string of the current diagram.

    subscribeToContentChanges(callback: (isDirty: boolean) ⇒ void): (isDirty: boolean) ⇒ void

    Sets a callback to be called when the content changes in the editor and returns the same callback to be used for unsubscription.

    unsubscribeToContentChanges(callback: (isDirty: boolean) ⇒ void): void

    Unsubscribes the passed callback when the content changes in the editor.

    markAsSaved(): void

    Resets the editor state that indicates that the content in the editor is saved. Also, it activates the subscribed callbacks related to content change.

    undo(): void

    Undoes the last change in the editor. Also, it activates the subscribed callbacks related to content change.

    redo(): void

    Redoes the last undone change in the editor. Also, it activates the subscribed callbacks related to content change.

    close(): void

    Closes the editor.

    getElementPosition(selector: string): Promise<Rect>

    Provides an alternative to extend the standard query selector when an element lives inside a canvas or a video component. The selector parameter must follow the <PROVIDER>:::<SELECT> format, such as Canvas:::MySquare or Video:::PresenterHand. This method returns a Rect representing the element position.

    envelopeApi: MessageBusClientApi<KogitoEditorEnvelopeApi>

    This is an advanced editor API. For more information about advanced editor API, see MessageBusClientApi and KogitoEditorEnvelopeApi.

Chapter 3. Creating and executing DMN and BPMN models using Maven

You can use Maven archetypes to develop DMN and BPMN models in VSCode using the Red Hat Decision Manager VSCode extension instead of Business Central. You can then integrate your archetypes with your Red Hat Decision Manager decision and process services in Business Central as needed. This method of developing DMN and BPMN models is helpful for building new business applications using the Red Hat Decision Manager VSCode extension.

Procedure

  1. In a command terminal, navigate to a local folder where you want to store the new Red Hat Decision Manager project.
  2. Enter the following command to generate a project within a defined folder using the following Maven archetype:

    Generating a project using Maven archetype

    mvn archetype:generate \
        -DarchetypeGroupId=org.kie \
        -DarchetypeArtifactId=kie-kjar-archetype \
        -DarchetypeVersion=7.52.0.Final-redhat-00007

    This command generates a Maven project with required dependencies and generates required directories and files to build your business application. You can set up and use Git version-control system (recommended) when developing a project.

    If you want to generate multiple projects in the same directory, you can specify the artifactId and groupId of the generated business application by adding -DgroupId=<groupid> -DartifactId=<artifactId> to the previous command.

  3. In your VSCode IDE, click File, select Open Folder, and navigate to the folder that is generated using the previous command.
  4. Before creating the first asset, set a package for your business application, for example, org.kie.businessapp, and create respective directories in the following paths:

    • PROJECT_HOME/src/main/java
    • PROJECT_HOME/src/main/resources
    • PROJECT_HOME/src/test/resources

    For example, you can create PROJECT_HOME/src/main/java/org/kie/businessapp for org.kie.businessapp package.

  5. Use VSCode to create assets for your business application. You can create the assets supported by Red Hat Decision Manager VSCode extension using the following ways:

    • To create a business process, create a new file with .bpmn or .bpmn2 in PROJECT_HOME/src/main/java/org/kie/businessapp directory, such as Process.bpmn.
    • To create a DMN model, create a new file with .dmn in PROJECT_HOME/src/main/java/org/kie/businessapp directory, such as AgeDecision.dmn.
    • To create a test scenario simulation model, create a new file with .scesim in PROJECT_HOME/src/main/java/org/kie/businessapp directory, such as TestAgeScenario.scesim.
  6. After you create the assets in your Maven archetype, navigate to the root directory (contains pom.xml) of the project in the command line and run the following command to build the knowledge JAR (KJAR) of your project:

    mvn clean install

    If the build fails, address any problems described in the command line error messages and try again to validate the project until the build is successful. However, if the build is successful, you can find the artifact of your business application in PROJECT_HOME/target directory.

    Note

    Use mvn clean install command often to validate your project after each major change during development.

You can deploy the generated knowledge JAR (KJAR) of your business application on a running KIE Server using the REST API. For more information about using REST API, see Interacting with Red Hat Decision Manager using KIE APIs.

Chapter 4. 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.

4.1. 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 list summarizes 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.

Red Hat Decision Manager provides design and runtime support for DMN 1.2 models at conformance level 3, and runtime-only support for DMN 1.1 and 1.3 models at conformance level 3. You can design your DMN models directly in Business Central or with the Red Hat Decision Manager DMN modeler in VSCode, or import existing DMN models into your Red Hat Decision Manager projects for deployment and execution. Any DMN 1.1 and 1.3 models (do not contain DMN 1.3 features) that you import into Business Central, open in the DMN designer, and save are converted to DMN 1.2 models.

4.2. DMN decision requirements diagram (DRD) components

A decision requirements diagram (DRD) is a visual representation of your DMN model. A DRD can represent part or all of the overall decision requirements graph (DRG) for the DMN model. DRDs 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 4.1. DRD components

ComponentDescriptionNotation

Elements

Decision

Node where one or more input elements determine an output based on defined decision logic.

dmn decision node

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.

dmn bkm node

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.

dmn knowledge source node

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.

dmn input data node

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.

dmn decision service node

Requirement connectors

Information requirement

Connection from an input data node or decision node to another decision node that requires the information.

dmn info connector

Knowledge requirement

Connection from a business knowledge model to a decision node or to another business knowledge model that invokes the decision logic.

dmn knowledge connector

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.

dmn authority connector

Artifacts

Text annotation

Explanatory note associated with an input data node, decision node, business knowledge model, or knowledge source.

dmn annotation node

Association

Connection from an input data node, decision node, business knowledge model, or knowledge source to a text annotation.

dmn association connector

The following table summarizes the permitted connectors between DRD elements:

Table 4.2. DRD connector rules

Starts fromConnects toConnection typeExample

Decision

Decision

Information requirement

dmn decision to decision

Business knowledge model

Decision

Knowledge requirement

dmn bkm to decision

Business knowledge model

dmn bkm to bkm

Decision service

Decision

Knowledge requirement

dmn decision service to decision

Business knowledge model

dmn decision service to bkm

Input data

Decision

Information requirement

dmn input to decision

Knowledge source

Authority requirement

dmn input to knowledge source

Knowledge source

Decision

Authority requirement

dmn knowledge source to decision

Business knowledge model

dmn knowledge source to bkm

Knowledge source

dmn knowledge source to knowledge source

Decision

Text annotation

Association

dmn decision to annotation

Business knowledge model

dmn bkm to annotation

Knowledge source

dmn knowledge source to annotation

Input data

dmn input to annotation

The following example DRD illustrates some of these DMN components in practice:

Figure 4.1. Example DRD: Loan prequalification

dmn example drd

The following example DRD illustrates DMN components that are part of a reusable decision service:

Figure 4.2. Example DRD: Phone call handling as a decision service

dmn example drd3

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.

4.3. 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.

4.3.1. 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
Note

The DMN specification currently does not provide an explicit way of declaring a variable as a function, context, range, or list, but Red Hat Decision Manager extends the DMN built-in types to support variables of these types.

The following list describes 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.

Red Hat Decision Manager 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.

Important

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" )
Important

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" )
Important

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 Red Hat Decision Manager 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 Red Hat Decision Manager 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 Red Hat Decision Manager 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 Red Hat Decision Manager 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 Red Hat Decision Manager 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]

Elements in a list can also be counted by the function count, which uses the list of elements as the parameter.

For example, the following expression returns 4:

count([ 2, 3, 4, 5 ])

4.3.2. Built-in functions in FEEL

To promote interoperability with other platforms and systems, Friendly Enough Expression Language (FEEL) includes a library of built-in functions. The built-in FEEL functions are implemented in the Drools Decision Model and Notation (DMN) engine so that you can use the functions in your DMN decision services.

The following sections describe each built-in FEEL function, listed in the format NAME( PARAMETERS ). For more information about FEEL functions in DMN, see the OMG Decision Model and Notation specification.

4.3.2.1. Conversion functions

The following functions support conversion between values of different types. Some of these functions use specific string formats, such as the following examples:

  • date string: Follows the format defined in the XML Schema Part 2: Datatypes document, such as 2020-06-01
  • time string: Follows one of the following formats:

    • Format defined in the XML Schema Part 2: Datatypes document, such as 23:59:00z
    • Format for a local time defined by ISO 8601 followed by @ and an IANA Timezone, such as 00:01:00@Etc/UTC
  • date time string: Follows the format of a date string followed by T and a time string, such as 2012-12-25T11:00:00Z
  • duration string: Follows the format of days and time duration and years and months duration defined in the XQuery 1.0 and XPath 2.0 Data Model, such as P1Y2M
date( from ) - using date

Converts from to a date value.

Table 4.3. Parameters

ParameterTypeFormat

from

string

date string

Example

date( "2012-12-25" ) - date( "2012-12-24" ) = duration( "P1D" )

date( from ) - using date and time

Converts from to a date value and sets time components to null.

Table 4.4. Parameters

ParameterType

from

date and time

Example

date(date and time( "2012-12-25T11:00:00Z" )) = date( "2012-12-25" )

date( year, month, day )

Produces a date from the specified year, month, and day values.

Table 4.5. Parameters

ParameterType

year

number

month

number

day

number

Example

date( 2012, 12, 25 ) = date( "2012-12-25" )

date and time( date, time )

Produces a date and time from the specified date and ignores any time components and the specified time.

Table 4.6. Parameters

ParameterType

date

date or date and time

time

time

Example

date and time ( "2012-12-24T23:59:00" ) = date and time(date( "2012-12-24" ), time( "23:59:00" ))

date and time( from )

Produces a date and time from the specified string.

Table 4.7. Parameters

ParameterTypeFormat

from

string

date time string

Example

date and time( "2012-12-24T23:59:00" ) + duration( "PT1M" ) = date and time( "2012-12-25T00:00:00" )

time( from )

Produces a time from the specified string.

Table 4.8. Parameters

ParameterTypeFormat

from

string

time string

Example

time( "23:59:00z" ) + duration( "PT2M" ) = time( "00:01:00@Etc/UTC" )

time( from )

Produces a time from the specified parameter and ignores any date components.

Table 4.9. Parameters

ParameterType

from

time or date and time

Example

time(date and time( "2012-12-25T11:00:00Z" )) = time( "11:00:00Z" )

time( hour, minute, second, offset? )

Produces a time from the specified hour, minute, and second component values.

Table 4.10. Parameters

ParameterType

hour

number

minute

number

second

number

offset (Optional)

days and time duration or null

Example

time( "23:59:00z" ) = time(23, 59, 0, duration( "PT0H" ))

number( from, grouping separator, decimal separator )

Converts from to a number using the specified separators.

Table 4.11. Parameters

ParameterType

from

string representing a valid number

grouping separator

Space ( ), comma (,), period (.), or null

decimal separator

Same types as grouping separator, but the values cannot match

Example

number( "1 000,0", " ", "," ) = number( "1,000.0", ",", "." )

string( from )

Provides a string representation of the specified parameter.

Table 4.12. Parameters

ParameterType

from

Non-null value

Examples

string( 1.1 ) = "1.1"
string( null ) = null

duration( from )

Converts from to a days and time duration value or years and months duration value.

Table 4.13. Parameters

ParameterTypeFormat

from

string

duration string

Examples

date and time( "2012-12-24T23:59:00" ) - date and time( "2012-12-22T03:45:00" ) = duration( "P2DT20H14M" )
duration( "P2Y2M" ) = duration( "P26M" )

years and months duration( from, to )

Calculates the years and months duration between the two specified parameters.

Table 4.14. Parameters

ParameterType

from

date or date and time

to

date or date and time

Example

years and months duration( date( "2011-12-22" ), date( "2013-08-24" ) ) = duration( "P1Y8M" )

4.3.2.2. Boolean functions

The following functions support Boolean operations.

not( negand )

Performs the logical negation of the negand operand.

Table 4.15. Parameters

ParameterType

negand

boolean

Examples

not( true ) = false
not( null ) = null

4.3.2.3. String functions

The following functions support string operations.

Note

In FEEL, Unicode characters are counted based on their code points.

substring( string, start position, length? )

Returns the substring from the start position for the specified length. The first character is at position value 1.

Table 4.16. Parameters

ParameterType

string

string

start position

number

length (Optional)

number

Examples

substring( "testing",3 ) = "sting"
substring( "testing",3,3 ) = "sti"
substring( "testing", -2, 1 ) = "n"
substring( "\U01F40Eab", 2 ) = "ab"

Note

In FEEL, the string literal "\U01F40Eab" is the 🐎ab string (horse symbol followed by a and b).

string length( string )

Calculates the length of the specified string.

Table 4.17. Parameters

ParameterType

string

string

Examples

string length( "tes" ) = 3
string length( "\U01F40Eab" ) = 3

upper case( string )

Produces an uppercase version of the specified string.

Table 4.18. Parameters

ParameterType

string

string

Example

upper case( "aBc4" ) = "ABC4"

lower case( string )

Produces a lowercase version of the specified string.

Table 4.19. Parameters

ParameterType

string

string

Example

lower case( "aBc4" ) = "abc4"

substring before( string, match )

Calculates the substring before the match.

Table 4.20. Parameters

ParameterType

string

string

match

string

Examples

substring before( "testing", "ing" ) = "test"
substring before( "testing", "xyz" ) = ""

substring after( string, match )

Calculates the substring after the match.

Table 4.21. Parameters

ParameterType

string

string

match

string

Examples

substring after( "testing", "test" ) = "ing"
substring after( "", "a" ) = ""

replace( input, pattern, replacement, flags? )

Calculates the regular expression replacement.

Table 4.22. Parameters

ParameterType

input

string

pattern

string

replacement

string

flags (Optional)

string

Note

This function uses regular expression parameters as defined in XQuery 1.0 and XPath 2.0 Functions and Operators.

Example

replace( "abcd", "(ab)|(a)", "[1=$1][2=$2]" ) = "[1=ab][2=]cd"

contains( string, match )

Returns true if the string contains the match.

Table 4.23. Parameters

ParameterType

string

string

match

string

Example

contains( "testing", "to" ) = false

starts with( string, match )

Returns true if the string starts with the match

Table 4.24. Parameters

ParameterType

string

string

match

string

Example

starts with( "testing", "te" ) = true

ends with( string, match )

Returns true if the string ends with the match.

Table 4.25. Parameters

ParameterType

string

string

match

string

Example

ends with( "testing", "g" ) = true

matches( input, pattern, flags? )

Returns true if the input matches the regular expression.

Table 4.26. Parameters

ParameterType

input

string

pattern

string

flags (Optional)

string

Note

This function uses regular expression parameters as defined in XQuery 1.0 and XPath 2.0 Functions and Operators.

Example

matches( "teeesting", "^te*sting" ) = true

split( string, delimiter )

Returns a list of the original string and splits it at the delimiter regular expression pattern.

Table 4.27. Parameters

ParameterType

string

string

delimiter

string for a regular expression pattern

Note

This function uses regular expression parameters as defined in XQuery 1.0 and XPath 2.0 Functions and Operators.

Examples

split( "John Doe", "\\s" ) = ["John", "Doe"]
split( "a;b;c;;", ";" ) = ["a","b","c","",""]

4.3.2.4. List functions

The following functions support list operations.

Note

In FEEL, the index of the first element in a list is 1. The index of the last element in a list can be identified as -1.

list contains( list, element )

Returns true if the list contains the element.

Table 4.28. Parameters

ParameterType

list

list

element

Any type, including null

Example

list contains( [1,2,3], 2 ) = true

count( list )

Counts the elements in the list.

Table 4.29. Parameters

ParameterType

list

list

Examples

count( [1,2,3] ) = 3
count( [] ) = 0
count( [1,[2,3]] ) = 2

min( list )

Returns the minimum comparable element in the list.

Table 4.30. Parameters

ParameterType

list

list

Alternative signature

min( e1, e2, ..., eN )

Examples

min( [1,2,3] ) = 1
min( 1 ) = 1
min( [1] ) = 1

max( list )

Returns the maximum comparable element in the list.

Table 4.31. Parameters

ParameterType

list

list

Alternative signature

max( e1, e2, ..., eN )

Examples

max( 1,2,3 ) = 3
max( [] ) = null

sum( list )

Returns the sum of the numbers in the list.

Table 4.32. Parameters

ParameterType

list

list of number elements

Alternative signature

sum( n1, n2, ..., nN )

Examples

sum( [1,2,3] ) = 6
sum( 1,2,3 ) = 6
sum( 1 ) = 1
sum( [] ) = null

mean( list )

Calculates the average (arithmetic mean) of the elements in the list.

Table 4.33. Parameters

ParameterType

list

list of number elements

Alternative signature

mean( n1, n2, ..., nN )

Examples

mean( [1,2,3] ) = 2
mean( 1,2,3 ) = 2
mean( 1 ) = 1
mean( [] ) = null

all( list )

Returns true if all elements in the list are true.

Table 4.34. Parameters

ParameterType

list

list of boolean elements

Alternative signature

all( b1, b2, ..., bN )

Examples

all( [false,null,true] ) = false
all( true ) = true
all( [true] ) = true
all( [] ) = true
all( 0 ) = null

any( list )

Returns true if any element in the list is true.

Table 4.35. Parameters

ParameterType

list

list of boolean elements

Alternative signature

any( b1, b2, ..., bN )

Examples

any( [false,null,true] ) = true
any( false ) = false
any( [] ) = false
any( 0 ) = null

sublist( list, start position, length? )

Returns the sublist from the start position, limited to the length elements.

Table 4.36. Parameters

ParameterType

list

list

start position

number

length (Optional)

number

Example

sublist( [4,5,6], 1, 2 ) = [4,5]

append( list, item )

Creates a list that is appended to the item or items.

Table 4.37. Parameters

ParameterType

list

list

item

Any type

Example

append( [1], 2, 3 ) = [1,2,3]

concatenate( list )

Creates a list that is the result of the concatenated lists.

Table 4.38. Parameters

ParameterType

list

list

Example

concatenate( [1,2],[3] ) = [1,2,3]

insert before( list, position, newItem )

Creates a list with the newItem inserted at the specified position.

Table 4.39. Parameters

ParameterType

list

list

position

number

newItem

Any type

Example

insert before( [1,3],1,2 ) = [2,1,3]

remove( list, position )

Creates a list with the removed element excluded from the specified position.

Table 4.40. Parameters

ParameterType

list

list

position

number

Example

remove( [1,2,3], 2 ) = [1,3]

reverse( list )

Returns a reversed list.

Table 4.41. Parameters

ParameterType

list

list

Example

reverse( [1,2,3] ) = [3,2,1]

index of( list, match )

Returns indexes matching the element.

Parameters

  • list of type list
  • match of any type

Table 4.42. Parameters

ParameterType

list

list

match

Any type

Example

index of( [1,2,3,2],2 ) = [2,4]

union( list )

Returns a list of all the elements from multiple lists and excludes duplicates.

Table 4.43. Parameters

ParameterType

list

list

Example

union( [1,2],[2,3] ) = [1,2,3]

distinct values( list )

Returns a list of elements from a single list and excludes duplicates.

Table 4.44. Parameters

ParameterType

list

list

Example

distinct values( [1,2,3,2,1] ) = [1,2,3]

flatten( list )

Returns a flattened list.

Table 4.45. Parameters

ParameterType

list

list

Example

flatten( [[1,2],[[3]], 4] ) = [1,2,3,4]

product( list )

Returns the product of the numbers in the list.

Table 4.46. Parameters

ParameterType

list

list of number elements

Alternative signature

product( n1, n2, ..., nN )

Examples

product( [2, 3, 4] ) = 24
product( 2, 3, 4 ) = 24

median( list )

Returns the median of the numbers in the list. If the number of elements is odd, the result is the middle element. If the number of elements is even, the result is the average of the two middle elements.

Table 4.47. Parameters

ParameterType

list

list of number elements

Alternative signature

median( n1, n2, ..., nN )

Examples

median( 8, 2, 5, 3, 4 ) = 4
median( [6, 1, 2, 3] ) = 2.5
median( [ ] ) = null

stddev( list )

Returns the standard deviation of the numbers in the list.

Table 4.48. Parameters

ParameterType

list

list of number elements

Alternative signature

stddev( n1, n2, ..., nN )

Examples

stddev( 2, 4, 7, 5 ) = 2.081665999466132735282297706979931
stddev( [47] ) = null
stddev( 47 ) = null
stddev( [ ] ) = null

mode( list )

Returns the mode of the numbers in the list. If multiple elements are returned, the numbers are sorted in ascending order.

Table 4.49. Parameters

ParameterType

list

list of number elements

Alternative signature

mode( n1, n2, ..., nN )

Examples

mode( 6, 3, 9, 6, 6 ) = [6]
mode( [6, 1, 9, 6, 1] ) = [1, 6]
mode( [ ] ) = [ ]

4.3.2.5. Numeric functions

The following functions support number operations.

decimal( n, scale )

Returns a number with the specified scale.

Table 4.50. Parameters

ParameterType

n

number

scale

number in the range [−6111..6176]

Note

This function is implemented to be consistent with the FEEL:number definition for rounding decimal numbers to the nearest even decimal number.

Examples

decimal( 1/3, 2 ) = .33
decimal( 1.5, 0 ) = 2
decimal( 2.5, 0 ) = 2
decimal( 1.035, 2 ) = 1.04
decimal( 1.045, 2 ) = 1.04
decimal( 1.055, 2 ) = 1.06
decimal( 1.065, 2 ) = 1.06

floor( n )

Returns the greatest integer that is less than or equal to the specified number.

Table 4.51. Parameters

ParameterType

n

number

Examples

floor( 1.5 ) = 1
floor( -1.5 ) = -2

ceiling( n )

Returns the smallest integer that is greater than or equal to the specified number.

Table 4.52. Parameters

ParameterType

n

number

Examples

ceiling( 1.5 ) = 2
ceiling( -1.5 ) = -1

abs( n )

Returns the absolute value.

Table 4.53. Parameters

ParameterType

n

number, days and time duration, or years and months duration

Examples

abs( 10 ) = 10
abs( -10 ) = 10
abs( @"PT5H" ) = @"PT5H"
abs( @"-PT5H" ) = @"PT5H"

modulo( dividend, divisor )

Returns the remainder of the division of the dividend by the divisor. If either the dividend or divisor is negative, the result is of the same sign as the divisor.

Note

This function is also expressed as modulo(dividend, divisor) = dividend - divisor*floor(dividen d/divisor).

Table 4.54. Parameters

ParameterType

dividend

number

divisor

number

Examples

modulo( 12, 5 ) = 2
modulo( -12,5 )= 3
modulo( 12,-5 )= -3
modulo( -12,-5 )= -2
modulo( 10.1, 4.5 )= 1.1
modulo( -10.1, 4.5 )= 3.4
modulo( 10.1, -4.5 )= -3.4
modulo( -10.1, -4.5 )= -1.1

sqrt( number )

Returns the square root of the specified number.

Table 4.55. Parameters

ParameterType

n

number

Example

sqrt( 16 ) = 4

log( number )

Returns the logarithm of the specified number.

Table 4.56. Parameters

ParameterType

n

number

Example

decimal( log( 10 ), 2 ) = 2.30

exp( number )

Returns Euler’s number e raised to the power of the specified number.

Table 4.57. Parameters

ParameterType

n

number

Example

decimal( exp( 5 ), 2 ) = 148.41

odd( number )

Returns true if the specified number is odd.

Table 4.58. Parameters

ParameterType

n

number

Examples

odd( 5 ) = true
odd( 2 ) = false

even( number )

Returns true if the specified number is even.

Table 4.59. Parameters

ParameterType

n

number

Examples

even( 5 ) = false
even ( 2 ) = true

4.3.2.6. Date and time functions

The following functions support date and time operations.

is( value1, value2 )

Returns true if both values are the same element in the FEEL semantic domain.

Table 4.60. Parameters

ParameterType

value1

Any type

value2

Any type

Examples

is( date( "2012-12-25" ), time( "23:00:50" ) ) = false
is( date( "2012-12-25" ), date( "2012-12-25" ) ) = true
is( time( "23:00:50z" ), time( "23:00:50" ) ) = false

4.3.2.7. Range functions

The following functions support temporal ordering operations to establish relationships between single scalar values and ranges of such values. These functions are similar to the components in the Health Level Seven (HL7) International Clinical Quality Language (CQL) 1.4 syntax.

before( )

Returns true when an element A is before an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. before( point1 point2 )
  2. before( point range )
  3. before( range point )
  4. before( range1,range2 )

Requirements for evaluating to true

  1. point1 < point2
  2. point < range.start or ( point = range.start and not(range.start included) )
  3. range.end < point or ( range.end = point and not(range.end included) )
  4. range1.end < range2.start or (( not(range1.end included) or not(range2.start included) ) and range1.end = range2.start )

Examples

before( 1, 10 ) = true
before( 10, 1 ) = false
before( 1, [1..10] ) = false
before( 1, (1..10] ) = true
before( 1, [5..10] ) = true
before( [1..10], 10 ) = false
before( [1..10), 10 ) = true
before( [1..10], 15 ) = true
before( [1..10], [15..20] ) = true
before( [1..10], [10..20] ) = false
before( [1..10), [10..20] ) = true
before( [1..10], (10..20] ) = true

after( )

Returns true when an element A is after an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. after( point1 point2 )
  2. after( point range )
  3. after( range, point )
  4. after( range1 range2 )

Requirements for evaluating to true

  1. point1 > point2
  2. point > range.end or ( point = range.end and not(range.end included) )
  3. range.start > point or ( range.start = point and not(range.start included) )
  4. range1.start > range2.end or (( not(range1.start included) or not(range2.end included) ) and range1.start = range2.end )

Examples

after( 10, 5 ) = true
after( 5, 10 ) = false
after( 12, [1..10] ) = true
after( 10, [1..10) ) = true
after( 10, [1..10] ) = false
after( [11..20], 12 ) = false
after( [11..20], 10 ) = true
after( (11..20], 11 ) = true
after( [11..20], 11 ) = false
after( [11..20], [1..10] ) = true
after( [1..10], [11..20] ) = false
after( [11..20], [1..11) ) = true
after( (11..20], [1..11] ) = true

meets( )

Returns true when an element A meets an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. meets( range1, range2 )

Requirements for evaluating to true

  1. range1.end included and range2.start included and range1.end = range2.start

Examples

meets( [1..5], [5..10] ) = true
meets( [1..5), [5..10] ) = false
meets( [1..5], (5..10] ) = false
meets( [1..5], [6..10] ) = false

met by( )

Returns true when an element A is met by an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. met by( range1, range2 )

Requirements for evaluating to true

  1. range1.start included and range2.end included and range1.start = range2.end

Examples

met by( [5..10], [1..5] ) = true
met by( [5..10], [1..5) ) = false
met by( (5..10], [1..5] ) = false
met by( [6..10], [1..5] ) = false

overlaps( )

Returns true when an element A overlaps an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. overlaps( range1, range2 )

Requirements for evaluating to true

  1. ( range1.end > range2.start or (range1.end = range2.start and (range1.end included or range2.end included)) ) and ( range1.start < range2.end or (range1.start = range2.end and range1.start included and range2.end included) )

Examples

overlaps( [1..5], [3..8] ) = true
overlaps( [3..8], [1..5] ) = true
overlaps( [1..8], [3..5] ) = true
overlaps( [3..5], [1..8] ) = true
overlaps( [1..5], [6..8] ) = false
overlaps( [6..8], [1..5] ) = false
overlaps( [1..5], [5..8] ) = true
overlaps( [1..5], (5..8] ) = false
overlaps( [1..5), [5..8] ) = false
overlaps( [1..5), (5..8] ) = false
overlaps( [5..8], [1..5] ) = true
overlaps( (5..8], [1..5] ) = false
overlaps( [5..8], [1..5) ) = false
overlaps( (5..8], [1..5) ) = false

overlaps before( )

Returns true when an element A overlaps before an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. overlaps before( range1 range2 )

Requirements for evaluating to true

  1. ( range1.start < range2.start or (range1.start = range2.start and range1.start included and range2.start included) ) and ( range1.end > range2.start or (range1.end = range2.start and range1.end included and range2.start included) ) and ( range1.end < range2.end or (range1.end = range2.end and (not(range1.end included) or range2.end included )) )

Examples

overlaps before( [1..5], [3..8] ) = true
overlaps before( [1..5], [6..8] ) = false
overlaps before( [1..5], [5..8] ) = true
overlaps before( [1..5], (5..8] ) = false
overlaps before( [1..5), [5..8] ) = false
overlaps before( [1..5), (1..5] ) = true
overlaps before( [1..5], (1..5] ) = true
overlaps before( [1..5), [1..5] ) = false
overlaps before( [1..5], [1..5] ) = false

overlaps after( )

Returns true when an element A overlaps after an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. overlaps after( range1 range2 )

Requirements for evaluating to true

  1. ( range2.start < range1.start or (range2.start = range1.start and range2.start included and not( range1.start included)) ) and ( range2.end > range1.start or (range2.end = range1.start and range2.end included and range1.start included) ) and ( range2.end < range1.end or (range2.end = range1.end and (not(range2.end included) or range1.end included)) )

Examples

overlaps after( [3..8], [1..5] )= true
overlaps after( [6..8], [1..5] )= false
overlaps after( [5..8], [1..5] )= true
overlaps after( (5..8], [1..5] )= false
overlaps after( [5..8], [1..5) )= false
overlaps after( (1..5], [1..5) )= true
overlaps after( (1..5], [1..5] )= true
overlaps after( [1..5], [1..5) )= false
overlaps after( [1..5], [1..5] )= false
overlaps after( (1..5), [1..5] )= false
overlaps after( (1..5], [1..6] )= false
overlaps after( (1..5], (1..5] )= false
overlaps after( (1..5], [2..5] )= false

finishes( )

Returns true when an element A finishes an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. finishes( point, range )
  2. finishes( range1, range2 )

Requirements for evaluating to true

  1. range.end included and range.end = point
  2. range1.end included = range2.end included and range1.end = range2.end and ( range1.start > range2.start or (range1.start = range2.start and (not(range1.start included) or range2.start included)) )

Examples

finishes( 10, [1..10] ) = true
finishes( 10, [1..10) ) = false
finishes( [5..10], [1..10] ) = true
finishes( [5..10), [1..10] ) = false
finishes( [5..10), [1..10) ) = true
finishes( [1..10], [1..10] ) = true
finishes( (1..10], [1..10] ) = true

finished by( )

Returns true when an element A is finished by an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. finished by( range, point )
  2. finished by( range1 range2 )

Requirements for evaluating to true

  1. range.end included and range.end = point
  2. range1.end included = range2.end included and range1.end = range2.end and ( range1.start < range2.start or (range1.start = range2.start and (range1.start included or not(range2.start included))) )

Examples

finished by( [1..10], 10 ) = true
finished by( [1..10), 10 ) = false
finished by( [1..10], [5..10] ) = true
finished by( [1..10], [5..10) ) = false
finished by( [1..10), [5..10) ) = true
finished by( [1..10], [1..10] ) = true
finished by( [1..10], (1..10] ) = true

includes( )

Returns true when an element A includes an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. includes( range, point )
  2. includes( range1, range2 )

Requirements for evaluating to true

  1. (range.start < point and range.end > point) or (range.start = point and range.start included) or (range.end = point and range.end included)
  2. ( range1.start < range2.start or (range1.start = range2.start and (range1.start included or not(range2.start included))) ) and ( range1.end > range2.end or (range1.end = range2.end and (range1.end included or not(range2.end included))) )

Examples

includes( [1..10], 5 ) = true
includes( [1..10], 12 ) = false
includes( [1..10], 1 ) = true
includes( [1..10], 10 ) = true
includes( (1..10], 1 ) = false
includes( [1..10), 10 ) = false
includes( [1..10], [4..6] ) = true
includes( [1..10], [1..5] ) = true
includes( (1..10], (1..5] ) = true
includes( [1..10], (1..10) ) = true
includes( [1..10), [5..10) ) = true
includes( [1..10], [1..10) ) = true
includes( [1..10], (1..10] ) = true
includes( [1..10], [1..10] ) = true

during( )

Returns true when an element A is during an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. during( point, range )
  2. during( range1 range2 )

Requirements for evaluating to true

  1. (range.start < point and range.end > point) or (range.start = point and range.start included) or (range.end = point and range.end included)
  2. ( range2.start < range1.start or (range2.start = range1.start and (range2.start included or not(range1.start included))) ) and ( range2.end > range1.end or (range2.end = range1.end and (range2.end included or not(range1.end included))) )

Examples

during( 5, [1..10] ) = true
during( 12, [1..10] ) = false
during( 1, [1..10] ) = true
during( 10, [1..10] ) = true
during( 1, (1..10] ) = false
during( 10, [1..10) ) = false
during( [4..6], [1..10] ) = true
during( [1..5], [1..10] ) = true
during( (1..5], (1..10] ) = true
during( (1..10), [1..10] ) = true
during( [5..10), [1..10) ) = true
during( [1..10), [1..10] ) = true
during( (1..10], [1..10] ) = true
during( [1..10], [1..10] ) = true

starts( )

Returns true when an element A starts an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. starts( point, range )
  2. starts( range1, range2 )

Requirements for evaluating to true

  1. range.start = point and range.start included
  2. range1.start = range2.start and range1.start included = range2.start included and ( range1.end < range2.end or (range1.end = range2.end and (not(range1.end included) or range2.end included)) )

Examples

starts( 1, [1..10] ) = true
starts( 1, (1..10] ) = false
starts( 2, [1..10] ) = false
starts( [1..5], [1..10] ) = true
starts( (1..5], (1..10] ) = true
starts( (1..5], [1..10] ) = false
starts( [1..5], (1..10] ) = false
starts( [1..10], [1..10] ) = true
starts( [1..10), [1..10] ) = true
starts( (1..10), (1..10) ) = true

started by( )

Returns true when an element A is started by an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. started by( range, point )
  2. started by( range1, range2 )

Requirements for evaluating to true

  1. range.start = point and range.start included
  2. range1.start = range2.start and range1.start included = range2.start included and ( range2.end < range1.end or (range2.end = range1.end and (not(range2.end included) or range1.end included)) )

Examples

started by( [1..10], 1 ) = true
started by( (1..10], 1 ) = false
started by( [1..10], 2 ) = false
started by( [1..10], [1..5] ) = true
started by( (1..10], (1..5] ) = true
started by( [1..10], (1..5] ) = false
started by( (1..10], [1..5] ) = false
started by( [1..10], [1..10] ) = true
started by( [1..10], [1..10) ) = true
started by( (1..10), (1..10) ) = true

coincides( )

Returns true when an element A coincides with an element B and when the relevant requirements for evaluating to true are also met.

Signatures

  1. coincides( point1, point2 )
  2. coincides( range1, range2 )

Requirements for evaluating to true

  1. point1 = point2
  2. range1.start = range2.start and range1.start included = range2.start included and range1.end = range2.end and range1.end included = range2.end included

Examples

coincides( 5, 5 ) = true
coincides( 3, 4 ) = false
coincides( [1..5], [1..5] ) = true
coincides( (1..5), [1..5] ) = false
coincides( [1..5], [2..6] ) = false

4.3.2.8. Temporal functions

The following functions support general temporal operations.

day of year( date )

Returns the Gregorian number of the day of the year.

Table 4.61. Parameters

ParameterType

date

date or date and time

Example

day of year( date(2019, 9, 17) ) = 260

day of week( date )

Returns the Gregorian day of the week: "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", or "Sunday".

Table 4.62. Parameters

ParameterType

date

date or date and time

Example

day of week( date(2019, 9, 17) ) = "Tuesday"

month of year( date )

Returns the Gregorian month of the year: "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", or "December".

Table 4.63. Parameters

ParameterType

date

date or date and time

Example

month of year( date(2019, 9, 17) ) = "September"

month of year( date )

Returns the Gregorian week of the year as defined by ISO 8601.

Table 4.64. Parameters

ParameterType

date

date or date and time

Examples

week of year( date(2019, 9, 17) ) = 38
week of year( date(2003, 12, 29) ) = 1
week of year( date(2004, 1, 4) ) = 1
week of year( date(2005, 1, 1) ) = 53
week of year( date(2005, 1, 3) ) = 1
week of year( date(2005, 1, 9) ) = 1

4.3.2.9. Sort functions

The following functions support sorting operations.

sort( list, precedes )

Returns a list of the same elements but ordered according to the sorting function.

Table 4.65. Parameters

ParameterType

list

list

precedes

function

Example

sort( list: [3,1,4,5,2], precedes: function(x,y) x < y ) = [1,2,3,4,5]

4.3.2.10. Context functions

The following functions support context operations.

get value( m, key )

Returns the value from the context for the specified entry key.

Table 4.66. Parameters

ParameterType

m

context

key

string

Examples

get value( {key1 : "value1"}, "key1" ) = "value1"
get value( {key1 : "value1"}, "unexistent-key" ) = null

get entries( m )

Returns a list of key-value pairs for the specified context.

Table 4.67. Parameters

ParameterType

m

context

Example

get entries( {key1 : "value1", key2 : "value2"} ) = [ { key : "key1", value : "value1" }, {key : "key2", value : "value2"} ]

4.3.3. 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, Red Hat Decision Manager removes the limitation on the use of consecutive spaces. Red Hat Decision Manager supports variable names with any number of consecutive spaces, but normalizes them into a single space. For example, the variable references First Name with one space and First Name with two spaces are both acceptable in Red Hat Decision Manager.

Red Hat Decision Manager 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 Red Hat Decision Manager 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.

4.4. 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). 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 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
Note

Red Hat Decision Manager 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 Red Hat Decision Manager, see Section 4.3.1, “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.

4.4.1. 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 4.3. Decision table for credit score rating

dmn decision table example

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 4.4. Decision table for lending strategy

dmn decision table example2

The following decision table determines applicant qualification for a loan as the concluding decision node in a loan prequalification decision model:

Figure 4.5. Decision table for loan prequalification

dmn decision table example3

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).

Important

Red Hat Decision Manager 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 Red Hat Decision Manager, see Designing a decision service using spreadsheet decision tables.

4.4.1.1. 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 decision table hit policies are supported in DMN:

  • 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.

4.4.2. 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 4.6. Boxed literal expression for minimum PITI value

dmn literal expression example2

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 4.7. Boxed literal expression for matching online dating candidates

dmn literal expression example3b

4.4.3. 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 4.8. Boxed context expression for flight passenger waiting list

dmn context expression example

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 4.9. Boxed context expression for front-end client PITI ratio

dmn context expression example2

4.4.4. 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 4.10. Boxed relation expression with employee information

dmn relation expression example

4.4.5. 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 4.11. Boxed function expression for flight capacity

dmn function expression example

The following boxed function expression contains a basic Java function as a context expression for determining absolute value in a decision model calculation:

Figure 4.12. Boxed function expression for absolute value

dmn function expression example2

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 4.13. Boxed function expression for installment calculation in business knowledge model

dmn function expression example3

The following boxed function expression uses a PMML model included in the DMN file to define the minimum acceptable PITI calculation (principal, interest, taxes, and insurance) in a lending decision:

Figure 4.14. Boxed function expression with an included PMML model in business knowledge model

dmn function expression example5

4.4.6. 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 4.15. Boxed invocation expression to reassign flight passengers

dmn invocation example

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 4.16. Boxed invocation expression for required monthly installment

dmn invocation example2

4.4.7. Boxed list expressions

A boxed list expression in DMN represents a FEEL list of items. You use boxed lists to define lists of relevant items for a particular node in a decision. You can also use literal FEEL expressions for list items in cells to create more complex lists.

For example, the following boxed list expression identifies approved credit score agencies in a loan application decision service:

Figure 4.17. Boxed list expression for approved credit score agencies

dmn list expression example

The following boxed list expression also identifies approved credit score agencies but uses FEEL logic to define the agency status (Inc., LLC, SA, GA) based on a DMN input node:

Figure 4.18. Boxed list expression using FEEL logic for approved credit score agency status

dmn list expression example2
dmn list expression example2a

4.5. 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 4.19. DRD for flight rebooking

dmn passenger rebooking drd

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 4.20. Boxed context expression example for prioritized wait list

dmn context expression example

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 4.21. Boxed function expression for passenger reassignment

dmn reassign passenger

The following is the DMN source file for this decision model:

<dmn:definitions xmlns="https://www.drools.org/kie-dmn/Flight-rebooking" xmlns:dmn="http://www.omg.org/spec/DMN/20151101/dmn.xsd" xmlns:feel="http://www.omg.org/spec/FEEL/20140401" id="_0019_flight_rebooking" name="0019-flight-rebooking" namespace="https://www.drools.org/kie-dmn/Flight-rebooking">
  <dmn:itemDefinition id="_tFlight" name="tFlight">
    <dmn:itemComponent id="_tFlight_Flight" name="Flight Number">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_From" name="From">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_To" name="To">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Dep" name="Departure">
      <dmn:typeRef>feel:dateTime</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Arr" name="Arrival">
      <dmn:typeRef>feel:dateTime</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Capacity" name="Capacity">
      <dmn:typeRef>feel:number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Status" name="Status">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tFlightTable" isCollection="true" name="tFlightTable">
    <dmn:typeRef>tFlight</dmn:typeRef>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tPassenger" name="tPassenger">
    <dmn:itemComponent id="_tPassenger_Name" name="Name">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tPassenger_Status" name="Status">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tPassenger_Miles" name="Miles">
      <dmn:typeRef>feel:number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tPassenger_Flight" name="Flight Number">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tPassengerTable" isCollection="true" name="tPassengerTable">
    <dmn:typeRef>tPassenger</dmn:typeRef>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tFlightNumberList" isCollection="true" name="tFlightNumberList">
    <dmn:typeRef>feel:string</dmn:typeRef>
  </dmn:itemDefinition>
  <dmn:inputData id="i_Flight_List" name="Flight List">
    <dmn:variable name="Flight List" typeRef="tFlightTable"/>
  </dmn:inputData>
  <dmn:inputData id="i_Passenger_List" name="Passenger List">
    <dmn:variable name="Passenger List" typeRef="tPassengerTable"/>
  </dmn:inputData>
  <dmn:decision name="Prioritized Waiting List" id="d_PrioritizedWaitingList">
    <dmn:variable name="Prioritized Waiting List" typeRef="tPassengerTable"/>
    <dmn:informationRequirement>
      <dmn:requiredInput href="#i_Passenger_List"/>
    </dmn:informationRequirement>
    <dmn:informationRequirement>
      <dmn:requiredInput href="#i_Flight_List"/>
    </dmn:informationRequirement>
    <dmn:knowledgeRequirement>
      <dmn:requiredKnowledge href="#b_PassengerPriority"/>
    </dmn:knowledgeRequirement>
    <dmn:context>
      <dmn:contextEntry>
        <dmn:variable name="Cancelled Flights" typeRef="tFlightNumberList"/>
        <dmn:literalExpression>
          <dmn:text>Flight List[ Status = "cancelled" ].Flight Number</dmn:text>
        </dmn:literalExpression>
      </dmn:contextEntry>
      <dmn:contextEntry>
        <dmn:variable name="Waiting List" typeRef="tPassengerTable"/>
        <dmn:literalExpression>
          <dmn:text>Passenger List[ list contains( Cancelled Flights, Flight Number ) ]</dmn:text>
        </dmn:literalExpression>
      </dmn:contextEntry>
      <dmn:contextEntry>
        <dmn:literalExpression>
          <dmn:text>sort( Waiting List, passenger priority )</dmn:text>
        </dmn:literalExpression>
      </dmn:contextEntry>
    </dmn:context>
  </dmn:decision>
  <dmn:decision name="Rebooked Passengers" id="d_RebookedPassengers">
    <dmn:variable name="Rebooked Passengers" typeRef="tPassengerTable"/>
    <dmn:informationRequirement>
      <dmn:requiredDecision href="#d_PrioritizedWaitingList"/>
    </dmn:informationRequirement>
    <dmn:informationRequirement>
      <dmn:requiredInput href="#i_Flight_List"/>
    </dmn:informationRequirement>
    <dmn:knowledgeRequirement>
      <dmn:requiredKnowledge href="#b_ReassignNextPassenger"/>
    </dmn:knowledgeRequirement>
    <dmn:invocation>
      <dmn:literalExpression>
        <dmn:text>reassign next passenger</dmn:text>
      </dmn:literalExpression>
      <dmn:binding>
        <dmn:parameter name="Waiting List"/>
        <dmn:literalExpression>
          <dmn:text>Prioritized Waiting List</dmn:text>
        </dmn:literalExpression>
      </dmn:binding>
      <dmn:binding>
        <dmn:parameter name="Reassigned Passengers List"/>
        <dmn:literalExpression>
          <dmn:text>[]</dmn:text>
        </dmn:literalExpression>
      </dmn:binding>
      <dmn:binding>
        <dmn:parameter name="Flights"/>
        <dmn:literalExpression>
          <dmn:text>Flight List</dmn:text>
        </dmn:literalExpression>
      </dmn:binding>
    </dmn:invocation>
  </dmn:decision>
  <dmn:businessKnowledgeModel id="b_PassengerPriority" name="passenger priority">
    <dmn:encapsulatedLogic>
      <dmn:formalParameter name="Passenger1" typeRef="tPassenger"/>
      <dmn:formalParameter name="Passenger2" typeRef="tPassenger"/>
      <dmn:decisionTable hitPolicy="UNIQUE">
        <dmn:input id="b_Passenger_Priority_dt_i_P1_Status" label="Passenger1.Status">
          <dmn:inputExpression typeRef="feel:string">
            <dmn:text>Passenger1.Status</dmn:text>
          </dmn:inputExpression>
          <dmn:inputValues>
            <dmn:text>"gold", "silver", "bronze"</dmn:text>
          </dmn:inputValues>
        </dmn:input>
        <dmn:input id="b_Passenger_Priority_dt_i_P2_Status" label="Passenger2.Status">
          <dmn:inputExpression typeRef="feel:string">
            <dmn:text>Passenger2.Status</dmn:text>
          </dmn:inputExpression>
          <dmn:inputValues>
            <dmn:text>"gold", "silver", "bronze"</dmn:text>
          </dmn:inputValues>
        </dmn:input>
        <dmn:input id="b_Passenger_Priority_dt_i_P1_Miles" label="Passenger1.Miles">
          <dmn:inputExpression typeRef="feel:string">
            <dmn:text>Passenger1.Miles</dmn:text>
          </dmn:inputExpression>
        </dmn:input>
        <dmn:output id="b_Status_Priority_dt_o" label="Passenger1 has priority">
          <dmn:outputValues>
            <dmn:text>true, false</dmn:text>
          </dmn:outputValues>
          <dmn:defaultOutputEntry>
            <dmn:text>false</dmn:text>
          </dmn:defaultOutputEntry>
        </dmn:output>
        <dmn:rule id="b_Passenger_Priority_dt_r1">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r1_i1">
            <dmn:text>"gold"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r1_i2">
            <dmn:text>"gold"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r1_i3">
            <dmn:text>>= Passenger2.Miles</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r1_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r2">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r2_i1">
            <dmn:text>"gold"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r2_i2">
            <dmn:text>"silver","bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r2_i3">
            <dmn:text>-</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r2_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r3">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r3_i1">
            <dmn:text>"silver"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r3_i2">
            <dmn:text>"silver"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r3_i3">
            <dmn:text>>= Passenger2.Miles</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r3_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r4">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r4_i1">
            <dmn:text>"silver"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r4_i2">
            <dmn:text>"bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r4_i3">
            <dmn:text>-</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r4_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r5">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r5_i1">
            <dmn:text>"bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r5_i2">
            <dmn:text>"bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r5_i3">
            <dmn:text>>= Passenger2.Miles</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r5_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
      </dmn:decisionTable>
    </dmn:encapsulatedLogic>
    <dmn:variable name="passenger priority" typeRef="feel:boolean"/>
  </dmn:businessKnowledgeModel>
  <dmn:businessKnowledgeModel id="b_ReassignNextPassenger" name="reassign next passenger">
    <dmn:encapsulatedLogic>
      <dmn:formalParameter name="Waiting List" typeRef="tPassengerTable"/>
      <dmn:formalParameter name="Reassigned Passengers List" typeRef="tPassengerTable"/>
      <dmn:formalParameter name="Flights" typeRef="tFlightTable"/>
      <dmn:context>
        <dmn:contextEntry>
          <dmn:variable name="Next Passenger" typeRef="tPassenger"/>
          <dmn:literalExpression>
            <dmn:text>Waiting List[1]</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Original Flight" typeRef="tFlight"/>
          <dmn:literalExpression>
            <dmn:text>Flights[ Flight Number = Next Passenger.Flight Number ][1]</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Best Alternate Flight" typeRef="tFlight"/>
          <dmn:literalExpression>
            <dmn: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]</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Reassigned Passenger" typeRef="tPassenger"/>
          <dmn:context>
            <dmn:contextEntry>
              <dmn:variable name="Name" typeRef="feel:string"/>
              <dmn:literalExpression>
                <dmn:text>Next Passenger.Name</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
            <dmn:contextEntry>
              <dmn:variable name="Status" typeRef="feel:string"/>
              <dmn:literalExpression>
                <dmn:text>Next Passenger.Status</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
            <dmn:contextEntry>
              <dmn:variable name="Miles" typeRef="feel:number"/>
              <dmn:literalExpression>
                <dmn:text>Next Passenger.Miles</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
            <dmn:contextEntry>
              <dmn:variable name="Flight Number" typeRef="feel:string"/>
              <dmn:literalExpression>
                <dmn:text>Best Alternate Flight.Flight Number</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
          </dmn:context>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Remaining Waiting List" typeRef="tPassengerTable"/>
          <dmn:literalExpression>
            <dmn:text>remove( Waiting List, 1 )</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Updated Reassigned Passengers List" typeRef="tPassengerTable"/>
          <dmn:literalExpression>
            <dmn:text>append( Reassigned Passengers List, Reassigned Passenger )</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:literalExpression>
            <dmn:text>if count( Remaining Waiting List ) > 0 then reassign next passenger( Remaining Waiting List, Updated Reassigned Passengers List, Flights ) else Updated Reassigned Passengers List</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
      </dmn:context>
    </dmn:encapsulatedLogic>
    <dmn:variable name="reassign next passenger" typeRef="tPassengerTable"/>
    <dmn:knowledgeRequirement>
      <dmn:requiredKnowledge href="#b_HasCapacity"/>
    </dmn:knowledgeRequirement>
  </dmn:businessKnowledgeModel>
  <dmn:businessKnowledgeModel id="b_HasCapacity" name="has capacity">
    <dmn:encapsulatedLogic>
      <dmn:formalParameter name="flight" typeRef="tFlight"/>
      <dmn:formalParameter name="rebooked list" typeRef="tPassengerTable"/>
      <dmn:literalExpression>
        <dmn:text>flight.Capacity > count( rebooked list[ Flight Number = flight.Flight Number ] )</dmn:text>
      </dmn:literalExpression>
    </dmn:encapsulatedLogic>
    <dmn:variable name="has capacity" typeRef="feel:boolean"/>
  </dmn:businessKnowledgeModel>
</dmn:definitions>

Chapter 5. DMN support in Red Hat Decision Manager

Red Hat Decision Manager provides design and runtime support for DMN 1.2 models at conformance level 3, and runtime-only support for DMN 1.1 and 1.3 models at conformance level 3. You can integrate DMN models with your Red Hat Decision Manager 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). Any DMN 1.1 and 1.3 models (do not contain DMN 1.3 features) that you import into Business Central, open in the DMN designer, and save are converted to DMN 1.2 models.
  • Package DMN files as part of your project knowledge JAR (KJAR) file without Business Central.

The following table summarizes the design and runtime support for each DMN version in Red Hat Decision Manager:

Table 5.1. DMN support in Red Hat Decision Manager

DMN version

DMN engine support

DMN modeler support

Execution

Open

Save

DMN 1.1

grn check

grn check

bk x

DMN 1.2

grn check

grn check

grn check

DMN 1.3

grn check

grn check

bk x

In addition to all DMN conformance level 3 requirements, Red Hat Decision Manager also includes enhancements and fixes to FEEL and DMN model components to optimize the experience of implementing DMN decision services with Red Hat Decision Manager. From a platform perspective, DMN models are like any other business asset in Red Hat Decision Manager, such as DRL files or spreadsheet decision tables, that you can include in your Red Hat Decision Manager project and deploy to KIE Server in order to start your DMN decision services.

For more information about including external DMN files with your Red Hat Decision Manager project packaging and deployment method, see Packaging and deploying a Red Hat Decision Manager project.

5.1. Configurable DMN properties in Red Hat Decision Manager

Red Hat Decision Manager provides the following DMN properties that you can configure when you execute your DMN models on KIE Server or on your client application. You can configure some of these properties using the kmodule.xml file in your Red Hat Decision Manager project when you deploy your project on KIE Server.

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 decision 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 decision 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.runtime.listeners.$LISTENER_NAME

When valorized with a Java fully qualified name, this property loads and registers a DMN Runtime Listener onto the decision engine at start time. You can use this property to register a DMN listener in order to be notified of several events during DMN model evaluations.

To configure this property when deploying your project on KIE Server, modify this property in the kmodule.xml file of your project. This approach is helpful when the listener is specific to your project and when the configuration must be applied in KIE Server only to your deployed project.

<kmodule xmlns="http://www.drools.org/xsd/kmodule">
  <configuration>
    <property key="org.kie.dmn.runtime.listeners.mylistener" value="org.acme.MyDMNListener"/>
  </configuration>
</kmodule>

To configure this property globally for your Red Hat Decision Manager environment, modify this property using a command terminal or any other global application configuration mechanism. This approach is helpful when the decision engine is embedded as part of your Java application.

-Dorg.kie.dmn.runtime.listeners.mylistener=org.acme.MyDMNListener
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 decision engine, but subsequent compilations are more efficient.

Default value: false

-Dorg.kie.dmn.compiler.execmodel=true

5.2. Configurable DMN validation in Red Hat Decision Manager

By default, the kie-maven-plugin component in the pom.xml file of your Red Hat Decision Manager project uses the following <validateDMN> configurations to perform pre-compilation validation of DMN model assets and to perform DMN decision table static analysis:

  • VALIDATE_SCHEMA: DMN model files are verified against the DMN specification XSD schema to ensure that the files are valid XML and compliant with the specification.
  • VALIDATE_MODEL: The pre-compilation analysis is performed for the DMN model to ensure that the basic semantic is aligned with the DMN specification.
  • ANALYZE_DECISION_TABLE: DMN decision tables are statically analyzed for gaps or overlaps and to ensure that the semantic of the decision table follows best practices.

You can modify the default DMN validation and DMN decision table analysis behavior to perform only a specified validation during the project build, or you can disable this default behavior completely, as shown in the following examples:

Default configuration for DMN validation and decision table analysis

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>VALIDATE_SCHEMA,VALIDATE_MODEL,ANALYZE_DECISION_TABLE</validateDMN>
  </configuration>
</plugin>

Configuration to perform only the DMN decision table static analysis

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>ANALYZE_DECISION_TABLE</validateDMN>
  </configuration>
</plugin>

Configuration to perform only the XSD schema validation

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>VALIDATE_SCHEMA</validateDMN>
  </configuration>
</plugin>

Configuration to perform only the DMN model validation

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>VALIDATE_MODEL</validateDMN>
  </configuration>
</plugin>

Configuration to disable all DMN validation

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>disable</validateDMN>
  </configuration>
</plugin>

Note

If you enter an unrecognized <validateDMN> configuration flag, all pre-compilation validation is disabled and the Maven plugin emits related log messages.

Chapter 6. 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. Red Hat Decision Manager 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 Red Hat Decision Manager. Red Hat Decision Manager also provides runtime-only support for DMN 1.1 and 1.3 models at conformance level 3, but any DMN 1.1 and 1.3 models (do not contain DMN 1.3 features) that you import into Business Central, open in the DMN designer, and save are converted to DMN 1.2 models.

Procedure

  1. In Business Central, go to MenuDesignProjects and click the project name.
  2. Create or import a DMN file in your Business Central project.

    To create a DMN file, click Add AssetDMN, 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.

    Note

    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.

    If an imported DRD is not automatically formatted, you can select the Perform automatic layout icon in the upper-right toolbar in the DMN designer to format the DRD.

  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:

    Figure 6.1. Adding DRD components

    dmn drag decision node

    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 6.2. Opening a new decision node boxed expression

    dmn decision edit

    Figure 6.3. Opening a new business knowledge model boxed expression

    dmn bkm edit

    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.

    Figure 6.4. Selecting the logic type for a decision node

    dmn decision boxed expression options

    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.

    Figure 6.5. Selecting the function or other logic type for a business knowledge model

    dmn bkm define
  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 6.6. Decision node decision table for credit score rating

    dmn decision table example1a

    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 6.7. Business knowledge model function for PITI calculation

    dmn function expression example4
  7. After you define the decision logic for the selected node, click Back to "<MODEL_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 6.8. Connecting credit score input to the credit score rating decision

    dmn input connection example
    dmn input connection example2
  9. Continue adding and defining the remaining DRD components of your decision model. Periodically click Save in the DMN designer to save your work.

    Note

    As you periodically save a DRD, the DMN designer performs a static validation of the DMN model and might produce error messages until the model is defined completely. After you finish defining the DMN model completely, if any errors remain, troubleshoot the specified problems accordingly.

  10. After you add and define all components of the DRD, click Save to save and validate the completed DRD.

    To adjust the DRD layout, you can select the Perform automatic layout icon in the upper-right toolbar of the DMN designer.

    The following is an example DRD for a loan prequalification decision model:

    Figure 6.9. Completed DRD for loan prequalification

    dmn example drd

    The following is an example DRD for a phone call handling decision model using a reusable decision service:

    Figure 6.10. Completed DRD for phone call handling with a decision service

    dmn example drd3

    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.

6.1. 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). 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 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

  • A DMN file is created or imported in Business Central.

Procedure

  1. In Business Central, go to MenuDesignProjects, 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 node that you want to define and click the Edit icon to open the DMN boxed expression designer:

    Figure 6.11. Opening a new decision node boxed expression

    dmn decision edit

    Figure 6.12. Opening a new business knowledge model boxed expression

    dmn bkm edit

    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.

    Figure 6.13. Selecting the logic type for a decision node

    dmn decision boxed expression options

    For business knowledge model nodes, 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.

    Figure 6.14. Selecting the function or other logic type for a business knowledge model

    dmn bkm define
  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.

    Figure 6.15. Managing data types for a column header value

    dmn manage data types
  6. On the Data Types page, click New Data Type to add a new data type or click Import Data Object to import an existing data object from your project that you want to use as a DMN data type.

    If you import a data object from your project as a DMN data type and then that object is updated, you must re-import the data object as a DMN data type to apply the changes in your DMN model.

    For this example, click New Data Type and create a Credit_Score_Rating data type as a string:

    Figure 6.16. Adding a new data type

    dmn custom data type add
  7. Click Add Constraints, select Enumeration from the drop-down options, and add the following constraints:

    • "Excellent"
    • "Good"
    • "Fair"
    • "Poor"
    • "Bad"

    Figure 6.17. Adding constraints to the new data type

    dmn custom data type constraints

    To change the order of data type constraints, you can click the left end of the constraint row and drag the row as needed:

    Figure 6.18. Dragging constraints to change constraint order

    dmn custom data type constraints drag

    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 the check mark to the right of the data type 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 6.19. Decision node decision table for credit score rating

    dmn decision table example1a
  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.

    Figure 6.20. Defining the decision table hit policy

    dmn hit policies

    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 6.21. Decision table for loan prequalification

    dmn decision table example3

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 6.22. Boxed context expression for front-end client PITI ratio

dmn context expression example2

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 6.23. Boxed function expression for installment calculation in business knowledge model

dmn function expression example3

For more information and examples of each boxed expression type, see Section 4.4, “DMN decision logic in boxed expressions”.

6.2. 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

  • A DMN file is created or imported in Business Central.

Procedure

  1. In Business Central, go to MenuDesignProjects, 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.

    Figure 6.24. Selecting the logic type for a decision node

    dmn decision boxed expression options
  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.

    Figure 6.25. Managing data types for a column header value

    dmn manage data types

    You can also set and manage custom data types for a specified decision node or business knowledge model node by selecting the Properties icon in the upper-right corner of the DMN designer:

    Figure 6.26. Managing data types in decision requirements diagram (DRD) properties

    dmn manage data types1a

    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 New Data Type to add a new data type or click Import Data Object to import an existing data object from your project that you want to use as a DMN data type.

    If you import a data object from your project as a DMN data type and then that object is updated, you must re-import the data object as a DMN data type to apply the changes in your DMN model.

    For this example, click New Data Type and create a Credit_Score_Rating data type as a string:

    Figure 6.27. Adding a new data type

    dmn custom data type add

    If the data type requires a list of items, enable the List setting.

  6. Click Add Constraints, select Enumeration from the drop-down options, and add the following constraints:

    • "Excellent"
    • "Good"
    • "Fair"
    • "Poor"
    • "Bad"

    Figure 6.28. Adding constraints to the new data type

    dmn custom data type constraints

    To change the order of data type constraints, you can click the left end of the constraint row and drag the row as needed:

    Figure 6.29. Dragging constraints to change constraint order

    dmn custom data type constraints drag

    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 the check mark to the right of the data type 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 6.30. Decision table for credit score rating

    dmn decision table example1a

    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:

    Figure 6.31. Decision table for loan prequalification

    dmn manage data types blank
  9. Continuing with this example, return to the Data Types window, click New Data Type, and create a Loan_Qualification data type as a Structure with no constraints.

    When you save the new structured data type, the first sub-field appears so that you can begin defining nested data fields in 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.

    For additional sub-fields, select the addition icon next to the Loan_Qualification data type:

    Figure 6.32. Adding a new structured data type with nested fields

    dmn manage data types structured
  10. 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 the check mark to the right of each data type that you create to save your changes.

    Figure 6.33. Adding nested data types with constraints

    dmn manage data types structured2

    To change the order or nesting of data types, you can click the left end of the data type row and drag the row as needed:

    Figure 6.34. Dragging data types to change data type order or nesting

    dmn manage data types structured2 drag
  11. 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 6.35. Decision table for loan prequalification

    dmn decision table example3

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 6.36. Boxed function expression for online dating compatibility

dmn manage data types structured3

Figure 6.37. Custom data type definitions for online dating compatibility

dmn manage data types structured3a

Figure 6.38. Parameter definitions with custom data types for online dating compatibility

dmn manage data types structured3b

6.3. Included models in DMN files in Business Central

In the DMN designer in Business Central, you can use the Included Models tab to include other DMN models and Predictive Model Markup Language (PMML) models from your project in a specified DMN file. When you include a DMN model within another DMN file, you can use all of the nodes and logic from both models in the same decision requirements diagram (DRD). When you include a PMML model within a DMN file, you can invoke that PMML model as a boxed function expression for a DMN decision node or business knowledge model node.

You cannot include DMN or PMML models from other projects in Business Central.

6.3.1. Including other DMN models within a DMN file in Business Central

In Business Central, you can include other DMN models from your project in a specified DMN file. When you include a DMN model within another DMN file, you can use all of the nodes and logic from both models in the same decision requirements diagram (DRD), but you cannot edit the nodes from the included model. To edit nodes from included models, you must update the source file for the included model directly. If you update the source file for an included DMN model, open the DMN file where the DMN model is included (or close an re-open) to verify the changes.

You cannot include DMN models from other projects in Business Central.

Prerequisites

  • The DMN models are created or imported (as .dmn files) in the same project in Business Central as the DMN file in which you want to include the models.

Procedure

  1. In Business Central, go to MenuDesignProjects, click the project name, and select the DMN file you want to modify.
  2. In the DMN designer, click the Included Models tab.
  3. Click Include Model, select a DMN model from your project in the Models list, enter a unique name for the included model, and click Include:

    Figure 6.39. Including a DMN model

    dmn include model

    The DMN model is added to this DMN file, and all DRD nodes from the included model are listed under Decision Components in the Decision Navigator view:

    Figure 6.40. DMN file with decision components from the included DMN model

    dmn include model list

    All data types from the included model are also listed in read-only mode in the Data Types tab for the DMN file:

    Figure 6.41. DMN file with data types from the included DMN model

    dmn include model data types
  4. In the Model tab of the DMN designer, click and drag the included DRD components onto the canvas to begin implementing them in your DRD:

    Figure 6.42. Adding DRD components from the included DMN model

    dmn include model drd

    To edit DRD nodes or data types from included models, you must update the source file for the included model directly. If you update the source file for an included DMN model, open the DMN file where the DMN model is included (or close an re-open) to verify the changes.

    To edit the included model name or to remove the included model from the DMN file, use the Included Models tab in the DMN designer.

    Important

    When you remove an included model, any nodes from that included model that are currently used in the DRD are also removed.

6.3.2. Including PMML models within a DMN file in Business Central

In Business Central, you can include Predictive Model Markup Language (PMML) models from your project in a specified DMN file. When you include a PMML model within a DMN file, you can invoke that PMML model as a boxed function expression for a DMN decision node or business knowledge model node. If you update the source file for an included PMML model, you must remove and re-include the PMML model in the DMN file to apply the source changes.

You cannot include PMML models from other projects in Business Central.

Prerequisites

  • The PMML models are imported (as .pmml files) in the same project in Business Central as the DMN file in which you want to include the models.

Procedure

  1. In your DMN project, add the following dependencies to the project pom.xml file to enable PMML evaluation:

    <!-- Required for the PMML compiler -->
    <dependency>
      <groupId>org.drools</groupId>
      <artifactId>kie-pmml</artifactId>
      <version>${rhdm.version}</version>
      <scope>provided</scope>
    </dependency>
    
    <!-- Alternative dependencies for JPMML Evaluator, override `kie-pmml` dependency -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-dmn-jpmml</artifactId>
      <version>${rhdm.version}</version>
      <scope>provided</scope>
    </dependency>
    <dependency>
      <groupId>org.jpmml</groupId>
      <artifactId>pmml-evaluator</artifactId>
      <version>1.5.1</version>
      <scope>provided</scope>
    </dependency>
    <dependency>
      <groupId>org.jpmml</groupId>
      <artifactId>pmml-evaluator-extension</artifactId>
      <version>1.5.1</version>
      <scope>provided</scope>
    </dependency>

    To access the project pom.xml file in Business Central, you can select any existing asset in the project and then in the Project Explorer menu on the left side of the screen, click the Customize View gear icon and select Repository Viewpom.xml.

    If you want to use the full PMML specification implementation with the Java Evaluator API for PMML (JPMML), use the alternative set of JPMML dependencies in your DMN project. If the JPMML dependencies and the standard kie-pmml dependency are both present, the kie-pmml dependency is disabled. For information about JPMML licensing terms, see Openscoring.io.

    Important

    The legacy kie-pmml dependency is deprecated with Red Hat Decision Manager 7.10.0 and will be replaced by kie-pmml-trusty dependency in a future Red Hat Decision Manager release.

    Note

    Instead of specifying a Red Hat Decision Manager <version> for individual dependencies, consider adding the Red Hat Business Automation bill of materials (BOM) dependency to dependencyManagement section of your project pom.xml file. The Red Hat Business Automation BOM applies to both Red Hat Decision Manager and Red Hat Process Automation Manager. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

    Example BOM dependency:

    <dependency>
      <groupId>com.redhat.ba</groupId>
      <artifactId>ba-platform-bom</artifactId>
      <version>7.11.0.redhat-00005</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>

    For more information about the Red Hat Business Automation BOM, see What is the mapping between RHDM product and maven library version?.

  2. If you added the JPMML dependencies in your DMN project to use the JPMML Evaluator, download the following JAR files and add them to the ~/kie-server.war/WEB-INF/lib and ~/business-central.war/WEB-INF/lib directories in your Red Hat Decision Manager distribution:

    • kie-dmn-jpmml JAR file in the Red Hat Decision Manager 7.11.0 Maven Repository distribution (rhdm-7.11.0-maven-repository/maven-repository/org/kie/kie-dmn-jpmml/7.52.0.Final-redhat-00007/kie-dmn-jpmml-7.52.0.Final-redhat-00007.jar) from the Red Hat Customer Portal
    • JPMML Evaluator 1.5.1 JAR file from the online Maven repository
    • JPMML Evaluator Extensions 1.5.1 JAR file from the online Maven repository

    These artifacts are required to enable JPMML evaluation in KIE Server and Business Central.

    Important

    Red Hat supports integration with the Java Evaluator API for PMML (JPMML) for PMML execution in Red Hat Decision Manager. However, Red Hat does not support the JPMML libraries directly. If you include JPMML libraries in your Red Hat Decision Manager distribution, see the Openscoring.io licensing terms for JPMML.

  3. In Business Central, go to MenuDesignProjects, click the project name, and select the DMN file you want to modify.
  4. In the DMN designer, click the Included Models tab.
  5. Click Include Model, select a PMML model from your project in the Models list, enter a unique name for the included model, and click Include:

    Figure 6.43. Including a PMML model

    dmn include model pmml

    The PMML model is added to this DMN file:

    Figure 6.44. DMN file with included PMML model

    dmn include model list pmml
  6. In the Model tab of the DMN designer, select or create the decision node or business knowledge model node in which you want to invoke the PMML model and click the Edit icon to open the DMN boxed expression designer:

    Figure 6.45. Opening a new decision node boxed expression

    dmn decision edit

    Figure 6.46. Opening a new business knowledge model boxed expression

    dmn bkm edit
  7. Set the expression type to Function (default for business knowledge model nodes), click the top-left function cell, and select PMML.
  8. In the document and model rows in the table, double-click the undefined cells to specify the included PMML document and the relevant PMML model within that document:

    Figure 6.47. Adding a PMML model in a DMN business knowledge model

    dmn include model expression pmml

    Figure 6.48. Example PMML definition in a DMN business knowledge model

    dmn function expression example5

    If you update the source file for an included PMML model, you must remove and re-include the PMML model in the DMN file to apply the source changes.

    To edit the included model name or to remove the included model from the DMN file, use the Included Models tab in the DMN designer.

6.4. Creating DMN models with multiple diagrams in Business Central

For complex DMN models, you can use the DMN designer in Business Central to design multiple DMN decision requirements diagrams (DRDs) that represent parts of the overall decision requirements graph (DRG) for the DMN decision model. In simple cases, you can use a single DRD to represent all of the overall DRG for the decision model, but in complex cases, a single DRD can become large and difficult to follow. Therefore, to better organize DMN decision models with many decision requirements, you can divide the model into smaller nested DRDs that constitute the larger central DRD representation of the overall DRG.

Prerequisites

Procedure

  1. In Business Central, navigate to your DMN project and create or import a DMN file in the project.
  2. Open the new or imported DMN file to view the DRD in the DMN designer, and begin designing or modifying the DRD using the DMN nodes in the left toolbar.
  3. For any DMN nodes that you want to define in a separate nested DRD, select the node, click the DRD Actions icon, and select from the available options.

    Figure 6.49. DRD actions icon for subdividing a DRD

    dmn drd actions

    The following options are available:

    • Create: Use this option to create a nested DRD where you can separately define the DMN components and diagram for the selected node.
    • Add to: If you already created a nested DRD, use this option to add the selected node to an existing DRD.
    • Remove: If the node that you selected is already within a nested DRD, use this option to remove the node from that nested DRD.

    After you create a nested DRD within your DMN decision model, the new DRD opens in a separate DRD canvas and the available DRD and components are listed in the Decision Navigator left menu. You can use the Decision Navigator menu to rename or remove a nested DRD.

    Figure 6.50. Rename new nested DRD in the Decision Navigator menu

    dmn drd actions rename
  4. In the separate canvas for the new nested DRD, design the flow and logic for all required components in this portion of the DMN model, as usual.
  5. Continue adding and defining any other nested DRDs for your decision model and save the completed DMN file.

    For example, the following DRD for a loan prequalification decision model contains all DMN components for the model without any nested DRDs. This example relies on the single DRD for all components and logic, resulting in a large and complex diagram.

    Figure 6.51. Single DRD for loan prequalification

    dmn example drd

    Alternatively, by following the steps in this procedure, you can divide this example DRD into multiple nested DRDs to better organize the decision requirements, as shown in the following example:

    Figure 6.52. Multiple nested DRDs for loan prequalification

    dmn drd multiple

    Figure 6.53. Overview of front end ratio DRD

    dmn drd multiple front end

    Figure 6.54. DRD for front end ratio

    dmn drd multiple front end details

    Figure 6.55. Overview of credit score rating DRD

    dmn drd multiple credit score

    Figure 6.56. DRD for credit score rating

    dmn drd multiple credit score details

    Figure 6.57. Overview of back end ratio DRD

    dmn drd multiple back end

    Figure 6.58. DRD for back end ratio

    dmn drd multiple back end details

6.5. DMN model documentation in Business Central

In the DMN designer in Business Central, you can use the Documentation tab to generate a report of your DMN model that you can print or download as an HTML file for offline use. The DMN model report contains all decision requirements diagrams (DRDs), data types, and boxed expressions in your DMN model. You can use this report to share your DMN model details or as part of your internal reporting workflow.

Figure 6.59. Example DMN model report

dmn documentation

6.6. DMN designer navigation and properties in Business Central

The DMN designer in Business Central 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 decision components, graphs, and boxed expressions of a selected DRD:

Figure 6.60. Project Explorer view

dmn designer project view

Figure 6.61. Decision Navigator view

dmn designer nav view
dmn designer nav view2
Note

The DRD components from any DMN models included in the DMN file (in the Included Models tab) are also listed in the Decision Components panel for the DMN file.

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 6.62. Explore diagram view

dmn designer preview
DRD properties and design

In the upper-right corner of the DMN designer, select the Properties icon to modify the identifying information, data types, and appearance of a selected DRD, DRD node, or boxed expression cell:

Figure 6.63. DRD node properties

dmn designer properties

To view the properties of the entire DRD, click the DRD canvas background instead of a specific node.

DRD search

In the upper-right corner of the DMN designer, use the search bar to search for text that appears in your DRD. The search feature is especially helpful in complex DRDs with many nodes:

Figure 6.64. DRD search

dmn designer search
DMN decision service details

Select a decision service node in the DMN designer to view additional properties, including Input Data, Encapsulated Decisions, and Output Decisions in the Properties panel.

Figure 6.65. Decision Service details

dmn decision service details

Chapter 7. DMN model execution

You can create or import DMN files in your Red Hat Decision Manager 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 Red Hat Decision Manager 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 Packaging and deploying a Red Hat Decision Manager project.

7.1. 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

  • You have built the DMN project as a KJAR artifact and deployed it to a Maven repository, or you have included your DMN assets as part of your project classpath. Ideally, you have built the DMN project 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 Packaging and deploying a Red Hat Decision Manager project.

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>${rhdm.version}</version>
    </dependency>
    
    <!-- Required if not using classpath KIE container -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-ci</artifactId>
      <version>${rhdm.version}</version>
    </dependency>

    The <version> is the Maven artifact version for Red Hat Decision Manager currently used in your project (for example, 7.52.0.Final-redhat-00007).

    Note

    Instead of specifying a Red Hat Decision Manager <version> for individual dependencies, consider adding the Red Hat Business Automation bill of materials (BOM) dependency to your project pom.xml file. The Red Hat Business Automation BOM applies to both Red Hat Decision Manager and Red Hat Process Automation Manager. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

    Example BOM dependency:

    <dependency>
      <groupId>com.redhat.ba</groupId>
      <artifactId>ba-platform-bom</artifactId>
      <version>7.11.0.redhat-00005</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>

    For more information about the Red Hat Business Automation BOM, see What is the mapping between RHDM product and maven library version?.

  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

7.2. 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 Interacting with Red Hat Decision Manager using 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 Planning a Red Hat Decision Manager installation.
  • You have built the DMN project as a KJAR artifact and deployed it to KIE Server. Ideally, you have built the DMN project 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 Packaging and deploying a Red Hat Decision Manager project.

  • You have the 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 dependency to the relevant classpath of your Java project:

    <!-- Required for the KIE Server Java client API -->
    <dependency>
      <groupId>org.kie.server</groupId>
      <artifactId>kie-server-client</artifactId>
      <version>${rhdm.version}</version>
    </dependency>

    The <version> is the Maven artifact version for Red Hat Decision Manager currently used in your project (for example, 7.52.0.Final-redhat-00007).

    Note

    Instead of specifying a Red Hat Decision Manager <version> for individual dependencies, consider adding the Red Hat Business Automation bill of materials (BOM) dependency to your project pom.xml file. The Red Hat Business Automation BOM applies to both Red Hat Decision Manager and Red Hat Process Automation Manager. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

    Example BOM dependency:

    <dependency>
      <groupId>com.redhat.ba</groupId>
      <artifactId>ba-platform-bom</artifactId>
      <version>7.11.0.redhat-00005</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>

    For more information about the Red Hat Business Automation BOM, see What is the mapping between RHDM product and maven library version?.

  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.

    Note

    If the KIE container only contains one DMN model, you can omit $modelNamespace and $modelName because the KIE Server API selects it by default.

7.3. 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.

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

Example POJO Java class

@javax.xml.bind.annotation.XmlAccessorType(javax.xml.bind.annotation.XmlAccessType.FIELD)
public class Person implements java.io.Serializable {

    static final long serialVersionUID = 1L;

    private java.lang.String id;
    private java.lang.String name;
    @javax.xml.bind.annotation.adapters.XmlJavaTypeAdapter(org.kie.internal.jaxb.LocalDateXmlAdapter.class)
    private java.time.LocalDate dojoining;

    public Person() {
    }

    public java.lang.String getId() {
        return this.id;
    }

    public void setId(java.lang.String id) {
        this.id = id;
    }

    public java.lang.String getName() {
        return this.name;
    }

    public void setName(java.lang.String name) {
        this.name = name;
    }

    public java.time.LocalDate getDojoining() {
        return this.dojoining;
    }

    public void setDojoining(java.time.LocalDate dojoining) {
        this.dojoining = dojoining;
    }

    public Person(java.lang.String id, java.lang.String name,
            java.time.LocalDate dojoining) {
        this.id = id;
        this.name = name;
        this.dojoining = dojoining;
    }

}

For more information about the KIE Server REST API, see Interacting with Red Hat Decision Manager using 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 Planning a Red Hat Decision Manager installation.
  • You have built the DMN project as a KJAR artifact and deployed it to KIE Server. Ideally, you have built the DMN project 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 Packaging and deploying a Red Hat Decision Manager project.

  • You have the 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>
    Note

    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>

7.4. REST endpoints for specific DMN models

Red Hat Decision Manager provides model-specific DMN KIE Server endpoints that you can use to interact with your specific DMN model without using the Business Central user interface.

For each DMN model in a container in Red Hat Decision Manager, the following REST endpoints of the KIE Server are automatically generated based on the content of the DMN model:

  • POST /server/containers/{containerId}/dmn/models/{modelname}: A business-domain endpoint for evaluating a specified DMN model in a container
  • POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}: A business-domain endpoint for evaluating a specified decision service component in a specific DMN model available in a container
  • POST /server/containers/{containerId}/dmn/models/{modelname}/dmnresult: An endpoint for evaluating a specified DMN model containing customized body payload and returning a DMNResult response, including business-domain context, helper messages, and helper decision pointers
  • POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}/dmnresult: An endpoint for evaluating a specified decision service component in a specific DMN model and returning a DMNResult response, including the business-domain context, helper messages, and help decision pointers for the decision service
  • GET /server/containers/{containerId}/dmn/models/{modelname}: An endpoint for returning standard DMN XML without decision logic and containing the inputs and decisions of the specified DMN model
  • GET /server/containers/{containerId}/dmn/openapi.json (|.yaml): An endpoint for retrieving Swagger or OAS for the DMN models in a specified container

You can use these endpoints to interact with a DMN model or a specific decision service within a model. As you decide between using business-domain and dmnresult variants of these REST endpoints, review the following considerations:

  • REST business-domain endpoints: Use this endpoint type if a client application is only concerned with a positive evaluation outcome, is not interested in parsing Info or Warn messages, and only needs an HTTP 5xx response for any errors. This type of endpoint is also helpful for single-page application-like clients, due to singleton coercion of decision service results that resemble the DMN modeling behavior.
  • REST dmnresult endpoints: Use this endpoint type if a client needs to parse Info, Warn, or Error messages in all cases.

For each endpoint, use a REST client or curl utility to send requests with the following components:

  • Base URL: http://HOST:PORT/kie-server/services/rest/
  • Path parameters:

    • {containerId}: The string identifier of the container, such as mykjar-project
    • {modelName}: The string identifier of the DMN model, such as Traffic Violation
    • {decisionServiceName}: The string identifier of the decision service component in the DMN DRG, such as TrafficViolationDecisionService
    • dmnresult: The string identifier that enables the endpoint to return a full DMNResult response with more detailed Info, Warn, and Error messaging
  • HTTP headers: For POST requests only:

    • accept: application/json
    • content-type: application/json
  • HTTP methods: GET or POST

The examples in the following endpoints are based on a mykjar-project container that contains a Traffic Violation DMN model, containing a TrafficViolationDecisionService decision service component.

For all of these endpoints, if a DMN evaluation Error message occurs, a DMNResult response is returned along with an HTTP 5xx error. If a DMN Info or Warn message occurs, the relevant response is returned along with the business-domain REST body, in the X-Kogito-decision-messages extended HTTP header, to be used for client-side business logic. When there is a requirement of more refined client-side business logic, the client can use the dmnresult variant of the endpoints.

Retrieve Swagger or OAS for DMN models in a specified container

GET /server/containers/{containerId}/dmn/openapi.json (|.yaml)

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/openapi.json (|.yaml)

Return the DMN XML without decision logic

GET /server/containers/{containerId}/dmn/models/{modelname}

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation

Example curl request

curl -u wbadmin:wbadmin -X GET "http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic%20Violation" -H  "accept: application/xml"

Example response (XML)

<?xml version='1.0' encoding='UTF-8'?>
<dmn:definitions xmlns:dmn="http://www.omg.org/spec/DMN/20180521/MODEL/" xmlns="https://github.com/kiegroup/drools/kie-dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF" xmlns:di="http://www.omg.org/spec/DMN/20180521/DI/" xmlns:kie="http://www.drools.org/kie/dmn/1.2" xmlns:feel="http://www.omg.org/spec/DMN/20180521/FEEL/" xmlns:dmndi="http://www.omg.org/spec/DMN/20180521/DMNDI/" xmlns:dc="http://www.omg.org/spec/DMN/20180521/DC/" id="_1C792953-80DB-4B32-99EB-25FBE32BAF9E" name="Traffic Violation" expressionLanguage="http://www.omg.org/spec/DMN/20180521/FEEL/" typeLanguage="http://www.omg.org/spec/DMN/20180521/FEEL/" namespace="https://github.com/kiegroup/drools/kie-dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF">
  <dmn:extensionElements/>
  <dmn:itemDefinition id="_63824D3F-9173-446D-A940-6A7F0FA056BB" name="tDriver" isCollection="false">
    <dmn:itemComponent id="_9DAB5DAA-3B44-4F6D-87F2-95125FB2FEE4" name="Name" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_856BA8FA-EF7B-4DF9-A1EE-E28263CE9955" name="Age" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_FDC2CE03-D465-47C2-A311-98944E8CC23F" name="State" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_D6FD34C4-00DC-4C79-B1BF-BBCF6FC9B6D7" name="City" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_7110FE7E-1A38-4C39-B0EB-AEEF06BA37F4" name="Points" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_40731093-0642-4588-9183-1660FC55053B" name="tViolation" isCollection="false">
    <dmn:itemComponent id="_39E88D9F-AE53-47AD-B3DE-8AB38D4F50B3" name="Code" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_1648EA0A-2463-4B54-A12A-D743A3E3EE7B" name="Date" isCollection="false">
      <dmn:typeRef>date</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_9F129EAA-4E71-4D99-B6D0-84EEC3AC43CC" name="Type" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
      <dmn:allowedValues kie:constraintType="enumeration" id="_626A8F9C-9DD1-44E0-9568-0F6F8F8BA228">
        <dmn:text>"speed", "parking", "driving under the influence"</dmn:text>
      </dmn:allowedValues>
    </dmn:itemComponent>
    <dmn:itemComponent id="_DDD10D6E-BD38-4C79-9E2F-8155E3A4B438" name="Speed Limit" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_229F80E4-2892-494C-B70D-683ABF2345F6" name="Actual Speed" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_2D4F30EE-21A6-4A78-A524-A5C238D433AE" name="tFine" isCollection="false">
    <dmn:itemComponent id="_B9F70BC7-1995-4F51-B949-1AB65538B405" name="Amount" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_F49085D6-8F08-4463-9A1A-EF6B57635DBD" name="Points" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:inputData id="_1929CBD5-40E0-442D-B909-49CEDE0101DC" name="Violation">
    <dmn:variable id="_C16CF9B1-5FAB-48A0-95E0-5FCD661E0406" name="Violation" typeRef="tViolation"/>
  </dmn:inputData>
  <dmn:decision id="_4055D956-1C47-479C-B3F4-BAEB61F1C929" name="Fine">
    <dmn:variable id="_8C1EAC83-F251-4D94-8A9E-B03ACF6849CD" name="Fine" typeRef="tFine"/>
    <dmn:informationRequirement id="_800A3BBB-90A3-4D9D-BA5E-A311DED0134F">
      <dmn:requiredInput href="#_1929CBD5-40E0-442D-B909-49CEDE0101DC"/>
    </dmn:informationRequirement>
  </dmn:decision>
  <dmn:inputData id="_1F9350D7-146D-46F1-85D8-15B5B68AF22A" name="Driver">
    <dmn:variable id="_A80F16DF-0DB4-43A2-B041-32900B1A3F3D" name="Driver" typeRef="tDriver"/>
  </dmn:inputData>
  <dmn:decision id="_8A408366-D8E9-4626-ABF3-5F69AA01F880" name="Should the driver be suspended?">
    <dmn:question>Should the driver be suspended due to points on his license?</dmn:question>
    <dmn:allowedAnswers>"Yes", "No"</dmn:allowedAnswers>
    <dmn:variable id="_40387B66-5D00-48C8-BB90-E83EE3332C72" name="Should the driver be suspended?" typeRef="string"/>
    <dmn:informationRequirement id="_982211B1-5246-49CD-BE85-3211F71253CF">
      <dmn:requiredInput href="#_1F9350D7-146D-46F1-85D8-15B5B68AF22A"/>
    </dmn:informationRequirement>
    <dmn:informationRequirement id="_AEC4AA5F-50C3-4FED-A0C2-261F90290731">
      <dmn:requiredDecision href="#_4055D956-1C47-479C-B3F4-BAEB61F1C929"/>
    </dmn:informationRequirement>
  </dmn:decision>
  <dmndi:DMNDI>
    <dmndi:DMNDiagram>
      <di:extension/>
      <dmndi:DMNShape id="dmnshape-_1929CBD5-40E0-442D-B909-49CEDE0101DC" dmnElementRef="_1929CBD5-40E0-442D-B909-49CEDE0101DC" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="708" y="350" width="100" height="50"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNShape id="dmnshape-_4055D956-1C47-479C-B3F4-BAEB61F1C929" dmnElementRef="_4055D956-1C47-479C-B3F4-BAEB61F1C929" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="709" y="210" width="100" height="50"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNShape id="dmnshape-_1F9350D7-146D-46F1-85D8-15B5B68AF22A" dmnElementRef="_1F9350D7-146D-46F1-85D8-15B5B68AF22A" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="369" y="344" width="100" height="50"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNShape id="dmnshape-_8A408366-D8E9-4626-ABF3-5F69AA01F880" dmnElementRef="_8A408366-D8E9-4626-ABF3-5F69AA01F880" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="534" y="83" width="133" height="63"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNEdge id="dmnedge-_800A3BBB-90A3-4D9D-BA5E-A311DED0134F" dmnElementRef="_800A3BBB-90A3-4D9D-BA5E-A311DED0134F">
        <di:waypoint x="758" y="375"/>
        <di:waypoint x="759" y="235"/>
      </dmndi:DMNEdge>
      <dmndi:DMNEdge id="dmnedge-_982211B1-5246-49CD-BE85-3211F71253CF" dmnElementRef="_982211B1-5246-49CD-BE85-3211F71253CF">
        <di:waypoint x="419" y="369"/>
        <di:waypoint x="600.5" y="114.5"/>
      </dmndi:DMNEdge>
      <dmndi:DMNEdge id="dmnedge-_AEC4AA5F-50C3-4FED-A0C2-261F90290731" dmnElementRef="_AEC4AA5F-50C3-4FED-A0C2-261F90290731">
        <di:waypoint x="759" y="235"/>
        <di:waypoint x="600.5" y="114.5"/>
      </dmndi:DMNEdge>
    </dmndi:DMNDiagram>
  </dmndi:DMNDI>

Evaluate a specified DMN model in a specified container

POST /server/containers/{containerId}/dmn/models/{modelname}

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation

Example curl request

curl -u wbadmin:wbadmin-X POST "http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation" -H  "accept: application/json" -H  "Content-Type: application/json" -d "{\"Driver\":{\"Points\":15},\"Violation\":{\"Date\":\"2021-04-08\",\"Type\":\"speed\",\"Actual Speed\":135,\"Speed Limit\":100}}"

Example POST request body with input data

{
  "Driver": {
    "Points": 15
  },
  "Violation": {
    "Date": "2021-04-08",
    "Type": "speed",
    "Actual Speed": 135,
    "Speed Limit": 100
  }
}

Example response (JSON)

{
  "Violation": {
    "Type": "speed",
    "Speed Limit": 100,
    "Actual Speed": 135,
    "Code": null,
    "Date": "2021-04-08"
  },
  "Driver": {
    "Points": 15,
    "State": null,
    "City": null,
    "Age": null,
    "Name": null
  },
  "Fine": {
    "Points": 7,
    "Amount": 1000
  },
  "Should the driver be suspended?": "Yes"
}

Evaluate a specified decision service within a specified DMN model in a container

POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}

For this endpoint, the request body must contain all the requirements of the decision service. The response is the resulting DMN context of the decision service, including the decision values, the original input values, and all other parametric DRG components in serialized form. For example, a business knowledge model is available in string-serialized form in its signature.

If the decision service is composed of a single-output decision, the response is the resulting value of that specific decision. This behavior provides an equivalent value at the API level of a specification feature when invoking the decision service in the model itself. As a result, you can, for example, interact with a DMN decision service from single-page web applications.

Figure 7.1. Example TrafficViolationDecisionService decision service with single-output decision

Image of decision service in Traffic Violation DMN model

Figure 7.2. Example TrafficViolationDecisionService decision service with multiple-output decision

Image of decision service in Traffic Violation DMN model

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService

Example POST request body with input data

{
  "Driver": {
    "Points": 2
  },
  "Violation": {
    "Type": "speed",
    "Actual Speed": 120,
    "Speed Limit": 100
  }
}

Example curl request

curl -X POST http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService -H 'content-type: application/json' -H 'accept: application/json' -d '{"Driver": {"Points": 2}, "Violation": {"Type": "speed", "Actual Speed": 120, "Speed Limit": 100}}'

Example response for single-output decision (JSON)

"No"

Example response for multiple-output decision (JSON)

{
  "Violation": {
    "Type": "speed",
    "Speed Limit": 100,
    "Actual Speed": 120
  },
  "Driver": {
    "Points": 2
  },
  "Fine": {
    "Points": 3,
    "Amount": 500
  },
  "Should the driver be suspended?": "No"
}

Evaluate a specified DMN model in a specified container and return a DMNResult response

POST /server/containers/{containerId}/dmn/models/{modelname}/dmnresult

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/dmnresult

Example POST request body with input data

{
  "Driver": {
    "Points": 2
  },
  "Violation": {
    "Type": "speed",
    "Actual Speed": 120,
    "Speed Limit": 100
  }
}

Example curl request

curl -X POST http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/dmnresult -H 'content-type: application/json' -H 'accept: application/json' -d '{"Driver": {"Points": 2}, "Violation": {"Type": "speed", "Actual Speed": 120, "Speed Limit": 100}}'

Example response (JSON)

{
  "namespace": "https://github.com/kiegroup/drools/kie-dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF",
  "modelName": "Traffic Violation",
  "dmnContext": {
    "Violation": {
      "Type": "speed",
      "Speed Limit": 100,
      "Actual Speed": 120,
      "Code": null,
      "Date": null
    },
    "Driver": {
      "Points": 2,
      "State": null,
      "City": null,
      "Age": null,
      "Name": null
    },
    "Fine": {
      "Points": 3,
      "Amount": 500
    },
    "Should the driver be suspended?": "No"
  },
  "messages": [],
  "decisionResults": [
    {
      "decisionId": "_4055D956-1C47-479C-B3F4-BAEB61F1C929",
      "decisionName": "Fine",
      "result": {
        "Points": 3,
        "Amount": 500
      },
      "messages": [],
      "evaluationStatus": "SUCCEEDED"
    },
    {
      "decisionId": "_8A408366-D8E9-4626-ABF3-5F69AA01F880",
      "decisionName": "Should the driver be suspended?",
      "result": "No",
      "messages": [],
      "evaluationStatus": "SUCCEEDED"
    }
  ]
}

Evaluate a specified decision service within a DMN model in a specified container and return a DMNResult response

POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}/dmnresult

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService/dmnresult

Example POST request body with input data

{
  "Driver": {
    "Points": 2
  },
  "Violation": {
    "Type": "speed",
    "Actual Speed": 120,
    "Speed Limit": 100
  }
}

Example curl request

curl -X POST http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService/dmnresult -H 'content-type: application/json' -H 'accept: application/json' -d '{"Driver": {"Points": 2}, "Violation": {"Type": "speed", "Actual Speed": 120, "Speed Limit": 100}}'

Example response (JSON)

{
  "namespace": "https://github.com/kiegroup/drools/kie-dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF",
  "modelName": "Traffic Violation",
  "dmnContext": {
    "Violation": {
      "Type": "speed",
      "Speed Limit": 100,
      "Actual Speed": 120,
      "Code": null,
      "Date": null
    },
    "Driver": {
      "Points": 2,
      "State": null,
      "City": null,
      "Age": null,
      "Name": null
    },
    "Should the driver be suspended?": "No"
  },
  "messages": [],
  "decisionResults": [
    {
      "decisionId": "_8A408366-D8E9-4626-ABF3-5F69AA01F880",
      "decisionName": "Should the driver be suspended?",
      "result": "No",
      "messages": [],
      "evaluationStatus": "SUCCEEDED"
    }
  ]
}

Chapter 8. Additional resources

Part II. Designing a decision service using PMML models

As a business rules developer, you can use Predictive Model Markup Language (PMML) to define statistical or data-mining models that you can integrate with your decision services in Red Hat Decision Manager. Red Hat Decision Manager includes consumer conformance support of PMML 4.2.1 for Regression, Scorecard, Tree, and Mining models. Red Hat Decision Manager does not include a built-in PMML model editor, but you can use an XML or PMML-specific authoring tool to create PMML models and then integrate them with your Red Hat Decision Manager projects.

For more information about PMML, see the DMG PMML specification.

Note

You can also design your decision service using Decision Model and Notation (DMN) models and include your PMML models as part of your DMN service. For information about DMN support in Red Hat Decision Manager 7.11, see the following resources:

Chapter 9. Decision-authoring assets in Red Hat Decision Manager

Red Hat Decision Manager 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 Red Hat Decision Manager projects to help you decide or confirm the best method for defining decisions in your decision service.

Table 9.1. Decision-authoring assets supported in Red Hat Decision Manager

AssetHighlightsAuthoring toolsDocumentation

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) that represent part or all of the overall decision requirements graph (DRG) 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

Designing a decision service using DMN models

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

Designing a decision service using guided decision tables

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

Designing a decision service using spreadsheet 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

Designing a decision service using guided rules

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

Designing a decision service using guided rule templates

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 Red Hat Decision Manager
  • 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)

Designing a decision service using DRL rules

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 Red Hat Decision Manager project or imported into a project in Business Central
  • Are optimal for incorporating predictive data into decision services in Red Hat Decision Manager

PMML or XML editor

Designing a decision service using PMML models

Chapter 10. Predictive Model Markup Language (PMML)

Predictive Model Markup Language (PMML) is an XML-based standard established by the Data Mining Group (DMG) for defining statistical and data-mining models. PMML models can be shared between PMML-compliant platforms and across organizations so that business analysts and developers are unified in designing, analyzing, and implementing PMML-based assets and services.

For more information about the background and applications of PMML, see the DMG PMML specification.

10.1. PMML conformance levels

The PMML specification defines producer and consumer conformance levels in a software implementation to ensure that PMML models are created and integrated reliably. For the formal definitions of each conformance level, see the DMG PMML conformance page.

The following list summarizes the PMML conformance levels:

Producer conformance
A tool or application is producer conforming if it generates valid PMML documents for at least one type of model. Satisfying PMML producer conformance requirements ensures that a model definition document is syntactically correct and defines a model instance that is consistent with semantic criteria that are defined in model specifications.
Consumer conformance
An application is consumer conforming if it accepts valid PMML documents for at least one type of model. Satisfying consumer conformance requirements ensures that a PMML model created according to producer conformance can be integrated and used as defined. For example, if an application is consumer conforming for Regression model types, then valid PMML documents defining models of this type produced by different conforming producers would be interchangeable in the application.

Red Hat Decision Manager includes consumer conformance support for the following PMML 4.2.1 model types:

For a list of all PMML model types, including those not supported in Red Hat Decision Manager, see the DMG PMML specification.

Chapter 11. PMML model examples

PMML defines an XML schema that enables PMML models to be used between different PMML-compliant platforms. The PMML specification enables multiple software platforms to work with the same file for authoring, testing, and production execution, assuming producer and consumer conformance are met.

The following are examples of PMML Regression, Scorecard, Tree, and Mining models. These examples illustrate the supported types of models that you can integrate with your decision services in Red Hat Decision Manager.

For more PMML examples, see the DMG PMML Sample Files page.

Example PMML Regression model

<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2-1/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
  <Header copyright="JBoss"/>
  <DataDictionary numberOfFields="5">
    <DataField dataType="double" name="fld1" optype="continuous"/>
    <DataField dataType="double" name="fld2" optype="continuous"/>
    <DataField dataType="string" name="fld3" optype="categorical">
      <Value value="x"/>
      <Value value="y"/>
    </DataField>
    <DataField dataType="double" name="fld4" optype="continuous"/>
    <DataField dataType="double" name="fld5" optype="continuous"/>
  </DataDictionary>
  <RegressionModel algorithmName="linearRegression" functionName="regression" modelName="LinReg" normalizationMethod="logit" targetFieldName="fld4">
    <MiningSchema>
      <MiningField name="fld1"/>
      <MiningField name="fld2"/>
      <MiningField name="fld3"/>
      <MiningField name="fld4" usageType="predicted"/>
      <MiningField name="fld5" usageType="target"/>
    </MiningSchema>
    <RegressionTable intercept="0.5">
      <NumericPredictor coefficient="5" exponent="2" name="fld1"/>
      <NumericPredictor coefficient="2" exponent="1" name="fld2"/>
      <CategoricalPredictor coefficient="-3" name="fld3" value="x"/>
      <CategoricalPredictor coefficient="3" name="fld3" value="y"/>
      <PredictorTerm coefficient="0.4">
        <FieldRef field="fld1"/>
        <FieldRef field="fld2"/>
      </PredictorTerm>
    </RegressionTable>
  </RegressionModel>
</PMML>

Example PMML Scorecard model

<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2-1/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
  <Header copyright="JBoss"/>
  <DataDictionary numberOfFields="4">
    <DataField name="param1" optype="continuous" dataType="double"/>
    <DataField name="param2" optype="continuous" dataType="double"/>
    <DataField name="overallScore" optype="continuous" dataType="double" />
    <DataField name="finalscore" optype="continuous" dataType="double" />
  </DataDictionary>
  <Scorecard modelName="ScorecardCompoundPredicate" useReasonCodes="true" isScorable="true" functionName="regression"    baselineScore="15" initialScore="0.8" reasonCodeAlgorithm="pointsAbove">
    <MiningSchema>
      <MiningField name="param1" usageType="active" invalidValueTreatment="asMissing">
      </MiningField>
      <MiningField name="param2" usageType="active" invalidValueTreatment="asMissing">
      </MiningField>
      <MiningField name="overallScore" usageType="target"/>
      <MiningField name="finalscore" usageType="predicted"/>
    </MiningSchema>
    <Characteristics>
      <Characteristic name="ch1" baselineScore="50" reasonCode="reasonCh1">
        <Attribute partialScore="20">
          <SimplePredicate field="param1" operator="lessThan" value="20"/>
        </Attribute>
        <Attribute partialScore="100">
          <CompoundPredicate booleanOperator="and">
            <SimplePredicate field="param1" operator="greaterOrEqual" value="20"/>
            <SimplePredicate field="param2" operator="lessOrEqual" value="25"/>
          </CompoundPredicate>
        </Attribute>
        <Attribute partialScore="200">
          <CompoundPredicate booleanOperator="and">
            <SimplePredicate field="param1" operator="greaterOrEqual" value="20"/>
            <SimplePredicate field="param2" operator="greaterThan" value="25"/>
          </CompoundPredicate>
        </Attribute>
      </Characteristic>
      <Characteristic name="ch2" reasonCode="reasonCh2">
        <Attribute partialScore="10">
          <CompoundPredicate booleanOperator="or">
            <SimplePredicate field="param2" operator="lessOrEqual" value="-5"/>
            <SimplePredicate field="param2" operator="greaterOrEqual" value="50"/>
          </CompoundPredicate>
        </Attribute>
        <Attribute partialScore="20">
          <CompoundPredicate booleanOperator="and">
            <SimplePredicate field="param2" operator="greaterThan" value="-5"/>
            <SimplePredicate field="param2" operator="lessThan" value="50"/>
          </CompoundPredicate>
        </Attribute>
      </Characteristic>
    </Characteristics>
  </Scorecard>
</PMML>

Example PMML Tree model

<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2-1/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.dmg.org/PMML-4_2">
  <Header copyright="JBOSS"/>
  <DataDictionary numberOfFields="5">
    <DataField dataType="double" name="fld1" optype="continuous"/>
    <DataField dataType="double" name="fld2" optype="continuous"/>
    <DataField dataType="string" name="fld3" optype="categorical">
      <Value value="true"/>
      <Value value="false"/>
    </DataField>
    <DataField dataType="string" name="fld4" optype="categorical">
      <Value value="optA"/>
      <Value value="optB"/>
      <Value value="optC"/>
    </DataField>
    <DataField dataType="string" name="fld5" optype="categorical">
      <Value value="tgtX"/>
      <Value value="tgtY"/>
      <Value value="tgtZ"/>
    </DataField>
  </DataDictionary>
  <TreeModel functionName="classification" modelName="TreeTest">
    <MiningSchema>
      <MiningField name="fld1"/>
      <MiningField name="fld2"/>
      <MiningField name="fld3"/>
      <MiningField name="fld4"/>
      <MiningField name="fld5" usageType="predicted"/>
    </MiningSchema>
    <Node score="tgtX">
      <True/>
      <Node score="tgtX">
        <SimplePredicate field="fld4" operator="equal" value="optA"/>
        <Node score="tgtX">
          <CompoundPredicate booleanOperator="surrogate">
            <SimplePredicate field="fld1" operator="lessThan" value="30.0"/>
            <SimplePredicate field="fld2" operator="greaterThan" value="20.0"/>
          </CompoundPredicate>
          <Node score="tgtX">
            <SimplePredicate field="fld2" operator="lessThan" value="40.0"/>
          </Node>
          <Node score="tgtZ">
            <SimplePredicate field="fld2" operator="greaterOrEqual" value="10.0"/>
          </Node>
        </Node>
        <Node score="tgtZ">
          <CompoundPredicate booleanOperator="or">
            <SimplePredicate field="fld1" operator="greaterOrEqual" value="60.0"/>
            <SimplePredicate field="fld1" operator="lessOrEqual" value="70.0"/>
          </CompoundPredicate>
          <Node score="tgtZ">
            <SimpleSetPredicate booleanOperator="isNotIn" field="fld4">
              <Array type="string">optA optB</Array>
            </SimpleSetPredicate>
          </Node>
        </Node>
      </Node>
      <Node score="tgtY">
        <CompoundPredicate booleanOperator="or">
          <SimplePredicate field="fld4" operator="equal" value="optA"/>
          <SimplePredicate field="fld4" operator="equal" value="optC"/>
        </CompoundPredicate>
        <Node score="tgtY">
          <CompoundPredicate booleanOperator="and">
            <SimplePredicate field="fld1" operator="greaterThan" value="10.0"/>
            <SimplePredicate field="fld1" operator="lessThan" value="50.0"/>
            <SimplePredicate field="fld4" operator="equal" value="optA"/>
            <SimplePredicate field="fld2" operator="lessThan" value="100.0"/>
            <SimplePredicate field="fld3" operator="equal" value="false"/>
          </CompoundPredicate>
        </Node>
        <Node score="tgtZ">
          <CompoundPredicate booleanOperator="and">
            <SimplePredicate field="fld4" operator="equal" value="optC"/>
            <SimplePredicate field="fld2" operator="lessThan" value="30.0"/>
          </CompoundPredicate>
        </Node>
      </Node>
    </Node>
  </TreeModel>
</PMML>

Example PMML Mining model (modelChain)

<PMML version="4.2" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2-1/pmml-4-2.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"   xmlns="http://www.dmg.org/PMML-4_2">
  <Header>
    <Application name="Drools-PMML" version="7.0.0-SNAPSHOT" />
  </Header>
  <DataDictionary numberOfFields="7">
    <DataField name="age" optype="continuous" dataType="double" />
    <DataField name="occupation" optype="categorical" dataType="string">
      <Value value="SKYDIVER" />
      <Value value="ASTRONAUT" />
      <Value value="PROGRAMMER" />
      <Value value="TEACHER" />
      <Value value="INSTRUCTOR" />
    </DataField>
    <DataField name="residenceState" optype="categorical" dataType="string">
      <Value value="AP" />
      <Value value="KN" />
      <Value value="TN" />
    </DataField>
    <DataField name="validLicense" optype="categorical" dataType="boolean" />
    <DataField name="overallScore" optype="continuous" dataType="double" />
    <DataField name="grade" optype="categorical" dataType="string">
      <Value value="A" />
      <Value value="B" />
      <Value value="C" />
      <Value value="D" />
      <Value value="F" />
    </DataField>
    <DataField name="qualificationLevel" optype="categorical" dataType="string">
      <Value value="Unqualified" />
      <Value value="Barely" />
      <Value value="Well" />
      <Value value="Over" />
    </DataField>
  </DataDictionary>
  <MiningModel modelName="SampleModelChainMine" functionName="classification">
    <MiningSchema>
      <MiningField name="age" />
      <MiningField name="occupation" />
      <MiningField name="residenceState" />
      <MiningField name="validLicense" />
      <MiningField name="overallScore" />
      <MiningField name="qualificationLevel" usageType="target"/>
    </MiningSchema>
    <Segmentation multipleModelMethod="modelChain">
      <Segment id="1">
        <True />
        <Scorecard modelName="Sample Score 1" useReasonCodes="true" isScorable="true" functionName="regression"               baselineScore="0.0" initialScore="0.345">
          <MiningSchema>
            <MiningField name="age" usageType="active" invalidValueTreatment="asMissing" />
            <MiningField name="occupation" usageType="active" invalidValueTreatment="asMissing" />
            <MiningField name="residenceState" usageType="active" invalidValueTreatment="asMissing" />
            <MiningField name="validLicense" usageType="active" invalidValueTreatment="asMissing" />
            <MiningField name="overallScore" usageType="predicted" />
          </MiningSchema>
          <Output>
            <OutputField name="calculatedScore" displayName="Final Score" dataType="double" feature="predictedValue"                     targetField="overallScore" />
          </Output>
          <Characteristics>
            <Characteristic name="AgeScore" baselineScore="0.0" reasonCode="ABZ">
              <Extension name="cellRef" value="$B$8" />
              <Attribute partialScore="10.0">
                <Extension name="cellRef" value="$C$10" />
                <SimplePredicate field="age" operator="lessOrEqual" value="5" />
              </Attribute>
              <Attribute partialScore="30.0" reasonCode="CX1">
                <Extension name="cellRef" value="$C$11" />
                <CompoundPredicate booleanOperator="and">
                  <SimplePredicate field="age" operator="greaterOrEqual" value="5" />
                  <SimplePredicate field="age" operator="lessThan" value="12" />
                </CompoundPredicate>
              </Attribute>
              <Attribute partialScore="40.0" reasonCode="CX2">
                <Extension name="cellRef" value="$C$12" />
                <CompoundPredicate booleanOperator="and">
                  <SimplePredicate field="age" operator="greaterOrEqual" value="13" />
                  <SimplePredicate field="age" operator="lessThan" value="44" />
                </CompoundPredicate>
              </Attribute>
              <Attribute partialScore="25.0">
                <Extension name="cellRef" value="$C$13" />
                <SimplePredicate field="age" operator="greaterOrEqual" value="45" />
              </Attribute>
            </Characteristic>
            <Characteristic name="OccupationScore" baselineScore="0.0">
              <Extension name="cellRef" value="$B$16" />
              <Attribute partialScore="-10.0" reasonCode="CX2">
                <Extension name="description" value="skydiving is a risky occupation" />
                <Extension name="cellRef" value="$C$18" />
                <SimpleSetPredicate field="occupation" booleanOperator="isIn">
                  <Array n="2" type="string">SKYDIVER ASTRONAUT</Array>
                </SimpleSetPredicate>
              </Attribute>
              <Attribute partialScore="10.0">
                <Extension name="cellRef" value="$C$19" />
                <SimpleSetPredicate field="occupation" booleanOperator="isIn">
                  <Array n="2" type="string">TEACHER INSTRUCTOR</Array>
                </SimpleSetPredicate>
              </Attribute>
              <Attribute partialScore="5.0">
                <Extension name="cellRef" value="$C$20" />
                <SimplePredicate field="occupation" operator="equal" value="PROGRAMMER" />
              </Attribute>
            </Characteristic>
            <Characteristic name="ResidenceStateScore" baselineScore="0.0" reasonCode="RES">
              <Extension name="cellRef" value="$B$22" />
              <Attribute partialScore="-10.0">
                <Extension name="cellRef" value="$C$24" />
                <SimplePredicate field="residenceState" operator="equal" value="AP" />
              </Attribute>
              <Attribute partialScore="10.0">
                <Extension name="cellRef" value="$C$25" />
                <SimplePredicate field="residenceState" operator="equal" value="KN" />
              </Attribute>
              <Attribute partialScore="5.0">
                <Extension name="cellRef" value="$C$26" />
                <SimplePredicate field="residenceState" operator="equal" value="TN" />
              </Attribute>
            </Characteristic>
            <Characteristic name="ValidLicenseScore" baselineScore="0.0">
              <Extension name="cellRef" value="$B$28" />
              <Attribute partialScore="1.0" reasonCode="LX00">
                <Extension name="cellRef" value="$C$30" />
                <SimplePredicate field="validLicense" operator="equal" value="true" />
              </Attribute>
              <Attribute partialScore="-1.0" reasonCode="LX00">
                <Extension name="cellRef" value="$C$31" />
                <SimplePredicate field="validLicense" operator="equal" value="false" />
              </Attribute>
            </Characteristic>
          </Characteristics>
        </Scorecard>
      </Segment>
      <Segment id="2">
        <True />
        <TreeModel modelName="SampleTree" functionName="classification" missingValueStrategy="lastPrediction" noTrueChildStrategy="returnLastPrediction">
          <MiningSchema>
            <MiningField name="age" usageType="active" />
            <MiningField name="validLicense" usageType="active" />
            <MiningField name="calculatedScore" usageType="active" />
            <MiningField name="qualificationLevel" usageType="predicted" />
          </MiningSchema>
          <Output>
            <OutputField name="qualification" displayName="Qualification Level" dataType="string" feature="predictedValue"                     targetField="qualificationLevel" />
          </Output>
          <Node score="Well" id="1">
            <True/>
            <Node score="Barely" id="2">
              <CompoundPredicate booleanOperator="and">
                <SimplePredicate field="age" operator="greaterOrEqual" value="16" />
                <SimplePredicate field="validLicense" operator="equal" value="true" />
              </CompoundPredicate>
              <Node score="Barely" id="3">
                <SimplePredicate field="calculatedScore" operator="lessOrEqual" value="50.0" />
              </Node>
              <Node score="Well" id="4">
                <CompoundPredicate booleanOperator="and">
                  <SimplePredicate field="calculatedScore" operator="greaterThan" value="50.0" />
                  <SimplePredicate field="calculatedScore" operator="lessOrEqual" value="60.0" />
                </CompoundPredicate>
              </Node>
              <Node score="Over" id="5">
                <SimplePredicate field="calculatedScore" operator="greaterThan" value="60.0" />
              </Node>
            </Node>
            <Node score="Unqualified" id="6">
              <CompoundPredicate booleanOperator="surrogate">
                <SimplePredicate field="age" operator="lessThan" value="16" />
                <SimplePredicate field="calculatedScore" operator="lessOrEqual" value="40.0" />
                <True />
              </CompoundPredicate>
            </Node>
          </Node>
        </TreeModel>
      </Segment>
    </Segmentation>
  </MiningModel>
</PMML>

Chapter 12. PMML support in Red Hat Decision Manager

Red Hat Decision Manager includes consumer conformance support for the following PMML model types:

For a list of all PMML model types, including those not supported in Red Hat Decision Manager, see the DMG PMML specification.

Red Hat Decision Manager offers two implementations including PMML legacy and PMML trusty.

Important

The PMML legacy implementation is deprecated with Red Hat Decision Manager 7.10.0 and will be replaced by PMML trusty implementation in a future Red Hat Decision Manager release.

Red Hat Decision Manager does not include a built-in PMML model editor, but you can use an XML or PMML-specific authoring tool to create PMML models and then integrate the PMML models in your decision services in Red Hat Decision Manager. You can import PMML files into your project in Business Central (Menu → Design → Projects → Import Asset) or package the PMML files as part of your project knowledge JAR (KJAR) file without Business Central.

For more information about including assets such as PMML files with your project packaging and deployment method, see Packaging and deploying a Red Hat Decision Manager project.

12.1. PMML trusty support and naming conventions in Red Hat Decision Manager

When you add a PMML file to a project in Red Hat Decision Manager, multiple assets are generated. The tree and scorecard models are translated to rules, and regression and mining models are translated to Java classes. Each type of PMML model generates a different set of assets, but all PMML model types generate at least the following set of assets:

  • A root package whose name is derived from the PMML file name
  • In the root package, a Java factory class that is used to instantiate the model
  • A subpackage specific to the model whose name is derived from the model name
  • For rule models, two rule-mapper classes that are used to instantiate the rule network
  • For mining models, children model packages and classes are nested in the parent model
Note

Currently, only one model for each PMML file is allowed. Also, extensions are temporarily not supported.

The following are naming conventions for generated PMML packages and classes:

  • The root package name is the name of the original PMML file in lowercase and without space, for example, sampleregression.
  • The name of the generated factory Java class is the PMML file name with Factory added to it in the format fileName+"Factory" and first uppercase letter, for example, SampleRegressionFactory.
  • The subpackage name of a model is the name of the original model in lowercase and without space, for example, compoundnestedpredicatescorecard.
  • The names of the generated data classes are determined by the model type:

    • Rules models: A top-level PMMLRuleMappersImpl is generated including references to PMMLRuleMapperImpl classes that are nested in the subpackages.
    • Mining models:

      • The name of the created segmentation subpackage is the name of the original model in lowercase, without space, and segmentation added to it in the format modelName+”segmentation”, for example, mixedminingsegmentation.
      • In the segmentation subpackage, a segmentation Java class is created that contains the references to the nested models. The name of the created segmentation Java class is the model name with Segmentation added to it in the format modelName+Segmentation, for example, MixedMiningSegmentation.
      • For each segment, a specific subpackage is created. The name of the segment specific subpackage is the original model name in lowercase with segment and a progressive integer starting from 0 added to it in the format modelName+segment+integer. For example, mixedminingsegment0, mixedminingsegment1.

Known limitations of PMML trusty implementation

The following list shows elements that are not implemented for PMML trusty:

  • Target element is not implemented
  • Extension element is not implemented
  • MiningSchema or MiningField elements that are not implemented, include:

    • importance
    • outliers
    • lowValue
    • highValue
    • invalidValueTreatment
    • invalidValueReplacement
  • OutputField elements that are not implemented, include:

    • Decisions
    • Value
    • Rule feature
    • Algorithm
    • isMultiValued
    • segmentId
    • isFinalResult
  • TransformationDictionary or LocalTransformation expressions that are not supported, include:

    • NormContinuous
    • NormDiscrete
    • MapValues
    • TextIndex
    • Aggregate
    • Lag
  • ModelStats, ModelExplanation, and ModelExplanation element is not implemented in all models including regression, tree, scorecard, and mining
  • verification element is not implemented in tree, scorecard, and mining model
  • VariableWeight element is not implemented in mining model
  • Tree model elements that are not implemented, include:

    • IsMissing or IsNotMissing
    • Surrogate in CompoundPredicate
    • missingValuePenalty
    • splitCharacteristic
    • isScorable

12.2. PMML legacy support and naming conventions in Red Hat Decision Manager

When you add a PMML file to a project in Red Hat Decision Manager, multiple assets are generated. Each type of PMML model generates a different set of assets, but all PMML model types generate at least the following set of assets:

  • A DRL file that contains all of the rules associated with your PMML model
  • At least two Java classes:

    • A data class that is used as the default object type for the model type
    • A RuleUnit class that is used to manage data sources and rule execution

If a PMML file has MiningModel as the root model, multiple instances of each of these files are generated.

The following are naming conventions for generated PMML legacy packages, classes, and rules:

  • If no package name is given in a PMML model file, then the default package name org.kie.pmml.pmml_4_2 is prefixed to the model name for the generated rules in the format "org.kie.pmml.pmml_4_2"+modelName.
  • The package name for the generated RuleUnit Java class is the same as the package name for the generated rules.
  • The name of the generated RuleUnit Java class is the model name with RuleUnit added to it in the format modelName+"RuleUnit".
  • Each PMML model has at least one data class that is generated. The package name for these classes is org.kie.pmml.pmml_4_2.model.
  • The names of generated data classes are determined by the model type, prefixed with the model name:

    • Regression models: One data class named modelName+"RegressionData"
    • Scorecard models: One data class named modelName+"ScoreCardData"
    • Tree models: Two data classes, the first named modelName+"TreeNode" and the second named modelName+"TreeToken"
    • Mining models: One data class named modelName+"MiningModelData"
Note

The mining model also generates all of the rules and classes that are within each of its segments.

12.2.1. PMML extensions in Red Hat Decision Manager

The PMML legacy specification supports Extension elements that extend the content of a PMML model. You can use extensions at almost every level of a PMML model definition, and as the first and last child in the main element of a model for maximum flexibility. For more information about PMML extensions, see the DMG PMML Extension Mechanism.

To optimize PMML integration, Red Hat Decision Manager supports the following additional PMML extensions:

  • modelPackage: Designates a package name for the generated rules and Java classes. Include this extension in the Header section of the PMML model file.
  • adapter: Designates the type of construct (bean or trait) that is used to contain input and output data for rules. Insert this extension in the MiningSchema or Output section (or both) of the PMML model file.
  • externalClass: Used in conjunction with the adapter extension in defining a MiningField or OutputField. This extension contains a class with an attribute name that matches the name of the MiningField or OutputField element.

Chapter 13. PMML model execution

You can import PMML files into your Red Hat Decision Manager project using Business Central (Menu → Design → Projects → Import Asset) or package the PMML files as part of your project knowledge JAR (KJAR) file without Business Central. After you implement your PMML files in your Red Hat Decision Manager project, you can execute the PMML-based decision service by embedding PMML calls directly in your Java application or by sending an ApplyPmmlModelCommand command to a configured KIE Server.

For more information about including PMML assets with your project packaging and deployment method, see Packaging and deploying a Red Hat Decision Manager project.

Note

You can also include a PMML model as part of a Decision Model and Notation (DMN) service in Business Central. When you include a PMML model within a DMN file, you can invoke that PMML model as a boxed function expression for a DMN decision node or business knowledge model node. For more information about including PMML models in a DMN service, see Designing a decision service using DMN models.

13.1. Embedding a PMML trusty 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 embed knowledge assets directly into a project if there is a tight relationship between the version of the code and the version of the PMML 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.

Prerequisites

Procedure

  1. In your client application, add the following dependencies to the relevant classpath of your Java project:

    <!-- Required for the PMML compiler -->
    <dependency>
      <groupId>org.drools</groupId>
      <artifactId>kie-pmml-dependencies</artifactId>
      <version>${rhdm.version}</version>
    </dependency>
    
    <!-- Required for the KIE public API -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-api</artifactId>
      <version>${rhdm.version}</version>
    </dependencies>
    
    <!-- Required if not using classpath KIE container -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-ci</artifactId>
      <version>${rhdm.version}</version>
    </dependency>

    The <version> is the Maven artifact version for Red Hat Decision Manager currently used in your project (for example, 7.52.0.Final-redhat-00007).

    Note

    Instead of specifying a Red Hat Decision Manager <version> for individual dependencies, consider adding the Red Hat Business Automation bill of materials (BOM) dependency to your project pom.xml file. The Red Hat Business Automation BOM applies to both Red Hat Decision Manager and Red Hat Process Automation Manager. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

    Example BOM dependency:

    <dependency>
      <groupId>com.redhat.ba</groupId>
      <artifactId>ba-platform-bom</artifactId>
      <version>7.11.0.redhat-00005</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>

    For more information about the Red Hat Business Automation BOM, see What is the mapping between RHDM product and maven library version?.

  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. Create an instance of the PMMLRuntime that is used to execute the model:

    PMMLRuntime pmmlRuntime = KieRuntimeFactory.of(kieContainer.getKieBase()).get(PMMLRuntime.class);
  4. Create an instance of the PMMLRequestData class that applies your PMML model to a data set:

    PMMLRequestData pmmlRequestData = new PMMLRequestData({correlation_id}, {model_name});
    pmmlRequestData.addRequestParam({parameter_name}, {parameter_value})
    ...
  5. Create an instance of the PMMLContext class that contains the input data:

    PMMLContext pmmlContext = new PMMLContextImpl(pmmlRequestData);
  6. Retrieve the PMML4Result while executing the PMML model with the required PMML class instances that you created:

    PMML4Result pmml4Result = pmmlRuntime.evaluate({model_name}, pmmlContext);

13.2. Embedding a PMML legacy 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 embed knowledge assets directly into a project if there is a tight relationship between the version of the code and the version of the PMML 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

Procedure

  1. In your client application, add the following dependencies to the relevant classpath of your Java project:

    <!-- Required for the PMML compiler -->
    <dependency>
      <groupId>org.drools</groupId>
      <artifactId>kie-pmml</artifactId>
      <version>${rhdm.version}</version>
    </dependency>
    
    <!-- Required for the KIE public API -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-api</artifactId>
      <version>${rhdm.version}</version>
    </dependencies>
    
    <!-- Required if not using classpath KIE container -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-ci</artifactId>
      <version>${rhdm.version}</version>
    </dependency>

    The <version> is the Maven artifact version for Red Hat Decision Manager currently used in your project (for example, 7.52.0.Final-redhat-00007).

    Note

    Instead of specifying a Red Hat Decision Manager <version> for individual dependencies, consider adding the Red Hat Business Automation bill of materials (BOM) dependency to your project pom.xml file. The Red Hat Business Automation BOM applies to both Red Hat Decision Manager and Red Hat Process Automation Manager. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

    Example BOM dependency:

    <dependency>
      <groupId>com.redhat.ba</groupId>
      <artifactId>ba-platform-bom</artifactId>
      <version>7.11.0.redhat-00005</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>

    For more information about the Red Hat Business Automation BOM, see What is the mapping between RHDM product and maven library version?.

    Important

    To use the legacy implementation, ensure that the kie-pmml-implementation system property is set as legacy.

  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. Create an instance of the PMMLRequestData class, which applies your PMML model to a set of data:

    public class PMMLRequestData {
        private String correlationId; 1
        private String modelName; 2
        private String source; 3
        private List<ParameterInfo<?>> requestParams; 4
        ...
    }
    1
    Identifies data that is associated with a particular request or result
    2
    The name of the model that should be applied to the request data
    3
    Used by internally generated PMMLRequestData objects to identify the segment that generated the request
    4
    The default mechanism for sending input data points
  4. Create an instance of the PMML4Result class, which holds the output information that is the result of applying the PMML-based rules to the input data:

    public class PMML4Result {
        private String correlationId;
        private String segmentationId; 1
        private String segmentId; 2
        private int segmentIndex; 3
        private String resultCode; 4
        private Map<String, Object> resultVariables; 5
        ...
    }
    1
    Used when the model type is MiningModel. The segmentationId is used to differentiate between multiple segmentations.
    2
    Used in conjunction with the segmentationId to identify which segment generated the results.
    3
    Used to maintain the order of segments.
    4
    Used to determine whether the model was successfully applied, where OK indicates success.
    5
    Contains the name of a resultant variable and its associated value.

    In addition to the normal getter methods, the PMML4Result class also supports the following methods for directly retrieving the values for result variables:

    public <T> Optional<T> getResultValue(String objName, String objField, Class<T> clazz, Object...params)
    
    public Object getResultValue(String objName, String objField, Object...params)
  5. Create an instance of the ParameterInfo class, which serves as a wrapper for basic data type objects used as part of the PMMLRequestData class:

    public class ParameterInfo<T> { 1
        private String correlationId;
        private String name; 2
        private String capitalizedName;
        private Class<T> type; 3
        private T value; 4
        ...
    }
    1
    The parameterized class to handle many different types
    2
    The name of the variable that is expected as input for the model
    3
    The class that is the actual type of the variable
    4
    The actual value of the variable
  6. Execute the PMML model based on the required PMML class instances that you have created:

    public void executeModel(KieBase kbase,
                             Map<String,Object> variables,
                             String modelName,
                             String correlationId,
                             String modelPkgName) {
        RuleUnitExecutor executor = RuleUnitExecutor.create().bind(kbase);
        PMMLRequestData request = new PMMLRequestData(correlationId, modelName);
        PMML4Result resultHolder = new PMML4Result(correlationId);
        variables.entrySet().forEach( es -> {
            request.addRequestParam(es.getKey(), es.getValue());
        });
    
        DataSource<PMMLRequestData> requestData = executor.newDataSource("request");
        DataSource<PMML4Result> resultData = executor.newDataSource("results");
        DataSource<PMMLData> internalData = executor.newDataSource("pmmlData");
    
        requestData.insert(request);
        resultData.insert(resultHolder);
    
        List<String> possiblePackageNames = calculatePossiblePackageNames(modelName,
                                                                        modelPkgName);
        Class<? extends RuleUnit> ruleUnitClass = getStartingRuleUnit("RuleUnitIndicator",
                                                                    (InternalKnowledgeBase)kbase,
                                                                    possiblePackageNames);
    
        if (ruleUnitClass != null) {
            executor.run(ruleUnitClass);
            if ( "OK".equals(resultHolder.getResultCode()) ) {
              // extract result variables here
            }
        }
    }
    
    protected Class<? extends RuleUnit> getStartingRuleUnit(String startingRule, InternalKnowledgeBase ikb, List<String> possiblePackages) {
        RuleUnitRegistry unitRegistry = ikb.getRuleUnitRegistry();
        Map<String,InternalKnowledgePackage> pkgs = ikb.getPackagesMap();
        RuleImpl ruleImpl = null;
        for (String pkgName: possiblePackages) {
          if (pkgs.containsKey(pkgName)) {
              InternalKnowledgePackage pkg = pkgs.get(pkgName);
              ruleImpl = pkg.getRule(startingRule);
              if (ruleImpl != null) {
                  RuleUnitDescr descr = unitRegistry.getRuleUnitFor(ruleImpl).orElse(null);
                  if (descr != null) {
                      return descr.getRuleUnitClass();
                  }
              }
          }
        }
        return null;
    }
    
    protected List<String> calculatePossiblePackageNames(String modelId, String...knownPackageNames) {
        List<String> packageNames = new ArrayList<>();
        String javaModelId = modelId.replaceAll("\\s","");
        if (knownPackageNames != null && knownPackageNames.length > 0) {
            for (String knownPkgName: knownPackageNames) {
                packageNames.add(knownPkgName + "." + javaModelId);
            }
        }
        String basePkgName = PMML4UnitImpl.DEFAULT_ROOT_PACKAGE+"."+javaModelId;
        packageNames.add(basePkgName);
        return packageNames;
    }

    Rules are executed by the RuleUnitExecutor class. The RuleUnitExecutor class creates KIE sessions and adds the required DataSource objects to those sessions, and then executes the rules based on the RuleUnit that is passed as a parameter to the run() method. The calculatePossiblePackageNames and the getStartingRuleUnit methods determine the fully qualified name of the RuleUnit class that is passed to the run() method.

To facilitate your PMML model execution, you can also use a PMML4ExecutionHelper class supported in Red Hat Decision Manager. For more information about the PMML helper class, see Section 13.2.1, “PMML execution helper class”.

13.2.1. PMML execution helper class

Red Hat Decision Manager provides a PMML4ExecutionHelper class that helps create the PMMLRequestData class required for PMML model execution and that helps execute rules using the RuleUnitExecutor class.

The following are examples of a PMML model execution without and with the PMML4ExecutionHelper class, as a comparison:

Example PMML model execution without using PMML4ExecutionHelper

public void executeModel(KieBase kbase,
                         Map<String,Object> variables,
                         String modelName,
                         String correlationId,
                         String modelPkgName) {
    RuleUnitExecutor executor = RuleUnitExecutor.create().bind(kbase);
    PMMLRequestData request = new PMMLRequestData(correlationId, modelName);
    PMML4Result resultHolder = new PMML4Result(correlationId);
    variables.entrySet().forEach( es -> {
        request.addRequestParam(es.getKey(), es.getValue());
    });

    DataSource<PMMLRequestData> requestData = executor.newDataSource("request");
    DataSource<PMML4Result> resultData = executor.newDataSource("results");
    DataSource<PMMLData> internalData = executor.newDataSource("pmmlData");

    requestData.insert(request);
    resultData.insert(resultHolder);

    List<String> possiblePackageNames = calculatePossiblePackageNames(modelName,
                                                                    modelPkgName);
    Class<? extends RuleUnit> ruleUnitClass = getStartingRuleUnit("RuleUnitIndicator",
                                                                (InternalKnowledgeBase)kbase,
                                                                possiblePackageNames);

    if (ruleUnitClass != null) {
        executor.run(ruleUnitClass);
        if ( "OK".equals(resultHolder.getResultCode()) ) {
          // extract result variables here
        }
    }
}

protected Class<? extends RuleUnit> getStartingRuleUnit(String startingRule, InternalKnowledgeBase ikb, List<String> possiblePackages) {
    RuleUnitRegistry unitRegistry = ikb.getRuleUnitRegistry();
    Map<String,InternalKnowledgePackage> pkgs = ikb.getPackagesMap();
    RuleImpl ruleImpl = null;
    for (String pkgName: possiblePackages) {
      if (pkgs.containsKey(pkgName)) {
          InternalKnowledgePackage pkg = pkgs.get(pkgName);
          ruleImpl = pkg.getRule(startingRule);
          if (ruleImpl != null) {
              RuleUnitDescr descr = unitRegistry.getRuleUnitFor(ruleImpl).orElse(null);
              if (descr != null) {
                  return descr.getRuleUnitClass();
              }
          }
      }
    }
    return null;
}

protected List<String> calculatePossiblePackageNames(String modelId, String...knownPackageNames) {
    List<String> packageNames = new ArrayList<>();
    String javaModelId = modelId.replaceAll("\\s","");
    if (knownPackageNames != null && knownPackageNames.length > 0) {
        for (String knownPkgName: knownPackageNames) {
            packageNames.add(knownPkgName + "." + javaModelId);
        }
    }
    String basePkgName = PMML4UnitImpl.DEFAULT_ROOT_PACKAGE+"."+javaModelId;
    packageNames.add(basePkgName);
    return packageNames;
}

Example PMML model execution using PMML4ExecutionHelper

public void executeModel(KieBase kbase,
                         Map<String,Object> variables,
                         String modelName,
                         String modelPkgName,
                         String correlationId) {
   PMML4ExecutionHelper helper = PMML4ExecutionHelperFactory.getExecutionHelper(modelName, kbase);
   helper.addPossiblePackageName(modelPkgName);

   PMMLRequestData request = new PMMLRequestData(correlationId, modelName);
   variables.entrySet().forEach(entry -> {
     request.addRequestParam(entry.getKey(), entry.getValue);
   });

   PMML4Result resultHolder = helper.submitRequest(request);
   if ("OK".equals(resultHolder.getResultCode)) {
     // extract result variables here
   }
}

When you use the PMML4ExecutionHelper, you do not need to specify the possible package names nor the RuleUnit class as you would in a typical PMML model execution.

To construct a PMML4ExecutionHelper class, you use the PMML4ExecutionHelperFactory class to determine how instances of PMML4ExecutionHelper are retrieved.

The following are the available PMML4ExecutionHelperFactory class methods for constructing a PMML4ExecutionHelper class:

PMML4ExecutionHelperFactory methods for PMML assets in a KIE base

Use these methods when PMML assets have already been compiled and are being used from an existing KIE base:

public static PMML4ExecutionHelper getExecutionHelper(String modelName, KieBase kbase)

public static PMML4ExecutionHelper getExecutionHelper(String modelName, KieBase kbase, boolean includeMiningDataSources)
PMML4ExecutionHelperFactory methods for PMML assets on the project classpath

Use these methods when PMML assets are on the project classpath. The classPath argument is the project classpath location of the PMML file:

public static PMML4ExecutionHelper getExecutionHelper(String modelName,  String classPath, KieBaseConfiguration kieBaseConf)

public static PMML4ExecutionHelper getExecutionHelper(String modelName,String classPath, KieBaseConfiguration kieBaseConf, boolean includeMiningDataSources)
PMML4ExecutionHelperFactory methods for PMML assets in a byte array

Use these methods when PMML assets are in the form of a byte array:

public static PMML4ExecutionHelper getExecutionHelper(String modelName, byte[] content, KieBaseConfiguration kieBaseConf)

public static PMML4ExecutionHelper getExecutionHelper(String modelName, byte[] content, KieBaseConfiguration kieBaseConf, boolean includeMiningDataSources)
PMML4ExecutionHelperFactory methods for PMML assets in a Resource

Use these methods when PMML assets are in the form of an org.kie.api.io.Resource object:

public static PMML4ExecutionHelper getExecutionHelper(String modelName, Resource resource, KieBaseConfiguration kieBaseConf)

public static PMML4ExecutionHelper getExecutionHelper(String modelName, Resource resource, KieBaseConfiguration kieBaseConf, boolean includeMiningDataSources)
Note

The classpath, byte array, and resource PMML4ExecutionHelperFactory methods create a KIE container for the generated rules and Java classes. The container is used as the source of the KIE base that the RuleUnitExecutor uses. The container is not persisted. The PMML4ExecutionHelperFactory method for PMML assets that are already in a KIE base does not create a KIE container in this way.

13.3. Executing a PMML model using KIE Server

You can execute PMML models that have been deployed to KIE Server by sending the ApplyPmmlModelCommand command to the configured KIE Server. When you use this command, a PMMLRequestData object is sent to the KIE Server and a PMML4Result result object is received as a reply. You can send PMML requests to KIE Server through the KIE Server REST API from a configured Java class or directly from a REST client.

Prerequisites

Procedure

  1. In your client application, add the following dependencies to the relevant classpath of your Java project:

    Example of legacy implementation

    <!-- Required for the PMML compiler -->
    <dependency>
      <groupId>org.drools</groupId>
      <artifactId>kie-pmml</artifactId>
      <version>${rhdm.version}</version>
    </dependency>
    
    <!-- Required for the KIE public API -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-api</artifactId>
      <version>${rhdm.version}</version>
    </dependencies>
    
    <!-- Required for the KIE Server Java client API -->
    <dependency>
      <groupId>org.kie.server</groupId>
      <artifactId>kie-server-client</artifactId>
      <version>${rhdm.version}</version>
    </dependency>
    
    <!-- Required if not using classpath KIE container -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-ci</artifactId>
      <version>${rhdm.version}</version>
    </dependency>

    Important

    To use the legacy implementation, ensure that the kie-pmml-implementation system property is set as legacy.

    Example of trusty implementation

    <!-- Required for the PMML compiler -->
    <dependency>
      <groupId>org.drools</groupId>
      <artifactId>kie-pmml-dependencies</artifactId>
      <version>${rhdm.version}</version>
    </dependency>
    
    <!-- Required for the KIE public API -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-api</artifactId>
      <version>${rhdm.version}</version>
    </dependencies>
    
    <!-- Required for the KIE Server Java client API -->
    <dependency>
      <groupId>org.kie.server</groupId>
      <artifactId>kie-server-client</artifactId>
      <version>${rhdm.version}</version>
    </dependency>
    
    <!-- Required if not using classpath KIE container -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-ci</artifactId>
      <version>${rhdm.version}</version>
    </dependency>

    The <version> is the Maven artifact version for Red Hat Decision Manager currently used in your project (for example, 7.52.0.Final-redhat-00007).

    Note

    Instead of specifying a Red Hat Decision Manager <version> for individual dependencies, consider adding the Red Hat Business Automation bill of materials (BOM) dependency to your project pom.xml file. The Red Hat Business Automation BOM applies to both Red Hat Decision Manager and Red Hat Process Automation Manager. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

    Example BOM dependency:

    <dependency>
      <groupId>com.redhat.ba</groupId>
      <artifactId>ba-platform-bom</artifactId>
      <version>7.11.0.redhat-00005</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>

    For more information about the Red Hat Business Automation BOM, see What is the mapping between RHDM product and maven library version?.

  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. Create a class for sending requests to KIE Server and receiving responses:

    public class ApplyScorecardModel {
      private static final ReleaseId releaseId =
              new ReleaseId("org.acme","my-kjar","1.0.0");
      private static final String containerId = "SampleModelContainer";
      private static KieCommands commandFactory;
      private static ClassLoader kjarClassLoader; 1
      private RuleServicesClient serviceClient; 2
    
      // Attributes specific to your class instance
      private String rankedFirstCode;
      private Double score;
    
      // Initialization of non-final static attributes
      static {
        commandFactory = KieServices.Factory.get().getCommands();
    
        // Specifications for kjarClassLoader, if used
        KieMavenRepository kmp = KieMavenRepository.getMavenRepository();
        File artifactFile = kmp.resolveArtifact(releaseId).getFile();
        if (artifactFile != null) {
          URL urls[] = new URL[1];
          try {
            urls[0] = artifactFile.toURI().toURL();
            classLoader = new KieURLClassLoader(urls,PMML4Result.class.getClassLoader());
          } catch (MalformedURLException e) {
            logger.error("Error getting classLoader for "+containerId);
            logger.error(e.getMessage());
          }
        } else {
          logger.warn("Did not find the artifact file for "+releaseId.toString());
        }
      }
    
      public ApplyScorecardModel(KieServicesConfiguration kieConfig) {
        KieServicesClient clientFactory = KieServicesFactory.newKieServicesClient(kieConfig);
        serviceClient = clientFactory.getServicesClient(RuleServicesClient.class);
      }
      ...
      // Getters and setters
      ...
    
      // Method for executing the PMML model on KIE Server
      public void applyModel(String occupation, int age) {
        PMMLRequestData input = new PMMLRequestData("1234","SampleModelName"); 3
        input.addRequestParam(new ParameterInfo("1234","occupation",String.class,occupation));
        input.addRequestParam(new ParameterInfo("1234","age",Integer.class,age));
    
        CommandFactoryServiceImpl cf = (CommandFactoryServiceImpl)commandFactory;
        ApplyPmmlModelCommand command = (ApplyPmmlModelCommand) cf.newApplyPmmlModel(request); 4
    
        ServiceResponse<ExecutionResults> results =
            ruleClient.executeCommandsWithResults(CONTAINER_ID, command); 5
    
        if (results != null) {  6
          PMML4Result resultHolder = (PMML4Result)results.getResult().getValue("results");
          if (resultHolder != null && "OK".equals(resultHolder.getResultCode())) {
            this.score = resultHolder.getResultValue("ScoreCard","score",Double.class).get();
            Map<String,Object> rankingMap =
                 (Map<String,Object>)resultHolder.getResultValue("ScoreCard","ranking");
            if (rankingMap != null && !rankingMap.isEmpty()) {
              this.rankedFirstCode = rankingMap.keySet().iterator().next();
            }
          }
        }
      }
    }
    1
    Defines the class loader if you did not include the KJAR in your client project dependencies
    2
    Identifies the service client as defined in the configuration settings, including KIE Server REST API access credentials
    3
    Initializes a PMMLRequestData object
    4
    Creates an instance of the ApplyPmmlModelCommand
    5
    Sends the command using the service client
    6
    Retrieves the results of the executed PMML model
  4. Execute the class instance to send the PMML invocation request to KIE Server.

    Alternatively, you can use JMS and REST interfaces to send the ApplyPmmlModelCommand command to KIE Server. For REST requests, you use the ApplyPmmlModelCommand command as a POST request to http://SERVER:PORT/kie-server/services/rest/server/containers/instances/{containerId} in JSON, JAXB, or XStream request format.

    Example POST endpoint

    http://localhost:8080/kie-server/services/rest/server/containers/instances/SampleModelContainer

    Example JSON request body

    {
      "commands": [ {
          "apply-pmml-model-command": {
            "outIdentifier": null,
            "packageName": null,
            "hasMining": false,
            "requestData": {
              "correlationId": "123",
              "modelName": "SimpleScorecard",
              "source": null,
              "requestParams": [
                {
                  "correlationId": "123",
                  "name": "param1",
                  "type": "java.lang.Double",
                  "value": "10.0"
                },
                {
                  "correlationId": "123",
                  "name": "param2",
                  "type": "java.lang.Double",
                  "value": "15.0"
                }
              ]
            }
          }
        }
      ]
    }

    Example curl request with endpoint and body

    curl -X POST "http://localhost:8080/kie-server/services/rest/server/containers/instances/SampleModelContainer" -H "accept: application/json" -H "content-type: application/json" -d "{ \"commands\": [ { \"apply-pmml-model-command\": { \"outIdentifier\": null, \"packageName\": null, \"hasMining\": false, \"requestData\": { \"correlationId\": \"123\", \"modelName\": \"SimpleScorecard\", \"source\": null, \"requestParams\": [ { \"correlationId\": \"123\", \"name\": \"param1\", \"type\": \"java.lang.Double\", \"value\": \"10.0\" }, { \"correlationId\": \"123\", \"name\": \"param2\", \"type\": \"java.lang.Double\", \"value\": \"15.0\" } ] } } } ]}"

    Example JSON response

    {
      "results" : [ {
        "value" : {"org.kie.api.pmml.DoubleFieldOutput":{
      "value" : 40.8,
      "correlationId" : "123",
      "segmentationId" : null,
      "segmentId" : null,
      "name" : "OverallScore",
      "displayValue" : "OverallScore",
      "weight" : 1.0
    }},
        "key" : "OverallScore"
      }, {
        "value" : {"org.kie.api.pmml.PMML4Result":{
      "resultVariables" : {
        "OverallScore" : {
          "value" : 40.8,
          "correlationId" : "123",
          "segmentationId" : null,
          "segmentId" : null,
          "name" : "OverallScore",
          "displayValue" : "OverallScore",
          "weight" : 1.0
        },
        "ScoreCard" : {
          "modelName" : "SimpleScorecard",
          "score" : 40.8,
          "holder" : {
            "modelName" : "SimpleScorecard",
            "correlationId" : "123",
            "voverallScore" : null,
            "moverallScore" : true,
            "vparam1" : 10.0,
            "mparam1" : false,
            "vparam2" : 15.0,
            "mparam2" : false
          },
          "enableRC" : true,
          "pointsBelow" : true,
          "ranking" : {
            "reasonCh1" : 5.0,
            "reasonCh2" : -6.0
          }
        }
      },
      "correlationId" : "123",
      "segmentationId" : null,
      "segmentId" : null,
      "segmentIndex" : 0,
      "resultCode" : "OK",
      "resultObjectName" : null
    }},
        "key" : "results"
      } ],
      "facts" : [ ]
    }

Chapter 14. Additional resources

Part III. Designing a decision service using DRL rules

As a business rules developer, you can define business rules using the DRL (Drools Rule Language) designer in Business Central. DRL rules are defined directly in free-form .drl text files instead of in a guided or tabular format like other types of rule assets in Business Central. These DRL files form the core of the decision service for your project.

Note

You can also design your decision service using Decision Model and Notation (DMN) models instead of rule-based or table-based assets. For information about DMN support in Red Hat Decision Manager 7.11, see the following resources:

Prerequisites

  • The space and project for the DRL rules have been created in Business Central. Each asset is associated with a project assigned to a space. For details, see Getting started with decision services.

Chapter 15. Decision-authoring assets in Red Hat Decision Manager

Red Hat Decision Manager 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 Red Hat Decision Manager projects to help you decide or confirm the best method for defining decisions in your decision service.

Table 15.1. Decision-authoring assets supported in Red Hat Decision Manager

AssetHighlightsAuthoring toolsDocumentation

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) that represent part or all of the overall decision requirements graph (DRG) 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

Designing a decision service using DMN models

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

Designing a decision service using guided decision tables

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

Designing a decision service using spreadsheet 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

Designing a decision service using guided rules

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

Designing a decision service using guided rule templates

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 Red Hat Decision Manager
  • 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)

Designing a decision service using DRL rules

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 Red Hat Decision Manager project or imported into a project in Business Central
  • Are optimal for incorporating predictive data into decision services in Red Hat Decision Manager

PMML or XML editor

Designing a decision service using PMML models

Chapter 16. DRL (Drools Rule Language) rules

DRL (Drools Rule Language) rules are business rules that you define directly in .drl text files. These DRL files are the source in which all other rule assets in Business Central are ultimately rendered. You can create and manage DRL files within the Business Central interface, or create them externally as part of a Maven or Java project using Red Hat CodeReady Studio or another integrated development environment (IDE). A DRL file can contain one or more rules that define at a minimum the rule conditions (when) and actions (then). The DRL designer in Business Central provides syntax highlighting for Java, DRL, and XML.

DRL files consist of the following components:

Components in a DRL file

package

import

function  // Optional

query  // Optional

declare   // Optional

global   // Optional

rule "rule name"
    // Attributes
    when
        // Conditions
    then
        // Actions
end

rule "rule2 name"

...

The following example DRL rule determines the age limit in a loan application decision service:

Example rule for loan application age limit

rule "Underage"
  salience 15
  agenda-group "applicationGroup"
  when
    $application : LoanApplication()
    Applicant( age < 21 )
  then
    $application.setApproved( false );
    $application.setExplanation( "Underage" );
end

A DRL file can contain single or multiple rules, queries, and functions, and can define resource declarations such as imports, globals, and attributes that are assigned and used by your rules and queries. The DRL package must be listed at the top of a DRL file and the rules are typically listed last. All other DRL components can follow any order.

Each rule must have a unique name within the rule package. If you use the same rule name more than once in any DRL file in the package, the rules fail to compile. Always enclose rule names with double quotation marks (rule "rule name") to prevent possible compilation errors, especially if you use spaces in rule names.

All data objects related to a DRL rule must be in the same project package as the DRL file in Business Central. Assets in the same package are imported by default. Existing assets in other packages can be imported with the DRL rule.

16.1. Packages in DRL

A package is a folder of related assets in Red Hat Decision Manager, such as data objects, DRL files, decision tables, and other asset types. A package also serves as a unique namespace for each group of rules. A single rule base can contain multiple packages. You typically store all the rules for a package in the same file as the package declaration so that the package is self-contained. However, you can import objects from other packages that you want to use in the rules.

The following example is a package name and namespace for a DRL file in a mortgage application decision service:

Example package definition in a DRL file

package org.mortgages;

16.2. Import statements in DRL

Similar to import statements in Java, imports in DRL files identify the fully qualified paths and type names for any objects that you want to use in the rules. You specify the package and data object in the format packageName.objectName, with multiple imports on separate lines. The decision engine automatically imports classes from the Java package with the same name as the DRL package and from the package java.lang.

The following example is an import statement for a loan application object in a mortgage application decision service:

Example import statement in a DRL file

import org.mortgages.LoanApplication;

16.3. Functions in DRL

Functions in DRL files put semantic code in your rule source file instead of in Java classes. Functions are especially useful if an action (then) part of a rule is used repeatedly and only the parameters differ for each rule. Above the rules in the DRL file, you can declare the function or import a static method from a helper class as a function, and then use the function by name in an action (then) part of the rule.

The following examples illustrate a function that is either declared or imported in a DRL file:

Example function declaration with a rule (option 1)

function String hello(String applicantName) {
    return "Hello " + applicantName + "!";
}

rule "Using a function"
  when
    // Empty
  then
    System.out.println( hello( "James" ) );
end

Example function import with a rule (option 2)

import function my.package.applicant.hello;

rule "Using a function"
  when
    // Empty
  then
    System.out.println( hello( "James" ) );
end

16.4. Queries in DRL

Queries in DRL files search the working memory of the decision engine for facts related to the rules in the DRL file. You add the query definitions in DRL files and then obtain the matching results in your application code. Queries search for a set of defined conditions and do not require when or then specifications. Query names are global to the KIE base and therefore must be unique among all other rule queries in the project. To return the results of a query, you construct a QueryResults definition using ksession.getQueryResults("name"), where "name" is the query name. This returns a list of query results, which enable you to retrieve the objects that matched the query. You define the query and query results parameters above the rules in the DRL file.

The following example is a query definition in a DRL file for underage applicants in a mortgage application decision service, with the accompanying application code:

Example query definition in a DRL file

query "people under the age of 21"
    $person : Person( age < 21 )
end

Example application code to obtain query results

QueryResults results = ksession.getQueryResults( "people under the age of 21" );
System.out.println( "we have " + results.size() + " people under the age  of 21" );

You can also iterate over the returned QueryResults using a standard for loop. Each element is a QueryResultsRow that you can use to access each of the columns in the tuple.

Example application code to obtain and iterate over query results

QueryResults results = ksession.getQueryResults( "people under the age of 21" );
System.out.println( "we have " + results.size() + " people under the age of 21" );

System.out.println( "These people are under the age of 21:" );

for ( QueryResultsRow row : results ) {
    Person person = ( Person ) row.get( "person" );
    System.out.println( person.getName() + "\n" );
}

16.5. Type declarations and metadata in DRL

Declarations in DRL files define new fact types or metadata for fact types to be used by rules in the DRL file:

  • New fact types: The default fact type in the java.lang package of Red Hat Decision Manager is Object, but you can declare other types in DRL files as needed. Declaring fact types in DRL files enables you to define a new fact model directly in the decision engine, without creating models in a lower-level language like Java. You can also declare a new type when a domain model is already built and you want to complement this model with additional entities that are used mainly during the reasoning process.
  • Metadata for fact types: You can associate metadata in the format @key(value) with new or existing facts. Metadata can be any kind of data that is not represented by the fact attributes and is consistent among all instances of that fact type. The metadata can be queried at run time by the decision engine and used in the reasoning process.

16.5.1. Type declarations without metadata in DRL

A declaration of a new fact does not require any metadata, but must include a list of attributes or fields. If a type declaration does not include identifying attributes, the decision engine searches for an existing fact class in the classpath and raises an error if the class is missing.

The following example is a declaration of a new fact type Person with no metadata in a DRL file:

Example declaration of a new fact type with a rule

declare Person
  name : String
  dateOfBirth : java.util.Date
  address : Address
end

rule "Using a declared type"
  when
    $p : Person( name == "James" )
  then   // Insert Mark, who is a customer of James.
    Person mark = new Person();
    mark.setName( "Mark" );
    insert( mark );
end

In this example, the new fact type Person has the three attributes name, dateOfBirth, and address. Each attribute has a type that can be any valid Java type, including another class that you create or a fact type that you previously declared. The dateOfBirth attribute has the type java.util.Date, from the Java API, and the address attribute has the previously defined fact type Address.

To avoid writing the fully qualified name of a class every time you declare it, you can define the full class name as part of the import clause:

Example type declaration with the fully qualified class name in the import

import java.util.Date

declare Person
    name : String
    dateOfBirth : Date
    address : Address
end

When you declare a new fact type, the decision engine generates at compile time a Java class representing the fact type. The generated Java class is a one-to-one JavaBeans mapping of the type definition.

For example, the following Java class is generated from the example Person type declaration:

Generated Java class for the Person fact type declaration

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` and `hashCode`
    // `toString`
}

You can then use the generated class in your rules like any other fact, as illustrated in the previous rule example with the Person type declaration:

Example rule that uses the declared Person fact type

rule "Using a declared type"
  when
    $p : Person( name == "James" )
  then   // Insert Mark, who is a customer of James.
    Person mark = new Person();
    mark.setName( "Mark" );
    insert( mark );
end

16.5.2. Enumerative type declarations in DRL

DRL supports the declaration of enumerative types in the format declare enum <factType>, followed by a comma-separated list of values ending with a semicolon. You can then use the enumerative list in the rules in the DRL file.

For example, the following enumerative type declaration defines days of the week for an employee scheduling rule:

Example enumerative type declaration with a scheduling rule

declare enum DaysOfWeek
   SUN("Sunday"),MON("Monday"),TUE("Tuesday"),WED("Wednesday"),THU("Thursday"),FRI("Friday"),SAT("Saturday");

   fullName : String
end

rule "Using a declared Enum"
when
   $emp : Employee( dayOff == DaysOfWeek.MONDAY )
then
   ...
end

16.5.3. Extended type declarations in DRL

DRL supports type declaration inheritance in the format declare <factType1> extends <factType2>. To extend a type declared in Java by a subtype declared in DRL, you repeat the parent type in a declaration statement without any fields.

For example, the following type declarations extend a Student type from a top-level Person type, and a LongTermStudent type from the Student subtype:

Example extended type declarations

import org.people.Person

declare Person end

declare Student extends Person
    school : String
end

declare LongTermStudent extends Student
    years : int
    course : String
end

16.5.4. Type declarations with metadata in DRL

You can associate metadata in the format @key(value) (the value is optional) with fact types or fact attributes. Metadata can be any kind of data that is not represented by the fact attributes and is consistent among all instances of that fact type. The metadata can be queried at run time by the decision engine and used in the reasoning process. Any metadata that you declare before the attributes of a fact type are assigned to the fact type, while metadata that you declare after an attribute are assigned to that particular attribute.

In the following example, the two metadata attributes @author and @dateOfCreation are declared for the Person fact type, and the two metadata items @key and @maxLength are declared for the name attribute. The @key metadata attribute has no required value, so the parentheses and the value are omitted.

Example metadata declaration 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

For declarations of metadata attributes for existing types, you can identify the fully qualified class name as part of the import clause for all declarations or as part of the individual declare clause:

Example metadata declaration for an imported type

import org.drools.examples.Person

declare Person
    @author( Bob )
    @dateOfCreation( 01-Feb-2009 )
end

Example metadata declaration for a declared type

declare org.drools.examples.Person
    @author( Bob )
    @dateOfCreation( 01-Feb-2009 )
end

16.5.5. Metadata tags for fact type and attribute declarations in DRL

Although you can define custom metadata attributes in DRL declarations, the decision engine also supports the following predefined metadata tags for declarations of fact types or fact type attributes.

Note

The examples in this section that refer to the VoiceCall class assume that the sample application domain model includes the following class details:

VoiceCall fact class in an example Telecom domain model

public class VoiceCall {
  private String  originNumber;
  private String  destinationNumber;
  private Date    callDateTime;
  private long    callDuration;  // in milliseconds

  // Constructors, getters, and setters
}

@role

This tag determines whether a given fact type is handled as a regular fact or an event in the decision engine during complex event processing.

Default parameter: fact

Supported parameters: fact, event

@role( fact | event )

Example: Declare VoiceCall as event type

declare VoiceCall
  @role( event )
end

@timestamp

This tag is automatically assigned to every event in the decision engine. By default, the time is provided by the session clock and assigned to the event when it is inserted into the working memory of the decision engine. You can specify a custom time stamp attribute instead of the default time stamp added by the session clock.

Default parameter: The time added by the decision engine session clock

Supported parameters: Session clock time or custom time stamp attribute

@timestamp( <attributeName> )

Example: Declare VoiceCall timestamp attribute

declare VoiceCall
  @role( event )
  @timestamp( callDateTime )
end

@duration

This tag determines the duration time for events in the decision engine. Events can be interval-based events or point-in-time events. Interval-based events have a duration time and persist in the working memory of the decision engine until their duration time has lapsed. Point-in-time events have no duration and are essentially interval-based events with a duration of zero. By default, every event in the decision engine has a duration of zero. You can specify a custom duration attribute instead of the default.

Default parameter: Null (zero)

Supported parameters: Custom duration attribute

@duration( <attributeName> )

Example: Declare VoiceCall duration attribute

declare VoiceCall
  @role( event )
  @timestamp( callDateTime )
  @duration( callDuration )
end

@expires

This tag determines the time duration before an event expires in the working memory of the decision engine. By default, an event expires when the event can no longer match and activate any of the current rules. You can define an amount of time after which an event should expire. This tag definition also overrides the implicit expiration offset calculated from temporal constraints and sliding windows in the KIE base. This tag is available only when the decision engine is running in stream mode.

Default parameter: Null (event expires after event can no longer match and activate rules)

Supported parameters: Custom timeOffset attribute in the format [#d][#h][#m][#s][[ms]]

@expires( <timeOffset> )

Example: Declare expiration offset for VoiceCall events

declare VoiceCall
  @role( event )
  @timestamp( callDateTime )
  @duration( callDuration )
  @expires( 1h35m )
end

@typesafe

This tab determines whether a given fact type is compiled with or without type safety. By default, all type declarations are compiled with type safety enabled. You can override this behavior to type-unsafe evaluation, where all constraints are generated as MVEL constraints and executed dynamically. This is useful when dealing with collections that do not have any generics or mixed type collections.

Default parameter: true

Supported parameters: true, false

@typesafe( <boolean> )

Example: Declare VoiceCall for type-unsafe evaluation

declare VoiceCall
  @role( fact )
  @typesafe( false )
end

@serialVersionUID

This tag defines an identifying serialVersionUID value for a serializable class in a fact declaration. If a serializable class does not explicitly declare a serialVersionUID, the serialization run time calculates a default serialVersionUID value for that class based on various aspects of the class, as described in the Java Object Serialization Specification. However, for optimal deserialization results and for greater compatibility with serialized KIE sessions, set the serialVersionUID as needed in the relevant class or in your DRL declarations.

Default parameter: Null

Supported parameters: Custom serialVersionUID integer

@serialVersionUID( <integer> )

Example: Declare serialVersionUID for a VoiceCall class

declare VoiceCall
  @serialVersionUID( 42 )
end

@key

This tag enables a fact type attribute to be used as a key identifier for the fact type. The generated class can then implement the equals() and hashCode() methods to determine if two instances of the type are equal to each other. The decision engine can also generate a constructor using all the key attributes as parameters.

Default parameter: None

Supported parameters: None

<attributeDefinition> @key

Example: Declare Person type attributes as keys

declare Person
    firstName : String @key
    lastName : String @key
    age : int
end

For this example, the decision engine checks the firstName and lastName attributes to determine if two instances of Person are equal to each other, but it does not check the age attribute. The decision engine also implicitly generates three constructors: one without parameters, one with the @key fields, and one with all fields:

Example constructors from the key declarations

Person() // Empty constructor

Person( String firstName, String lastName )

Person( String firstName, String lastName, int age )

You can then create instances of the type based on the key constructors, as shown in the following example:

Example instance using the key constructor

Person person = new Person( "John", "Doe" );

@position

This tag determines the position of a declared fact type attribute or field in a positional argument, overriding the default declared order of attributes. You can use this tag to modify positional constraints in patterns while maintaining a consistent format in your type declarations and positional arguments. You can use this tag only for fields in classes on the classpath. If some fields in a single class use this tag and some do not, the attributes without this tag are positioned last, in the declared order. Inheritance of classes is supported, but not interfaces of methods.

Default parameter: None

Supported parameters: Any integer

<attributeDefinition> @position ( <integer> )

Example: Declare a fact type and override declared order

declare Person
    firstName : String @position( 1 )
    lastName : String @position( 0 )
    age : int @position( 2 )
    occupation: String
end

In this example, the attributes are prioritized in positional arguments in the following order:

  1. lastName
  2. firstName
  3. age
  4. occupation

In positional arguments, you do not need to specify the field name because the position maps to a known named field. For example, the argument Person( lastName == "Doe" ) is the same as Person( "Doe"; ), where the lastName field has the highest position annotation in the DRL declaration. The semicolon ; indicates that everything before it is a positional argument. You can mix positional and named arguments on a pattern by using the semicolon to separate them. Any variables in a positional argument that have not yet been bound are bound to the field that maps to that position.

The following example patterns illustrate different ways of constructing positional and named arguments. The patterns have two constraints and a binding, and the semicolon differentiates the positional section from the named argument section. Variables and literals and expressions using only literals are supported in positional arguments, but not variables alone.

Example patterns with positional and named arguments

Person( "Doe", "John", $a; )

Person( "Doe", "John"; $a : age )

Person( "Doe"; firstName == "John", $a : age )

Person( lastName == "Doe"; firstName == "John", $a : age )

Positional arguments can be classified as input arguments or output arguments. Input arguments contain a previously declared binding and constrain against that binding using unification. Output arguments generate the declaration and bind it to the field represented by the positional argument when the binding does not yet exist.

In extended type declarations, use caution when defining @position annotations because the attribute positions are inherited in subtypes. This inheritance can result in a mixed attribute order that can be confusing in some cases. Two fields can have the same @position value and consecutive values do not need to be declared. If a position is repeated, the conflict is solved using inheritance, where position values in the parent type have precedence, and then using the declaration order from the first to last declaration.

For example, the following extended type declarations result in mixed positional priorities:

Example extended fact type with mixed position annotations

declare Person
    firstName : String @position( 1 )
    lastName : String @position( 0 )
    age : int @position( 2 )
    occupation: String
end

declare Student extends Person
    degree : String @position( 1 )
    school : String @position( 0 )
    graduationDate : Date
end

In this example, the attributes are prioritized in positional arguments in the following order:

  1. lastName (position 0 in the parent type)
  2. school (position 0 in the subtype)
  3. firstName (position 1 in the parent type)
  4. degree (position 1 in the subtype)
  5. age (position 2 in the parent type)
  6. occupation (first field with no position annotation)
  7. graduationDate (second field with no position annotation)

16.5.6. Property-change settings and listeners for fact types

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

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

  • ALWAYS: (Default) All types are property reactive, but you can disable property reactivity for a specific type by using the @classReactive property-change setting.
  • ALLOWED: No types are property reactive, but you can enable property reactivity for a specific type by using the @propertyReactive property-change setting.
  • DISABLED: No types are property reactive. All property-change listeners are ignored.

Example property reactivity setting in KnowledgeBuilderConfiguration

KnowledgeBuilderConfiguration config = KnowledgeBuilderFactory.newKnowledgeBuilderConfiguration();
config.setOption(PropertySpecificOption.ALLOWED);
KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder(config);

Alternatively, you can update the drools.propertySpecific system property in the standalone.xml file of your Red Hat Decision Manager distribution:

Example property reactivity setting in system properties

<system-properties>
  ...
  <property name="drools.propertySpecific" value="ALLOWED"/>
  ...
</system-properties>

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

@classReactive

If property reactivity is set to ALWAYS in the decision engine (all types are property reactive), this tag disables the default property reactivity behavior for a specific Java class or a declared DRL fact type. You can use this tag if you want the decision engine to re-evaluate all fact patterns for the specified fact type each time the rule is triggered, instead of reacting only to modified properties that are constrained or bound inside a given pattern.

Example: Disable default property reactivity in a DRL type declaration

declare Person
  @classReactive
    firstName : String
    lastName : String
end

Example: Disable default property reactivity in a Java class

@classReactive
public static class Person {
    private String firstName;
    private String lastName;
}

@propertyReactive

If property reactivity is set to ALLOWED in the decision engine (no types are property reactive unless specified), this tag enables property reactivity for a specific Java class or a declared DRL fact type. You can use this tag if you want the decision engine to react only to modified properties that are constrained or bound inside a given pattern for the specified fact type, instead of re-evaluating all fact patterns for the fact each time the rule is triggered.

Example: Enable property reactivity in a DRL type declaration (when reactivity is disabled globally)

declare Person
  @propertyReactive
    firstName : String
    lastName : String
end

Example: Enable property reactivity in a Java class (when reactivity is disabled globally)

@propertyReactive
public static class Person {
    private String firstName;
    private String lastName;
}

@watch

This tag enables property reactivity for additional properties that you specify in-line in fact patterns in DRL rules. This tag is supported only if property reactivity is set to ALWAYS in the decision engine, or if property reactivity is set to ALLOWED and the relevant fact type uses the @propertyReactive tag. You can use this tag in DRL rules to add or exclude specific properties in fact property reactivity logic.

Default parameter: None

Supported parameters: Property name, * (all), ! (not), !* (no properties)

<factPattern> @watch ( <property> )

Example: Enable or disable property reactivity in fact patterns

// Listens for changes in both `firstName` (inferred) and `lastName`:
Person(firstName == $expectedFirstName) @watch( lastName )

// Listens for changes in all properties of the `Person` fact:
Person(firstName == $expectedFirstName) @watch( * )

// Listens for changes in `lastName` and explicitly excludes changes in `firstName`:
Person(firstName == $expectedFirstName) @watch( lastName, !firstName )

// Listens for changes in all properties of the `Person` fact except `age`:
Person(firstName == $expectedFirstName) @watch( *, !age )

// Excludes changes in all properties of the `Person` fact (equivalent to using `@classReactivity` tag):
Person(firstName == $expectedFirstName) @watch( !* )

The decision engine generates a compilation error if you use the @watch tag for properties in a fact type that uses the @classReactive tag (disables property reactivity) or when property reactivity is set to ALLOWED in the decision engine and the relevant fact type does not use the @propertyReactive tag. Compilation errors also arise if you duplicate properties in listener annotations, such as @watch( firstName, ! firstName ).

@propertyChangeSupport

For facts that implement support for property changes as defined in the JavaBeans Specification, this tag enables the decision engine to monitor changes in the fact properties.

Example: Declare property change support in JavaBeans object

declare Person
    @propertyChangeSupport
end

16.5.7. Access to DRL declared types in application code

Declared types in DRL are typically used within the DRL files while Java models are typically used when the model is shared between rules and applications. Because declared types are generated at KIE base compile time, an application cannot access them until application run time. In some cases, an application needs to access and handle facts directly from the declared types, especially when the application wraps the decision engine and provides higher-level, domain-specific user interfaces for rules management.

To handle declared types directly from the application code, you can use the org.drools.definition.type.FactType API in Red Hat Decision Manager. Through this API, you can instantiate, read, and write fields in the declared fact types.

The following example code modifies a Person fact type directly from an application:

Example application code to handle a declared fact type through the FactType API

import java.util.Date;

import org.kie.api.definition.type.FactType;
import org.kie.api.KieBase;
import org.kie.api.runtime.KieSession;

...

// Get a reference to a KIE base with the declared type:
KieBase kbase = ...

// Get the declared fact type:
FactType personType = kbase.getFactType("org.drools.examples", "Person");

// Create instances:
Object bob = personType.newInstance();

// Set attribute values:
personType.set(bob, "name", "Bob" );
personType.set(bob, "dateOfBirth", new Date());
personType.set(bob, "address", new Address("King's Road","London","404"));

// Insert the fact into a KIE session:
KieSession ksession = ...
ksession.insert(bob);
ksession.fireAllRules();

// Read attributes:
String name = (String) personType.get(bob, "name");
Date date = (Date) personType.get(bob, "dateOfBirth");

The API also includes other helpful methods, such as setting all the attributes at once, reading values from a Map collection, or reading all attributes at once into a Map collection.

Although the API behavior is similar to Java reflection, the API does not use reflection and relies on more performant accessors that are implemented with generated bytecode.

16.6. Global variables in DRL

Global variables in DRL files typically provide data or services for the rules, such as application services used in rule consequences, and return data from rules, such as logs or values added in rule consequences. You set the global value in the working memory of the decision engine through a KIE session configuration or REST operation, declare the global variable above the rules in the DRL file, and then use it in an action (then) part of the rule. For multiple global variables, use separate lines in the DRL file.

The following example illustrates a global variable list configuration for the decision engine and the corresponding global variable definition in the DRL file:

Example global list configuration for the decision engine

List<String> list = new ArrayList<>();
KieSession kieSession = kiebase.newKieSession();
kieSession.setGlobal( "myGlobalList", list );

Example global variable definition with a rule

global java.util.List myGlobalList;

rule "Using a global"
  when
    // Empty
  then
    myGlobalList.add( "My global list" );
end

Warning

Do not use global variables to establish conditions in rules unless a global variable has a constant immutable value. Global variables are not inserted into the working memory of the decision engine, so the decision engine cannot track value changes of variables.

Do not use global variables to share data between rules. 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 of the decision engine.

A use case for a global variable might be an instance of an email service. In your integration code that is calling the decision engine, you obtain your emailService object and then set it in the working memory of the decision engine. In the DRL file, you declare that you have a global of type emailService and give it the name "email", and then in your rule consequences, you can use actions such as email.sendSMS(number, message).

If you declare global variables with the same identifier in multiple packages, then you must set all the packages with the same type so that they all reference the same global value.

16.7. Rule attributes in DRL

Rule attributes are additional specifications that you can add to business rules to modify rule behavior. In DRL files, you typically define rule attributes above the rule conditions and actions, with multiple attributes on separate lines, in the following format:

rule "rule_name"
    // Attribute
    // Attribute
    when
        // Conditions
    then
        // Actions
end

The following table lists the names and supported values of the attributes that you can assign to rules:

Table 16.1. Rule attributes

AttributeValue

salience

An integer defining the priority of the rule. Rules with a higher salience value are given higher priority when ordered in the activation queue.

Example: salience 10

enabled

A Boolean value. When the option is selected, the rule is enabled. When the option is not selected, the rule is disabled.

Example: enabled true

date-effective

A string containing a date and time definition. The rule can be activated only if the current date and time is after a date-effective attribute.

Example: date-effective "4-Sep-2018"

date-expires

A string containing a date and time definition. The rule cannot be activated if the current date and time is after the date-expires attribute.

Example: date-expires "4-Oct-2018"

no-loop

A Boolean value. When the option is selected, the rule cannot be reactivated (looped) if a consequence of the rule re-triggers a previously met condition. When the condition is not selected, the rule can be looped in these circumstances.

Example: no-loop true

agenda-group

A string identifying an agenda group to which you want to assign the rule. Agenda groups allow you to partition the agenda to provide more execution control over groups of rules. Only rules in an agenda group that has acquired a focus are able to be activated.

Example: agenda-group "GroupName"

activation-group

A string identifying an activation (or XOR) group to which you want to assign the rule. In activation groups, only one rule can be activated. The first rule to fire will cancel all pending activations of all rules in the activation group.

Example: activation-group "GroupName"

duration

A long integer value defining the duration of time in milliseconds after which the rule can be activated, if the rule conditions are still met.

Example: duration 10000

timer

A string identifying either int (interval) or cron timer definitions for scheduling the rule.

Example: timer ( cron:* 0/15 * * * ? ) (every 15 minutes)

calendar

A Quartz calendar definition for scheduling the rule.

Example: calendars "* * 0-7,18-23 ? * *" (exclude non-business hours)

auto-focus

A Boolean value, applicable only to rules within agenda groups. When the option is selected, the next time the rule is activated, a focus is automatically given to the agenda group to which the rule is assigned.

Example: auto-focus true

lock-on-active

A Boolean value, applicable only to rules within rule flow groups or agenda groups. When the option is selected, the next time the ruleflow group for the rule becomes active or the agenda group for the rule receives a focus, the rule cannot be activated again until the ruleflow group is no longer active or the agenda group loses the focus. This is a stronger version of the no-loop attribute, because the activation of a matching rule is discarded regardless of the origin of the update (not only by the rule itself). This attribute is ideal for calculation rules where you have a number of rules that modify a fact and you do not want any rule re-matching and firing again.

Example: lock-on-active true

ruleflow-group

A string identifying a rule flow group. In rule flow groups, rules can fire only when the group is activated by the associated rule flow.

Example: ruleflow-group "GroupName"

dialect

A string identifying either JAVA or MVEL as the language to be used for code expressions in the rule. By default, the rule uses the dialect specified at the package level. Any dialect specified here overrides the package dialect setting for the rule.

Example: dialect "JAVA"

Note

When you use Red Hat Decision Manager without the executable model, the dialect "JAVA" rule consequences support only Java 5 syntax. For more information about executable models, see Packaging and deploying a Red Hat Decision Manager project.

16.7.1. Timer and calendar rule attributes in DRL

Timers and calendars are DRL rule attributes that enable you to apply scheduling and timing constraints to your DRL rules. These attributes require additional configurations depending on the use case.

The timer attribute in DRL rules is a string identifying either int (interval) or cron timer definitions for scheduling a rule and supports the following formats:

Timer attribute formats

timer ( int: <initial delay> <repeat interval> )

timer ( cron: <cron expression> )

Example interval timer attributes

// Run after a 30-second delay
timer ( int: 30s )

// Run every 5 minutes after a 30-second delay each time
timer ( int: 30s 5m )

Example cron timer attribute

// Run every 15 minutes
timer ( cron:* 0/15 * * * ? )

Interval timers follow the semantics of java.util.Timer objects, with an initial delay and an optional repeat interval. Cron timers follow standard Unix cron expressions.

The following example DRL rule uses a cron timer to send an SMS text message every 15 minutes:

Example DRL rule with a cron timer

rule "Send SMS message 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

Generally, a rule that is controlled by a timer becomes active when the rule is triggered and the rule consequence is executed repeatedly, according to the timer settings. The execution stops when the rule condition no longer matches incoming facts. However, the way the decision engine handles rules with timers depends on whether the decision engine is in active mode or in passive mode.

By default, the decision engine runs in passive mode and evaluates rules, according to the defined timer settings, when a user or an application explicitly calls fireAllRules(). Conversely, if a user or application calls fireUntilHalt(), the decision engine starts in active mode and evaluates rules continually until the user or application explicitly calls halt().

When the decision engine is in active mode, rule consequences are executed even after control returns from a call to fireUntilHalt() and the decision engine remains reactive to any changes made to the working memory. For example, removing a fact that was involved in triggering the timer rule execution causes the repeated execution to terminate, and inserting a fact so that some rule matches causes that rule to be executed. However, the decision engine is not continually active, but is active only after a rule is executed. Therefore, the decision engine does not react to asynchronous fact insertions until the next execution of a timer-controlled rule. Disposing a KIE session terminates all timer activity.

When the decision engine is in passive mode, rule consequences of timed rules are evaluated only when fireAllRules() is invoked again. However, you can change the default timer-execution behavior in passive mode by configuring the KIE session with a TimedRuleExecutionOption option, as shown in the following example:

KIE session configuration to automatically execute timed rules in passive mode

KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();
ksconf.setOption( TimedRuleExecutionOption.YES );
KSession ksession = kbase.newKieSession(ksconf, null);

You can additionally set a FILTERED specification on the TimedRuleExecutionOption option that enables you to define a callback to filter those rules, as shown in the following example:

KIE session configuration to filter which timed rules are 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 interval timers, you can also use an expression timer with expr instead of int to define both the delay and interval as an expression instead of a fixed value.

The following example DRL file declares a fact type with a delay and period that are then used in the subsequent rule with an expression timer:

Example rule with an expression timer

declare Bean
  delay   : String = "30s"
  period  : long = 60000
end

rule "Expression timer"
  timer ( expr: $d, $p )
  when
    Bean( $d : delay, $p : period )
  then
    // Actions
end

The expressions, such as $d and $p in this example, can use any variable defined in the pattern-matching part of the rule. The variable can be any String value that can be parsed into a time duration or any numeric value that is internally converted in a long value for a duration in milliseconds.

Both interval and expression timers can use the following optional parameters:

  • start and end: A Date or a String representing a Date or a long value. The value can also be a Number that is transformed into a Java Date in the format new Date( ((Number) n).longValue() ).
  • repeat-limit: An integer that defines the maximum number of repetitions allowed by the timer. If both the end and the repeat-limit parameters are set, the timer stops when the first of the two is reached.

Example timer attribute with optional start, end, and repeat-limit parameters

timer (int: 30s 1h; start=3-JAN-2020, end=4-JAN-2020, repeat-limit=50)

In this example, the rule is scheduled for every hour, after a delay of 30 seconds each hour, beginning on 3 January 2020 and ending either on 4 January 2020 or when the cycle repeats 50 times.

If the system is paused (for example, the session is serialized and then later deserialized), the rule is scheduled only one time to recover from missing activations regardless of how many activations were missed during the pause, and then the rule is subsequently scheduled again to continue in sync with the timer setting.

The calendar attribute in DRL rules is a Quartz calendar definition for scheduling a rule and supports the following format:

Calendar attribute format

calendars "<definition or registered name>"

Example calendar attributes

// Exclude non-business hours
calendars "* * 0-7,18-23 ? * *"

// Weekdays only, as registered in the KIE session
calendars "weekday"

You can adapt a Quartz calendar based on the Quartz calendar API and then register the calendar in the KIE session, as shown in the following example:

Adapting a Quartz Calendar

Calendar weekDayCal = QuartzHelper.quartzCalendarAdapter(org.quartz.Calendar quartzCal)

Registering the calendar in the KIE session

ksession.getCalendars().set( "weekday", weekDayCal );

You can use calendars with standard rules and with rules that use timers. The calendar attribute can contain one or more comma-separated calendar names written as String literals.

The following example rules use both calendars and timers to schedule the rules:

Example rules with calendars and timers

rule "Weekdays are high priority"
  calendars "weekday"
  timer ( int:0 1h )
  when
    Alarm()
  then
    send( "priority high - we have an alarm" );
end

rule "Weekends are low priority"
  calendars "weekend"
  timer ( int:0 4h )
  when
    Alarm()
  then
    send( "priority low - we have an alarm" );
end

16.8. Rule conditions in DRL (WHEN)

The when part of a DRL rule (also known as the Left Hand Side (LHS) of the rule) contains the conditions that must be met to execute an action. Conditions consist of a series of stated patterns and constraints, with optional bindings and supported rule condition elements (keywords), based on the available data objects in the package. For example, if a bank requires loan applicants to have over 21 years of age, then the when condition of an "Underage" rule would be Applicant( age < 21 ).

Note

DRL uses when instead of if because if is typically part of a procedural execution flow during which a condition is checked at a specific point in time. In contrast, when indicates that the condition evaluation is not limited to a specific evaluation sequence or point in time, but instead occurs continually at any time. Whenever the condition is met, the actions are executed.

If the when section is empty, then the conditions are considered to be true and the actions in the then section are executed the first time a fireAllRules() call is made in the decision engine. This is useful if you want to use rules to set up the decision engine state.

The following example rule uses empty conditions to insert a fact every time the rule is executed:

Example rule without conditions

rule "Always insert applicant"
  when
    // Empty
  then   // Actions to be executed once
    insert( new Applicant() );
end

// The rule is internally rewritten in the following way:

rule "Always insert applicant"
  when
    eval( true )
  then
    insert( new Applicant() );
end

If rule conditions use multiple patterns with no defined keyword conjunctions (such as and, or, or not), the default conjunction is and:

Example rule without keyword conjunctions

rule "Underage"
  when
    application : LoanApplication()
    Applicant( age < 21 )
  then
    // Actions
end

// The rule is internally rewritten in the following way:

rule "Underage"
  when
    application : LoanApplication()
    and Applicant( age < 21 )
  then
    // Actions
end

16.8.1. Patterns and constraints

A pattern in a DRL rule condition is the segment to be matched by the decision engine. A pattern can potentially match each fact that is inserted into the working memory of the decision engine. A pattern can also contain constraints to further define the facts to be matched.

In the simplest form, with no constraints, a pattern matches a fact of the given type. In the following example, the type is Person, so the pattern will match against all Person objects in the working memory of the decision engine:

Example pattern for a single fact type

Person()

The type does not need to be the actual class of some fact object. Patterns can refer to superclasses or even interfaces, potentially matching facts from many different classes. For example, the following pattern matches all objects in the working memory of the decision engine:

Example pattern for all objects

Object() // Matches all objects in the working memory

The parentheses of a pattern enclose the constraints, such as the following constraint on the person’s age:

Example pattern with a constraint

Person( age == 50 )

A constraint is an expression that returns true or false. Pattern constraints in DRL are essentially Java expressions with some enhancements, such as property access, and some differences, such as equals() and !equals() semantics for == and != (instead of the usual same and not same semantics).

Any JavaBeans property can be accessed directly from pattern constraints. A bean property is exposed internally using a standard JavaBeans getter that takes no arguments and returns something. For example, the age property is written as age in DRL instead of the getter getAge():

DRL constraint syntax with JavaBeans properties

Person( age == 50 )

// This is the same as the following getter format:

Person( getAge() == 50 )

Red Hat Decision Manager uses the standard JDK Introspector class to achieve this mapping, so it follows the standard JavaBeans specification. For optimal decision engine performance, use the property access format, such as age, instead of using getters explicitly, such as getAge().

Warning

Do not use property accessors to change the state of the object in a way that might affect the rules because the decision engine caches the results of the match between invocations for higher efficiency.

For example, do not use property accessors in the following ways:

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);
}

Instead of following the second example, insert a fact that wraps the current date in the working memory and update that fact between fireAllRules() as needed.

However, if the getter of a property cannot be found, the compiler uses the property name as a fallback method name, without arguments:

Fallback method if object is not found

Person( age == 50 )

// If `Person.getAge()` does not exist, the compiler uses the following syntax:

Person( age() == 50 )

You can also nest access properties in patterns, as shown in the following example. Nested properties are indexed by the decision engine.

Example pattern with nested property access

Person( address.houseNumber == 50 )

// This is the same as the following format:

Person( getAddress().getHouseNumber() == 50 )

Warning

In stateful KIE sessions, use nested accessors carefully because the working memory of the decision engine is not aware of any of the nested values and does not detect when they change. Either consider the nested values immutable while any of their parent references are inserted into the working memory, or, if you want to modify a nested value, mark all of the outer facts as updated. In the previous example, when the houseNumber property changes, any Person with that Address must be marked as updated.

You can use any Java expression that returns a boolean value as a constraint inside the parentheses of a pattern. Java expressions can be mixed with other expression enhancements, such as property access:

Example pattern with a constraint using property access and Java expression

Person( age == 50 )

You can change the evaluation priority by using parentheses, as in any logical or mathematical expression:

Example evaluation order of constraints

Person( age > 100 && ( age % 10 == 0 ) )

You can also reuse Java methods in constraints, as shown in the following example:

Example constraints with reused Java methods

Person( Math.round( weight / ( height * height ) ) < 25.0 )

Warning

Do not use constraints to change the state of the object in a way that might affect the rules because the decision engine caches the results of the match between invocations for higher efficiency. Any method that is executed on a fact in the rule conditions must be a read-only method. Also, the state of a fact should not change between rule invocations unless those facts are marked as updated in the working memory on every change.

For example, do not use a pattern constraint in the following ways:

Person( incrementAndGetAge() == 10 ) // Do not do this.
Person( System.currentTimeMillis() % 1000 == 0 ) // Do not do this.

Standard Java operator precedence applies to constraint operators in DRL, and DRL operators follow standard Java semantics except for the == and != operators.

The == operator uses null-safe equals() semantics instead of the usual same semantics. For example, the pattern Person( firstName == "John" ) is similar to java.util.Objects.equals(person.getFirstName(), "John"), and because "John" is not null, the pattern is also similar to "John".equals(person.getFirstName()).

The != operator uses null-safe !equals() semantics instead of the usual not same semantics. For example, the pattern Person( firstName != "John" ) is similar to !java.util.Objects.equals(person.getFirstName(), "John").

If the field and the value of a constraint are of different types, the decision engine uses type coercion to resolve the conflict and reduce compilation errors. For instance, if "ten" is provided as a string in a numeric evaluator, a compilation error occurs, whereas "10" is coerced to a numeric 10. In coercion, the field type always takes precedence over the value type:

Example constraint with a value that is coerced

Person( age == "10" ) // "10" is coerced to 10

For groups of constraints, you can use a delimiting comma , to use implicit and connective semantics:

Example patterns with multiple constraints

// Person is at least 50 years old and weighs at least 80 kilograms:
Person( age > 50, weight > 80 )

// Person is at least 50 years old, weighs at least 80 kilograms, and is taller than 2 meters:
Person( age > 50, weight > 80, height > 2 )

Note

Although the && and , operators have the same semantics, they are resolved with different priorities. The && operator precedes the || operator, and both the && and || operators together precede the , operator. Use the comma operator at the top-level constraint for optimal decision engine performance and human readability.

You cannot embed a comma operator in a composite constraint expression, such as in parentheses:

Example of misused comma in composite constraint expression

// Do not use the following format:
Person( ( age > 50, weight > 80 ) || height > 2 )

// Use the following format instead:
Person( ( age > 50 && weight > 80 ) || height > 2 )

16.8.2. Bound variables in patterns and constraints

You can bind variables to patterns and constraints to refer to matched objects in other portions of a rule. Bound variables can help you define rules more efficiently or more consistently with how you annotate facts in your data model. To differentiate more easily between variables and fields in a rule, use the standard format $variable for variables, especially in complex rules. This convention is helpful but not required in DRL.

For example, the following DRL rule uses the variable $p for a pattern with the Person fact:

Pattern with a bound variable

rule "simple rule"
  when
    $p : Person()
  then
    System.out.println( "Person " + $p );
end

Similarly, you can also bind variables to properties in pattern constraints, as shown in the following example:

// Two persons of the same age:
Person( $firstAge : age ) // Binding
Person( age == $firstAge ) // Constraint expression
Note

Ensure that you separate constraint bindings and constraint expressions for clearer and more efficient rule definitions. Although mixed bindings and expressions are supported, they can complicate patterns and affect evaluation efficiency.

// Do not use the following format:
Person( $age : age * 2 < 100 )

// Use the following format instead:
Person( age * 2 < 100, $age : age )

The decision engine does not support bindings to the same declaration, but does support unification of arguments across several properties. While positional arguments are always processed with unification, the unification symbol := exists for named arguments.

The following example patterns unify the age property across two Person facts:

Example pattern with unification

Person( $age := age )
Person( $age := age )

Unification declares a binding for the first occurrence and constrains to the same value of the bound field for sequence occurrences.

16.8.3. Nested constraints and inline casts

In some cases, you might need to access multiple properties of a nested object, as shown in the following example:

Example pattern to access multiple properties

Person( name == "mark", address.city == "london", address.country == "uk" )

You can group these property accessors to nested objects with the syntax .( <constraints> ) for more readable rules, as shown in the following example:

Example pattern with grouped constraints

Person( name == "mark", address.( city == "london", country == "uk") )

Note

The period prefix . differentiates the nested object constraints from a method call.

When you work with nested objects in patterns, you can use the syntax <type>#<subtype> to cast to a subtype and make the getters from the parent type available to the subtype. You can use either the object name or fully qualified class name, and you can cast to one or multiple subtypes, as shown in the following examples:

Example patterns with inline casting to a subtype

// Inline casting with subtype name:
Person( name == "mark", address#LongAddress.country == "uk" )

// Inline casting with fully qualified class name:
Person( name == "mark", address#org.domain.LongAddress.country == "uk" )

// Multiple inline casts:
Person( name == "mark", address#LongAddress.country#DetailedCountry.population > 10000000 )

These example patterns cast Address to LongAddress, and additionally to DetailedCountry in the last example, making the parent getters available to the subtypes in each case.

You can use the instanceof operator to infer the results of the specified type in subsequent uses of that field with the pattern, as shown in the following example:

Person( name == "mark", address instanceof LongAddress, address.country == "uk" )

If an inline cast is not possible (for example, if instanceof returns false), the evaluation is considered false.

16.8.4. Date literal in constraints

By default, the decision engine supports the date format dd-mmm-yyyy. You can customize the date format, including a time format mask if needed, by providing an alternative format mask with the system property drools.dateformat="dd-mmm-yyyy hh:mm". You can also customize the date format by changing the language locale with the drools.defaultlanguage and drools.defaultcountry system properties (for example, the locale of Thailand is set as drools.defaultlanguage=th and drools.defaultcountry=TH).

Example pattern with a date literal restriction

Person( bornBefore < "27-Oct-2009" )

16.8.5. Supported operators in DRL pattern constraints

DRL supports standard Java semantics for operators in pattern constraints, with some exceptions and with some additional operators that are unique in DRL. The following list summarizes the operators that are handled differently in DRL constraints than in standard Java semantics or that are unique in DRL constraints.

.(), #

Use the .() operator to group property accessors to nested objects, and use the # operator to cast to a subtype in nested objects. Casting to a subtype makes the getters from the parent type available to the subtype. You can use either the object name or fully qualified class name, and you can cast to one or multiple subtypes.

Example patterns with nested objects

// Ungrouped property accessors:
Person( name == "mark", address.city == "london", address.country == "uk" )

// Grouped property accessors:
Person( name == "mark", address.( city == "london", country == "uk") )

Note

The period prefix . differentiates the nested object constraints from a method call.

Example patterns with inline casting to a subtype

// Inline casting with subtype name:
Person( name == "mark", address#LongAddress.country == "uk" )

// Inline casting with fully qualified class name:
Person( name == "mark", address#org.domain.LongAddress.country == "uk" )

// Multiple inline casts:
Person( name == "mark", address#LongAddress.country#DetailedCountry.population > 10000000 )

!.

Use this operator to dereference a property in a null-safe way. The value to the left of the !. operator must be not null (interpreted as != null) in order to give a positive result for pattern matching.

Example constraint with null-safe dereferencing

Person( $streetName : address!.street )

// This is internally rewritten in the following way:

Person( address != null, $streetName : address.street )

[]

Use this operator to access a List value by index or a Map value by key.

Example constraints with List and Map access

// The following format is the same as `childList(0).getAge() == 18`:
Person(childList[0].age == 18)

// The following format is the same as `credentialMap.get("jdoe").isValid()`:
Person(credentialMap["jdoe"].valid)

<, <=, >, >=

Use these operators on properties with natural ordering. For example, for Date fields, the < operator means before, and for String fields, the operator means alphabetically before. These properties apply only to comparable properties.

Example constraints with before operator

Person( birthDate < $otherBirthDate )

Person( firstName < $otherFirstName )

==, !=

Use these operators as equals() and !equals() methods in constraints, instead of the usual same and not same semantics.

Example constraint with null-safe equality

Person( firstName == "John" )

// This is similar to the following formats:

java.util.Objects.equals(person.getFirstName(), "John")
"John".equals(person.getFirstName())

Example constraint with null-safe not equality

Person( firstName != "John" )

// This is similar to the following format:

!java.util.Objects.equals(person.getFirstName(), "John")

&&, ||

Use these operators to create an abbreviated combined relation condition that adds more than one restriction on a field. You can group constraints with parentheses () to create a recursive syntax pattern.

Example constraints with abbreviated combined relation

// 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")

matches, not matches

Use these operators to indicate that a field matches or does not match a specified Java regular expression. Typically, the regular expression is a String literal, but variables that resolve to a valid regular expression are also supported. These operators apply only to String properties. If you use matches against a null value, the resulting evaluation is always false. If you use not matches against a null value, the resulting evaluation is always true. As in Java, regular expressions that you write as String literals must use a double backslash \\ to escape.

Example constraint to match or not match a regular expression

Person( country matches "(USA)?\\S*UK" )

Person( country not matches "(USA)?\\S*UK" )

contains, not contains

Use these operators to verify whether a field that is an Array or a Collection contains or does not contain a specified value. These operators apply to Array or Collection properties, but you can also use these operators in place of String.contains() and !String.contains() constraints checks.

Example constraints with contains and not contains for a Collection

// Collection with a specified field:
FamilyTree( countries contains "UK" )

FamilyTree( countries not contains "UK" )


// Collection with a variable:
FamilyTree( countries contains $var )

FamilyTree( countries not contains $var )

Example constraints with contains and not contains for a String literal

// Sting literal with a specified field:
Person( fullName contains "Jr" )

Person( fullName not contains "Jr" )


// String literal with a variable:
Person( fullName contains $var )

Person( fullName not contains $var )

Note

For backward compatibility, the excludes operator is a supported synonym for not contains.

memberOf, not memberOf

Use these operators to verify whether a field is a member of or is not a member of an Array or a Collection that is defined as a variable. The Array or Collection must be a variable.

Example constraints with memberOf and not memberOf with a Collection

FamilyTree( person memberOf $europeanDescendants )

FamilyTree( person not memberOf $europeanDescendants )

soundslike

Use this operator to verify whether a word has almost the same sound, using English pronunciation, as the given value (similar to the matches operator). This operator uses the Soundex algorithm.

Example constraint with soundslike

// Match firstName "Jon" or "John":
Person( firstName soundslike "John" )

str

Use this operator to verify whether a field that is a String starts with or ends with a specified value. You can also use this operator to verify the length of the String.

Example constraints with str

// Verify what the String starts with:
Message( routingValue str[startsWith] "R1" )

// Verify what the String ends with:
Message( routingValue str[endsWith] "R2" )

// Verify the length of the String:
Message( routingValue str[length] 17 )

in, notin

Use these operators to specify more than one possible value to match in a constraint (compound value restriction). This functionality of compound value restriction is supported only in the in and not in operators. The second operand of these operators must be a comma-separated list of values enclosed in parentheses. You can provide values as variables, literals, return values, or qualified identifiers. These operators are internally rewritten as a list of multiple restrictions using the operators == or !=.

Example constraints with in and notin

Person( $color : favoriteColor )
Color( type in ( "red", "blue", $color ) )

Person( $color : favoriteColor )
Color( type notin ( "red", "blue", $color ) )

16.8.6. Operator precedence in DRL pattern constraints

DRL supports standard Java operator precedence for applicable constraint operators, with some exceptions and with some additional operators that are unique in DRL. The following table lists DRL operator precedence where applicable, from highest to lowest precedence:

Table 16.2. Operator precedence in DRL pattern constraints

Operator typeOperatorsNotes

Nested or null-safe property access

., .(), !.

Not standard Java semantics

List or Map access

[]

Not standard Java semantics

Constraint binding

:

Not standard Java semantics

Multiplicative

*, /%

 

Additive

+, -

 

Shift

>>, >>>, <<

 

Relational

<, <=, >, >=, instanceof

 

Equality

== !=

Uses equals() and !equals() semantics, not standard Java same and not same semantics

Non-short-circuiting AND

&

 

Non-short-circuiting exclusive OR

^

 

Non-short-circuiting inclusive OR

|

 

Logical AND

&&

 

Logical OR

||

 

Ternary

? :

 

Comma-separated AND

,

Not standard Java semantics

16.8.7. Supported rule condition elements in DRL (keywords)

DRL supports the following rule condition elements (keywords) that you can use with the patterns that you define in DRL rule conditions:

and

Use this to group conditional components into a logical conjunction. Infix and prefix and are supported. You can group patterns explicitly with parentheses (). By default, all listed patterns are combined with and when no conjunction is specified.

Example patterns with and

//Infix `and`:
Color( colorType : type ) and Person( favoriteColor == colorType )

//Infix `and` with grouping:
(Color( colorType : type ) and (Person( favoriteColor == colorType ) or Person( favoriteColor == colorType ))

// Prefix `and`:
(and Color( colorType : type ) Person( favoriteColor == colorType ))

// Default implicit `and`:
Color( colorType : type )
Person( favoriteColor == colorType )

Note

Do not use a leading declaration binding with the and keyword (as you can with or, for example). A declaration can only reference a single fact at a time, and if you use a declaration binding with and, then when and is satisfied, it matches both facts and results in an error.

Example misuse of and

// Causes compile error:
$person : (Person( name == "Romeo" ) and Person( name == "Juliet"))

or

Use this to group conditional components into a logical disjunction. Infix and prefix or are supported. You can group patterns explicitly with parentheses (). You can also use pattern binding with or, but each pattern must be bound separately.

Example patterns with or

//Infix `or`:
Color( colorType : type ) or Person( favoriteColor == colorType )

//Infix `or` with grouping:
(Color( colorType : type ) or (Person( favoriteColor == colorType ) and Person( favoriteColor == colorType ))

// Prefix `or`:
(or Color( colorType : type ) Person( favoriteColor == colorType ))

Example patterns with or and pattern binding

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 ))

The behavior of the or condition element is different from the connective || operator for constraints and restrictions in field constraints. The decision engine does not directly interpret the or element but uses logical transformations to rewrite a rule with or as a number of sub-rules. This process ultimately results in a rule that has a single or as the root node and one sub-rule for each of its condition elements. Each sub-rule is activated and executed like any normal rule, with no special behavior or interaction between the sub-rules.

Therefore, consider the or condition element a shortcut for generating two or more similar rules that, in turn, can create multiple activations when two or more terms of the disjunction are true.

exists

Use this to specify facts and constraints that must exist. This option is triggered on only the first match, not subsequent matches. If you use this element with multiple patterns, enclose the patterns with parentheses ().

Example patterns with exists

exists Person( firstName == "John")

exists (Person( firstName == "John", age == 42 ))

exists (Person( firstName == "John" ) and
        Person( lastName == "Doe" ))

not

Use this to specify facts and constraints that must not exist. If you use this element with multiple patterns, enclose the patterns with parentheses ().

Example patterns with not

not Person( firstName == "John")

not (Person( firstName == "John", age == 42 ))

not (Person( firstName == "John" ) and
     Person( lastName == "Doe" ))

forall

Use this to verify whether all facts that match the first pattern match all the remaining patterns. When a forall construct is satisfied, the rule evaluates to true. This element is a scope delimiter, so it can use any previously bound variable, but no variable bound inside of it is available for use outside of it.

Example rule with forall

rule "All full-time employees have red ID badges"
  when
    forall( $emp : Employee( type == "fulltime" )
                   Employee( this == $emp, badgeColor = "red" ) )
  then
    // True, all full-time employees have red ID badges.
end

In this example, the rule selects all Employee objects whose type is "fulltime". For each fact that matches this pattern, the rule evaluates the patterns that follow (badge color) and if they match, the rule evaluates to true.

To state that all facts of a given type in the working memory of the decision engine must match a set of constraints, you can use forall with a single pattern for simplicity.

Example rule with forall and a single pattern

rule "All full-time employees have red ID badges"
  when
    forall( Employee( badgeColor = "red" ) )
  then
    // True, all full-time employees have red ID badges.
end

You can use forall constructs with multiple patterns or nest them with other condition elements, such as inside a not element construct.

Example rule with forall and multiple patterns

rule "All employees have health and dental care programs"
  when
    forall( $emp : Employee()
            HealthCare( employee == $emp )
            DentalCare( employee == $emp )
          )
  then
    // True, all employees have health and dental care.
end

Example rule with forall and not

rule "Not all employees have health and dental care"
  when
    not ( forall( $emp : Employee()
                  HealthCare( employee == $emp )
                  DentalCare( employee == $emp ) )
        )
  then
    // True, not all employees have health and dental care.
end

Note

The format forall( p1 p2 p3 …​) is equivalent to not( p1 and not( and p2 p3 …​ ) ).

from

Use this to specify a data source for a pattern. This enables the decision engine to reason over data that is not in the working memory. The data source can be a sub-field on a bound variable or the result of a method call. The expression used to define the object source is any expression that follows regular MVEL syntax. Therefore, the from element enables you to easily use object property navigation, execute method calls, and access maps and collection elements.

Example rule with from and pattern binding

rule "Validate zipcode"
  when
    Person( $personAddress : address )
    Address( zipcode == "23920W" ) from $personAddress
  then
    // Zip code is okay.
end

Example rule with from and a graph notation

rule "Validate zipcode"
  when
    $p : Person()
    $a : Address( zipcode == "23920W" ) from $p.address
  then
    // Zip code is okay.
end

Example rule with from to iterate over all objects

rule "Apply 10% discount to all items over US$ 100 in an order"
  when
    $order : Order()
    $item  : OrderItem( value > 100 ) from $order.items
  then
    // Apply discount to `$item`.
end

Note

For large collections of objects, instead of adding an object with a large graph that the decision engine must iterate over frequently, add the collection directly to the KIE session and then join the collection in the condition, as shown in the following example:

when
  $order : Order()
  OrderItem( value > 100, order == $order )

Example rule with from and lock-on-active rule attribute

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 the person to sales region 1.
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 the person.
end

Important

Using from with lock-on-active rule attribute can result in rules not being executed. You can address this issue in one of the following ways:

  • Avoid using the from element when you can insert all facts into the working memory of the decision engine or use nested object references in your constraint expressions.
  • Place the variable used in the modify() block as the last sentence in your rule condition.
  • Avoid using the lock-on-active rule attribute when you can explicitly manage how rules within the same ruleflow group place activations on one another.

The pattern that contains a from clause cannot be followed by another pattern starting with a parenthesis. The reason for this restriction is that the DRL parser reads the from expression as "from $l (String() or Number())" and it cannot differentiate this expression from a function call. The simplest workaround to this is to wrap the from clause in parentheses, as shown in the following example:

Example rules with from used incorrectly and correctly

// Do not use `from` in this way:
rule R
  when
    $l : List()
    String() from $l
    (String() or Number())
  then
    // Actions
end

// Use `from` in this way instead:
rule R
  when
    $l : List()
    (String() from $l)
    (String() or Number())
  then
    // Actions
end

entry-point

Use this to define an entry point, or event stream, corresponding to a data source for the pattern. This element is typically used with the from condition element. You can declare an entry point for events so that the decision engine uses data from only that entry point to evaluate the rules. You can declare an entry point either implicitly by referencing it in DRL rules or explicitly in your Java application.

Example rule with from entry-point

rule "Authorize withdrawal"
  when
    WithdrawRequest( $ai : accountId, $am : amount ) from entry-point "ATM Stream"
    CheckingAccount( accountId == $ai, balance > $am )
  then
    // Authorize withdrawal.
end

Example Java application code with EntryPoint object and inserted facts

import org.kie.api.runtime.KieSession;
import org.kie.api.runtime.rule.EntryPoint;

// Create your KIE base and KIE session as usual:
KieSession session = ...

// Create a reference to the entry point:
EntryPoint atmStream = session.getEntryPoint("ATM Stream");

// Start inserting your facts into the entry point:
atmStream.insert(aWithdrawRequest);

collect

Use this to define a collection of objects that the rule can use as part of the condition. The rule obtains the collection either from a specified source or from the working memory of the decision engine. The result pattern of the collect element can be any concrete class that implements the java.util.Collection interface and provides a default no-arg public constructor. You can use Java collections like List, LinkedList, and HashSet, or your own class. If variables are bound before the collect element in a condition, you can use the variables to constrain both your source and result patterns. However, any binding made inside the collect element is not available for use outside of it.

Example rule with collect

import java.util.List

rule "Raise priority when system has more than three pending alarms"
  when
    $system : System()
    $alarms : List( size >= 3 )
              from collect( Alarm( system == $system, status == 'pending' ) )
  then
    // Raise priority because `$system` has three or more `$alarms` pending.
end

In this example, the rule assesses all pending alarms in the working memory of the decision engine for each given system and groups them in a List. If three or more alarms are found for a given system, the rule is executed.

You can also use the collect element with nested from elements, as shown in the following example:

Example rule with collect and nested from

import java.util.LinkedList;

rule "Send a message to all parents"
  when
    $town : Town( name == 'Paris' )
    $mothers : LinkedList()
               from collect( Person( children > 0 )
                             from $town.getPeople()
                           )
  then
    // Send a message to all parents.
end

accumulate

Use this to iterate over a collection of objects, execute custom actions for each of the elements, and return one or more result objects (if the constraints evaluate to true). This element is a more flexible and powerful form of the collect condition element. You can use predefined functions in your accumulate conditions or implement custom functions as needed. You can also use the abbreviation acc for accumulate in rule conditions.

Use the following format to define accumulate conditions in rules:

Preferred format for accumulate

accumulate( <source pattern>; <functions> [;<constraints>] )

Note

Although the decision engine supports alternate formats for the accumulate element for backward compatibility, this format is preferred for optimal performance in rules and applications.

The decision engine supports the following predefined accumulate functions. These functions accept any expression as input.

  • average
  • min
  • max
  • count
  • sum
  • collectList
  • collectSet

In the following example rule, min, max, and average are accumulate functions that calculate the minimum, maximum, and average temperature values over all the readings for each sensor:

Example rule with accumulate to calculate temperature values

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

The following example rule uses the average function with accumulate to calculate the average profit for all items in an order:

Example rule with accumulate to calculate average profit

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

To use custom, domain-specific functions in accumulate conditions, create a Java class that implements the org.kie.api.runtime.rule.AccumulateFunction interface. For example, the following Java class defines a custom implementation of an AverageData function:

Example Java class with custom implementation of 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;
    }

}

To use the custom function in a DRL rule, import the function using the import accumulate statement:

Format to import a custom function

import accumulate <class_name> <function_name>

Example rule with the imported average function

import accumulate AverageAccumulateFunction.AverageData average

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

16.8.8. OOPath syntax with graphs of objects in DRL rule conditions

OOPath is an object-oriented syntax extension of XPath that is designed for browsing graphs of objects in DRL rule condition constraints. OOPath uses the compact notation from XPath for navigating through related elements while handling collections and filtering constraints, and is specifically useful for graphs of objects.

When the field of a fact is a collection, you can use the from condition element (keyword) to bind and reason over all the items in that collection one by one. If you need to browse a graph of objects in the rule condition constraints, the extensive use of the from condition element results in a verbose and repetitive syntax, as shown in the following example:

Example rule that browses a graph of objects with from

rule "Find all grades for Big Data exam"
  when
    $student: Student( $plan: plan )
    $exam: Exam( course == "Big Data" ) from $plan.exams
    $grade: Grade() from $exam.grades
  then
    // Actions
end

In this example, the domain model contains a Student object with a Plan of study. The Plan can have zero or more Exam instances and an Exam can have zero or more Grade instances. Only the root object of the graph, the Student in this case, needs to be in the working memory of the decision engine for this rule setup to function.

As a more efficient alternative to using extensive from statements, you can use the abbreviated OOPath syntax, as shown in the following example:

Example rule that browses a graph of objects with OOPath syntax

rule "Find all grades for Big Data exam"
  when
    Student( $grade: /plan/exams[course == "Big Data"]/grades )
  then
    // Actions
end

Formally, the core grammar of an OOPath expression is defined in extended Backus-Naur form (EBNF) notation in the following way:

EBNF notation for OOPath expressions

OOPExpr = [ID ( ":" | ":=" )] ( "/" | "?/" ) OOPSegment { ( "/" | "?/" | "." ) OOPSegment } ;
OOPSegment = ID ["#" ID] ["[" ( Number | Constraints ) "]"]

In practice, an OOPath expression has the following features and capabilities:

  • Starts with a forward slash / or with a question mark and forward slash ?/ if it is a non-reactive OOPath expression (described later in this section).
  • Can dereference a single property of an object with the period . operator.
  • Can dereference multiple properties of an object with the forward slash / operator. If a collection is returned, the expression iterates over the values in the collection.
  • Can filter out traversed objects that do not satisfy one or more constraints. The constraints are written as predicate expressions between square brackets, as shown in the following example:

    Constraints as a predicate expression

    Student( $grade: /plan/exams[ course == "Big Data" ]/grades )

  • Can downcast a traversed object to a subclass of the class declared in the generic collection. Subsequent constraints can also safely access the properties declared only in that subclass, as shown in the following example. Objects that are not instances of the class specified in this inline cast are automatically filtered out.

    Constraints with downcast objects

    Student( $grade: /plan/exams#AdvancedExam[ course == "Big Data", level > 3 ]/grades )

  • Can backreference an object of the graph that was traversed before the currently iterated graph. For example, the following OOPath expression matches only the grades that are above the average for the passed exam:

    Constraints with backreferenced object

    Student( $grade: /plan/exams/grades[ result > ../averageResult ] )

  • Can recursively be another OOPath expression, as shown in the following example:

    Recursive constraint expression

    Student( $exam: /plan/exams[ /grades[ result > 20 ] ] )

  • Can access objects by their index between square brackets [], as shown in the following example. To adhere to Java convention, OOPath indexes are 0-based, while XPath indexes are 1-based.

    Constraints with access to objects by index

    Student( $grade: /plan/exams[0]/grades )

OOPath expressions can be reactive or non-reactive. The decision engine does not react to updates involving a deeply nested object that is traversed during the evaluation of an OOPath expression.

To make these objects reactive to changes, modify the objects to extend the class org.drools.core.phreak.ReactiveObject. After you modify an object to extend the ReactiveObject class, the domain object invokes the inherited method notifyModification to notify the decision engine when one of the fields has been updated, as shown in the following example:

Example object method to notify the decision engine that an exam has been moved to a different course

public void setCourse(String course) {
        this.course = course;
        notifyModification(this);
        }

With the following corresponding OOPath expression, when an exam is moved to a different course, the rule is re-executed and the list of grades matching the rule is recomputed:

Example OOPath expression from "Big Data" rule

Student( $grade: /plan/exams[ course == "Big Data" ]/grades )

You can also use the ?/ separator instead of the / separator to disable reactivity in only one sub-portion of an OOPath expression, as shown in the following example:

Example OOPath expression that is partially non-reactive

Student( $grade: /plan/exams[ course == "Big Data" ]?/grades )

With this example, the decision engine reacts to a change made to an exam or if an exam is added to the plan, but not if a new grade is added to an existing exam.

If an OOPath portion is non-reactive, all remaining portions of the OOPath expression also become non-reactive. For example, the following OOPath expression is completely non-reactive:

Example OOPath expression that is completely non-reactive

Student( $grade: ?/plan/exams[ course == "Big Data" ]/grades )

For this reason, you cannot use the ?/ separator more than once in the same OOPath expression. For example, the following expression causes a compilation error:

Example OOPath expression with duplicate non-reactivity markers

Student( $grade: /plan?/exams[ course == "Big Data" ]?/grades )

Another alternative for enabling OOPath expression reactivity is to use the dedicated implementations for List and Set interfaces in Red Hat Decision Manager. These implementations are the ReactiveList and ReactiveSet classes. A ReactiveCollection class is also available. The implementations also provide reactive support for performing mutable operations through the Iterator and ListIterator classes.

The following example class uses these classes to configure OOPath expression reactivity:

Example Java class to configure OOPath expression reactivity

public class School extends AbstractReactiveObject {
    private String name;
    private final List<Child> children = new ReactiveList<Child>(); 1

    public void setName(String name) {
        this.name = name;
        notifyModification(); 2
    }

    public void addChild(Child child) {
        children.add(child); 3
        // No need to call `notifyModification()` here
    }
  }

1
Uses the ReactiveList instance for reactive support over the standard Java List instance.
2
Uses the required notifyModification() method for when a field is changed in reactive support.
3
The children field is a ReactiveList instance, so the notifyModification() method call is not required. The notification is handled automatically, like all other mutating operations performed over the children field.

16.9. Rule actions in DRL (THEN)

The then part of the rule (also known as the Right Hand Side (RHS) of the rule) contains the actions to be performed when the conditional part of the rule has been met. Actions consist of one or more methods that execute consequences based on the rule conditions and on available data objects in the package. For example, if a bank requires loan applicants to be over 21 years of age (with a rule condition Applicant( age < 21 )) and a loan applicant is under 21 years old, the then action of an "Underage" rule would be setApproved( false ), declining the loan because the applicant is under age.

The main purpose of rule actions is to insert, delete, or modify data in the working memory of the decision engine. Effective rule actions are small, declarative, and readable. If you need to use imperative or conditional code in rule actions, then divide the rule into multiple smaller and more declarative rules.

Example rule for loan application age limit

rule "Underage"
  when
    application : LoanApplication()
    Applicant( age < 21 )
  then
    application.setApproved( false );
    application.setExplanation( "Underage" );
end

16.9.1. Supported rule action methods in DRL

DRL supports the following rule action methods that you can use in DRL rule actions. You can use these methods to modify the working memory of the decision engine without having to first reference a working memory instance. These methods act as shortcuts to the methods provided by the KnowledgeHelper class in your Red Hat Decision Manager distribution.

For all rule action methods, download the Red Hat Decision Manager 7.11.0 Source Distribution ZIP file from the Red Hat Customer Portal and navigate to ~/rhdm-7.11.0-sources/src/drools-$VERSION/drools-core/src/main/java/org/drools/core/spi/KnowledgeHelper.java.

set

Use this to set the value of a field.

set<field> ( <value> )

Example rule action to set the values of a loan application approval

$application.setApproved ( false );
$application.setExplanation( "has been bankrupt" );

modify

Use this to specify fields to be modified for a fact and to notify the decision engine of the change. This method provides a structured approach to fact updates. It combines the update operation with setter calls to change object fields.

modify ( <fact-expression> ) {
    <expression>,
    <expression>,
    ...
}

Example rule action to modify a loan application amount and approval

modify( LoanApplication ) {
        setAmount( 100 ),
        setApproved ( true )
}

update

Use this to specify fields and the entire related fact to be updated and to notify the decision engine of the change. After a fact has changed, you must call update before changing another fact that might be affected by the updated values. To avoid this added step, use the modify method instead.

update ( <object, <handle> )  // Informs the decision engine that an object has changed

update ( <object> )  // Causes `KieSession` to search for a fact handle of the object

Example rule action to update a loan application amount and approval

LoanApplication.setAmount( 100 );
update( LoanApplication );

Note

If you provide property-change listeners, you do not need to call this method when an object changes. For more information about property-change listeners, see Decision engine in Red Hat Decision Manager.

insert

Use this to insert a new fact into the working memory of the decision engine and to define resulting fields and values as needed for the fact.

insert( new <object> );

Example rule action to insert a new loan applicant object

insert( new Applicant() );

insertLogical

Use this to insert a new fact logically into the decision engine. The decision engine is responsible for logical decisions on insertions and retractions of facts. After regular or stated insertions, facts must be retracted explicitly. After logical insertions, the facts that were inserted are automatically retracted when the conditions in the rules that inserted the facts are no longer true.

insertLogical( new <object> );

Example rule action to logically insert a new loan applicant object

insertLogical( new Applicant() );

delete

Use this to remove an object from the decision engine. The keyword retract is also supported in DRL and executes the same action, but delete is typically preferred in DRL code for consistency with the keyword insert.

delete( <object> );

Example rule action to delete a loan applicant object

delete( Applicant );

16.9.2. Other rule action methods from drools and kcontext variables

In addition to the standard rule action methods, the decision engine supports methods in conjunction with the predefined drools and kcontext variables that you can also use in rule actions.

You can use the drools variable to call methods from the KnowledgeHelper class in your Red Hat Decision Manager distribution, which is also the class that the standard rule action methods are based on. For all drools rule action options, download the Red Hat Decision Manager 7.11.0 Source Distribution ZIP file from the Red Hat Customer Portal and navigate to ~/rhdm-7.11.0-sources/src/drools-$VERSION/drools-core/src/main/java/org/drools/core/spi/KnowledgeHelper.java.

The following examples are common methods that you can use with the drools variable:

  • drools.halt(): Terminates rule execution if a user or application has previously called fireUntilHalt(). When a user or application calls fireUntilHalt(), the decision engine starts in active mode and evaluates rules continually until the user or application explicitly calls halt(). Otherwise, by default, the decision engine runs in passive mode and evaluates rules only when a user or an application explicitly calls fireAllRules().
  • drools.getWorkingMemory(): Returns the WorkingMemory object.
  • drools.setFocus( "<agenda_group>" ): Sets the focus to a specified agenda group to which the rule belongs.
  • drools.getRule().getName(): Returns the name of the rule.
  • drools.getTuple(), drools.getActivation(): Returns the Tuple that matches the currently executing rule and then delivers the corresponding Activation. These calls are useful for logging and debugging purposes.

You can use the kcontext variable with the getKieRuntime() method to call other methods from the KieContext class and, by extension, the RuleContext class in your Red Hat Decision Manager distribution. The full Knowledge Runtime API is exposed through the kcontext variable and provides extensive rule action methods. For all kcontext rule action options, download the Red Hat Decision Manager 7.11.0 Source Distribution ZIP file from the Red Hat Customer Portal and navigate to ~/rhdm-7.11.0-sources/src/kie-api-parent-$VERSION/kie-api/src/main/java/org/kie/api/runtime/rule/RuleContext.java.

The following examples are common methods that you can use with the kcontext.getKieRuntime() variable-method combination:

  • kcontext.getKieRuntime().halt(): Terminates rule execution if a user or application has previously called fireUntilHalt(). This method is equivalent to the drools.halt() method. When a user or application calls fireUntilHalt(), the decision engine starts in active mode and evaluates rules continually until the user or application explicitly calls halt(). Otherwise, by default, the decision engine runs in passive mode and evaluates rules only when a user or an application explicitly calls fireAllRules().
  • kcontext.getKieRuntime().getAgenda(): Returns a reference to the KIE session Agenda, and in turn provides access to rule activation groups, rule agenda groups, and ruleflow groups.

    Example call to access agenda group "CleanUp" and set the focus

    kcontext.getKieRuntime().getAgenda().getAgendaGroup( "CleanUp" ).setFocus();

    This example is equivalent to drools.setFocus( "CleanUp" ).

  • kcontext.getKieRuntime().getQueryResults(<string> query): Runs a query and returns the results. This method is equivalent to drools.getKieRuntime().getQueryResults().
  • kcontext.getKieRuntime().getKieBase(): Returns the KieBase object. The KIE base is the source of all the knowledge in your rule system and the originator of the current KIE session.
  • kcontext.getKieRuntime().setGlobal(), ~.getGlobal(), ~.getGlobals(): Sets or retrieves global variables.
  • kcontext.getKieRuntime().getEnvironment(): Returns the runtime Environment, similar to your operating system environment.

16.9.3. Advanced rule actions with conditional and named consequences

In general, effective rule actions are small, declarative, and readable. However, in some cases, the limitation of having a single consequence for each rule can be challenging and lead to verbose and repetitive rule syntax, as shown in the following example rules:

Example rules with verbose and repetitive syntax

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

A partial solution to the repetition is to make the second rule extend the first rule, as shown in the following modified example:

Partially enhanced example rules with an extended condition

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

As a more efficient alternative, you can consolidate the two rules into a single rule with modified conditions and labelled corresponding rule actions, as shown in the following consolidated example:

Consolidated example rule with conditional and named consequences

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 example rule uses two actions: the usual default action and another action named giveDiscount. The giveDiscount action is activated in the condition with the keyword do when a customer older than 60 years old is found in the KIE base, regardless of whether or not the customer owns a car.

You can configure the activation of a named consequence with an additional condition, such as the if statement in the following example. The condition in the if statement is always evaluated on the pattern that immediately precedes it.

Consolidated example rule with an additional condition

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

You can also evaluate different rule conditions using a nested if and else if construct, as shown in the following more complex example:

Consolidated example rule with more complex conditions

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

This example rule gives a 10% discount and free parking to Golden customers over 60, but only a 5% discount without free parking to Silver customers. The rule activates the consequence named giveDiscount5 with the keyword break instead of do. The keyword do schedules a consequence in the decision engine agenda, enabling the remaining part of the rule conditions to continue being evaluated, while break blocks any further condition evaluation. If a named consequence does not correspond to any condition with do but is activated with break, the rule fails to compile because the conditional part of the rule is never reached.

16.10. Comments in DRL files

DRL supports single-line comments prefixed with a double forward slash // and multi-line comments enclosed with a forward slash and asterisk /* …​ */. You can use DRL comments to annotate rules or any related components in DRL files. DRL comments are ignored by the decision engine when the DRL file is processed.

Example rule with comments

rule "Underage"
  // This is a single-line comment.
  when
    $application : LoanApplication()  // This is an in-line comment.
    Applicant( age < 21 )
  then
    /* This is a multi-line comment
    in the rule actions. */
    $application.setApproved( false );
    $application.setExplanation( "Underage" );
end

Important

The hash symbol # is not supported for DRL comments.

16.11. Error messages for DRL troubleshooting

Red Hat Decision Manager provides standardized messages for DRL errors to help you troubleshoot and resolve problems in your DRL files. The error messages use the following format:

Figure 16.1. Error message format for DRL file problems

error message
  • 1st Block: Error code
  • 2nd Block: Line and column in the DRL source where the error occurred
  • 3rd Block: Description of the problem
  • 4th Block: Component in the DRL source (rule, function, query) where the error occurred
  • 5th Block: Pattern in the DRL source where the error occurred (if applicable)

Red Hat Decision Manager supports the following standardized error messages:

101: no viable alternative

Indicates that the parser reached a decision point but could not identify an alternative.

Example rule with incorrect spelling

1: rule "simple rule"
2:   when
3:     exists Person()
4:     exits Student()  // Must be `exists`
5:   then
6: end

Error message

[ERR 101] Line 4:4 no viable alternative at input 'exits' in rule "simple rule"

Example rule without a rule name

1: package org.drools.examples;
2: rule    // Must be `rule "rule name"` (or `rule rule_name` if no spacing)
3:   when
4:     Object()
5:   then
6:     System.out.println("A RHS");
7: end

Error message

[ERR 101] Line 3:2 no viable alternative at input 'when'

In this example, the parser encountered the keyword when but expected the rule name, so it flags when as the incorrect expected token.

Example rule with incorrect syntax

1: rule "simple rule"
2:   when
3:     Student( name == "Andy )  // Must be `"Andy"`
4:   then
5: end

Error message

[ERR 101] Line 0:-1 no viable alternative at input '<eof>' in rule "simple rule" in pattern Student

Note

A line and column value of 0:-1 means the parser reached the end of the source file (<eof>) but encountered incomplete constructs, usually due to missing quotation marks "…​", apostrophes '…​', or parentheses (…​).

102: mismatched input

Indicates that the parser expected a particular symbol that is missing at the current input position.

Example rule with an incomplete rule statement

1: rule simple_rule
2:   when
3:     $p : Person(
        // Must be a complete rule statement

Error message

[ERR 102] Line 0:-1 mismatched input '<eof>' expecting ')' in rule "simple rule" in pattern Person

Note

A line and column value of 0:-1 means the parser reached the end of the source file (<eof>) but encountered incomplete constructs, usually due to missing quotation marks "…​", apostrophes '…​', or parentheses (…​).

Example rule with incorrect syntax

1: package org.drools.examples;
2:
3: rule "Wrong syntax"
4:   when
5:     not( Car( ( type == "tesla", price == 10000 ) || ( type == "kia", price == 1000 ) ) from $carList )
       // Must use `&&` operators instead of commas `,`
6:   then
7:     System.out.println("OK");
8: end

Error messages

[ERR 102] Line 5:36 mismatched input ',' expecting ')' in rule "Wrong syntax" in pattern Car
[ERR 101] Line 5:57 no viable alternative at input 'type' in rule "Wrong syntax"
[ERR 102] Line 5:106 mismatched input ')' expecting 'then' in rule "Wrong syntax"

In this example, the syntactic problem results in multiple error messages related to each other. The single solution of replacing the commas , with && operators resolves all errors. If you encounter multiple errors, resolve one at a time in case errors are consequences of previous errors.

103: failed predicate

Indicates that a validating semantic predicate evaluated to false. These semantic predicates are typically used to identify component keywords in DRL files, such as declare, rule, exists, not, and others.

Example rule with an invalid keyword

 1: package nesting;
 2:
 3: import org.drools.compiler.Person
 4: import org.drools.compiler.Address
 5:
 6: Some text  // Must be a valid DRL keyword
 7:
 8: rule "test something"
 9:   when
10:     $p: Person( name=="Michael" )
11:   then
12:     $p.name = "other";
13:     System.out.println(p.name);
14: end

Error message

[ERR 103] Line 6:0 rule 'rule_key' failed predicate: {(validateIdentifierKey(DroolsSoftKeywords.RULE))}? in rule

The Some text line is invalid because it does not begin with or is not a part of a DRL keyword construct, so the parser fails to validate the rest of the DRL file.

Note

This error is similar to 102: mismatched input, but usually involves DRL keywords.

104: trailing semi-colon not allowed

Indicates that an eval() clause in a rule condition uses a semicolon ; but must not use one.

Example rule with eval() and trailing semicolon

1: rule "simple rule"
2:   when
3:     eval( abc(); )  // Must not use semicolon `;`
4:   then
5: end

Error message

[ERR 104] Line 3:4 trailing semi-colon not allowed in rule "simple rule"

105: did not match anything

Indicates that the parser reached a sub-rule in the grammar that must match an alternative at least once, but the sub-rule did not match anything. The parser has entered a branch with no way out.

Example rule with invalid text in an empty condition

1: rule "empty condition"
2:   when
3:     None  // Must remove `None` if condition is empty
4:   then
5:      insert( new Person() );
6: end

Error message

[ERR 105] Line 2:2 required (...)+ loop did not match anything at input 'WHEN' in rule "empty condition"

In this example, the condition is intended to be empty but the word None is used. This error is resolved by removing None, which is not a valid DRL keyword, data type, or pattern construct.

Note

If you encounter other DRL error messages that you cannot resolve, contact your Red Hat Technical Account Manager.

16.12. Rule units in DRL rule sets

Rule units are groups of data sources, global variables, and DRL rules that function together for a specific purpose. 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. Rule units are an enhanced alternative to rule-grouping DRL attributes such as rule agenda groups or activation groups for execution control.

Rule units are helpful when you want to coordinate rule execution so that the complete execution of one rule unit triggers the start of another rule unit and so on. For example, assume that you have a set of rules for data enrichment, another set of rules that processes that data, and another set of rules that extract the output from the processed data. If you add these rule sets into three distinct rule units, you can coordinate those rule units so that complete execution of the first unit triggers the start of the second unit and the complete execution of the second unit triggers the start of third unit.

To define a rule unit, implement 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 data source of `Persons` in this rule unit:
    public DataSource<Person> getPersons() {
        return persons;
    }

    // A global variable 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. A rule unit data source is a source of the data processed by a given rule unit and represents the entry point that the decision engine uses to evaluate the rule unit. 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 decision engine.

The decision engine supports the following optional life-cycle methods for rule units:

Table 16.3. Rule unit life-cycle methods

MethodInvoked when

onStart()

Rule unit execution starts

onEnd()

Rule unit execution ends

onSuspend()

Rule unit execution is suspended (used only with runUntilHalt())

onResume()

Rule unit execution is resumed (used only with runUntilHalt())

onYield(RuleUnit other)

The consequence of a rule in the rule unit triggers the execution of a different rule unit

You can add one or more rules to a rule unit. By default, all the rules in a DRL file are automatically associated with a rule unit that follows the naming convention of the DRL file name. If the DRL file is in the same package and has the same name as a class that implements the RuleUnit interface, then all of the rules in that DRL file implicitly belong to that rule unit. For example, all the rules in the AdultUnit.drl file in the org.mypackage.myunit package are automatically part of the rule unit org.mypackage.myunit.AdultUnit.

To override this naming convention and explicitly declare the rule unit that the rules in a DRL file belong to, use the unit keyword in the DRL file. The unit declaration must immediately follow the package declaration and contain the name of the class in that package that the rules in the DRL file are part of.

Example rule unit declaration in a DRL file

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

Warning

Do not mix rules with and without a rule unit in the same KIE base. Mixing two rule paradigms in a KIE base results in a compilation error.

You can also rewrite the same pattern in a more convenient way using OOPath notation, as shown in the following example:

Example rule unit declaration in a DRL file that uses 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

Note

OOPath is an object-oriented syntax extension of XPath that is designed for browsing graphs of objects in DRL rule condition constraints. OOPath uses the compact notation from XPath for navigating through related elements while handling collections and filtering constraints, and is specifically useful for graphs of objects.

In this example, any matching facts in the rule conditions are retrieved from the persons data source defined in the DataSource definition in the rule unit class. The rule condition and action use the adultAge variable in the same way that a global variable is defined at the DRL file level.

To execute one or more rule units defined in a KIE base, create a new RuleUnitExecutor class bound to the KIE base, create the rule unit from the relevant data source, and run the rule unit executer:

Example rule unit execution

// Create a `RuleUnitExecutor` class and bind it to the KIE base:
KieBase kbase = kieContainer.getKieBase();
RuleUnitExecutor executor = RuleUnitExecutor.create().bind( kbase );

// Create the `AdultUnit` rule unit using the `persons` data source and run the executor:
RuleUnit adultUnit = new AdultUnit(persons, 18);
executor.run( adultUnit );

Rules are executed by the RuleUnitExecutor class. The RuleUnitExecutor class creates KIE sessions and adds the required DataSource objects to those sessions, and then executes the rules based on the RuleUnit that is passed as a parameter to the run() method.

The example execution code produces the following output when the relevant Person facts are inserted in the persons data source:

Example rule unit execution output

org.mypackage.myunit.AdultUnit started.
Jane is adult and greater than 18
John is adult and greater than 18
org.mypackage.myunit.AdultUnit ended.

Instead of explicitly creating the rule unit instance, you can register the rule unit variables in the executor and pass to the executor the rule unit class that you want to run, and then the executor creates an instance of the rule unit. You can then set the DataSource definition and other variables as needed before running the rule unit.

Alternate rule unit execution option with registered variables

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

The name that you pass 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. In the previous example, the RuleUnitExecutor inserts into the new rule unit the data source bound to the "persons" name and inserts the value 18 bound to the String "adultAge" into the fields with the corresponding names inside the AdultUnit class.

To override this default variable-binding behavior, use the @UnitVar annotation to explicitly define a logical binding name for each field of the rule unit class. For example, the field bindings in the following class are redefined with alternative names:

Example code to modify variable binding names with @UnitVar

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 rule unit:

Example rule unit execution with modified variable names

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

You can execute a rule unit in passive mode by using the run() method (equivalent to invoking fireAllRules() on a KIE session) or in active mode using the runUntilHalt() method (equivalent to invoking fireUntilHalt() on a KIE session). By default, the decision engine runs in passive mode and evaluates rule units only when a user or an application explicitly calls run() (or fireAllRules() for standard rules). If a user or application calls runUntilHalt() for rule units (or fireUntilHalt() for standard rules), the decision engine starts in active mode and evaluates rule units continually until the user or application explicitly calls halt().

If you use the runUntilHalt() method, invoke the method on a separate execution thread to avoid blocking the main thread:

Example rule unit execution with runUntilHalt() on a separate thread

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

16.12.1. Data sources for rule units

A rule unit data source is a source of the data processed by a given rule unit and represents the entry point that the decision engine uses to evaluate the rule unit. A rule unit can have zero or more data sources and each DataSource definition declared inside a rule unit can correspond to a different entry point into the rule unit executor. Multiple rule units can share a single data source, but each rule unit must use different entry points through which the same objects are inserted.

You can create a DataSource definition with a fixed set of data in a rule unit class, as shown in the following example:

Example data source definition

DataSource<Person> persons = DataSource.create( new Person( "John", 42 ),
                                                new Person( "Jane", 44 ),
                                                new Person( "Sally", 4 ) );

Because a data source represents the entry point of the rule unit, you can insert, update, or delete facts in a rule unit:

Example code to insert, modify, and delete a fact in a rule unit

// Insert a fact:
Person john = new Person( "John", 42 );
FactHandle johnFh = persons.insert( john );

// Modify the fact and optionally specify modified properties (for property reactivity):
john.setAge( 43 );
persons.update( johnFh, john, "age" );

// Delete the fact:
persons.delete( johnFh );

16.12.2. Rule unit execution control

Rule units are helpful when you want to coordinate rule execution so that the execution of one rule unit triggers the start of another rule unit and so on.

To facilitate rule unit execution control, the decision engine supports the following rule unit methods that you can use in DRL rule actions to coordinate the execution of rule units:

  • drools.run(): Triggers the execution of a specified rule unit class. This method imperatively interrupts the execution of the rule unit and activates the other specified rule unit.
  • drools.guard(): Prevents (guards) a specified rule unit class from being executed until the associated rule condition is met. This method declaratively schedules the execution of the other specified rule unit. When the decision engine produces at least one match for the condition in the guarding rule, the guarded rule unit is considered active. A rule unit can contain multiple guarding rules.

As an example of the drools.run() method, consider the following DRL rules that each belong to a specified rule unit. The NotAdult rule uses the drools.run( AdultUnit.class ) method to trigger the execution of the AdultUnit rule unit:

Example DRL rules with controlled execution using drools.run()

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

The example also uses a RuleUnitExecutor class created from the KIE base that was built from these rules and a DataSource definition of persons bound to it:

Example rule executor and data source definitions

RuleUnitExecutor executor = RuleUnitExecutor.create().bind( kbase );
DataSource<Person> persons = executor.newDataSource( "persons",
                                                     new Person( "John", 42 ),
                                                     new Person( "Jane", 44 ),
                                                     new Person( "Sally", 4 ) );

In this case, the example creates the DataSource definition directly from the RuleUnitExecutor class and binds it to the "persons" variable in a single statement.

The example execution code produces the following output when the relevant Person facts are inserted in the persons data source:

Example rule unit execution output

Sally is NOT adult
John is adult
Jane is adult
Sally is adult

The NotAdult rule detects a match when evaluating the person "Sally", who is under 18 years old. The rule then modifies her age to 18 and uses the drools.run( AdultUnit.class ) method to trigger the execution of the AdultUnit rule unit. The AdultUnit rule unit contains a rule that can now be executed for all of the 3 persons in the DataSource definition.

As an example of the drools.guard() method, consider the following BoxOffice class and BoxOfficeUnit rule unit class:

Example 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;
    }
}

Example BoxOfficeUnit rule unit class

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

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

The example also uses the following TicketIssuerUnit rule unit class to keep selling box office tickets for the event as long as at least one box office is open. This rule unit uses DataSource definitions of persons and tickets:

Example TicketIssuerUnit rule unit class

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;
    }
}

The BoxOfficeUnit rule unit contains a BoxOfficeIsOpen DRL rule that uses the drools.guard( TicketIssuerUnit.class ) method to guard the execution of the TicketIssuerUnit rule unit that distributes the event tickets, as shown in the following DRL rule examples:

Example DRL rules with controlled execution using drools.guard()

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

package org.mypackage.myunit;
unit BoxOfficeUnit;

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

In this example, so long as at least one box office is open, the guarded TicketIssuerUnit rule unit is active and distributes event tickets. When no more box offices are in open state, the guarded TicketIssuerUnit rule unit is prevented from being executed.

The following example class illustrates a more complete box office scenario:

Example class for the box office scenario

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

persons.insert(new Person("John", 40));

// Execute `BoxOfficeIsOpen` rule, run `TicketIssuerUnit` rule unit, and execute `RegisterAdultTicket` rule:
executor.run(BoxOfficeUnit.class);

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

persons.insert(new Person("Matteo", 30));

// Execute `RegisterAdultTicket` rule:
executor.run(BoxOfficeUnit.class);

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

// One box office is closed, the other is 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 closed:
office2.setOpen(false);
boxOffices.update(officeFH2, office2); // Guarding rule is no longer true.
persons.insert(new Person("Edson", 35));
executor.run(BoxOfficeUnit.class); // No execution

assertEquals( 0, list.size() );

16.12.3. Rule unit identity conflicts

In rule unit execution scenarios with guarded rule units, a rule can guard multiple rule units and at the same time a rule unit can be guarded and then activated by multiple rules. For these two-way guarding scenarios, rule units must have a clearly defined identity to avoid identity conflicts.

By default, the identity of a rule unit is the rule unit class name and is treated as a singleton class by the RuleUnitExecutor. This identification behavior is encoded in the getUnitIdentity() default method of the RuleUnit interface:

Default identity method in the RuleUnit interface

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

In some cases, you may need to override this default identification behavior to avoid conflicting identities between rule units.

For example, the following RuleUnit class contains a DataSource definition that accepts any kind of object:

Example Unit0 rule unit class

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

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

This rule unit contains the following DRL rule that guards another rule unit based on two conditions (in OOPath notation):

Example GuardAgeCheck DRL rule in the rule unit

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

The guarded AgeCheckUnit rule unit verifies the age of a set of persons. The AgeCheckUnit contains a DataSource definition of the persons to check, a minAge variable that it verifies against, and a List for gathering the results:

Example AgeCheckUnit rule unit

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;
    }
}

The AgeCheckUnit rule unit contains the following DRL rule that performs the verification of the persons in the data source:

Example CheckAge DRL rule in the rule unit

package org.mypackage.myunit
unit AgeCheckUnit

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

This example creates a RuleUnitExecutor class, binds the class to the KIE base that contains these two rule units, and creates the two DataSource definitions for the same rule units:

Example executor and data source definitions

RuleUnitExecutor executor = RuleUnitExecutor.create().bind( kbase );

DataSource<Object> input = executor.newDataSource( "input" );
DataSource<Person> persons = executor.newDataSource( "persons",
                                                     new Person( "John", 42 ),
                                                     new Person( "Sally", 4 ) );

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

You can now insert some objects into the input data source and execute the Unit0 rule unit:

Example rule unit execution with inserted objects

ds.insert("test");
ds.insert(3);
ds.insert(4);
executor.run(Unit0.class);

Example results list from the execution

[Sally>3, John>3]

In this example, the rule unit named AgeCheckUnit is considered a singleton class and then executed only once, with the minAge variable set to 3. Both the String "test" and the Integer 4 inserted into the input data source can also trigger a second execution with the minAge variable set to 4. However, the second execution does not occur because another rule unit with the same identity has already been evaluated.

To resolve this rule unit identity conflict, override the getUnitIdentity() method in the AgeCheckUnit class to include also the minAge variable in the rule unit identity:

Modified AgeCheckUnit rule unit to override the getUnitIdentity() method

public class AgeCheckUnit implements RuleUnit {

    ...

    @Override
    public Identity getUnitIdentity() {
        return new Identity(getClass(), minAge);
    }
}

With this override in place, the previous example rule unit execution produces the following output:

Example results list from executing the modified rule unit

[John>4, Sally>3, John>3]

The rule units with minAge set to 3 and 4 are now considered two different rule units and both are executed.

Chapter 17. Data objects

Data objects are the building blocks for the rule assets that you create. Data objects are custom data types implemented as Java objects in specified packages of your project. For example, you might create a Person object with data fields Name, Address, and DateOfBirth to specify personal details for loan application rules. These custom data types determine what data your assets and your decision services are based on.

17.1. Creating data objects

The following procedure is a generic overview of creating data objects. It is not specific to a particular business asset.

Procedure

  1. In Business Central, go to MenuDesignProjects and click the project name.
  2. Click Add AssetData Object.
  3. Enter a unique Data Object name and select the Package where you want the data object to be available for other rule assets. Data objects with the same name cannot exist in the same package. In the specified DRL file, you can import a data object from any package.

    Importing data objects from other packages

    You can import an existing data object from another package directly into the asset designers like guided rules or guided decision table designers. Select the relevant rule asset within the project and in the asset designer, go to Data Objects → New item to select the object to be imported.

  4. To make your data object persistable, select the Persistable checkbox. Persistable data objects are able to be stored in a database according to the JPA specification. The default JPA is Hibernate.
  5. Click Ok.
  6. In the data object designer, click add field to add a field to the object with the attributes Id, Label, and Type. Required attributes are marked with an asterisk (*).

    • Id: Enter the unique ID of the field.
    • Label: (Optional) Enter a label for the field.
    • Type: Enter the data type of the field.
    • List: (Optional) Select this check box to enable the field to hold multiple items for the specified type.

      Figure 17.1. Add data fields to a data object

      Add data fields to a data object
  7. Click Create to add the new field, or click Create and continue to add the new field and continue adding other fields.

    Note

    To edit a field, select the field row and use the general properties on the right side of the screen.

Chapter 18. Creating DRL rules in Business Central

You can create and manage DRL rules for your project in Business Central. In each DRL rule file, you define rule conditions, actions, and other components related to the rule, based on the data objects you create or import in the package.

Procedure

  1. In Business Central, go to MenuDesignProjects and click the project name.
  2. Click Add AssetDRL file.
  3. Enter an informative DRL file name and select the appropriate Package. The package that you specify must be the same package where the required data objects have been assigned or will be assigned.

    You can also select Show declared DSL sentences if any domain specific language (DSL) assets have been defined in your project. These DSL assets will then become usable objects for conditions and actions that you define in the DRL designer.

  4. Click Ok to create the rule asset.

    The new DRL file is now listed in the DRL panel of the Project Explorer, or in the DSLR panel if you selected the Show declared DSL sentences option. The package to which you assigned this DRL file is listed at the top of the file.

  5. In the Fact types list in the left panel of the DRL designer, confirm that all data objects and data object fields (expand each) required for your rules are listed. If not, you can either import relevant data objects from other packages by using import statements in the DRL file, or create data objects within your package.
  6. After all data objects are in place, return to the Model tab of the DRL designer and define the DRL file with any of the following components:

    Components in a DRL file

    package
    
    import
    
    function  // Optional
    
    query  // Optional
    
    declare   // Optional
    
    global   // Optional
    
    rule "rule name"
        // Attributes
        when
            // Conditions
        then
            // Actions
    end
    
    rule "rule2 name"
    
    ...

    • package: (automatic) This was defined for you when you created the DRL file and selected the package.
    • import: Use this to identify the data objects from either this package or another package that you want to use in the DRL file. Specify the package and data object in the format packageName.objectName, with multiple imports on separate lines.

      Importing data objects

      import org.mortgages.LoanApplication;

    • function: (optional) Use this to include a function to be used by rules in the DRL file. Functions in DRL files put semantic code in your rule source file instead of in Java classes. Functions are especially useful if an action (then) part of a rule is used repeatedly and only the parameters differ for each rule. Above the rules in the DRL file, you can declare the function or import a static method from a helper class as a function, and then use the function by name in an action (then) part of the rule.

      Declaring and using a function with a rule (option 1)

      function String hello(String applicantName) {
          return "Hello " + applicantName + "!";
      }
      
      rule "Using a function"
        when
          // Empty
        then
          System.out.println( hello( "James" ) );
      end

      Importing and using the function with a rule (option 2)

      import function my.package.applicant.hello;
      
      rule "Using a function"
        when
          // Empty
        then
          System.out.println( hello( "James" ) );
      end

    • query: (optional) Use this to search the decision engine for facts related to the rules in the DRL file. You add the query definitions in DRL files and then obtain the matching results in your application code. Queries search for a set of defined conditions and do not require when or then specifications. Query names are global to the KIE base and therefore must be unique among all other rule queries in the project. To return the results of a query, construct a traditional QueryResults definition using ksession.getQueryResults("name"), where "name" is the query name. This returns a list of query results, which enable you to retrieve the objects that matched the query. Define the query and query results parameters above the rules in the DRL file.

      Example query definition in a DRL file

      query "people under the age of 21"
          $person : Person( age < 21 )
      end

      Example application code to obtain query results

      QueryResults results = ksession.getQueryResults( "people under the age of 21" );
      System.out.println( "we have " + results.size() + " people under the age  of 21" );

    • declare: (optional) Use this to declare a new fact type to be used by rules in the DRL file. The default fact type in the java.lang package of Red Hat Decision Manager is Object, but you can declare other types in DRL files as needed. Declaring fact types in DRL files enables you to define a new fact model directly in the decision engine, without creating models in a lower-level language like Java.

      Declaring and using a new fact type

      declare Person
        name : String
        dateOfBirth : java.util.Date
        address : Address
      end
      
      rule "Using a declared type"
        when
          $p : Person( name == "James" )
        then   // Insert Mark, who is a customer of James.
          Person mark = new Person();
          mark.setName( "Mark" );
          insert( mark );
      end

    • global: (optional) Use this to include a global variable to be used by rules in the DRL file. Global variables typically provide data or services for the rules, such as application services used in rule consequences, and return data from rules, such as logs or values added in rule consequences. Set the global value in the working memory of the decision engine through a KIE session configuration or REST operation, declare the global variable above the rules in the DRL file, and then use it in an action (then) part of the rule. For multiple global variables, use separate lines in the DRL file.

      Setting the global list configuration for the decision engine

      List<String> list = new ArrayList<>();
      KieSession kieSession = kiebase.newKieSession();
      kieSession.setGlobal( "myGlobalList", list );

      Defining the global list in a rule

      global java.util.List myGlobalList;
      
      rule "Using a global"
        when
          // Empty
        then
          myGlobalList.add( "My global list" );
      end

      Warning

      Do not use global variables to establish conditions in rules unless a global variable has a constant immutable value. Global variables are not inserted into the working memory of the decision engine, so the decision engine cannot track value changes of variables.

      Do not use global variables to share data between rules. 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 of the decision engine.

    • rule: Use this to define each rule in the DRL file. Rules consist of a rule name in the format rule "name", followed by optional attributes that define rule behavior (such as salience or no-loop), followed by when and then definitions. Each rule must have a unique name within the rule package. The when part of the rule contains the conditions that must be met to execute an action. For example, if a bank requires loan applicants to have over 21 years of age, then the when condition for an "Underage" rule would be Applicant( age < 21 ). The then part of the rule contains the actions to be performed when the conditional part of the rule has been met. For example, when the loan applicant is under 21 years old, the then action would be setApproved( false ), declining the loan because the applicant is under age.

      Rule for loan application age limit

      rule "Underage"
        salience 15
        when
          $application : LoanApplication()
          Applicant( age < 21 )
        then
          $application.setApproved( false );
          $application.setExplanation( "Underage" );
      end

      At a minimum, each DRL file must specify the package, import, and rule components. All other components are optional.

      The following is an example DRL file in a loan application decision service:

      Example DRL file for a loan application

      package org.mortgages;
      
      import org.mortgages.LoanApplication;
      import org.mortgages.Bankruptcy;
      import org.mortgages.Applicant;
      
      rule "Bankruptcy history"
      	salience 10
      	when
      		$a : LoanApplication()
      		exists (Bankruptcy( yearOfOccurrence > 1990 || amountOwed > 10000 ))
      	then
      		$a.setApproved( false );
      		$a.setExplanation( "has been bankrupt" );
      		delete( $a );
      end
      
      rule "Underage"
      	salience 15
      	when
      		$application : LoanApplication()
      		Applicant( age < 21 )
      	then
      		$application.setApproved( false );
      		$application.setExplanation( "Underage" );
      		delete( $application );
      end

      Figure 18.1. Example DRL file for a loan application in Business Central

      Example DRL file with required components
  7. After you define all components of the rule, click Validate in the upper-right toolbar of the DRL designer to validate the DRL file. If the file validation fails, address any problems described in the error message, review all syntax and components in the DRL file, and try again to validate the file until the file passes.
  8. Click Save in the DRL designer to save your work.

18.1. Adding WHEN conditions in DRL rules

The when part of the rule contains the conditions that must be met to execute an action. For example, if a bank requires loan applicants to have over 21 years of age, then the when condition of an "Underage" rule would be Applicant( age < 21 ). Conditions consist of a series of stated patterns and constraints, with optional bindings and other supported DRL elements, based on the available data objects in the package.

Prerequisites

  • The package is defined at the top of the DRL file. This should have been done for you when you created the file.
  • The import list of data objects used in the rule is defined below the package line of the DRL file. Data objects can be from this package or from another package in Business Central.
  • The rule name is defined in the format rule "name" below the package, import, and other lines that apply to the entire DRL file. The same rule name cannot be used more than once in the same package. Optional rule attributes (such as salience or no-loop) that define rule behavior are below the rule name, before the when section.

Procedure

  1. In the DRL designer, enter when within the rule to begin adding condition statements. The when section consists of zero or more fact patterns that define conditions for the rule.

    If the when section is empty, then the conditions are considered to be true and the actions in the then section are executed the first time a fireAllRules() call is made in the decision engine. This is useful if you want to use rules to set up the decision engine state.

    Example rule without conditions

    rule "Always insert applicant"
      when
        // Empty
      then   // Actions to be executed once
        insert( new Applicant() );
    end
    
    // The rule is internally rewritten in the following way:
    
    rule "Always insert applicant"
      when
        eval( true )
      then
        insert( new Applicant() );
    end

  2. Enter a pattern for the first condition to be met, with optional constraints, bindings, and other supported DRL elements. A basic pattern format is <patternBinding> : <patternType> ( <constraints> ). Patterns are based on the available data objects in the package and define the conditions to be met in order to trigger actions in the then section.

    • Simple pattern: A simple pattern with no constraints matches against a fact of the given type. For example, the following condition is only that the applicant exists.

      when
        Applicant()
    • Pattern with constraints: A pattern with constraints matches against a fact of the given type and the additional restrictions in parentheses that are true or false. For example, the following condition is that the applicant is under the age of 21.

      when
        Applicant( age < 21 )
    • Pattern with binding: A binding on a pattern is a shorthand reference that other components of the rule can use to refer back to the defined pattern. For example, the following binding a on LoanApplication is used in a related action for underage applicants.

      when
        $a : LoanApplication()
        Applicant( age < 21 )
      then
        $a.setApproved( false );
        $a.setExplanation( "Underage" )
  3. Continue defining all condition patterns that apply to this rule. The following are some of the keyword options for defining DRL conditions:

    • and: Use this to group conditional components into a logical conjunction. Infix and prefix and are supported. By default, all listed patterns are combined with and when no conjunction is specified.

      // All of the following examples are interpreted the same way:
      $a : LoanApplication() and Applicant( age < 21 )
      
      $a : LoanApplication()
      and Applicant( age < 21 )
      
      $a : LoanApplication()
      Applicant( age < 21 )
      
      (and $a : LoanApplication() Applicant( age < 21 ))
    • or: Use this to group conditional components into a logical disjunction. Infix and prefix or are supported.

      // All of the following examples are interpreted the same way:
      Bankruptcy( amountOwed == 100000 ) or IncomeSource( amount == 20000 )
      
      Bankruptcy( amountOwed == 100000 )
      or IncomeSource( amount == 20000 )
      
      (or Bankruptcy( amountOwed == 100000 ) IncomeSource( amount == 20000 ))
    • exists: Use this to specify facts and constraints that must exist. This option is triggered on only the first match, not subsequent matches. If you use this element with multiple patterns, enclose the patterns with parentheses ().

      exists ( Bankruptcy( yearOfOccurrence > 1990 || amountOwed > 10000 ) )
    • not: Use this to specify facts and constraints that must not exist.

      not ( Applicant( age < 21 ) )
    • forall: Use this to verify whether all facts that match the first pattern match all the remaining patterns. When a forall construct is satisfied, the rule evaluates to true.

      forall( $app : Applicant( age < 21 )
                    Applicant( this == $app, status = 'underage' ) )
    • from: Use this to specify a data source for a pattern.

      Applicant( ApplicantAddress : address )
      Address( zipcode == "23920W" ) from ApplicantAddress
    • entry-point: Use this to define an Entry Point corresponding to a data source for the pattern. Typically used with from.

      Applicant() from entry-point "LoanApplication"
    • collect: Use this to define a collection of objects that the rule can use as part of the condition. In the example, all pending applications in the decision engine for each given mortgage are grouped in a List. If three or more pending applications are found, the rule is executed.

      $m : Mortgage()
      $a : List( size >= 3 )
          from collect( LoanApplication( Mortgage == $m, status == 'pending' ) )
    • accumulate: Use this to iterate over a collection of objects, execute custom actions for each of the elements, and return one or more result objects (if the constraints evaluate to true). This option is a more flexible and powerful form of collect. Use the format accumulate( <source pattern>; <functions> [;<constraints>] ). In the example, min, max, and average are accumulate functions that calculate the minimum, maximum, and average temperature values over all the readings for each sensor. Other supported functions include count, sum, variance, standardDeviation, collectList, and collectSet.

      $s : Sensor()
      accumulate( Reading( sensor == $s, $temp : temperature );
                  $min : min( $temp ),
                  $max : max( $temp ),
                  $avg : average( $temp );
                  $min < 20, $avg > 70 )
    Note

    For more information about DRL rule conditions, see Section 16.8, “Rule conditions in DRL (WHEN)”.

  4. After you define all condition components of the rule, click Validate in the upper-right toolbar of the DRL designer to validate the DRL file. If the file validation fails, address any problems described in the error message, review all syntax and components in the DRL file, and try again to validate the file until the file passes.
  5. Click Save in the DRL designer to save your work.

18.2. Adding THEN actions in DRL rules

The then part of the rule contains the actions to be performed when the conditional part of the rule has been met. For example, when a loan applicant is under 21 years old, the then action of an "Underage" rule would be setApproved( false ), declining the loan because the applicant is under age. Actions consist of one or more methods that execute consequences based on the rule conditions and on available data objects in the package. The main purpose of rule actions is to insert, delete, or modify data in the working memory of the decision engine.

Prerequisites

  • The package is defined at the top of the DRL file. This should have been done for you when you created the file.
  • The import list of data objects used in the rule is defined below the package line of the DRL file. Data objects can be from this package or from another package in Business Central.
  • The rule name is defined in the format rule "name" below the package, import, and other lines that apply to the entire DRL file. The same rule name cannot be used more than once in the same package. Optional rule attributes (such as salience or no-loop) that define rule behavior are below the rule name, before the when section.

Procedure

  1. In the DRL designer, enter then after the when section of the rule to begin adding action statements.
  2. Enter one or more actions to be executed on fact patterns based on the conditions for the rule.

    The following are some of the keyword options for defining DRL actions:

    • set: Use this to set the value of a field.

      $application.setApproved ( false );
      $application.setExplanation( "has been bankrupt" );
    • modify: Use this to specify fields to be modified for a fact and to notify the decision engine of the change. This method provides a structured approach to fact updates. It combines the update operation with setter calls to change object fields.

      modify( LoanApplication ) {
              setAmount( 100 ),
              setApproved ( true )
      }
    • update: Use this to specify fields and the entire related fact to be updated and to notify the decision engine of the change. After a fact has changed, you must call update before changing another fact that might be affected by the updated values. To avoid this added step, use the modify method instead.

      LoanApplication.setAmount( 100 );
      update( LoanApplication );
    • insert: Use this to insert a new fact into the decision engine.

      insert( new Applicant() );
    • insertLogical: Use this to insert a new fact logically into the decision engine. The decision engine is responsible for logical decisions on insertions and retractions of facts. After regular or stated insertions, facts must be retracted explicitly. After logical insertions, the facts that were inserted are automatically retracted when the conditions in the rules that inserted the facts are no longer true.

      insertLogical( new Applicant() );
    • delete: Use this to remove an object from the decision engine. The keyword retract is also supported in DRL and executes the same action, but delete is typically preferred in DRL code for consistency with the keyword insert.

      delete( Applicant );
    Note

    For more information about DRL rule actions, see Section 16.9, “Rule actions in DRL (THEN)”.

  3. After you define all action components of the rule, click Validate in the upper-right toolbar of the DRL designer to validate the DRL file. If the file validation fails, address any problems described in the error message, review all syntax and components in the DRL file, and try again to validate the file until the file passes.
  4. Click Save in the DRL designer to save your work.

Chapter 19. Executing rules

After you identify example rules or create your own rules in Business Central, you can build and deploy the associated project and execute rules locally or on KIE Server to test the rules.

Prerequisites

Procedure

  1. In Business Central, go to MenuDesignProjects and click the project name.
  2. In the upper-right corner of the project Assets page, click Deploy to build the project and deploy it to KIE Server. If the build fails, address any problems described in the Alerts panel at the bottom of the screen.

    For more information about project deployment options, see Packaging and deploying a Red Hat Decision Manager project.

    Note

    If the rule assets in your project are not built from an executable rule model by default, verify that the following dependency is in the pom.xml file of your project and rebuild the project:

    <dependency>
      <groupId>org.drools</groupId>
      <artifactId>drools-model-compiler</artifactId>
      <version>${rhdm.version}</version>
    </dependency>

    This dependency is required for rule assets in Red Hat Decision Manager to be built from executable rule models by default. This dependency is included as part of the Red Hat Decision Manager core packaging, but depending on your Red Hat Decision Manager upgrade history, you may need to manually add this dependency to enable the executable rule model behavior.

    For more information about executable rule models, see Packaging and deploying a Red Hat Decision Manager project.

  3. Create a Maven or Java project outside of Business Central, if not created already, that you can use for executing rules locally or that you can use as a client application for executing rules on KIE Server. The project must contain a pom.xml file and any other required components for executing the project resources.

    For example test projects, see "Other methods for creating and executing DRL rules".

  4. Open the pom.xml file of your test project or client application and add the following dependencies, if not added already:

    • kie-ci: Enables your client application to load Business Central project data locally using ReleaseId
    • kie-server-client: Enables your client application to interact remotely with assets on KIE Server
    • slf4j: (Optional) Enables your client application to use Simple Logging Facade for Java (SLF4J) to return debug logging information after you interact with KIE Server

    Example dependencies for Red Hat Decision Manager 7.11 in a client application pom.xml file:

    <!-- For local execution -->
    <dependency>
      <groupId>org.kie</groupId>
      <artifactId>kie-ci</artifactId>
      <version>7.52.0.Final-redhat-00007</version>
    </dependency>
    
    <!-- For remote execution on KIE Server -->
    <dependency>
      <groupId>org.kie.server</groupId>
      <artifactId>kie-server-client</artifactId>
      <version>7.52.0.Final-redhat-00007</version>
    </dependency>
    
    <!-- For debug logging (optional) -->
    <dependency>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-simple</artifactId>
      <version>1.7.25</version>
    </dependency>

    For available versions of these artifacts, search the group ID and artifact ID in the Nexus Repository Manager online.

    Note

    Instead of specifying a Red Hat Decision Manager <version> for individual dependencies, consider adding the Red Hat Business Automation bill of materials (BOM) dependency to your project pom.xml file. The Red Hat Business Automation BOM applies to both Red Hat Decision Manager and Red Hat Process Automation Manager. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

    Example BOM dependency:

    <dependency>
      <groupId>com.redhat.ba</groupId>
      <artifactId>ba-platform-bom</artifactId>
      <version>7.11.0.redhat-00005</version>
      <scope>import</scope>
      <type>pom</type>
    </dependency>

    For more information about the Red Hat Business Automation BOM, see What is the mapping between Red Hat Decision Manager and the Maven library version?.

  5. Ensure that the dependencies for artifacts containing model classes are defined in the client application pom.xml file exactly as they appear in the pom.xml file of the deployed project. If dependencies for model classes differ between the client application and your projects, execution errors can occur.

    To access the project pom.xml file in Business Central, select any existing asset in the project and then in the Project Explorer menu on the left side of the screen, click the Customize View gear icon and select Repository Viewpom.xml.

    For example, the following Person class dependency appears in both the client and deployed project pom.xml files:

    <dependency>
      <groupId>com.sample</groupId>
      <artifactId>Person</artifactId>
      <version>1.0.0</version>
    </dependency>
  6. If you added the slf4j dependency to the client application pom.xml file for debug logging, create a simplelogger.properties file on the relevant classpath (for example, in src/main/resources/META-INF in Maven) with the following content:

    org.slf4j.simpleLogger.defaultLogLevel=debug
  7. In your client application, create a .java main class containing the necessary imports and a main() method to load the KIE base, insert facts, and execute the rules.

    For example, a Person object in a project contains getter and setter methods to set and retrieve the first name, last name, hourly rate, and the wage of a person. The following Wage rule in a project calculates the wage and hourly rate values and displays a message based on the result:

    package com.sample;
    
    import com.sample.Person;
    
    dialect "java"
    
    rule "Wage"
      when
        Person(hourlyRate * wage > 100)
        Person(name : firstName, surname : lastName)
      then
        System.out.println("Hello" + " " + name + " " + surname + "!");
        System.out.println("You are rich!");
    end

    To test this rule locally outside of KIE Server (if needed), configure the .java class to import KIE services, a KIE container, and a KIE session, and then use the main() method to fire all rules against a defined fact model:

    Executing rules locally

    import org.kie.api.KieServices;
    import org.kie.api.builder.ReleaseId;
    import org.kie.api.runtime.KieContainer;
    import org.kie.api.runtime.KieSession;
    import org.drools.compiler.kproject.ReleaseIdImpl;
    
    public class RulesTest {
    
      public static final void main(String[] args) {
        try {
          // Identify the project in the local repository:
          ReleaseId rid = new ReleaseIdImpl("com.myspace", "MyProject", "1.0.0");
    
          // Load the KIE base:
          KieServices ks = KieServices.Factory.get();
          KieContainer kContainer = ks.newKieContainer(rid);
          KieSession kSession = kContainer.newKieSession();
    
          // Set up the fact model:
          Person p = new Person();
          p.setWage(12);
          p.setFirstName("Tom");
          p.setLastName("Summers");
          p.setHourlyRate(10);
    
          // Insert the person into the session:
          kSession.insert(p);
    
          // Fire all rules:
          kSession.fireAllRules();
          kSession.dispose();
        }
    
        catch (Throwable t) {
          t.printStackTrace();
        }
      }
    }

    To test this rule on KIE Server, configure the .java class with the imports and rule execution information similarly to the local example, and additionally specify KIE services configuration and KIE services client details:

    Executing rules on KIE Server

    package com.sample;
    
    import java.util.ArrayList;
    import java.util.HashSet;
    import java.util.List;
    import java.util.Set;
    
    import org.kie.api.command.BatchExecutionCommand;
    import org.kie.api.command.Command;
    import org.kie.api.KieServices;
    import org.kie.api.runtime.ExecutionResults;
    import org.kie.api.runtime.KieContainer;
    import org.kie.api.runtime.KieSession;
    import org.kie.server.api.marshalling.MarshallingFormat;
    import org.kie.server.api.model.ServiceResponse;
    import org.kie.server.client.KieServicesClient;
    import org.kie.server.client.KieServicesConfiguration;
    import org.kie.server.client.KieServicesFactory;
    import org.kie.server.client.RuleServicesClient;
    
    import com.sample.Person;
    
    public class RulesTest {
    
      private static final String containerName = "testProject";
      private static final String sessionName = "myStatelessSession";
    
      public static final void main(String[] args) {
        try {
          // Define KIE services configuration and client:
          Set<Class<?>> allClasses = new HashSet<Class<?>>();
          allClasses.add(Person.class);
          String serverUrl = "http://$HOST:$PORT/kie-server/services/rest/server";
          String username = "$USERNAME";
          String password = "$PASSWORD";
          KieServicesConfiguration config =
            KieServicesFactory.newRestConfiguration(serverUrl,
                                                    username,
                                                    password);
          config.setMarshallingFormat(MarshallingFormat.JAXB);
          config.addExtraClasses(allClasses);
          KieServicesClient kieServicesClient =
            KieServicesFactory.newKieServicesClient(config);
    
          // Set up the fact model:
          Person p = new Person();
          p.setWage(12);
          p.setFirstName("Tom");
          p.setLastName("Summers");
          p.setHourlyRate(10);
    
          // Insert Person into the session:
          KieCommands kieCommands = KieServices.Factory.get().getCommands();
          List<Command> commandList = new ArrayList<Command>();
          commandList.add(kieCommands.newInsert(p, "personReturnId"));
    
          // Fire all rules:
          commandList.add(kieCommands.newFireAllRules("numberOfFiredRules"));
          BatchExecutionCommand batch = kieCommands.newBatchExecution(commandList, sessionName);
    
          // Use rule services client to send request:
          RuleServicesClient ruleClient = kieServicesClient.getServicesClient(RuleServicesClient.class);
          ServiceResponse<ExecutionResults> executeResponse = ruleClient.executeCommandsWithResults(containerName, batch);
          System.out.println("number of fired rules:" + executeResponse.getResult().getValue("numberOfFiredRules"));
        }
    
        catch (Throwable t) {
          t.printStackTrace();
        }
      }
    }

  8. Run the configured .java class from your project directory. You can run the file in your development platform (such as Red Hat CodeReady Studio) or in the command line.

    Example Maven execution (within project directory):

    mvn clean install exec:java -Dexec.mainClass="com.sample.app.RulesTest"

    Example Java execution (within project directory)

    javac -classpath "./$DEPENDENCIES/*:." RulesTest.java
    java -classpath "./$DEPENDENCIES/*:." RulesTest
  9. Review the rule execution status in the command line and in the server log. If any rules do not execute as expected, review the configured rules in the project and the main class configuration to validate the data provided.

Chapter 20. Other methods for creating and executing DRL rules

As an alternative to creating and managing DRL rules within the Business Central interface, you can create DRL rule files externally as part of a Maven or Java project using Red Hat CodeReady Studio or another integrated development environment (IDE). These standalone projects can then be integrated as knowledge JAR (KJAR) dependencies in existing Red Hat Decision Manager projects in Business Central. The DRL files in your standalone project must contain at a minimum the required package specification, import lists, and rule definitions. Any other DRL components, such as global variables and functions, are optional. All data objects related to a DRL rule must be included with your standalone DRL project or deployment.

You can also use executable rule models in your Maven or Java projects to provide a Java-based representation of a rule set for execution at build time. The executable model is a more efficient alternative to the standard asset packaging in Red Hat Decision Manager and enables KIE containers and KIE bases to be created more quickly, especially when you have large lists of DRL (Drools Rule Language) files and other Red Hat Decision Manager assets.

20.1. Creating and executing DRL rules in Red Hat CodeReady Studio

You can use Red Hat CodeReady Studio to create DRL files with rules and integrate the files with your Red Hat Decision Manager decision service. This method of creating DRL rules is helpful if you already use Red Hat CodeReady Studio for your decision service and want to continue with the same workflow. If you do not already use this method, then the Business Central interface of Red Hat Decision Manager is recommended for creating DRL files and other rule assets.

Prerequisites

Procedure

  1. In the Red Hat CodeReady Studio, click FileNewProject.
  2. In the New Project window that opens, select DroolsDrools Project and click Next.
  3. Click the second icon to Create a project and populate it with some example files to help you get started quickly. Click Next.
  4. Enter a Project name and select the Maven radio button as the project building option. The GAV values are generated automatically. You can update these values as needed for your project:

    • Group ID: com.sample
    • Artifact ID: my-project
    • Version: 1.0.0-SNAPSHOT
  5. Click Finish to create the project.

    This configuration sets up a basic project structure, class path, and sample rules. The following is an overview of the project structure:

    my-project
     `-- src/main/java
        | `-- com.sample
        |    `-- DecisionTableTest.java
        |    `-- DroolsTest.java
        |    `-- ProcessTest.java
        |
     `-- src/main/resources
        | `-- dtables
        |    `-- Sample.xls
        | `-- process
        |    `-- sample.bpmn
        | `-- rules
        |    `-- Sample.drl
        | `-- META-INF
        |
     `-- JRE System Library
        |
     `-- Maven Dependencies
        |
     `-- Drools Library
        |
     `-- src
        |
     `-- target
        |
     `-- pom.xml

    Notice the following elements:

    • A Sample.drl rule file in the src/main/resources directory, containing an example Hello World and GoodBye rules.
    • A DroolsTest.java file under the src/main/java directory in the com.sample package. The DroolsTest class can be used to execute the Sample.drl rule.
    • The Drools Library directory, which acts as a custom class path containing JAR files necessary for execution.

    You can edit the existing Sample.drl file and DroolsTest.java files with new configurations as needed, or create new rule and object files. In this procedure, you are creating a new rule and new Java objects.

  6. Create a Java object on which the rule or rules will operate.

    In this example, a Person.java file is created in my-project/src/main/java/com.sample. The Person class contains getter and setter methods to set and retrieve the first name, last name, hourly rate, and the wage of a person:

      public class Person {
        private String firstName;
        private String lastName;
        private Integer hourlyRate;
        private Integer wage;
    
        public String getFirstName() {
          return firstName;
        }
    
        public void setFirstName(String firstName) {
          this.firstName = firstName;
        }
    
        public String getLastName() {
          return lastName;
        }
    
        public void setLastName(String lastName) {
          this.lastName = lastName;
        }
    
        public Integer getHourlyRate() {
          return hourlyRate;
        }
    
        public void setHourlyRate(Integer hourlyRate) {
          this.hourlyRate = hourlyRate;
        }
    
        public Integer getWage(){
          return wage;
        }
    
        public void setWage(Integer wage){
          this.wage = wage;
        }
      }
  7. Click FileSave to save the file.
  8. Create a rule file in .drl format in my-project/src/main/resources/rules. The DRL file must contain at a minimum a package specification, an import list of data objects to be used by the rule or rules, and one or more rules with when conditions and then actions.

    The following Wage.drl file contains a Wage rule that imports the Person class, calculates the wage and hourly rate values, and displays a message based on the result:

    package com.sample;
    
    import com.sample.Person;
    
    dialect "java"
    
    rule "Wage"
      when
        Person(hourlyRate * wage > 100)
        Person(name : firstName, surname : lastName)
      then
        System.out.println("Hello" + " " + name + " " + surname + "!");
        System.out.println("You are rich!");
    end
  9. Click FileSave to save the file.
  10. Create a main class and save it to the same directory as the Java object that you created. The main class will load the KIE base and execute rules.

    Note

    You can also add the main() method and Person class within a single Java object file, similar to the DroolsTest.java sample file.

  11. In the main class, add the required import statements to import KIE services, a KIE container, and a KIE session. Then load the KIE base, insert facts, and execute the rule from the main() method that passes the fact model to the rule.

    In this example, a RulesTest.java file is created in my-project/src/main/java/com.sample with the required imports and main() method:

    package com.sample;
    
    import org.kie.api.KieServices;
    import org.kie.api.runtime.KieContainer;
    import org.kie.api.runtime.KieSession;
    
    public class RulesTest {
      public static final void main(String[] args) {
        try {
          // Load the KIE base:
          KieServices ks = KieServices.Factory.get();
          KieContainer kContainer = ks.getKieClasspathContainer();
          KieSession kSession = kContainer.newKieSession();
    
          // Set up the fact model:
          Person p = new Person();
          p.setWage(12);
          p.setFirstName("Tom");
          p.setLastName("Summers");
          p.setHourlyRate(10);
    
          // Insert the person into the session:
          kSession.insert(p);
    
          // Fire all rules:
          kSession.fireAllRules();
          kSession.dispose();
        }
    
        catch (Throwable t) {
          t.printStackTrace();
        }
      }
    }
  12. Click FileSave to save the file.
  13. After you create and save all DRL assets in your project, right-click your project folder and select Run AsJava Application to build the project. If the project build fails, address any problems described in the Problems tab of the lower window in CodeReady Studio, and try again to validate the project until the project builds.
If the Run AsJava Application option is not available

If Java Application is not an option when you right-click your project and select Run As, then go to Run AsRun Configurations, right-click Java Application, and click New. Then in the Main tab, browse for and select your Project and the associated Main class. Click Apply and then click Run to test the project. The next time you right-click your project folder, the Java Application option will appear.

To integrate the new rule assets with an existing project in Red Hat Decision Manager, you can compile the new project as a knowledge JAR (KJAR) and add it as a dependency in the pom.xml file of the project in Business Central. To access the project pom.xml file in Business Central, you can select any existing asset in the project and then in the Project Explorer menu on the left side of the screen, click the Customize View gear icon and select Repository Viewpom.xml.

20.2. Creating and executing DRL rules using Java

You can use Java objects to create DRL files with rules and integrate the objects with your Red Hat Decision Manager decision service. This method of creating DRL rules is helpful if you already use external Java objects for your decision service and want to continue with the same workflow. If you do not already use this method, then the Business Central interface of Red Hat Decision Manager is recommended for creating DRL files and other rule assets.

Procedure

  1. Create a Java object on which the rule or rules will operate.

    In this example, a Person.java file is created in a directory my-project. The Person class contains getter and setter methods to set and retrieve the first name, last name, hourly rate, and the wage of a person:

      public class Person {
        private String firstName;
        private String lastName;
        private Integer hourlyRate;
        private Integer wage;
    
        public String getFirstName() {
          return firstName;
        }
    
        public void setFirstName(String firstName) {
          this.firstName = firstName;
        }
    
        public String getLastName() {
          return lastName;
        }
    
        public void setLastName(String lastName) {
          this.lastName = lastName;
        }
    
        public Integer getHourlyRate() {
          return hourlyRate;
        }
    
        public void setHourlyRate(Integer hourlyRate) {
          this.hourlyRate = hourlyRate;
        }
    
        public Integer getWage(){
          return wage;
        }
    
        public void setWage(Integer wage){
          this.wage = wage;
        }
      }
  2. Create a rule file in .drl format under the my-project directory. The DRL file must contain at a minimum a package specification (if applicable), an import list of data objects to be used by the rule or rules, and one or more rules with when conditions and then actions.

    The following Wage.drl file contains a Wage rule that calculates the wage and hourly rate values and displays a message based on the result:

    package com.sample;
    
    import com.sample.Person;
    
    dialect "java"
    
    rule "Wage"
      when
        Person(hourlyRate * wage > 100)
        Person(name : firstName, surname : lastName)
      then
        System.out.println("Hello" + " " + name + " " + surname + "!");
        System.out.println("You are rich!");
    end
  3. Create a main class and save it to the same directory as the Java object that you created. The main class will load the KIE base and execute rules.
  4. In the main class, add the required import statements to import KIE services, a KIE container, and a KIE session. Then load the KIE base, insert facts, and execute the rule from the main() method that passes the fact model to the rule.

    In this example, a RulesTest.java file is created in my-project with the required imports and main() method:

    import org.kie.api.KieServices;
    import org.kie.api.runtime.KieContainer;
    import org.kie.api.runtime.KieSession;
    
    public class RulesTest {
      public static final void main(String[] args) {
        try {
          // Load the KIE base:
          KieServices ks = KieServices.Factory.get();
          KieContainer kContainer = ks.getKieClasspathContainer();
          KieSession kSession = kContainer.newKieSession();
    
          // Set up the fact model:
          Person p = new Person();
          p.setWage(12);
          p.setFirstName("Tom");
          p.setLastName("Summers");
          p.setHourlyRate(10);
    
          // Insert the person into the session:
          kSession.insert(p);
    
          // Fire all rules:
          kSession.fireAllRules();
          kSession.dispose();
        }
    
        catch (Throwable t) {
          t.printStackTrace();
        }
      }
    }
  5. Download the Red Hat Decision Manager 7.11.0 Source Distribution ZIP file from the Red Hat Customer Portal and extract it under my-project/dm-engine-jars/.
  6. In the my-project/META-INF directory, create a kmodule.xml metadata file with the following content:

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

    This kmodule.xml file is a KIE module descriptor that selects resources to KIE bases and configures sessions. This file enables you to define and configure one or more KIE bases, and to include DRL files from specific packages in a specific KIE base. You can also create one or more KIE sessions from each KIE base.

    The following example shows a more advanced kmodule.xml file:

    <?xml version="1.0" encoding="UTF-8"?>
    <kmodule xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.drools.org/xsd/kmodule">
      <kbase name="KBase1" default="true" eventProcessingMode="cloud" equalsBehavior="equality" declarativeAgenda="enabled" packages="org.domain.pkg1">
        <ksession name="KSession1_1" type="stateful" default="true" />
        <ksession name="KSession1_2" type="stateful" default="true" 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="KSession2_1" type="stateless" default="true" clockType="realtime">
          <fileLogger file="debugInfo" threaded="true" interval="10" />
          <workItemHandlers>
            <workItemHandler name="name" type="new org.domain.WorkItemHandler()" />
          </workItemHandlers>
          <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>

    This example defines two KIE bases. Two KIE sessions are instantiated from the KBase1 KIE base, and one KIE session from KBase2. The KIE session from KBase2 is a stateless KIE session, which means that data from a previous invocation of the KIE session (the previous session state) is discarded between session invocations. Specific packages of rule assets are included with both KIE bases. When you specify packages in this way, you must organize your DRL files in a folder structure that reflects the specified packages.

  7. After you create and save all DRL assets in your Java object, navigate to the my-project directory in the command line and run the following command to build your Java files. Replace RulesTest.java with the name of your Java main class.

    javac -classpath "./dm-engine-jars/*:." RulesTest.java

    If the build fails, address any problems described in the command line error messages and try again to validate the Java object until the object passes.

  8. After your Java files build successfully, run the following command to execute the rules locally. Replace RulesTest with the prefix of your Java main class.

    java -classpath "./dm-engine-jars/*:." RulesTest
  9. Review the rules to ensure that they executed properly, and address any needed changes in the Java files.

To integrate the new rule assets with an existing project in Red Hat Decision Manager, you can compile the new Java project as a knowledge JAR (KJAR) and add it as a dependency in the pom.xml file of the project in Business Central. To access the project pom.xml file in Business Central, you can select any existing asset in the project and then in the Project Explorer menu on the left side of the screen, click the Customize View gear icon and select Repository Viewpom.xml.

20.3. Creating and executing DRL rules using Maven

You can use Maven archetypes to create DRL files with rules and integrate the archetypes with your Red Hat Decision Manager decision service. This method of creating DRL rules is helpful if you already use external Maven archetypes for your decision service and want to continue with the same workflow. If you do not already use this method, then the Business Central interface of Red Hat Decision Manager is recommended for creating DRL files and other rule assets.

Procedure

  1. Navigate to a directory where you want to create a Maven archetype and run the following command:

    mvn archetype:generate -DgroupId=com.sample.app -DartifactId=my-app -DarchetypeArtifactId=maven-archetype-quickstart -DinteractiveMode=false

    This creates a directory my-app with the following structure:

    my-app
    |-- pom.xml
    `-- src
        |-- main
        |   `-- java
        |       `-- com
        |           `-- sample
        |               `-- app
        |                   `-- App.java
        `-- test
            `-- java
                `-- com
                    `-- sample
                        `-- app
                            `-- AppTest.java

    The my-app directory contains the following key components:

    • A src/main directory for storing the application sources
    • A src/test directory for storing the test sources
    • A pom.xml file with the project configuration
  2. Create a Java object on which the rule or rules will operate within the Maven archetype.

    In this example, a Person.java file is created in the directory my-app/src/main/java/com/sample/app. The Person class contains getter and setter methods to set and retrieve the first name, last name, hourly rate, and the wage of a person:

    package com.sample.app;
    
      public class Person {
    
        private String firstName;
        private String lastName;
        private Integer hourlyRate;
        private Integer wage;
    
        public String getFirstName() {
          return firstName;
        }
    
        public void setFirstName(String firstName) {
          this.firstName = firstName;
        }
    
        public String getLastName() {
          return lastName;
        }
    
        public void setLastName(String lastName) {
          this.lastName = lastName;
        }
    
        public Integer getHourlyRate() {
          return hourlyRate;
        }
    
        public void setHourlyRate(Integer hourlyRate) {
          this.hourlyRate = hourlyRate;
        }
    
        public Integer getWage(){
          return wage;
        }
    
        public void setWage(Integer wage){
          this.wage = wage;
        }
      }
  3. Create a rule file in .drl format in my-app/src/main/resources/rules. The DRL file must contain at a minimum a package specification, an import list of data objects to be used by the rule or rules, and one or more rules with when conditions and then actions.

    The following Wage.drl file contains a Wage rule that imports the Person class, calculates the wage and hourly rate values, and displays a message based on the result:

    package com.sample.app;
    
    import com.sample.app.Person;
    
    dialect "java"
    
    rule "Wage"
      when
        Person(hourlyRate * wage > 100)
        Person(name : firstName, surname : lastName)
      then
        System.out.println("Hello " + name + " " + surname + "!");
        System.out.println("You are rich!");
    end
  4. In the my-app/src/main/resources/META-INF directory, create a kmodule.xml metadata file with the following content:

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

    This kmodule.xml file is a KIE module descriptor that selects resources to KIE bases and configures sessions. This file enables you to define and configure one or more KIE bases, and to include DRL files from specific packages in a specific KIE base. You can also create one or more KIE sessions from each KIE base.

    The following example shows a more advanced kmodule.xml file:

    <?xml version="1.0" encoding="UTF-8"?>
    <kmodule xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.drools.org/xsd/kmodule">
      <kbase name="KBase1" default="true" eventProcessingMode="cloud" equalsBehavior="equality" declarativeAgenda="enabled" packages="org.domain.pkg1">
        <ksession name="KSession1_1" type="stateful" default="true" />
        <ksession name="KSession1_2" type="stateful" default="true" 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="KSession2_1" type="stateless" default="true" clockType="realtime">
          <fileLogger file="debugInfo" threaded="true" interval="10" />
          <workItemHandlers>
            <workItemHandler name="name" type="new org.domain.WorkItemHandler()" />
          </workItemHandlers>
          <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>

    This example defines two KIE bases. Two KIE sessions are instantiated from the KBase1 KIE base, and one KIE session from KBase2. The KIE session from KBase2 is a stateless KIE session, which means that data from a previous invocation of the KIE session (the previous session state) is discarded between session invocations. Specific packages of rule assets are included with both KIE bases. When you specify packages in this way, you must organize your DRL files in a folder structure that reflects the specified packages.

  5. In the my-app/pom.xml configuration file, specify the libraries that your application requires. Provide the Red Hat Decision Manager dependencies as well as the group ID, artifact ID, and version (GAV) of your application.

    <?xml version="1.0" encoding="UTF-8"?>
    <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>
    <groupId>com.sample.app</groupId>
    <artifactId>my-app</artifactId>
    <version>1.0.0</version>
    <repositories>
      <repository>
        <id>jboss-ga-repository</id>
        <url>http://maven.repository.redhat.com/ga/</url>
      </repository>
    </repositories>
    <dependencies>
      <dependency>
        <groupId>org.drools</groupId>
        <artifactId>drools-compiler</artifactId>
        <version>VERSION</version>
      </dependency>
      <dependency>
        <groupId>org.kie</groupId>
        <artifactId>kie-api</artifactId>
        <version>VERSION</version>
      </dependency>
      <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>4.11</version>
        <scope>test</scope>
      </dependency>
    </dependencies>
    </project>

    For information about Maven dependencies and the BOM (Bill of Materials) in Red Hat Decision Manager, see What is the mapping between Red Hat Decision Manager and Maven library version?.

  6. Use the testApp method in my-app/src/test/java/com/sample/app/AppTest.java to test the rule. The AppTest.java file is created by Maven by default.
  7. In the AppTest.java file, add the required import statements to import KIE services, a KIE container, and a KIE session. Then load the KIE base, insert facts, and execute the rule from the testApp() method that passes the fact model to the rule.

    import org.kie.api.KieServices;
    import org.kie.api.runtime.KieContainer;
    import org.kie.api.runtime.KieSession;
    
    public void testApp() {
    
      // Load the KIE base:
      KieServices ks = KieServices.Factory.get();
      KieContainer kContainer = ks.getKieClasspathContainer();
      KieSession kSession = kContainer.newKieSession();
    
      // Set up the fact model:
      Person p = new Person();
      p.setWage(12);
      p.setFirstName("Tom");
      p.setLastName("Summers");
      p.setHourlyRate(10);
    
      // Insert the person into the session:
      kSession.insert(p);
    
      // Fire all rules:
      kSession.fireAllRules();
      kSession.dispose();
    }
  8. After you create and save all DRL assets in your Maven archetype, navigate to the my-app directory in the command line and run the following command to build your files:

    mvn clean install

    If the build fails, address any problems described in the command line error messages and try again to validate the files until the build is successful.

  9. After your files build successfully, run the following command to execute the rules locally. Replace com.sample.app with your package name.

    mvn exec:java -Dexec.mainClass="com.sample.app"
  10. Review the rules to ensure that they executed properly, and address any needed changes in the files.

To integrate the new rule assets with an existing project in Red Hat Decision Manager, you can compile the new Maven project as a knowledge JAR (KJAR) and add it as a dependency in the pom.xml file of the project in Business Central. To access the project pom.xml file in Business Central, you can select any existing asset in the project and then in the Project Explorer menu on the left side of the screen, click the Customize View gear icon and select Repository Viewpom.xml.

Chapter 21. Example decisions in Red Hat Decision Manager for an IDE

Red Hat Decision Manager provides example decisions distributed as Java classes that you can import into your integrated development environment (IDE). You can use these examples to better understand decision engine capabilities or use them as a reference for the decisions that you define in your own Red Hat Decision Manager projects.

The following example decision sets are some of the examples available in Red Hat Decision Manager:

  • Hello World example: Demonstrates basic rule execution and use of debug output
  • State example: Demonstrates forward chaining and conflict resolution through rule salience and agenda groups
  • Fibonacci example: Demonstrates recursion and conflict resolution through rule salience
  • Banking example: Demonstrates pattern matching, basic sorting, and calculation
  • Pet Store example: Demonstrates rule agenda groups, global variables, callbacks, and GUI integration
  • Sudoku example: Demonstrates complex pattern matching, problem solving, callbacks, and GUI integration
  • House of Doom example: Demonstrates backward chaining and recursion
Note

For optimization examples provided with Red Hat build of OptaPlanner, see Getting started with Red Hat build of OptaPlanner.

21.1. Importing and executing Red Hat Decision Manager example decisions in an IDE

You can import Red Hat Decision Manager example decisions into your integrated development environment (IDE) and execute them to explore how the rules and code function. You can use these examples to better understand decision engine capabilities or use them as a reference for the decisions that you define in your own Red Hat Decision Manager projects.

Prerequisites

  • Java 8 or later is installed.
  • Maven 3.5.x or later is installed.
  • An IDE is installed, such as Red Hat CodeReady Studio.

Procedure

  1. Download and unzip the Red Hat Decision Manager 7.11.0 Source Distribution from the Red Hat Customer Portal to a temporary directory, such as /rhdm-7.11.0-sources.
  2. Open your IDE and select FileImportMavenExisting Maven Projects, or the equivalent option for importing a Maven project.
  3. Click Browse, navigate to ~/rhdm-7.11.0-sources/src/drools-$VERSION/drools-examples (or, for the Conway’s Game of Life example, ~/rhdm-7.11.0-sources/src/droolsjbpm-integration-$VERSION/droolsjbpm-integration-examples), and import the project.
  4. Navigate to the example package that you want to run and find the Java class with the main method.
  5. Right-click the Java class and select Run AsJava Application to run the example.

    To run all examples through a basic user interface, run the DroolsExamplesApp.java class (or, for Conway’s Game of Life, the DroolsJbpmIntegrationExamplesApp.java class) in the org.drools.examples main class.

    Figure 21.1. Interface for all examples in drools-examples (DroolsExamplesApp.java)

    drools examples run all

    Figure 21.2. Interface for all examples in droolsjbpm-integration-examples (DroolsJbpmIntegrationExamplesApp.java)

    droolsjbpm examples run all

21.2. Hello World example decisions (basic rules and debugging)

The Hello World example decision set demonstrates how to insert objects into the decision engine working memory, how to match the objects using rules, and how to configure logging to trace the internal activity of the decision engine.

The following is an overview of the Hello World example:

  • Name: helloworld
  • Main class: org.drools.examples.helloworld.HelloWorldExample (in src/main/java)
  • Module: drools-examples
  • Type: Java application
  • Rule file: org.drools.examples.helloworld.HelloWorld.drl (in src/main/resources)
  • Objective: Demonstrates basic rule execution and use of debug output

In the Hello World example, a KIE session is generated to enable rule execution. All rules require a KIE session for execution.

KIE session for rule execution

KieServices ks = KieServices.Factory.get(); 1
KieContainer kc = ks.getKieClasspathContainer(); 2
KieSession ksession = kc.newKieSession("HelloWorldKS"); 3

1
Obtains the KieServices factory. This is the main interface that applications use to interact with the decision engine.
2
Creates a KieContainer from the project class path. This detects a /META-INF/kmodule.xml file from which it configures and instantiates a KieContainer with a KieModule.
3
Creates a KieSession based on the "HelloWorldKS" KIE session configuration defined in the /META-INF/kmodule.xml file.
Note

For more information about Red Hat Decision Manager project packaging, see Packaging and deploying a Red Hat Decision Manager project.

Red Hat Decision Manager has an event model that exposes internal engine activity. Two default debug listeners, DebugAgendaEventListener and DebugRuleRuntimeEventListener, print debug event information to the System.err output. The KieRuntimeLogger provides execution auditing, the result of which you can view in a graphical viewer.

Debug listeners and audit loggers

// Set up listeners.
ksession.addEventListener( new DebugAgendaEventListener() );
ksession.addEventListener( new DebugRuleRuntimeEventListener() );

// Set up a file-based audit logger.
KieRuntimeLogger logger = KieServices.get().getLoggers().newFileLogger( ksession, "./target/helloworld" );

// Set up a ThreadedFileLogger so that the audit view reflects events while debugging.
KieRuntimeLogger logger = ks.getLoggers().newThreadedFileLogger( ksession, "./target/helloworld", 1000 );

The logger is a specialized implementation built on the Agenda and RuleRuntime listeners. When the decision engine has finished executing, logger.close() is called.

The example creates a single Message object with the message "Hello World", inserts the status HELLO into the KieSession, executes rules with fireAllRules().

Data insertion and execution

// Insert facts into the KIE session.
final Message message = new Message();
message.setMessage( "Hello World" );
message.setStatus( Message.HELLO );
ksession.insert( message );

// Fire the rules.
ksession.fireAllRules();

Rule execution uses a data model to pass data as inputs and outputs to the KieSession. The data model in this example has two fields: the message, which is a String, and the status, which can be HELLO or GOODBYE.

Data model class

public static class Message {
    public static final int HELLO   = 0;
    public static final int GOODBYE = 1;

    private String          message;
    private int             status;
    ...
}

The two rules are located in the file src/main/resources/org/drools/examples/helloworld/HelloWorld.drl.

The when condition of the "Hello World" rule states that the rule is activated for each Message object inserted into the KIE session that has the status Message.HELLO. Additionally, two variable bindings are created: the variable message is bound to the message attribute and the variable m is bound to the matched Message object itself.

The then action of the rule specifies to print the content of the bound variable message to System.out, and then changes the values of the message and status attributes of the Message object bound to m. The rule uses the modify statement to apply a block of assignments in one statement and to notify the decision engine of the changes at the end of the block.

"Hello World" rule

rule "Hello World"
  when
    m : Message( status == Message.HELLO, message : message )
  then
    System.out.println( message );
    modify ( m ) { message = "Goodbye cruel world",
                   status = Message.GOODBYE };
end

The "Good Bye" rule is similar to the "Hello World" rule except that it matches Message objects that have the status Message.GOODBYE.

"Good Bye" rule

rule "Good Bye"
  when
    Message( status == Message.GOODBYE, message : message )
  then
    System.out.println( message );
end

To execute the example, run the org.drools.examples.helloworld.HelloWorldExample class as a Java application in your IDE. The rule writes to System.out, the debug listener writes to System.err, and the audit logger creates a log file in target/helloworld.log.

System.out output in the IDE console

Hello World
Goodbye cruel world

System.err output in the IDE console

==>[ActivationCreated(0): rule=Hello World;
                   tuple=[fid:1:1:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
[ObjectInserted: handle=[fid:1:1:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96];
                 object=org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]
[BeforeActivationFired: rule=Hello World;
                   tuple=[fid:1:1:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
==>[ActivationCreated(4): rule=Good Bye;
                   tuple=[fid:1:2:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
[ObjectUpdated: handle=[fid:1:2:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96];
                old_object=org.drools.examples.helloworld.HelloWorldExample$Message@17cec96;
                new_object=org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]
[AfterActivationFired(0): rule=Hello World]
[BeforeActivationFired: rule=Good Bye;
                   tuple=[fid:1:2:org.drools.examples.helloworld.HelloWorldExample$Message@17cec96]]
[AfterActivationFired(4): rule=Good Bye]

To better understand the execution flow of this example, you can load the audit log file from target/helloworld.log into your IDE debug view or Audit View, if available (for example, in WindowShow View in some IDEs).

In this example, the Audit view shows that the object is inserted, which creates an activation for the "Hello World" rule. The activation is then executed, which updates the Message object and causes the "Good Bye" rule to activate. Finally, the "Good Bye" rule is executed. When you select an event in the Audit View, the origin event, which is the "Activation created" event in this example, is highlighted in green.

Figure 21.3. Hello World example Audit View

helloworld auditview1

21.3. State example decisions (forward chaining and conflict resolution)

The State example decision set demonstrates how the decision engine uses forward chaining and any changes to facts in the working memory to resolve execution conflicts for rules in a sequence. The example focuses on resolving conflicts through salience values or through agenda groups that you can define in rules.

The following is an overview of the State example:

  • Name: state
  • Main classes: org.drools.examples.state.StateExampleUsingSalience, org.drools.examples.state.StateExampleUsingAgendaGroup (in src/main/java)
  • Module: drools-examples
  • Type: Java application
  • Rule files: org.drools.examples.state.*.drl (in src/main/resources)
  • Objective: Demonstrates forward chaining and conflict resolution through rule salience and agenda groups

A forward-chaining rule system is a data-driven system that starts with a fact in the working memory of the decision engine and reacts to changes to that fact. When objects are inserted into working memory, any rule conditions that become true as a result of the change are scheduled for execution by the agenda.

In contrast, a backward-chaining rule system is a goal-driven system that starts with a conclusion that the decision engine attempts to satisfy, often using recursion. If the system cannot reach the conclusion or goal, it searches for subgoals, which are conclusions that complete part of the current goal. The system continues this process until either the initial conclusion is satisfied or all subgoals are satisfied.

The decision engine in Red Hat Decision Manager uses both forward and backward chaining to evaluate rules.

The following diagram illustrates how the decision engine evaluates rules using forward chaining overall with a backward-chaining segment in the logic flow:

Figure 21.4. Rule evaluation logic using forward and backward chaining

RuleEvaluation Enterprise

In the State example, each State class has fields for its name and its current state (see the class org.drools.examples.state.State). The following states are the two possible states for each object:

  • NOTRUN
  • FINISHED

State class

public class State {
    public static final int NOTRUN   = 0;
    public static final int FINISHED = 1;

    private final PropertyChangeSupport changes =
        new PropertyChangeSupport( this );

    private String name;
    private int    state;

    ... setters and getters go here...
}

The State example contains two versions of the same example to resolve rule execution conflicts:

  • A StateExampleUsingSalience version that resolves conflicts by using rule salience
  • A StateExampleUsingAgendaGroups version that resolves conflicts by using rule agenda groups

Both versions of the state example involve four State objects: A, B, C, and D. Initially, their states are set to NOTRUN, which is the default value for the constructor that the example uses.

State example using salience

The StateExampleUsingSalience version of the State example uses salience values in rules to resolve rule execution conflicts. Rules with a higher salience value are given higher priority when ordered in the activation queue.

The example inserts each State instance into the KIE session and then calls fireAllRules().

Salience State example execution

final State a = new State( "A" );
final State b = new State( "B" );
final State c = new State( "C" );
final State d = new State( "D" );

ksession.insert( a );
ksession.insert( b );
ksession.insert( c );
ksession.insert( d );

ksession.fireAllRules();

// Dispose KIE session if stateful (not required if stateless).
ksession.dispose();

To execute the example, run the org.drools.examples.state.StateExampleUsingSalience class as a Java application in your IDE.

After the execution, the following output appears in the IDE console window:

Salience State example output in the IDE console

A finished
B finished
C finished
D finished

Four rules are present.

First, the "Bootstrap" rule fires, setting A to state FINISHED, which then causes B to change its state to FINISHED. Objects C and D are both dependent on B, causing a conflict that is resolved by the salience values.

To better understand the execution flow of this example, you can load the audit log file from target/state.log into your IDE debug view or Audit View, if available (for example, in WindowShow View in some IDEs).

In this example, the Audit View shows that the assertion of the object A in the state NOTRUN activates the "Bootstrap" rule, while the assertions of the other objects have no immediate effect.

Figure 21.5. Salience State example Audit View

state example audit1

Rule "Bootstrap" in salience State example

rule "Bootstrap"
  when
    a : State(name == "A", state == State.NOTRUN )
  then
    System.out.println(a.getName() + " finished" );
    a.setState( State.FINISHED );
end

The execution of the "Bootstrap" rule changes the state of A to FINISHED, which activates rule "A to B".

Rule "A to B" in salience State example

rule "A to B"
  when
    State(name == "A", state == State.FINISHED )
    b : State(name == "B", state == State.NOTRUN )
  then
    System.out.println(b.getName() + " finished" );
    b.setState( State.FINISHED );
end

The execution of rule "A to B" changes the state of B to FINISHED, which activates both rules "B to C" and "B to D", placing their activations onto the decision engine agenda.

Rules "B to C" and "B to D" in salience State example

rule "B to C"
    salience 10
  when
    State(name == "B", state == State.FINISHED )
    c : State(name == "C", state == State.NOTRUN )
  then
    System.out.println(c.getName() + " finished" );
    c.setState( State.FINISHED );
end

rule "B to D"
  when
    State(name == "B", state == State.FINISHED )
    d : State(name == "D", state == State.NOTRUN )
  then
    System.out.println(d.getName() + " finished" );
    d.setState( State.FINISHED );
end

From this point on, both rules may fire and, therefore, the rules are in conflict. The conflict resolution strategy enables the decision engine agenda to decide which rule to fire. Rule "B to C" has the higher salience value (10 versus the default salience value of 0), so it fires first, modifying object C to state FINISHED.

The Audit View in your IDE shows the modification of the State object in the rule "A to B", which results in two activations being in conflict.

You can also use the Agenda View in your IDE to investigate the state of the decision engine agenda. In this example, the Agenda View shows the breakpoint in the rule "A to B" and the state of the agenda with the two conflicting rules. Rule "B to D" fires last, modifying object D to state FINISHED.

Figure 21.6. Salience State example Agenda View

state example agenda1

State example using agenda groups

The StateExampleUsingAgendaGroups version of the State example uses agenda groups in rules to resolve rule execution conflicts. Agenda groups enable you to partition the decision engine agenda to provide more execution control over groups of rules. By default, all rules are in the agenda group MAIN. You can use the agenda-group attribute to specify a different agenda group for the rule.

Initially, a working memory has its focus on the agenda group MAIN. Rules in an agenda group only fire when the group receives the focus. You can set the focus either by using the method setFocus() or the rule attribute auto-focus. The auto-focus attribute enables the rule to be given a focus automatically for its agenda group when the rule is matched and activated.

In this example, the auto-focus attribute enables rule "B to C" to fire before "B to D".

Rule "B to C" in agenda group State example

rule "B to C"
    agenda-group "B to C"
    auto-focus true
  when
    State(name == "B", state == State.FINISHED )
    c : State(name == "C", state == State.NOTRUN )
  then
    System.out.println(c.getName() + " finished" );
    c.setState( State.FINISHED );
    kcontext.getKnowledgeRuntime().getAgenda().getAgendaGroup( "B to D" ).setFocus();
end

The rule "B to C" calls setFocus() on the agenda group "B to D", enabling its active rules to fire, which then enables the rule "B to D" to fire.

Rule "B to D" in agenda group State example

rule "B to D"
    agenda-group "B to D"
  when
    State(name == "B", state == State.FINISHED )
    d : State(name == "D", state == State.NOTRUN )
  then
    System.out.println(d.getName() + " finished" );
    d.setState( State.FINISHED );
end

To execute the example, run the org.drools.examples.state.StateExampleUsingAgendaGroups class as a Java application in your IDE.

After the execution, the following output appears in the IDE console window (same as the salience version of the State example):

Agenda group State example output in the IDE console

A finished
B finished
C finished
D finished

Dynamic facts in the State example

Another notable concept in this State example is the use of dynamic facts, based on objects that implement a PropertyChangeListener object. In order for the decision engine to see and react to changes of fact properties, the application must notify the decision engine that changes occurred. You can configure this communication explicitly in the rules by using the modify statement, or implicitly by specifying that the facts implement the PropertyChangeSupport interface as defined by the JavaBeans specification.

This example demonstrates how to use the PropertyChangeSupport interface to avoid the need for explicit modify statements in the rules. To make use of this interface, ensure that your facts implement PropertyChangeSupport in the same way that the class org.drools.example.State implements it, and then use the following code in the DRL rule file to configure the decision engine to listen for property changes on those facts:

Declaring a dynamic fact

declare type State
  @propertyChangeSupport
end

When you use PropertyChangeListener objects, each setter must implement additional code for the notification. For example, the following setter for state is in the class org.drools.examples:

Setter example with PropertyChangeSupport

public void setState(final int newState) {
    int oldState = this.state;
    this.state = newState;
    this.changes.firePropertyChange( "state",
                                     oldState,
                                     newState );
}

21.4. Fibonacci example decisions (recursion and conflict resolution)

The Fibonacci example decision set demonstrates how the decision engine uses recursion to resolve execution conflicts for rules in a sequence. The example focuses on resolving conflicts through salience values that you can define in rules.

The following is an overview of the Fibonacci example:

  • Name: fibonacci
  • Main class: org.drools.examples.fibonacci.FibonacciExample (in src/main/java)
  • Module: drools-examples
  • Type: Java application
  • Rule file: org.drools.examples.fibonacci.Fibonacci.drl (in src/main/resources)
  • Objective: Demonstrates recursion and conflict resolution through rule salience

The Fibonacci Numbers form a sequence starting with 0 and 1. The next Fibonacci number is obtained by adding the two preceding Fibonacci numbers: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, and so on.

The Fibonacci example uses the single fact class Fibonacci with the following two fields:

  • sequence
  • value

The sequence field indicates the position of the object in the Fibonacci number sequence. The value field shows the value of that Fibonacci object for that sequence position, where -1 indicates a value that still needs to be computed.

Fibonacci class

public static class Fibonacci {
    private int  sequence;
    private long value;

    public Fibonacci( final int sequence ) {
        this.sequence = sequence;
        this.value = -1;
    }

    ... setters and getters go here...
}

To execute the example, run the org.drools.examples.fibonacci.FibonacciExample class as a Java application in your IDE.

After the execution, the following output appears in the IDE console window:

Fibonacci example output in the IDE console

recurse for 50
recurse for 49
recurse for 48
recurse for 47
...
recurse for 5
recurse for 4
recurse for 3
recurse for 2
1 == 1
2 == 1
3 == 2
4 == 3
5 == 5
6 == 8
...
47 == 2971215073
48 == 4807526976
49 == 7778742049
50 == 12586269025

To achieve this behavior in Java, the example inserts a single Fibonacci object with a sequence field of 50. The example then uses a recursive rule to insert the other 49 Fibonacci objects.

Instead of implementing the PropertyChangeSupport interface to use dynamic facts, this example uses the MVEL dialect modify keyword to enable a block setter action and notify the decision engine of changes.

Fibonacci example execution

ksession.insert( new Fibonacci( 50 ) );
ksession.fireAllRules();

This example uses the following three rules:

  • "Recurse"
  • "Bootstrap"
  • "Calculate"

The rule "Recurse" matches each asserted Fibonacci object with a value of -1, creating and asserting a new Fibonacci object with a sequence of one less than the currently matched object. Each time a Fibonacci object is added while the one with a sequence field equal to 1 does not exist, the rule re-matches and fires again. The not conditional element is used to stop the rule matching once you have all 50 Fibonacci objects in memory. The rule also has a salience value because you need to have all 50 Fibonacci objects asserted before you execute the "Bootstrap" rule.

Rule "Recurse"

rule "Recurse"
    salience 10
  when
    f : Fibonacci ( value == -1 )
    not ( Fibonacci ( sequence == 1 ) )
  then
    insert( new Fibonacci( f.sequence - 1 ) );
    System.out.println( "recurse for " + f.sequence );
end

To better understand the execution flow of this example, you can load the audit log file from target/fibonacci.log into your IDE debug view or Audit View, if available (for example, in WindowShow View in some IDEs).

In this example, the Audit View shows the original assertion of the Fibonacci object with a sequence field of 50, done from Java code. From there on, the Audit View shows the continual recursion of the rule, where each asserted Fibonacci object causes the "Recurse" rule to become activated and to fire again.

Figure 21.7. Rule "Recurse" in Audit View

fibonacci1

When a Fibonacci object with a sequence field of 2 is asserted, the "Bootstrap" rule is matched and activated along with the "Recurse" rule. Notice the multiple restrictions on field sequence that test for equality with 1 or 2:

Rule "Bootstrap"

rule "Bootstrap"
  when
    f : Fibonacci( sequence == 1 || == 2, value == -1 ) // multi-restriction
  then
    modify ( f ){ value = 1 };
    System.out.println( f.sequence + " == " + f.value );
end

You can also use the Agenda View in your IDE to investigate the state of the decision engine agenda. The "Bootstrap" rule does not fire yet because the "Recurse" rule has a higher salience value.

Figure 21.8. Rules "Recurse" and "Bootstrap" in Agenda View 1

fibonacci agenda1

When a Fibonacci object with a sequence of 1 is asserted, the "Bootstrap" rule is matched again, causing two activations for this rule. The "Recurse" rule does not match and activate because the not conditional element stops the rule matching as soon as a Fibonacci object with a sequence of 1 exists.

Figure 21.9. Rules "Recurse" and "Bootstrap" in Agenda View 2

fibonacci agenda2

The "Bootstrap" rule sets the objects with a sequence of 1 and 2 to a value of 1. Now that you have two Fibonacci objects with values not equal to -1, the "Calculate" rule is able to match.

At this point in the example, nearly 50 Fibonacci objects exist in the working memory. You need to select a suitable triple to calculate each of their values in turn. If you use three Fibonacci patterns in a rule without field constraints to confine the possible cross products, the result would be 50x49x48 possible combinations, leading to about 125,000 possible rule firings, most of them incorrect.

The "Calculate" rule uses field constraints to evaluate the three Fibonacci patterns in the correct order. This technique is called cross-product matching.

The first pattern finds any Fibonacci object with a value != -1 and binds both the pattern and the field. The second Fibonacci object does the same thing, but adds an additional field constraint to ensure that its sequence is greater by one than the Fibonacci object bound to f1. When this rule fires for the first time, you know that only sequences 1 and 2 have values of 1, and the two constraints ensure that f1 references sequence 1 and that f2 references sequence 2.

The final pattern finds the Fibonacci object with a value equal to -1 and with a sequence one greater than f2.

At this point in the example, three Fibonacci objects are correctly selected from the available cross products, and you can calculate the value for the third Fibonacci object that is bound to f3.

Rule "Calculate"

rule "Calculate"
  when
    // Bind f1 and s1.
    f1 : Fibonacci( s1 : sequence, value != -1 )
    // Bind f2 and v2, refer to bound variable s1.
    f2 : Fibonacci( sequence == (s1 + 1), v2 : value != -1 )
    // Bind f3 and s3, alternative reference of f2.sequence.
    f3 : Fibonacci( s3 : sequence == (f2.sequence + 1 ), value == -1 )
  then
    // Note the various referencing techniques.
    modify ( f3 ) { value = f1.value + v2 };
    System.out.println( s3 + " == " + f3.value );
end

The modify statement updates the value of the Fibonacci object bound to f3. This means that you now have another new Fibonacci object with a value not equal to -1, which allows the "Calculate" rule to re-match and calculate the next Fibonacci number.

The debug view or Audit View of your IDE shows how the firing of the last "Bootstrap" rule modifies the Fibonacci object, enabling the "Calculate" rule to match, which then modifies another Fibonacci object that enables the "Calculate" rule to match again. This process continues until the value is set for all Fibonacci objects.

Figure 21.10. Rules in Audit View

fibonacci4

21.5. Pricing example decisions (decision tables)

The Pricing example decision set demonstrates how to use a spreadsheet decision table for calculating the retail cost of an insurance policy in tabular format instead of directly in a DRL file.

The following is an overview of the Pricing example:

  • Name: decisiontable
  • Main class: org.drools.examples.decisiontable.PricingRuleDTExample (in src/main/java)
  • Module: drools-examples
  • Type: Java application
  • Rule file: org.drools.examples.decisiontable.ExamplePolicyPricing.xls (in src/main/resources)
  • Objective: Demonstrates use of spreadsheet decision tables to define rules

Spreadsheet decision tables are XLS or XLSX spreadsheets that contain business rules defined in a tabular format. You can include spreadsheet decision tables with standalone Red Hat Decision Manager projects or upload them to projects in Business Central. Each row in a decision table is a rule, and each column is a condition, an action, or another rule attribute. After you create and upload your decision tables into your Red Hat Decision Manager project, the rules you defined are compiled into Drools Rule Language (DRL) rules as with all other rule assets.

The purpose of the Pricing example is to provide a set of business rules to calculate the base price and a discount for a car driver applying for a specific type of insurance policy. The driver’s age and history and the policy type all contribute to calculate the basic premium, and additional rules calculate potential discounts for which the driver might be eligible.

To execute the example, run the org.drools.examples.decisiontable.PricingRuleDTExample class as a Java application in your IDE.

After the execution, the following output appears in the IDE console window:

Cheapest possible
BASE PRICE IS: 120
DISCOUNT IS: 20

The code to execute the example follows the typical execution pattern: the rules are loaded, the facts are inserted, and a stateless KIE session is created. The difference in this example is that the rules are defined in an ExamplePolicyPricing.xls file instead of a DRL file or other source. The spreadsheet file is loaded into the decision engine using templates and DRL rules.

Spreadsheet decision table setup

The ExamplePolicyPricing.xls spreadsheet contains two decision tables in the first tab:

  • Base pricing rules
  • Promotional discount rules

As the example spreadsheet demonstrates, you can use only the first tab of a spreadsheet to create decision tables, but multiple tables can be within a single tab. Decision tables do not necessarily follow top-down logic, but are more of a means to capture data resulting in rules. The evaluation of the rules is not necessarily in the given order, because all of the normal mechanics of the decision engine still apply. This is why you can have multiple decision tables in the same tab of a spreadsheet.

The decision tables are executed through the corresponding rule template files BasePricing.drt and PromotionalPricing.drt. These template files reference the decision tables through their template parameter and directly reference the various headers for the conditions and actions in the decision tables.

BasePricing.drt rule template file

template header
age[]
profile
priorClaims
policyType
base
reason

package org.drools.examples.decisiontable;

template "Pricing bracket"
age
policyType
base

rule "Pricing bracket_@{row.rowNumber}"
  when
    Driver(age >= @{age0}, age <= @{age1}
        , priorClaims == "@{priorClaims}"
        , locationRiskProfile == "@{profile}"
    )
    policy: Policy(type == "@{policyType}")
  then
    policy.setBasePrice(@{base});
    System.out.println("@{reason}");
end
end template

PromotionalPricing.drt rule template file

template header
age[]
priorClaims
policyType
discount

package org.drools.examples.decisiontable;

template "discounts"
age
priorClaims
policyType
discount

rule "Discounts_@{row.rowNumber}"
  when
    Driver(age >= @{age0}, age <= @{age1}, priorClaims == "@{priorClaims}")
    policy: Policy(type == "@{policyType}")
  then
    policy.applyDiscount(@{discount});
end
end template

The rules are executed through the kmodule.xml reference of the KIE Session DTableWithTemplateKB, which specifically mentions the ExamplePolicyPricing.xls spreadsheet and is required for successful execution of the rules. This execution method enables you to execute the rules as a standalone unit (as in this example) or to include the rules in a packaged knowledge JAR (KJAR) file, so that the spreadsheet is packaged along with the rules for execution.

The following section of the kmodule.xml file is required for the execution of the rules and spreadsheet to work successfully:

    <kbase name="DecisionTableKB" packages="org.drools.examples.decisiontable">
        <ksession name="DecisionTableKS" type="stateless"/>
    </kbase>

    <kbase name="DTableWithTemplateKB" packages="org.drools.examples.decisiontable-template">
        <ruleTemplate dtable="org/drools/examples/decisiontable-template/ExamplePolicyPricingTemplateData.xls"
                      template="org/drools/examples/decisiontable-template/BasePricing.drt"
                      row="3" col="3"/>
        <ruleTemplate dtable="org/drools/examples/decisiontable-template/ExamplePolicyPricingTemplateData.xls"
                      template="org/drools/examples/decisiontable-template/PromotionalPricing.drt"
                      row="18" col="3"/>
        <ksession name="DTableWithTemplateKS"/>
    </kbase>

As an alternative to executing the decision tables using rule template files, you can use the DecisionTableConfiguration object and specify an input spreadsheet as the input type, such as DecisionTableInputType.xls:

DecisionTableConfiguration dtableconfiguration =
    KnowledgeBuilderFactory.newDecisionTableConfiguration();
        dtableconfiguration.setInputType( DecisionTableInputType.XLS );

        KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();

        Resource xlsRes = ResourceFactory.newClassPathResource( "ExamplePolicyPricing.xls",
                                                                getClass() );
        kbuilder.add( xlsRes,
                      ResourceType.DTABLE,
                      dtableconfiguration );

The Pricing example uses two fact types:

  • Driver
  • Policy.

The example sets the default values for both facts in their respective Java classes Driver.java and Policy.java. The Driver is 30 years old, has had no prior claims, and currently has a risk profile of LOW. The Policy that the driver is applying for is COMPREHENSIVE.

In any decision table, each row is considered a different rule and each column is a condition or an action. Each row is evaluated in a decision table unless the agenda is cleared upon execution.

Decision table spreadsheets (XLS or XLSX) require two key areas that define rule data:

  • A RuleSet area
  • A RuleTable area

The RuleSet area of the spreadsheet defines elements that you want to apply globally to all rules in the same package (not only the spreadsheet), such as a rule set name or universal rule attributes. The RuleTable area defines the actual rules (rows) and the conditions, actions, and other rule attributes (columns) that constitute that rule table within the specified rule set. A decision table spreadsheet can contain multiple RuleTable areas, but only one RuleSet area.

Figure 21.11. Decision table configuration

DT Config

The RuleTable area also defines the objects to which the rule attributes apply, in this case Driver and Policy, followed by constraints on the objects. For example, the Driver object constraint that defines the Age Bracket column is age >= $1, age <= $2, where the comma-separated range is defined in the table column values, such as 18,24.

Base pricing rules

The Base pricing rules decision table in the Pricing example evaluates the age, risk profile, number of claims, and policy type of the driver and produces the base price of the policy based on these conditions.

Figure 21.12. Base price calculation

DT Table1

The Driver attributes are defined in the following table columns:

  • Age Bracket: The age bracket has a definition for the condition age >=$1, age <=$2, which defines the condition boundaries for the driver’s age. This condition column highlights the use of $1 and $2, which is comma delimited in the spreadsheet. You can write these values as 18,24 or 18, 24 and both formats work in the execution of the business rules.
  • Location risk profile: The risk profile is a string that the example program passes always as LOW but can be changed to reflect MED or HIGH.
  • Number of prior claims: The number of claims is defined as an integer that the condition column must exactly equal to trigger the action. The value is not a range, only exact matches.

The Policy of the decision table is used in both the conditions and the actions of the rule and has attributes defined in the following table columns:

  • Policy type applying for: The policy type is a condition that is passed as a string that defines the type of coverage: COMPREHENSIVE, FIRE_THEFT, or THIRD_PARTY.
  • Base $ AUD: The basePrice is defined as an ACTION that sets the price through the constraint policy.setBasePrice($param); based on the spreadsheet cells corresponding to this value. When you execute the corresponding DRL rule for this decision table, the then portion of the rule executes this action statement on the true conditions matching the facts and sets the base price to the corresponding value.
  • Record Reason: When the rule successfully executes, this action generates an output message to the System.out console reflecting which rule fired. This is later captured in the application and printed.

The example also uses the first column on the left to categorize rules. This column is for annotation only and has no affect on rule execution.

Promotional discount rules

The Promotional discount rules decision table in the Pricing example evaluates the age, number of prior claims, and policy type of the driver to generate a potential discount on the price of the insurance policy.

Figure 21.13. Discount calculation

DT Table2

This decision table contains the conditions for the discount for which the driver might be eligible. Similar to the base price calculation, this table evaluates the Age, Number of prior claims of the driver, and the Policy type applying for to determine a Discount % rate to be applied. For example, if the driver is 30 years old, has no prior claims, and is applying for a COMPREHENSIVE policy, the driver is given a discount of 20 percent.

21.6. Pet Store example decisions (agenda groups, global variables, callbacks, and GUI integration)

The Pet Store example decision set demonstrates how to use agenda groups and global variables in rules and how to integrate Red Hat Decision Manager rules with a graphical user interface (GUI), in this case a Swing-based desktop application. The example also demonstrates how to use callbacks to interact with a running decision engine to update the GUI based on changes in the working memory at run time.

The following is an overview of the Pet Store example:

  • Name: petstore
  • Main class: org.drools.examples.petstore.PetStoreExample (in src/main/java)
  • Module: drools-examples
  • Type: Java application
  • Rule file: org.drools.examples.petstore.PetStore.drl (in src/main/resources)
  • Objective: Demonstrates rule agenda groups, global variables, callbacks, and GUI integration

In the Pet Store example, the sample PetStoreExample.java class defines the following principal classes (in addition to several classes to handle Swing events):

  • Petstore contains the main() method.
  • PetStoreUI is responsible for creating and displaying the Swing-based GUI. This class contains several smaller classes, mainly for responding to various GUI events, such as user mouse clicks.
  • TableModel holds the table data. This class is essentially a JavaBean that extends the Swing class AbstractTableModel.
  • CheckoutCallback enables the GUI to interact with the rules.
  • Ordershow keeps the items that you want to buy.
  • Purchase stores details of the order and the products that you are buying.
  • Product is a JavaBean containing details of the product available for purchase and its price.

Much of the Java code in this example is either plain JavaBean or Swing based. For more information about Swing components, see the Java tutorial on Creating a GUI with JFC/Swing.

Rule execution behavior in the Pet Store example

Unlike other example decision sets where the facts are asserted and fired immediately, the Pet Store example does not execute the rules until more facts are gathered based on user interaction. The example executes rules through a PetStoreUI object, created by a constructor, that accepts the Vector object stock for collecting the products. The example then uses an instance of the CheckoutCallback class containing the rule base that was previously loaded.

Pet Store KIE container and fact execution setup

// KieServices is the factory for all KIE services.
KieServices ks = KieServices.Factory.get();

// Create a KIE container on the class path.
KieContainer kc = ks.getKieClasspathContainer();

// Create the stock.
Vector<Product> stock = new Vector<Product>();
stock.add( new Product( "Gold Fish", 5 ) );
stock.add( new Product( "Fish Tank", 25 ) );
stock.add( new Product( "Fish Food", 2 ) );

// A callback is responsible for populating the working memory and for firing all rules.
PetStoreUI ui = new PetStoreUI( stoc