
  <dependencyManagement>

    <dependencies>

      <dependency>

        <groupId>org.drools</groupId>

        <artifactId>drools-bom</artifactId>

        <type>pom</type>

        <version>...</version>

        <scope>import</scope>

      </dependency>

      ...

    </dependencies>

  </dependencyManagement>

  <dependencies>

    <dependency>

      <groupId>org.kie</groupId>

      <artifactId>kie-api</artifactId>

    </dependency>

    <dependency>

      <groupId>org.drools</groupId>

      <artifactId>drools-compiler</artifactId>

      <scope>runtime</scope>

    </dependency>

    ...

  <dependencies>

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

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

1.3.1.3. Runtime

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

1.3.1.4. Installing IDE (Rule Workbench)

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

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

1.3.1.4.1. Installing GEF (a required dependency)

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

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

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

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

Next you choose the GEF plug-in:

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

1.3.1.4.2. Installing GEF from zip file

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

1.3.1.4.3. Installing Drools plug-in from zip file

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

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

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

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

1.3.1.4.4. Drools Runtimes

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

1.3.1.4.4.1. Defining a Drools runtime

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

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

In general, you have two options:

If you simply want to use the default JARs as included in the Drools Eclipse plugin, you can create a new Drools runtime automatically by clicking the "Create a new Drools 5 runtime ..." button. A file browser will show up, asking you to select the folder on your file system where you want this runtime to be created. The plugin will then automatically copy all required dependencies to the specified folder. After selecting this folder, the dialog should look like the figure shown below.
If you want to use one specific release of the Drools project, you should create a folder on your file system that contains all the necessary Drools libraries and dependencies. Instead of creating a new Drools runtime as explained above, give your runtime a name and select the location of this folder containing all the required JARs.

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

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

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

1.3.1.4.4.2. Selecting a runtime for your Drools project

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

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

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

1.3.2. Building from source

1.3.2.1. Getting the sources

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

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

https://github.com/droolsjbpm

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

1.3.2.2. Building the sources

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

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

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

1.3.3. Eclipse

1.3.3.1. Importing Eclipse Projects

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

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

Chapter 2. Release Notes

2.1. New and Noteworthy in KIE API 6.0.0

2.1.1. New KIE name

KIE is the new umbrella name used to group together our related projects; as the family continues to grow. KIE is also used for the generic parts of unified API; such as building, deploying and loading. This replaces the droolsjbpm and knowledge keywords that would have been used before.

Figure 2.1. KIE Anatomy

2.1.2. Maven aligned projects and modules and Maven Deployment

One of the biggest complaints during the 5.x series was the lack of defined methodology for deployment. The mechanism used by Drools and jBPM was very flexible, but it was too flexible. A big focus for 6.0 was streamlining the build, deploy and loading (utilization) aspects of the system. Building and deploying activities are now aligned with Maven and Maven repositories. The utilization for loading rules and processess is now convention and configuration oriented, instead of programmatic, with sane defaults to minimise the configuration.

Projects can be built with Maven and installed to the local M2_REPO or remote Maven repositories. Maven is then used to declare and build the classpath of dependencies, for KIE to access.

2.1.3. Configuration and convention based projects

The 'kmodule.xml' provides declarative configuration for KIE projects. Conventions and defaults are used to reduce the amount of configuration needed.

Example 2.1. Declare KieBases and KieSessions


<kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule">

  <kbase name="kbase1" packages="org.mypackages>

    <ksession name="ksession1"/>

  </kbase>

</kmodule>

Example 2.2. Utilize the KieSession

KieServices ks = KieServices.Factory.get();

KieContainer kContainer = ks.getKieClasspathContainer();


KieSession kSession = kContainer.newKieSession("ksession1");

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

kSession.fireAllRules();

2.1.4. KieBase Inclusion

It is possible to include all the KIE artifacts belonging to a KieBase into a second KieBase. This means that the second KieBase, in addition to all the rules, function and processes directly defined into it, will also contain the ones created in the included KieBase. This inclusion can be done declaratively in the kmodule.xml file

Example 2.3. Including a KieBase into another declaratively


<kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule">

  <kbase name="kbase2" includes="kbase1">

    <ksession name="ksession2"/>

  </kbase>

</kmodule>

or programmatically using the KieModuleModel.

Example 2.4. Including a KieBase into another programmatically

KieModuleModel kmodule = KieServices.Factory.get().newKieModuleModel();

KieBaseModel kieBaseModel1 = kmodule.newKieBaseModel("KBase2").addInclude("KBase1");

2.1.5. KieModules, KieContainer and KIE-CI

Any Maven produced JAR with a 'kmodule.xml' in it is considered a KieModule. This can be loaded from the classpath or dynamically at runtime from a Resource location. If the kie-ci dependency is on the classpath it embeds Maven and all resolving is done automatically using Maven and can access local or remote repositories. Settings.xml is obeyed for Maven configuration.

The KieContainer provides a runtime to utilize the KieModule, versioning is built in throughout, via Maven. Kie-ci will create a classpath dynamically from all the Maven declared dependencies for the artifact being loaded. Maven LATEST, SNAPSHOT, RELEASE and version ranges are supported.

Example 2.5. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieContainer kContainer = ks.newKieContainer(

        ks.newReleaseId("org.mygroup", "myartefact", "1.0") );


KieSession kSession = kContainer.newKieSession("ksession1");

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

kSession.fireAllRules();

KieContainers can be dynamically updated to a specific version, and resolved through Maven if KIE-CI is on the classpath. For stateful KieSessions the existing sessions are incrementally updated.

Example 2.6. Dynamically Update - Java

KieContainer kContainer.updateToVersion(

                ks.newReleaseId("org.mygroup", "myartefact", "1.1") );

2.1.6. KieScanner

The KieScanner is a Maven-oriented replacement of the KnowledgeAgent present in Drools 5. It continuously monitors your Maven repository to check if a new release of a Kie project has been installed and if so, deploys it in the KieContainer wrapping that project. The use of the KieScanner requires kie-ci.jar to be on the classpath.

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

Example 2.7. Registering and starting a KieScanner on a KieContainer

KieServices kieServices = KieServices.Factory.get();

ReleaseId releaseId = kieServices.newReleaseId( "org.acme", "myartifact", "1.0-SNAPSHOT" );

KieContainer kContainer = kieServices.newKieContainer( releaseId );

KieScanner kScanner = kieServices.newKieScanner( kContainer );


// Start the KieScanner polling the Maven repository every 10 seconds

kScanner.start( 10000L );

In this example the KieScanner is configured to run with a fixed time interval, but it is also possible to run it on demand by invoking the scanNow() method on it. If the KieScanner finds, in the Maven repository, an updated version of the Kie project used by that KieContainer it automatically downloads the new version and triggers an incremental build of the new project. From this moment all the new KieBases and KieSessions created from that KieContainer will use the new project version.

2.1.7. Hierarchical ClassLoader

The CompositeClassLoader is no longer used; as it was a constant source of performance problems and bugs. Traditional hierarchical classloaders are now used. The root classloader is at the KieContext level, with one child ClassLoader per namespace. This makes it cleaner to add and remove rules, but there can now be no referencing between namespaces in DRL files; i.e. functions can only be used by the namespaces that declared them. The recommendation is to use static Java methods in your project, which is visible to all namespaces; but those cannot (like other classes on the root KieContainer ClassLoader) be dynamically updated.

2.1.8. Legacy API Adapter

The 5.x API for building and running with Drools and jBPM is still available through Maven dependency "knowledge-api-legacy5-adapter". Because the nature of deployment has significantly changed in 6.0, it was not possible to provide an adapter bridge for the KnowledgeAgent. If any other methods are missing or problematic, please open a JIRA, and we'll fix for 6.1

2.1.9. KIE Documentation

While a lot of new documentation has been added for working with the new KIE API, the entire documentation has not yet been brought up to date. For this reason there will be continued references to old terminologies. Apologies in advance, and thank you for your patience. We hope those in the community will work with us to get the documentation updated throughout, for 6.1

2.2. What is New and Noteworthy in Drools 6.0.0

2.2.1. PHREAK - Lazy rule matching algorithm

The main work done for Drools in 6.0 involves the new PREAK algorithm. This is a lazy algorithm that should enable Drools to handle a larger number of rules and facts. AngendaGroups can now help improvement performance, as rules are not evaluated until it attempts to fire them.

Sequential mode continues to be supported for PHREAK but now 'modify' is allowed. While there is no 'inference' with sequential configuration, as rules are lazily evaluated, any rule not yet evaluated will see the more recent data as a result of 'modify'. This is more inline with how people intuitively think sequential works.

The conflict resolution order has been tweaked for PHREAK, and now is ordered by salience and then rule order; based on the rule position in the file.. Prior to Drools 6.0.0, after salience, it was considered arbitrary. When KieModules and updateToVersion are used for dynamic deployment, the rule order in the file is preserved via the diff processing.

2.2.2. Automatically firing timed rule in passive mode

When the rule engine runs in passive mode (i.e.: using fireAllRules) by default it doesn't fire consequences of timed rules unless fireAllRules isn't invoked again. Now it is possible to change this default behavior by configuring the KieSession with a TimedRuleExectionOption as shown in the following example.

Example 2.8. Configuring a KieSession to automatically execute timed rules

KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();

ksconf.setOption( TimedRuleExectionOption.YES );

KSession ksession = kbase.newKieSession(ksconf, null);

It is also possible to have a finer grained control on the timed rules that have to be automatically executed. To do this it is necessary to set a FILTERED TimedRuleExectionOption that allows to define a callback to filter those rules, as done in the next example.

Example 2.9. Configuring a filter to choose which timed rules should be automatically executed

KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();

conf.setOption( new TimedRuleExectionOption.FILTERED(new TimedRuleExecutionFilter() {

    public boolean accept(Rule[] rules) {

        return rules[0].getName().equals("MyRule");

    }

}) );

2.2.3. Expression Timers

It is now possible to define both the delay and interval of an interval timer as an expression instead of a fixed value. To do that it is necessary to declare the timer as an expression one (indicated by "expr:") as in the following example:

Example 2.10. An Expression Timer Example

declare Bean

    delay   : String = "30s"

    period  : long = 60000

end


rule "Expression timer"

    timer( expr: $d, $p )

when

    Bean( $d : delay, $p : period )

then

end

The expressions, $d and $p in this case, can use any variable defined in the pattern matching part of the rule and can be any String that can be parsed in a time duration or any numeric value that will be internally converted in a long representing a duration expressed in milliseconds.

Both interval and expression timers can have 3 optional parameters named "start", "end" and "repeat-limit". When one or more of these parameters are used the first part of the timer definition must be followed by a semicolon ';' and the parameters have to be separated by a comma ',' as in the following example:

Example 2.11. An Interval Timer with a start and an end

timer (int: 30s 10s; start=3-JAN-2010, end=5-JAN-2010)

The value for start and end parameters can be a Date, a String representing a Date or a long, or more in general any Number, that will be transformed in a Java Date applying the following conversion:

new Date( ((Number) n).longValue() )

Conversely the repeat-limit can be only an integer and it defines the maximum number of repetitions allowed by the timer. If both the end and the repeat-limit parameters are set the timer will stop when the first of the two will be matched.

The using of the start parameter implies the definition of a phase for the timer, where the beginning of the phase is given by the start itself plus the eventual delay. In other words in this case the timed rule will then be scheduled at times:

start + delay + n*period

for up to repeat-limit times and no later than the end timestamp (whichever first). For instance the rule having the following interval timer

timer ( int: 30s 1m; start="3-JAN-2010" )

will be scheduled at the 30th second of every minute after the midnight of the 3-JAN-2010. This also means that if for example you turn the system on at midnight of the 3-FEB-2010 it won't be scheduled immediately but will preserve the phase defined by the timer and so it will be scheduled for the first time 30 seconds after the midnight. If for some reason the system is paused (e.g. the session is serialized and then deserialized after a while) the rule will be scheduled only once to recover from missing activations (regardless of how many activations we missed) and subsequently it will be scheduled again in phase with the timer.

2.2.4. RuleFowGroup and AgendaGroups are merged

These two groups have been merged and now RuleFlowGroup's behave the same as AgendaGroups. The get methods have been left, for deprecation reasons, but both return the same underlying data. When jBPM activates a group it now just calls setFocus. RuleFlowGroups and AgendaGroups when used together was a continued source of errors. It also aligns the codebase, towards PHREAK and the multi-core explotation that is planned in the future.

2.3. New and Noteworthy in KIE Workbench 6.0.0

The workbench has had a big overhaul using a new base project called UberFire. UberFire is inspired by Eclipse and provides a clean, extensible and flexible framework for the workbench. The end result is not only a richer experience for our end users, but we can now develop more rapidly with a clean component based architecture. If you like he Workbench experience you can use UberFire today to build your own web based dashboard and console efforts.

As well as the move to a UberFire the other biggest change is the move from JCR to Git; there is an utility project to help with migration. Git is the most scalable and powerful source repository bar none. JGit provides a solid OSS implementation for Git. This addresses the continued performance problems with the various JCR implementations, which would slow down once the number of files and number of versions become too high. There has been a big "low tech" drive, to remove complexity. Everything is now stored as a file, including meta data. The database is only there to provide fast indexing and search. So importing and exporting is all standard Git and external sites, like GitHub, can be used to exchange repositories.

In 5.x developers would work with their own source repository and then push JCR, via the team provider. This team provider was not full featured and not available outside Eclipse. Git enables our repository to work any existing Git tool or team provider. While not yet supported in the UI, this will be added over time, it is possible to connect to the repo and tag and branch and restore things.

Figure 2.2. Workbench

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

The "Model Structure" diagram outlines the new project anatomy. The Drools workbench is called KIE-Drools-WB. KIE-WB is the uber workbench that combines all the Guvnor, Drools and jBPM plugins. The jBPM-WB is ghosted out, as it doesn't actually exist, being made redundant by KIE-WB.

Figure 2.3. Module Structure

Important

KIE Drools Workbench and KIE Workbench share a common set of components for generic workbench functionality such as Project navigation, Project definitions, Maven based Projects, Maven Artifact Repository. These common features are described in more detail throughout this documentation.

The two primary distributions consist of:

KIE Drools Workbench
- Drools Editors, for rules and supporting assets.
- jBPM Designer, for Rule Flow and supporting assets.
KIE Workbench
- Drools Editors, for rules and supporting assets.
- jBPM Designer, for BPMN2 and supporting assets.
- jBPM Console, runtime and Human Task support.
- jBPM Form Builder.
- BAM.

Workbench highlights:

New flexible Workbench environment, with perspectives and panels.
New packaging and build system following KIE API.
- Maven based projects.
- Maven Artifact Repository replaces Global Area, with full dependency support.
New Data Modeller replaces the declarative Fact Model Editor; bringing authoring of Java classes to the authoring environment. Java classes are packaged into the project and can be used within rules, processes etc and externally in your own applications.
Virtual File System replaces JCR with a default Git based implementation.
- Default Git based implementation supports remote operations.
- External modifications appear within the Workbench.
Incremental Build system showing, near real-time validation results of your project and assets.

The editors themselves are largely unchanged; however of note imports have moved from the package definition to individual editors so you need only import types used for an asset and not the package as a whole.

2.4. New and Noteworthy in Integration 6.0.0

2.4.1. CDI

CDI is now tightly integrated into the KIE API. It can be used to inject versioned KieSession and KieBases.

@Inject

@KSession("kbase1") 

@KReleaseId( groupId = "jar1", rtifactId = "art1", version = "1.0")

private KieBase kbase1v10;


@Inject

@KBase("kbase1") 

@KReleaseId( groupId = "jar1", rtifactId = "art1", version = "1.1")

private KieBase kbase1v10;

Figure 2.4. Side by side version loading for 'jar1.KBase1' KieBase

@Inject

@KSession("ksession1") 

@KReleaseId( groupId = "jar1", rtifactId = "art1", version = "1.0")

private KieSession ksessionv10;


@Inject

@KSession("ksession1") 

@KReleaseId( groupId = "jar1", rtifactId = "art1", version = "1.1")

private KieSession ksessionv11;

Figure 2.5. Side by side version loading for 'jar1.KBase1' KieBase

2.4.2. Spring

Spring has been revamped and now integrated with KIE. Spring can replace the 'kmodule.xml' with a more powerful spring version. The aim is for consistency with kmodule.xml

2.4.3. Aries Blueprints

Aries blueprints is now also supported, and follows the work done for spring. The aim is for consistency with spring and kmodule.xml

2.4.4. OSGi Ready

All modules have been refactored to avoid package splitting, which was a problem in 5.x. Testing has been moved to PAX.

Chapter 3. Compatibility matrix

Starting from KIE 6.0, Drools (including workbench), jBPM (including designer and console) and OptaPlanner follow the same version numbering.

Part II. KIE

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

Table of Contents

4. KIE

4.1. Overview

4.1.1. Anatomy of Projects
4.1.2. Lifecycles

4.2. Build, Deploy, Utilize and Run

4.2.1. Introduction
4.2.2. Building
4.2.3. Deploying
4.2.4. Running
4.2.5. Installation and Deployment Cheat Sheets
4.2.6. Build, Deploy and Utilize Examples

4.3. Security

4.3.1. Security Manager

Chapter 4. KIE

4.1. Overview

4.1.1. Anatomy of Projects
4.1.2. Lifecycles

4.2. Build, Deploy, Utilize and Run

4.2.1. Introduction
4.2.2. Building
4.2.3. Deploying
4.2.4. Running
4.2.5. Installation and Deployment Cheat Sheets
4.2.6. Build, Deploy and Utilize Examples

4.3. Security

4.3.1. Security Manager

4.1. Overview

4.1.1. Anatomy of Projects

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

KIE currently consists of the following subprojects:

Figure 4.1. KIE Anatomy

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

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

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

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

4.1.2. Lifecycles

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

Author
- Authoring of knowledge using a UI metaphor, such as: DRL, BPMN2, decision table, class models.
Build
- Builds the authored knowledge into deployable units.
- For KIE this unit is a JAR.
Test
- Test KIE knowedge before it's deployed to the application.
Deploy
- Deploys the unit to a location where applications may utilize (consume) them.
- KIE uses Maven style repository.
Utilize
Run
- System interaction with the KieSession, via API.
Work
- User interaction with the KieSession, via command line or UI.
Manage
- Manage any KieSession or KieContainer.

4.2. Build, Deploy, Utilize and Run

4.2.1. Introduction

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

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

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

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

Figure 4.2. Example project layout and Maven POM

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

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

Figure 4.3. Example project layout and Maven POM

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

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

4.2.2. Building

Figure 4.4. org.kie.api.core.builder

4.2.2.1. Creating and building a Kie Project

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

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

Example 4.1. An empty kmodule.xml file


<?xml version="1.0" encoding="UTF-8"?>

<kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule"/>

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

Figure 4.5. KieContainer

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

Example 4.2. Creating a KieContainer from the classpath

KieServices kieServices = KieServices.Factory.get();

KieContainer kContainer = kieServices.getKieClasspathContainer();

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

Figure 4.6. KieServices

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

4.2.2.2. The kmodule.xml file

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

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

Figure 4.7. KieBase

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

Figure 4.8. KieSession

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

Example 4.3. A sample kmodule.xml file


<kmodule xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

        xmlns="http://jboss.org/kie/6.0.0/kmodule">

  <kbase name="KBase1" default="true" eventProcessingMode="cloud" equalsBehavior="equality" declarativeAgenda="enabled" packages="org.domain.pkg1">

    <ksession name="KSession2_1" type="stateful" default="true/">

    <ksession name="KSession2_1" type="stateless" default="false/" beliefSystem="jtms">

  </kbase>

  <kbase name="KBase2" default="false" eventProcessingMode="stream" equalsBehavior="equality" declarativeAgenda="enabled" packages="org.domain.pkg2, org.domain.pkg3" includes="KBase1">

    <ksession name="KSession2_1" type="stateful" default="false" clockType="realtime">

      <fileLogger file="drools.log" threaded="true" interval="10"/>

      <workItemHandlers>

        <workItemHandler name="name" type="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>

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

Table 4.1. kbase Attributes

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

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

Table 4.2. ksession Attributes

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

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

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

Example 4.4. Retriving KieBases and KieSessions from the KieContainer

KieServices kieServices = KieServices.Factory.get();

KieContainer kContainer = kieServices.getKieClasspathContainer();


KieBase kBase1 = kContainer.getKieBase("KBase1");

KieSession kieSession1 = kContainer.newKieSession("KSession2_1");

StatelessKieSession kieSession2 = kContainer.newStatelessKieSession("KSession2_2");

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

Example 4.5. Retriving default KieBases and KieSessions from the KieContainer

KieContainer kContainer = ...


KieBase kBase1 = kContainer.getKieBase(); // returns KBase1

KieSession kieSession1 = kContainer.newKieSession(); // returns KSession2_1

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

Example 4.6. Creating a KieContainer of an existing project by ReleaseId

KieServices kieServices = KieServices.Factory.get();

ReleaseId releaseId = kieServices.newReleaseId( "org.acme", "myartifact", "1.0" );

KieContainer kieContainer = kieServices.newKieContainer( releaseId );

4.2.2.3. Building with Maven

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

Example 4.7. Adding the KIE plugin to a Maven pom.xml




  <build>

    <plugins>

      <plugin>

        <groupId>org.kie</groupId>

        <artifactId>kie-maven-plugin</artifactId>

        <version>${project.version}</version>

        <extensions>true</extensions>

      </plugin>

    </plugins>

  </build>

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

4.2.2.4. Defining a KieModule programmatically

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

Figure 4.9. KieFileSystem

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

Figure 4.10. KieModuleModel

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

Example 4.8. Creating a kmodule.xml programmatically and adding it to a KieFileSystem

KieServices kieServices = KieServices.Factory.get();

KieModuleModel kieModuleModel = kieServices.newKieModuleModel();


KieBaseModel kieBaseModel1 = kieModuleModel.newKieBaseModel( "KBase1 ")

        .setDefault( true )

        .setEqualsBehavior( EqualityBehaviorOption.EQUALITY )

        .setEventProcessingMode( EventProcessingOption.STREAM );


KieSessionModel ksessionModel1 = kieBaseModel1.newKieSessionModel( "KSession1" )

        .setDefault( true )

        .setType( KieSessionModel.KieSessionType.STATEFUL )

        .setClockType( ClockTypeOption.get("realtime") );


KieFileSystem kfs = kieServices.newKieFileSystem();

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

Example 4.9. Adding Kie artifacts to a KieFileSystem

KieFileSystem kfs = ...

kfs.write( "src/main/resources/KBase1/ruleSet1.drl", stringContainingAValidDRL )

        .write( "src/main/resources/dtable.xls",

                kieServices.getResources().newInputStreamResource( dtableFileStream ) );

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

Figure 4.11. KieResources

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

Example 4.10. Creating and adding a Resource with an explicit type

KieFileSystem kfs = ...

kfs.write( "src/main/resources/myDrl.txt",

           kieServices.getResources().newInputStreamResource( drlStream )

                      .setResourceType(ResourceType.DRL) );

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

Figure 4.12. KieBuilder

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

Figure 4.13. KieRepository

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

Example 4.11. Building the contents of a KieFileSystem and creating a KieContainer

KieServices kieServices = KieServices.Factory.get();

KieFileSystem kfs = ...

kieServices.newKieBuilder( kfs ).buildAll();

KieContainer kieContainer = kieServices.newKieContainer(kieServices.getRepository().getDefaultReleaseId());

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

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

Example 4.12. Checking that a compilation didn't produce any error

KieBuilder kieBuilder = kieServices.newKieBuilder( kfs ).buildAll();

assertEquals( 0, kieBuilder.getResults().getMessages( Message.Level.ERROR ).size() );

4.2.2.5. Changing the Default Build Result Severity

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

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

Example 4.13. Setting the severity using properties

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

4.2.3. Deploying

4.2.3.1. KieBase

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

Figure 4.14. KieBase

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

Example 4.14. Creating a new KieBase with a custom ClassLoader

KieServices kieServices = KieServices.Factory.get();

KieBaseConfiguration kbaseConf = kieServices.newKieBaseConfiguration( null, MyType.class.getClassLoader() );

KieBase kbase = kieContainer.newKieBase( kbaseConf );

4.2.3.2. KieSessions and KieBase Modifications

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

4.2.3.3. KieScanner

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

Figure 4.15. KieScanner

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

Example 4.15. Registering and starting a KieScanner on a KieContainer

KieServices kieServices = KieServices.Factory.get();

ReleaseId releaseId = kieServices.newReleaseId( "org.acme", "myartifact", "1.0-SNAPSHOT" );

KieContainer kContainer = kieServices.newKieContainer( releaseId );

KieScanner kScanner = kieServices.newKieScanner( kContainer );


// Start the KieScanner polling the Maven repository every 10 seconds

kScanner.start( 10000L );

In this example the KieScanner is configured to run with a fixed time interval, but it is also possible to run it on demand by invoking the scanNow() method on it. If the KieScanner finds in the Maven repository an updated version of the Kie project used by that KieContainer it automatically downloads the new version and triggers an incremental build of the new project. From this moment all the new KieBases and KieSessions created from that KieContainer will use the new project version.

4.2.3.4. Maven Versions and Dependencies

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

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

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

If you always want to use the newest version, Maven has two keywords you can use as an alternative to version ranges. You should use these options with care as you are no longer in control of the plugins/dependencies you are using.

When you depend on a plugin or a dependency, you can use the a version value of LATEST or RELEASE. LATEST refers to the latest released or snapshot version of a particular artifact, the most recently deployed artifact in a particular repository. RELEASE refers to the last non-snapshot release in the repository. In general, it is not a best practice to design software which depends on a non-specific version of an artifact. If you are developing software, you might want to use RELEASE or LATEST as a convenience so that you don't have to update version numbers when a new release of a third-party library is released. When you release software, you should always make sure that your project depends on specific versions to reduce the chances of your build or your project being affected by a software release not under your control. Use LATEST and RELEASE with caution, if at all.

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

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

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

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


<metadata>

  <groupId>com.foo</groupId>

  <artifactId>my-foo</artifactId>

  <version>2.0.0</version>

  <versioning>

    <release>1.1.1</release>

    <versions>

      <version>1.0</version>

      <version>1.0.1</version>

      <version>1.1</version>

      <version>1.1.1</version>

      <version>2.0.0</version>

    </versions>

    <lastUpdated>20090722140000</lastUpdated>

  </versioning>

</metadata>

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

<version>[1.0.1]</version>

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

<version>1.0.1</version>

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

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

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

<version>[1.0.0,)</version>

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

<version>LATEST</version>

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

<version>RELEASE</version>

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

4.2.3.5. Settings.xml and Remote Repository Ssetup

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

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

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

The Maven install: $M2_HOME/conf/settings.xml
A user's install: ${user.home}/.m2/settings.xml
Folder location specified by the system propert kie.maven.settings.custom

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


<profiles>

  <profile>

    <id>profile-1</id>

    <activation>

      <activeByDefault>true</activeByDefault>

    </activation>

    ...

  </profile>

</profiles>

Maven provides detailed documentation on using multiple remote repositories:

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

4.2.4. Running

4.2.4.1. KieBase

Example 4.16. Getting a KieBase from a KieContainer

KieBase kBase = kContainer.getKieBase();

4.2.4.2. KieSession

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

Figure 4.16. KieSession

Example 4.17. Create a KieSession from a KieBase

KieSession ksession = kbase.newKieSession();

4.2.4.3. KieRuntime

4.2.4.3.1. KieRuntime

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

Figure 4.17. KieRuntime

4.2.4.3.1.1. Globals

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

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

global java.util.List list

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

List list = new ArrayList();

ksession.setGlobal("list", list);

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

4.2.4.4. Event Model

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

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

Figure 4.18. KieRuntimeEventManager

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

Figure 4.19. RuleRuntimeEventManager

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

Example 4.18. Adding an AgendaEventListener

ksession.addEventListener( new DefaultAgendaEventListener() {

    public void afterMatchFired(AfterMatchFiredEvent event) {

        super.afterMatchFired( event );

        System.out.println( event );

    }

});

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

Example 4.19. Adding a DebugRuleRuntimeEventListener

ksession.addEventListener( new DebugRuleRuntimeEventListener() );

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

Figure 4.20. KieRuntimeEvent

The events currently supported are:

MatchCreatedEvent
MatchCancelledEvent
BeforeMatchFiredEvent
AfterMatchFiredEvent
AgendaGroupPushedEvent
AgendaGroupPoppedEvent
ObjectInsertEvent
ObjectDeletedEvent
ObjectUpdatedEvent
ProcessCompletedEvent
ProcessNodeLeftEvent
ProcessNodeTriggeredEvent
ProcessStartEvent

4.2.4.5. KieRuntimeLogger

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

Figure 4.21. KieLoggers

Example 4.20. FileLogger

KieRuntimeLogger logger =

  KieServices.Factory.get().newFileLogger(ksession, "logdir/mylogfile");

...

logger.close();

4.2.4.6. Commands and the CommandExecutor

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

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

Figure 4.22. CommandExecutor

Figure 4.23. ExecutionResults

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

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

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

Example 4.21. Set Global Command

<!-- <br/> --><span class="java_type">StatelessKieSession</span><!-- <br/> --><span class="java_plain">&nbsp;ksession&nbsp;</span><!-- <br/> --><span class="java_operator">=</span><!-- <br/> --><span class="java_plain">&nbsp;kbase</span><!-- <br/> --><span class="java_separator">.</span><!-- <br/> --><span class="java_plain">newStatelessKieSession</span><!-- <br/> --><span class="java_separator">();</span>
<!--  --><br/><span class="java_type">ExecutionResults</span><span class="java_plain">&nbsp;bresults&nbsp;</span><span class="java_operator">=</span>
<!--  --><br/><span class="java_plain">&nbsp;&nbsp;&nbsp;&nbsp;ksession</span><span class="java_separator">.</span><span class="java_plain">execute</span><span class="java_separator">(</span><span class="java_plain">&nbsp;</span><span class="java_type">CommandFactory</span><span class="java_separator">.</span><span class="java_plain">newSetGlobal</span><span class="java_separator">(</span><span class="java_plain">&nbsp;</span><span class="java_literal">&quot;stilton&quot;</span><span class="java_separator">,</span><span class="java_plain">&nbsp;</span><span class="java_keyword">new</span><span class="java_plain">&nbsp;</span><span class="java_type">Cheese</span><span class="java_separator">(</span><span class="java_plain">&nbsp;</span><span class="java_literal">&quot;stilton&quot;</span><span class="java_plain">&nbsp;</span><span class="java_separator">),</span><span class="java_plain">&nbsp;</span><span class="java_literal">true</span><span class="java_separator">);</span>
<!--  --><br/><span class="java_type">Cheese</span><span class="java_plain">&nbsp;stilton&nbsp;</span><span class="java_operator">=</span><span class="java_plain">&nbsp;bresults</span><span class="java_separator">.</span><span class="java_plain">getValue</span><span class="java_separator">(</span><span class="java_plain">&nbsp;</span><span class="java_literal">&quot;stilton&quot;</span><span class="java_plain">&nbsp;</span><span class="java_separator">);</span>

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

Example 4.22. Get Global Command

StatelessKieSession ksession = kbase.newStatelessKieSession();

ExecutionResults bresults =

    ksession.execute( CommandFactory.getGlobal( "stilton" );

Cheese stilton = bresults.getValue( "stilton" );

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

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

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

Example 4.23. BatchExecution Command

StatelessKieSession ksession = kbase.newStatelessKieSession();


List cmds = new ArrayList();

cmds.add( CommandFactory.newInsertObject( new Cheese( "stilton", 1), "stilton") );

cmds.add( CommandFactory.newStartProcess( "process cheeses" ) );

cmds.add( CommandFactory.newQuery( "cheeses" ) );

ExecutionResults bresults = ksession.execute( CommandFactory.newBatchExecution( cmds ) );

Cheese stilton = ( Cheese ) bresults.getValue( "stilton" );

QueryResults qresults = ( QueryResults ) bresults.getValue( "cheeses" );

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

All commands support XML and jSON marshalling using XStream, as well as JAXB marshalling. This is covered in section XXX.

4.2.4.7. StatelessKieSession

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

Figure 4.24. StatelessKieSession

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

Example 4.24. Simple StatelessKieSession execution with a Collection

StatelessKieSession ksession = kbase.newStatelessKieSession();

ksession.execute( collection );

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

Example 4.25. Simple StatelessKieSession execution with InsertElements Command

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

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

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

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

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

Example 4.26. Session scoped global

StatelessKieSession ksession = kbase.newStatelessKieSession();

// Set a global hbnSession, that can be used for DB interactions in the rules.

ksession.setGlobal( "hbnSession", hibernateSession );

// Execute while being able to resolve the "hbnSession" identifier.

ksession.execute( collection );

Using a delegate is another way of global resolution. Assigning a value to a global (with setGlobal(String, Object)) results in the value being stored in an internal collection mapping identifiers to values. Identifiers in this internal collection will have priority over any supplied delegate. Only if an identifier cannot be found in this internal collection, the delegate global (if any) will be used.
The third way of resolving globals is to have execution scoped globals. Here, a Command to set a global is passed to the CommandExecutor.

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

Example 4.27. Out identifiers

// Set up a list of commands

List cmds = new ArrayList();

cmds.add( CommandFactory.newSetGlobal( "list1", new ArrayList(), true ) );

cmds.add( CommandFactory.newInsert( new Person( "jon", 102 ), "person" ) );

cmds.add( CommandFactory.newQuery( "Get People" "getPeople" );


// Execute the list

ExecutionResults results =

  ksession.execute( CommandFactory.newBatchExecution( cmds ) );


// Retrieve the ArrayList

results.getValue( "list1" );

// Retrieve the inserted Person fact

results.getValue( "person" );

// Retrieve the query as a QueryResults instance.

results.getValue( "Get People" );

4.2.4.8. Marshalling

The KieMarshallers are used to marshal and unmarshal KieSessions.

Figure 4.25. KieMarshallers

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

Example 4.28. Simple Marshaller Example

// ksession is the KieSession

// kbase is the KieBase

ByteArrayOutputStream baos = new ByteArrayOutputStream();

Marshaller marshaller = KieServices.Factory.get().getMarshallers().newMarshaller( kbase );

marshaller.marshall( baos, ksession );

baos.close();

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

Example 4.29. IdentityMarshallingStrategy

ByteArrayOutputStream baos = new ByteArrayOutputStream();

KieMarshallers kMarshallers = KieServices.Factory.get().getMarshallers()

ObjectMarshallingStrategy oms = kMarshallers.newIdentityMarshallingStrategy()

Marshaller marshaller =

        kMarshallers.newMarshaller( kbase, new ObjectMarshallingStrategy[]{ oms } );

marshaller.marshall( baos, ksession );

baos.close();

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

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

Example 4.30. IdentityMarshallingStrategy with Acceptor

ByteArrayOutputStream baos = new ByteArrayOutputStream();

KieMarshallers kMarshallers = KieServices.Factory.get().getMarshallers()

ObjectMarshallingStrategyAcceptor identityAcceptor =

        kMarshallers.newClassFilterAcceptor( new String[] { "org.domain.pkg1.*" } );

ObjectMarshallingStrategy identityStrategy =

        kMarshallers.newIdentityMarshallingStrategy( identityAcceptor );

ObjectMarshallingStrategy sms = kMarshallers.newSerializeMarshallingStrategy();

Marshaller marshaller =

        kMarshallers.newMarshaller( kbase,

                                    new ObjectMarshallingStrategy[]{ identityStrategy, sms } );

marshaller.marshall( baos, ksession );

baos.close();

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

Also note that if you are using scheduled matches (i.e. some of your rules use timers or calendars) they are marshallable only if, before you use it, you configure your KieSession to use a trackable timer job factory manager as follows:

Example 4.31. Configuring a trackable timer job factory manager

KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();

ksconf.setOption(TimerJobFactoryOption.get("trackable"));

KSession ksession = kbase.newKieSession(ksconf, null);

4.2.4.9. Persistence and Transactions

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

Example 4.32. Simple example using transactions

KieServices kieServices = KieServices.Factory.get();

Environment env = kieServices.newEnvironment();

env.set( EnvironmentName.ENTITY_MANAGER_FACTORY,

         Persistence.createEntityManagerFactory( "emf-name" ) );

env.set( EnvironmentName.TRANSACTION_MANAGER,

         TransactionManagerServices.getTransactionManager() );


// KieSessionConfiguration may be null, and a default will be used

KieSession ksession =

        kieServices.getStoreServices().newKieSession( kbase, null, env );

int sessionId = ksession.getId();


UserTransaction ut =

  (UserTransaction) new InitialContext().lookup( "java:comp/UserTransaction" );

ut.begin();

ksession.insert( data1 );

ksession.insert( data2 );

ksession.startProcess( "process1" );

ut.commit();

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

Example 4.33. Loading a KieSession

KieSession ksession =

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

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

Example 4.34. Configuring JPA


<persistence-unit name="org.drools.persistence.jpa" transaction-type="JTA">

   <provider>org.hibernate.ejb.HibernatePersistence</provider>

   <jta-data-source>jdbc/BitronixJTADataSource</jta-data-source>

   <class>org.drools.persistence.info.SessionInfo</class>

   <class>org.drools.persistence.info.WorkItemInfo</class>

   <properties>

         <property name="hibernate.dialect" value="org.hibernate.dialect.H2Dialect"/>

         <property name="hibernate.max_fetch_depth" value="3"/>

         <property name="hibernate.hbm2ddl.auto" value="update" />

         <property name="hibernate.show_sql" value="true" />

         <property name="hibernate.transaction.manager_lookup_class"

                      value="org.hibernate.transaction.BTMTransactionManagerLookup" />

   </properties>

</persistence-unit>

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

Example 4.35. Configuring JTA DataSource

PoolingDataSource ds = new PoolingDataSource();

ds.setUniqueName( "jdbc/BitronixJTADataSource" );

ds.setClassName( "org.h2.jdbcx.JdbcDataSource" );

ds.setMaxPoolSize( 3 );

ds.setAllowLocalTransactions( true );

ds.getDriverProperties().put( "user", "sa" );

ds.getDriverProperties().put( "password", "sasa" );

ds.getDriverProperties().put( "URL", "jdbc:h2:mem:mydb" );

ds.init();

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

Example 4.36. JNDI properties

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

4.2.5. Installation and Deployment Cheat Sheets

Figure 4.26. Installation Overview

Figure 4.27. Deployment Overview

4.2.6. Build, Deploy and Utilize Examples

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

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

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

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

4.2.6.1. Default KieSession

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

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

Example 4.37. Author - kmodule.xml




<kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule"> </kmodule>

Example 4.38. Build and Install - Maven

mvn install

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

Example 4.39. Utilize and Run - Java



KieServices ks = KieServices.Factory.get();

KieContainer kContainer = ks.getKieClasspathContainer();


KieSession kSession = kContainer.newKieSession();

kSession.setGlobal("out", out);

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

kSession.fireAllRules();

4.2.6.2. Named KieSession

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

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

Example 4.40. Author - kmodule.xml


<kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule">

    <kbase name="kbase1">

        <ksession name="ksession1"/>

    </kbase>

</kmodule>

Example 4.41. Build and Install - Maven

mvn install

Example 4.42. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieContainer kContainer = ks.getKieClasspathContainer();


KieSession kSession = kContainer.newKieSession("ksession1");

kSession.setGlobal("out", out);

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

kSession.fireAllRules();

4.2.6.3. KieBase Inheritence

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

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

Example 4.43. Author - kmodule.xml


<kbase name="kbase2" includes="kbase1">

    <ksession name="ksession2"/>

</kbase>

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

Example 4.44. Author - pom.xml


<project xmlns="http://maven.apache.org/POM/4.0.0"

         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

  <modelVersion>4.0.0</modelVersion>

  <parent>

    <groupId>org.drools</groupId>

    <artifactId>drools-examples-api</artifactId>

    <version>6.0.0/version>

  </parent>



  <artifactId>kiebase-inclusion</artifactId>

  <name>Drools API examples - KieBase Inclusion</name>



  <dependencies>

    <dependency>

      <groupId>org.drools</groupId>

      <artifactId>drools-compiler</artifactId>

    </dependency>

    <dependency>

      <groupId>org.drools</groupId>

      <artifactId>named-kiesession</artifactId>

      <version>6.0.0</version>

    </dependency>

  </dependencies>



</project>

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

Example 4.45. Build and Install - Maven

mvn install

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

Example 4.46. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieContainer kContainer = ks.getKieClasspathContainer();

KieSession kSession = kContainer.newKieSession("ksession2");

kSession.setGlobal("out", out);


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

kSession.fireAllRules();


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

kSession.fireAllRules();

4.2.6.4. Multiple KieBases

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

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

Example 4.47. Author - kmodule.xml


<kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule">



  <kbase name="kbase1">

    <ksession name="ksession1"/>

  </kbase>



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

    <ksession name="ksession2"/>

  </kbase>



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

    <ksession name="ksession3"/>

  </kbase>



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

    <ksession name="ksession4"/>

  </kbase>



  <kbase name="kbase5" packages="org.*">

    <ksession name="ksession5"/>

  </kbase>



  <kbase name="kbase6" packages="org.some.*">

    <ksession name="ksession6"/>

  </kbase>

</kmodule>

Example 4.48. Build and Install - Maven

mvn install

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

Example 4.49. Utilize and Run - Java

@Test

public void testSimpleKieBase() {

    List<Integer> list = useKieSession("ksession1");

    // no packages imported means import everything

    assertEquals(4, list.size());

    assertTrue( list.containsAll( asList(0, 1, 2, 3) ) );

}


//.. other tests for ksession2 to ksession6 here


private List<Integer> useKieSession(String name) {

    KieServices ks = KieServices.Factory.get();

    KieContainer kContainer = ks.getKieClasspathContainer();

    KieSession kSession = kContainer.newKieSession(name);


    List<Integer> list = new ArrayList<Integer>();

    kSession.setGlobal("list", list);

    kSession.insert(1);

    kSession.fireAllRules();


    return list;

}

4.2.6.5. KieContainer from KieRepository

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

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

Example 4.50. Author - pom.xml


<project xmlns="http://maven.apache.org/POM/4.0.0"

         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

  <modelVersion>4.0.0</modelVersion>

  <parent>

    <groupId>org.drools</groupId>

    <artifactId>drools-examples-api</artifactId>

    <version>6.0.0</version>

  </parent>



  <artifactId>kiecontainer-from-kierepo</artifactId>

  <name>Drools API examples - KieContainer from KieRepo</name>



  <dependencies>

    <dependency>

      <groupId>org.kie</groupId>

      <artifactId>kie-ci</artifactId>

    </dependency>

  </dependencies>



</project>

Example 4.51. Build and Install - Maven

mvn install

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

Example 4.52. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();


// Install example1 in the local Maven repo before to do this

KieContainer kContainer = ks.newKieContainer(ks.newReleaseId("org.drools", "named-kiesession", "6.0.0-SNAPSHOT"));


KieSession kSession = kContainer.newKieSession("ksession1");

kSession.setGlobal("out", out);


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

kSession.insert(msg1);

kSession.fireAllRules();

4.2.6.6. Default KieSession from File

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

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

Example 4.53. Build and Install - Maven

mvn install

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

Example 4.54. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieRepository kr = ks.getRepository();


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


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


KieSession kSession = kContainer.newKieSession();

kSession.setGlobal("out", out);


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

kSession.insert(msg1);

kSession.fireAllRules();

4.2.6.7. Named KieSession from File

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

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

Example 4.55. Build and Install - Maven

mvn install

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

Example 4.56. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieRepository kr = ks.getRepository();


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


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


KieSession kSession = kContainer.newKieSession("ksession1");

kSession.setGlobal("out", out);


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

kSession.insert(msg1);

kSession.fireAllRules();

4.2.6.8. KieModule with Dependent KieModule

Project: kie-module-form-multiple-files
Summary: Programmatically provide the list of dependant KieModules, without using Maven to resolve anything.

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

Example 4.57. Build and Install - Maven

mvn install

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

Example 4.58. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieRepository kr = ks.getRepository();


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

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


KieModule kModule = kr.addKieModule(ex1Res, ex2Res);

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


KieSession kSession = kContainer.newKieSession("ksession2");

kSession.setGlobal("out", out);


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

kSession.insert(msg1);

kSession.fireAllRules();


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

kSession.insert(msg2);

kSession.fireAllRules();

4.2.6.9. Programmaticaly build a Simple KieModule with Defaults

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

Example 4.59. Build and Install - Maven

mvn install

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

Example 4.60. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieRepository kr = ks.getRepository();

KieFileSystem kfs = ks.newKieFileSystem();


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


KieBuilder kb = ks.newKieBuilder(kfs);


kb.buildAll(); // kieModule is automatically deployed to KieRepository if successfully built.

if (kb.getResults().hasMessages(Level.ERROR)) {

    throw new RuntimeException("Build Errors:\n" + kb.getResults().toString());

}


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


KieSession kSession = kContainer.newKieSession();

kSession.setGlobal("out", out);


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

kSession.fireAllRules();

4.2.6.10. Programmaticaly build a KieModule using Meta Models

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

Example 4.61. Build and Install - Maven

mvn install

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

Example 4.62. Utilize and Run - Java

KieServices ks = KieServices.Factory.get();

KieFileSystem kfs = ks.newKieFileSystem();


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

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


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

kfs.generateAndWritePomXML(rid);


KieModuleModel kModuleModel = ks.newKieModuleModel();

kModuleModel.newKieBaseModel("kiemodulemodel")

            .addInclude("kiebase1")

            .addInclude("kiebase2")

            .newKieSessionModel("ksession6");


kfs.writeKModuleXML(kModuleModel.toXML());

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


KieBuilder kb = ks.newKieBuilder(kfs);

kb.setDependencies(ex1Res, ex2Res);

kb.buildAll(); // kieModule is automatically deployed to KieRepository if successfully built.

if (kb.getResults().hasMessages(Level.ERROR)) {

    throw new RuntimeException("Build Errors:\n" + kb.getResults().toString());

}


KieContainer kContainer = ks.newKieContainer(rid);


KieSession kSession = kContainer.newKieSession("ksession6");

kSession.setGlobal("out", out);


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

kSession.insert(msg1);

kSession.fireAllRules();


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

kSession.insert(msg2);

kSession.fireAllRules();


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

kSession.insert(msg3);

kSession.fireAllRules();

4.3. Security

4.3.1. Security Manager

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

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

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

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

4.3.1.1. How to define a KIE Policy

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

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

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

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

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

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

Example 4.63. A sample engine.policy file

grant { 
    permission java.security.AllPermission;
}

An example security policy for rules could be:

Example 4.64. A sample rules.policy file

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

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

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

Table 4.3. Parameters

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

For instance:

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

Note

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

Note

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

Note

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

Part III. Drools Runtime and Language

Drools is a powerful Hybrid Reasoning System.

Table of Contents

5. Hybrid Reasoning

5.1. Artificial Intelligence

5.1.1. A Little History
5.1.2. Knowledge Representation and Reasoning
5.1.3. Rule Engines and Production Rule Systems (PRS)
5.1.4. Hybrid Reasoning Systems (HRS)
5.1.5. Expert Systems
5.1.6. Recommended Reading

5.2. Rete Algorithm

5.3. ReteOO Algorithm

5.4. PHREAK Algorithm

6. User Guide

6.1. The Basics

6.1.1. Stateless Knowledge Session
6.1.2. Stateful Knowledge Session
6.1.3. Methods versus Rules
6.1.4. Cross Products

6.2. Execution Control

6.2.1. Agenda
6.2.2. Rule Matches and Conflict Sets.
6.2.3. Declarative Agenda

6.3. Inference

6.3.1. Bus Pass Example

6.4. Truth Maintenance with Logical Objects

6.4.1. Overview

6.5. Decision Tables in Spreadsheets

6.5.1. When to Use Decision Tables
6.5.2. Overview
6.5.3. How Decision Tables Work
6.5.4. Spreadsheet Syntax
6.5.5. Creating and integrating Spreadsheet based Decision Tables
6.5.6. Managing Business Rules in Decision Tables
6.5.7. Rule Templates

6.6. Logging

7. Rule Language Reference

7.1. Overview

7.1.1. A rule file
7.1.2. What makes a rule

7.2. Keywords

7.3. Comments

7.3.1. Single line comment
7.3.2. Multi-line comment

7.4. Error Messages

7.4.1. Message format
7.4.2. Error Messages Description
7.4.3. Other Messages

7.5. Package

7.5.1. import
7.5.2. global

7.6. Function

7.7. Type Declaration

7.7.1. Declaring New Types
7.7.2. Declaring Metadata
7.7.3. Declaring Metadata for Existing Types
7.7.4. Parametrized constructors for declared types
7.7.5. Non Typesafe Classes
7.7.6. Accessing Declared Types from the Application Code
7.7.7. Type Declaration 'extends'
7.7.8. Traits

7.8. Rule

7.8.1. Rule Attributes
7.8.2. Timers and Calendars
7.8.3. Left Hand Side (when) syntax
7.8.4. The Right Hand Side (then)
7.8.5. Conditional named consequences
7.8.6. A Note on Auto-boxing and Primitive Types

7.9. Query

7.10. Domain Specific Languages

7.10.1. When to Use a DSL
7.10.2. DSL Basics
7.10.3. Adding Constraints to Facts
7.10.4. Developing a DSL
7.10.5. DSL and DSLR Reference

8. Complex Event Processing

8.1. Complex Event Processing

8.2. Drools Fusion

8.3. Event Semantics

8.4. Event Processing Modes

8.4.1. Cloud Mode
8.4.2. Stream Mode

8.5. Session Clock

8.5.1. Available Clock Implementations

8.6. Sliding Windows

8.6.1. Sliding Time Windows
8.6.2. Sliding Length Windows

8.7. Streams Support

8.7.1. Declaring and Using Entry Points

8.8. Memory Management for Events

8.8.1. Explicit expiration offset
8.8.2. Inferred expiration offset

8.9. Temporal Reasoning

8.9.1. Temporal Operators

Chapter 5. Hybrid Reasoning

5.1. Artificial Intelligence

5.1.1. A Little History
5.1.2. Knowledge Representation and Reasoning
5.1.3. Rule Engines and Production Rule Systems (PRS)
5.1.4. Hybrid Reasoning Systems (HRS)
5.1.5. Expert Systems
5.1.6. Recommended Reading

5.2. Rete Algorithm

5.3. ReteOO Algorithm

5.4. PHREAK Algorithm

5.1. Artificial Intelligence

5.1.1. A Little History

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

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

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

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

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

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

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

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

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

5.1.2. Knowledge Representation and Reasoning

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

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

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

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

5.1.3. Rule Engines and Production Rule Systems (PRS)

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

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

Ontology
Rules
Data

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

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

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

when

    <conditions>

then

    <actions>;

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

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

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

Figure 5.1. High-level View of a Production Rule System

5.1.4. Hybrid Reasoning Systems (HRS)

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

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

Figure 5.2. Forward Chaining

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

Figure 5.3. Backward Chaining

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

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

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

5.1.5. Expert Systems

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

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

Figure 5.4. Early History of Expert Systems

5.1.6. Recommended Reading

General AI, KRR and Expert System Books

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

Introduction to Expert Systems
- Peter Jackson

Expert Systems: Principles and Programming
- Joseph C. Giarratano, Gary D. Riley

Knowledge Representation and Reasoning
- Ronald J. Brachman, Hector J. Levesque

Artificial Intelligence : A Modern Approach.
- Stuart Russell and Peter Norvig

Figure 5.5. Recommended Reading

Papers

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

Production Matching for Large Learning Systems: Rete/UL (1993)
- Robert B. Doorenbos
Advances In Rete Pattern Matching
- Marshall Schor, Timothy P. Daly, Ho Soo Lee, Beth R. Tibbitts (AAAI 1986)
Collection-Oriented Match
- Anurag Acharya and Milind Tambe (1993)
The Leaps Algorithm
- Don Batery (1990)
Gator: An Optimized Discrimination Network for Active Database Rule Condition Testing
- Eric Hanson , Mohammed S. Hasan (1993)

Drools Books

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

JBoss Drools Business Rules
- Paul Browne
Drools JBoss Rules 5.0 Developers Guide
- Michal Bali
Drools Developer's Cookbook
- Lucas Amador

Figure 5.6. Recommended Reading

5.2. Rete Algorithm

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

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

Figure 5.7. Rete Nodes

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

Figure 5.8. ObjectTypeNodes

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

Figure 5.9. AlphaNodes

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

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

Figure 5.10. JoinNode

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

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

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

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

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

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

Figure 5.11. Node Sharing

5.3. ReteOO Algorithm

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

Node sharing

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

Alpha indexing

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

Beta indexing

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

Tree based graphs

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

Modify-in-place

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

Property reactive

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

Sub-networks

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

Backward Chaining

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

Lazy Truth Maintenance

Truth maintenance has a runtime cost, which is incurred whether TMS is used or not. Lazy TMS only turns it on, on first use. Further it's only turned on for that object type, so other object types do not incur the runtime cost.

Heap based agenda

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

Dynamic Rules

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

5.4. PHREAK Algorithm

Drools 6 introduces a new algorithm, that attempts to address some of the core issues of RETE. The algorithm is not a rewrite form scratch and incorporates all of the existing code from ReteOO, and all its enhancements. While PHREAK is an evolution of the RETE algorithm, it is no longer classified as a RETE implementation. In the same way that once an animal evolves beyond a certain point and key characteristics are changed, the animal becomes classified as new species. There are two key RETE characteristics that strongly identify any derivative strains, regardless of optimizations. That it is an eager, data oriented algorithm. Where all work is doing done the insert, update or delete actions; eagerly producing all partial matches for all rules. PHREAK in contrast is characterised as a lazy, goal oriented algorithm; where partial matching is aggressively delayed.

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

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

Three layers of contextual memory; Node, Segment and Rule memories.
Rule, segment and node based linking.
Lazy (delayed) rule evaluation.
Isolated rule evaluation.
Set oriented propagations.
Stack based evaluations, with pause and resume.

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

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

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

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

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

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

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

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

Figure 5.12. PHREAK 3 Layered memory system

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

Figure 5.13. Example1: Single rule, no sharing

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

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

Figure 5.14. Example 2: Two rules, with sharing

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

Figure 5.15. Example 3: Three rules, with sharing

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

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

Figure 5.16. Example 4 : Single rule, with sub-network and no sharing

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

Figure 5.17. Example 5: Two rules, one with a sub-network and sharing

Not nodes with constraints and accumulate nodes have special behaviour and can never unlink a segment, and are always considered to have their bits on.

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

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

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

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

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

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

RETE will also churn away producing partial machines for rules that do not have data in all the joins; where as PHREAK will avoid this.

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

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

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

Chapter 6. User Guide

6.1. The Basics

6.1.1. Stateless Knowledge Session

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

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

Validation
- Is this person eligible for a mortgage?
Calculation
- Compute a mortgage premium.
Routing and Filtering
- Filter incoming messages, such as emails, into folders.
- Send incoming messages to a destination.

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

public class Applicant {

    private String name;

    private int age;

    private boolean valid;

    // getter and setter methods here

}

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

package com.company.license

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

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

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

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


<?xml version="1.0" encoding="UTF-8"?>

<kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule"/>

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

KieServices kieServices = KieServices.Factory.get();

KieContainer kContainer = kieServices.getKieClasspathContainer();

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

StatelessKieSession kSession = kContainer.newStatelessKieSession();

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

assertTrue( applicant.isValid() );

ksession.execute( applicant );

assertFalse( applicant.isValid() );

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

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

public class Applicant {

    private String name;

    private int age;

    // getter and setter methods here

}


public class Application {

    private Date dateApplied;

    private boolean valid;

    // getter and setter methods here

}

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

package com.company.license

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

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

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

StatelessKieSession kSession = kContainer.newStatelessKieSession();

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

Application application = new Application();

assertTrue( application.isValid() );

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

assertFalse( application.isValid() );

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

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

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

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

KieCommands kieCommands = kieServices.getCommands();

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

cmds.add( kieCommands.newInsert( new Person( "Mr John Smith" ), "mrSmith", true, null ) );

cmds.add( kieCommands.newInsert( new Person( "Mr John Doe" ), "mrDoe", true, null ) );

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

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

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

6.1.2. Stateful Knowledge Session

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

Monitoring
- Stock market monitoring and analysis for semi-automatic buying.
Diagnostics
- Fault finding, medical diagnostics
Logistics
- Parcel tracking and delivery provisioning
Compliance
- Validation of legality for market trades.

In contrast to a Stateless Session, the dispose() method must be called afterwards to ensure there are no memory leaks, as the KieBase contains references to Stateful Knowledge Sessions when they are created. Since Stateful Knowledge Session is the most commonly used session type it is just named KieSession in the KIE API. KieSession also supports the BatchExecutor interface, like StatelessKieSession, the only difference being that the FireAllRules command is not automatically called at the end for a Stateful Session.

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

public class Room {

    private String name

    // getter and setter methods here

}

public class Sprinkler {

    private Room room;

    private boolean on;

    // getter and setter methods here

}

public class Fire {

    private Room room;

    // getter and setter methods here

}

public class Alarm {

}

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

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

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

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

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

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

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

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

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

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

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

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

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

KieServices kieServices = KieServices.Factory.get();

KieContainer kContainer = kieServices.getKieClasspathContainer();

KieSession ksession = kContainer.newKieSession();

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

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

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

for( String name: names ){

    Room room = new Room( name );

    name2room.put( name, room );

    ksession.insert( room );

    Sprinkler sprinkler = new Sprinkler( room );

    ksession.insert( sprinkler );

}


ksession.fireAllRules();

> Everything is ok

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

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

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


FactHandle kitchenFireHandle = ksession.insert( kitchenFire );

FactHandle officeFireHandle = ksession.insert( officeFire );


ksession.fireAllRules();

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

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

ksession.delete( kitchenFireHandle );

ksession.delete( officeFireHandle );


ksession.fireAllRules();

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

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

6.1.3. Methods versus Rules

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

public void helloWorld(Person person) {

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

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

    }

}

Methods are called directly.
Specific instances are passed.
One call results in a single execution.

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

Rules execute by matching against any data as long it is inserted into the engine.
Rules can never be called directly.
Specific instances cannot be passed to a rule.
Depending on the matches, a rule may fire once or several times, or not at all.

6.1.4. Cross Products

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

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

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

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

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

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

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

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

6.2. Execution Control

6.2.1. Agenda

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

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

The engine cycles repeatedly through two phases:

Rule Runtime Actions. This is where most of the work takes place, either in the Consequence (the RHS itself) or the main Java application process. Once the Consequence has finished or the main Java application process calls fireAllRules() the engine switches to the Agenda Evaluation phase.
Agenda Evaluation. This attempts to select a rule to fire. If no rule is found it exits, otherwise it fires the found rule, switching the phase back to Rule Runtime Actions.

Figure 6.1. Two Phase Execution

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

6.2.2. Rule Matches and Conflict Sets.

6.2.2.1. Cashflow Example

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

public class CashFlow {

    private Date   date;

    private double amount;

    private int    type;

    long           accountNo;

    // getter and setter methods here

}


public class Account {

    private long   accountNo;

    private double balance;

    // getter and setter methods here

}


public AccountPeriod {

    private Date start;

    private Date end;

    // getter and setter methods here

}

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

Figure 6.2. CashFlows and Account

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

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

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

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

select * from Account acc,
              Cashflow cf,
              AccountPeriod ap      
where acc.accountNo == cf.accountNo and 
      cf.type == CREDIT and
      cf.date >= ap.start and 
      cf.date <= ap.end

select * from Account acc, 
              Cashflow cf,
              AccountPeriod ap 
where acc.accountNo == cf.accountNo and 
      cf.type == DEBIT and
      cf.date >= ap.start and 
      cf.date <= ap.end

trigger : acc.balance += cf.amount

trigger : acc.balance -= cf.amount

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

Figure 6.3. AccountingPeriod, CashFlows and Account

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

Figure 6.4. CashFlows and Account

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

Figure 6.5. CashFlows and Account

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

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

Figure 6.6. CashFlows and Account

Figure 6.7. CashFlows and Account

6.2.2.2. Conflict Resolution

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

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

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

6.2.2.3. Salience

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

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

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

Figure 6.8. CashFlows and Account

6.2.2.4. Agenda Groups

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

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

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

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

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

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

Agenda agenda = ksession.getAgenda();

agenda.getAgendaGroup( "report" ).setFocus();

agenda.getAgendaGroup( "calculation" ).setFocus();

ksession.fireAllRules();

6.2.2.5. Rule Flow

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

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

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

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

6.2.3. Declarative Agenda

Warning

Declarative Agenda is experimental, and all aspects are highly likely to change in the future. @Eager and @Direct are temporary annotations to control the behaviour of rules, which will also change as Declarative Agenda evolves. Annotations instead of attributes where chosen, to reflect their experimental nature.

The declarative agenda allows to use rules to control which other rules can fire and when. While this will add a lot more overhead than the simple use of salience, the advantage is it is declarative and thus more readable and maintainable and should allow more use cases to be achieved in a simpler fashion.

This feature is off by default and must be explicitly enabled, that is because it is considered highly experimental for the moment and will be subject to change, but can be activated on a given KieBase by adding the declarativeAgenda='enabled' attribute in the corresponding kbase tag of the kmodule.xml file as in the following example.

Example 6.1. Enabling the Declarative Agenda


<kmodule xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

        xmlns="http://jboss.org/kie/6.0.0/kmodule">

  <kbase name="DeclarativeKBase" declarativeAgenda="enabled">

    <ksession name="KSession">

  </kbase>

</kmodule>

The basic idea is:

All rule's Matches are inserted into WorkingMemory as facts. So you can now do pattern matching against a Match. The rule's metadata and declarations are available as fields on the Match object.
You can use the kcontext.blockMatch( Match match ) for the current rule to block the selected match. Only when that rule becomes false will the match be eligible for firing. If it is already eligible for firing and is later blocked, it will be removed from the agenda until it is unblocked.
A match may have multiple blockers and a count is kept. All blockers must became false for the counter to reach zero to enable the Match to be eligible for firing.
kcontext.unblockAllMatches( Match match ) is an over-ride rule that will remove all blockers regardless
An activation may also be cancelled, so it never fires with cancelMatch
An unblocked Match is added to the Agenda and obeys normal salience, agenda groups, ruleflow groups etc.
The @Direct annotations allows a rule to fire as soon as it's matched, this is to be used for rules that block/unblock matches, it is not desirable for these rules to have side effects that impact else where.

Example 6.2. New RuleContext methods

void blockMatch(Match match);
void unblockAllMatches(Match match);
void cancelMatch(Match match);

Here is a basic example that will block all matches from rules that have metadata @department('sales'). They will stay blocked until the blockerAllSalesRules rule becomes false, i.e. "go2" is retracted.

Example 6.3. Block rules based on rule metadata

rule rule1 @Eager @department('sales') when
    $s : String( this == 'go1' )
then
    list.add( kcontext.rule.name + ':' + $s );
end
rule rule2 @Eager @department('sales') when
    $s : String( this == 'go1' )
then
    list.add( kcontext.rule.name + ':' + $s );
end
rule blockerAllSalesRules @Direct @Eager when
    $s : String( this == 'go2' )
    $i : Match( department == 'sales' )
then
    list.add( $i.rule.name + ':' + $s  );
    kcontext.blockMatch( $i );
end

Warning

Further than annotate the blocking rule with @Direct, it is also necessary to annotate all the rules that could be potentially blocked by it with @Eager. This is because, since the Match has to be evaluated by the pattern matching of the blocking rule, the potentially blocked ones cannot be evaluated lazily, otherwise won't be any Match to be evaluated.

This example shows how you can use active property to count the number of active or inactive (already fired) matches.

Example 6.4. Count the number of active/inactive Matches

rule rule1 @Eager @department('sales') when
    $s : String( this == 'go1' )
then
    list.add( kcontext.rule.name + ':' + $s );
end
rule rule2 @Eager @department('sales') when
    $s : String( this == 'go1' )
then
    list.add( kcontext.rule.name + ':' + $s );
end
rule rule3 @Eager @department('sales') when
    $s : String( this == 'go1' )
then
    list.add( kcontext.rule.name + ':' + $s );
end
rule countActivateInActive @Direct @Eager when
    $s : String( this == 'go2' )
    $active : Number( this == 1 ) from accumulate( $a : Match( department == 'sales', active == true ), count( $a ) )
    $inActive : Number( this == 2 ) from  accumulate( $a : Match( department == 'sales', active == false ), count( $a ) )
then
    kcontext.halt( );
end

6.3. Inference

6.3.1. Bus Pass Example

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bad
- Monolithic
- Leaky
Good

6.4. Truth Maintenance with Logical Objects

6.4.1. Overview

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

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

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

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

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

Figure 6.9. Stated Insertion

Figure 6.10. Logical Insertion

6.4.1.1. Bus Pass Example With Inference and TMS

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

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

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

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

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

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

Returning to the code to issue bus passes, these two rules can + logically insert the ChildBusPass and AdultBusPass facts, as the TMS + supports chaining of logical insertions for a cascading set of retracts.

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

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

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

6.4.1.2. Important note: Equality for Java objects

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

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

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

6.5. Decision Tables in Spreadsheets

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

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

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

6.5.1. When to Use Decision Tables

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

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

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

6.5.2. Overview

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

Figure 6.11. Using Excel to edit a decision table

Figure 6.12. Multiple actions for a rule row

Figure 6.13. Using OpenOffice.org

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

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

Note

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

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

Figure 6.14. A real world example using multiple tables for grouping like rules

6.5.3. How Decision Tables Work

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

Figure 6.15. Rows and columns

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

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

Figure 6.16. Expanded for rule templates

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

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

Warning

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

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

Note

In general the keywords make up name-value pairs.

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

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

Note

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

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

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

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

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

Note

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

Warning

Very large decision tables may have very large memory requirements.

6.5.4. Spreadsheet Syntax

6.5.4.1. Spreadsheet Structure

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

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

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

All keywords are case insensitive.

Only the first worksheet is examined for decision tables.

6.5.4.2. Rule Set Entries

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

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

Table 6.5. Entries in the Rule Set area

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

Warning

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

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

Table 6.6. Rule attribute entries in the Rule Set area

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

6.5.4.3. Rule Tables

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

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

Table 6.7. Column Headers in the Rule Table

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

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

Text in the first cell below CONDITION develops into a pattern for the rule condition, with the snippet in the next line becoming a constraint. If the cell is merged with one or more neighbours, a single pattern with multiple constraints is formed: all constraints are combined into a parenthesized list and appended to the text in this cell. The cell may be left blank, which means that the code snippet in the next row must result in a valid conditional element on its own.
To include a pattern without constraints, you can write the pattern in front of the text for another pattern.
The pattern may be written with or without an empty pair of parentheses. A "from" clause may be appended to the pattern.
If the pattern ends with "eval", code snippets are supposed to produce boolean expressions for inclusion into a pair of parentheses after "eval".
Text in the second cell below CONDITION is processed in two steps.
1. The code snippet in this cell is modified by interpolating values from cells farther down in the column. If you want to create a constraint consisting of a comparison using "==" with the value from the cells below, the field selector alone is sufficient. Any other comparison operator must be specified as the last item within the snippet, and the value from the cells below is appended. For all other constraint forms, you must mark the position for including the contents of a cell with the symbol $param. Multiple insertions are possible by using the symbols $1, $2, etc., and a comma-separated list of values in the cells below.
  A text according to the pattern forall(delimiter){snippet} is expanded by repeating the snippet once for each of the values of the comma-separated list of values in each of the cells below, inserting the value in place of the symbol $ and by joining these expansions by the given delimiter. Note that the forall construct may be surrounded by other text.
2. If the cell in the preceding row is not empty, the completed code snippet is added to the conditional element from that cell. A pair of parentheses is provided automatically, as well as a separating comma if multiple constraints are added to a pattern in a merged cell.
  If the cell above is empty, the interpolated result is used as is.
Text in the third cell below CONDITION is for documentation only. It should be used to indicate the column's purpose to a human reader.
From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the conditional element or constraint for this rule.

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

Text in the first cell below ACTION is optional. If present, it is interpreted as an object reference.
Text in the second cell below ACTION is processed in two steps.
1. The code snippet in this cell is modified by interpolating values from cells farther down in the column. For a singular insertion, mark the position for including the contents of a cell with the symbol $param. Multiple insertions are possible by using the symbols $1, $2, etc., and a comma-separated list of values in the cells below.
  A method call without interpolation can be achieved by a text without any marker symbols. In this case, use any non-blank entry in a row below to include the statement.
  The forall construct is available here, too.
2. If the first cell is not empty, its text, followed by a period, the text in the second cell and a terminating semicolon are stringed together, resulting in a method call which is added as an action statement for the consequence.
  If the cell above is empty, the interpolated result is used as is.
Text in the third cell below ACTION is for documentation only. It should be used to indicate the column's purpose to a human reader.
From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the action statement for this rule.

Note

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

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

Text in the first cell below METADATA is ignored.
Text in the second cell below METADATA is subject to interpolation, as described above, using values from the cells in the rule rows. The metadata marker character @ is prefixed automatically, and thus it should not be included in the text for this cell.
Text in the third cell below METADATA is for documentation only. It should be used to indicate the column's purpose to a human reader.
From the fourth row on, non-blank entries provide data for interpolation as described above. A blank cell results in the omission of the metadata annotation for this rule.

6.5.4.4. Examples

The various interpolations are illustrated in the following example.

Example 6.5. Interpolating cell data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Figure 6.17. Example usage of keywords for imports, headers, etc.

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

Figure 6.18. Example usage of keywords for functions, etc.

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

Note

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

6.5.5. Creating and integrating Spreadsheet based Decision Tables

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

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

Figure 6.19. Wizard in the IDE

6.5.6. Managing Business Rules in Decision Tables

6.5.6.1. Workflow and Collaboration

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

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

Business analyst takes a template decision table (from a repository, or from IT)
Decision table business language descriptions are entered in the table(s)
Decision table rules (rows) are entered (roughly)
Decision table is handed to a technical resource, who maps the business language (descriptions) to scripts (this may involve software development of course, if it is a new application or data model)
Technical person hands back and reviews the modifications with the business analyst.
The business analyst can continue editing the rule rows as needed (moving columns around is also fine etc).
In parallel, the technical person can develop test cases for the rules (liaising with business analysts) as these test cases can be used to verify rules and rule changes once the system is running.

6.5.6.2. Using spreadsheet features

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

<title> Wizard in the IDE </title>

Figure 6.20.

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

6.5.7. Rule Templates

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

store your data in a database (or any other format)
conditionally generate rules based on the values in the data
use data for any part of your rules (e.g. condition operator, class name, property name)
run different templates over the same data

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

Figure 6.21. Template data

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

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

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

Annotations to the preceding program listing:

Line 1: All rule templates start with template header.
Lines 2-4: Following the header is the list of columns in the order they appear in the data. In this case we are calling the first column age, the second type and the third log.
Line 5: An empty line signifies the end of the column definitions.
Lines 6-9: Standard rule header text. This is standard rule DRL and will appear at the top of the generated DRL. Put the package statement and any imports and global and function definitions into this section.
Line 10: The keyword template signals the start of a rule template. There can be more than one template in a template file, but each template should have a unique name.
Lines 11-18: The rule template - see below for details.
Line 20: The keywords end template signify the end of the template.

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

package org.drools.examples.templates;

global java.util.List list;

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

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

The code to run this is simple:



DecisionTableConfiguration dtableconfiguration =

    KnowledgeBuilderFactory.newDecisionTableConfiguration();

dtableconfiguration.setInputType( DecisionTableInputType.XLS );


KnowledgeBuilder kbuilder = KnowledgeBuilderFactory.newKnowledgeBuilder();


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

                                                    getClass() ),

              ResourceType.DTABLE,

              dtableconfiguration );

6.6. Logging

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

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


    <dependency>

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

      <artifactId>logback-classic</artifactId>

      <version>1.x</version>

    </dependency>

Note

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

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

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


<configuration>



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



    ...



<configuration>

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


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



    <category name="org.drools">

      <priority value="debug" />

    </category>



    ...



</log4j:configuration>

Chapter 7. Rule Language Reference

7.1. Overview

7.1.1. A rule file
7.1.2. What makes a rule

7.2. Keywords

7.3. Comments

7.3.1. Single line comment
7.3.2. Multi-line comment

7.4. Error Messages

7.4.1. Message format
7.4.2. Error Messages Description
7.4.3. Other Messages

7.5. Package

7.5.1. import
7.5.2. global

7.6. Function

7.7. Type Declaration

7.7.1. Declaring New Types
7.7.2. Declaring Metadata
7.7.3. Declaring Metadata for Existing Types
7.7.4. Parametrized constructors for declared types
7.7.5. Non Typesafe Classes
7.7.6. Accessing Declared Types from the Application Code
7.7.7. Type Declaration 'extends'
7.7.8. Traits

7.8. Rule

7.8.1. Rule Attributes
7.8.2. Timers and Calendars
7.8.3. Left Hand Side (when) syntax
7.8.4. The Right Hand Side (then)
7.8.5. Conditional named consequences
7.8.6. A Note on Auto-boxing and Primitive Types

7.9. Query

7.10. Domain Specific Languages

7.10.1. When to Use a DSL
7.10.2. DSL Basics
7.10.3. Adding Constraints to Facts
7.10.4. Developing a DSL
7.10.5. DSL and DSLR Reference

7.1. Overview

Drools has a "native" rule language. This format is very light in terms of punctuation, and supports natural and domain specific languages via "expanders" that allow the language to morph to your problem domain. This chapter is mostly concerted with this native rule format. The diagrams used to present the syntax are known as "railroad" diagrams, and they are basically flow charts for the language terms. The technically very keen may also refer to DRL.g which is the Antlr3 grammar for the rule language. If you use the Rule Workbench, a lot of the rule structure is done for you with content assistance, for example, type "ru" and press ctrl+space, and it will build the rule structure for you.

7.1.1. A rule file

A rule file is typically a file with a .drl extension. In a DRL file you can have multiple rules, queries and functions, as well as some resource declarations like imports, globals and attributes that are assigned and used by your rules and queries. However, you are also able to spread your rules across multiple rule files (in that case, the extension .rule is suggested, but not required) - spreading rules across files can help with managing large numbers of rules. A DRL file is simply a text file.

The overall structure of a rule file is:

Example 7.1. Rules file

package package-name

imports

globals

functions

queries

rules

The order in which the elements are declared is not important, except for the package name that, if declared, must be the first element in the rules file. All elements are optional, so you will use only those you need. We will discuss each of them in the following sections.

7.1.2. What makes a rule

For the inpatients, just as an early view, a rule has the following rough structure:

rule "name"
    attributes
    when
        LHS
    then
        RHS
end

It's really that simple. Mostly punctuation is not needed, even the double quotes for "name" are optional, as are newlines. Attributes are simple (always optional) hints to how the rule should behave. LHS is the conditional parts of the rule, which follows a certain syntax which is covered below. RHS is basically a block that allows dialect specific semantic code to be executed.

It is important to note that white space is not important, except in the case of domain specific languages, where lines are processed one by one and spaces may be significant to the domain language.

7.2. Keywords

Drools 5 introduces the concept of hard and soft keywords.

Hard keywords are reserved, you cannot use any hard keyword when naming your domain objects, properties, methods, functions and other elements that are used in the rule text.

Here is the list of hard keywords that must be avoided as identifiers when writing rules:

true
false
null

Soft keywords are just recognized in their context, enabling you to use these words in any other place if you wish, although, it is still recommended to avoid them, to avoid confusions, if possible. Here is a list of the soft keywords:

Of course, you can have these (hard and soft) words as part of a method name in camel case, like notSomething() or accumulateSomething() - there are no issues with that scenario.

Although the 3 hard keywords above are unlikely to be used in your existing domain models, if you absolutely need to use them as identifiers instead of keywords, the DRL language provides the ability to escape hard keywords on rule text. To escape a word, simply enclose it in grave accents, like this:

Holiday( `true` == "yes" ) // please note that Drools will resolve that reference to the method Holiday.isTrue()

7.3. Comments

Comments are sections of text that are ignored by the rule engine. They are stripped out when they are encountered, except inside semantic code blocks, like the RHS of a rule.

7.3.1. Single line comment

To create single line comments, you can use '//'. The parser will ignore anything in the line after the comment symbol. Example:

rule "Testing Comments"
when
    // this is a single line comment
    eval( true ) // this is a comment in the same line of a pattern
then
    // this is a comment inside a semantic code block
end

Warning

'#' for comments has been removed.

7.3.2. Multi-line comment

Figure 7.1. Multi-line comment

Multi-line comments are used to comment blocks of text, both in and outside semantic code blocks. Example:

rule "Test Multi-line Comments"
when
    /* this is a multi-line comment
       in the left hand side of a rule */
    eval( true )
then
    /* and this is a multi-line comment
       in the right hand side of a rule */
end

7.4. Error Messages

Drools 5 introduces standardized error messages. This standardization aims to help users to find and resolve problems in a easier and faster way. In this section you will learn how to identify and interpret those error messages, and you will also receive some tips on how to solve the problems associated with them.

7.4.1. Message format

The standardization includes the error message format and to better explain this format, let's use the following example:

Figure 7.2. Error Message Format

1st Block: This area identifies the error code.

2nd Block: Line and column information.

3rd Block: Some text describing the problem.

4th Block: This is the first context. Usually indicates the rule, function, template or query where the error occurred. This block is not mandatory.

5th Block: Identifies the pattern where the error occurred. This block is not mandatory.

7.4.2. Error Messages Description

7.4.2.1. 101: No viable alternative

Indicates the most common errors, where the parser came to a decision point but couldn't identify an alternative. Here are some examples:

Example 7.2.

1: rule one
2:   when
3:     exists Foo()
4:     exits Bar()
5:   then
6: end

The above example generates this message:

[ERR 101] Line 4:4 no viable alternative at input 'exits' in rule one

At first glance this seems to be valid syntax, but it is not (exits != exists). Let's take a look at next example:

Example 7.3.

1: package org.drools.examples;
2: rule
3:   when
4:     Object()
5:   then
6:     System.out.println("A RHS");
7: end

Now the above code generates this message:

[ERR 101] Line 3:2 no viable alternative at input 'WHEN'

This message means that the parser encountered the token WHEN, actually a hard keyword, but it's in the wrong place since the the rule name is missing.

The error "no viable alternative" also occurs when you make a simple lexical mistake. Here is a sample of a lexical problem:

Example 7.4.

1: rule simple_rule
2:   when
3:     Student( name == "Andy )
4:   then
5: end

The above code misses to close the quotes and because of this the parser generates this error message:

[ERR 101] Line 0:-1 no viable alternative at input '<eof>' in rule simple_rule in pattern Student

Note

Usually the Line and Column information are accurate, but in some cases (like unclosed quotes), the parser generates a 0:-1 position. In this case you should check whether you didn't forget to close quotes, apostrophes or parentheses.

7.4.2.2. 102: Mismatched input

This error indicates that the parser was looking for a particular symbol that it didn't ﬁnd at the current input position. Here are some samples:

Example 7.5.

1: rule simple_rule
2:   when
3:     foo3 : Bar(

The above example generates this message:

[ERR 102] Line 0:-1 mismatched input '<eof>' expecting ')' in rule simple_rule in pattern Bar

To fix this problem, it is necessary to complete the rule statement.

Note

Usually when you get a 0:-1 position, it means that parser reached the end of source.

The following code generates more than one error message:

Example 7.6.

1: package org.drools.examples;
2:
3: rule "Avoid NPE on wrong syntax"
4:   when
5:     not( Cheese( ( type == "stilton", price == 10 ) || ( type == "brie", price == 15 ) ) from $cheeseList )
6:   then
7:     System.out.println("OK");
8: end

These are the errors associated with this source:

[ERR 102] Line 5:36 mismatched input ',' expecting ')' in rule "Avoid NPE on wrong syntax" in pattern Cheese
[ERR 101] Line 5:57 no viable alternative at input 'type' in rule "Avoid NPE on wrong syntax"
[ERR 102] Line 5:106 mismatched input ')' expecting 'then' in rule "Avoid NPE on wrong syntax"

Note that the second problem is related to the first. To fix it, just replace the commas (',') by AND operator ('&&').

Note

In some situations you can get more than one error message. Try to fix one by one, starting at the first one. Some error messages are generated merely as consequences of other errors.

7.4.2.3. 103: Failed predicate

A validating semantic predicate evaluated to false. Usually these semantic predicates are used to identify soft keywords. This sample shows exactly this situation:

Example 7.7.

 1: package nesting;
 2: dialect "mvel"
 3:
 4: import org.drools.compiler.Person
 5: import org.drools.compiler.Address
 6: 
 7: fdsfdsfds
 8: 
 9: rule "test something"
10:   when
11:     p: Person( name=="Michael" )
12:   then
13:     p.name = "other";
14:     System.out.println(p.name);
15: end

With this sample, we get this error message:

[ERR 103] Line 7:0 rule 'rule_key' failed predicate: {(validateIdentifierKey(DroolsSoftKeywords.RULE))}? in rule

The fdsfdsfds text is invalid and the parser couldn't identify it as the soft keyword rule.

Note

This error is very similar to 102: Mismatched input, but usually involves soft keywords.

7.4.2.4. 104: Trailing semi-colon not allowed

This error is associated with the eval clause, where its expression may not be terminated with a semicolon. Check this example:

Example 7.8.

1: rule simple_rule
2:   when
3:     eval(abc();)
4:   then
5: end

Due to the trailing semicolon within eval, we get this error message:

[ERR 104] Line 3:4 trailing semi-colon not allowed in rule simple_rule

This problem is simple to fix: just remove the semi-colon.

7.4.2.5. 105: Early Exit

The recognizer came to a subrule in the grammar that must match an alternative at least once, but the subrule did not match anything. Simply put: the parser has entered a branch from where there is no way out. This example illustrates it:

Example 7.9.

1: template test_error
2:   aa s  11;
3: end

This is the message associated to the above sample:

[ERR 105] Line 2:2 required (...)+ loop did not match anything at input 'aa' in template test_error

To fix this problem it is necessary to remove the numeric value as it is neither a valid data type which might begin a new template slot nor a possible start for any other rule file construct.

7.4.3. Other Messages

Any other message means that something bad has happened, so please contact the development team.

7.5. Package

A package is a collection of rules and other related constructs, such as imports and globals. The package members are typically related to each other - perhaps HR rules, for instance. A package represents a namespace, which ideally is kept unique for a given grouping of rules. The package name itself is the namespace, and is not related to files or folders in any way.

It is possible to assemble rules from multiple rule sources, and have one top level package configuration that all the rules are kept under (when the rules are assembled). Although, it is not possible to merge into the same package resources declared under different names. A single Rulebase may, however, contain multiple packages built on it. A common structure is to have all the rules for a package in the same file as the package declaration (so that is it entirely self-contained).

The following railroad diagram shows all the components that may make up a package. Note that a package must have a namespace and be declared using standard Java conventions for package names; i.e., no spaces, unlike rule names which allow spaces. In terms of the order of elements, they can appear in any order in the rule file, with the exception of the package statement, which must be at the top of the file. In all cases, the semicolons are optional.

Figure 7.3. package

Notice that any rule attribute (as described the section Rule Attributes) may also be written at package level, superseding the attribute's default value. The modified default may still be replaced by an attribute setting within a rule.

7.5.1. import

Figure 7.4. import

Import statements work like import statements in Java. You need to specify the fully qualified paths and type names for any objects you want to use in the rules. Drools automatically imports classes from the Java package of the same name, and also from the package java.lang.

7.5.2. global

Figure 7.5. global

With global you define global variables. They are used to make application objects available to the rules. Typically, they are used to provide data or services that the rules use, especially application services used in rule consequences, and to return data from the rules, like logs or values added in rule consequences, or for the rules to interact with the application, doing callbacks. Globals are not inserted into the Working Memory, and therefore a global should never be used to establish conditions in rules except when it has a constant immutable value. The engine cannot be notified about value changes of globals and does not track their changes. Incorrect use of globals in constraints may yield surprising results - surprising in a bad way.

If multiple packages declare globals with the same identifier they must be of the same type and all of them will reference the same global value.

In order to use globals you must:

Declare your global variable in your rules file and use it in rules. Example:

global java.util.List myGlobalList;

rule "Using a global"
when
    eval( true )
then
    myGlobalList.add( "Hello World" );
end

Set the global value on your working memory. It is a best practice to set all global values before asserting any fact to the working memory. Example:
```
List list = new ArrayList();

WorkingMemory wm = rulebase.newStatefulSession();

wm.setGlobal( "myGlobalList", list );
```

Note that these are just named instances of objects that you pass in from your application to the working memory. This means you can pass in any object you want: you could pass in a service locator, or perhaps a service itself. With the new from element it is now common to pass a Hibernate session as a global, to allow from to pull data from a named Hibernate query.

One example may be an instance of a Email service. In your integration code that is calling the rule engine, you obtain your emailService object, and then set it in the working memory. In the DRL, you declare that you have a global of type EmailService, and give it the name "email". Then in your rule consequences, you can use things like email.sendSMS(number, message).

Globals are not designed to share data between rules and they should never be used for that purpose. Rules always reason and react to the working memory state, so if you want to pass data from rule to rule, assert the data as facts into the working memory.

It is strongly discouraged to set or change a global value from inside your rules. We recommend to you always set the value from your application using the working memory interface.

7.6. Function

Figure 7.6. function

Functions are a way to put semantic code in your rule source file, as opposed to in normal Java classes. They can't do anything more than what you can do with helper classes. (In fact, the compiler generates the helper class for you behind the scenes.) The main advantage of using functions in a rule is that you can keep the logic all in one place, and you can change the functions as needed (which can be a good or a bad thing). Functions are most useful for invoking actions on the consequence (then) part of a rule, especially if that particular action is used over and over again, perhaps with only differing parameters for each rule.

A typical function declaration looks like:

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

Note that the function keyword is used, even though its not really part of Java. Parameters to the function are defined as for a method, and you don't have to have parameters if they are not needed. The return type is defined just like in a regular method.

Alternatively, you could use a static method in a helper class, e.g., Foo.hello(). Drools supports the use of function imports, so all you would need to do is:

import function my.package.Foo.hello

Irrespective of the way the function is defined or imported, you use a function by calling it by its name, in the consequence or inside a semantic code block. Example:

rule "using a static function"
when 
    eval( true )
then
    System.out.println( hello( "Bob" ) );
end

7.7. Type Declaration

Figure 7.7. meta_data

Figure 7.8. type_declaration

Type declarations have two main goals in the rules engine: to allow the declaration of new types, and to allow the declaration of metadata for types.

Declaring new types: Drools works out of the box with plain Java objects as facts. Sometimes, however, users may want to define the model directly to the rules engine, without worrying about creating models in a lower level language like Java. At other times, there is a domain model already built, but eventually the user wants or needs to complement this model with additional entities that are used mainly during the reasoning process.
Declaring metadata: facts may have meta information associated to them. Examples of meta information include any kind of data that is not represented by the fact attributes and is consistent among all instances of that fact type. This meta information may be queried at runtime by the engine and used in the reasoning process.

7.7.1. Declaring New Types

To declare a new type, all you need to do is use the keyword declare, followed by the list of fields, and the keyword end. A new fact must have a list of fields, otherwise the engine will look for an existing fact class in the classpath and raise an error if not found.

Example 7.10. Declaring a new fact type: Address

declare Address
   number : int
   streetName : String
   city : String
end

The previous example declares a new fact type called Address. This fact type will have three attributes: number, streetName and city. Each attribute has a type that can be any valid Java type, including any other class created by the user or even other fact types previously declared.

For instance, we may want to declare another fact type Person:

Example 7.11. declaring a new fact type: Person

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

As we can see on the previous example, dateOfBirth is of type java.util.Date, from the Java API, while address is of the previously defined fact type Address.

You may avoid having to write the fully qualified name of a class every time you write it by using the import clause, as previously discussed.

Example 7.12. Avoiding the need to use fully qualified class names by using import

import java.util.Date

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

When you declare a new fact type, Drools will, at compile time, generate bytecode that implements a Java class representing the fact type. The generated Java class will be a one-to-one Java Bean mapping of the type definition. So, for the previous example, the generated Java class would be:

Example 7.13. generated Java class for the previous 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/hashCode

    // toString

}

Since the generated class is a simple Java class, it can be used transparently in the rules, like any other fact.

Example 7.14. Using the declared types in rules

rule "Using a declared Type"
when 
    $p : Person( name == "Bob" )
then
    // Insert Mark, who is Bob's mate.
    Person mark = new Person();
    mark.setName("Mark");
    insert( mark );
end

7.7.2. Declaring Metadata

Metadata may be assigned to several different constructions in Drools: fact types, fact attributes and rules. Drools uses the at sign ('@') to introduce metadata, and it always uses the form:

@metadata_key( metadata_value )

The parenthesized metadata_value is optional.

For instance, if you want to declare a metadata attribute like author, whose value is Bob, you could simply write:

Example 7.15. Declaring a metadata attribute

@author( Bob )

Drools allows the declaration of any arbitrary metadata attribute, but some will have special meaning to the engine, while others are simply available for querying at runtime. Drools allows the declaration of metadata both for fact types and for fact attributes. Any metadata that is declared before the attributes of a fact type are assigned to the fact type, while metadata declared after an attribute are assigned to that particular attribute.

Example 7.16. Declaring metadata attributes for fact types and attributes

import java.util.Date

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

    name : String @key @maxLength( 30 )
    dateOfBirth : Date 
    address : Address
end

In the previous example, there are two metadata items declared for the fact type (@author and @dateOfCreation) and two more defined for the name attribute (@key and @maxLength). Please note that the @key metadata has no required value, and so the parentheses and the value were omitted.:

7.7.2.1. Predefined class level annotations

Some annotations have predefined semantics that are interpreted by the engine. The following is a list of some of these predefined annotations and their meaning.

7.7.2.1.1. @role( <fact | event> )

The @role annotation defines how the engine should handle instances of that type: either as regular facts or as events. It accepts two possible values:

fact : this is the default, declares that the type is to be handled as a regular fact.
event : declares that the type is to be handled as an event.

The following example declares that the fact type StockTick in a stock broker application is to be handled as an event.

Example 7.17. declaring a fact type as an event

import some.package.StockTick

declare StockTick
    @role( event )
end

The same applies to facts declared inline. If StockTick was a fact type declared in the DRL itself, instead of a previously existing class, the code would be:

Example 7.18. declaring a fact type and assigning it the event role

declare StockTick 
    @role( event )

    datetime : java.util.Date
    symbol : String
    price : double
end

7.7.2.1.2. @typesafe( <boolean> )

By default all type declarations are compiled with type safety enabled; @typesafe( false ) provides a means to override this behaviour by permitting a fall-back, to type unsafe evaluation where all constraints are generated as MVEL constraints and executed dynamically. This can be important when dealing with collections that do not have any generics or mixed type collections.

7.7.2.1.3. @timestamp( <attribute name> )

Every event has an associated timestamp assigned to it. By default, the timestamp for a given event is read from the Session Clock and assigned to the event at the time the event is inserted into the working memory. Although, sometimes, the event has the timestamp as one of its own attributes. In this case, the user may tell the engine to use the timestamp from the event's attribute instead of reading it from the Session Clock.

@timestamp( <attributeName> )

To tell the engine what attribute to use as the source of the event's timestamp, just list the attribute name as a parameter to the @timestamp tag.

Example 7.19. declaring the VoiceCall timestamp attribute

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

7.7.2.1.4. @duration( <attribute name> )

Drools supports both event semantics: point-in-time events and interval-based events. A point-in-time event is represented as an interval-based event whose duration is zero. By default, all events have duration zero. The user may attribute a different duration for an event by declaring which attribute in the event type contains the duration of the event.

@duration( <attributeName> )

So, for our VoiceCall fact type, the declaration would be:

Example 7.20. declaring the VoiceCall duration attribute

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

7.7.2.1.5. @expires( <time interval> )

Important

This tag is only considered when running the engine in STREAM mode. Also, additional discussion on the effects of using this tag is made on the Memory Management section. It is included here for completeness.

Events may be automatically expired after some time in the working memory. Typically this happens when, based on the existing rules in the knowledge base, the event can no longer match and activate any rules. Although, it is possible to explicitly define when an event should expire.

@expires( <timeOffset> )

The value of timeOffset is a temporal interval in the form:

[#d][#h][#m][#s][#[ms]]

Where [ ] means an optional parameter and # means a numeric value.

So, to declare that the VoiceCall facts should be expired after 1 hour and 35 minutes after they are inserted into the working memory, the user would write:

Example 7.21. declaring the expiration offset for the VoiceCall events

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

The @expires policy will take precedence and override the implicit expiration offset calculated from temporal constraints and sliding windows in the knowledge base.

7.7.2.1.6. @propertyChangeSupport

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

Example 7.22. @propertyChangeSupport

declare Person
  @propertyChangeSupport
end

7.7.2.1.7. @propertyReactive

Make this type property reactive. See Fine grained property change listeners section for details.

7.7.2.2. Predefined attribute level annotations

As noted before, Drools also supports annotations in type attributes. Here is a list of predefined attribute annotations.

7.7.2.2.1. @key

Declaring an attribute as a key attribute has 2 major effects on generated types:

The attribute will be used as a key identifier for the type, and as so, the generated class will implement the equals() and hashCode() methods taking the attribute into account when comparing instances of this type.
Drools will generate a constructor using all the key attributes as parameters.

For instance:

Example 7.23. example of @key declarations for a type

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

For the previous example, Drools will generate equals() and hashCode() methods that will check the firstName and lastName attributes to determine if two instances of Person are equal to each other, but will not check the age attribute. It will also generate a constructor taking firstName and lastName as parameters, allowing one to create instances with a code like this:

Example 7.24. creating an instance using the key constructor

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

7.7.2.2.2. @position

Patterns support positional arguments on type declarations.

Positional arguments are ones where you don't need to specify the field name, as the position maps to a known named field. i.e. Person( name == "mark" ) can be rewritten as Person( "mark"; ). The semicolon ';' is important so that the engine knows that everything before it is a positional argument. Otherwise we might assume it was a boolean expression, which is how it could be interpreted after the semicolon. You can mix positional and named arguments on a pattern by using the semicolon ';' to separate them. Any variables used in a positional that have not yet been bound will be bound to the field that maps to that position.

declare Cheese
    name : String
    shop : String
    price : int
end

The default order is the declared order, but this can be overridden using @position

declare Cheese
    name : String @position(1)
    shop : String @position(2)
    price : int @position(0)
end

Example patterns, with two constraints and a binding. Remember semicolon ';' is used to differentiate the positional section from the named argument section. Variables and literals and expressions using just literals are supported in positional arguments, but not variables.

Cheese( "stilton", "Cheese Shop", p; )
Cheese( "stilton", "Cheese Shop"; p : price )
Cheese( "stilton"; shop == "Cheese Shop", p : price )
Cheese( name == "stilton"; shop == "Cheese Shop", p : price )

@Position is inherited when beans extend each other; while not recommended, two fields may have the same @position value, and not all consecutive values need be declared. If a @position is repeated, the conflict is solved using inheritance (fields in the superclass have the precedence) and the declaration order. If a @position value is missing, the first field without an explicit @position (if any) is selected to fill the gap. As always, conflicts are resolved by inheritance and declaration order.

declare Cheese
    name : String 
    shop : String @position(2)
    price : int @position(0)
end

declare SeasonedCheese extends Cheese
    year : Date @position(0)
    origin : String @position(6)
    country : String    
end

In the example, the field order would be : price (@position 0 in the superclass), year (@position 0 in the subclass), name (first field with no @position), shop (@position 2), country (second field without @position), origin.

7.7.3. Declaring Metadata for Existing Types

Drools allows the declaration of metadata attributes for existing types in the same way as when declaring metadata attributes for new fact types. The only difference is that there are no fields in that declaration.

For instance, if there is a class org.drools.examples.Person, and one wants to declare metadata for it, it's possible to write the following code:

Example 7.25. Declaring metadata for an existing type

import org.drools.examples.Person

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

Instead of using the import, it is also possible to reference the class by its fully qualified name, but since the class will also be referenced in the rules, it is usually shorter to add the import and use the short class name everywhere.

Example 7.26. Declaring metadata using the fully qualified class name

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

7.7.4. Parametrized constructors for declared types

Generate constructors with parameters for declared types.

Example: for a declared type like the following:

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

The compiler will implicitly generate 3 constructors: one without parameters, one with the @key fields, and one with all fields.

Person() // parameterless constructor
Person( String firstName, String lastName )
Person( String firstName, String lastName, int age )

7.7.5. Non Typesafe Classes

@typesafe( <boolean>) has been added to type declarations. By default all type declarations are compiled with type safety enabled; @typesafe( false ) provides a means to override this behaviour by permitting a fall-back, to type unsafe evaluation where all constraints are generated as MVEL constraints and executed dynamically. This can be important when dealing with collections that do not have any generics or mixed type collections.

7.7.6. Accessing Declared Types from the Application Code

Declared types are usually used inside rules files, while Java models are used when sharing the model between rules and applications. Although, sometimes, the application may need to access and handle facts from the declared types, especially when the application is wrapping the rules engine and providing higher level, domain specific user interfaces for rules management.

In such cases, the generated classes can be handled as usual with the Java Reflection API, but, as we know, that usually requires a lot of work for small results. Therefore, Drools provides a simplified API for the most common fact handling the application may want to do.

The first important thing to realize is that a declared fact will belong to the package where it was declared. So, for instance, in the example below, Person will belong to the org.drools.examples package, and so the fully qualified name of the generated class will be org.drools.examples.Person.

Example 7.27. Declaring a type in the org.drools.examples package

package org.drools.examples

import java.util.Date

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

Declared types, as discussed previously, are generated at knowledge base compilation time, i.e., the application will only have access to them at application run time. Therefore, these classes are not available for direct reference from the application.

Drools then provides an interface through which users can handle declared types from the application code: org.drools.definition.type.FactType. Through this interface, the user can instantiate, read and write fields in the declared fact types.

Example 7.28. Handling declared fact types through the API

// get a reference to a knowledge base with a declared type:
KieBase kbase = ...

// get the declared FactType
FactType personType = kbase.getFactType( "org.drools.examples",
                                         "Person" );

// handle the type as necessary:
// create instances:
Object bob = personType.newInstance();

// set attributes values
personType.set( bob,
                "name",
                "Bob" );
personType.set( bob,
                "age",
                42 );

// insert fact into a session
KieSession ksession = ...
ksession.insert( bob );
ksession.fireAllRules();

// read attributes
String name = personType.get( bob, "name" );
int age = personType.get( bob, "age" );

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

Although the API is similar to Java reflection (yet much simpler to use), it does not use reflection underneath, relying on much more performant accessors implemented with generated bytecode.

7.7.7. Type Declaration 'extends'

Type declarations now support 'extends' keyword for inheritance

In order to extend a type declared in Java by a DRL declared subtype, repeat the supertype in a declare statement without any fields.

b org.people.Person

declare Person end

declare Student extends Person
    school : String
end

declare LongTermStudent extends Student
    years : int
    course : String
end

7.7.8. Traits

WARNING : this feature is still experimental and subject to changes

The same fact may have multiple dynamic types which do not fit naturally in a class hierarchy. Traits allow to model this very common scenario. A trait is an interface that can be applied (and eventually removed) to an individual object at runtime. To create a trait rather than a traditional bean, one has to declare them explicitly as in the following example:

Example 7.29.

declare trait GoldenCustomer
    // fields will map to getters/setters
    code     : String
    balance  : long
    discount : int
    maxExpense : long
end

At runtime, this declaration results in an interface, which can be used to write patterns, but can not be instantiated directly. In order to apply a trait to an object, we provide the new don keyword, which can be used as simply as this:

Example 7.30.

when
    $c : Customer()
then
    GoldenCustomer gc = don( $c, GoldenCustomer.class );
end

when a core object dons a trait, a proxy class is created on the fly (one such class will be generated lazily for each core/trait class combination). The proxy instance, which wraps the core object and implements the trait interface, is inserted automatically and will possibly activate other rules. An immediate advantage of declaring and using interfaces, getting the implementation proxy for free from the engine, is that multiple inheritance hierarchies can be exploited when writing rules. The core classes, however, need not implement any of those interfaces statically, also facilitating the use of legacy classes as cores. In fact, any object can don a trait, provided that they are declared as @Traitable. Notice that this annotation used to be optional, but now is mandatory.

Example 7.31.

import org.drools.core.factmodel.traits.Traitable;
declare Customer
    @Traitable
    code    : String
    balance : long
end

The only connection between core classes and trait interfaces is at the proxy level: a trait is not specifically tied to a core class. This means that the same trait can be applied to totally different objects. For this reason, the trait does not transparently expose the fields of its core object. So, when writing a rule using a trait interface, only the fields of the interface will be available, as usual. However, any field in the interface that corresponds to a core object field, will be mapped by the proxy class:

Example 7.32.

when
    $o: OrderItem( $p : price, $code : custCode )
    $c: GoldenCustomer( code == $code, $a : balance, $d: discount )
then
    $c.setBalance( $a - $p*$d );
end

In this case, the code and balance would be read from the underlying Customer object. Likewise, the setAccount will modify the underlying object, preserving a strongly typed access to the data structures. A hard field must have the same name and type both in the core class and all donned interfaces. The name is used to establish the mapping: if two fields have the same name, then they must also have the same declared type. The annotation @org.drools.core.factmodel.traits.Alias allows to relax this restriction. If an @Alias is provided, its value string will be used to resolve mappings instead of the original field name. @Alias can be applied both to traits and core beans.

Example 7.33.

import org.drools.core.factmodel.traits.*;
declare trait GoldenCustomer
    balance : long @Alias( "org.acme.foo.accountBalance" )
end

declare Person
    @Traitable
    name : String
    savings : long @Alias( "org.acme.foo.accountBalance" )
end

when
    GoldenCustomer( balance > 1000 ) // will react to new Person( 2000 )
then
end

More work is being done on reaxing this constraint (see the experimental section on "logical" traits later). Now, one might wonder what happens when a core class does NOT provide the implementation for a field defined in an interface. We call hard fields those trait fields which are also core fields and thus readily available, while we define soft those fields which are NOT provided by the core class. Hidden fields, instead, are fields in the core class not exposed by the interface.

So, while hard field management is intuitive, there remains the problem of soft and hidden fields. Hidden fields are normally only accessible using the core class directly. However, the "fields" Map can be used on a trait interface to access a hidden field. If the field can't be resolved, null will be returned. Notice that this feature is likely to change in the future.

Example 7.34.

when
    $sc : GoldenCustomer( fields[ "age" ] > 18 )  // age is declared by the underlying core class, but not by GoldenCustomer
then

Soft fields, instead, are stored in a Map-like data structure that is specific to each core object and referenced by the proxy(es), so that they are effectively shared even when an object dons multiple traits.

Example 7.35.

when
    $sc : GoldenCustomer( $c : code, // hard getter
                          $maxExpense : maxExpense > 1000 // soft getter
    )
then
    $sc.setDiscount( ... ); // soft setter
end

A core object also holds a reference to all its proxies, so that it is possible to track which type(s) have been added to an object, using a sort of dynamic "instanceof" operator, which we called isA. The operator can accept a String, a class literal or a list of class literals. In the latter case, the constraint is satisfied only if all the traits have been donned.

Example 7.36.

$sc : GoldenCustomer( $maxExpense : maxExpense > 1000,
                      this isA "SeniorCustomer", this isA [ NationalCustomer.class, OnlineCustomer.class ]
)

Eventually, the business logic may require that a trait is removed from a wrapped object. To this end, we provide two options. The first is a "logical don", which will result in a logical insertion of the proxy resulting from the traiting operation. The TMS will ensure that the trait is removed when its logical support is removed in the first place.

Example 7.37.

then
    don( $x, // core object
         Customer.class, // trait class
         true // optional flag for logical insertion
    )

The second is the use of the "shed" keyword, which causes the removal of any type that is a subtype (or equivalent) of the one passed as an argument. Notice that, as of version 5.5, shed would only allow to remove a single specific trait.

Example 7.38.

then
    Thing t = shed( $x, GoldenCustomer.class ) // also removes any trait that

This operation returns another proxy implementing the org.drools.core.factmodel.traits.Thing interface, where the getFields() and getCore() methods are defined. Internally, in fact, all declared traits are generated to extend this interface (in addition to any others specified). This allows to preserve the wrapper with the soft fields which would otherwise be lost.

A trait and its proxies are also correlated in another way. Starting from version 5.6, whenever a core object is "modified", its proxies are "modified" automatically as well, to allow trait-based patterns to react to potential changes in hard fields. Likewise, whenever a trait proxy (mached by a trait pattern) is modified, the modification is propagated to the core class and the other traits. Morover, whenever a don operation is performed, the core object is also modified automatically, to reevaluate any "isA" operation which may be triggered.

Potentially, this may result in a high number of modifications, impacting performance (and correctness) heavily. So two solutions are currently implemented. First, whenever a core object is modified, only the most specific traits (in the sense of inheritance between trait interfaces) are updated and an internal blocking mechanism is in place to ensure that each potentially matching pattern is evaluated once and only once. So, in the following situation:

declare trait GoldenCustomer end
declare trait NationalGoldenustomer extends GoldenCustomer end
declare trait SeniorGoldenCustomer extends GoldenCustomer end

a modification of an object that is both a GoldenCustomer, a NationalGoldenCustomer and a SeniorGoldenCustomer wold cause only the latter two proxies to be actually modified. The first would match any pattern for GoldenCustomer and NationalGoldenCustomer; the latter would instead be prevented from rematching GoldenCustomer, but would be allowed to match SeniorGoldenCustomer patterns. It is not necessary, instead, to modify the GoldenCustomer proxy since it is already covered by at least one other more specific trait.

The second method, up to the usr, is to mark traits as @PropertyReactive. Property reactivity is trait-enabled and takes into account the trait field mappings, so to block unnecessary propagations.

7.7.8.1. Cascading traits

WARNING : This feature is extremely experimental and subject to changes

Normally, a hard field must be exposed with its original type by all traits donned by an object, to prevent situations such as

Example 7.39.

declare Person
  @Traitable
  name : String
  id : String
end

declare trait Customer
  id : String
end

declare trait Patient
  id : long  // Person can't don Patient, or an exception will be thrown
end

Should a Person don both Customer and Patient, the type of the hard field id would be ambiguous. However, consider the following example, where GoldenCustomers refer their best friends so that they become Customers as well:

Example 7.40.

declare Person
  @Traitable( logical=true )
  bestFriend : Person
end

declare trait Customer end
        
declare trait GoldenCustomer extends Customer
  refers : Customer @Alias( "bestFriend" )
end

Aside from the @Alias, a Person-as-GoldenCustomer's best friend might be compatible with the requirements of the trait GoldenCustomer, provided that they are some kind of Customer themselves. Marking a Person as "logically traitable" - i.e. adding the annotation @Traitable( logical = true ) - will instruct the engine to try and preserve the logical consistency rather than throwing an exception due to a hard field with different type declarations (Person vs Customer). The following operations would then work:

Example 7.41.

Person p1 = new Person();
Person p2 = new Person();
p1.setBestFriend( p2 );
...
Customer c2 = don( p2, Customer.class );
...
GoldenCustomer gc1 = don( p1, GoldenCustomer.class );
...
p1.getBestFriend(); // returns p2
gc1.getRefers(); // returns c2, a Customer proxy wrapping p2

Notice that, by the time p1 becomes GoldenCustomer, p2 must have already become a Customer themselves, otherwise a runtime exception will be thrown since the very definition of GoldenCustomer would have been violated.

In some cases, however, one might want to infer, rather than verify, that p2 is a Customer by virtue that p1 is a GoldenCustomer. This modality can be enabled by marking Customer as "logical", using the annotation @org.drools.core.factmodel.traits.Trait( logical = true ). In this case, should p2 not be a Customer by the time that p1 becomes a GoldenCustomer, it will be automatically don the trait Customer to preserve the logical integrity of the system.

Notice that the annotation on the core class enables the dynamic type management for its fields, whereas the annotation on the traits determines whether they will be enforced as integrity constraints or cascaded dynamically.

Example 7.42.

import org.drools.factmodel.traits.*;

declare trait Customer
    @Trait( logical = true )
end

7.8. Rule

Figure 7.9. rule

A rule specifies that when a particular set of conditions occur, specified in the Left Hand Side (LHS), then do what queryis specified as a list of actions in the Right Hand Side (RHS). A common question from users is "Why use when instead of if?" "When" was chosen over "if" because "if" is normally part of a procedural execution flow, where, at a specific point in time, a condition is to be checked. In contrast, "when" indicates that the condition evaluation is not tied to a specific evaluation sequence or point in time, but that it happens continually, at any time during the life time of the engine; whenever the condition is met, the actions are executed.

A rule must have a name, unique within its rule package. If you define a rule twice in the same DRL it produces an error while loading. If you add a DRL that includes a rule name already in the package, it replaces the previous rule. If a rule name is to have spaces, then it will need to be enclosed in double quotes (it is best to always use double quotes).

Attributes - described below - are optional. They are best written one per line.

The LHS of the rule follows the when keyword (ideally on a new line), similarly the RHS follows the then keyword (again, ideally on a newline). The rule is terminated by the keyword end. Rules cannot be nested.

Example 7.43. Rule Syntax Overview

rule "<name>"
    <attribute>*
when
    <conditional element>*
then
    <action>*
end

Example 7.44. A simple rule

rule "Approve if not rejected"
  salience -100 
  agenda-group "approval"
    when
        not Rejection() 
        p : Policy(approved == false, policyState:status)
        exists Driver(age > 25)
        Process(status == policyState)
    then
        log("APPROVED: due to no objections."); 
        p.setApproved(true);
end

7.8.1. Rule Attributes

Rule attributes provide a declarative way to influence the behavior of the rule. Some are quite simple, while others are part of complex subsystems such as ruleflow. To get the most from Drools you should make sure you have a proper understanding of each attribute.

Figure 7.10. rule attributes

no-loop

default value: false

type: Boolean

When a rule's consequence modifies a fact it may cause the rule to activate again, causing an infinite loop. Setting no-loop to true will skip the creation of another Activation for the rule with the current set of facts.

ruleflow-group

default value: N/A

type: String

Ruleflow is a Drools feature that lets you exercise control over the firing of rules. Rules that are assembled by the same ruleflow-group identifier fire only when their group is active.

lock-on-active

default value: false

type: Boolean

Whenever a ruleflow-group becomes active or an agenda-group receives the focus, any rule within that group that has lock-on-active set to true will not be activated any more; irrespective of the origin of the update, the activation of a matching rule is discarded. This is a stronger version of no-loop, because the change could now be caused not only by the rule itself. It's ideal for calculation rules where you have a number of rules that modify a fact and you don't want any rule re-matching and firing again. Only when the ruleflow-group is no longer active or the agenda-group loses the focus those rules with lock-on-active set to true become eligible again for their activations to be placed onto the agenda.

salience

default value: 0

type: integer

Each rule has an integer salience attribute which defaults to zero and can be negative or positive. Salience is a form of priority where rules with higher salience values are given higher priority when ordered in the Activation queue.

Drools also supports dynamic salience where you can use an expression involving bound variables.

Example 7.45. Dynamic Salience

rule "Fire in rank order 1,2,.."
        salience( -$rank )
    when
        Element( $rank : rank,... )
    then
        ...
end

agenda-group

default value: MAIN

type: String

Agenda groups allow the user to partition the Agenda providing more execution control. Only rules in the agenda group that has acquired the focus are allowed to fire.

auto-focus

default value: false

type: Boolean

When a rule is activated where the auto-focus value is true and the rule's agenda group does not have focus yet, then it is given focus, allowing the rule to potentially fire.

activation-group

default value: N/A

type: String

Rules that belong to the same activation-group, identified by this attribute's string value, will only fire exclusively. More precisely, the first rule in an activation-group to fire will cancel all pending activations of all rules in the group, i.e., stop them from firing.

Note: This used to be called Xor group, but technically it's not quite an Xor. You may still hear people mention Xor group; just swap that term in your mind with activation-group.

dialect

default value: as specified by the package

type: String

possible values: "java" or "mvel"

The dialect species the language to be used for any code expressions in the LHS or the RHS code block. Currently two dialects are available, Java and MVEL. While the dialect can be specified at the package level, this attribute allows the package definition to be overridden for a rule.

date-effective

default value: N/A

type: String, containing a date and time definition

A rule can only activate if the current date and time is after date-effective attribute.

date-expires

default value: N/A

type: String, containing a date and time definition

A rule cannot activate if the current date and time is after the date-expires attribute.

duration

default value: no default value

type: long

The duration dictates that the rule will fire after a specified duration, if it is still true.

Example 7.46. Some attribute examples

rule "my rule"
  salience 42
  agenda-group "number 1"
    when ...

7.8.2. Timers and Calendars

Rules now support both interval and cron based timers, which replace the now deprecated duration attribute.

Example 7.47. Sample timer attribute uses

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

timer ( int: 30s )

timer ( int: 30s 5m )


timer ( cron: <cron expression> )

timer ( cron:* 0/15 * * * ? )

Interval (indicated by "int:") timers follow the semantics of java.util.Timer objects, with an initial delay and an optional repeat interval. Cron (indicated by "cron:") timers follow standard Unix cron expressions:

Example 7.48. A Cron Example

rule "Send SMS every 15 minutes"

    timer (cron:* 0/15 * * * ?)

when

    $a : Alarm( on == true )

then

    channels[ "sms" ].insert( new Sms( $a.mobileNumber, "The alarm is still on" );

end

A rule controlled by a timer becomes active when it matches, and once for each individual match. Its consequence is executed repeatedly, according to the timer's settings. This stops as soon as the condition doesn't match any more.

Consequences are executed even after control returns from a call to fireUntilHalt. Moreover, the Engine remains reactive to any changes made to the Working Memory. For instance, removing a fact that was involved in triggering the timer rule's execution causes the repeated execution to terminate, or inserting a fact so that some rule matches will cause that rule to fire. But the Engine is not continually active, only after a rule fires, for whatever reason. Thus, reactions to an insertion done asynchronously will not happen until the next execution of a timer-controlled rule. Disposing a session puts an end to all timer activity.

Conversely when the rule engine runs in passive mode (i.e.: using fireAllRules instead of fireUntilHalt) by default it doesn't fire consequences of timed rules unless fireAllRules isn't invoked again. However it is possible to change this default behavior by configuring the KieSession with a TimedRuleExectionOption as shown in the following example.

Example 7.49. Configuring a KieSession to automatically execute timed rules

KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();

ksconf.setOption( TimedRuleExectionOption.YES );

KSession ksession = kbase.newKieSession(ksconf, null);

Example 7.50. Configuring a filter to choose which timed rules should be automatically executed

KieSessionConfiguration ksconf = KieServices.Factory.get().newKieSessionConfiguration();

conf.setOption( new TimedRuleExectionOption.FILTERED(new TimedRuleExecutionFilter() {

    public boolean accept(Rule[] rules) {

        return rules[0].getName().equals("MyRule");

    }

}) );

For what regards interval timers it is also possible to define both the delay and interval as an expression instead of a fixed value. To do that it is necessary to use an expression timer (indicated by "expr:") as in the following example:

Example 7.51. An Expression Timer Example

declare Bean

    delay   : String = "30s"

    period  : long = 60000

end


rule "Expression timer"

    timer( expr: $d, $p )

when

    Bean( $d : delay, $p : period )

then

end

Example 7.52. An Interval Timer with a start and an end

timer (int: 30s 10s; start=3-JAN-2010, end=5-JAN-2010)

The value for start and end parameters can be a Date, a String representing a Date or a long, or more in general any Number, that will be transformed in a Java Date applying the following conversion:

new Date( ((Number) n).longValue() )

start + delay + n*period

for up to repeat-limit times and no later than the end timestamp (whichever first). For instance the rule having the following interval timer

timer ( int: 30s 1m; start="3-JAN-2010" )

Calendars are used to control when rules can fire. The Calendar API is modelled on Quartz:

Example 7.53. Adapting a Quartz Calendar

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

Calendars are registered with the KieSession:

Example 7.54. Registering a Calendar

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

They can be used in conjunction with normal rules and rules including timers. The rule attribute "calendars" may contain one or more comma-separated calendar names written as string literals.

Example 7.55. Using Calendars and Timers together

rule "weekdays are high priority"

   calendars "weekday"

   timer (int:0 1h)

when 

    Alarm()

then

    send( "priority high - we have an alarm );

end 


rule "weekend are low priority"

   calendars "weekend"

   timer (int:0 4h)

when 

    Alarm()

then

    send( "priority low - we have an alarm );

end

7.8.3. Left Hand Side (when) syntax

7.8.3.1. What is the Left Hand Side?

The Left Hand Side (LHS) is a common name for the conditional part of the rule. It consists of zero or more Conditional Elements. If the LHS is empty, it will be considered as a condition element that is always true and it will be activated once, when a new WorkingMemory session is created.

Figure 7.11. Left Hand Side

Example 7.56. Rule without a Conditional Element

rule "no CEs"
when
    // empty
then
    ... // actions (executed once)
end

// The above rule is internally rewritten as:

rule "eval(true)"
when
    eval( true )
then
    ... // actions (executed once)
end

Conditional elements work on one or more patterns (which are described below). The most common conditional element is "and". Therefore it is implicit when you have multiple patterns in the LHS of a rule that are not connected in any way:

Example 7.57. Implicit and

rule "2 unconnected patterns"
when
    Pattern1()
    Pattern2()
then
    ... // actions
end

// The above rule is internally rewritten as:

rule "2 and connected patterns"
when
    Pattern1()
    and Pattern2()
then
    ... // actions
end

Note

An "and" cannot have a leading declaration binding (unlike for example or). This is obvious, since a declaration can only reference a single fact at a time, and when the "and" is satisfied it matches both facts - so which fact would the declaration bind to?

// Compile error
$person : (Person( name == "Romeo" ) and Person( name == "Juliet"))

7.8.3.2. Pattern (conditional element)

7.8.3.2.1. What is a pattern?

A pattern element is the most important Conditional Element. It can potentially match on each fact that is inserted in the working memory.

A pattern contains of zero or more constraints and has an optional pattern binding. The railroad diagram below shows the syntax for this.

Figure 7.12. Pattern

In its simplest form, with no constraints, a pattern matches against a fact of the given type. In the following case the type is Cheese, which means that the pattern will match against all Person objects in the Working Memory:

Person()

The type need not be the actual class of some fact object. Patterns may refer to superclasses or even interfaces, thereby potentially matching facts from many different classes.

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

Inside of the pattern parenthesis is where all the action happens: it defines the constraints for that pattern. For example, with a age related constraint:

Person( age == 100 )

Note

For backwards compatibility reasons it's allowed to suffix patterns with the ; character. But it is not recommended to do that.

7.8.3.2.2. Pattern binding

For referring to the matched object, use a pattern binding variable such as $p.

Example 7.58. Pattern with a binding variable

rule ...
when
    $p : Person()
then
    System.out.println( "Person " + $p );
end

The prefixed dollar symbol ($) is just a convention; it can be useful in complex rules where it helps to easily differentiate between variables and fields, but it is not mandatory.

7.8.3.3. Constraint (part of a pattern)

7.8.3.3.1. What is a constraint?

A constraint is an expression that returns true or false. This example has a constraint that states 5 is smaller than 6:

Person( 5 < 6 )  // just an example, as constraints like this would be useless in a real pattern

In essence, it's a Java expression with some enhancements (such as property access) and a few differences (such as equals() semantics for ==). Let's take a deeper look.

7.8.3.3.2. Property access on Java Beans (POJO's)

Any bean property can be used directly. A bean property is exposed using a standard Java bean getter: a method getMyProperty() (or isMyProperty() for a primitive boolean) which takes no arguments and return something. For example: the age property is written as age in DRL instead of the getter getAge():

Person( age == 50 )

// this is the same as:
Person( getAge() == 50 )

Drools uses the standard JDK Introspector class to do this mapping, so it follows the standard Java bean specification.

Note

We recommend using property access (age) over using getters explicitly (getAge()) because of performance enhancements through field indexing.

Warning

Property accessors must not change the state of the object in a way that may effect the rules. Remember that the rule engine effectively caches the results of its matching in between invocations to make it faster.

public int getAge() {
    age++; // Do NOT do this
    return age;
}

public int getAge() {
    Date now = DateUtil.now(); // Do NOT do this
    return DateUtil.differenceInYears(now, birthday);
}

To solve this latter case, insert a fact that wraps the current date into working memory and update that fact between fireAllRules as needed.

Note

The following fallback applies: if the getter of a property cannot be found, the compiler will resort to using the property name as a method name and without arguments:

Person( age == 50 )

// If Person.getAge() does not exists, this falls back to:
Person( age() == 50 )

Nested property access is also supported:

Person( address.houseNumber == 50 )

// this is the same as:
Person( getAddress().getHouseNumber() == 50 )

Nested properties are also indexed.

Warning

In a stateful session, care should be taken when using nested accessors as the Working Memory is not aware of any of the nested values, and does not know when they change. Either consider them immutable while any of their parent references are inserted into the Working Memory. Or, instead, if you wish to modify a nested value you should mark all of the outer facts as updated. In the above example, when the houseNumber changes, any Person with that Address must be marked as updated.

7.8.3.3.3. Java expression

You can use any Java expression that returns a boolean as a constraint inside the parentheses of a pattern. Java expressions can be mixed with other expression enhancements, such as property access:

Person( age == 50 )

It is possible to change the evaluation priority by using parentheses, as in any logic or mathematical expression:

Person( age > 100 && ( age % 10 == 0 ) )

It is possible to reuse Java methods:

Person( Math.round( weight / ( height * height ) ) < 25.0 )

Warning

As for property accessors, methods must not change the state of the object in a way that may affect the rules. Any method executed on a fact in the LHS should be a read only method.

Person( incrementAndGetAge() == 10 ) // Do NOT do this

Warning

The state of a fact should not change between rule invocations (unless those facts are marked as updated to the working memory on every change):

Person( System.currentTimeMillis() % 1000 == 0 ) // Do NOT do this

Normal Java operator precedence applies, see the operator precedence list below.

Important

All operators have normal Java semantics except for == and !=.

The == operator has null-safe equals() semantics:

// Similar to: java.util.Objects.equals(person.getFirstName(), "John")
// so (because "John" is not null) similar to:
// "John".equals(person.getFirstName())
Person( firstName == "John" )

The != operator has null-safe !equals() semantics:

// Similar to: !java.util.Objects.equals(person.getFirstName(), "John")
Person( firstName != "John" )

Type coercion is always attempted if the field and the value are of different types; exceptions will be thrown if a bad coercion is attempted. For instance, if "ten" is provided as a string in a numeric evaluator, an exception is thrown, whereas "10" would coerce to a numeric 10. Coercion is always in favor of the field type and not the value type:

Person( age == "10" ) // "10" is coerced to 10

7.8.3.3.4. Comma separated AND

The comma character (',') is used to separate constraint groups. It has implicit AND connective semantics.

// Person is at least 50 and weighs at least 80 kg
Person( age > 50, weight > 80 )

// Person is at least 50, weighs at least 80 kg and is taller than 2 meter.
Person( age > 50, weight > 80, height > 2 )

Note

Although the && and , operators have the same semantics, they are resolved with different priorities: The && operator precedes the || operator. Both the && and || operator precede the , operator. See the operator precedence list below.

The comma operator should be preferred at the top level constraint, as it makes constraints easier to read and the engine will often be able to optimize them better.

The comma (,) operator cannot be embedded in a composite constraint expression, such as parentheses:

Person( ( age > 50, weight > 80 ) || height > 2 ) // Do NOT do this: compile error

// Use this instead
Person( ( age > 50 && weight > 80 ) || height > 2 )

7.8.3.3.5. Binding variables

A property can be bound to a variable:

// 2 persons of the same age
Person( $firstAge : age ) // binding
Person( age == $firstAge ) // constraint expression

The prefixed dollar symbol ($) is just a convention; it can be useful in complex rules where it helps to easily differentiate between variables and fields.

Note

For backwards compatibility reasons, It's allowed (but not recommended) to mix a constraint binding and constraint expressions as such:

// Not recommended
Person( $age : age * 2 < 100 )

// Recommended (separates bindings and constraint expressions)
Person( age * 2 < 100, $age : age )

Bound variable restrictions using the operator == provide for very fast execution as it use hash indexing to improve performance.

7.8.3.3.6. Unification

Drools does not allow bindings to the same declaration. However this is an important aspect to derivation query unification. While positional arguments are always processed with unification a special unification symbol, ':=', was introduced for named arguments named arguments. The following "unifies" the age argument across two people.

Person( $age := age ) 
Person( $age := age)

In essence unification will declare a binding for the first occurrence and constrain to the same value of the bound field for sequence occurrences.

7.8.3.3.7. Grouped accessors for nested objects

Often it happens that it is necessary to access multiple properties of a nested object as in the following example

Person( name == "mark", address.city == "london", address.country == "uk" )

These accessors to nested objects can be grouped with a '.(...)' syntax providing more readable rules as in

Person( name==  "mark", address.( city == "london", country ==  "uk") )

Note the '.' prefix, this is necessary to differentiate the nested object constraints from a method call.

7.8.3.3.8. Inline casts and coercion

When dealing with nested objects, it also quite common the need to cast to a subtype. It is possible to do that via the # symbol as in:

Person( name=="mark", address#LongAddress.country == "uk" )

This example casts Address to LongAddress, making its getters available. If the cast is not possible (instanceof returns false), the evaluation will be considered false. Also fully qualified names are supported:

Person( name=="mark", address#org.domain.LongAddress.country == "uk" )

It is possible to use multiple inline casts in the same expression:

Person( name == "mark", address#LongAddress.country#DetailedCountry.population > 10000000 )

moreover, since we also support the instanceof operator, if that is used we will infer its results for further uses of that field, within that pattern:

Person( name=="mark", address instanceof LongAddress, address.country == "uk" )

7.8.3.3.9. Special literal support

Besides normal Java literals (including Java 5 enums), this literal is also supported:

7.8.3.3.9.1. Date literal

The date format dd-mmm-yyyy is supported by default. You can customize this by providing an alternative date format mask as the System property named drools.dateformat. If more control is required, use a restriction.

Example 7.59. Date Literal Restriction

Cheese( bestBefore < "27-Oct-2009" )

7.8.3.3.10. List and Map access

It's possible to directly access a List value by index:

// Same as childList(0).getAge() == 18
Person( childList[0].age == 18 )

It's also possible to directly access a Map value by key:

// Same as credentialMap.get("jsmith").isValid()
Person( credentialMap["jsmith"].valid )

7.8.3.3.11. Abbreviated combined relation condition

This allows you to place more than one restriction on a field using the restriction connectives && or ||. Grouping via parentheses is permitted, resulting in a recursive syntax pattern.

Figure 7.13. Abbreviated combined relation condition

Figure 7.14. Abbreviated combined relation condition with parentheses

// 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" )

7.8.3.3.12. Special DRL operators

Figure 7.15. Operators

Coercion to the correct value for the evaluator and the field will be attempted.

7.8.3.3.12.1. The operators `<` `<=` `>` `>=`

These operators can be used on properties with natural ordering. For example, for Date fields, < means before, for String fields, it means alphabetically lower.

Person( firstName < $otherFirstName )

Person( birthDate < $otherBirthDate )

Only applies on Comparable properties.

7.8.3.3.12.2. Null-safe dereferencing operator

The !. operator allows to derefencing in a null-safe way. More in details the matching algorithm requires the value to the left of the !. operator to be not null in order to give a positive result for pattern matching itself. In other words the pattern:

Person( $streetName : address!.street )

will be internally translated in:

Person( address != null, $streetName : address.street )

7.8.3.3.12.3. The operator `matches`

Matches a field against any valid Java Regular Expression. Typically that regexp is a string literal, but variables that resolve to a valid regexp are also allowed.

Example 7.60. Regular Expression Constraint

Cheese( type matches "(Buffalo)?\\S*Mozarella" )

Note

Like in Java, regular expressions written as string literals need to escape '\'.

Only applies on String properties. Using matches against a null value always evaluates to false.

7.8.3.3.12.4. The operator `not matches`

The operator returns true if the String does not match the regular expression. The same rules apply as for the matches operator. Example:

Example 7.61. Regular Expression Constraint

Cheese( type not matches "(Buffulo)?\\S*Mozarella" )

Only applies on String properties. Using not matches against a null value always evaluates to true.

7.8.3.3.12.5. The operator `contains`

The operator contains is used to check whether a field that is a Collection or elements contains the specified value.

Example 7.62. Contains with Collections

CheeseCounter( cheeses contains "stilton" ) // contains with a String literal
CheeseCounter( cheeses contains $var ) // contains with a variable

Only applies on Collection properties.

7.8.3.3.12.6. The operator `not contains`

The operator not contains is used to check whether a field that is a Collection or elements does not contain the specified value.

Example 7.63. Literal Constraint with Collections

CheeseCounter( cheeses not contains "cheddar" ) // not contains with a String literal
CheeseCounter( cheeses not contains $var ) // not contains with a variable

Only applies on Collection properties.

Note
For backward compatibility, the excludes operator is supported as a synonym for not contains.

7.8.3.3.12.7. The operator `memberOf`

The operator memberOf is used to check whether a field is a member of a collection or elements; that collection must be a variable.

Example 7.64. Literal Constraint with Collections

CheeseCounter( cheese memberOf $matureCheeses )

7.8.3.3.12.8. The operator `not memberOf`

The operator not memberOf is used to check whether a field is not a member of a collection or elements; that collection must be a variable.

Example 7.65. Literal Constraint with Collections

CheeseCounter( cheese not memberOf $matureCheeses )

7.8.3.3.12.9. The operator `soundslike`

This operator is similar to matches, but it checks whether a word has almost the same sound (using English pronunciation) as the given value. This is based on the Soundex algorithm (see http://en.wikipedia.org/wiki/Soundex).

Example 7.66. Test with soundslike

// match cheese "fubar" or "foobar"
Cheese( name soundslike 'foobar' )

7.8.3.3.12.10. The operator `str`

This operator str is used to check whether a field that is a String starts with or ends with a certain value. It can also be used to check the length of the String.

Message( routingValue str[startsWith] "R1" )

Message( routingValue str[endsWith] "R2" )

Message( routingValue str[length] 17 )

7.8.3.3.12.11. The operators `in` and `not in` (compound value restriction)

The compound value restriction is used where there is more than one possible value to match. Currently only the in and not in evaluators support this. The second operand of this operator must be a comma-separated list of values, enclosed in parentheses. Values may be given as variables, literals, return values or qualified identifiers. Both evaluators are actually syntactic sugar, internally rewritten as a list of multiple restrictions using the operators != and ==.

Figure 7.16. compoundValueRestriction

Example 7.67. Compound Restriction using "in"

Person( $cheese : favouriteCheese )
Cheese( type in ( "stilton", "cheddar", $cheese ) )

7.8.3.3.13. Inline eval operator (deprecated)

Figure 7.17. Inline Eval Expression

An inline eval constraint can use any valid dialect expression as long as it results to a primitive boolean. The expression must be constant over time. Any previously bound variable, from the current or previous pattern, can be used; autovivification is also used to auto-create field binding variables. When an identifier is found that is not a current variable, the builder looks to see if the identifier is a field on the current object type, if it is, the field binding is auto-created as a variable of the same name. This is called autovivification of field variables inside of inline eval's.

This example will find all male-female pairs where the male is 2 years older than the female; the variable age is auto-created in the second pattern by the autovivification process.

Example 7.68. Return Value operator

Person( girlAge : age, sex = "F" )
Person( eval( age == girlAge + 2 ), sex = 'M' ) // eval() is actually obsolete in this example

Note

Inline eval's are effectively obsolete as their inner syntax is now directly supported. It's recommended not to use them. Simply write the expression without wrapping eval() around it.

7.8.3.3.14. Operator precedence

The operators are evaluated in this precedence:

Table 7.1. Operator precedence

Operator type	Operators	Notes
(nested / null safe) property access	`.` `!.`	Not normal Java semantics
List/Map access	`[ ]`	Not normal Java semantics
constraint binding	`:`	Not normal Java semantics
multiplicative	`*` `/` `%`
additive	`+` `-`
shift	`<<` `>>` `>>>`
relational	`<` `>` `<=` `>=` `instanceof`
equality	`==` `!=`	Does not use normal Java (not) same semantics: uses (not) equals semantics instead.
non-short circuiting AND	`&`
non-short circuiting exclusive OR	`^`
non-short circuiting inclusive OR	`\|`
logical AND	`&&`
logical OR	`\|\|`
ternary	`? :`
Comma separated AND	`,`	Not normal Java semantics

7.8.3.4. Positional Arguments

Patterns now support positional arguments on type declarations.

declare Cheese
    name : String
    shop : String
    price : int
end

Cheese( "stilton", "Cheese Shop", p; )
Cheese( "stilton", "Cheese Shop"; p : price )
Cheese( "stilton"; shop == "Cheese Shop", p : price )
Cheese( name == "stilton"; shop == "Cheese Shop", p : price )

Positional arguments that are given a previously declared binding will constrain against that using unification; these are referred to as input arguments. If the binding does not yet exist, it will create the declaration binding it to the field represented by the position argument; these are referred to as output arguments.

7.8.3.5. Fine grained property change listeners

When you call modify() (see the modify statement section) on a given object it will trigger a revaluation of all patterns of the matching object type in the knowledge base. This can can lead to unwanted and useless evaluations and in the worst cases to infinite recursions. The only workaround to avoid it was to split up your objects into smaller ones having a 1 to 1 relationship with the original object.

This feature allows the pattern matching to only react to modification of properties actually constrained or bound inside of a given pattern. That will help with performance and recursion and avoid artificial object splitting.

By default this feature is off in order to make the behavior of the rule engine backward compatible with the former releases. When you want to activate it on a specific bean you have to annotate it with @propertyReactive. This annotation works both on DRL type declarations:

declare Person
@propertyReactive
    firstName : String
    lastName : String
end

and on Java classes:

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

In this way, for instance, if you have a rule like the following:

rule "Every person named Mario is a male" when
    $person : Person( firstName == "Mario" )
then
    modify ( $person )  { setMale( true ) }
end

you won't have to add the no-loop attribute to it in order to avoid an infinite recursion because the engine recognizes that the pattern matching is done on the 'firstName' property while the RHS of the rule modifies the 'male' one. Note that this feature does not work for update(), and this is one of the reasons why we promote modify() since it encapsulates the field changes within the statement. Moreover, on Java classes, you can also annotate any method to say that its invocation actually modifies other properties. For instance in the former Person class you could have a method like:

@Modifies( { "firstName", "lastName" } )
public void setName(String name) {
    String[] names = name.split("\\s");
    this.firstName = names[0];
    this.lastName = names[1];
}

That means that if a rule has a RHS like the following:

modify($person) { setName("Mario Fusco") }

it will correctly recognize that the values of both properties 'firstName' and 'lastName' could have potentially been modified and act accordingly, not missing of reevaluating the patterns constrained on them. At the moment the usage of @Modifies is not allowed on fields but only on methods. This is coherent with the most common scenario where the @Modifies will be used for methods that are not related with a class field as in the Person.setName() in the former example. Also note that @Modifies is not transitive, meaning that if another method internally invokes the Person.setName() one it won't be enough to annotate it with @Modifies( { "name" } ), but it is necessary to use @Modifies( { "firstName", "lastName" } ) even on it. Very likely @Modifies transitivity will be implemented in the next release.

For what regards nested accessors, the engine will be notified only for top level fields. In other words a pattern matching like:

Person ( address.city.name == "London )

will be revaluated only for modification of the 'address' property of a Person object. In the same way the constraints analysis is currently strictly limited to what there is inside a pattern. Another example could help to clarify this. An LHS like the following:

$p : Person( )
Car( owner = $p.name )

will not listen on modifications of the person's name, while this one will do:

Person( $name : name )
Car( owner = $name )

To overcome this problem it is possible to annotate a pattern with @watch as it follows:

$p : Person( ) @watch ( name )
Car( owner = $p.name )

Indeed, annotating a pattern with @watch allows you to modify the inferred set of properties for which that pattern will react. Note that the properties named in the @watch annotation are actually added to the ones automatically inferred, but it is also possible to explicitly exclude one or more of them prepending their name with a ! and to make the pattern to listen for all or none of the properties of the type used in the pattern respectively with the wildcrds * and !*. So, for example, you can annotate a pattern in the LHS of a rule like:

// listens for changes on both firstName (inferred) and lastName
Person( firstName == $expectedFirstName ) @watch( lastName )

// listens for all the properties of the Person bean
Person( firstName == $expectedFirstName ) @watch( * )

// listens for changes on lastName and explicitly exclude firstName
Person( firstName == $expectedFirstName ) @watch( lastName, !firstName )

// listens for changes on all the properties except the age one
Person( firstName == $expectedFirstName ) @watch( *, !age )

Since doesn't make sense to use this annotation on a pattern using a type not annotated with @PropertyReactive the rule compiler will raise a compilation error if you try to do so. Also the duplicated usage of the same property in @watch (for example like in: @watch( firstName, ! firstName ) ) will end up in a compilation error. In a next release we will make the automatic detection of the properties to be listened smarter by doing analysis even outside of the pattern.

It also possible to enable this feature by default on all the types of your model or to completely disallow it by using on option of the KnowledgeBuilderConfiguration. In particular this new PropertySpecificOption can have one of the following 3 values:

- DISABLED => the feature is turned off and all the other related annotations are just ignored
- ALLOWED => this is the default behavior: types are not property reactive unless they are not annotated with @PropertySpecific
- ALWAYS => all types are property reactive by default

So, for example, to have a KnowledgeBuilder generating property reactive types by default you could do:

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

In this last case it will be possible to disable the property reactivity feature on a specific type by annotating it with @ClassReactive.

7.8.3.6. Basic conditional elements

7.8.3.6.1. Conditional Element `and`

The Conditional Element "and" is used to group other Conditional Elements into a logical conjunction. Drools supports both prefix and and infix and.

Figure 7.18. infixAnd

Traditional infix and is supported:

//infixAnd
Cheese( cheeseType : type ) and Person( favouriteCheese == cheeseType )

Explicit grouping with parentheses is also supported:

//infixAnd with grouping
( Cheese( cheeseType : type ) and
  ( Person( favouriteCheese == cheeseType ) or 
    Person( favouriteCheese == cheeseType ) )

Note

The symbol && (as an alternative to and) is deprecated. But it is still supported in the syntax for backwards compatibility.

Figure 7.19. prefixAnd

Prefix and is also supported:

(and Cheese( cheeseType : type )
     Person( favouriteCheese == cheeseType ) )

The root element of the LHS is an implicit prefix and and doesn't need to be specified:

Example 7.69. implicit root prefixAnd

when
    Cheese( cheeseType : type )
    Person( favouriteCheese == cheeseType )
then
    ...

7.8.3.6.2. Conditional Element `or`

The Conditional Element or is used to group other Conditional Elements into a logical disjunction. Drools supports both prefix or and infix or.

Figure 7.20. infixOr

Traditional infix or is supported:

//infixOr
Cheese( cheeseType : type ) or Person( favouriteCheese == cheeseType )

Explicit grouping with parentheses is also supported:

//infixOr with grouping
( Cheese( cheeseType : type ) or
  ( Person( favouriteCheese == cheeseType ) and
    Person( favouriteCheese == cheeseType ) )

Note

The symbol || (as an alternative to or) is deprecated. But it is still supported in the syntax for backwards compatibility.

Figure 7.21. prefixOr

Prefix or is also supported:

(or Person( sex == "f", age > 60 )
    Person( sex == "m", age > 65 )

Note

The behavior of the Conditional Element or is different from the connective || for constraints and restrictions in field constraints. The engine actually has no understanding of the Conditional Element or. Instead, via a number of different logic transformations, a rule with or is rewritten as a number of subrules. This process ultimately results in a rule that has a single or as the root node and one subrule for each of its CEs. Each subrule can activate and fire like any normal rule; there is no special behavior or interaction between these subrules. - This can be most confusing to new rule authors.

The Conditional Element or also allows for optional pattern binding. This means that each resulting subrule will bind its pattern to the pattern binding. Each pattern must be bound separately, using eponymous variables:

pensioner : ( Person( sex == "f", age > 60 ) or Person( sex == "m", age > 65 ) )

(or pensioner : Person( sex == "f", age > 60 ) 
    pensioner : Person( sex == "m", age > 65 ) )

Since the conditional element or results in multiple subrule generation, one for each possible logically outcome, the example above would result in the internal generation of two rules. These two rules work independently within the Working Memory, which means both can match, activate and fire - there is no shortcutting.

The best way to think of the conditional element or is as a shortcut for generating two or more similar rules. When you think of it that way, it's clear that for a single rule there could be multiple activations if two or more terms of the disjunction are true.

7.8.3.6.3. Conditional Element `not`

Figure 7.22. not

The CE not is first order logic's non-existential quantifier and checks for the non-existence of something in the Working Memory. Think of "not" as meaning "there must be none of...".

The keyword not may be followed by parentheses around the CEs that it applies to. In the simplest case of a single pattern (like below) you may optionally omit the parentheses.

Example 7.70. No Busses

not Bus()

Example 7.71. No red Busses

// Brackets are optional:
not Bus(color == "red")
// Brackets are optional:
not ( Bus(color == "red", number == 42) )
// "not" with nested infix and - two patterns,
// brackets are requires:
not ( Bus(color == "red") and
      Bus(color == "blue") )

7.8.3.6.4. Conditional Element `exists`

Figure 7.23. exists

The CE exists is first order logic's existential quantifier and checks for the existence of something in the Working Memory. Think of "exists" as meaning "there is at least one..". It is different from just having the pattern on its own, which is more like saying "for each one of...". If you use exists with a pattern, the rule will only activate at most once, regardless of how much data there is in working memory that matches the condition inside of the exists pattern. Since only the existence matters, no bindings will be established.

The keyword exists must be followed by parentheses around the CEs that it applies to. In the simplest case of a single pattern (like below) you may omit the parentheses.

Example 7.72. At least one Bus

exists Bus()

Example 7.73. At least one red Bus

exists Bus(color == "red")
// brackets are optional:
exists ( Bus(color == "red", number == 42) )
// "exists" with nested infix and,
// brackets are required:
exists ( Bus(color == "red") and
         Bus(color == "blue") )

7.8.3.7. Advanced conditional elements

7.8.3.7.1. Conditional Element `forall`

Figure 7.24. forall

The Conditional Element forall completes the First Order Logic support in Drools. The Conditional Element forall evaluates to true when all facts that match the first pattern match all the remaining patterns. Example:

rule "All English buses are red"
when
    forall( $bus : Bus( type == 'english') 
                   Bus( this == $bus, color = 'red' ) )
then
    // all English buses are red
end

In the above rule, we "select" all Bus objects whose type is "english". Then, for each fact that matches this pattern we evaluate the following patterns and if they match, the forall CE will evaluate to true.

To state that all facts of a given type in the working memory must match a set of constraints, forall can be written with a single pattern for simplicity. Example:

Example 7.74. Single Pattern Forall

rule "All Buses are Red"
when
    forall( Bus( color == 'red' ) )
then
    // all Bus facts are red
end

Another example shows multiple patterns inside the forall:

Example 7.75. Multi-Pattern Forall

rule "all employees have health and dental care programs"
when
    forall( $emp : Employee()
            HealthCare( employee == $emp )
            DentalCare( employee == $emp )
          )
then
    // all employees have health and dental care
end

Forall can be nested inside other CEs. For instance, forall can be used inside a not CE. Note that only single patterns have optional parentheses, so that with a nested forall parentheses must be used:

Example 7.76. Combining Forall with Not CE

rule "not all employees have health and dental care"
when 
    not ( forall( $emp : Employee()
                  HealthCare( employee == $emp )
                  DentalCare( employee == $emp ) ) 
        )
then
    // not all employees have health and dental care
end

As a side note, forall( p1 p2 p3...) is equivalent to writing:

not(p1 and not(and p2 p3...))

Also, it is important to note that forall is a scope delimiter. Therefore, it can use any previously bound variable, but no variable bound inside it will be available for use outside of it.

7.8.3.7.2. Conditional Element `from`

Figure 7.25. from

The Conditional Element from enables users to specify an arbitrary source for data to be matched by LHS patterns. This allows the engine to reason over data not in the Working Memory. The data source could be a sub-field on a bound variable or the results of a method call. It is a powerful construction that allows out of the box integration with other application components and frameworks. One common example is the integration with data retrieved on-demand from databases using hibernate named queries.

The expression used to define the object source is any expression that follows regular MVEL syntax. Therefore, it allows you to easily use object property navigation, execute method calls and access maps and collections elements.

Here is a simple example of reasoning and binding on another pattern sub-field:

rule "validate zipcode"
when
    Person( $personAddress : address ) 
    Address( zipcode == "23920W") from $personAddress 
then
    // zip code is ok
end

With all the flexibility from the new expressiveness in the Drools engine you can slice and dice this problem many ways. This is the same but shows how you can use a graph notation with the 'from':

rule "validate zipcode"
when
    $p : Person( ) 
    $a : Address( zipcode == "23920W") from $p.address 
then
    // zip code is ok
end

Previous examples were evaluations using a single pattern. The CE from also support object sources that return a collection of objects. In that case, from will iterate over all objects in the collection and try to match each of them individually. For instance, if we want a rule that applies 10% discount to each item in an order, we could do:

rule "apply 10% discount to all items over US$ 100,00 in an order"
when
    $order : Order()
    $item  : OrderItem( value > 100 ) from $order.items
then
    // apply discount to $item
end

The above example will cause the rule to fire once for each item whose value is greater than 100 for each given order.

You must take caution, however, when using from, especially in conjunction with the lock-on-active rule attribute as it may produce unexpected results. Consider the example provided earlier, but now slightly modified as follows:

rule "Assign people in North Carolina (NC) to sales region 1"
ruleflow-group "test"
lock-on-active true
when
    $p : Person( ) 
    $a : Address( state == "NC") from $p.address 
then
    modify ($p) {} // Assign person to sales region 1 in a modify block
end

rule "Apply a discount to people in the city of Raleigh"
ruleflow-group "test"
lock-on-active true
when
    $p : Person( ) 
    $a : Address( city == "Raleigh") from $p.address 
then
    modify ($p) {} // Apply discount to person in a modify block
end

In the above example, persons in Raleigh, NC should be assigned to sales region 1 and receive a discount; i.e., you would expect both rules to activate and fire. Instead you will find that only the second rule fires.

If you were to turn on the audit log, you would also see that when the second rule fires, it deactivates the first rule. Since the rule attribute lock-on-active prevents a rule from creating new activations when a set of facts change, the first rule fails to reactivate. Though the set of facts have not changed, the use of from returns a new fact for all intents and purposes each time it is evaluated.

First, it's important to review why you would use the above pattern. You may have many rules across different rule-flow groups. When rules modify working memory and other rules downstream of your RuleFlow (in different rule-flow groups) need to be reevaluated, the use of modify is critical. You don't, however, want other rules in the same rule-flow group to place activations on one another recursively. In this case, the no-loop attribute is ineffective, as it would only prevent a rule from activating itself recursively. Hence, you resort to lock-on-active.

There are several ways to address this issue:

Avoid the use of from when you can assert all facts into working memory or use nested object references in your constraint expressions (shown below).
Place the variable assigned used in the modify block as the last sentence in your condition (LHS).
Avoid the use of lock-on-active when you can explicitly manage how rules within the same rule-flow group place activations on one another (explained below).

The preferred solution is to minimize use of from when you can assert all your facts into working memory directly. In the example above, both the Person and Address instance can be asserted into working memory. In this case, because the graph is fairly simple, an even easier solution is to modify your rules as follows:

rule "Assign people in North Carolina (NC) to sales region 1"
ruleflow-group "test"
lock-on-active true
when
    $p : Person(address.state == "NC" )  
then
    modify ($p) {} // Assign person to sales region 1 in a modify block
end

rule "Apply a discount to people in the city of Raleigh"
ruleflow-group "test"
lock-on-active true
when
    $p : Person(address.city == "Raleigh" )  
then
    modify ($p) {} //Apply discount to person in a modify block
end

Now, you will find that both rules fire as expected. However, it is not always possible to access nested facts as above. Consider an example where a Person holds one or more Addresses and you wish to use an existential quantifier to match people with at least one address that meets certain conditions. In this case, you would have to resort to the use of from to reason over the collection.

There are several ways to use from to achieve this and not all of them exhibit an issue with the use of lock-on-active. For example, the following use of from causes both rules to fire as expected:

rule "Assign people in North Carolina (NC) to sales region 1"
ruleflow-group "test"
lock-on-active true
when
    $p : Person($addresses : addresses)
    exists (Address(state == "NC") from $addresses)  
then
    modify ($p) {} // Assign person to sales region 1 in a modify block
end

rule "Apply a discount to people in the city of Raleigh"
ruleflow-group "test"
lock-on-active true
when
    $p : Person($addresses : addresses)
    exists (Address(city == "Raleigh") from $addresses)  
then
    modify ($p) {} // Apply discount to person in a modify block
end

However, the following slightly different approach does exhibit the problem:

rule "Assign people in North Carolina (NC) to sales region 1"
ruleflow-group "test"
lock-on-active true
when
    $assessment : Assessment()
    $p : Person()
    $addresses : List() from $p.addresses
    exists (Address( state == "NC") from $addresses) 
then
    modify ($assessment) {} // Modify assessment in a modify block
end

rule "Apply a discount to people in the city of Raleigh"
ruleflow-group "test"
lock-on-active true
when
    $assessment : Assessment()
    $p : Person()
    $addresses : List() from $p.addresses 
    exists (Address( city == "Raleigh") from $addresses)
then
    modify ($assessment) {} // Modify assessment in a modify block
end

In the above example, the $addresses variable is returned from the use of from. The example also introduces a new object, assessment, to highlight one possible solution in this case. If the $assessment variable assigned in the condition (LHS) is moved to the last condition in each rule, both rules fire as expected.

Though the above examples demonstrate how to combine the use of from with lock-on-active where no loss of rule activations occurs, they carry the drawback of placing a dependency on the order of conditions on the LHS. In addition, the solutions present greater complexity for the rule author in terms of keeping track of which conditions may create issues.

A better alternative is to assert more facts into working memory. In this case, a person's addresses may be asserted into working memory and the use of from would not be necessary.

There are cases, however, where asserting all data into working memory is not practical and we need to find other solutions. Another option is to reevaluate the need for lock-on-active. An alternative to lock-on-active is to directly manage how rules within the same rule-flow group activate one another by including conditions in each rule that prevent rules from activating each other recursively when working memory is modified. For example, in the case above where a discount is applied to citizens of Raleigh, a condition may be added to the rule that checks whether the discount has already been applied. If so, the rule does not activate.

7.8.3.7.3. Conditional Element `collect`

Figure 7.26. collect

The Conditional Element collect allows rules to reason over a collection of objects obtained from the given source or from the working memory. In First Oder Logic terms this is the cardinality quantifier. A simple example:

import java.util.ArrayList

rule "Raise priority if system has more than 3 pending alarms"
when
    $system : System()
    $alarms : ArrayList( size >= 3 )
              from collect( Alarm( system == $system, status == 'pending' ) )
then
    // Raise priority, because system $system has
    // 3 or more alarms pending. The pending alarms
    // are $alarms.
end

In the above example, the rule will look for all pending alarms in the working memory for each given system and group them in ArrayLists. If 3 or more alarms are found for a given system, the rule will fire.

The result pattern of collect can be any concrete class that implements the java.util.Collection interface and provides a default no-arg public constructor. This means that you can use Java collections like ArrayList, LinkedList, HashSet, etc., or your own class, as long as it implements the java.util.Collection interface and provide a default no-arg public constructor.

Both source and result patterns can be constrained as any other pattern.

Variables bound before the collect CE are in the scope of both source and result patterns and therefore you can use them to constrain both your source and result patterns. But note that collect is a scope delimiter for bindings, so that any binding made inside of it is not available for use outside of it.

Collect accepts nested from CEs. The following example is a valid use of "collect":

import java.util.LinkedList;

rule "Send a message to all mothers"
when
    $town : Town( name == 'Paris' )
    $mothers : LinkedList() 
               from collect( Person( gender == 'F', children > 0 ) 
                             from $town.getPeople() 
                           )
then
    // send a message to all mothers
end

7.8.3.7.4. Conditional Element `accumulate`

Figure 7.27. accumulate

The Conditional Element accumulate is a more flexible and powerful form of collect, in the sense that it can be used to do what collect does and also achieve results that the CE collect is not capable of achieving. Accumulate allows a rule to iterate over a collection of objects, executing custom actions for each of the elements, and at the end, it returns a result object.

Accumulate supports both the use of pre-defined accumulate functions, or the use of inline custom code. Inline custom code should be avoided though, as it is harder for rule authors to maintain, and frequently leads to code duplication. Accumulate functions are easier to test and reuse.

The Accumulate CE also supports multiple different syntaxes. The preferred syntax is the top level accumulate, as noted bellow, but all other syntaxes are supported for backward compatibility.

7.8.3.7.4.1. Accumulate CE (preferred syntax)

The top level accumulate syntax is the most compact and flexible syntax. The simplified syntax is as follows:

accumulate( <source pattern>; <functions> [;<constraints>] )

For instance, a rule to calculate the minimum, maximum and average temperature reading for a given sensor and that raises an alarm if the minimum temperature is under 20C degrees and the average is over 70C degrees could be written in the following way, using Accumulate:

Note

The DRL language defines "acc" as a synonym of "accumulate". The author might prefer to use "acc" as a less verbose keyword or the full keyword "accumulate" for legibility.

rule "Raise alarm"
when
    $s : Sensor()
    accumulate( Reading( sensor == $s, $temp : temperature );
                $min : min( $temp ),
                $max : max( $temp ),
                $avg : average( $temp );
                $min < 20, $avg > 70 )
then
    // raise the alarm
end

In the above example, min, max and average are Accumulate Functions and will calculate the minimum, maximum and average temperature values over all the readings for each sensor.

Drools ships with several built-in accumulate functions, including:

average
min
max
count
sum
collectList
collectSet

These common functions accept any expression as input. For instance, if someone wants to calculate the average profit on all items of an order, a rule could be written using the average function:

rule "Average profit"
when
    $order : Order()
    accumulate( OrderItem( order == $order, $cost : cost, $price : price );
                $avgProfit : average( 1 - $cost / $price ) )
then
    // average profit for $order is $avgProfit
end

Accumulate Functions are all pluggable. That means that if needed, custom, domain specific functions can easily be added to the engine and rules can start to use them without any restrictions. To implement a new Accumulate Function all one needs to do is to create a Java class that implements the org.drools.core.runtime.rule.TypedAccumulateFunction interface. As an example of an Accumulate Function implementation, the following is the implementation of the average function:

/**

 * An implementation of an accumulator capable of calculating average values

 */

public class AverageAccumulateFunction implements org.drools.core.runtime.rule.TypedAccumulateFunction {


    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.drools.base.accumulators.AccumulateFunction#createContext()

     */

    public Serializable createContext() {

        return new AverageData();

    }


    /* (non-Javadoc)

     * @see org.drools.core.base.accumulators.AccumulateFunction#init(java.lang.Object)

     */

    public void init(Serializable context) throws Exception {

        AverageData data = (AverageData) context;

        data.count = 0;

        data.total = 0;

    }


    /* (non-Javadoc)

     * @see org.drools.core.base.accumulators.AccumulateFunction#accumulate(java.lang.Object, java.lang.Object)

     */

    public void accumulate(Serializable context,

                           Object value) {

        AverageData data = (AverageData) context;

        data.count++;

        data.total += ((Number) value).doubleValue();

    }


    /* (non-Javadoc)

     * @see org.drools.core.base.accumulators.AccumulateFunction#reverse(java.lang.Object, java.lang.Object)

     */

    public void reverse(Serializable context,

                        Object value) throws Exception {

        AverageData data = (AverageData) context;

        data.count--;

        data.total -= ((Number) value).doubleValue();

    }


    /* (non-Javadoc)

     * @see org.drools.core.base.accumulators.AccumulateFunction#getResult(java.lang.Object)

     */

    public Object getResult(Serializable context) throws Exception {

        AverageData data = (AverageData) context;

        return new Double( data.count == 0 ? 0 : data.total / data.count );

    }


    /* (non-Javadoc)

     * @see org.drools.core.base.accumulators.AccumulateFunction#supportsReverse()

     */

    public boolean supportsReverse() {

        return true;

    }


    /**

     * {@inheritDoc}

     */

    public Class< ? > getResultType() {

        return Number.class;

    }


}

The code for the function is very simple, as we could expect, as all the "dirty" integration work is done by the engine. Finally, to use the function in the rules, the author can import it using the "import accumulate" statement:

import accumulate <class_name> <function_name>

For instance, if one implements the class some.package.VarianceFunction function that implements the variance function and wants to use it in the rules, he would do the following:

Example 7.77. Example of importing and using the custom "variance" accumulate function

import accumulate some.package.VarianceFunction variance

rule "Calculate Variance"
when
    accumulate( Test( $s : score ), $v : variance( $s ) )
then
    // the variance of the test scores is $v
end

Note

The built in functions (sum, average, etc) are imported automatically by the engine. Only user-defined custom accumulate functions need to be explicitly imported.

Note

For backward compatibility, Drools still supports the configuration of accumulate functions through configuration files and system properties, but this is a deprecated method. In order to configure the variance function from the previous example using the configuration file or system property, the user would set a property like this:

drools.accumulate.function.variance = some.package.VarianceFunction

Please note that "drools.accumulate.function." is a prefix that must always be used, "variance" is how the function will be used in the drl files, and "some.package.VarianceFunction" is the fully qualified name of the class that implements the function behavior.

7.8.3.7.4.2. Alternate Syntax: single function with return type

The accumulate syntax evolved over time with the goal of becoming more compact and expressive. Nevertheless, Drools still supports previous syntaxes for backward compatibility purposes.

In case the rule is using a single accumulate function on a given accumulate, the author may add a pattern for the result object and use the "from" keyword to link it to the accumulate result. Example: a rule to apply a 10% discount on orders over $100 could be written in the following way:

rule "Apply 10% discount to orders over US$ 100,00"
when
    $order : Order()
    $total : Number( doubleValue > 100 ) 
             from accumulate( OrderItem( order == $order, $value : value ),
                              sum( $value ) )
then
    # apply discount to $order
end

In the above example, the accumulate element is using only one function (sum), and so, the rules author opted to explicitly write a pattern for the result type of the accumulate function (Number) and write the constraints inside it. There are no problems in using this syntax over the compact syntax presented before, except that is is a bit more verbose. Also note that it is not allowed to use both the return type and the functions binding in the same accumulate statement.

7.8.3.7.4.3. Accumulate with inline custom code

Warning

The use of accumulate with inline custom code is not a good practice for several reasons, including difficulties on maintaining and testing rules that use them, as well as the inability of reusing that code. Implementing your own accumulate functions is very simple and straightforward, they are easy to unit test and to use. This form of accumulate is supported for backward compatibility only.

Another possible syntax for the accumulate is to define inline custom code, instead of using accumulate functions. As noted on the previous warned, this is discouraged though for the stated reasons.

The general syntax of the accumulate CE with inline custom code is:

<result pattern> from accumulate( <source pattern>,
                                  init( <init code> ),
                                  action( <action code> ),
                                  reverse( <reverse code> ),
                                  result( <result expression> ) )

The meaning of each of the elements is the following:

<source pattern>: the source pattern is a regular pattern that the engine will try to match against each of the source objects.
<init code>: this is a semantic block of code in the selected dialect that will be executed once for each tuple, before iterating over the source objects.
<action code>: this is a semantic block of code in the selected dialect that will be executed for each of the source objects.
<reverse code>: this is an optional semantic block of code in the selected dialect that if present will be executed for each source object that no longer matches the source pattern. The objective of this code block is to undo any calculation done in the <action code> block, so that the engine can do decremental calculation when a source object is modified or deleted, hugely improving performance of these operations.
<result expression>: this is a semantic expression in the selected dialect that is executed after all source objects are iterated.
<result pattern>: this is a regular pattern that the engine tries to match against the object returned from the <result expression>. If it matches, the accumulate conditional element evaluates to true and the engine proceeds with the evaluation of the next CE in the rule. If it does not matches, the accumulate CE evaluates to false and the engine stops evaluating CEs for that rule.

It is easier to understand if we look at an example:

rule "Apply 10% discount to orders over US$ 100,00"
when
    $order : Order()
    $total : Number( doubleValue > 100 ) 
             from accumulate( OrderItem( order == $order, $value : value ),
                              init( double total = 0; ),
                              action( total += $value; ),
                              reverse( total -= $value; ),
                              result( total ) )
then
    # apply discount to $order
end

In the above example, for each Order in the Working Memory, the engine will execute the init code initializing the total variable to zero. Then it will iterate over all OrderItem objects for that order, executing the action for each one (in the example, it will sum the value of all items into the total variable). After iterating over all OrderItem objects, it will return the value corresponding to the result expression (in the above example, the value of variable total). Finally, the engine will try to match the result with the Number pattern, and if the double value is greater than 100, the rule will fire.

The example used Java as the semantic dialect, and as such, note that the usage of the semicolon as statement delimiter is mandatory in the init, action and reverse code blocks. The result is an expression and, as such, it does not admit ';'. If the user uses any other dialect, he must comply to that dialect's specific syntax.

As mentioned before, the reverse code is optional, but it is strongly recommended that the user writes it in order to benefit from the improved performance on update and delete.

The accumulate CE can be used to execute any action on source objects. The following example instantiates and populates a custom object:

rule "Accumulate using custom objects"
when
    $person   : Person( $likes : likes )
    $cheesery : Cheesery( totalAmount > 100 )
                from accumulate( $cheese : Cheese( type == $likes ),
                                 init( Cheesery cheesery = new Cheesery(); ),
                                 action( cheesery.addCheese( $cheese ); ),
                                 reverse( cheesery.removeCheese( $cheese ); ),
                                 result( cheesery ) );
then
    // do something
end

7.8.3.8. Conditional Element `eval`

Figure 7.28. eval

The conditional element eval is essentially a catch-all which allows any semantic code (that returns a primitive boolean) to be executed. This code can refer to variables that were bound in the LHS of the rule, and functions in the rule package. Overuse of eval reduces the declarativeness of your rules and can result in a poorly performing engine. While eval can be used anywhere in the patterns, the best practice is to add it as the last conditional element in the LHS of a rule.

Evals cannot be indexed and thus are not as efficient as Field Constraints. However this makes them ideal for being used when functions return values that change over time, which is not allowed within Field Constraints.

For folks who are familiar with Drools 2.x lineage, the old Drools parameter and condition tags are equivalent to binding a variable to an appropriate type, and then using it in an eval node.

p1 : Parameter()
p2 : Parameter()
eval( p1.getList().containsKey( p2.getItem() ) )

p1 : Parameter()
p2 : Parameter()
// call function isValid in the LHS
eval( isValid( p1, p2 ) )

7.8.3.9. Railroad diagrams

7.8.4. The Right Hand Side (then)

7.8.4.1. Usage

The Right Hand Side (RHS) is a common name for the consequence or action part of the rule; this part should contain a list of actions to be executed. It is bad practice to use imperative or conditional code in the RHS of a rule; as a rule should be atomic in nature - "when this, then do this", not "when this, maybe do this". The RHS part of a rule should also be kept small, thus keeping it declarative and readable. If you find you need imperative and/or conditional code in the RHS, then maybe you should be breaking that rule down into multiple rules. The main purpose of the RHS is to insert, delete or modify working memory data. To assist with that there are a few convenience methods you can use to modify working memory; without having to first reference a working memory instance.

update(object, handle); will tell the engine that an object has changed (one that has been bound to something on the LHS) and rules may need to be reconsidered.

update(object); can also be used; here the Knowledge Helper will look up the facthandle for you, via an identity check, for the passed object. (Note that if you provide Property Change Listeners to your Java beans that you are inserting into the engine, you can avoid the need to call update() when the object changes.). After a fact's field values have changed you must call update before changing another fact, or you will cause problems with the indexing within the rule engine. The modify keyword avoids this problem.

insert(new Something()); will place a new object of your creation into the Working Memory.

insertLogical(new Something()); is similar to insert, but the object will be automatically deleted when there are no more facts to support the truth of the currently firing rule.

delete(handle); removes an object from Working Memory.

These convenience methods are basically macros that provide short cuts to the KnowledgeHelper instance that lets you access your Working Memory from rules files. The predefined variable drools of type KnowledgeHelper lets you call several other useful methods. (Refer to the KnowledgeHelper interface documentation for more advanced operations).

The call drools.halt() terminates rule execution immediately. This is required for returning control to the point whence the current session was put to work with fireUntilHalt().
Methods insert(Object o), update(Object o) and delete(Object o) can be called on drools as well, but due to their frequent use they can be called without the object reference.
drools.getWorkingMemory() returns the WorkingMemory object.
drools.setFocus( String s) sets the focus to the specified agenda group.
drools.getRule().getName(), called from a rule's RHS, returns the name of the rule.
drools.getTuple() returns the Tuple that matches the currently executing rule, and drools.getActivation() delivers the corresponding Activation. (These calls are useful for logging and debugging purposes.)

The full Knowledge Runtime API is exposed through another predefined variable, kcontext, of type KieContext. Its method getKieRuntime() delivers an object of type KieRuntime, which, in turn, provides access to a wealth of methods, many of which are quite useful for coding RHS logic.

The call kcontext.getKieRuntime().halt() terminates rule execution immediately.
The accessor getAgenda() returns a reference to this session's Agenda, which in turn provides access to the various rule groups: activation groups, agenda groups, and rule flow groups. A fairly common paradigm is the activation of some agenda group, which could be done with the lengthy call:
```
// give focus to the agenda group CleanUp

kcontext.getKieRuntime().getAgenda().getAgendaGroup( "CleanUp" ).setFocus();
```
(You can achieve the same using drools.setFocus( "CleanUp" ).)
To run a query, you call getQueryResults(String query), whereupon you may process the results, as explained in section Query.
A set of methods dealing with event management lets you, among other things, add and remove event listeners for the Working Memory and the Agenda.
Method getKieBase() returns the KieBase object, the backbone of all the Knowledge in your system, and the originator of the current session.
You can manage globals with setGlobal(...), getGlobal(...) and getGlobals().
Method getEnvironment() returns the runtime's Environment which works much like what you know as your operating system's environment.

7.8.4.2. The `modify` Statement

This language extension provides a structured approach to fact updates. It combines the update operation with a number of setter calls to change the object's fields. This is the syntax schema for the modify statement:

modify ( <fact-expression> ) {
    <expression> [ , <expression> ]*
}

The parenthesized <fact-expression> must yield a fact object reference. The expression list in the block should consist of setter calls for the given object, to be written without the usual object reference, which is automatically prepended by the compiler.

The example illustrates a simple fact modification.

Example 7.78. A modify statement

rule "modify stilton"
when
    $stilton : Cheese(type == "stilton")
then
    modify( $stilton ){
        setPrice( 20 ),
        setAge( "overripe" )
    }
end

The advantages in using the modify statment are particularly clear when used in conjuction with fine grained property change listeners. See the corresponding section for more details.

7.8.5. Conditional named consequences

Sometimes the constraint of having one single consequence for each rule can be somewhat limiting and leads to verbose and difficult to be maintained repetitions like in the following example:

rule "Give 10% discount to customers older than 60"
when
    $customer : Customer( age > 60 )
then
    modify($customer) { setDiscount( 0.1 ) };
end

rule "Give free parking to customers older than 60"
when
    $customer : Customer( age > 60 )
    $car : Car ( owner == $customer )
then
    modify($car) { setFreeParking( true ) };
end

It is already possible to partially overcome this problem by making the second rule extending the first one like in:

rule "Give 10% discount to customers older than 60"
when
    $customer : Customer( age > 60 )
then
    modify($customer) { setDiscount( 0.1 ) };
end

rule "Give free parking to customers older than 60"
    extends "Give 10% discount to customers older than 60"
when
    $car : Car ( owner == $customer )
then
    modify($car) { setFreeParking( true ) };
end

Anyway this feature makes it possible to define more labelled consequences other than the default one in a single rule, so, for example, the 2 former rules can be compacted in only one like it follows:

rule "Give 10% discount and free parking to customers older than 60"
when
    $customer : Customer( age > 60 )
    do[giveDiscount]
    $car : Car ( owner == $customer )
then
    modify($car) { setFreeParking( true ) };
then[giveDiscount]
    modify($customer) { setDiscount( 0.1 ) };
end

This last rule has 2 consequences, the usual default one, plus another one named "giveDiscount" that is activated, using the keyword do, as soon as a customer older than 60 is found in the knowledge base, regardless of the fact that he owns a car or not. The activation of a named consequence can be also guarded by an additional condition like in this further example:

rule "Give free parking to customers older than 60 and 10% discount to golden ones among them"
when
    $customer : Customer( age > 60 )
    if ( type == "Golden" ) do[giveDiscount]
    $car : Car ( owner == $customer )
then
    modify($car) { setFreeParking( true ) };
then[giveDiscount]
    modify($customer) { setDiscount( 0.1 ) };
end

The condition in the if statement is always evaluated on the pattern immediately preceding it. In the end this last, a bit more complicated, example shows how it is possible to switch over different conditions using a nested if/else statement:

rule "Give free parking and 10% discount to over 60 Golden customer and 5% to Silver ones"
when
    $customer : Customer( age > 60 )
    if ( type == "Golden" ) do[giveDiscount10]
    else if ( type == "Silver" ) break[giveDiscount5]
    $car : Car ( owner == $customer )
then
    modify($car) { setFreeParking( true ) };
then[giveDiscount10]
    modify($customer) { setDiscount( 0.1 ) };
then[giveDiscount5]
    modify($customer) { setDiscount( 0.05 ) };
end

Here the purpose is to give a 10% discount AND a free parking to Golden customers over 60, but only a 5% discount (without free parking) to the Silver ones. This result is achieved by activating the consequence named "giveDiscount5" using the keyword break instead of do. In fact do just schedules a consequence in the agenda, allowing the remaining part of the LHS to continue of being evaluated as per normal, while break also blocks any further pattern matching evaluation. Note, of course, that the activation of a named consequence not guarded by any condition with break doesn't make sense (and generates a compile time error) since otherwise the LHS part following it would be never reachable.

7.8.6. A Note on Auto-boxing and Primitive Types

Drools attempts to preserve numbers in their primitive or object wrapper form, so a variable bound to an int primitive when used in a code block or expression will no longer need manual unboxing; unlike Drools 3.0 where all primitives were autoboxed, requiring manual unboxing. A variable bound to an object wrapper will remain as an object; the existing JDK 1.5 and JDK 5 rules to handle auto-boxing and unboxing apply in this case. When evaluating field constraints, the system attempts to coerce one of the values into a comparable format; so a primitive is comparable to an object wrapper.

7.9. Query

Figure 7.29. query

A query is a simple way to search the working memory for facts that match the stated conditions. Therefore, it contains only the structure of the LHS of a rule, so that you specify neither "when" nor "then". A query has an optional set of parameters, each of which can be optionally typed. If the type is not given, the type Object is assumed. The engine will attempt to coerce the values as needed. Query names are global to the KieBase; so do not add queries of the same name to different packages for the same RuleBase.

To return the results use ksession.getQueryResults("name"), where "name" is the query's name. This returns a list of query results, which allow you to retrieve the objects that matched the query.

The first example presents a simple query for all the people over the age of 30. The second one, using parameters, combines the age limit with a location.

Example 7.79. Query People over the age of 30

query "people over the age of 30" 
    person : Person( age > 30 )
end

Example 7.80. Query People over the age of x, and who live in y

query "people over the age of x"  (int x, String y)
    person : Person( age > x, location == y )
end

We iterate over the returned QueryResults using a standard "for" loop. Each element is a QueryResultsRow which we can use to access each of the columns in the tuple. These columns can be accessed by bound declaration name or index position.

Example 7.81. Query People over the age of 30

QueryResults results = ksession.getQueryResults( "people over the age of 30" );
System.out.println( "we have " + results.size() + " people over the age  of 30" );

System.out.println( "These people are are over 30:" );

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

Support for positional syntax has been added for more compact code. By default the declared type order in the type declaration matches the argument position. But it possible to override these using the @position annotation. This allows patterns to be used with positional arguments, instead of the more verbose named arguments.

declare Cheese
    name : String @position(1)
    shop : String @position(2)
    price : int @position(0)
end

The @Position annotation, in the org.drools.definition.type package, can be used to annotate original pojos on the classpath. Currently only fields on classes can be annotated. Inheritance of classes is supported, but not interfaces or methods. The isContainedIn query below demonstrates the use of positional arguments in a pattern; Location(x, y;) instead of Location( thing == x, location == y).

Queries can now call other queries, this combined with optional query arguments provides derivation query style backward chaining. Positional and named syntax is supported for arguments. It is also possible to mix both positional and named, but positional must come first, separated by a semi colon. Literal expressions can be passed as query arguments, but at this stage you cannot mix expressions with variables. Here is an example of a query that calls another query. Note that 'z' here will always be an 'out' variable. The '?' symbol means the query is pull only, once the results are returned you will not receive further results as the underlying data changes.

declare Location
    thing : String 
    location : String 
end

query isContainedIn( String x, String y ) 
    Location(x, y;)
    or 
    ( Location(z, y;) and ?isContainedIn(x, z;) )
end

As previously mentioned you can use live "open" queries to reactively receive changes over time from the query results, as the underlying data it queries against changes. Notice the "look" rule calls the query without using '?'.

query isContainedIn( String x, String y ) 
    Location(x, y;)
    or 
    ( Location(z, y;) and isContainedIn(x, z;) )
end

rule look when 
    Person( $l : likes ) 
    isContainedIn( $l, 'office'; )
then
   insertLogical( $l 'is in the office' );
end

Drools supports unification for derivation queries, in short this means that arguments are optional. It is possible to call queries from Java leaving arguments unspecified using the static field org.drools.core.runtime.rule.Variable.v - note you must use 'v' and not an alternative instance of Variable. These are referred to as 'out' arguments. Note that the query itself does not declare at compile time whether an argument is in or an out, this can be defined purely at runtime on each use. The following example will return all objects contained in the office.

results = ksession.getQueryResults( "isContainedIn", new Object[] {  Variable.v, "office" } );
l = new ArrayList<List<String>>();
for ( QueryResultsRow r : results ) {
    l.add( Arrays.asList( new String[] { (String) r.get( "x" ), (String) r.get( "y" ) } ) );
}

The algorithm uses stacks to handle recursion, so the method stack will not blow up.

The following is not yet supported:

List and Map unification
Variables for the fields of facts
Expression unification - pred( X, X + 1, X * Y / 7 )

7.10. Domain Specific Languages

Domain Specific Languages (or DSLs) are a way of creating a rule language that is dedicated to your problem domain. A set of DSL definitions consists of transformations from DSL "sentences" to DRL constructs, which lets you use of all the underlying rule language and engine features. Given a DSL, you write rules in DSL rule (or DSLR) files, which will be translated into DRL files.

DSL and DSLR files are plain text files, and you can use any text editor to create and modify them. But there are also DSL and DSLR editors, both in the IDE as well as in the web based BRMS, and you can use those as well, although they may not provide you with the full DSL functionality.

7.10.1. When to Use a DSL

DSLs can serve as a layer of separation between rule authoring (and rule authors) and the technical intricacies resulting from the modelling of domain object and the rule engine's native language and methods. If your rules need to be read and validated by domain experts (such as business analysts, for instance) who are not programmers, you should consider using a DSL; it hides implementation details and focuses on the rule logic proper. DSL sentences can also act as "templates" for conditional elements and consequence actions that are used repeatedly in your rules, possibly with minor variations. You may define DSL sentences as being mapped to these repeated phrases, with parameters providing a means for accommodating those variations.

DSLs have no impact on the rule engine at runtime, they are just a compile time feature, requiring a special parser and transformer.

7.10.2. DSL Basics

The Drools DSL mechanism allows you to customise conditional expressions and consequence actions. A global substitution mechanism ("keyword") is also available.

Example 7.82. Example DSL mapping

[when]Something is {colour}=Something(colour=="{colour}")

In the preceding example, [when] indicates the scope of the expression, i.e., whether it is valid for the LHS or the RHS of a rule. The part after the bracketed keyword is the expression that you use in the rule; typically a natural language expression, but it doesn't have to be. The part to the right of the equal sign ("=") is the mapping of the expression into the rule language. The form of this string depends on its destination, RHS or LHS. If it is for the LHS, then it ought to be a term according to the regular LHS syntax; if it is for the RHS then it might be a Java statement.

Whenever the DSL parser matches a line from the rule file written in the DSL with an expression in the DSL definition, it performs three steps of string manipulation. First, it extracts the string values appearing where the expression contains variable names in braces (here: {colour}). Then, the values obtained from these captures are then interpolated wherever that name, again enclosed in braces, occurs on the right hand side of the mapping. Finally, the interpolated string replaces whatever was matched by the entire expression in the line of the DSL rule file.

Note that the expressions (i.e., the strings on the left hand side of the equal sign) are used as regular expressions in a pattern matching operation against a line of the DSL rule file, matching all or part of a line. This means you can use (for instance) a '?' to indicate that the preceding character is optional. One good reason to use this is to overcome variations in natural language phrases of your DSL. But, given that these expressions are regular expression patterns, this also means that all "magic" characters of Java's pattern syntax have to be escaped with a preceding backslash ('\').

It is important to note that the compiler transforms DSL rule files line by line. In the above example, all the text after "Something is " to the end of the line is captured as the replacement value for "{colour}", and this is used for interpolating the target string. This may not be exactly what you want. For instance, when you intend to merge different DSL expressions to generate a composite DRL pattern, you need to transform a DSLR line in several independent operations. The best way to achieve this is to ensure that the captures are surrounded by characteristic text - words or even single characters. As a result, the matching operation done by the parser plucks out a substring from somewhere within the line. In the example below, quotes are used as distinctive characters. Note that the characters that surround the capture are not included during interpolation, just the contents between them.

As a rule of thumb, use quotes for textual data that a rule editor may want to enter. You can also enclose the capture with words to ensure that the text is correctly matched. Both is illustrated by the following example. Note that a single line such as Something is "green" and another solid thing is now correctly expanded.

Example 7.83. Example with quotes

[when]something is "{colour}"=Something(colour=="{colour}")
[when]another {state} thing=OtherThing(state=="{state}"

It is a good idea to avoid punctuation (other than quotes or apostrophes) in your DSL expressions as much as possible. The main reason is that punctuation is easy to forget for rule authors using your DSL. Another reason is that parentheses, the period and the question mark are magic characters, requiring escaping in the DSL definition.

In a DSL mapping, the braces "{" and "}" should only be used to enclose a variable definition or reference, resulting in a capture. If they should occur literally, either in the expression or within the replacement text on the right hand side, they must be escaped with a preceding backslash ("\"):

[then]do something= if (foo) \{ doSomething(); \}

Note

If braces "{" and "}" should appear in the replacement string of a DSL definition, escape them with a backslash ('\').

Example 7.84. Examples of DSL mapping entries

# This is a comment to be ignored.
[when]There is a person with name of "{name}"=Person(name=="{name}")
[when]Person is at least {age} years old and lives in "{location}"=
      Person(age >= {age}, location=="{location}")
[then]Log "{message}"=System.out.println("{message}");
[when]And = and

Given the above DSL examples, the following examples show the expansion of various DSLR snippets:

Example 7.85. Examples of DSL expansions

There is a person with name of "Kitty"
   ==> Person(name="Kitty")
Person is at least 42 years old and lives in "Atlanta"
   ==> Person(age >= 42, location="Atlanta")
Log "boo"
   ==> System.out.println("boo");
There is a person with name of "Bob" and Person is at least 30 years old and lives in "Utah"
   ==> Person(name="Bob") and Person(age >= 30, location="Utah")

Note

Don't forget that if you are capturing plain text from a DSL rule line and want to use it as a string literal in the expansion, you must provide the quotes on the right hand side of the mapping.

You can chain DSL expressions together on one line, as long as it is clear to the parser where one ends and the next one begins and where the text representing a parameter ends. (Otherwise you risk getting all the text until the end of the line as a parameter value.) The DSL expressions are tried, one after the other, according to their order in the DSL definition file. After any match, all remaining DSL expressions are investigated, too.

The resulting DRL text may consist of more than one line. Line ends are in the replacement text are written as \n.

7.10.3. Adding Constraints to Facts

A common requirement when writing rule conditions is to be able to add an arbitrary combination of constraints to a pattern. Given that a fact type may have many fields, having to provide an individual DSL statement for each combination would be plain folly.

The DSL facility allows you to add constraints to a pattern by a simple convention: if your DSL expression starts with a hyphen (minus character, "-") it is assumed to be a field constraint and, consequently, is is added to the last pattern line preceding it.

For an example, lets take look at class Cheese, with the following fields: type, price, age and country. We can express some LHS condition in normal DRL like the following

Cheese(age < 5, price == 20, type=="stilton", country=="ch")

The DSL definitions given below result in three DSL phrases which may be used to create any combination of constraint involving these fields.

[when]There is a Cheese with=Cheese()
[when]- age is less than {age}=age<{age}
[when]- type is '{type}'=type=='{type}'
[when]- country equal to '{country}'=country=='{country}'

You can then write rules with conditions like the following:

There is a Cheese with
        - age is less than 42
        - type is 'stilton'

The parser will pick up a line beginning with "-" and add it as a constraint to the preceding pattern, inserting a comma when it is required. For the preceding example, the resulting DRL is:

Cheese(age<42, type=='stilton')

Combining all all numeric fields with all relational operators (according to the DSL expression "age is less than..." in the preceding example) produces an unwieldy amount of DSL entries. But you can define DSL phrases for the various operators and even a generic expression that handles any field constraint, as shown below. (Notice that the expression definition contains a regular expression in addition to the variable name.)

[when][]is less than or equal to=<=
[when][]is less than=<
[when][]is greater than or equal to=>=
[when][]is greater than=>
[when][]is equal to===
[when][]equals===
[when][]There is a Cheese with=Cheese()
[when][]- {field:\w*} {operator} {value:\d*}={field} {operator} {value}

Given these DSL definitions, you can write rules with conditions such as:

There is a Cheese with
   - age is less than 42
   - rating is greater than 50
   - type equals 'stilton'

In this specific case, a phrase such as "is less than" is replaced by <, and then the line matches the last DSL entry. This removes the hyphen, but the final result is still added as a constraint to the preceding pattern. After processing all of the lines, the resulting DRL text is:

Cheese(age<42, rating > 50, type=='stilton')

Note

The order of the entries in the DSL is important if separate DSL expressions are intended to match the same line, one after the other.

7.10.4. Developing a DSL

A good way to get started is to write representative samples of the rules your application requires, and to test them as you develop. This will provide you with a stable framework of conditional elements and their constraints. Rules, both in DRL and in DSLR, refer to entities according to the data model representing the application data that should be subject to the reasoning process defined in rules. Notice that writing rules is generally easier if most of the data model's types are facts.

Given an initial set of rules, it should be possible to identify recurring or similar code snippets and to mark variable parts as parameters. This provides reliable leads as to what might be a handy DSL entry. Also, make sure you have a full grasp of the jargon the domain experts are using, and base your DSL phrases on this vocabulary.

You may postpone implementation decisions concerning conditions and actions during this first design phase by leaving certain conditional elements and actions in their DRL form by prefixing a line with a greater sign (">"). (This is also handy for inserting debugging statements.)

During the next development phase, you should find that the DSL configuration stabilizes pretty quickly. New rules can be written by reusing the existing DSL definitions, or by adding a parameter to an existing condition or consequence entry.

Try to keep the number of DSL entries small. Using parameters lets you apply the same DSL sentence for similar rule patterns or constraints. But do not exaggerate: authors using the DSL should still be able to identify DSL phrases by some fixed text.

7.10.5. DSL and DSLR Reference

A DSL file is a text file in a line-oriented format. Its entries are used for transforming a DSLR file into a file according to DRL syntax.

A line starting with "#" or "//" (with or without preceding white space) is treated as a comment. A comment line starting with "#/" is scanned for words requesting a debug option, see below.
Any line starting with an opening bracket ("[") is assumed to be the first line of a DSL entry definition.
Any other line is appended to the preceding DSL entry definition, with the line end replaced by a space.

A DSL entry consists of the following four parts:

A scope definition, written as one of the keywords "when" or "condition", "then" or "consequence", "*" and "keyword", enclosed in brackets ("[" and "]"). This indicates whether the DSL entry is valid for the condition or the consequence of a rule, or both. A scope indication of "keyword" means that the entry has global significance, i.e., it is recognized anywhere in a DSLR file.
A type definition, written as a Java class name, enclosed in brackets. This part is optional unless the the next part begins with an opening bracket. An empty pair of brackets is valid, too.
A DSL expression consists of a (Java) regular expression, with any number of embedded variable definitions, terminated by an equal sign ("="). A variable definition is enclosed in braces ("{" and "}"). It consists of a variable name and two optional attachments, separated by colons (":"). If there is one attachment, it is a regular expression for matching text that is to be assigned to the variable; if there are two attachments, the first one is a hint for the GUI editor and the second one the regular expression.
Note that all characters that are "magic" in regular expressions must be escaped with a preceding backslash ("\") if they should occur literally within the expression.
The remaining part of the line after the delimiting equal sign is the replacement text for any DSLR text matching the regular expression. It may contain variable references, i.e., a variable name enclosed in braces. Optionally, the variable name may be followed by an exclamation mark ("!") and a transformation function, see below.
Note that braces ("{" and "}") must be escaped with a preceding backslash ("\") if they should occur literally within the replacement string.

Debugging of DSL expansion can be turned on, selectively, by using a comment line starting with "#/" which may contain one or more words from the table presented below. The resulting output is written to standard output.

Table 7.2. Debug options for DSL expansion

Word	Description
result	Prints the resulting DRL text, with line numbers.
steps	Prints each expansion step of condition and consequence lines.
keyword	Dumps the internal representation of all DSL entries with scope "keyword".
when	Dumps the internal representation of all DSL entries with scope "when" or "*".
then	Dumps the internal representation of all DSL entries with scope "then" or "*".
usage	Displays a usage statistic of all DSL entries.

Below are some sample DSL definitions, with comments describing the language features they illustrate.

# Comment: DSL examples

#/ debug: display result and usage

# keyword definition: replaces "regula" by "rule"
[keyword][]regula=rule

# conditional element: "T" or "t", "a" or "an", convert matched word
[when][][Tt]here is an? {entity:\w+}=
        ${entity!lc}: {entity!ucfirst} ()

# consequence statement: convert matched word, literal braces
[then][]update {entity:\w+}=modify( ${entity!lc} )\{ \}

The transformation of a DSLR file proceeds as follows:

The text is read into memory.
Each of the "keyword" entries is applied to the entire text. First, the regular expression from the keyword definition is modified by replacing white space sequences with a pattern matching any number of white space characters, and by replacing variable definitions with a capture made from the regular expression provided with the definition, or with the default (".*?"). Then, the DSLR text is searched exhaustively for occurrences of strings matching the modified regular expression. Substrings of a matching string corresponding to variable captures are extracted and replace variable references in the corresponding replacement text, and this text replaces the matching string in the DSLR text.
Sections of the DSLR text between "when" and "then", and "then" and "end", respectively, are located and processed in a uniform manner, line by line, as described below.
For a line, each DSL entry pertaining to the line's section is taken in turn, in the order it appears in the DSL file. Its regular expression part is modified: white space is replaced by a pattern matching any number of white space characters; variable definitions with a regular expression are replaced by a capture with this regular expression, its default being ".*?". If the resulting regular expression matches all or part of the line, the matched part is replaced by the suitably modified replacement text.
Modification of the replacement text is done by replacing variable references with the text corresponding to the regular expression capture. This text may be modified according to the string transformation function given in the variable reference; see below for details.
If there is a variable reference naming a variable that is not defined in the same entry, the expander substitutes a value bound to a variable of that name, provided it was defined in one of the preceding lines of the current rule.
If a DSLR line in a condition is written with a leading hyphen, the expanded result is inserted into the last line, which should contain a pattern CE, i.e., a type name followed by a pair of parentheses. if this pair is empty, the expanded line (which should contain a valid constraint) is simply inserted, otherwise a comma (",") is inserted beforehand.
If a DSLR line in a consequence is written with a leading hyphen, the expanded result is inserted into the last line, which should contain a "modify" statement, ending in a pair of braces ("{" and "}"). If this pair is empty, the expanded line (which should contain a valid method call) is simply inserted, otherwise a comma (",") is inserted beforehand.

Note

It is currently not possible to use a line with a leading hyphen to insert text into other conditional element forms (e.g., "accumulate") or it may only work for the first insertion (e.g., "eval").

All string transformation functions are described in the following table.

Table 7.3. String transformation functions

Name	Description
uc	Converts all letters to upper case.
lc	Converts all letters to lower case.
ucfirst	Converts the first letter to upper case, and all other letters to lower case.
num	Extracts all digits and "-" from the string. If the last two digits in the original string are preceded by "." or ",", a decimal period is inserted in the corresponding position.
a?b/c	Compares the string with string a, and if they are equal, replaces it with b, otherwise with c. But c can be another triplet a, b, c, so that the entire structure is, in fact, a translation table.

The following DSL examples show how to use string transformation functions.

# definitions for conditions
[when][]There is an? {entity}=${entity!lc}: {entity!ucfirst}()
[when][]- with an? {attr} greater than {amount}={attr} <= {amount!num}
[when][]- with a {what} {attr}={attr} {what!positive?>0/negative?%lt;0/zero?==0/ERROR}

A file containing a DSL definition has to be put under the resources folder or any of its subfolders like any other drools artifact. It must have the extension .dsl, or alternatively be marked with type ResourceType.DSL. when programmatically added to a KieFileSystem. For a file using DSL definition, the extension .dslr should be used, while it can be added to a KieFileSystem with type ResourceType.DSLR.

For parsing and expanding a DSLR file the DSL configuration is read and supplied to the parser. Thus, the parser can "recognize" the DSL expressions and transform them into native rule language expressions.

Chapter 8. Complex Event Processing

8.1. Complex Event Processing

8.2. Drools Fusion

8.3. Event Semantics

8.4. Event Processing Modes

8.4.1. Cloud Mode
8.4.2. Stream Mode

8.5. Session Clock

8.5.1. Available Clock Implementations

8.6. Sliding Windows

8.6.1. Sliding Time Windows
8.6.2. Sliding Length Windows

8.7. Streams Support

8.7.1. Declaring and Using Entry Points

8.8. Memory Management for Events

8.8.1. Explicit expiration offset
8.8.2. Inferred expiration offset

8.9. Temporal Reasoning

8.9.1. Temporal Operators

8.1. Complex Event Processing

There is no broadly accepted definition on the term Complex Event Processing. The term Event by itself is frequently overloaded and used to refer to several different things, depending on the context it is used. Defining terms is not the goal of this guide and as so, lets adopt a loose definition that, although not formal, will allow us to proceed with a common understanding.

So, in the scope of this guide:

Important

Event, is a record of a significant change of state in the application domain at a given point in time.

For instance, on a Stock Broker application, when a sale operation is executed, it causes a change of state in the domain. This change of state can be observed on several entities in the domain, like the price of the securities that changed to match the value of the operation, the ownership of the traded assets that changed from the seller to the buyer, the balance of the accounts from both seller and buyer that are credited and debited, etc. Depending on how the domain is modelled, this change of state may be represented by a single event, multiple atomic events or even hierarchies of correlated events. In any case, in the context of this guide, Event is the record of the change of a particular piece of data in the domain.

Events are processed by computer systems since they were invented, and throughout the history, systems responsible for that were given different names and different methodologies were employed. It wasn't until the 90's though, that a more focused work started on EDA (Event Driven Architecture) with a more formal definition on the requirements and goals for event processing. Old messaging systems started to change to address such requirements and new systems started to be developed with the single purpose of event processing. Two trends were born under the names of Event Stream Processing and Complex Event Processing.

In the very beginnings, Event Stream Processing was focused on the capabilities of processing streams of events in (near) real time, while the main focus of Complex Event Processing was on the correlation and composition of atomic events into complex (compound) events. An important (maybe the most important) milestone was the publishing of Dr. David Luckham's book "The Power of Events" in 2002. In the book, Dr Luckham introduces the concept of Complex Event Processing and how it can be used to enhance systems that deal with events. Over the years, both trends converged to a common understanding and today these systems are all referred to as CEP systems.

This is a very simplistic explanation to a really complex and fertile field of research, but sets a high level and common understanding of the concepts that this guide will introduce.

The current understanding of what Complex Event Processing is may be briefly described as the following quote from Wikipedia:

Important

	"Complex Event Processing, or CEP, is primarily an event processing concept that deals with the task of processing multiple events with the goal of identifying the meaningful events within the event cloud. CEP employs techniques such as detection of complex patterns of many events, event correlation and abstraction, event hierarchies, and relationships between events such as causality, membership, and timing, and event-driven processes."
	--Wikipedia

In other words, CEP is about detecting and selecting the interesting events (and only them) from an event cloud, finding their relationships and inferring new data from them and their relationships.

Note

For the remaining of this guide, we will use the terms Complex Event Processing and CEP as a broad reference for any of the related technologies and techniques, including but not limited to, CEP, Complex Event Processing, ESP, Event Stream Processing and Event Processing in general.

8.2. Drools Fusion

Event Processing use cases, in general, share several requirements and goals with Business Rules use cases. These overlaps happen both on the business side and on the technical side.

On the Business side:

Business rules are frequently defined based on the occurrence of scenarios triggered by events. Examples could be:
- On an algorithmic trading application: take an action if the security price increases X% compared to the day opening price, where the price increases are usually denoted by events on a Stock Trade application.
- On a monitoring application: take an action if the temperature on the server room increases X degrees in Y minutes, where sensor readings are usually denoted by events.
Both business rules and event processing queries change frequently and require immediate response for the business to adapt itself to new market conditions, new regulations and new enterprise policies.

From a technical perspective:

Both require seamless integration with the enterprise infrastructure and applications, specially on autonomous governance, including, but not limited to, lifecycle management, auditing, security, etc.
Both have functional requirements like pattern matching and non-functional requirements like response time and query/rule explanation.

Even sharing requirements and goals, historically, both fields were born appart and although the industry evolved and one can find good products on the market, they either focus on event processing or on business rules management. That is due not only because of historical reasons but also because, even overlapping in part, use cases do have some different requirements.

Important

Drools was also born as a rules engine several years ago, but following the vision of becoming a single platform for behavioral modelling, it soon realized that it could only achieve this goal by crediting the same importance to the three complementary business modelling techniques:

Business Rules Management
Business Processes Management
Complex Event Processing

In this context, Drools Fusion is the module responsible for adding event processing capabilities into the platform.

Supporting Complex Event Processing, though, is much more than simply understanding what an event is. CEP scenarios share several common and distinguishing characteristics:

Usually required to process huge volumes of events, but only a small percentage of the events are of real interest.
Events are usually immutable, since they are a record of state change.
Usually the rules and queries on events must run in reactive modes, i.e., react to the detection of event patterns.
Usually there are strong temporal relationships between related events.
Individual events are usually not important. The system is concerned about patterns of related events and their relationships.
Usually, the system is required to perform composition and aggregation of events.

Based on this general common characteristics, Drools Fusion defined a set of goals to be achieved in order to support Complex Event Processing appropriately:

Support Events, with their proper semantics, as first class citizens.
Allow detection, correlation, aggregation and composition of events.
Support processing of Streams of events.
Support temporal constraints in order to model the temporal relationships between events.
Support sliding windows of interesting events.
Support a session scoped unified clock.
Support the required volumes of events for CEP use cases.
Support to (re)active rules.
Support adapters for event input into the engine (pipeline).

The above list of goals are based on the requirements not covered by Drools Expert itself, since in a unified platform, all features of one module are leveraged by the other modules. This way, Drools Fusion is born with enterprise grade features like Pattern Matching, that is paramount to a CEP product, but that is already provided by Drools Expert. In the same way, all features provided by Drools Fusion are leveraged by Drools Flow (and vice-versa) making process management aware of event processing and vice-versa.

For the remaining of this guide, we will go through each of the features Drools Fusion adds to the platform. All these features are available to support different use cases in the CEP world, and the user is free to select and use the ones that will help him model his business use case.

8.3. Event Semantics

An event is a fact that present a few distinguishing characteristics:

Usually immutables: since, by the previously discussed definition, events are a record of a state change in the application domain, i.e., a record of something that already happened, and the past can not be "changed", events are immutables. This constraint is an important requirement for the development of several optimizations and for the specification of the event lifecycle. This does not mean that the Java object representing the object must be immutable. Quite the contrary, the engine does not enforce immutability of the object model, because one of the most common use cases for rules is event data enrichment.
Note
As a best practice, the application is allowed to populate un-populated event attributes (to enrich the event with inferred data), but already populated attributes should never be changed.
Strong temporal constraints: rules involving events usually require the correlation of multiple events, specially temporal correlations where events are said to happen at some point in time relative to other events.
Managed lifecycle: due to their immutable nature and the temporal constraints, events usually will only match other events and facts during a limited window of time, making it possible for the engine to manage the lifecycle of the events automatically. In other words, one an event is inserted into the working memory, it is possible for the engine to find out when an event can no longer match other facts and automatically delete it, releasing its associated resources.
Use of sliding windows: since all events have timestamps associated to them, it is possible to define and use sliding windows over them, allowing the creation of rules on aggregations of values over a period of time. Example: average of an event value over 60 minutes.

Drools supports the declaration and usage of events with both semantics: point-in-time events and interval-based events.

Note

A simplistic way to understand the unitification of the semantics is to consider a point-in-time event as an interval-based event whose duration is zero.

8.4. Event Processing Modes

Rules engines in general have a well known way of processing data and rules and provide the application with the results. Also, there is not many requirements on how facts should be presented to the rules engine, specially because in general, the processing itself is time independent. That is a good assumption for most scenarios, but not for all of them. When the requirements include the processing of real time or near real time events, time becomes and important variable of the reasoning process.

The following sections will explain the impact of time on rules reasoning and the two modes provided by Drools for the reasoning process.

8.4.1. Cloud Mode

The CLOUD processing mode is the default processing mode. Users of rules engine are familiar with this mode because it behaves in exactly the same way as any pure forward chaining rules engine, including previous versions of Drools.

When running in CLOUD mode, the engine sees all facts in the working memory, does not matter if they are regular facts or events, as a whole. There is no notion of flow of time, although events have a timestamp as usual. In other words, although the engine knows that a given event was created, for instance, on January 1st 2009, at 09:35:40.767, it is not possible for the engine to determine how "old" the event is, because there is no concept of "now".

In this mode, the engine will apply its usual many-to-many pattern matching algorithm, using the rules constraints to find the matching tuples, activate and fire rules as usual.

This mode does not impose any kind of additional requirements on facts. So for instance:

There is no notion of time. No requirements clock synchronization.
There is no requirement on event ordering. The engine looks at the events as an unordered cloud against which the engine tries to match rules.

On the other hand, since there is no requirements, some benefits are not available either. For instance, in CLOUD mode, it is not possible to use sliding windows, because sliding windows are based on the concept of "now" and there is no concept of "now" in CLOUD mode.

Since there is no ordering requirement on events, it is not possible for the engine to determine when events can no longer match and as so, there is no automatic life-cycle management for events. I.e., the application must explicitly delete events when they are no longer necessary, in the same way the application does with regular facts.

Cloud mode is the default execution mode for Drools, but in any case, as any other configuration in Drools, it is possible to change this behavior either by setting a system property, using configuration property files or using the API. The corresponding property is:

KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();

config.setOption( EventProcessingOption.CLOUD );

The equivalent property is:

drools.eventProcessingMode = cloud

8.4.2. Stream Mode

The STREAM processing mode is the mode of choice when the application needs to process streams of events. It adds a few common requirements to the regular processing, but enables a whole lot of features that make stream event processing a lot simpler.

The main requirements to use STREAM mode are:

Events in each stream must be time-ordered. I.e., inside a given stream, events that happened first must be inserted first into the engine.
The engine will force synchronization between streams through the use of the session clock, so, although the application does not need to enforce time ordering between streams, the use of non-time-synchronized streams may result in some unexpected results.

Given that the above requirements are met, the application may enable the STREAM mode using the following API:

KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();

config.setOption( EventProcessingOption.STREAM );

Or, the equivalent property:

drools.eventProcessingMode = stream

When using the STREAM, the engine knows the concept of flow of time and the concept of "now", i.e., the engine understands how old events are based on the current timestamp read from the Session Clock. This characteristic allows the engine to provide the following additional features to the application:

Sliding Window support
Automatic Event Lifecycle Management
Automatic Rule Delaying when using Negative Patterns

All these features are explained in the following sections.

8.4.2.1. Role of Session Clock in Stream mode

When running the engine in CLOUD mode, the session clock is used only to time stamp the arriving events that don't have a previously defined timestamp attribute. Although, in STREAM mode, the Session Clock assumes an even more important role.

In STREAM mode, the session clock is responsible for keeping the current timestamp, and based on it, the engine does all the temporal calculations on event's aging, synchronizes streams from multiple sources, schedules future tasks and so on.

Check the documentation on the Session Clock section to know how to configure and use different session clock implementations.

8.4.2.2. Negative Patterns in Stream Mode

Negative patterns behave different in STREAM mode when compared to CLOUD mode. In CLOUD mode, the engine assumes that all facts and events are known in advance (there is no concept of flow of time) and so, negative patterns are evaluated immediately.

When running in STREAM mode, negative patterns with temporal constraints may require the engine to wait for a time period before activating a rule. The time period is automatically calculated by the engine in a way that the user does not need to use any tricks to achieve the desired result.

For instance:

Example 8.1. a rule that activates immediately upon matching

rule "Sound the alarm"
when
    $f : FireDetected( )
    not( SprinklerActivated( ) )
then
    // sound the alarm
end

The above rule has no temporal constraints that would require delaying the rule, and so, the rule activates immediately. The following rule on the other hand, must wait for 10 seconds before activating, since it may take up to 10 seconds for the sprinklers to activate:

Example 8.2. a rule that automatically delays activation due to temporal constraints

rule "Sound the alarm"
when
    $f : FireDetected( )
    not( SprinklerActivated( this after[0s,10s] $f ) )
then
    // sound the alarm
end

This behaviour allows the engine to keep consistency when dealing with negative patterns and temporal constraints at the same time. The above would be the same as writing the rule as below, but does not burden the user to calculate and explicitly write the appropriate duration parameter:

Example 8.3. same rule with explicit duration parameter

rule "Sound the alarm"
    duration( 10s )
when
    $f : FireDetected( )
    not( SprinklerActivated( this after[0s,10s] $f ) )
then
    // sound the alarm
end

The following rule expects every 10 seconds at least one “Heartbeat” event, if not the rule fires. The special case in this rule is that we use the same type of the object in the first pattern and in the negative pattern. The negative pattern has the temporal constraint to wait between 0 to 10 seconds before firing and it excludes the Heartbeat bound to $h. Excluding the bound Heartbeat is important since the temporal constraint [0s, ...] does not exclude by itself the bound event $h from being matched again, thus preventing the rule to fire.

Example 8.4. excluding bound events in negative patterns

rule "Sound the alarm"
when
    $h: Heartbeat( ) from entry-point "MonitoringStream"
    not( Heartbeat( this != $h, this after[0s,10s] $h ) from entry-point "MonitoringStream" )
then
    // Sound the alarm
end

8.5. Session Clock

Reasoning over time requires a reference clock. Just to mention one example, if a rule reasons over the average price of a given stock over the last 60 minutes, how the engine knows what stock price changes happened over the last 60 minutes in order to calculate the average? The obvious response is: by comparing the timestamp of the events with the "current time". How the engine knows what time is now? Again, obviously, by querying the Session Clock.

The session clock implements a strategy pattern, allowing different types of clocks to be plugged and used by the engine. This is very important because the engine may be running in an elements of different scenarios that may require different clock implementations. Just to mention a few:

Rules testing: testing always requires a controlled environment, and when the tests include rules with temporal constraints, it is necessary to not only control the input rules and facts, but also the flow of time.
Regular execution: usually, when running rules in production, the application will require a real time clock that allows the rules engine to react immediately to the time progression.
Special environments: specific environments may have specific requirements on time control. Cluster environments may require clock synchronization through heart beats, or JEE environments may require the use of an AppServer provided clock, etc.
Rules replay or simulation: to replay scenarios or simulate scenarios it is necessary that the application also controls the flow of time.

8.5.1. Available Clock Implementations

Drools 5 provides 2 clock implementations out of the box. The default real time clock, based on the system clock, and an optional pseudo clock, controlled by the application.

8.5.1.1. Real Time Clock

By default, Drools uses a real time clock implementation that internally uses the system clock to determine the current timestamp.

To explicitly configure the engine to use the real time clock, just set the session configuration parameter to real time:

KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();

config.setOption( ClockTypeOption.get("realtime") );

8.5.1.2. Pseudo Clock

Drools also offers out of the box an implementation of a clock that is controlled by the application that is called Pseudo Clock. This clock is specially useful for unit testing temporal rules since it can be controlled by the application and so the results become deterministic.

To configure the pseudo session clock, do:

KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();

config.setOption( ClockTypeOption.get("pseudo") );

As an example of how to control the pseudo session clock:

KieSessionConfiguration config = KieServices.Factory.get().newKieSessionConfiguration();

conf.setOption( ClockTypeOption.get( "pseudo" ) );

KieSession session = kbase.newKieSession( conf, null );

        

SessionPseudoClock clock = session.getSessionClock();


// then, while inserting facts, advance the clock as necessary:

FactHandle handle1 = session.insert( tick1 );

clock.advanceTime( 10, TimeUnit.SECONDS );

FactHandle handle2 = session.insert( tick2 );

clock.advanceTime( 30, TimeUnit.SECONDS );

FactHandle handle3 = session.insert( tick3 );

8.6. Sliding Windows

Sliding Windows are a way to scope the events of interest by defining a window that is constantly moving. The two most common types of sliding window implementations are time based windows and length based windows.

The next sections will detail each of them.

Important

Sliding Windows are only available when running the engine in STREAM mode. Check the Event Processing Mode section for details on how the STREAM mode works.

Important

Sliding windows start to match immediately and defining a sliding window does not imply that the rule has to wait for the sliding window to be "full" in order to match. For instance, a rule that calculates the average of an event property on a window:length(10) will start calculating the average immediately, and it will start at 0 (zero) for no-events, and will update the average as events arrive one by one.

8.6.1. Sliding Time Windows

Sliding Time Windows allow the user to write rules that will only match events occurring in the last X time units.

For instance, if the user wants to consider only the Stock Ticks that happened in the last 2 minutes, the pattern would look like this:

StockTick() over window:time( 2m )

Drools uses the "over" keyword to associate windows to patterns.

On a more elaborate example, if the user wants to sound an alarm in case the average temperature over the last 10 minutes read from a sensor is above the threshold value, the rule would look like:

Example 8.5. aggregating values over time windows

rule "Sound the alarm in case temperature rises above threshold"
when
    TemperatureThreshold( $max : max )
    Number( doubleValue > $max ) from accumulate(
        SensorReading( $temp : temperature ) over window:time( 10m ),
        average( $temp ) )
then
    // sound the alarm
end

The engine will automatically disregard any SensorReading older than 10 minutes and keep the calculated average consistent.

Important

Please note that time based windows are considered when calculating the interval an event remains in the working memory before being expired, but an event falling off a sliding window does not mean by itself that the event will be discarded from the working memory, as there might be other rules that depend on that event. The engine will discard events only when no other rules depend on that event and the expiration policy for that event type is fulfilled.

8.6.2. Sliding Length Windows

Sliding Length Windows work the same way as Time Windows, but consider events based on order of their insertion into the session instead of flow of time.

For instance, if the user wants to consider only the last 10 RHT Stock Ticks, independent of how old they are, the pattern would look like this:

StockTick( company == "RHT" ) over window:length( 10 )

As you can see, the pattern is similar to the one presented in the previous section, but instead of using window:time to define the sliding window, it uses window:length.

Example 8.6. aggregating values over length windows

rule "Sound the alarm in case temperature rises above threshold"
when
    TemperatureThreshold( $max : max )
    Number( doubleValue > $max ) from accumulate(
        SensorReading( $temp : temperature ) over window:length( 100 ),
        average( $temp ) )
then
    // sound the alarm
end

The engine will keep only consider the last 100 readings to calculate the average temperature.

Important

Please note that falling off a length based window is not criteria for event expiration in the session. The engine disregards events that fall off a window when calculating that window, but does not remove the event from the session based on that condition alone as there might be other rules that depend on that event.

Important

Please note that length based windows do not define temporal constraints for event expiration from the session, and the engine will not consider them. If events have no other rules defining temporal constraints and no explicit expiration policy, the engine will keep them in the session indefinitely.

8.7. Streams Support

Most CEP use cases have to deal with streams of events. The streams can be provided to the application in various forms, from JMS queues to flat text files, from database tables to raw sockets or even through web service calls. In any case, the streams share a common set of characteristics:

events in the stream are ordered by a timestamp. The timestamp may have different semantics for different streams but they are always ordered internally.
volumes of events are usually high.
atomic events are rarely useful by themselves. Usually meaning is extracted from the correlation between multiple events from the stream and also from other sources.
streams may be homogeneous, i.e. contain a single type of events, or heterogeneous, i.e. contain multiple types of events.

Drools generalized the concept of a stream as an "entry point" into the engine. An entry point is for drools a gate from which facts come. The facts may be regular facts or special facts like events.

In Drools, facts from one entry point (stream) may join with facts from any other entry point or event with facts from the working memory. Although, they never mix, i.e., they never lose the reference to the entry point through which they entered the engine. This is important because one may have the same type of facts coming into the engine through several entry points, but one fact that is inserted into the engine through entry point A will never match a pattern from a entry point B, for example.

8.7.1. Declaring and Using Entry Points

Entry points are declared implicitly in Drools by directly making use of them in rules. I.e. referencing an entry point in a rule will make the engine, at compile time, to identify and create the proper internal structures to support that entry point.

So, for instance, lets imagine a banking application, where transactions are fed into the system coming from streams. One of the streams contains all the transactions executed in ATM machines. So, if one of the rules says: a withdraw is authorized if and only if the account balance is over the requested withdraw amount, the rule would look like:

Example 8.7. Example of Stream Usage

rule "authorize withdraw"
when
    WithdrawRequest( $ai : accountId, $am : amount ) from entry-point "ATM Stream"
    CheckingAccount( accountId == $ai, balance > $am )
then
    // authorize withdraw
end

In the previous example, the engine compiler will identify that the pattern is tied to the entry point "ATM Stream" and will both create all the necessary structures for the rulebase to support the "ATM Stream" and will only match WithdrawRequests coming from the "ATM Stream". In the previous example, the rule is also joining the event from the stream with a fact from the main working memory (CheckingAccount).

Now, lets imagine a second rule that states that a fee of $2 must be applied to any account for which a withdraw request is placed at a bank branch:

Example 8.8. Using a different Stream

rule "apply fee on withdraws on branches"
when
    WithdrawRequest( $ai : accountId, processed == true ) from entry-point "Branch Stream"
    CheckingAccount( accountId == $ai )
then
    // apply a $2 fee on the account
end

The previous rule will match events of the exact same type as the first rule (WithdrawRequest), but from two different streams, so an event inserted into "ATM Stream" will never be evaluated against the pattern on the second rule, because the rule states that it is only interested in patterns coming from the "Branch Stream".

So, entry points, besides being a proper abstraction for streams, are also a way to scope facts in the working memory, and a valuable tool for reducing cross products explosions. But that is a subject for another time.

Inserting events into an entry point is equally simple. Instead of inserting events directly into the working memory, insert them into the entry point as shown in the example below:

Example 8.9. Inserting facts into an entry point

// create your rulebase and your session as usual

KieSession session = ...


// get a reference to the entry point

EntryPoint atmStream = session.getEntryPoint( "ATM Stream" );


// and start inserting your facts into the entry point

atmStream.insert( aWithdrawRequest );

The previous example shows how to manually insert facts into a given entry point. Although, usually, the application will use one of the many adapters to plug a stream end point, like a JMS queue, directly into the engine entry point, without coding the inserts manually. The Drools pipeline API has several adapters and helpers to do that as well as examples on how to do it.

8.8. Memory Management for Events

Important

The automatic memory management for events is only performed when running the engine in STREAM mode. Check the Event Processing Mode section for details on how the STREAM mode works.

One of the benefits of running the engine in STREAM mode is that the engine can detect when an event can no longer match any rule due to its temporal constraints. When that happens, the engine can safely delete the event from the session without side effects and release any resources used by that event.

There are basically 2 ways for the engine to calculate the matching window for a given event:

explicitly, using the expiration policy
implicitly, analyzing the temporal constraints on events

8.8.1. Explicit expiration offset

The first way of allowing the engine to calculate the window of interest for a given event type is by explicitly setting it. To do that, just use the declare statement and define an expiration for the fact type:

Example 8.10. explicitly defining an expiration offset of 30 minutes for StockTick events

declare StockTick
    @expires( 30m )
end

The above example declares an expiration offset of 30 minutes for StockTick events. After that time, assuming no rule still needs the event, the engine will expire and remove the event from the session automatically.

8.8.2. Inferred expiration offset

Another way for the engine to calculate the expiration offset for a given event is implicitly, by analyzing the temporal constraints in the rules. For instance, given the following rule:

Example 8.11. example rule with temporal constraints

rule "correlate orders"
when
    $bo : BuyOrderEvent( $id : id ) 
    $ae : AckEvent( id == $id, this after[0,10s] $bo )
then
    // do something
end

Analyzing the above rule, the engine automatically calculates that whenever a BuyOrderEvent matches, it needs to store it for up to 10 seconds to wait for matching AckEvent's. So, the implicit expiration offset for BuyOrderEvent will be 10 seconds. AckEvent, on the other hand, can only match existing BuyOrderEvent's, and so its expiration offset will be zero seconds.

The engine will make this analysis for the whole rulebase and find the offset for every event type. Whenever an implicit expiration offset clashes with the explicit expiration offset, then engine will use the greater of the two.

8.9. Temporal Reasoning

Temporal reasoning is another requirement of any CEP system. As discussed previously, one of the distinguishing characteristics of events is their strong temporal relationships.

Temporal reasoning is an extensive field of research, from its roots on Temporal Modal Logic to its more practical applications in business systems. There are hundreds of papers and thesis written and approaches are described for several applications. Drools once more takes a pragmatic and simple approach based on several sources, but specially worth noting the following papers:

[ALLEN81] Allen, J.F.. An Interval-based Representation of Temporal Knowledge. 1981.

[ALLEN83] Allen, J.F.. Maintaining knowledge about temporal intervals. 1983.

[BENNE00] by Bennet, Brandon and Galton, Antony P.. A Unifying Semantics for Time and Events. 2005.

[YONEK05] by Yoneki, Eiko and Bacon, Jean. Unified Semantics for Event Correlation Over Time and Space in Hybrid Network Environments. 2005.

Drools implements the Interval-based Time Event Semantics described by Allen, and represents Point-in-Time Events as Interval-based evens with duration 0 (zero).

$eventA : EventA( this after[ 3m30s, 4m ] $eventB )

The previous pattern will match if and only if the temporal distance between the time when $eventB finished and the time when $eventA started is between ( 3 minutes and 30 seconds ) and ( 4 minutes ). In other words:

 3m30s <= $eventA.startTimestamp - $eventB.endTimeStamp <= 4m

The temporal distance interval for the after operator is optional:

If two values are defined (like in the example below), the interval starts on the first value and finishes on the second.
If only one value is defined, the interval starts on the value and finishes on the positive infinity.
If no value is defined, it is assumed that the initial value is 1ms and the final value is the positive infinity.

Note

It is possible to define negative distances for this operator. Example:

$eventA : EventA( this after[ -3m30s, -2m ] $eventB )

Note

If the first value is greater than the second value, the engine automatically reverses them, as there is no reason to have the first value greater than the second value. Example: the following two patterns are considered to have the same semantics:

$eventA : EventA( this after[ -3m30s, -2m ] $eventB ) 
$eventA : EventA( this after[ -2m, -3m30s ] $eventB )

Note

The after, before and coincides operators can be used to define constraints between events, java.util.Date attributes, and long attributes (interpreted as timestamps since epoch) in any combination. Example:

EventA( this after $someDate )

8.9.1.2. Before

The before evaluator correlates two events and matches when the temporal distance from the event being correlated to the current correlated belongs to the distance range declared for the operator.

Lets look at an example:

$eventA : EventA( this before[ 3m30s, 4m ] $eventB )

The previous pattern will match if and only if the temporal distance between the time when $eventA finished and the time when $eventB started is between ( 3 minutes and 30 seconds ) and ( 4 minutes ). In other words:

 3m30s <= $eventB.startTimestamp - $eventA.endTimeStamp <= 4m

The temporal distance interval for the before operator is optional:

If two values are defined (like in the example below), the interval starts on the first value and finishes on the second.
If only one value is defined, then the interval starts on the value and finishes on the positive infinity.
If no value is defined, it is assumed that the initial value is 1ms and the final value is the positive infinity.

Note

It is possible to define negative distances for this operator. Example:

$eventA : EventA( this before[ -3m30s, -2m ] $eventB )

Note

$eventA : EventA( this before[ -3m30s, -2m ] $eventB ) 
$eventA : EventA( this before[ -2m, -3m30s ] $eventB )

Note

EventA( this after $someDate )

8.9.1.3. Coincides

The coincides evaluator correlates two events and matches when both happen at the same time. Optionally, the evaluator accept thresholds for the distance between events' start and finish timestamps.

Lets look at an example:

$eventA : EventA( this coincides $eventB )

The previous pattern will match if and only if the start timestamps of both $eventA and $eventB are the same AND the end timestamp of both $eventA and $eventB also are the same.

Optionally, this operator accepts one or two parameters. These parameters are the thresholds for the distance between matching timestamps.

If only one parameter is given, it is used for both start and end timestamps.
If two parameters are given, then the first is used as a threshold for the start timestamp and the second one is used as a threshold for the end timestamp.

In other words:

$eventA : EventA( this coincides[15s, 10s] $eventB )

Above pattern will match if and only if:

abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 15s && 
abs( $eventA.endTimestamp - $eventB.endTimestamp ) <= 10s

Warning

It makes no sense to use negative interval values for the parameters and the engine will raise an error if that happens.

Note

EventA( this after $someDate )

8.9.1.4. During

The during evaluator correlates two events and matches when the current event happens during the occurrence of the event being correlated.

Lets look at an example:

$eventA : EventA( this during $eventB )

The previous pattern will match if and only if the $eventA starts after $eventB starts and finishes before $eventB finishes.

In other words:

$eventB.startTimestamp < $eventA.startTimestamp <= $eventA.endTimestamp < $eventB.endTimestamp

The during operator accepts 1, 2 or 4 optional parameters as follow:

If one value is defined, this will be the maximum distance between the start timestamp of both event and the maximum distance between the end timestamp of both events in order to operator match. Example:
```
$eventA : EventA( this during[ 5s ] $eventB )
```
Will match if and only if:
```
0 < $eventA.startTimestamp - $eventB.startTimestamp <= 5s && 
0 < $eventB.endTimestamp - $eventA.endTimestamp <= 5s
```

If two values are defined, the first value will be the minimum distance between the timestamps of both events, while the second value will be the maximum distance between the timestamps of both events. Example:
```
$eventA : EventA( this during[ 5s, 10s ] $eventB )
```
Will match if and only if:
```
5s <= $eventA.startTimestamp - $eventB.startTimestamp <= 10s && 
5s <= $eventB.endTimestamp - $eventA.endTimestamp <= 10s
```

If four values are defined, the first two values will be the minimum and maximum distances between the start timestamp of both events, while the last two values will be the minimum and maximum distances between the end timestamp of both events. Example:
```
$eventA : EventA( this during[ 2s, 6s, 4s, 10s ] $eventB )
```
Will match if and only if:
```
2s <= $eventA.startTimestamp - $eventB.startTimestamp <= 6s && 
4s <= $eventB.endTimestamp - $eventA.endTimestamp <= 10s
```

8.9.1.5. Finishes

The finishes evaluator correlates two events and matches when the current event's start timestamp happens after the correlated event's start timestamp, but both end timestamps occur at the same time.

Lets look at an example:

$eventA : EventA( this finishes $eventB )

The previous pattern will match if and only if the $eventA starts after $eventB starts and finishes at the same time $eventB finishes.

In other words:

$eventB.startTimestamp < $eventA.startTimestamp && 
$eventA.endTimestamp == $eventB.endTimestamp

The finishes evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of both events in order for the operator to match. Example:

$eventA : EventA( this finishes[ 5s ] $eventB )

Will match if and only if:

$eventB.startTimestamp < $eventA.startTimestamp && 
abs( $eventA.endTimestamp - $eventB.endTimestamp ) <= 5s

Warning

It makes no sense to use a negative interval value for the parameter and the engine will raise an exception if that happens.

8.9.1.6. Finished By

The finishedby evaluator correlates two events and matches when the current event start timestamp happens before the correlated event start timestamp, but both end timestamps occur at the same time. This is the symmetrical opposite of finishes evaluator.

Lets look at an example:

$eventA : EventA( this finishedby $eventB )

The previous pattern will match if and only if the $eventA starts before $eventB starts and finishes at the same time $eventB finishes.

In other words:

$eventA.startTimestamp < $eventB.startTimestamp && 
$eventA.endTimestamp == $eventB.endTimestamp

The finishedby evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of both events in order for the operator to match. Example:

$eventA : EventA( this finishedby[ 5s ] $eventB )

Will match if and only if:

$eventA.startTimestamp < $eventB.startTimestamp && 
abs( $eventA.endTimestamp - $eventB.endTimestamp ) <= 5s

Warning

It makes no sense to use a negative interval value for the parameter and the engine will raise an exception if that happens.

8.9.1.7. Includes

The includes evaluator correlates two events and matches when the event being correlated happens during the current event. It is the symmetrical opposite of during evaluator.

Lets look at an example:

$eventA : EventA( this includes $eventB )

The previous pattern will match if and only if the $eventB starts after $eventA starts and finishes before $eventA finishes.

In other words:

$eventA.startTimestamp < $eventB.startTimestamp <= $eventB.endTimestamp < $eventA.endTimestamp

The includes operator accepts 1, 2 or 4 optional parameters as follow:

If one value is defined, this will be the maximum distance between the start timestamp of both event and the maximum distance between the end timestamp of both events in order to operator match. Example:
```
$eventA : EventA( this includes[ 5s ] $eventB )
```
Will match if and only if:
```
0 < $eventB.startTimestamp - $eventA.startTimestamp <= 5s && 
0 < $eventA.endTimestamp - $eventB.endTimestamp <= 5s
```

If two values are defined, the first value will be the minimum distance between the timestamps of both events, while the second value will be the maximum distance between the timestamps of both events. Example:
```
$eventA : EventA( this includes[ 5s, 10s ] $eventB )
```
Will match if and only if:
```
5s <= $eventB.startTimestamp - $eventA.startTimestamp <= 10s && 
5s <= $eventA.endTimestamp - $eventB.endTimestamp <= 10s
```

If four values are defined, the first two values will be the minimum and maximum distances between the start timestamp of both events, while the last two values will be the minimum and maximum distances between the end timestamp of both events. Example:
```
$eventA : EventA( this includes[ 2s, 6s, 4s, 10s ] $eventB )
```
Will match if and only if:
```
2s <= $eventB.startTimestamp - $eventA.startTimestamp <= 6s && 
4s <= $eventA.endTimestamp - $eventB.endTimestamp <= 10s 
```

8.9.1.8. Meets

The meets evaluator correlates two events and matches when the current event's end timestamp happens at the same time as the correlated event's start timestamp.

Lets look at an example:

$eventA : EventA( this meets $eventB )

The previous pattern will match if and only if the $eventA finishes at the same time $eventB starts.

In other words:

abs( $eventB.startTimestamp - $eventA.endTimestamp ) == 0

The meets evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of current event and the start timestamp of the correlated event in order for the operator to match. Example:

$eventA : EventA( this meets[ 5s ] $eventB )

Will match if and only if:

abs( $eventB.startTimestamp - $eventA.endTimestamp) <= 5s

Warning

It makes no sense to use a negative interval value for the parameter and the engine will raise an exception if that happens.

8.9.1.9. Met By

The metby evaluator correlates two events and matches when the current event's start timestamp happens at the same time as the correlated event's end timestamp.

Lets look at an example:

$eventA : EventA( this metby $eventB )

The previous pattern will match if and only if the $eventA starts at the same time $eventB finishes.

In other words:

abs( $eventA.startTimestamp - $eventB.endTimestamp ) == 0

The metby evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the end timestamp of the correlated event and the start timestamp of the current event in order for the operator to match. Example:

$eventA : EventA( this metby[ 5s ] $eventB )

Will match if and only if:

abs( $eventA.startTimestamp - $eventB.endTimestamp) <= 5s

Warning

It makes no sense to use a negative interval value for the parameter and the engine will raise an exception if that happens.

8.9.1.10. Overlaps

The overlaps evaluator correlates two events and matches when the current event starts before the correlated event starts and finishes after the correlated event starts, but before the correlated event finishes. In other words, both events have an overlapping period.

Lets look at an example:

$eventA : EventA( this overlaps $eventB )

The previous pattern will match if and only if:

$eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp

The overlaps operator accepts 1 or 2 optional parameters as follow:

If one parameter is defined, this will be the maximum distance between the start timestamp of the correlated event and the end timestamp of the current event. Example:
```
$eventA : EventA( this overlaps[ 5s ] $eventB )
```
Will match if and only if:
```
$eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp && 
0 <= $eventA.endTimestamp - $eventB.startTimestamp <= 5s
```

If two values are defined, the first value will be the minimum distance and the second value will be the maximum distance between the start timestamp of the correlated event and the end timestamp of the current event. Example:
```
$eventA : EventA( this overlaps[ 5s, 10s ] $eventB )
```
Will match if and only if:
```
$eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp && 
5s <= $eventA.endTimestamp - $eventB.startTimestamp <= 10s 
```

8.9.1.11. Overlapped By

The overlappedby evaluator correlates two events and matches when the correlated event starts before the current event starts and finishes after the current event starts, but before the current event finishes. In other words, both events have an overlapping period.

Lets look at an example:

$eventA : EventA( this overlappedby $eventB )

The previous pattern will match if and only if:

$eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp

The overlappedby operator accepts 1 or 2 optional parameters as follow:

If one parameter is defined, this will be the maximum distance between the start timestamp of the current event and the end timestamp of the correlated event. Example:
```
$eventA : EventA( this overlappedby[ 5s ] $eventB )
```
Will match if and only if:
```
$eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp && 
0 <= $eventB.endTimestamp - $eventA.startTimestamp <= 5s
```

If two values are defined, the first value will be the minimum distance and the second value will be the maximum distance between the start timestamp of the current event and the end timestamp of the correlated event. Example:
```
$eventA : EventA( this overlappedby[ 5s, 10s ] $eventB )
```
Will match if and only if:
```
$eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp && 
5s <= $eventB.endTimestamp - $eventA.startTimestamp <= 10s
```

8.9.1.12. Starts

The starts evaluator correlates two events and matches when the current event's end timestamp happens before the correlated event's end timestamp, but both start timestamps occur at the same time.

Lets look at an example:

$eventA : EventA( this starts $eventB )

The previous pattern will match if and only if the $eventA finishes before $eventB finishes and starts at the same time $eventB starts.

In other words:

$eventA.startTimestamp == $eventB.startTimestamp && 
$eventA.endTimestamp < $eventB.endTimestamp

The starts evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the start timestamp of both events in order for the operator to match. Example:

$eventA : EventA( this starts[ 5s ] $eventB )

Will match if and only if:

abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 5s && 
$eventA.endTimestamp < $eventB.endTimestamp

Warning

It makes no sense to use a negative interval value for the parameter and the engine will raise an exception if that happens.

8.9.1.13. Started By

The startedby evaluator correlates two events and matches when the correlating event's end timestamp happens before the current event's end timestamp, but both start timestamps occur at the same time. Lets look at an example:

$eventA : EventA( this startedby $eventB )

The previous pattern will match if and only if the $eventB finishes before $eventA finishes and starts at the same time $eventB starts.

In other words:

$eventA.startTimestamp == $eventB.startTimestamp && 
$eventA.endTimestamp > $eventB.endTimestamp

The startedby evaluator accepts one optional parameter. If it is defined, it determines the maximum distance between the start timestamp of both events in order for the operator to match. Example:

$eventA : EventA( this starts[ 5s ] $eventB )

Will match if and only if:

abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 5s && 
$eventA.endTimestamp > $eventB.endTimestamp

Warning

It makes no sense to use a negative interval value for the parameter and the engine will raise an exception if that happens.

Part IV. Drools Integration

Integration Documentation

Table of Contents

9. Drools Commands

9.1. API

9.1.1. XStream
9.1.2. JSON
9.1.3. JAXB

9.2. Commands supported

9.2.1. BatchExecutionCommand
9.2.2. InsertObjectCommand
9.2.3. RetractCommand
9.2.4. ModifyCommand
9.2.5. GetObjectCommand
9.2.6. InsertElementsCommand
9.2.7. FireAllRulesCommand
9.2.8. StartProcessCommand
9.2.9. SignalEventCommand
9.2.10. CompleteWorkItemCommand
9.2.11. AbortWorkItemCommand
9.2.12. QueryCommand
9.2.13. SetGlobalCommand
9.2.14. GetGlobalCommand
9.2.15. GetObjectsCommand

10. CDI

10.1. Introduction

10.2. Annotations

10.2.1. @KReleaseId
10.2.2. @KContainer
10.2.3. @KBase
10.2.4. @KSession for KieSession
10.2.5. @KSession for StatelessKieSession

10.3. API Example Comparison

11. Integration with Spring

11.1. Important Changes for Drools 6.0

11.2. Integration with Drools Expert

11.2.1. KieModule
11.2.2. KieBase
11.2.3. IMPORTANT NOTE
11.2.4. KieSessions
11.2.5. Event Listeners
11.2.6. Loggers
11.2.7. Defining Batch Commands
11.2.8. Persistence

11.3. Integration with jBPM Human Task

11.3.1. How to configure Spring with jBPM Human task

12. Apache Camel Integration

12.1. Camel

13. Drools Camel Server

13.1. Introduction

13.2. Deployment

13.3. Configuration

13.3.1. REST/Camel Services configuration

14. JMX monitoring with RHQ/JON

14.1. Introduction

14.1.1. Enabling JMX monitoring in a Drools application
14.1.2. Installing and running the RHQ/JON plugin

Chapter 9. Drools Commands

9.1. API

XML marshalling/unmarshalling of the Drools Commands requires the use of special classes, which are going to be described in the following sections.

The following urls show sample script examples for jaxb, xstream and json marshalling using:

http://fisheye.jboss.org/browse/JBossRules/trunk/drools-camel/src/test/resources/org/drools/camel/component/jaxb.mvt?r=HEAD
http://fisheye.jboss.org/browse/JBossRules/trunk/drools-camel/src/test/resources/org/drools/camel/component/jaxb.mvt?r=HEAD
http://fisheye.jboss.org/browse/JBossRules/trunk/drools-camel/src/test/resources/org/drools/camel/component/xstream.mvt?r=HEAD

9.1.1. XStream

To use the XStream commands marshaller you need to use the DroolsHelperProvider to obtain an XStream instance. We need to use this because it has the commands converters registered.

Marshalling
BatchExecutionHelperProviderImpl.newXStreamMarshaller().toXML(command);
Unmarshalling
BatchExecutionHelperProviderImpl.newXStreamMarshaller().fromXML(xml)

9.1.2. JSON

JSON API to marshalling/unmarshalling is similar to XStream API:

Marshalling
BatchExecutionHelper.newJSonMarshaller().toXML(command);
Unmarshalling
BatchExecutionHelper.newJSonMarshaller().fromXML(xml)

9.1.3. JAXB

There are two options for using JAXB, you can define your model in an XSD file or you can have a POJO model. In both cases you have to declare your model inside JAXBContext, and in order to do that you need to use Drools Helper classes. Once you have the JAXBContext you need to create the Unmarshaller/Marshaller as needed.

9.1.3.1. Using an XSD file to define the model

With your model defined in a XSD file you need to have a KnowledgeBase that has your XSD model added as a resource.

To do this, the XSD file must be added as a XSD ResourceType into the KnowledgeBuilder. Finally you can create the JAXBContext using the KnowledgeBase created with the KnowledgeBuilder  

Options xjcOpts = new Options();

xjcOpts.setSchemaLanguage(Language.XMLSCHEMA);

JaxbConfiguration jaxbConfiguration = KnowledgeBuilderFactory.newJaxbConfiguration( xjcOpts, "xsd" );

kbuilder.add(ResourceFactory.newClassPathResource("person.xsd", getClass()), ResourceType.XSD, jaxbConfiguration);

KnowledgeBase kbase = kbuilder.newKnowledgeBase();


List<String> classesName = new ArrayList<String>();

classesName.add("org.drools.compiler.test.Person");

   

JAXBContext jaxbContext = KnowledgeBuilderHelper.newJAXBContext(classesName.toArray(new String[classesName.size()]), kbase);

9.1.3.2. Using a POJO model

In this case you need to use DroolsJaxbHelperProviderImpl to create the JAXBContext. This class has two parameters:

classNames: A List with the canonical name of the classes that you want to use in the marshalling/unmarshalling process.
properties: JAXB custom properties

List<String> classNames = new ArrayList<String>();

classNames.add("org.drools.compiler.test.Person");

JAXBContext jaxbContext = DroolsJaxbHelperProviderImpl.createDroolsJaxbContext(classNames, null);

Marshaller marshaller = jaxbContext.createMarshaller();

9.2. Commands supported

Currently, the following commands are supported:

BatchExecutionCommand
InsertObjectCommand
RetractCommand
ModifyCommand
GetObjectCommand
InsertElementsCommand
FireAllRulesCommand
StartProcessCommand
SignalEventCommand
CompleteWorkItemCommand
AbortWorkItemCommand
QueryCommand
SetGlobalCommand
GetGlobalCommand
GetObjectsCommand

Note

In the next snippets code we are going to use a POJO org.drools.compiler.test.Person that has two fields

name: String
age: Integer

Note

In the next examples, to marshall the commands we have used the next snippet codes:

XStream

String xml = BatchExecutionHelper.newXStreamMarshaller().toXML(command);

JSON

String xml = BatchExecutionHelper.newJSonMarshaller().toXML(command);

JAXB

Marshaller marshaller = jaxbContext.createMarshaller();

StringWriter xml = new StringWriter();

marshaller.setProperty(Marshaller.JAXB_FORMATTED_OUTPUT, true);

marshaller.marshal(command, xml);

9.2.1. BatchExecutionCommand

Description: The command that contains a list of commands, which will be sent and executed.
Attributes
Table 9.1. BatchExecutionCommand attributes
Name Description required
lookup Sets the knowledge session id on which the commands are going to be executed true
commands List of commands to be executed false

Name	Description	required
lookup	Sets the knowledge session id on which the commands are going to be executed	true
commands	List of commands to be executed	false

Command creation

BatchExecutionCommand command = new BatchExecutionCommand();

command.setLookup("ksession1");

InsertObjectCommand insertObjectCommand = new InsertObjectCommand(new Person("john", 25));

FireAllRulesCommand fireAllRulesCommand = new FireAllRulesCommand();

command.getCommands().add(insertObjectCommand);

command.getCommands().add(fireAllRulesCommand);

XML output

XStream


<batch-execution lookup="ksession1">

  <insert>

    <org.drools.compiler.test.Person>

      <name>john</name>

      <age>25</age>

    </org.drools.compiler.test.Person>

  </insert>

  <fire-all-rules/>

</batch-execution>

JSON

{"batch-execution":{"lookup":"ksession1","commands":[{"insert":{"object":{"org.drools.compiler.test.Person":{"name":"john","age":25}}}},{"fire-all-rules":""}]}}

JAXB


<?xml version="1.0" encoding="UTF-8" standalone="yes"?>

<batch-execution lookup="ksession1">

    <insert>

        <object xsi:type="person" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">

            <age>25</age>

            <name>john</name>

        </object>

    </insert>

    <fire-all-rules max="-1"/>

</batch-execution>

9.2.2. InsertObjectCommand

Description: Insert an object in the knowledge session.

Attributes

Table 9.2. InsertObjectCommand attributes

Name	Description	required
object	The object to be inserted	true
outIdentifier	Id to identify the FactHandle created in the object insertion and added to the execution results	false
returnObject	Boolean to establish if the object must be returned in the execution results. Default value: true	false
entryPoint	Entrypoint for the insertion	false

Command creation

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


Command insertObjectCommand = CommandFactory.newInsert(new Person("john", 25), "john", false, null);

cmds.add