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Drools expert user guide pdf free download
Activiti is distributed under the Apache V2 license. The distribution contains most of the sources as jar files. Activiti runs on a JDK higher than or equal to version 7. There are installation instructions on that page as well. To verify that your installation was successful, run java -version on the command line. That should print the installed version of your JDK. Activiti development can be done with the IDE of your choice.
Download the eclipse distribution of drools expert user guide pdf free download choice from the Eclipse download page. Unzip the downloaded file and then you should be able to start it with the eclipse file in the directory eclipse. Further in this user guide, there is a section on installing our eclipse designer plugin. Every self-respecting developer should have read How to ask questions the smart way.
All classes that have. However, if the user guide mentions those classes as configuration перейти на источник, they are supported and can be considered stable. In the jar file, all classes in packages that have.
No stability guarantees are given on classes or interfaces that are in implementation classes. After downloading the Activiti UI WAR file from the Activiti websitefollow these steps to get the demo setup running with default settings.
But we test on Tomcat primarily. Login with admin and password test. The Activiti UI application uses an in-memory H2 database by default, if you want to use another database configuration please read the longer version. The way to do this depends on your operating system. By default the UI application runs with an in-memory database.
The process drools expert user guide pdf free download user console. Use this tool to start new processes, assign tasks, view and claim tasks, etc. Note that the Activiti UI app demo setup is a way of showing the capabilities and functionality of Activiti as easily and as fast as possible. This does however, not mean that it is the only way of using Activiti.
Or you could very well choose to run Activiti as a typical, standalone BPM server. If it is possible in Java, it is possible with Activiti! As said in the one minute demo setup, the Activiti UI app runs по этой ссылке in-memory H2 database by default.
To run the Activiti UI app with a standalone H2 or another drools expert user guide pdf free download the activiti-app. To include the Activiti mouse download center 10 windows control hp and its dependent libraries, we advise using Maven or Ivyas it simplifies dependency management on both our and your side a lot.
The Activiti download zip contains a folder libs which contain all the Activiti jars and the source jars. The dependencies are not shipped this way. The required dependencies of the Activiti engine are generated using mvn dependency:tree :. Note: the mail jars are only needed ссылка на подробности you are using the mail service task.
All the dependencies can easily be downloaded using mvn dependency:copy-dependencies on a module of the Activiti source code. Playing around with the Activiti UI web application is a good way to get familiar with the Activiti concepts and functionality.
However, the main purpose of Activiti is of course to enable powerful BPM and workflow capabilities in your own application. The following chapters will help you to get перейти на страницу with how to use Activiti programmatically in your environment:.
The chapter on configuration will teach you how to set up Activiti and how to obtain an instance of the ProcessEngine class which is your central access point to all the engine functionality of Activiti. These services offer the Activiti engine functionality in a convenient yet powerful way and can be used in any Java environment.
Then continue on to the BPMN drools expert user guide pdf free download. The Activiti process engine is configured through an XML file called activiti. The easiest way to obtain a ProcessEngineis to use the org. ProcessEngines class:. This will look for an activiti.
The following snippet shows an example configuration. The following sections will give a detailed overview of the configuration properties. Note that the configuration XML drools expert user guide pdf free download in fact a Spring configuration. This does not mean that Activiti can only be used in a Spring environment! We are recovery iso 10 download for free easyre windows leveraging the parsing and dependency injection capabilities of Spring internally for building up the engine.
The ProcessEngineConfiguration object can also be created programmatically using the configuration file. It is also possible to use a different bean id e.
It is also приведенная ссылка not to use a configuration file, and create a drools expert user guide pdf free download based on defaults see the different supported classes for more information. All these ProcessEngineConfiguration. After calling the buildProcessEngine operation, a ProcessEngine is created:. The activiti. This bean is then used to construct the ProcessEngine.
There are multiple classes available that can be used to define the processEngineConfiguration. These classes represent different environments, and set defaults accordingly. The following classes are currently available more will follow in future releases :. StandaloneProcessEngineConfiguration : the process engine is used in a standalone way. Activiti will take care of the transactions. By default, the database will only be checked when the engine boots and an exception is thrown if there is no Activiti schema or the schema version is incorrect.
An H2 in-memory database is used by default. The database will be created and dropped when the engine boots and shuts down. When читать далее this, probably no additional configuration is needed except when using for example the job executor or mail capabilities.
SpringProcessEngineConfiguration : To be used when the process engine is used in a Spring environment. See the Spring integration section for more information. There are two ways to configure the database that the Activiti engine will use. The first option is to define the JDBC properties of the database:. The data source that is constructed based on the provided JDBC properties will have the default MyBatis connection pool settings.
The following attributes can optionally be set to tweak that connection pool taken from the MyBatis documentation :. Default is Default is 20 seconds.
Our benchmarks have по ссылке that the MyBatis connection pool is not the most efficient or resilient when dealing with a lot of concurrent requests.
As such, it is advised to us a javax. Note that Activiti does not ship with a library that allows to define such a data source. So you have to make sure that the libraries are on your classpath. The following properties can be set, regardless of whether you are using the JDBC or data source approach:. Should only be specified in case automatic detection fails. See the supported databases section for an overview of which types are supported. By default, the database configuration for Activiti is contained within the drools expert user guide pdf free download.
By using JNDI Java Naming and Directory Interface to obtain the database connection, the connection is fully managed by the Servlet Container and the configuration can be managed outside the war deployment.
This also allows more control over the connection parameters than what is provided by the db. Configuration of читать далее JNDI datasource will differ depending on what servlet container application you are using. The instructions below will work for Tomcat, but for other container applications, please refer to the documentation for your container app.
The default context is copied from the Activiti war file when the application is first deployed, so if it already exists, источник статьи will need to replace it.
Default is “true”. Add an Activiti configuration file activiti. However, often only database administrators can execute DDL statements on a database. On a production system, this is also the wisest of choices.
The scripts are also in the engine jar activiti-engine-x. The SQL files are of the form. Where db is any of the supported databases and type is. These tables are optional and should be drools expert user guide pdf free download when using the default identity management as shipped with the engine.
Optional: not needed when history level is set to none. Note that this will also disable some features such as commenting on drools expert user guide pdf free download which store the data in the history database. When using the DDL file approach, both a regular version and a special file with mysql55 in it are available this applies on anything lower than 5.
This latter file will have column types with no millisecond precision in it.
Drools expert user guide pdf free download
Forgy’s paper, he described 4 basic nodes: root, 1-input, 2-input and terminal. 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. AlphaNodes are used to evaluate literal conditions. Although the paper only covers equality conditions, many RETE implementations support other operations. For example, Account. 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.
Forgy’s paper, he refers to these as IntraElement conditions. 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.
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.
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:. 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. The KnowledgeBuilder is responsible for taking source files, such as a DRL file or an Excel file, and turning them into a Knowledge Package of rule and process definitions which a Knowledge Base can consume. An object of the class ResourceType indicates the type of resource it is being asked to build. Binaries, such as decision tables Excel. A configuration can be created using the KnowledgeBuilderFactory.
This allows the behavior of the Knowledge Builder to be modified. The most common usage is to provide a custom class loader so that the KnowledgeBuilder object can resolve classes that are not in the default classpath. The first parameter is for properties and is optional, i. The options parameter can be used for things like changing the dialect or registering new accumulator functions. Resources of any type can be added iteratively. Below, a DRL file is added.
Unlike Drools 4. It is best practice to always check the hasErrors method after an addition. You should not add more resources or retrieve the Knowledge Packages if there are errors.
When all the resources have been added and there are no errors the collection of Knowledge Packages can be retrieved. It is a Collection because there is one Knowledge Package per package namespace. These Knowledge Packages are serializable and often used as a unit of deployment. Instead of adding the resources to create definitions programmatically it is also possible to do it by configuration, via the ChangeSet XML. The following XML schema is not normative and intended for illustration only.
Currently only the add element is supported, but the others will be implemented to support iterative changes. The following example loads a single DRL file. Notice the file: prefix, which signifies the protocol for the resource. The Change Set supports all the protocols provided by java.
URL, such as “file” and “http”, as well as an additional “classpath”. Using the ClassPath resource loader in Java allows you to specify the Class Loader to be used to locate the resource but this is not possible from XML. Currently you still need to use the API to load that ChangeSet, but we will add support for containers such as Spring in the future, so that the process of creating a Knowledge Base can be done completely by XML configuration.
Loading resources using an XML file couldn’t be simpler, as it’s just another resource type. ChangeSets can include any number of resources, and they even support additional configuration information, which currently is only needed for decision tables. Below, the example is expanded to load rules from a http URL location, and an Excel decision table from the classpath. The ChangeSet is especially useful when working with a Knowledge Agent, as it allows for change notification and automatic rebuilding of the Knowledge Base, which is covered in more detail in the section on the Knowledge Agent, under Deploying.
Directories can also be specified, to add all resources in that folder. Currently it is expected that all resources in that folder are of the same type. If you use the Knowledge Agent it will provide a continous scanning for added, modified or removed resources and rebuild the cached Knowledge Base. The KnowledgeAgent provides more information on this. A Knowledge Package is a collection of Knowledge Definitions, such as rules and processes.
It is created by the Knowledge Builder, as described in the chapter “Building”. Knowledge Packages are self-contained and serializable, and they currently form the basic deployment unit. Knowledge Packages are added to the Knowledge Base. However, a Knowledge Package instance cannot be reused once it’s added to the Knowledge Base. If you need to add it to another Knowledge Base, try serializing it first and using the “cloned” result.
We hope to fix this limitation in future versions of Drools. The Knowlege Base is a repository of all the application’s knowledge definitions. It may contain rules, processes, functions, and type models. The Knowledge Base itself does not contain instance data, known as facts; instead, sessions are created from the Knowledge Base into which data can be inserted and where process instances may be started.
Creating the Knowlege Base can be heavy, whereas session creation is very light, so it is recommended that Knowledge Bases be cached where possible to allow for repeated session creation. A KnowledgeBase object is also serializable, and some people may prefer to build and then store a KnowledgeBase , treating it also as a unit of deployment, instead of the Knowledge Packages.
If a custom class loader was used with the KnowledgeBuilder to resolve types not in the default class loader, then that must also be set on the KnowledgeBase. The technique for this is the same as with the KnowledgeBuilder. This is the simplest form of deployment. This approach requires drools-core. Note that the addKnowledgePackages kpkgs method can be called iteratively to add additional knowledge. Both the KnowledgeBase and the KnowledgePackage are units of deployment and serializable.
This means you can have one machine do any necessary building, requiring drools-compiler. Although serialization is standard Java, we present an example of how one machine might write out the deployment unit and how another machine might read in and use that deployment unit.
The KnowledgeBase is also serializable and some people may prefer to build and then store the KnowledgeBase itself, instead of the Knowledge Packages. Drools Guvnor, our server side management system, uses this deployment approach. Stateful Knowledge Sessions will be discussed in more detail in section “Running”. The KnowledgeBase creates and returns StatefulKnowledgeSession objects, and it may optionally keep references to those.
When KnowledgeBase 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. The KnowlegeAgent provides automatic loading, caching and re-loading of resources and is configured from a properties files.
The Knowledge Agent will continuously scan all the added resources, using a default polling interval of 60 seconds. If their date of the last modification is updated it will rebuild the cached Knowledge Base using the new resources. The agent must specify a name, which is used in the log files to associate a log entry with the corresponding agent. The following example constructs an agent that will build a new KnowledgeBase from the specified ChangeSet.
See section “Building” for more details on the ChangeSet format. Note that the method can be called iteratively to add new resources over time. The Knowledge Agent polls the resources added from the ChangeSet every 60 seconds, the default interval, to see if they are updated.
Whenever changes are found it will construct a new Knowledge Base or apply the modifications to the existing Knowledge Base according to its configuration. If the change set specifies a resource that is a directory its contents will be scanned for changes, too. Resource scanning is not on by default, it’s a service and must be started, and the same is true for notification. Both can be done via the ResourceFactory. The default resource scanning period may be changed via the ResourceChangeScannerService.
A suitably updated ResourceChangeScannerConfiguration object is passed to the service’s configure method, which allows for the service to be reconfigured on demand.
Knowledge Agents can take an empty Knowledge Base or a populated one. If a populated Knowledge Base is provided, the Knowledge Agent will run an iterator from Knowledge Base and subscribe to the resources that it finds.
While it is possible for the Knowledge Builder to build all resources found in a directory, that information is lost by the Knowledge Builder so that those directories will not be continuously scanned.
Only directories specified as part of the applyChangeSet Resource method are monitored. One of the advantages of providing KnowledgeBase as the starting point is that you can provide it with a KnowledgeBaseConfiguration. When resource changes are detected and a new KnowledgeBase object is instantiated, it will use the KnowledgeBaseConfiguration of the previous KnowledgeBase object. In the above example getKnowledgeBase will return the same provided kbase instance until resource changes are detected and a new Knowledge Base is built.
When the new Knowledge Base is built, it will be done with the KnowledgeBaseConfiguration that was provided to the previous KnowledgeBase. As mentioned previously, a ChangeSet XML can specify a directory and all of its contents will be added. When the directory scan detects an additional file, it will be added to the Knowledge Base; any removed file is removed from the Knowledge Base, and modified files will be removed from the Knowledge Base.
Note that for the resource type PKG the drools-compiler dependency is not needed as the Knowledge Agent is able to handle those with just drools-core. You could use this to load the resources from a directory, while inhibiting the continuous scan for changes of that directory. Taken together, this forms an importanty deployment scenario for the Knowledge Agent. The KnowlegeBase is a repository of all the application’s knowledge definitions.
It will contain rules, processes, functions, and type models. The Knowledge Base itself does not contain data; instead, sessions are created from the KnowledgeBase into which data can be inserted and from which process instances may be started. Creating the KnowlegeBase can be heavy, whereas session creation is very light, so it is recommended that Knowle Bases be cached where possible to allow for repeated session creation.
The StatefulKnowledgeSession stores and executes on the runtime data. It is created from the KnowledgeBase. The WorkingMemoryEntryPoint provides the methods around inserting, updating and retrieving facts.
The term “entry point” is related to the fact that we have multiple partitions in a Working Memory and you can choose which one you are inserting into, although this use case is aimed at event processing and covered in more detail in the Fusion manual. Most rule based applications will work with the default entry point alone. The KnowledgeRuntime interface provides the main interaction with the engine.
It is available in rule consequences and process actions. In this manual the focus is on the methods and interfaces related to rules, and the methods pertaining to processes will be ignored for now.
But you’ll notice that the KnowledgeRuntime inherits methods from both the WorkingMemory and the ProcessRuntime , thereby providing a unified API to work with processes and rules. Insertion is the act of telling the WorkingMemory about a fact, which you do by ksession. When you insert a fact, it is examined for matches against the rules. This means all of the work for deciding about firing or not firing a rule is done during insertion; no rule, however, is executed until you call fireAllRules , which you call after you have finished inserting your facts.
It is a common misunderstanding for people to think the condition evaluation happens when you call fireAllRules. Expert systems typically use the term assert or assertion to refer to facts made available to the system.
However, due to “assert” being a keyword in most languages, we have decided to use the insert keyword; so expect to hear the two used interchangeably. When an Object is inserted it returns a FactHandle. This FactHandle is the token used to represent your inserted object within the WorkingMemory. It is also used for interactions with the WorkingMemory when you wish to retract or modify an object.
As mentioned in the Knowledge Base section, a Working Memory may operate in two assertion modes, i. New instance assertions always result in the return of a new FactHandle , but if an instance is asserted again then it returns the original fact handle, i. New instance assertions will only return a new FactHandle if no equal objects have been asserted. Retraction is the removal of a fact from Working Memory, which means that it will no longer track and match that fact, and any rules that are activated and dependent on that fact will be cancelled.
Note that it is possible to have rules that depend on the nonexistence of a fact, in which case retracting a fact may cause a rule to activate.
See the not and exist keywords. Retraction is done using the FactHandle that was returned by the insert call. The Rule Engine must be notified of modified facts, so that they can be reprocessed. Internally, modification is actually a retract followed by an insert; the Rule Engine removes the fact from the WorkingMemory and inserts it again.
You must use the update method to notify the WorkingMemory of changed objects for those objects that are not able to notify the WorkingMemory themselves.
Notice that update always takes the modified object as a second parameter, which allows you to specify new instances for immutable objects. The update method can only be used with objects that have shadow proxies turned on.
The update method is only available within Java code. On the right hand side of a rule, also the modify statement is supported, providing simplified calls to the object’s setters. The WorkingMemory provides access to the Agenda, permits query executions, and lets you access named Enty Points. Queries are used to retrieve fact sets based on patterns, as they are used in rules. Patterns may make use of optional parameters. Queries can be defined in the Knowlege Base, from where they are called up to return the matching results.
While iterating over the result collection, any bound identifier in the query can be accessed using the get String identifier method and any FactHandle for that identifier can be retrieved using getFactHandle String identifier. Drools has always had query support, but the result was returned as an iterable set; this makes it hard to monitor changes over time.
We have now complimented this with Live Querries, which has a listener attached instead of returning an iterable result set. These live querries stay open creating a view and publish change events for the contents of this view.
So now you can execute your query, with parameters and listen to changes in the resulting view. The KnowledgeRuntime provides further methods that are applicable to both rules and processes, such as setting globals and registering Channels previously exit points, some references may remain in docs for a while.
Globals are named objects that can be passed to the rule engine, without needing to insert them. Most often these are used for static information, or for services that are used in the RHS of a rule, or perhaps as a means to return objects from the rule engine.
If you use a global on the LHS of a rule, make sure it is immutable. A global must first be declared in a rules file before it can be set on the session. With the Knowledge Base now aware of the global identifier and its type, it is now possible to call ksession. Failure to declare the global type and identifier first will result in an exception being thrown. To set the global on the session use ksession.
If a rule evaluates on a global before you set it you will get a NullPointerException. The StatefulRuleSession is inherited by the StatefulKnowledgeSession and provides the rule related methods that are relevant from outside of the engine. AgendaFilter objects are optional implementations of the filter interface which are used to allow or deny the firing of an activation.
What you filter on is entirely up to the implementation. Drools 4. To use a filter specify it while calling fireAllRules. The following example permits only rules ending in the string “Test”.
All others will be filtered out. The Agenda is a Rete feature. During actions on the WorkingMemory , rules may become fully matched and eligible for execution; a single Working Memory Action can result in multiple eligible rules. When a rule is fully matched an Activation is created, referencing the rule and the matched facts, and placed onto the Agenda.
The Agenda controls the execution order of these Activations using a Conflict Resolution strategy. Working Memory Actions. This is where most of the work takes place, either in the Consequence the RHS itself or the main Java application process. Once the Consequence has finished or the main Java application process calls fireAllRules the 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 Working Memory Actions.
The process repeats until the agenda is clear, in which case control returns to the calling application. When Working Memory Actions are taking place, no rules are being fired.
Conflict resolution is required when there are multiple rules on the agenda. The basics to this are covered in chapter “Quick Start”. As firing a rule may have side effects on the working memory, the rule engine needs to know in what order the rules should fire for instance, firing ruleA may cause ruleB to be removed from the agenda. The most visible one is salience or priority , in which case a user can specify that a certain rule has a higher priority by giving it a higher number than other rules.
In that case, the rule with higher salience will be preferred. LIFO priorities are based on the assigned Working Memory Action counter value, with all rules created during the same action receiving the same value.
The execution order of a set of firings with the same priority value is arbitrary. As a general rule, it is a good idea not to count on rules firing in any particular order, and to author the rules without worrying about a “flow”. These are discussed in later sections. Agenda groups are a way to partition rules activations, actually on the agenda.
At any one time, only one group has “focus” which means that activations for rules in that group only will take effect. You can also have rules with “auto focus” which means that the focus is taken for its agenda group when that rule’s conditions are true. While it best to design rules that do not need control flow, this is not always possible.
Agenda groups provide a handy way to create a “flow” between grouped rules. You can switch the group which has focus either from within the rule engine, or via the API.
If your rules have a clear need for multiple “phases” or “sequences” of processing, consider using agenda-groups for this purpose. Each time setFocus is called it pushes that Agenda Group onto a stack.
When the focus group is empty it is popped from the stack and the focus group that is now on top evaluates. An Agenda Group can appear in multiple locations on the stack.
It is also always the first group on the stack, given focus initially, by default. An activation group is a set of rules bound together by the same “activation-group” rule attribute.
In this group only one rule can fire, and after that rule has fired all the other rules are cancelled from the agenda. The clear method can be called at any time, which cancels all of the activations before one has had a chance to fire.
A rule flow group is a group of rules associated by the “ruleflow-group” rule attribute. These rules can only fire when the group is activate. The group itself can only become active when the elaboration of the ruleflow diagram reaches the node representing the group.
Here too, the clear method can be called at any time to cancels all activations still remaining on the Agenda. The event package provides means to be notified of rule engine events, including rules firing, objects being asserted, etc.
This allows you, for instance, to separate logging and auditing activities from the main part of your application and the rules. We will only cover the WorkingMemoryEventManager here.
The WorkingMemoryEventManager allows for listeners to be added and removed, so that events for the working memory and the agenda can be listened to.
The following code snippet shows how a simple agenda listener is declared and attached to a session. It will print activations after they have fired. To print all Working Memory events, you add a listener like this:. All emitted events implement the KnowlegeRuntimeEvent interface which can be used to retrieve the actual KnowlegeRuntime the event originated from.
The KnowledgeRuntimeLogger 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.
Its main focus is on decision service type scenarios. It avoids the need to call dispose. Stateless sessions do not support iterative insertions and the method call fireAllRules from Java code; the act of calling execute is a single-shot method that will internally instantiate a StatefulKnowledgeSession , 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. 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. Simple StatelessKnowledgeSession execution with a Collection. If you wanted to insert the collection itself, and the collection’s individual elements, then CommandFactory. Methods of the CommandFactory create the supported commands, all of which can be marshalled using XStream and the BatchExecutionHelper. StatelessKnowledgeSession supports globals, scoped in a number of ways.
I’ll cover the non-command way first, as commands are scoped to a specific execution call. Globals can be resolved in three ways. The Stateless Knowledge Session 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. 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. With Rete you have a stateful session where objects can be asserted and modified over time, and where rules can also be added and removed.
Now what happens if we assume a stateless session, where after the initial data set no more data can be asserted or modified and rules cannot be added or removed? Certainly it won’t be necessary to re-evaluate rules, and the engine will be able to operate in a simplified way.
Order the Rules by salience and position in the ruleset by setting a sequence attribute on the rule terminal node. Create an array, one element for each possible rule activation; element position indicates firing order. Disconnect the Left Input Adapter Node propagation, and let the Object plus the Node be referenced in a Command object, which is added to a list on the Working Memory for later execution. Assert all objects, and, when all assertions are finished and thus right-input node memories are populated, check the Command list and execute each in turn.
All resulting Activations should be placed in the array, based upon the determined sequence number of the Rule. Record the first and last populated elements, to reduce the iteration range.
If we have a maximum number of allowed rule executions, we can exit our network evaluations early to fire all the rules in the array. This stops any left-input propagations at insertion time, so that we know that a right-input propagation will never need to attempt a join with the left-inputs removing the need for left-input memory. All nodes have their memory turned off, including the left-input Tuple memory but excluding the right-input object memory, which means that the only node remembering an insertion propagation is the right-input object memory.
Once all the assertions are finished and all right-input memories populated, we can then iterate the list of LeftInputAdatperNode Command objects calling each in turn. They will propagate down the network attempting to join with the right-input objects, but they won’t be remembered in the left input as we know there will be no further object assertions and thus propagations into the right-input memory. There is no longer an Agenda, with a priority queue to schedule the Tuples; instead, there is simply an array for the number of rules.
The sequence number of the RuleTerminalNode indicates the element within the array where to place the Activation. Once all Command objects have finished we can iterate our array, checking each element in turn, and firing the Activations if they exist. To improve performance, we remember the first and the last populated cell in the array. The network is constructed, with each RuleTerminalNode being given a sequence number based on a salience number and its order of being added to the network.
Typically the right-input node memories are Hash Maps, for fast object retraction; here, as we know there will be no object retractions, we can use a list when the values of the object are not indexed. For larger numbers of objects indexed Hash Maps provide a performance increase; if we know an object type has only a few instances, indexing is probably not advantageous, and a list can be used.
Sequential mode can only be used with a Stateless Session and is off by default. To turn it on, either call RuleBaseConfiguration. Sequential mode can fall back to a dynamic agenda by calling setSequentialAgenda with SequentialAgenda.
You may also set the “drools. Drools has the concept of stateful or stateless sessions. We’ve already covered stateful sessions, which use the standard working memory that can be worked with iteratively over time. Stateless is a one-off execution of a working memory 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 a rule engine like a function call with optional return results. In Drools 4 we supported these two paradigms but the way the user interacted with them was different. StatelessSession used an execute StatefulSession didn’t have this method, and insert used the more traditional insert The other issue was that the StatelessSession did not return any results, so that users themselves had to map globals to get results, and it wasn’t possible to do anything besides inserting objects; users could not start processes or execute queries.
Drools 5. The foundation for this is the CommandExecutor interface, which both the stateful and stateless interfaces extend, creating consistency and ExecutionResults :. The CommandFactory allows for commands to be executed on those sessions, the only difference being that the Stateless Knowledge Session executes fireAllRules at the end before disposing the session.
The currently supported commands are:. InsertObject will insert a single object, with an optional “out” identifier. InsertElements will iterate an Iterable, inserting each of the elements.
What this means is that a Stateless Knowledge Session is no longer limited to just inserting objects, it can now start processes or execute queries, and do this in any order. The execute method only allows for a single command. That’s where BatchExecution comes in, which represents a composite command, created from a list of commands.
Now, execute will iterate over the list and execute each command in turn. This means you can insert some objects, start a process, call fireAllRules and execute a query, all in a single execute As mentioned previosly, the Stateless Knowledge Session 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 StatelessKnowledgeSession. 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.
Commands support out identifiers. Any command that has an out identifier set on it will add its results to the returned ExecutionResults instance. Let’s look at a simple example to see how this works. 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.
The CommandExecutor returns an ExecutionResults , and this is handled by the pipeline code snippet as well. Configured converters only exist for the commands supported via the Command Factory.
The user may add other converters for their user objects. This is very useful for scripting stateless or stateful knowledge sessions, especially when services are involved. There is currently no XML schema to support schema validation. The basic format is outlined here, and the drools-pipeline module has an illustrative unit test in the XStreamBatchExecutionTest unit test.
This contains a list of elements that represent commands, the supported commands is limited to those Commands provided by the Command Factory. The contents of the insert element is the user object, as dictated by XStream. The insert element features an “out-identifier” attribute, demanding that the inserted object will also be returned as part of the result payload. This command does not support an out-identifier. The org. UserClass is just an illustrative user object that XStream would serialize.
While the out attribute is useful in returning specific instances as a result payload, we often wish to run actual queries. Both parameter and parameterless queries are supported. Other process related methods will be added later, like interacting with work items. However, with marshalling you need more flexibility when dealing with referenced user data.
To achieve this we have the ObjectMarshallingStrategy interface. Two implementations are provided, but users can implement their own.
SerializeMarshallingStrategy is the default, as used in the example above, and it just calls the Serializable or Externalizable methods on a user instance.
IdentityMarshallingStrategy instead creates an 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 he code to use an Identity Marshalling Strategy. For added flexability we can’t assume that a single strategy is suitable. The Marshaller has a chain of strategies, and when it attempts to read or write a user object it iterates the strategies asking if they accept responsability for marshalling the user object.
One of the provided implementations is ClassFilterAcceptor. This allows strings and wild cards to be used to match class names. Assuming that we want to serialize all classes except for one given package, where we will use identity lookup, we could do the following:. For development purposes we recommend the Bitronix Transaction Manager, as it’s simple to set up and works embedded, but for production use JBoss Transactions is recommended. If rollback occurs the ksession state is also rolled back, so you can continue to use it after a rollback.
To load a previously persisted Stateful Knowledge Session you’ll need the id, as shown below:. To enable persistence several classes must be added to your persistence.
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 contsulted for details. For a quick start, here is the programmatic approach:. Bitronix also provides a simple embedded JNDI service, ideal for testing. To use it add a jndi. Drools Clips is an alpha level research project to provide a Clips like front end ot Drools.
Deftemplates are working, the knowledge base handles multiple name spaces and you can attach the knoweldge base to the session for interative building, to provide a more “shell” like environment suitable for Clips. This project is very early stages and in need of love. If you want to help, open up eclipse import api, core, compiler and clips and you should be good to go.
The unit tests should be self explanatory. 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. A rule file is typically a file with a.
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. A DRL file is simply a text file. 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. 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. 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.
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:. 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. The parser will ignore anything in the line after the comment symbol. Multi-line comments are used to comment blocks of text, both in and outside semantic code blocks.
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.
The standardization includes the error message format and to better explain this format, let’s use the following example:. Usually indicates the rule, function, template or query where the error occurred. This block is not mandatory. Indicates the most common errors, where the parser came to a decision point but couldn’t identify an alternative. Here are some examples:. At first glance this seems to be valid syntax, but it is not exits!
Let’s take a look at next example:. 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:. The above code misses to close the quotes and because of this the parser generates this error message:.
Usually the Line and Column information are accurate, but in some cases like unclosed quotes , the parser generates a position. In this case you should check whether you didn’t forget to close quotes, apostrophes or parentheses. Here are some samples:. Note that the second problem is related to the first. 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. A validating semantic predicate evaluated to false. Usually these semantic predicates are used to identify soft keywords. This sample shows exactly this situation:. This error is associated with the eval clause, where its expression may not be terminated with a semicolon. Check this example:. 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:. 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. Any other message means that something bad has happened, so please contact the development team.
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.
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. Notice that any rule atttribute 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. 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.
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. 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.
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. Then in your rule consequences, you can use things like email.
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. 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.
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. Drools supports the use of function imports, so all you would need to do is:.
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.
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. Launching this will open a new window, with Guvnor in debug mode, ready to go.
This allows Fact Types to be defined as events. 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. The options are to use a DRL file, or the name of a class that you have written and which is available on the classpath. It will poll those files every 60 seconds, which is the default, to see if they are updated. In the case lf Jaxb it adds additional Drools related path info and with XStream it registers Drools related converters and aliases.
This is an implementation of capacitated vehicle routing: Value to only fire the drolls if mdcl is not null. Literal expressions can passed as query arguments, but at this stage you cannot mix expressions with variables. Fact types that are available in your model will be shown in the left-hand listbox. Once the WAR file is in the target directory, the Guvnor is ready to go.
The idea for next releases is to let users to plug their custom Constraints too. Adding your own logos or styles to Guvnor web GUI The following is a list of the most interesting changes. What was once previously static code is now defined by compile-time configuration. You can download the generated repository. Source code Example 9. With category rules it is possible to make all the rules in a certain category extend a rule.
Yes, it should support importing rules so all you need to do is open up a text editor and start typing. For what regards nested accessors, the engine will be notified only for top level fields.
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Drools expert user guide pdf free download.Drools Tutorial: Architecture of Drools Rule Engine and Example
New and Noteworthy in drools 7. Release Notes. Distribution zip contains binaries, examples, sources and javadocs. Distribution ZIP. Drools and jBPM integration with third party project like Spring. Distribution zip contains binaries, examples and sources. See documentation for details about installation.
WildFly 19 WAR. Distribution zip contains WAR files for all supported containers. WildFly 10 WAR. Tomcat 7 WAR. Weblogic 12 WAR. Guvnor 5. Distribution zip contains binaries and sources. Older community releases can be found in the release archive. Red Hat customers can download the product platforms from the product downloads site.
Business Central Workbench. Business Central Workbench Showcase. For more info about the Drools Docker images see this blog post. The latest releases, including 7.
Final are available in the Maven Central Repository. To use them, first configure the JBoss Maven repository. We stopped to release Eclipse tools synchronously with Drools, thus the recent version of Drools and of latest released Eclipse tools updatesite may differ. Click here to go to the Drools and jBPM update site latest released version 7. Alternatively, you can download the latest released version of Drools and jBPM tools zip 7. Download Latest final version: 7.
Final Release date: New and Noteworthy in drools 7. Latest 6. Other downloads: Older community releases can be found in the release archive.
Maven Repository The latest releases, including 7. Eclipse update site We stopped to release Eclipse tools synchronously with Drools, thus the recent version of Drools and of latest released Eclipse tools updatesite may differ.
Upcoming events. Drools Engine. Drools and jBPM integration. KIE Execution Server. WAR files for all supported containers. Drools Workbench. Drools and jBPM tools.