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Drools Fusion User Guide

Version 5.5.0.CR1

In the Drools vision of a unified behavioral modelling platform, Drools Fusion is the module responsible for enabling event processing behavior.

Although several tries were made, there isn't up to date any 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:

For instance, on a Stock Broker application, when a sell 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 owner of the individual traded assets that change 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 on a particular 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, where 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 the 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 as CEP systems.

This is a very simplistic explanation to a really complex and fertile field of research, but sets a very high level and common understanding for the concepts this guide will introduce.

The current understanding of what Complex Event Processing is may be briefly described as the following quote from 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.

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:

From a technical perspective:

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.

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:

Based on this general common characteristics, Drools Fusion defined a set of goals to be achieved in order to support Complex Event Processing appropriately:

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.

Events, from a Drools perspective are just a special type of fact. In this way, we can say that all events are facts, but not all facts are events. In the next few sections the specific differences that characterize an event are presented.

An event is a fact that present a few distinguishing characteristics:

Drools supports the declaration and usage of events with both semantics: point-in-time events and interval-based events.

All events have a set of metadata associated to them. Most of the metadata values have defaults that are automatically assigned to each event when they are inserted into the working memory, but it is possible to change the default on an event type basis, using the metadata tags listed below.

For the examples, lets assume the user has the following class in the application domain model:

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 array of different scenarios that may require different clock implementations. Just to mention a few:

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.

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:

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.

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:

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:

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:

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.

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:

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).

Drools implements all 13 operators defined by Allen and also their logical complement (negation). This section details each of the operators and their parameters.

The after evaluator correlates two events and matches when the temporal distance from the current event to the event being correlated belongs to the distance range declared for the operator.

Lets look at an example:

$eventA : EventA( this after[ 3m30s, 4m ] $eventB ) 

The previous pattern will match if and only if the temporal distance between the time when $eventB finished and the time when $eventA started is between ( 3 minutes and 30 seconds ) and ( 4 minutes ). In other words:

 3m30s <= $eventA.startTimestamp - $eventB.endTimeStamp <= 4m 

The temporal distance interval for the after operator is optional:

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:

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:

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:

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.

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:

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 retract 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:

KnowledgeBaseConfiguration config = KnowledgeBaseFactory.newKnowledgeBaseConfiguration();

config.setOption( EventProcessingOption.CLOUD );

The equivalent property is:

drools.eventProcessingMode = cloud

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:

Given that the above requirements are met, the application may enable the STREAM mode using the following API:

KnowledgeBaseConfiguration config = KnowledgeBaseFactory.newKnowledgeBaseConfiguration();

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:

All these features are explained in the following sections.

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:

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:

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:

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.

Sliding Windows are a way to scope the events of interest by defining a window that is constantly moving. The two most common types of sliding window implementations are time based windows and length based windows.

The next sections will detail each of them.

Sliding Length Windows work the same way as Time Windows, but consider events based on order of their insertion into the session instead of flow of time.

For instance, if the user wants to consider only the last 10 RHT Stock Ticks, independent of how old they are, the pattern would look like this:

StockTick( company == "RHT" ) over window:length( 10 )

As you can see, the pattern is similar to the one presented in the previous section, but instead of using window:time to define the sliding window, it uses window:length.

Using a similar example to the one in the previous section, if the user wants to sound an alarm in case the average temperature over the last 100 readings from a sensor is above the threshold value, the rule would look like:

The engine will keep only consider the last 100 readings to calculate the average temperature.


Please note that falling off a length based window is not criteria for event expiration in the session. The engine disregards events that fall off a window when calculating that window, but does not remove the event from the session based on that condition alone as there might be other rules that depend on that event.


Please note that length based windows do not define temporal constraints for event expiration from the session, and the 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.

The classic Rete algorithm is usually executed using a single thread. Although, as confirmed in several opportunities by Dr. Forgy, the algorithm itself is parallelizable. Drools implementation of the ReteOO algorithm supports coarse grained parallelization through rulebase partitioning.

When this option is enabled, the rulebase will be partitioned in several independent partitions and a pool of worker threads will be used to propagate facts through the partitions. The implementation guarantees that at most one worker thread will be executing tasks for a given partition, but multiple partitions may be "active" at a single point in time.

Everything should be transparent to the user, except that all working memory actions (insert/retract/modify) are executed asynchronously.

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 retract 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: