The document discusses JBoss Drools and Drools Fusion, which provide business rules and complex event processing capabilities. It describes the history and vision of Drools, an overview of its rule engine and management tools, and how it has evolved from a standalone framework to an integrated platform. It also introduces jBPM for business process management and how it integrates with Drools for rules and event processing.

1. JBoss Drools and Drools Fusion (CEP): Making Business Rules react to RTE Tihomir Surdilovic [email_address] JBoss, a Division of Red Hat

2. Agenda Project (Drools) vs. Product (BRMS)

3. Drools Vision

4. Drools History

5. Drools at a glance Expert

6. Guvnor

7. JBPM5 Focus on Drools Fusion (CEP Extensions) Event Declaration and Semantics

8. Event Cloud, Streams and the Session Clock

9. Temporal Reasoning

10. Sliding Window Support

11. Streams Support

12. Memory Management Questions & Answers

13. Project vs. Product Repository Rules and Metadata JBoss Enterprise BRMS Platform JBoss Rules Rules Engine/ Execution Rules Development & Management CEP Complex Events Jboss EWP

14. Project vs. Product Cont. Stand-alone Framework

15. Innovation

16. Fast and Frequent Releases

17. Tested on developer environments

18. Suitable for development and prototype

19. No Patches – On to next big thing

20. Non SLA bound forum support Fully Integrated Platform

21. Same Innovated Code + Stability

22. Stability – Includes usability, performance and security issues identified during productization

23. Certified for Multi-OS, Multi-DB

24. Best for Development and Production

25. 5 Yr lifecycle – Includes patches, updates, backward compatibility

26. SLA based full support from dedicated support engineers

27. Open Source Assurance – legal protection derived

28. Jboss community and Enterprise Changes Community / Enterprise Platform Delta BRMS Drools Enterprise Platforms - Performance, Scalability, Security and Quality improves over time without compromising long-term compatibility. 08/09 12/10 03/11 05/11 Over 150+ defect fixes & 35+ OS/JVM/DB certifications (& counting) between Drools Community 5.1 & JBoss BRMS 5.1.x

29. Evolution of Jboss BRMS 05/08 04/09 12/10 ? Lightweight Business Rules Engine Business Rules Engine

30. Authoring and Management App

31. Repository

32. EAP Certified

33. DBs, JVMs Business Rules Engine

34. Authoring and Management App

35. Choice of Repository

36. Integrated Container

37. CEP – Tech Preview

38. EAP, EWS, EWP, JVMs, Dbs and more

39. Performance Repository JBoss Enterprise BRMS Platform 5.0 JBoss Rules BRM Repository JBoss Enterprise BRMS Platform 5.1 JBoss Rules BRM CEP Jboss EWP

40. “ A common platform to model and govern the business logic of the enterprise.” Drools Vision

41. Drools Vision Cont. Event Processes Semantic Ontologies Workflows Rules Rules Workflows Event Processes Semantic Ontologies

42. Drools History – Business Rule Engines 1980s 2010s 1990s 2000s OPS5 ART Clips Jess Drools 2 JRules Drools 3 Drools 4 Drools 5

43. Drools History Drools 2 Rete like XML Scripting language Drools 3 Based on Clips functionality

44. Iterative improves to JRules syntax with Clips functionality Drools 4 More declarative

45. Basic functional programming feature with “from”

46. Basic Rule Flow

47. Basic BRMS Drools 5 Improved functional programming with 'accumulate'

48. More Advanced Rule Flow integration

49. Complex Event Process Temporal Comparators

50. Sliding Time Windows Production ready BRMS (Guvnor)

51. Drools History Cont. Drools 5.1 Differential Diff (true modify) Drools 5.2 (this month, 5.2.0.CR1 available for download) Freeform expressions between patterns

52. Multi-function accumulates

53. Prolog like derivation queries

54. Decision tables and rule templates (Guvnor)

55. Pure GWT (Guvnor)

56. Sample Industries and Users Investment Millennium Investment Group (MIG) Logistics Fedex Airline Sabre Mortgage Franklin American Healthcare OSDE

57. Drools at a glance - Declarative programming Production Rule Systems PRD (forward chaining) Reactive

58. when Alarm( status == “alert” ) then send( “warning” ) Logic Programming LP (backward chaining) Query

59. descendant( “mary”, “jane”) Functional Programming FP Map,Fold, Filter

60. avg([12, 16, 4, 6]) Returns single value 9.5 round([10.3, 4.7, 7.8] ) Returns List [10, 5, 8] Description Logic (model concepts, roles, and individuals) Person Has Name and lives at Address

61. Drools Expert at a glance – Example A - SQL comparison example select * from Account acc, Cashflow cf, AccountPeriod ap where acc.accountNo == cf.accountNo and cf.type == CREDIT cf.date >= ap.start and cf.date <= ap.end trigger : acc.balance += cf.amount rule “increase balance for AccountPeriod Credits” when ap : AccountPeriod() acc : Account( $accountNo : accountNo ) CashFlow( type == CREDIT, accountNo == $accountNo, date >= ap.start && <= ap.end, $ammount : ammount ) then acc.balance += $amount; end

62. Drools Expert at a glance – Rules as a view rule “increase balance for AccountPeriod Credits” when ap : AccountPeriod() acc : Account( $accountNo : accountNo ) CashFlow( type == CREDIT, accountNo == $accountNo, date >= ap.start && <= ap.end, $ammount : ammount ) then acc.balance += $amount; end rule “decrease balance for AccountPeriod Debits” when ap : AccountPeriod() acc : Account( $accountNo : accountNo ) CashFlow( type == DEBIT, accountNo == $accountNo, date >= ap.start && <= ap.end, $ammount : ammount ) then acc.balance -= $amount; end

63. Drools Expert at a glance – Example A - Building and executing rules

64. Drools Expert at a glance – Example A Building and executing rules cont. Building Executing

65. Drools Expert at a glance – Example B - Building and executing rules

66. Drools Expert at a glance – Example B cont. No Fires Add some Fires

67. Drools Expert at a glance – Example B cont. Fires are put out after a while

68. Drools Expert at a glance – Conditional Elements

69. Drools Expert at a glance – Accumulate CE Bus Example Accumulate general syntax Bus Example Revisited (no accumulate functions)

70. Drools Expert at a glance – Conditional Elements From

71. Drools Expert at a glance – Timers and Calendars Interval Timers Field Name Mandatory? Allowed Values Allowed Special Characters Seconds YES 0-59 , - * / Minutes YES 0-59 , - * / Hours YES 0-23 , - * / Day of month YES 1-31 , - * ? / L W Month YES 1-12 or JAN-DEC , - * / Day of week YES 1-7 or SUN-SAT , - * ? / L # Year NO empty, 1970-2099 , - * / Cron-based Timers

72. Drools Expert at a glance – Timers and Calendars Calendars Using Timers and Calendars together Execute now and after 1 hour duration on week days Execute now and after 4 hour duration on weekend days

73. Drools Expert at a glance – Truth maintenance and Inference Issuing Buss passes – typical rule implementation Good practice to do: De-couple knowledge responsibilities

74. Encapsulate knowledge

75. Provide semantic abstractions for those encapsulation

76. Integrity robustness – truth maintenance These types of implementations are considered Bad

77. Monolithic

78. Leaky

79. Brittle integrity - manual maintenance Couples the logic What happens when the Child becomes 16?

80. Drools Expert at a glance – Truth maintenance and Inference cont. De-coupling knowledge responsibilities De-couples the logic Maintains the truth by automatically retracting. Rule “logically” inserts an object. When the rule is no longer true, the object is retracted. The truth maintenance cascades

81. Drools Expert at a glance – Truth maintenance and Inference cont. Notification upon automatic fact retraction Executing The truth maintenance cascades

82. Drools Expert at a glance – Roadmap Improved backward chaining (open queries)

83. Optimistic backward chaining.

84. Business Semantics (ontologies)

85. Simulation Testing

86. High Availability

87. Distributed (Grid) platform

88. Runtime Management BAM (Business Activity Monitoring)

89. Drools Guvnor at a glance Technology JCR 2.0 (JSR-170) backend

90. Seam 2 + GWT frontend

91. WebDav

92. Eclipse synchronisation plugin

93. Role based security Authoring Decision Tables

94. Guided Editor QA Scenario Testing + Rule Verification

95. Drools Guvnor at a glance – Business asset storage

96. Drools Guvnor at a glance – Guided Editor

97. Drools Guvnor at a glance – Decision Tables

98. Drools Guvnor at a glance – Scenario Testing

99. Drools Guvnor at a glance – BPMN2 processes

100. Drools Guvnor at a glance – Compiling and exposing rule packages

101. jBPM5 at a glance – What is BPM? Business Process Management A business process is a process that describes the order in which a series of steps need to be executed, using a flow chart.

102. jBPM5 at a glance – Why BPM? Visibility

103. Monitoring

104. Higher-level Continuous improvement

105. Speed of development

106. Increased agility

107. jBPM5 at a glance – Key Characteristics Open-source business process management project offering: generic process engine supporting native BPMN 2.0 execution

108. targeting developers and business users

109. collaboration, management and monitoring using web-based consoles

110. powerful rules and event integration Core engine is a workflow engine in pure Java state transitions

111. lightweight

112. embeddable

113. generic, extensible Core Engine

114. jBPM5 at a glance – Core Engine // (1) Create knowledge base and add process definition KnowledgeBuilder kbuilder = ... kbuilder.add( ..., &quot;sample.bpmn&quot;, ResourceType.BPMN2); KnowledgeBase kbase = kbuilder.newKnowledgeBase(); kbase.addKnowledgePackages(kbuilder.getKnowledgePackages()); // (2) Create new stateful knowledge session StatefulKnowledgeSession ksession = kbase.newStatefulKnowledgeSession(); // (3) Start a new process instance ksession.startProcess(“com.sample.bpmn.hello”); Knowledge Base Stateful Knowledge Session Process Definition Process Instance

115. jBPM5 at a glance – BPMN 2.0 OMG specification Model

116. Notation

117. Execution semantics Understandable by all business users

118. Process, collaboration, choreography

119. Extensible < definitions ... > < process id=&quot;com.sample.bpmn.hello&quot; name=&quot;Hello World&quot; > < startEvent id=&quot;_1&quot; name=&quot;StartProcess&quot; /> < sequenceFlow sourceRef=&quot;_1&quot; targetRef=&quot;_2&quot; /> < scriptTask id=&quot;_2&quot; name=&quot;Hello&quot; > < script >System.out.println(&quot;Hello World&quot;);</ script > </ scriptTask > < sequenceFlow sourceRef=&quot;_2&quot; targetRef=&quot;_3&quot; /> < endEvent id=&quot;_3&quot; name=&quot;EndProcess&quot; /> </ process > </ definitions > Core Engine BPMN 2.0 XML

120. jBPM5 at a glance – Persistence and Transactions Persistence (JPA, pluggable) Runtime persistence

121. History logging

122. Services Transactions (JTA, pluggable) Command-scoped

123. User-defined Core Engine BPMN 2.0 XML Persistence Trans- actions JPA JTA

124. jBPM5 at a glance – Console Web-based management

125. Business user

126. Features Process instance management

127. User task lists / forms

128. Reporting Core Engine BPMN 2.0 XML Persistence Trans- actions Events History Log Management Console BAM

129. jBPM5 at a glance – Console Domain-specific Processes: Extend palette with

130. domain-specific, declarative

131. service nodes define input / output parameters

132. runtime binding User task

133. Human task service (WS-HT) Task lists

134. Task life cycle Task clients Task forms Core Engine BPMN 2.0 XML Persistence Trans- actions Events Integration Domain-specific Processes Human Task Service ESB

135. jBPM5 at a glance – Roadmap jBPM 5.0: February 2011

136. jBPM 5.1: NOW ... Improve Oryx BPMN2 designer to support full round-tripping

137. New Eclipse BPMN2 editor

138. Lots of small feature improvements and bug fixes jBPM 5.2 – jBPM 6.x Simulation / testing / replay

139. Flexible, adaptive, goal-oriented processes

140. Integrated “no-code” tooling (form builder, etc.)

141. Focus on Drools Fusion (CEP) Brief introduction on CEP and Terminology

142. Drools Vision

143. Drools Fusion: Complex Event Processing extensions Event Declaration and Semantics

144. Event Cloud, Streams and the Session Clock

145. Temporal Reasoning

146. Sliding Window Support

147. Streams Support

148. Memory Management

149. Drools Fusion – What is an Event? “ An event is an observable occurrence .”

150. “ An event in the Unified Modelling Language is a notable occurrence at a particular point in time .”

151. http://www.wikipedia.org

152. “ Anything that happens , or is contemplated as happening .”

153. “ An object that represents, encodes or records an event , generally for the purpose of computer processing ”

154. http://complexevents.com For the scope of this presentation:

155. “ An event is a significant change of state at a particular point in time ”

156. Drools Fusion – What is a Complex Event? “ Complex Event , is an abstraction of other events called its members.” Examples : The 1929 stock market crash – an abstraction denoting many thousands of member events, including individual stock trades)

157. The 2004 Indonesian Tsunami – an abstraction of many natural events

158. A completed stock purchase -an abstraction of the events in a transaction to purchase the stock

159. A successful on-line shopping cart checkout – an abstraction of shopping cart events on an on-line website Source: http://complexevents.com

160. Drools Fusion – What is Complex Event Processing (CEP)? “ 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.

161. 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.”

162. -- wikipedia

163. Drools Fusion – CEP Scenarios Examples: Emergency Response Systems

164. Credit Card Fraud Detection

165. Logistics Real-Time Awareness solution

166. Neonatal ICU: infant vital signs monitoring A few characteristics of common CEP scenarios: Huge volume of events, but only a few of real interest Usually events are immutable

167. Usually queries/rules have to run in reactive mode

168. Strong temporal relationships between events Individual events are usually not important The composition and aggregation of events is important

169. Drools Fusion – CEP vs. ESP Complex Event Processing , or CEP, and Event Stream Processing , or ESP, are two technologies that were born separate, but converged . An oversimplification : In their origins... Event Stream Processing focused on the ability to process high volume streams of events.

170. Complex Event Processing focused on defining, detecting and processing the relationships among events. For the scope of this presentation:

171. “ CEP is used as a common term meaning both CEP and ESP .”

172. Drools Fusion – EDA “ Event Driven Architecture (EDA) is a software architecture pattern promoting the production , detection , consumption of, and reaction to events. An event can be defined as &quot;a significant change in state&quot;[1]. For example, when a consumer purchases a car, the car's state changes from &quot;for sale&quot; to &quot;sold&quot;. A car dealer's system architecture may treat this state change as an event to be produced, published, detected and consumed by various applications within the architecture.” http://en.wikipedia.org/wiki/Event_Driven_Architecture CEP is a component of the EDA

173. Drools Fusion – EDA vs SOA EDA is **not** SOA 2.0

174. Complementary architectures

175. Metaphor In our body : SOA is used to build our muscles and organs (what's needed to DO things)

176. EDA is used to build our sensory system (what's needed to REACT TO things)

177. Drools Fusion – CEP Characteristics A few characteristics of common CEP scenarios: Huge volume of events, but only a few of real interest Usually events are immutable

178. Usually queries/rules have to run in reactive mode

179. Strong temporal relationships between events Individual events are usually not important The composition and aggregation of events is important

180. Drools Fusion – Enables... Event Detection: From an event cloud or set of streams, select all the meaningful events, and only them. [Temporal] Event Correlation: Ability to correlate events and facts declaring both temporal and non-temporal constraints between them.

181. Ability to reason over event aggregation Event Abstraction: Ability to compose complex events from atomic events AND reason over them

182. Drools Fusion – Features Features: Event Semantics as First Class Citizens

183. Allow Detection, Correlation and Composition

184. Temporal Constraints

185. Session Clock

186. Stream Processing

187. Sliding Windows

188. CEP volumes (scalability)

189. (Re)Active Rules

190. Data Loaders for Input

191. Drools Fusion – Demo Twitter Stream CEP Demo: Listen to the Twitter Stream API Twitter4J API

192. Listens to a random sample of tweets Detects patterns and reacts Drools Fusion Simple one process (multi-thread) demo Focus on specific features

193. Drools Fusion – Event Declaration and Semantics Event semantics: Point-in-time and Interval An event is a fact with a few special characteristics: Usually immutable , but not enforced

194. Strong temporal relationships Lifecycle may be managed Allow use of sliding windows “ All events are facts , but not all facts are events.”

195. Demo twitter rules part 1, 2, 3) // declaring existing class import some.package.VoiceCall declare VoiceCall @role ( event ) @timestamp ( calltime ) @duration ( duration ) end // generating an event class declare StockTick @role ( event ) symbol : String price : double end

196. Drools Fusion – Temporal Reasoning Semantics for: time: discrete

197. events: point-in-time and interval Ability to express temporal relationships: Allen’s 13 temporal operators James F. Allen defined the 13 possible temporal relations between two events.

198. Eiko Yoneki and Jean Bacon defined a unified semantics for event correlation over time and space.

199. Drools Fusion – Temporal Relationships rule “Shipment not picked up in time” when Shipment ( $pickupTime : scheduledPickupTime ) not ShipmentPickup ( this before $pickupTime ) then // shipment not picked up... Action required. end Temporal Relationship

200. Drools Fusion – Allen’s 13 Temporal Operators Point-Point Point-Interval Interval-Interval A before B A meets B A overlaps B A finishes B A includes B A starts B A coincides B A B A B A B A B A B A B A B

201. Drools Fusion – Allen’s 13 Temporal Operators Point-Point Point-Interval Interval-Interval A after B A metBy B A overlapedBy B A finishedBy B A during B A finishes B Demo twitter rules part 4 A B A B A B A B A B A B

202. Drools Fusion – Streams : Simple Example Scenario

203. Drools Fusion – Stream Support (entry-points) A scoping abstraction for stream support Rule compiler gather all entry-point declarations and expose them through the session API

204. Engine manages all the scoping and synchronization behind the scenes. rule “Stock Trade Correlation” when

205. $c : Customer( type == “VIP” )

206. BuyOrderEvent( customer == $c , $id : id ) from entry-point “Home Broker Stream”

207. BuyAckEvent( sourceEvent == $id ) from entry-point “Stock Trader Stream”

208. then

209. // take some action

210. end

211. No notion of “flow of time” : the engine sees all facts without regard to time

212. No attached Session Clock

213. No requirements on event ordering

214. No automatic event lifecycle management

215. No sliding window support Notion of “flow of time” : concept of “now”

216. Session Clock has an active role synchronizing the reasoning

217. Event Streams must be ordered

218. Automatic event lifecycle management

219. Sliding window support

220. Automatic rule delaying on absence of facts Drools Fusion – Cloud Mode, Stream Mode, Session Clock CLOUD STREAM

221. Drools Fusion – Reference Clock Reference clock defines the flow of time Named Session Clock is assigned to each session created Synchronizes time sensitive operations duration rules

222. event streams

223. process timers

224. sliding windows

225. Drools Fusion – Session Clock Uses the strategy pattern and multiple implementations: Real-time operation

226. Tests

227. Simulations

228. etc

229. Drools Fusion – Session Clock Selecting the session clock: API: KnowledgeSessionConfiguration conf = ...

230. conf.setOption( ClockTypeOption.get( “realtime” ) ); System Property or Configuration File: drools.clockType = pseudo

231. Drools Fusion – Sliding Window Support Allows reasoning over a moving window of “interest” Time

232. Length Sliding window 1 Sliding window 2

233. Drools Fusion – Sliding Window Support Allows reasoning over a moving window of “interest” Time

234. Length Sliding window 1 Sliding window 2 Joined window

235. Drools Fusion – Sliding Window Support Allows reasoning over a moving window of “interest” Time

236. Length Demo twitter rules part 5 rule “Average Order Value over 12 hours” when $c : Customer() $a : Number() from accumulate ( BuyOrder( customer == $c , $p : price ) over window:time ( 12h ),

237. average ( $p ) )

238. then

239. // do something

240. end

241. Drools Fusion – Delaying Rules Negative patterns may require rule firings to be delayed. rule “Order timeout” when $bse : BuyShares ( $id : id ) not BuySharesAck( id == $id , this after[0s,30s] $bse ) then // Buy order was not acknowledged. Cancel operation // by timeout. end Forces the rule to wait for 30 seconds before firing, because the acknowledgement may arrive at any time!

242. Drools Fusion – Temporal Dimension Requires the support to the temporal dimension A rule/query might match in a given point in time, and not match in the subsequent point in time That is the single most difficult requirement to support in a way that the engine: stays deterministic

243. stays a high-performance engine Achieved mostly by compile time optimizations that enable: constraint tightening

244. match space narrowing

245. memory management

246. Drools Fusion – Temporal Dimension Support CEP scenarios are stateful by nature.

247. Events usually are only interesting during a short period of time .

248. Hard for applications to know when events are not necessary anymore Temporal constraints and sliding windows describe such “ window of interest ”

249. Drools Fusion – Simple Example Rule rule “Bag was lost” when $b : BagScannedEvent() from entry-point “check-in” not BagScannedEvent( id == $b.id , this after[0s,5m] $b ) from entry-point “pre-load”

250. then

251. // Bag was lost, do something

252. end Easy to “see” that the only temporal relationship between the events defines a 5 minutes interest window.

253. Drools Fusion – Abstract Example Rule rule “Abstract event relationship example” when $a : A() $b : B( this after[-2, 2] $a ) $c : C( this after[-3, 4] $a ) $d : D( this after[ 1, 2] $b, this after[2,3] $c ) not E( this after[ 1,10] $d ) then // Bag was lost, do something end How about now? What is the temporal relationship between A and E?

254. Drools Fusion – Temporal Dependency Matrix A B C D E A [ 0, 0 ] [ -2, 2 ] [ -3, 2 ] [ -1, 4 ] [ 0, 14 ] B [ -2, 2 ] [ 0, 0 ] [ -2, 0 ] [ 1, 2 ] [ 2, 12 ] C [ -2, 3 ] [ 0, 2 ] [ 0, 0 ] [ 2, 3 ] [ 3, 13 ] D [ -4, 1 ] [ -2, -1 ] [ -3, -2 ] [ 0, 0 ] [ 1, 10 ] E [ -14, 0 ] [ -12, -2 ] [ -13, -3 ] [-10,-1 ] [ 0, 0 ] Constraint tightening Demo twitter rules part 6 [-2,2] [-3,4] [2,3] [1,2] [1,10] A D B E C

255. Q&A Drools project site: http://www.drools.org ( http://www.jboss.org/drools/ ) Documentation: http://www.jboss.org/drools/documentation.html Tihomir Surdilovic – tsurdilo @redhat.com