Rules Engine are being increasingly used in the enterprise applications to write complex business processing tasks in configurable manner without sacrificing performance. This session will focus on sharing experiences of using Open source Drools rules engine to write business logic for some banking applications. The session will also explore ways to write DSLs to make business rules very end user friendly and use of decision tables for users to give rules in excel

1. Developing
Configurable and
High Performance
Apps in Drools
Ajay Mahajan
Lead Architect
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2. Agenda
What, Where, Why, When
Drools Eco-System & A Use Case
Rule Definitions
Usage - Deployment Modes
Best Practices
Value from Session
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3. What, Where, Why, When
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4. What is a Rule Engine
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5. Where Does It Fit
Parameterization
Code
Config Files
Database
Rules Engine
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6. Why Should I Bother
• Many ways to define Rules
Flexibility & • Group rules and define priorities
Configurability • Plethora of functions that help in decisioning
Rule Engine
• User friendly and Business Like
Manageability • Better Tooling Support
• Easier Understanding & Visualization
• Just define your rules not execution details
Declarative v/s • Execution is handled by Rules Engine
Imperial • Sequencing and Re-entry
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7. FUDs – Fear, Uncertainty and Doubt
• True, as compared to if-else statements in code
Slow • However, Rules are precompiled
• In Some Cases, execution is faster if designed correctly
• Yes, they do have a learning curve
Difficult • Start small and Limit the features to those you really need
• You don’t have to learn each feature and function offered
More • Yes, rules engine do need more space than a java class file
• Precompiled Rules form a Rete graph
Memory • Use stateless models where use case allows
• Stateful models – Follow the optimizations and mind your memory
• Commercial Tools can get expensive, e.g. Blaze and Jrules
Expensive • Open Source Drools has evolved over time, in its version 5.5
• Drools used in high volume, mission critical systems
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8. When Should I Use a Rule Engine
IF (Requirements== expressed as rules)
IF (Rules == many OR complex OR changing frequently)
IF (Rules == managed separately from application code)
IF (memory != very low)
IF (Application == evolving)
IF (Developers == have skills OR ok with learning curve)
IF (Business Users == like to see / experiment with rules)
IF ( Additional Complexity < Benefits in Flexibility + Configurability)
THEN use Rules Engine
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9. Drools Ecosystem
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10. Jboss - Drools
Expert
• Main Rules Engine Component
• Have Stabilized after going through product cycles
• Highly Successful and widely used
Rule Flow (replaced by jBPM)
• Group into Rule sets and define flow chart to execute them
• Graphical Environment to define work flows
• jBPM using BPMN 2.0 is the way to go for any serious BPM
Guvnor
• Web Based GUI to manage the Rules
• Split out as separate component in V5
• Read Only in Production, but modify in test/UAT for business
Planner
• Resources– travelling salesman, scheduling, routing
• Heuristic Rules
• Relatively new
Fusion
• Event Processing- ESP / CEP use cases
• Concept of Sliding Time window
• Other products such as Esper and Twitter Storm
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11. Drools Expert – Steps
• DRL, Decision
Define Tables, DSL
Compile • To Knowledge base
Create • Uses Knowledge base
Session
Insert • Causes Activations
Facts
Fire • RHS
Rules Executes
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12. Real Use Case
Trades
Matching Engine Match Statuses
T1, T4, T3, T2
T1 C3
T2 C1 + C3
T3+T4 C4
Confirmation
C1, C2, C3, C4
100’s of Trades and Confirmations inflowing per second at peak hr
Flowing in any order, not necessarily one after the other
One trade can exactly match to one confirmation
A trade can match to more than one confirmation
One or more trades can match to one Confirmation
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13. DRL – Drools Rules Language
rule "Perfect Match"
salience 100
when
t:Trade()
c:Confirm(qty == t.qty , confirmId == t.cusip , amt == t.amt , price == t.price )
then
log("Perfect Match for " + t.toString() + c.toString());
Match perfectMatch = new Match(t,c, "Perfect");
// retract perfect matches
retract(t);
retract(c);
end Trade to Confirm Matching
Why is this
The base language for Rules Definition blazing
All other Forms compile to this language fast ??
Rich and Versatile
Entire Syntax, features and Rules definition is available
Least User Friendly
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14. DSL - Domain Specific Language
expander Match.dsl
rule "Exact Match Trade to Confirm"
when
Match Trade and Confirm
- on cusip with confirmId
- on amt
- on price
then
Log "Perfect Match“
Create Match with Status “Perfect”
Remove Matched Elements
end
This is real code not Pseudo code
DSL combines with grammar definition and translates to DRL
Very Easy to Understand for Users and Visualize
Power of creating new business vocabulary
Encourages reuse
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15. Decision Table
RuleTable TradeRequests
NAME CONDITION
CONDITION ACTION ACTION ACTION ACTION ACTION ACTION
event:Event
event:Event
eventGroup
$param != null TradeRequest tr = ne
tr.setStartCaptureDate(DateUtils.getBusinessDate(event.get$1(), $2+1));
tr.setEndCaptureDate(DateUtils.getBusinessDate(event.get$1(), $2+1));
tr.setStartTradeDate(DateUtils.getBusinessDate(event.get$1(), $2));
tr.setEndTradeDate(Date
tr.setSettlementF
Trade Condition Event Start Capture End Capture Settlement
(Comment Column) Name Group Date Present Trade Type Date Date Traded After Traded Before Flag
TD < ED ; F = F IncOpenFail I effectiveDate Open Fails FreezeDate, 0 EffectiveDate, -1 "F"
TD < ED ; SD <= FD ; F = O IncRegOpen I effectiveDate Regular Open EffectiveDate, -1 FreezeDate, 0 EffectiveDate, -1 "O"
TD < ED ; FD < SD IncExtSet I effectiveDate Extended Settle EffectiveDate, -1 FreezeDate, 0 EffectiveDate, -1
ED<=TD ; SD <=FD ; CD <= FD IncShortSet I effectiveDate Short Settle EffectiveDate, 0 FreezeDate, 0 EffectiveDate, 0
TD < ED ; FD < PD IncAsOf I effectiveDate As of Trades FreezeDate, 1 EffectiveDate, -1
Corporate Actions
Excel columns are designated as Conditions or Actions
Top few control rows are hidden from users,
Control rows help translate the excel into DRL
Easy to understand for Users, once basic Excel formats are given
Fit for use cases where there is need for intense parameterization
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16. Learning & Best Practices
Experiences, Usage Models, Performance
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17. Real Life Experiences
Complex Matching Engines
• Multiple Engines used for various functional matching
• Performed at 600 transactions / sec on one instance of execution
• If the I/o (messaging / database) were commented, got 8k executions / sec
• Stateful models used, but memory was conversed through optimizations
Corporate Actions – Event Validations, Trade Extraction
• Stateless model that evaluates each event separately through set of rules
• 100’s of rules based on event types defined in Decision Tables
• Increase in number of rules barely dent the performance
• For 10k executions, 2 rules take 320 ms, and 100 rules take 328 ms
Risk Analysis & Calculations
• Calculations have lot of parameters, such as credit rating, product type, etc.
• Calculations segmented into small number of individual steps
• The decision of which formulae to use, was done by a Rules Engine
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18. Usage Models
Synchronous Execution
Request – Response Style Your Rule
Application Engine
Can act on the decision immediately
Asynchronous Pipeline
Rule
Messaging Style Events Actions
Engine
Very scalable and resilient
In Process with the Application
Jar file as part of the application
Excellent for Stateless execution, as reduces I/o without increasing memory
Stateful executions are challenge in clustered environment & need memory sizing
Out of Process as a runtime component
Central Deployment & Management
Overheads in Communication, and hence affects performance
Could become bottleneck / central point of failure
Needs sophisticated scaling models (e.g. functional split based on Hash or some key)
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19. Improving Performance ..1
Keep Separate Deployable Units rather than a giant rule engine component
Divide and Conquer
Use Stateless Sessions where Business case allows
You can cluster and load balance your services seamlessly
You can use in-process deployments easily
Limit the number of facts in Stateful Executions
The degradation is exponential beyond 400k objects in memory
If higher volumes anticipated, than plan for multi deployments using sharding concepts
Limit the Size of the objects checked in memory
Use DTO (Data Transfer Object) pattern
Use Batched Mode of Execution
Check in more objects if you can in one go into the memory
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20. Improving Performance ..2
Use Drools only for decisions, not performing actual actions
Let the decisions be communicated to a downstream component or by the caller to Rules Engine
Avoid using evals(), --- use only as a last resort
The java code inside eval is difficult to optimize into rete tree
Work on aggregates where possible
Rather than Checking Individual facts into the memory
If you want to dig deeper
Read more on the Rete Algorithm
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21. Development Tips
Use the IDE
Syntax Validations
DSL conversions
Drools Debugging
Use events
Understanding how rules activate and fire
Helpful for troubleshooting
Remember to turn off in production
Keep individual rules small, simple and atomic
Avoid cyclic triggering of rules when you update the facts
Use Agenda groups & Activation groups wherever applicable
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22. Ajay Mahajan
ajay.mahajan@wipro.com
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