The document presents VIATRA 3, a reactive model transformation platform that allows for both batch and incremental transformations using a framework based on observed and controlled events. It discusses the main concepts, query language capabilities, conflict resolution, and the performance evaluation of the platform across various scenarios. Additionally, it highlights the flexibility and scalability of VIATRA for diverse applications in model-driven development.

1. VIATRA 3:
A Reactive Model Transformation
Platform
Gábor Bergmann, István Dávid, Ábel Hegedüs, Ákos
Horváth, István Ráth, Zoltán Ujhelyi and Dániel
Varró
IncQuery Labs Ltd.
Budapest University of Technology and Economics
University of Antwerp
7/22/2015 ICMT 2015, L’Aquila, Italy 1

2. Reactive model-driven applications
Interference between functions
Commonalities
• Queries, rules, scheduling, conflicts
User
interaction
(modify)
SRC TRG
Batch
transformation
Incremental
transformation
Live validation Live views
Derived features
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3. Separation of concerns
Reactive
Transformation
Framework
Observed
events
Controlled
events
Actions
What has changed?
When to react?
Perform in
consistent state
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4. Examples of events and actions
Reactive
Transformation
Framework
Observed
events
Controlled
events
Actions
• Model modified
• Match appeared
• Event sequence identified
• „Run” button pushed
• Consistent state reached
after editing
• Transaction committed
• Modify model
• Add error marker
• Update view
• Send e-mail
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5. Main concepts
Reactive
Transformation
Framework
Observed
events
Controlled
events
Actions
• Event source
• Event occurrence
(type, data)
• Life cycle
• Jobs
• Scheduler
Rule
specifications
• Agenda
• Executor
• Conflict resolver
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6. Query language
Xtend (Java)
Language example
pattern someCondition( param1, param2 ) {...}
val rule = createRule().precondition(someCondition).
action[ match | // perform action ].build
val incrRule = createRule().precondition(someCondition).
lifecycle(ActivationLifecycles.incremental).
action(::Appeared)[
match | // perform action].
action(::Disappeared)[
match | // perform action].
build
Event data
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7. Query language
Xtend (Java)
pattern someCondition( param1, param2 ) {...}
val rule = createRule().precondition(someCondition).
action[ match | // perform action ].build
val incrRule = createRule().precondition(someCondition).
lifecycle(ActivationLifecycles.incremental).
action(::Appeared)[
match | // perform action].
action(::Disappeared)[
match | // perform action].
build
Language example
Rule specification
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8. Query language
Xtend (Java)
pattern someCondition( param1, param2 ) {...}
val rule = createRule().precondition(someCondition).
action[ match | // perform action ].build
val incrRule = createRule().precondition(someCondition).
lifecycle(ActivationLifecycles.incremental).
action(::Appeared)[
match | // perform action].
action(::Disappeared)[
match | // perform action].
build
Language example
Observed events
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9. Query language
Xtend (Java)
pattern someCondition( param1, param2 ) {...}
val rule = createRule().precondition(someCondition).
action[ match | // perform action ].build
val incrRule = createRule().precondition(someCondition).
lifecycle(ActivationLifecycles.incremental).
action(::Appeared)[
match | // perform action].
action(::Disappeared)[
match | // perform action].
build
Language example
Job specification
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10. Query language
Xtend (Java)
pattern someCondition( param1, param2 ) {...}
val rule = createRule().precondition(someCondition).
action[ match | // perform action ].build
val incrRule = createRule().precondition(someCondition).
lifecycle(ActivationLifecycles.incremental).
action(::Appeared)[
match | // perform action].
action(::Disappeared)[
match | // perform action].
build
Language example
Activation
state-event
transitions
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11. Query language
Xtend (Java)
pattern someCondition( param1, param2 ) {...}
val rule = createRule().precondition(someCondition).
action[ match | // perform action ].build
val incrRule = createRule().precondition(someCondition).
lifecycle(ActivationLifecycles.incremental).
action(::Appeared)[
match | // perform action].
action(::Disappeared)[
match | // perform action].
build
Language example
Jobs associated
with event types
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12. Batch transformation
Event-driven transformation
Activation life cycles
Phases
Disabled
Appear
Update
Fire
Disappear
Initial
Enabled
Transitions
Updated
/updateJob
/appearJobAppeared
Disappeared
Fired
/disappearJob
/action
Appeared
Only feature of
event data object
has changed
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13. Conflict resolution
Multiple actions available
• Different activations in the same rule
• Activations of different rules
Which activation to execute next?
Conflict resolver can be selected
• Global conflict set: deals with all rules
• Scoped conflict set: selected rules
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14. VIATRA Features
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15. EMF-IncQuery and VIATRA
EMF-IncQuery
• Latest release 1.0: 2015. 06. 30 (graduation)
• “an incremental model query engine”
New VIATRA
• Latest release 0.7: 2015. 06. 30
• “an event-driven and reactive
model transformation platform”
• High-performance model transformation engine
o built on IncQuery technology
• Unique feature:
o supports batch + incremental transformation
o over a unified virtual machine infrastructure (EVM)
• Full Java and EMF compatibility
• Tightly integrated into Xtend
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16. But there is more…
Complex Event-Processing Engine
• Specify reactions upon detecting
complex sequences of events
• Xtext based language
Design Space Exploration Framework
• Explore design candidates as models
satisfying multiple criteria
Model obfuscator
• Remove sensitive information from confidential models
Coming soon
• MWE2 integration for transformation chains
• Debugger for reactive transformations
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17. Evaluation
Performance benchmarks
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18. Test scenario
Different transformation variants
• Batch
o Simple and optimized
o Plain EMF-IncQuery and VIATRA
• Incremental
o Change monitor based
o EVM: explicit traceability and query
result based (QRT)
o VIATRA: query based
Executions
• First transformation execution
• Small modification + (re)execution
Scale SRC Objects SRC References TRG Objects TRG References Trace Objects Trace References
SUM
Objects
SUM
References
1 395 772 366 736 354 720 1 115 2 228
2 849 1 821 773 1 535 762 1 535 2 384 4 891
4 1 694 4 697 1 534 2 972 1 522 3 056 4 750 10 725
8 3 604 17 111 3 266 6 108 3 254 6 520 10 124 29 739
16 7 820 89 193 7 124 12 395 7 112 14 236 22 056 115 824
32 17 714 594 181 16 308 24 837 16 297 32 605 50 319 651 623
64 43 795 4 424 529 40 960 50 028 40 948 81 908 125 703 4 556 465
Different model scenarios
• Industrial (presented below)
• Client-Server
• Publish-Subscribe
Environment
• New machine with 16 GB RAM
Parameters
• 10 GB Heap
• Maximum 10 minutes execution times
for complete chain
Trace model’s size
similar to target model
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19. Runtime for first transformation
Batch IncQuery + Batch VIATRA
are the fastest
Incremental approaches
are within ~200%
Logarithmic scale!
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20. Modification and second transformation
As expected
incremental transformation
does well in (re)execution
Logarithmic scale!
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21. Conclusions - New VIATRA
Reactive Model Transformation Platform
Flexibility and wide range of possible applications
Performance and scalability
Internal DSLs and easy integration
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22. Final points
22
VIATRA
• Version 0.7 is available
• Generic transformation engine for batch and incremental
transformation chains
• http://www.eclipse.org/viatra/
The examples with all test results and more details are
available form
• https://github.com/IncQueryLabs/incquery-examples-cps/
Your contributions (feedback, forum posts, ideas, bugzillas,
patches) are very welcome!
• To what direction should we enhance the demonstrator and the
VIATRA transformation API?
7/22/2015 ICMT 2015, L’Aquila, Italy