05/02/2023 1ICMT 2015, L’Aquila, Italy

VIATRA 3:A Reactive Model

Transformation PlatformGábor 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

05/02/2023 2ICMT 2015, L’Aquila, Italy

Reactive model-driven Applications

Interference between functionsCommonalities• Queries, rules, scheduling, conflicts

User interaction(modify)

SRC TRGBatch transformation

Incremental transformation

Live validation Live views

Derived features

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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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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

Main concepts

Reactive Transformation

Framework

Observed events

Controlled events

Actions

• Event source• Event

occurrence(type, data)

• Life cycle • Jobs

• Scheduler

Rule specification

s

• Agenda• Executor

• Conflict resolver

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Query languageXtend (Java)

Language examplepattern someCondition( param1, param2 ) {...}

val rule = createRule().precondition(someCondition). action[ match | // perform action ].buildval incrRule = createRule().precondition(someCondition). lifecycle(ActivationLifecycles.incremental). action(::Appeared)[ match | // perform action]. action(::Disappeared)[ match | // perform action]. build

Event data

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Query languageXtend (Java)

pattern someCondition( param1, param2 ) {...}

val rule = createRule().precondition(someCondition). action[ match | // perform action ].buildval incrRule = createRule().precondition(someCondition). lifecycle(ActivationLifecycles.incremental). action(::Appeared)[ match | // perform action]. action(::Disappeared)[ match | // perform action]. build

Language exampleRule specification

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Query languageXtend (Java)

pattern someCondition( param1, param2 ) {...}

val rule = createRule().precondition(someCondition). action[ match | // perform action ].buildval 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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Query languageXtend (Java)

pattern someCondition( param1, param2 ) {...}

val rule = createRule().precondition(someCondition). action[ match | // perform action ].buildval 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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Query languageXtend (Java)

pattern someCondition( param1, param2 ) {...}

val rule = createRule().precondition(someCondition). action[ match | // perform action ].buildval 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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Query languageXtend (Java)

pattern someCondition( param1, param2 ) {...}

val rule = createRule().precondition(someCondition). action[ match | // perform action ].buildval 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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Batch transformation

Event-driven transformation

Activation life cyclesPhases

Disabled

AppearUpdate

FireDisappear

Initial

Enabled

Transitions

Updated

/updateJob

/appearJobAppeared

Disappeared

Fired/disappearJob

/action

Appeared

Only feature of event data object has changed

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Conflict resolutionMultiple actions available• Different activations in the same rule• Activations of different rulesWhich 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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VIATRA Features

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EMF-IncQuery and VIATRAEMF-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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But there is more…Complex Event-Processing Engine• Specify reactions upon detecting

complex sequences of events• Xtext based languageDesign Space Exploration Framework• Explore design candidates as models

satisfying multiple criteriaModel obfuscator• Remove sensitive information from confidential modelsComing soon• MWE2 integration for transformation chains• Debugger for reactive transformations

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Evaluation

Performance benchmarks

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Test scenarioDifferent transformation variants• Batch

o Simple and optimizedo Plain EMF-IncQuery and VIATRA

• Incremental o Change monitor basedo 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 ReferencesSUM Objects

SUM References

1 395 772 366 736 354 720 1 115 2 2282 849 1 821 773 1 535 762 1 535 2 384 4 8914 1 694 4 697 1 534 2 972 1 522 3 056 4 750 10 7258 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 82432 17 714 594 181 16 308 24 837 16 297 32 605 50 319 651 62364 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-SubscribeEnvironment• New machine with 16 GB RAMParameters• 10 GB Heap• Maximum 10 minutes execution

times for complete chain

Trace model’s size similar to target model

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Runtime for first transformation

Batch IncQuery + Batch VIATRA are the fastest

Incremental approaches are within

~200%

Logarithmic scale!

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Modification and second transformation

As expected incremental transformationdoes well in (re)execution

Logarithmic scale!

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Conclusions - New VIATRAReactive Model Transformation Platform

Flexibility and wide range of possible applications

Performance and scalabilityInternal DSLs and easy integration

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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?