The document presents an overview of integrating efficient model queries in state-of-the-art Eclipse Modeling Framework (EMF) tools, focusing on EMF-IncQuery, a model query engine optimized for incremental queries. It addresses scalability issues in model-driven engineering (MDE), highlights the performance benefits of EMF-IncQuery, and introduces new features such as an expressive query language and on-the-fly validations. The document concludes with performance benchmarks showcasing the speed advantages of EMF-IncQuery over traditional OCL methods, emphasizing its potential for large instance models.

1. Integrating Efficient Model Queries
in State-of-the-art EMF Tools
Gábor Bergmann, Ábel Hegedüs, Ákos
Horváth, István Ráth, Zoltán Ujhelyi and
Dániel Varró
Budapest University of Technology and Economics TOOLS Europe 2012
Fault Tolerant Systems Research Group Prague, Czech Republic, 2012. 05. 30.

2. Overview
 Introduction
 EMF-INCQUERY overview
 Transparent query integration
 Performance considerations
 Conclusion

3. MDE today
Modeling techniques
Integrated model-driven tools
REQ
Model- SD
based early
analysis Architecture
IMP
modeling,
DSMLs TST
Code
generation,
IDE model-assisted Model-based
development test generation

4. MDE today
Modeling techniques
Eclipse Modeling Framework
REQ
SD
IMP
TST
IDE - Eclipse

5. A key problem of MDE
 Scalability vs. modeling tools
o Issues encountered by several industrial partners using
tools based on the Eclipse Modeling Framework (EMF)
o Modeling scenarios can get really complex really quickly
• Instance models of size 1-2M and beyond
• Performance issues with model transformations and code
generators have an adverse effect on everyday development tasks
 Scalability? Recognized e.g. by AUTOSAR tool vendors
o Complex http://wiki.eclipse.org/Auto_IWG_WP2
(meta)models
o Large instance models
o Complex query and manipulation scenarios

6. Focus: model queries
 Model queries: “a piece of code that retrieves a given set of the
model”
 Queries are at the heart of MDE
o Every model access/read is a (simple) query
o More complex: Views, content providers
o Most complex: Model transformations, code generators, …
 Query performance (= the speed of content retrieval) is crucial
o First vs. consecutive vs. throughput
o Query result vs. query contents
 Core problems
1. Slow query engines
2. Considerable programming effort necessary to get complex
queries right

7. EMF-INCQUERY

8. Overview
 EMF-INCQUERY: a model query engine
o Supports batch queries
o Optimized for incremental queries!
 Incremental evaluation
o Based on the RETE algorithm
o Compute once, update afterwards
o Gain: Instant re-evaluation
o Price: Uses some more memory
• Manageable with proper life cycles

9. EMF instance
model
Input = Model contents + changes (EMF notifications)
Input RETE network
nodes
Query definition
Intermediate
nodes
Output Delta
nodes monitor
Generated query
components
Query engine
Output = Query results + (subsequent) Query result deltas

10. Benefits of EMF-INCQUERY
 Makes on-the-fly well-formedness validation, view
maintenance, … feasible over really large instance
models
 Simplifies writing really complex queries
o Graph pattern language
o Highly reusable  query libraries
 Easy-to-integrate into existing apps
o Works with any EMF domain metamodel
o Integrates through Eclipse standards  works with
many EMF-based apps out-of-the-box

11. DEMO INCQUERY Pattern Language
 School metamodel in EMF

12. INCQUERY Pattern Language
Expressive declarative query language
by graph patterns
Capture local + global queries
Compositionality + Reusabilility
„Arbitrary” Recursion, Negation

13. New features with version 0.6
 Tools
o Xtext2-based language
• Content assist (code completion), validation, outline, …
o Unique query language features
• Unlimited recursion and transitive closure
• Aggregate functions
• Match/exceed the expressive power of OCL, while providing more
flexible re-use
 Runtime
o Build and execute queries on-the-fly
o Strong generative bindings to Java
o Efficient incremental transitive closure

14. INCQUERY Development Tools
• Works with most EMF-
based editors out-of-
the-box
• Reveals matches as
selection
Pattern Editor
Queries are applied
& updates on-the-
fly
Query Explorer

15. INCQUERY Model Validation
• Works with most EMF-
based tools out-of-the-
box
• Manages error-warning
markers on-the-fly as the
Standard Eclipse BPMN Editor
user is editing the model
= Instantaneous feedback
Markers in the Problems View

16. TRANSPARENT QUERY
INTEGRATION
… beyond the development tools

17. Derived features in EMF
Derived features: Derived attributes:
• Supported by auxiliary • Typed scalar values
mechanisms (e.g. Java, OCL) • Supported by
• Supported by EMF-based INCQUERY derivatives
<<derived>>
tools out-of-the-box
<<derived>>
<<derived>>
Derived edges:
• Binary relationships
• Supported by binary
INCQUERY patterns

18. INCQUERY derivatives
You can combine IQPL
with (side-effect free,
deterministic) Xbase
Key benefits:
• Automatic
dependency-aware
change notifications Derived attribute
for derived features “hasPrimeWeight”:
• Instantaneous result True iff pattern
retrieval “coursePrimeWeight”
matches to host C

19. PERFORMANCE BENCHMARK
Instantaneous result retrieval?

20. Benchmarking scenario
 In-memory models
o Embedded railways control software system design domain
o Instance models up to 2.7M elements
 Scenario: well-formedness validation
o 5 rules, from simple to very complex
o Batch validation: loading models + executing queries
o On-the-fly revalidation: modifying models + re-executing
queries instantaneously
 Tools
o Eclipse tools (OCL, INCQUERY, EMF-Java)
o RDF/SPARQL engines (for comparison)
 http://viatra.inf.mit.bme.hu/publications/trainbenchmark

21. Revalidation on-the-fly
SPARQL tools are
• Incremental EMF engines
generally 1-2 Orders-of-
typically 5-10x faster
Magnitude (OM) slower
• sub-100ms response times
than top EMF tools
for up to 1.5M elements

22. A closer look at the top
Performance advantage
of OCL-IA over OCL is
not significant with
complex queries
INCQUERY is 1-1.5 orders-
of-magnitude faster
than OCL-IA

23. Memory usage
• Incremental engines
impose a linear memory
consumption overhead
• INCQUERY overhead is
larger than OCL-IA
• Even the largest models
fit into 1GB of RAM

24. Final points
 EMF-INCQUERY has been proposed as an
Eclipse.org project under EMFT
http://www.eclipse.org/proposals/modeling.emf.incquery/
 EMF-INCQUERY 0.6 preview release is available
immediately
o Pointers:
http://viatra.inf.mit.bme.hu/incquery
http://viatra.inf.mit.bme.hu/incquery/getting_started
http://viatra.inf.mit.bme.hu/performance
 Release 0.6 is scheduled shortly after the Eclipse
Juno Launch (beginning of July)

25. Acknowledgements
 Additional development team members
o Márk Czotter, Tamás Szabó
 Performance benchmarking
o Benedek Izsó, Zoltán Szatmári
 Testing, examples
o Attila Csicsely, Tamás Csurgó, Dániel Kávássy, Gábor
Szárnyas, Tamás Tóth
 Thank you very much!