Gremlin-ATL presentation at ASE 2017.

1. Gremlin-ATL: a Scalable Model
Transformation Framework
Gwendal DANIEL
Inria, IMT Atlantique, LS2N
gwendal.daniel@inria.fr
Frédéric JOUAULT
ESEO
frederic.jouault@eseo.fr
Gerson SUNYE
Inria, IMT Atlantique, LS2N
gerson.sunye@inria.fr
Jordi CABOT
ICREA - UOC
jordi.cabot@icrea.cat

2. Introduction
• Complex and large models
• Civil Engineering
• Biology
• Reverse Engineering
• Need to provide solutions to
• Store large models
• Efficiently query them
• Compute complex transformations
2G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

3. Model Persistence
• Default serialization mechanism: XMI
• Scalable model persistence frameworks
• Use databases to store models
• Relational: CDO
• NoSQL: Morsa, NeoEMF
• Low memory footprint
3G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework
Modeling API
Modeling
Framework
DatabaseModeler

4. Model Persistence
API Call1
…
API Calln
Model
Transformation
Transformation
Engine
Modeling
Framework
Database
rule myRule {
from pp : In!Person
to ee: Out!Entity (
name <- p.name
)
}
get(pp1)
get(pp1, name)
create(ee1, Entity)
set(ee1, name)
…
get(ppn)
get(ppn, name)
create(een, Entity)
set(een, name)
Typical Model
Transformation Tool
Under the hood
4
Low-level modeling API
→ Not aligned with the
database capabilities
Fragmented queries
→ Not efficient
→ Remote database
Intermediate objects
→ Memory consumption
→ Execution time overhead
G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

5. Model Persistence
• Not efficient to compute model queries and transformations
• Why can’t we write database queries manually?
• Modern persistence frameworks typically rely on NoSQL databases
• Multiple query languages, data representation, etc
• Low-level queries are hard to understand and maintain
• Modeling expertise vs. Database expertise
• Solution: generate them!
5G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

6. Gremlin-ATL
• Generate database queries from model transformations
• Bypass modeling framework APIs
• Benefit of all the query capabilities of the backend
6
ATLtoGremlin
Transformation
Gremlin
Traversal
DatabaseATL Transformation
G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework
rule myRule {
from pp : In!Person
to ee: Out!Entity (
name <- pp.name
)
}
g.idx(‘’metaclasses’’)[[name:Person]].transform{
ee = g.createVertex(Entity);
e.addEdge(‘’instanceof’’,
g.idx(‘’metaclasses’’)[[name:Entity]]
ee.name = it.name
}.iterate();
Database
Gremlin-ATL
Transformation Tool
Under the hood

7. Gremlin-ATL
• Input: ATL
• Well-known in the modeling
community
• Hybrid language
• Transformation rules
• Embeds OCL
rule myRule {
from pp : In!Person (
not pp.name.isEmpty()
)
to ee : Out!Entity(
name ← pp.name
}
7G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

8. Gremlin-ATL
• Output: Gremlin
• Multi-database query language
• Graph traversals
• Easily extensible
• Native Neo4j support, OrientDB …
• NeoEMF/Graph
g.idx(‘metaclasses’)[[name:Person]].inE(‘instanceOf’).outV
.filter{!it.name.isEmtpy()}
.transform{
ee = g.createVertex();
ee.addEdge(‘instanceOf’, g.idx(‘metaclasses’)[[name:Entity]]);
ee.name = it.name;
}.iterate()
8G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

9. rule myRule {
from pp : In!Person (
not pp.name.isEmpty()
)
to ee : Out!Entity(
name ← pp.name
)
}
Gremlin-ATL
• 3 steps process
• Map ATL constructs to their Gremlin equivalent
• Merge the created Gremlin constructs into a script
• Send the script to the database
9
g.idx(‘metaclasses’)[[name:Person]].inE(‘instanceOf’).outV
.filter{!it.name.isEmtpy()}
.transform{
ee = g.createVertex();
ee.addEdge(‘instanceOf’, g.idx(‘metaclasses’)[[name:Entity]]);
ee.name = it.name;
}.iterate()
G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

10. Evaluation
10
0
50000
100000
150000
200000
250000
300000
set1 set2 set3 set4
Java2KDM Execution Time (ms)
ATL Gremlin-ATL
OutOfMemory
G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework
Models containing 6000 to 3,5M elements
Java2KDM Transformation
NeoEMF/Graph

11. Evaluation
11G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework
Models containing 6000 to 3,5M elements
Java2KDM Transformation
NeoEMF/Graph
0
100
200
300
400
500
600
set1 set2 set3 set4
Java2KDM Memory Consumption (MB)
ATL Gremlin-ATL
OutOfMemory

12. Conclusion
• Benefits
• No query fragmentation / intermediate objects
• Positive results on large models
• Extensible architecture (see our article)
• Drawbacks
• Less efficient than existing solutions for small / in-memory models
• Need to be manually integrated
• Tightly coupled to the model persistence framework
12G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

13. Conclusion
• Future Work
• Extend our mapping
• Reuse schema inferrence approaches
• Ease the integration of other persistence frameworks / data sources
• Gremlin-ATL to express data migration operations
• Promising results on the Neo4j panama paper database (see our article)
13G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework

14. Question?
Thank you for your attention!
Websites / Repositories
NeoEMF: neoemf.com
Gremlin-ATL: github.com/atlanmod/mogwai
https://github.com/SOM-Research
https://github.com/AtlanMod
14G. Daniel - Gremlin-ATL: a Scalable Model Transformation Framework