Neo4EMF : big models made easier! @ EclipseCon France 2014 - Ignite Talks Session
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Ignite talk teaser about Neo4EMF, an Eclipse tool to build and handle EMF models on top of Neo4j graph databases
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Neo4EMF : big models made easier! @ EclipseCon France 2014 - Ignite Talks Session
- 1. Neo4EMF: When big models are no longer an issue! Amine Benelallam, Hugo Brunelière AtlanMod Team (Inria, Mines Nantes, LINA)
- 2. © AtlanMod - atlanmod-contact@mines-nantes.fr 2 Big data, big models…
- 3. © AtlanMod - atlanmod-contact@mines-nantes.fr Time Vs. Memory 3 Finding the right balance? Image by Krzysztof Szkurlatowski 12frames.eu
- 4. © AtlanMod - atlanmod-contact@mines-nantes.fr 4 …Neo4EMF! = EMF + Neo4j
- 5. © AtlanMod - atlanmod-contact@mines-nantes.fr 5 Neo4EMF features Java code generation On-demand loading Improved querying
- 6. © AtlanMod - atlanmod-contact@mines-nantes.fr 6 Create / save data Load on-demand data Check changesNotify set Neo4EMF Resource Dirty save Neo4EMF overview
- 7. © AtlanMod - atlanmod-contact@mines-nantes.fr 7 Using Neo4EMF… Dedicated Ecore model importer
- 8. © AtlanMod - atlanmod-contact@mines-nantes.fr 8 Install Neo4J runtime if not there Using Neo4EMF…
- 9. © AtlanMod - atlanmod-contact@mines-nantes.fr 9 Using Neo4EMF… Dedicated Genmodel metadata
- 10. © AtlanMod - atlanmod-contact@mines-nantes.fr 10 Using Neo4EMF… Neo4EMF generated code
- 11. © AtlanMod - atlanmod-contact@mines-nantes.fr 11 Using Neo4EMF… Handling the Neo4EMF resource
- 12. © AtlanMod - atlanmod-contact@mines-nantes.fr 12 Using Neo4EMF… Using Neo4EMF within another tool: Example of Java model discovery with Eclipse MoDisco
- 13. © AtlanMod - atlanmod-contact@mines-nantes.fr 13 Time Vs. Memory (2) Load + full navigation & Save (performance for a 400Mb model) 0 20 40 60 80 100 120 140 160 Load+navigateAll/modify (s) Save (s) XMI CDO Neo4EMF 0 200 400 600 800 1000 1200 1400 1600 Load+navigateAll/modify (Mb) Save (Mb)
- 14. © AtlanMod - atlanmod-contact@mines-nantes.fr 14 Time Vs. Memory (3) Advanced navigation/querying (performance for a 1Gb model) 0 200 400 600 800 1000 1200 1400 1600 Grabats (Mb) Unused methods (Mb) 0 10 20 30 40 50 60 70 Grabats (s) Unused methods (s) XMI CDO Neo4EMF
- 15. © AtlanMod - atlanmod-contact@mines-nantes.fr 15 Check us out : www.neo4emf.com Contact us : neo4emf@gmail.com Fork us at : https://github.com/neo 4emf/Neo4EMF Don’t forget to…
Notas del editor
- - EMF-based tools, for instance, reverse engineering ones have reached their limits in terms of scalability - Models up to 10^6 elements - Robust persistence backend is a key success to model management operations (e.g. query and transformations) - Existing backends focus more on time performance
- - Provides a persistence layer for EMF-based models on top of Graph-DB (neo4j) - Balances time performance and memory optimization - Enables Light-weight first time loading and change tracking - Enables dirty saving and and unloading mechanisms - Provides adapted java code generation
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --
- Traversal: Traversing the whole resource and changing name values Save: Saving the resource Model size : 400 MB Thanks to dirty save, we can maintain a good memory footprint, akin to XMI. In time performance we notice that neo4EMF is consuming more time during traversal, this is due to the periodical dirty save to the resource. During save, Neo4EMF is consuming less time, since it only performs a cleaning of the of database – the save is done --