Speed up the access and queries on large models thanks to our language (and execution environment) to define access plans for specific scenarios. This talk was awarded the best paper award at the Models 2016 conference Read more at http://modeling-languages.com/prefetchml-dsl-prefetching-caching-emf-models/

1. PrefetchML: a Framework for
Prefetching and Caching Models
Jordi Cabot
ICREA-UOC
jordi.cabot@icrea.cat
Gwendal Daniel
Inria - Mines Nantes & Lina
gwendal.daniel@inria.fr
Gerson Sunyé
Inria - Mines Nantes & Lina
gerson.sunye@inria.fr

2. 07/10/2016 PrefetchML: a Framework for Prefetching and Caching Models - G. Daniel 2
Introduction
● Prefetching
– Bring objects into memory before they are requested
● Caching
– Retain objects in memory to speed-up their access
● Integrated in databases and file systems
– Speeds-up I/O intensive applications

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Introduction
● Database prefetchers & caches
– Lack of fine-grained configuration
● Elements to load
● Usage scenario
– Tightly coupled to data representation
● Low level of abstraction
● Not reusable
– Not a common in NoSQL stores

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Introduction
● We need to prefetch and cache models
– Scalable Persistence Frameworks
● Databases to store large models (relational, NoSQL, ...)
● Latencies to bring elements from the database
● I/O intensive

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Introduction
● We need to prefetch and cache models
– Metamodel informations
● High-level prefetching rules
● Decoupled from data representation

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PrefetchML
● A prefetching and caching framework at the model level
PrefetchML DSL
PrefetchML Engine
– Metamodel and model level
– Use case dependent
– Readable
– Datastore-independent
– Transparent for the persistence solution
rule definition
rule execution

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PrefetchML DSL
● Define prefetching and caching rules grouped in plans
● Event-based language
– Model loading
– Element access, update, deletion
– OCL guards
● OCL loading instructions
● Cache management
– Define cache policy
– Remove cached elements

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PrefetchML DSL
import ''http://www.example.com/Java''
plan samplePlan {
rule r1: on starting
fetch Package.allInstances()
}
Package
+ name : String
ClassDeclaration
+ name : String
BodyDeclaration
+ name : String
Modifier
+ kind : VisibilityKind
*
1
ownedElements
*
bodyDeclarations
modifier

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PrefetchML DSL
import ''http://www.example.com/Java''
plan samplePlan {
rule r1: on starting
fetch Package.allInstances()
rule r2: on access type ClassDeclaration
fetch self.bodyDeclarations.modifier
}
Package
+ name : String
ClassDeclaration
+ name : String
BodyDeclaration
+ name : String
Modifier
+ kind : VisibilityKind
*
1
ownedElements
*
bodyDeclarations
modifier

10. 07/10/2016 PrefetchML: a Framework for Prefetching and Caching Models - G. Daniel 10
PrefetchML DSL
import ''http://www.example.com/Java''
plan samplePlan {
use cache LRU[size=100, chunk=10]
rule r1: on starting
fetch Package.allInstances()
rule r2: on access type ClassDeclaration
fetch self.bodyDeclarations.modifier
}
Package
+ name : String
ClassDeclaration
+ name : String
BodyDeclaration
+ name : String
Modifier
+ kind : VisibilityKind
*
1
ownedElements
*
bodyDeclarations
modifier

11. 07/10/2016 PrefetchML: a Framework for Prefetching and Caching Models - G. Daniel 11
PrefetchML DSL
import ''http://www.example.com/Java''
plan samplePlan {
use cache LRU[size=100, chunk=10]
rule r1: on starting
fetch Package.allInstances()
rule r2: on access type ClassDeclaration (self.name = 'MyClass')
fetch self.bodyDeclarations.modifier
}
Package
+ name : String
ClassDeclaration
+ name : String
BodyDeclaration
+ name : String
Modifier
+ kind : VisibilityKind
*
1
ownedElements
*
bodyDeclarations
modifier

12. 07/10/2016 PrefetchML: a Framework for Prefetching and Caching Models - G. Daniel 12
PrefetchML DSL
import ''http://www.example.com/Java''
plan samplePlan {
use cache LRU[size=100, chunk=10]
rule r1: on starting
fetch Package.allInstances()
rule r2: on access type ClassDeclaration (self.name = 'MyClass')
fetch self.bodyDeclarations.modifier
remove type Package
}
Package
+ name : String
ClassDeclaration
+ name : String
BodyDeclaration
+ name : String
Modifier
+ kind : VisibilityKind
*
1
ownedElements
*
bodyDeclarations
modifier

13. 07/10/2016 PrefetchML: a Framework for Prefetching and Caching Models - G. Daniel 13
PrefetchML Engine
● Rule execution engine
● Executes loading instructions
● Captures events
● Transparent to the modeling framework
● Database independent
– Specialized connectors

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PrefetchML Engine

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PrefetchML Engine

16. 07/10/2016 PrefetchML: a Framework for Prefetching and Caching Models - G. Daniel 16
PrefetchML Engine

17. 07/10/2016 PrefetchML: a Framework for Prefetching and Caching Models - G. Daniel 17
PrefetchML Engine

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Tooling
● PrefetchML Editor
– XText editor
– Navigation hints
– Rule validation

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Tooling

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Evaluation
Q1 Q2 Q3
0
2
4
6
8
10
12
14
16
18
No Pref.
EMF Pref.
NeoEMF Pref.
ExecutionTime(s)
Q1 Q2 Q3
0
1
2
3
4
5
6
7
8
9
ExecutionTime(s)
● Model containing 2 million elements
● MDT OCL over NeoEMF/Graph
Cold Execution (empty cache) Warmed Execution

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Conclusion
● Model-level prefetching & caching framework
● PrefetchML DSL: defines prefetching and caching rules at
a high level of abstraction
● PrefetchML Engine: captures events, trigger rules, cache
model elements

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Conclusion
● Future work
– Automatic generation of PrefetchML plans
● ATL transformations
● Code analysis
– Dynamic discovery of frequent access patterns
● Log mining
● Switch on/off plans when needed
● Add/remove rules at runtime

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Questions?
Websites / Repositories
PrefetchML: https://github.com/atlanmod/Prefetching_Caching_DSL
NeoEMF: neoemf.com
https://github.com/atlanmod
Thank you for your attention!
https://github.com/SOM-Research

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PrefetchML Engine