IncQuery-D is a distributed incremental graph query engine in the cloud, which is able to evaluate complex queries on large graphs.

1. Budapest University of Technology and Economics
Department of Measurement and Information Systems
DISTRIBUTED INCREMENTAL GRAPH QUERIES
Gábor Szárnyas, Dániel Varró
2 February, 2015
22nd Minisymposium of the
Department of Measurement and Information Systems

2. MOTIVATION

3. Performance
issues
Agile Model-Driven Development
Modeling
Code
generation
Testing
Early validations
Transformations
Scalability
challenges

4. Model Sizes
 Models = graphs with 100M–1B elements
o Car industry
o Avionics
o Software analysis
o Cyber-physical systems
Source: Markus Scheidgen, Automated andTransparent
Model Fragmentation for Persisting Large Models, 2012
application model size
software models 108
sensor data 109
geo-spatial models 1012
Validation may take hours

5. MDE
Scalability
Incrementality
Incremental
queries
Incremental
transformation
Storing partial
results
Tracking
changes

6. Motivating Example
 Pattern for an AUTOSAR validation constraint
Communication
channel
Logical signal Mapping Physical signal
Invalid submodel
 Validation
Valid submodel

7. Antijoin
Join
Join
Fill indexer nodesStore interim resultsRead result setEdit modelPropagating changesRead result set
Rete Algorithm
Communication
channel
Logical signal Mapping Physical signal
Result set

8. CURRENT STATE OF RESEARCH

9. EMF-INCQUERY
 Rete-based incremental graph query engine
 Open source Eclipse project
 Typical use cases
o Validation
o Incremental model transformation
o Model synchronization

10. Single Workstation Limitations
 Majority of tools mostly work for <1M model
elements due to resource exhaustion
 Best tools: <10M model elements
 JVM limitations: cannot handle 15+ GB heap
memory efficiently
 Proposed solution
o Horizontal scaling: distributed system

11. Problem Statement
Scalability
Scalable
storage
Scalable
query engine
Distributed NoSQL
databases
Distributed
INCQUERY:
INCQUERY-D
Complex
queries
Big
models

12. Goals of INCQUERY-D
 Objectives
o Distributed incremental pattern matching
o Adapting EMF-INCQUERY’s tooling to distributed DBs
o Executed over a cloud infrastructure (COTS hardware)
 Achieve scalability by avoiding memory bottleneck
o Sharding separately
• Data
• Indexers
• Query network
o In memory
• Index + query

13. RESEARCH QUESTIONS AND
RESULTS

14. Architecture and Data Representation
 Is it possible to build a query engine which works
on various backends using different data
representation formats?
 Is it possible to serve multiple users concurrently?

15. INCQUERY-D Architecture
Server 1
Database
shard 1
Server 2
Database
shard 2
Server 3
Database
shard 3
Transaction
In-memory
EMF model
Database
shard 0
Server 0
Rete net
Indexer
layer
EMF-INCQUERY INCQUERY-D
Distributed query evaluation network
Distributed indexer Model access adapter
Indexing
Indexer Indexer Indexer Indexer
Join
Join
Antijoin
In-memory storage
Distributed indexing,
notification
Production network
• Stores intermediate query results
• Propagates changes
Distributed persistent
storage
Distributed production network
• Each intermediate node can be allocated
to a different host
• Remote internode communication

16. Scalable Incremental Query Evaluation
 Is it possible to utilise an incremental query
evaluation algorithm in a distributed system for
high performance query evaluation?
 How can we benchmark a distributed system in a
reproducible manner?

17. Benchmark Results for Revalidation
Quick response time
for models with 88M elements
Different characteristics

18. Dimensions of Scalability
 Infrastructure
o Number of machines
o Available memory / CPU
o Network performance
o Number of concurrent users
 Model
o Model size
o Model characteristics
 Queries
o Number of queries
o Query complexity

19. Optimisation and Dynamic Reconfiguration
 How can we scale and optimise such a system?
 How can the system adapt to the changes
o in the system?
o in the cloud environment?
 How can we estimate the resources required by a
certain setup?

20. Dynamic Resource Allocation
Server 1 Server 2 Server 3Server 0
Indexer Indexer Indexer Indexer
Join
Join
Antijoin
10% 70% 60%
Δ
80%90%
Join
25%75%
Δ
Δ
Memory usage

21. Conclusion
 MDE provides Big Data questions for research
 Horizontal scaling is a way for querying large models
 Theoretical challenges
o Distributed pattern matching algorithm
o Data representation
o Dynamic resource allocation
 Practical challenges
o Integrating technologies: database, messaging
framework, monitoring, user interface, etc.
o High performance query evaluation

22. Ω