Stress-testing remote model querying APIs for relational and graph-based stores


Recent research in scalable model-driven engineering now allows very large models to be stored and queried. Due to their size, rather than transferring such models over the network in their entirety, it is typically more efficient to access them remotely using networked services (e.g. model repositories, model indexes). Little attention has been paid so far to the nature of these services, and whether they remain responsive with an increasing number of concurrent clients. This paper extends a previous empirical study on the impact of certain key decisions on the scalability of concurrent model queries on two domains, using an Eclipse Connected Data Objects model repository, four configurations of the Hawk model index and a Neo4j-based configuration of the NeoEMF model store. The study evaluates the impact of the network protocol, the API design, the caching layer, the query language and the type of database and analyses the reasons for their varying levels of performance. The design of the API was shown to make a bigger difference compared to the network protocol (HTTP/TCP) used. Where available, the query-specific indexed and derived attributes in Hawk outperformed the comprehensive generic caching in CDO. Finally, the results illustrate the still ongoing evolution of graph databases: two tools using different versions of the same backend had very different performance, with one slower than CDO and the other faster than it.

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Divisions: College of Engineering & Physical Sciences
Additional Information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funding: EPSRC (EP/F001096/1); and EU FP7 (611125).
Uncontrolled Keywords: API design,Collaborative modelling,Model persistence,NoSQL storage,Relational databases,Remote model querying,Stress testing,Software,Modelling and Simulation
Publication ISSN: 1619-1374
Last Modified: 25 Mar 2024 08:22
Date Deposited: 10 Jul 2017 14:20
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2019-04-04
Published Online Date: 2017-06-30
Accepted Date: 2017-06-19
Submitted Date: 1800-11-14
Authors: García-Domínguez, Antonio (ORCID Profile 0000-0002-4744-9150)
Barmpis, Konstantinos
Kolovos, Dimitrios S.
Wei, Ran
Paige, Richard F.



Version: Published Version

License: Creative Commons Attribution

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