Domain-Specific Languages for Algorithmic Graph Processing: A Systematic Literature Review

Graph analytics has grown increasingly popular as a model for data analytics across a variety of domains. This has prompted an emergence of solutions for large-scale graph analytics, many of which integrate user-facing domain-specific languages (DSLs) to support graph processing operations. These DS...

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Bibliographic Details
Published in:Algorithms Vol. 18; no. 7; p. 445
Main Authors: Boukham, Houda, Younsi Dahbi, Kawtar, Chiadmi, Dalila
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2025
Subjects:
Algorithms
Case studies
Data collection
Digital libraries
Documentation
Domain specific languages
graph algorithms
graph analytics
Graph matching
Graphs
high-performance computing
Keywords
Literature reviews
Pattern matching
Systematic review
United States
Switzerland
Germany
Washington, D.C
ISSN:1999-4893, 1999-4893
Online Access:Get full text
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Summary:Graph analytics has grown increasingly popular as a model for data analytics across a variety of domains. This has prompted an emergence of solutions for large-scale graph analytics, many of which integrate user-facing domain-specific languages (DSLs) to support graph processing operations. These DSLs fall into two categories: query-based DSLs for graph-pattern matching and graph algorithm DSLs. While graph query DSLs are now standardized, research on DSLs for algorithmic graph processing remains fragmented and lacks a cohesive framework. To address this gap, we conduct a systematic literature review of algorithmic graph processing DSLs aimed at large-scale graph analytics. Our findings reveal the prevalence of property graphs (with 60% of surveyed DSLs explicitly adopting this model), as well as notable similarities in syntax and features. This allows us to identify a common template that can serve as the foundation for a standardized graph algorithm model, improving portability and unifying design between different DSLs and graph analytics toolkits. We additionally find that, despite achieving remarkable performance and scalability, only 20% of surveyed DSLs see real-life adoption. Incidentally, all DSLs for which user documentation is available are developed as part of academia–industry collaborations or in fully industrial contexts. Based on these results, we provide a comprehensive overview of the current research landscape, along with a roadmap of recommendations and future directions to enhance reusability and interoperability in large-scale graph analytics across industry and academia.
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ObjectType-Literature Review-2
ISSN:1999-4893
1999-4893
DOI:10.3390/a18070445