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 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.