Abstract
Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. However, due to the novelty of such systems, no standard data model or query language has yet emerged. Consequently, migrating datasets or applications even between related technologies often requires a large amount of manual work or ad-hoc solutions, thus subjecting the users to the possibility of vendor lock-in. To avoid this threat, vendors are working on supporting existing standard languages (e.g. SQL) or standardising languages.
In this paper, we present a formal specification for openCypher, a high-level declarative graph query language with an ongoing standardisation effort. We introduce relational graph algebra, which extends relational operators by adapting graph-specific operators and define a mapping from core openCypher constructs to this algebra. We propose an algorithm that allows systematic compilation of openCypher queries.
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Notes
- 1.
- 2.
Requiring uniqueness of edges is called edge isomorphic matching. Other query languages and execution engines might use vertex isomorphic matching (requiring uniqueness of vertices), isomorphic matching (requiring uniqueness of both vertices and edges) or homomorphic matching (not requiring uniqueness of either) [8].
- 3.
The term result set refers to the result collection, which can be a set, a bag or a list.
- 4.
The
functions return a single scalar value, while 
returns a list. - 5.
Decision on grouping semantics is due after the camera ready submission deadline. The semantics presented in this paper is one of the possible approaches.
- 6.
In openCypher, the filtering
operation is a subclause of 
and 
. When used in 
as illustrated on line 3 of List. 3.13, 
is similar to the 
construct of SQL with the major difference that, in openCypher it is also allowed when no aggregation was specified in the query. - 7.
Patterns in the openCypher query might contain anonymous vertices and edges. In the algebraic form, we denote this with names starting with an underscore, such as \(\mathtt {\_v1}\) and \(\mathtt {\_e2}\).
- 8.
Label and type constraints can be omitted for the get-vertices operator and the expand operators. For example,
returns all vertices, while 
traverses all outgoing edges e from vertices v to w, regardless of their labels/types. - 9.
Matching semantics might use value equality of attributes that share a common name (similarly to natural join) to use an arbitrary condition (similarly to \(\theta \)-join).
- 10.
SQL implementations offer the OFFSET and the LIMIT/TOP keywords.
- 11.
Our prototype, ingraph, is available at: http://docs.inf.mit.bme.hu/ingraph/.
functions return a single scalar value, while 
returns a list.
operation is a subclause of 
and 
. When used in 
as illustrated on line 3 of List. 3.13, 
is similar to the 
construct of SQL with the major difference that, in openCypher it is also allowed when no aggregation was specified in the query.
returns all vertices, while 
traverses all outgoing edges e from vertices v to w, regardless of their labels/types.References
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Acknowledgements
Gábor Szárnyas and Dániel Varró were supported by the MTA-BME Lendület Research Group on Cyber-Physical Systems and the NSERC RGPIN-04573-16 project. The authors would like to thank Gábor Bergmann and János Maginecz for their comments on the draft of this paper.
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Marton, J., Szárnyas, G., Varró, D. (2017). Formalising openCypher Graph Queries in Relational Algebra. In: Kirikova, M., Nørvåg, K., Papadopoulos, G. (eds) Advances in Databases and Information Systems. ADBIS 2017. Lecture Notes in Computer Science(), vol 10509. Springer, Cham. https://doi.org/10.1007/978-3-319-66917-5_13
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