Foundations of Modern Query Languages for Graph Databases

TL;DR

This paper surveys the foundational features of modern graph query languages, covering data models, query functionalities, semantics, and complexity, with examples from SPARQL, Cypher, and Gremlin, emphasizing the importance of formalisation.

Contribution

It provides a comprehensive overview of core concepts, semantics, and expressivity of modern graph query languages, highlighting future research directions.

Findings

Graph data models include edge-labelled graphs and property graphs.

Query functionalities encompass graph patterns and navigational expressions.

Formalisation is crucial for understanding expressivity and complexity.

Abstract

We survey foundational features underlying modern graph query languages. We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges; and property graphs, where nodes and edges can further have attributes. Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions. We start with graph patterns, in which a graph-structured query is matched against the data. Thereafter we discuss navigational expressions, in which patterns can be matched recursively against the graph to navigate paths of arbitrary length; we give an overview of what kinds of expressions have been proposed, and how they can be combined with graph patterns. We also discuss several semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, and offer examples of such features in three modern languages that are used to query graphs: SPARQL, Cypher and Gremlin. We conclude by discussing the importance of formalisation for graph query languages; a summary of what is known about SPARQL, Cypher and Gremlin in terms of expressivity and complexity; and an outline of possible future directions for the area.