8 prerequisites of a graph query language
Graph query language is the key to unleash the value from connected data. Mingxi Wu outlines the eight prerequisites of a practical graph query language, drawn from six years' experience dealing with real-world graph analytical use cases. Along the way, Mingxi compares GSQL, Gremlin, Cypher, and SPARQL, pointing out their respective pros and cons.
| Talk Title | 8 prerequisites of a graph query language |
| Speakers | Mingxi Wu (TigerGraph) |
| Conference | Strata Data Conference |
| Conf Tag | Making Data Work |
| Location | London, United Kingdom |
| Date | April 30-May 2, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Graph query language is the key to unleash the value from connected data. Mingxi Wu outlines the eight prerequisites of a practical graph query language, drawn from six years’ experience dealing with real-world graph analytical use cases. Along the way, Mingxi compares GSQL, Gremlin, Cypher, and SPARQL, pointing out their respective pros and cons. You’ll learn why Gremlin isn’t ideal for complicated real-life use cases—mainly due to its programming model requiring a runtime traversal tree; the benefits of Cypher’s pattern match style programming model; and why SPARQL is insufficient for property graph analytics. Mingxi concludes with an overview of GSQL, a Turing-complete query language that has the merits of Cypher—pattern match style query model plus another easy-to-think programming model—and provides multiple passes on a static topology with runtime attributes decoration capability.
