Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

8 prerequisites of a graph query language

Mingxi Wu (TigerGraph)
14:0514:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Secondary topics:  Graph technologies and analytics
Average rating: **...
(2.50, 2 ratings)

Who is this presentation for?

  • Data scientists

Level

Intermediate

Prerequisite knowledge

  • Familiarity with Gremlin, Cypher, GSQL, and SQL

What you'll learn

  • Understand the differences between Gremlin, Cypher, and GSQL

Description

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.

Photo of Mingxi Wu

Mingxi Wu

TigerGraph

Mingxi Wu is the vice president of engineering at TigerGraph, a Silicon Valley-based startup building a world-leading real-time graph database. Over his career, Mingxi has focused on database research and data management software. Previously, he worked in Microsoft’s SQL Server group, Oracle’s Relational Database Optimizer group, and Turn Inc.’s Big Data Management group. Lately, his interest has turned to building an easy-to-use and highly expressive graph query language. He has won research awards from the most prestigious publication venues in database and data mining, including SIGMOD, KDD, and VLDB and has authored five US patents with three more international patents pending. Mingxi holds a PhD from the University of Florida, specializing in both database and data mining.

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