Write a Blog >>
ASE 2021
Sun 14 - Sat 20 November 2021 Australia
Wed 17 Nov 2021 12:40 - 12:50 at Kangaroo - Learning II Chair(s): John Grundy

GraphQL is a query language for APIs and a runtime for executing those queries, fetching the requested data from existing microservices, REST APIs, databases, or other sources. Its expressiveness and its flexibility have made it an attractive candidate for API providers in many industries especially through the web. A major drawback to blindly servicing a client’s query in GraphQL is that the cost of a query can be unexpectedly large, creating computation and resource overload for the provider, and API rate-limit overages and infrastructure overload for the client. To mitigate these drawbacks, it is necessary to efficiently estimate the cost of a query before executing it. Estimating query cost is challenging because GraphQL queries have a nested structure, GraphQL APIs follow different design conventions, and the underlying data sources are hidden. Estimates based on worst-case static query analysis have had limited success because they tend to grossly overestimate cost. We propose a machine-learning approach to efficiently and accurately estimate the query cost. We also demonstrate the power of this approach by testing it on query-response data from publicly available commercial APIs. Our framework is efficient and predicts query costs with high accuracy, consistently outperforming the static analysis by a large margin.

Wed 17 Nov

Displayed time zone: Hobart change

12:00 - 13:00
Learning IIResearch Papers / Industry Showcase at Kangaroo
Chair(s): John Grundy Monash University
On Multi-Modal Learning of Editing Source Code
Research Papers
Saikat Chakraborty Columbia University, Baishakhi Ray Columbia University
Learning Highly Recursive Input Grammars
Research Papers
Neil Kulkarni University of California, Berkeley, Caroline Lemieux Microsoft Research, Koushik Sen University of California at Berkeley
Link to publication Pre-print
Learning GraphQL Query Cost
Industry Showcase
Georgios Mavroudeas Rensselaer Polytechnic Institute, Guillaume Baudart Inria; ENS; PSL University, Alan Cha IBM Research, USA, Martin Hirzel IBM Research, Jim A. Laredo IBM Research, Malik Magdon-Ismail Rensselaer Polytechnic Institute, Louis Mandel IBM Research, USA, Erik Wittern IBM Research