GRAPHiQL: A graph intuitive query language for relational databases
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Madden_GRAPHiQL.pdf
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343.35 KB
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Author(s)
Jindal, Alekh
Madden, Samuel R.
Date Issued
October 2014
Journal
Proceedings of the 2014 IEEE International Conference on Big Data (Big Data)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Jindal, Alekh, and Samuel Madden. “GRAPHiQL: A Graph Intuitive Query Language for Relational Databases.” 2014 IEEE International Conference on Big Data (Big Data) (October 2014).
Version
Author's final manuscript
Abstract
Graph analytics is becoming increasingly popular, driving many important business applications from social network analysis to machine learning. Since most graph data is collected in a relational database, it seems natural to attempt to perform graph analytics within the relational environment. However, SQL, the query language for relational databases, makes it difficult to express graph analytics operations. This is because SQL requires programmers to think in terms of tables and joins, rather than the more natural representation of graphs as collections of nodes and edges. As a result, even relatively simple graph operations can require very complex SQL queries. In this paper, we present GRAPHiQL, an intuitive query language for graph analytics, which allows developers to reason in terms of nodes and edges. GRAPHiQL provides key graph constructs such as looping, recursion, and neighborhood operations. At runtime, GRAPHiQL compiles graph programs into efficient SQL queries that can run on any relational database. We demonstrate the applicability of GRAPHiQL on several applications and compare the performance of GRAPHiQL queries with those of Apache Giraph (a popular `vertex centric' graph programming language).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/BigData.2014.7004261
