Graph analytics using vertica relational database
Name
Madden_Graph analytics.pdf
Size
1.28 MB
Format
Adobe PDF
Checksum (MD5)
7d75f5f09b83dc2c50124e381cd763e5
Author(s)
Castellanos, Malu
Hsu, Meichun
Jindal, Alekh
Madden, Samuel R
Date Issued
December 2015
Journal
2015 IEEE International Conference on Big Data (Big Data)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Jindal, Alekh et al. “Graph Analytics Using Vertica Relational Database.” 2015 IEEE International Conference on Big Data (Big Data), October 29 - November 1 2015, Santa Clara, California, USA, Institute of Electrical and Electronics Engineers (IEEE), December 2015: 1191-1200 © 2015 Institute of Electrical and Electronics Engineers (IEEE)
Version
Original manuscript
Abstract
Graph analytics is becoming increasingly popular, with a number of new applications and systems developed in the past few years. In this paper, we study Vertica relational database as a platform for graph analytics. We show that vertex-centric graph analysis can be translated to SQL queries, typically involving table scans and joins, and that modern column-oriented databases are very well suited to running such queries. Furthermore, we show how developers can trade memory footprint for significantly reduced I/O costs in Vertica. We present an experimental evaluation of the Vertica relational database system on a variety of graph analytics, including iterative analysis, a combination of graph and relational analyses, and more complex 1-hop neighborhood graph analytics, showing that it is competitive to two popular vertex-centric graph analytics systems, namely Giraph and GraphLab.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/BigData.2015.7363873
