Scalable Analysis of Massive Graphs on a Parallel Data Flow System

Author

Yoo, Andy

Author_Institution

Center for Appl. Sci. Comput., Lawrence Livermore Nat. Lab., Livermore, CA, USA

fYear

2010

Firstpage

1

Lastpage

6

Abstract

The feasibility of using dataflow systems for running complex graph queries is studied in this paper. A general query optimization framework for parallel dataflow systems is also proposed. The proposed methods are used to optimize a suite of benchmark queries, and their effectiveness is evaluated. The performance of the optimized queries is measured on an actual parallel dataflow machine using a large semantic graph and compared to that of equivalent SQL queries on a high-end parallel relational database system. The study has revealed that dataflow system can achieve significant performance improvement over state-of-art database systems and can be a viable and scalable alternative to run large complex graph queries.

Keywords

SQL; data flow graphs; graphs; query processing; relational databases; equivalent SQL queries; high-end parallel relational database system; massive graphs; parallel data flow system; query optimization framework; scalable analysis; Computer architecture; Concurrent computing; Data flow computing; Flow graphs; Optimization methods; Parallel processing; Performance analysis; Query processing; Relational databases; Scientific computing;

fLanguage

English

Publisher

ieee

Conference_Titel

System Sciences (HICSS), 2010 43rd Hawaii International Conference on

Conference_Location

Honolulu, HI

ISSN

1530-1605

Print_ISBN

978-1-4244-5509-6

Electronic_ISBN

1530-1605

Type

conf

DOI

10.1109/HICSS.2010.325

Filename

5428331