A comparison of systems to large-scale data access

Abstract

With the amount of data produced in several application domains, it is increasingly difficult to manage and query related large data repositories (https://www.lsstcorp.org/sciencewiki/images/DC- Handbook-v1.1.pdf). Within the PetaSky project, we focus on the problem of managing scientific data in the field of cosmology. The data we consider are those of the LSST project. The overall expected size of the database that will be produced will exceed 60 PB. This paper presents preliminary results of experiments conducted on PT1.1 (http://lsst1.ncsa.uiuc.edu/schema/index.php?sVer=PT1-1 (with a size of 90 GB.)) and PT1.2 (http://lsst1.ncsa.uiuc.edu/schema/index.php?sVer=PT1-1 (with a size of 145 GB.)) data sets in order to compare the performances of both centralized and distributed database management systems. As for centralized systems, we have deployed three different DBMSs: Mysql, Postgresql and DBMS-X (a commercial relational database). Regarding distributed systems, we have deployed HadoopDB and Hive. The goal of these experiments is to report on the ability of these systems to support large scale declarative queries. We mainly investigate the impact of data partitioning, indexing and compression on query execution performances. © 2014 Springer-Verlag Berlin Heidelberg.