With the amount of data produced in several application domains, it becomes, in many cases, difficult to manage and query large data repositories. Within the PetaSky project (http://com.isima.fr/Petasky), we focus on the problem of managing scientific data in the field of cosmology. The data we consider are those of the LSST project (http://www.lsst.org/). The overall expected size of the database that will be produced will exceed 60 PB (http://www.lsst.org/lsst/science/concept data). In order to evaluate the performances of existing SQL On MapReduce data management systems, we conducted experiments by using data and queries from the area of corpuscular physics and cosmology. The goal of this work is to report on the ability of such systems to support large scale declarative queries. We mainly investigated the impact of data partitioning, indexing and compression on query execution performances.