  The size of astronomical observational data is increasing yearly. For
example, while Atacama Large Millimeter/submillimeter Array is expected to
generate 200 TB raw data every year, Large Synoptic Survey Telescope is
estimated to produce 15 TB raw data every night. Since the increasing rate of
computing is much lower than that of astronomical data, to provide high
performance computing (HPC) resources together with scientific data will be
common in the next decade. However, the installation and maintenance costs of a
HPC system can be burdensome for the provider. I note public cloud computing
for an alternative way to get sufficient computing resources inexpensively. I
build Hadoop and Hive clusters by utilizing a virtual private server (VPS)
service and Amazon Elastic MapReduce (EMR), and measure their performances. The
VPS cluster behaves differently day by day, while the EMR clusters are
relatively stable. Since partitioning is essential for Hive, several
partitioning algorithms are evaluated. In this paper, I report the results of
the benchmarks and the performance optimizations in cloud computing
environment.
