  In recent years, much research has focused on how to optimize Hadoop jobs.
Their approaches are diverse, ranging from improving HDFS and Hadoop job
scheduler to optimizing parameters in Hadoop configurations. Despite their
success in improving the performance of Hadoop jobs, however, very little is
known about the limit of their optimization performance. That is, how optimal
is a given Hadoop optimization? When a Hadoop optimization method X improves
the performance of a job by Y %, how do we know if this improvement is as good
as it can be? To answer this question, in this paper, we first examine the
ideal best case, the lower bound, of running time for Hadoop jobs and develop a
measure to accurately estimate how optimal a given Hadoop optimization is with
respect to the lower bound. Then, we demonstrate how one may exploit the
proposed measure to improve the optimization of Hadoop jobs.
