  Handling skew is one of the major challenges in query processing. In
distributed computational environments such as MapReduce, uneven distribution
of the data to the servers is not desired. One of the dominant measures that we
want to optimize in distributed environments is communication cost. In a
MapReduce job this is the amount of data that is transferred from the mappers
to the reducers. In this paper we will introduce a novel technique for handling
skew when we want to compute a multiway join in one MapReduce round with
minimum communication cost. This technique is actually an adaptation of the
Shares algorithm [Afrati et. al, TKDE 2011].
