  When dealing with massive data sorting, we usually use Hadoop which is a
framework that allows for the distributed processing of large data sets across
clusters of computers using simple programming models. A common approach in
implement of big data sorting is to use shuffle and sort phase in MapReduce
based on Hadoop. However, if we use it directly, the efficiency could be very
low and the load imbalance can be a big problem. In this paper we carry out an
experimental study of an optimization and analysis of large scale data sorting
algorithm based on hadoop. In order to reach optimization, we use more than 2
rounds MapReduce. In the first round, we use a MapReduce to take sample
randomly. Then we use another MapReduce to order the data uniformly, according
to the results of the first round. If the data is also too big, it will turn
back to the first round and keep on. The experiments show that, it is better to
use the optimized algorithm than shuffle of MapReduce to sort large scale data.
