Paramount and vast applications such as social networks deal with big graphs. For this reason, big graph analysis and processing is currently a necessity. Detection of connected components is one of the analysis of graphs which is utilized as a sub-part in many graph algorithms, such as clustering. The goal of this paper is to propose a parallel preprocess algorithm with MapReduce to decrease graph volume for rapid detection of connected components. Suggested method is able to lessen the volume up to more than 99% quickly by just two rounds of MapReduce. Our evaluation shows that the combination of the preprocess with detection of connected components has a significant impact on: reduction of execution time up to 7 times, decrease in data transmission of processing nodes in network and MapReduce rounds.