With the advent of next-generation sequencing, traditional bioinformatics tools are challenged by massive raw metagenomic datasets. One of the bottlenecks of metagenomic studies is lack of large-scale and cloud computing suitable data analysis tools. In this paper, we proposed a Spark based tool, called MetaSpark, to recruit metagenomic reads to reference genomes. MetaSpark benefits from the distributed data set (RDD) of Spark, which makes it able to cache data set in memory across cluster nodes and scale well with the datasets. Compared with previous metagenomics recruitment tools, MetaSpark recruited significantly more reads than many programs such as SOAP2, BWA and LAST and increased recruited reads by xe2x88xbc4% compared with FR-HIT when there were 1 million reads and 0.75xe2x80x89GB references. Different test cases demonstrate MetaSparks scalability and overall high performance.