In　recent　years,　the　application　of　laser　point　cloud　data　has　increased　dramatically.　How　to　efficiently　store　and　fast　process　the　data　becomes　an　important　research　direction　at　present.　Point　cloud　data　contain　a　wealth　of　geographic　information,　belonging　to　the　category　of　spatial　data.　Traditional　relational　databases　are　relatively　weak　in　massive　spatial　data　storage　and　processing,　while　the　application　of　non-relational　databases　provides　a　new　perspective　of　study　for　this　fact.　Sharding　technology　is　a　solution　for　database　level　extension.　In　this　thesis,　sharding　cluster　for　MongoDB　is　established　under　distributed　environment,　while　distributed　storage,　spatial　query　and　MapReduce　operation　test　for　numerous　laser-point　cloud　data　will　be　implemented　through　scope　sharding　and　Hash-based　sharding,　which　completely　reflects　huge　advantages　of　MongoDB　under　distribution　in　storage　and　processing　for　spatial　data.