Density-based　spatial　clustering　of　applications　with　noise　(DBSCAN)　is　a　density-based　clustering　algorithm　that　has　the　characteristics　of　being　able　to　discover　clusters　of　any　shape,　effectively　distinguishing　noise　points　and　naturally　supporting　spatial　databases.　DBSCAN　has　been　widely　used　in　the　field　of　spatial　data　mining.　This　paper　studies　the　parallelization　design　and　realization　of　the　DBSCAN　algorithm　based　on　the　Spark　platform,　and　solves　the　following　problems　that　arise　when　computing　macro　data:　the　requirement　of　a　great　deal　of　calculation　using　the　single-node　algorithm;　the　low　level　of　resource-utilization　with　the　multi-node　algorithm;　the　large　time　consumption;　and　the　lack　of　instantaneity.　The　experimental　results　indicate　that　the　proposed　parallel　algorithm　design　is　able　to　achieve　more　stable　speedup　at　an　increased　involved　spatial　data　scale.