Apache　Spark　is　a　fast　and　general　engine　for　large-scale　data　processing　using　distributed　memory.　It　provides　different　deploy　modes　to　meet　the　needs　of　different　users　and　Spark　on　YARN　is　the　most　popular　deploy　mode.　Different　deploy　modes　have　different　scheduling　mechanisms.　Spark　on　YARN　has　three　different　schedulers,　including　FIFO　Scheduler,　Fair　Scheduler,　and　Capacity　Scheduler.　However,　these　three　schedulers　cannot　fit　hard　real-time　application　scenarios.　With　the　application　of　Apache　Spark　more　widely,　the　needs　of　hard　real-time　scheduling　will　increase　quickly.　In　this　paper,　we　proposed　a　novel　hard　real-time　scheduling　algorithm　called　DVDA　(Deadline　and　Value　Density-Aware)　in　order　to　meet　the　requirements　of　hard　real-time　scheduling.　Compared　with　traditional　EDF　(Earliest　Deadline　First)　algorithm　which　only　considers　the　deadline,　the　DVDA　algorithm　considers　both　the　deadline　and　value　density　of　the　application.　Furthermore,　we　implement　a　DVDA　Scheduler　for　Spark　on　YARN　based　on　the　DVDA　algorithm.　Finally,　the　experiments　are　conducted　to　verify　the　effectiveness　of　the　algorithm.　Experimental　results　show　that　the　proposed　algorithm　can　increase　the　application　completed　rate　by　18%　and　6%,　Value　Income　by　78%　and　32%　compared　with　default　Capacity　scheduler　and　EDF-Capacity　scheduler　respectively.