With　the　arrival　of　the　era　of　big　data,　the　data　generated　by　the　monitoring　system　grows　rapidly,　the　storage　and　quick　retrieval　of　massive　monitoring　data　pose　a　challenge　to　the　database.　HBase　is　an　open-source　version　of　Bigtable,　built　on　top　of　HDFS,　based　design　and　query　on　rowkey　is　quick,　but　with　the　development　of　database　application,　when　retrieving　non-rowkey　fields,　HBase　can　only　query　by　scanning　the　full　table,　which　obviously　cannot　meet　the　actual　requirements,　and　the　query　of　the　monitoring　data　is　mostly　based　on　the　most　recent　data,　so　for　this　problem,　this　article　presents　a　secondary　indexing　approach　based　on　coprocessor　and　Redis.　This　method　realizes　the　rapid　creation　and　automatic　update　of　secondary　indexes　in　HBase　and　Redis　through　the　coprocessor.　It　gets　the　corresponding　rowkey　quickly　during　retrieval,　and　query　the　corresponding　data　in　the　data　table　according　to　the　rowkey.　The　former　stores　the　index　of　the　data　table,　while　the　latter　stores　the　index　of　the　latest　data　in　the　data　table,　to　improve　the　real-time　performance　of　data　retrieval.　©　Published　under　licence　by　IOP　Publishing　Ltd.