The　trace　analysis　for　datacenter　holds　a　prominent　importance　for　the　datacenter　performance　optimization.　However,　due　to　the　error　and　low　execution　priority　of　trace　collection　tasks,　modern　datacenter　traces　suffer　from　the　serious　data　missing　problem.　Previous　works　handle　the　trace　data　recovery　via　the　statistical　imputation　methods.　However,　such　methods　either　recover　the　missing　data　with　fixed　values　or　require　users　to　decide　the　relationship　model　among　trace　attributes,　which　are　not　feasible　or　accurate　when　dealing　with　the　two　missing　data　trends　in　datacenter　traces:　the　data　sparsity　and　the　complex　correlations　among　trace　attributes.　To　this　end,　we　focus　on　a　trace　released　by　Alibaba　and　propose　a　tensor-based　trace　data　recovery　model　to　facilitate　the　efficient　and　accurate　data　recovery　for　large-scale,　sparse　datacenter　traces.　The　proposed　model　consists　of　two　main　phases.　First,　the　data　discretization　and　attribute　selection　methods　work　together　to　select　the　trace　attributes　with　strong　correlations　with　the　value-missing　attribute.　Then,　a　tensor　is　constructed　and　the　missing　values　are　recovered　by　employing　the　CANDECOMP/PARAFAC　decomposition　based　tensor　completion　method.　The　experimental　results　demonstrate　that　our　model　achieves　higher　accuracy　than　six　statistical　or　machine　learning-based　methods.