Abstract: Jobs in MapReduce workflow need to materialize intermediate data into HDFS (Hadoop distributed file system), which causes a large amount of I/O overhead and low efficiency. Based on existing representative work Hive, this paper proposes a strategy to match and reuse the MapReduce calculation results by extracting and storing the characteristics of the results. Firstly, we define Join-Graph, Join-Object and other structures according to the query condition, which can be used to find reusable results. Based on the abstract syntax tree generated by HiveQL (Hive query language) parser, an algorithm is proposed to generate Join-Object of the query. Followed by traversing the candidate Join-Object list, an algorithm is provided to generate the best reuse solution including single Join-Object and multiple Join-Objects reuse. In addition, we provide three methods to increase the reuse probability, including multi-key selection, arithmetic delay and semantic understanding. Finally, we conduct the experiments using TPC-H and SSB benchmarks. The results show that the efficiency is improved by 28%-52% when reusing single Join-Object by TPC-H, while it is improved by up to 75% when reusing multiple Join-Objects, and the efficiency of all the 22 queries is improved by 15.7% on average. By SSB, the efficiency is improved by 40% to 76%, 55% on average.