Abstract: With increasing data scale in the “big data era”,in-memory computing frameworks have grown significantly.The mainstream in-memory computing framework Apache Spark uses memory to cache intermediate results,which greatly improves data processing performance.At the same time,non-volatile memory (NVM) with fast read and write performance has great development prospects in the field of in-memory computing,so there is huge promise in building Spark's cache with a mix of DRAM and NVM.In this paper,a Spark cache system based on DRAM-NVM hybrid memory is proposed,which selects the flat hybrid cache model as the design scheme,and then designs a dedicated data structure for the cache block management system,and proposes the overall design architecture of the hybrid cache system for Spark.In addition,in order to save frequently accessed cache blocks in the DRAM cache,a hybrid cache management strategy based on the minimum reuse cost of cache blocks is proposed.First,the future reuse of RDD is obtained from the DAG information,and the cache blocks with high future reuse times will be stored in the DRAM cache first,and the migration cost is considered when the cache block is migrated.The design experiments show that the DRAM-NVM hybrid cache has an average performance improvement of 53.06% compared to the original cache system,and the proposed strategy has an average improvement of 35.09%compared to the default cache strategy for the same hybrid memory.At the same time,the hybrid system designed in this paper only needs 1/4 of the DRAM and 3/4 of the NVM as the cache,and the running time of the total DRAM cache can be achieved by 85.49%.

Key words: Spark, Cache management strategy, NVM, Hybrid memory