Abstract: In the context of the big data era where the amount of data is growing rapidly,log-structured merge-tree-based(LSM-Tree-based) key-value storage systems are widely deployed in many NoSQL systems because of their excellent flexibility and scalability.However,when querying data in the traditional LSM-Tree based key-value storage system,the read amplification problem,which is caused by searching through multiple SSTables,generates additional I/O overhead.In this paper,we present FCLI-LSM,a new optimization design for key-value storage systems.FCLI-LSM improves the performance of LSM-Tree-based key-value storage by combining the optimization methods of fine-grained key-value pair caching and learned index.By analyzing data access hotspots.FCLI-LSM implements three-level classification of hot,warm,and cold key-value data.FCLI-LSM designs a fine-grained caching mechanism for hot data based on key-value separation,effectively reducing the additional I/O overhead caused by the read amplification problem.In addition,FCLI-LSM also designs a cache affinity optimization for learned index,which further improves the query performance of storage system for warm key-value data.Experimental results show that,compared with existing read optimization solutions,FCLI-LSM can reduce average read latency by more than 40% and increase system throughput by more than 1.7 times.

Key words: Big data, Key-Value storage, LSM tree, Learned index, Caching