Elevating Performance of LSM-Tree-Based Key-Value Stores with Gradient Data Hierarchy
Hui Sun, Jinfeng Xu, Xiao Qin · 2023
Key-value stores are a key player of managing large-scale unstructured data in storage systems. Performance improvement of the LSM-tree structure has been extensively investigated, but current work primarily focuses on cache structural optimization rather than hot-and-cold data properties. Moreover, existing and external memory components of LSM-tree rarely have uniform hot and cold attributions. In this study, we make use of the gradient and hierarchy mechanism to optimize the components catering for cache data. We design an adaptive data migration method according to hot and cold data in the cache. We reform and expand a gradient cold-hot data hierarchy (GDH) mechanism that replaces the in-memory data structure to address the problem of missing hot and cold data attributes. The hot and cold data are placed in separate cache partitions to store hot data as far the high hierarchy as possible, reducing$\mathrm{I}/\mathrm{O}$accesses. When it comes to frequently accessed hot data, we advocate for a hotness-aware technique for data stored on a disk, where read-write performance and the cache hit rate are revamped. The experiment results reveal that our proposed GDH achieves a high cache-hit ratio and low access latency under a wide range of workloads.