An efficient flattened index structure with lazy restructuring and hotness awareness

Abstract

Hotspot issue is ubiquitous in the in-memory key-value stores, which seriously affects its performance. The efficiency of the index structure is the key to the performance of the in-memory key-value store, and the access of hotspot data must be through the search of the index structure. However, the existing index structures do not consider the hotspot issue, they treat and manage all the data equally, and cannot break through the performance bottleneck of the system. Index structures of type B＋-Tree are common in in-memory key-value stores, but the existing B＋-tree index structures do not consider the frequent structure change caused by node restructuring, which will seriously degrade the performance of the index structure. In this paper, we propose LH-Index, an efficient flattened index structure with lazy restructuring and hotness awareness. First, we use a search method combining linear search and binary search based on a partitioning strategy and endow nodes in LH-Index with a hotness awareness ability to search hotspot data quickly. Meanwhile, we use a lazy restructuring strategy for LH-Index to reduce the performance degradation due to frequent structure change. In addition, in order to meet the requirements of most scenarios, LH-Index is extended to DRAM/NVM hybrid memory by separate storage. Our evaluation shows that LH-Index achieves up to 1.85× search throughput and the lowest tail latency relative to other index structures under highly-skewed workloads, and it also has the highest throughput under mixed workloads.