Tlbtree: A Read/Write-Optimized Tree Index For Non-Volatile Memory

With the rapid advance of Non-Volatile Memory (NVM), it has been a hot topic to improve traditional tree indices like B+-tree for NVM. However, due to the high cost of the writing operations on NVM, few existing tree indices can offer high performance for both read and write operations. For example, the WB-tree with unsorted leaf nodes is write-optimized but has poor search performance. To address this problem, in this paper, we propose a read/write-optimized tree index called TLBtree (Two-Layer B+-tree) for NVM. TLBtree consists of a read-optimized top layer and a write-optimized bottom layer. We notice that the top levels of a B+-tree are read frequently, while the bottom levels are written frequently. Motivated by such an observation, we propose to design a read-optimized top layer and a write-optimized layer for the TLBtree index. We offer several read optimizations to implement the top layer and employ write-optimized structures to organize the bottom layer. With this mechanism, we can alleviate the read and write tradeoff of the index on NVM. We conduct extensive experiments on a server with Intel Optane DC Persistent Memory and compare TLBtree with state-of-the-art NVM-based tree indices, including WB-tree, Fast&fair, and FPtree. The results show that TLBtree outperforms other indices in write-intensive workloads by up to 1.7x throughput and achieves comparable read-only performance with read-optimized indices.