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Two Birds With One Stone: Boosting Both Search and Write Performance for Tree Indices on Persistent Memory

Two Birds With One Stone: Boosting Both Search and Write Performance for Tree Indices on... The advance of byte-addressable persistent memory (PM) makes it a hot topic to revisit traditional tree indices such as B+-tree and radix tree, and a few new persistent memory-friendly tree indices have been proposed. However, due to the special features of persistent memory compared to DRAM and the limitations of B+-tree-like indices, it is much harder to optimize both search and write performance for tree indices on persistent memory. As a result, most existing indices for persistent memory, e.g., WB-tree, proposed to improve write performance while sacrificing search performance. Aiming to optimize both write and search performance for tree indices on persistent memory, in this paper, we first propose a novel Two-Layer Architecture (TLA) for constructing tree indices on persistent memory. The key idea, of TLA is to organize the index with a search-optimized top layer and a write-optimized bottom layer, letting the top layer optimize search performance and the bottom layer improve write performance. By adopting efficient structures for the two layers, TLA can boost both write and search performance for tree indices on persistent memory. Following the TLA architecture, we present a new index called TLBtree (Two-Layer B+-tree) offering high search and write performance for persistent memory. Moreover, we develop a concurrent TLBtree to support non-blocking read operations in multi-core environment. We evaluate our proposals under a server equipped with real Intel Optane persistent memory. The results show that TLBtree outperforms the state-of-the-art tree indices, including WB-tree, Fast&Fair, and FPTree, in both search and write performance. Also, the concurrent TLBtree can achieve up to 3.7x speedup than its competitors under the multi-core environment. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

Two Birds With One Stone: Boosting Both Search and Write Performance for Tree Indices on Persistent Memory

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2021 Association for Computing Machinery.
ISSN
1539-9087
eISSN
1558-3465
DOI
10.1145/3476981
Publisher site
See Article on Publisher Site

Abstract

The advance of byte-addressable persistent memory (PM) makes it a hot topic to revisit traditional tree indices such as B+-tree and radix tree, and a few new persistent memory-friendly tree indices have been proposed. However, due to the special features of persistent memory compared to DRAM and the limitations of B+-tree-like indices, it is much harder to optimize both search and write performance for tree indices on persistent memory. As a result, most existing indices for persistent memory, e.g., WB-tree, proposed to improve write performance while sacrificing search performance. Aiming to optimize both write and search performance for tree indices on persistent memory, in this paper, we first propose a novel Two-Layer Architecture (TLA) for constructing tree indices on persistent memory. The key idea, of TLA is to organize the index with a search-optimized top layer and a write-optimized bottom layer, letting the top layer optimize search performance and the bottom layer improve write performance. By adopting efficient structures for the two layers, TLA can boost both write and search performance for tree indices on persistent memory. Following the TLA architecture, we present a new index called TLBtree (Two-Layer B+-tree) offering high search and write performance for persistent memory. Moreover, we develop a concurrent TLBtree to support non-blocking read operations in multi-core environment. We evaluate our proposals under a server equipped with real Intel Optane persistent memory. The results show that TLBtree outperforms the state-of-the-art tree indices, including WB-tree, Fast&Fair, and FPTree, in both search and write performance. Also, the concurrent TLBtree can achieve up to 3.7x speedup than its competitors under the multi-core environment.

Journal

ACM Transactions on Embedded Computing Systems (TECS)Association for Computing Machinery

Published: Sep 22, 2021

Keywords: B+-tree

References