TY - GEN
T1 - Hybrid DRAM-NVM R-Trees with Consistency Guarantee
AU - Zhang, Kaiqi
AU - Shen, Chengyou
AU - Zhang, Siyuan
AU - Shi, Shengfei
AU - Gao, Hong
AU - Tu, Yaofeng
AU - Li, Jianzhong
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The non-volatile memory (NVM) with DRAM-like performance and disk-like persistency has attracted considerable attention in a variety of index structures, including hash table, B-Tree and R-Tree. However, existing NVM-optimized consistent R-Tree is still suboptimal because its single level system neglects the potential boost that DRAM can bring. In this paper, we first propose a hybrid DRAM-NVM consistent R-Tree (HR-Tree), which separately stores internal nodes in DRAM and leaf nodes in NVM. To avoid inconsistency, HR-Tree uses several auxiliary flag bits and pointers to record the process of writes to NVM and employs persistence operations to strictly control the order of writes to NVM. To reduce DRAM consumption, which mainly depends on the metadata size of a leaf node, we present a shared byte strategy to abolish restrictions on metadata size while still keeping HR-Tree consistency. Next, for further shortening search time, we propose an alternative Hilbert-curve-based hybrid R-Tree (HHR-Tree). It has better search efficiency yet leads to insertion performance degradation. Contrary to in-place update in HR-Tree, HHR-Tree applies out-of-place mechanism to enforce data consistency. We conduct comprehensive evaluations on Intel Optane DC Persistent Memory. The proposed HR-Tree outperforms FBR-Tree in terms of insertion, deletion and search throughput while HHR-Tree exhibits a significant improvement for search performance by sacrificing insertion efficiency.
AB - The non-volatile memory (NVM) with DRAM-like performance and disk-like persistency has attracted considerable attention in a variety of index structures, including hash table, B-Tree and R-Tree. However, existing NVM-optimized consistent R-Tree is still suboptimal because its single level system neglects the potential boost that DRAM can bring. In this paper, we first propose a hybrid DRAM-NVM consistent R-Tree (HR-Tree), which separately stores internal nodes in DRAM and leaf nodes in NVM. To avoid inconsistency, HR-Tree uses several auxiliary flag bits and pointers to record the process of writes to NVM and employs persistence operations to strictly control the order of writes to NVM. To reduce DRAM consumption, which mainly depends on the metadata size of a leaf node, we present a shared byte strategy to abolish restrictions on metadata size while still keeping HR-Tree consistency. Next, for further shortening search time, we propose an alternative Hilbert-curve-based hybrid R-Tree (HHR-Tree). It has better search efficiency yet leads to insertion performance degradation. Contrary to in-place update in HR-Tree, HHR-Tree applies out-of-place mechanism to enforce data consistency. We conduct comprehensive evaluations on Intel Optane DC Persistent Memory. The proposed HR-Tree outperforms FBR-Tree in terms of insertion, deletion and search throughput while HHR-Tree exhibits a significant improvement for search performance by sacrificing insertion efficiency.
KW - Data consistency
KW - Non-volatile memory
KW - R-Tree
UR - https://www.scopus.com/pages/publications/105015578379
U2 - 10.1109/ICDE65448.2025.00249
DO - 10.1109/ICDE65448.2025.00249
M3 - 会议稿件
AN - SCOPUS:105015578379
T3 - Proceedings - International Conference on Data Engineering
SP - 3329
EP - 3341
BT - Proceedings - 2025 IEEE 41st International Conference on Data Engineering, ICDE 2025
PB - IEEE Computer Society
T2 - 41st IEEE International Conference on Data Engineering, ICDE 2025
Y2 - 19 May 2025 through 23 May 2025
ER -