Name
Abstract | ||
|---|---|---|
Emerging Non-volatile Memory (NVM) is attractive because of its byte-addressability, durability, and DRAM-scale latency. Hashing indexes have been extensively used to provide fast query services in the storage system. Recent research proposes crash-consistent and write-optimized hashing indexes for NVM. However, existing NVM-based hashing indexes suffer from limited scalability when running on a Commercial Non-Volatile Memory product, named Intel Optane DC Persistent Memory Module (DCPMM), due to the limited bandwidth of Optane DCPMM. To achieve a high load factor, existing NVM-based hashing indexes often evict an existing item to its alternative position, which incurs extra write and will consume the limited bandwidth. Moreover, the lock operations and metadata updates further saturate the limited bandwidth and prevent the hash table from scaling. In order to achieve scalability performance as well as a high load factor for the NVM-based hashing index, we design a new persistent hashing index, called REH, based on extendible hashing. REH (1) proposes a selective persistence scheme that stores buckets in NVM and places directory and metadata in DRAM to reduce both unnecessary NVM reads and writes, (2) uses 256B sized-buckets, as 256B is the internal data access size in Optane DCPMM, and the buckets are directly pointed to by directory entries, (3) leverages fingerprinting to further reduce unnecessary NVM reads, (4) employs failure-atomic bucket split to reduce bucket split overhead. Evaluations show that REH outperforms the state-of-the-art NVM-based hashing indexes by up to 1.68 similar to 7.78x. In the meantime, REH can achieve a high load factor. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.23919/DATE54114.2022.9774546 | PROCEEDINGS OF THE 2022 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2022) |
DocType | ISSN | Citations |
Conference | 1530-1591 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengtao Li | 1 | 0 | 0.34 |
| Zhipeng Tan | 2 | 33 | 10.18 |
| Jianxi Chen | 3 | 153 | 10.60 |