Name
Abstract | ||
|---|---|---|
ABSTRACTPerfect hashing is a hash function that maps a set of distinct keys to a set of continuous integers without collision. However,most existing perfect hash schemes are static, which means that they cannot support incremental updates, while most datasets in practice are dynamic. To address this issue, we propose a novel hashing scheme, namely MapEmbed Hashing. Inspired by divide-and-conquer and map-and-reduce, our key idea is named map-and-embed and includes two phases: 1) Map all keys into many small virtual tables; 2) Embed all small tables into a large table by circular move. Our experimental results show that under the same experimental setting, the state-of-the-art perfect hashing (dynamic perfect hashing) can achieve around 15% load factor, around 0.3 Mops update speed, while our MapEmbed achieves around 90% ~ 95% load factor, and around 8.0 Mops update speed per thread. All codes of ours and other algorithms are open-sourced at GitHub. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3447548.3467240 | Knowledge Discovery and Data Mining |
Keywords | DocType | Citations |
Perfect Hashing, Hash Tables, KV Stores | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhan Wu | 1 | 1 | 2.38 |
| Zirui Liu | 2 | 0 | 1.01 |
| Xiang Yu | 3 | 0 | 0.34 |
| Jie Gui | 4 | 67 | 6.08 |
| Haochen Gan | 5 | 0 | 0.34 |
| Yuhao Han | 6 | 0 | 0.34 |
| Tao Li | 7 | 38 | 7.33 |
| Ori Rottenstreich | 8 | 9 | 7.31 |
| Tong Yang | 9 | 208 | 37.35 |