Congratulations to Baotong LU (a PhD candidate supervised by Prof. Eric Lo) and co-authors (Tianzheng Wang, Xiangpeng Hao, Eric Lo) won the 2021 ACM SIGMOD Research Highlight Award for the paper titled “Dash: Scalable Hashing on Persistent Memory“.
The SIGMOD Research Highlight Award aims to showcase a set of research projects that exemplify core database research. In particular, these projects address an important problem, represent a definitive milestone in solving the problem, and have the potential of significant impact. The initiative of the SIGMOD Research Highlights also aims to make the selected works widely known in the database community, to our industry partners, and potentially to the broader ACM community.
Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules(DCPMM) further accelerates this trend. Many new hash table designs have been proposed, but most of them were based on emulation and perform sub-optimally on real PM. They were also piece-wise and partial solutions that side-step many important properties, in particular good scalability, high load factor and instant recovery.
We present Dash, a holistic approach to building dynamic and scalable hash tables on real PM hardware with all the aforementioned properties. Based on Dash, we adapted two popular dynamic hashing schemes (extendible hashing and linear hashing). On a 24-core machine with Intel Optane DCPMM, we show that compared to state-of-the-art, Dash-enabled hash tables can achieve up to∼3.9×higher performance with up to over 90% load factor and an instant recovery time of 57ms regardless of data size.