An Efficient In-Memory Analytics System Based on Persistent Memory.

With the development of big data applications, online analytics systems that aim to offer decision support for various businesses and tasks have become a research focus, which calls for efficient approaches to handling OLAP queries. However, traditional OLAP systems suffer from the costly interactions with disks or SSDs, making them hard to deliver high performance for OLAP query processing. In this paper, we propose to use the emerging persistent memory to construct an efficient in-memory analytics system to improve the performance for OLAP query processing. We present the overall architecture as well as the detailed algorithms for the proposed system, which is named PM-Picker, and finally discuss the implementation issue of the system.