Non-Volatile RAM (NVRAM) is a novel class of hardware technology which is an interesting blend of two storage paradigms: byte-addressable DRAM and block-addressable storage (e.g. HDD/SSD). Most of the existing enterprise relational data management systems such as SAP HANA have their internal architecture based on the inherent assumption that memory is volatile and base their persistence on explicit handling of block-oriented storage devices. In this paper, we present the early adoption of Non-Volatile Memory within the SAP HANA Database, from the architectural and technical angles. We discuss our architectural choices, dive deeper into a few challenges of the NVRAM integration and their solutions, and share our experimental results. As we present our solutions for the NVRAM integration, we also give, as a basis, a detailed description of the relevant HANA internals.

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With the proliferation of mobile devices and explosive growth of spatio-temporal data, Location-Based Services (LBS) have become an indispensable technology in our daily lives. The key characteristics of the LBS applications include a high rate of time-stamped location updates, and many concurrent historical, present and predictive queries. The commercial providers of LBS must support all three kinds of queries and address the high update rates. While they employ relational databases for this purpose, traditional databases are unable to cope with the growing demands of many LBS systems. Support for spatio-temporal indexes within these databases are limited to R-tree based approaches. Although a number of advanced spatiotemporal indexes have been proposed by the research community, only a few of them support historical queries. These indexing techniques, with support for historical queries, are unable to sustain high update and query throughput typical in LBS.Technological trends involving increasingly large main memory and core footprints offer opportunities to address some of these issues. We present several key ideas to support high performance commercial LBS by exploiting in-memory database techniques. Taking advantage of very large memory available in modern machines, our system maintains the location data and index for the past N days in memory. Older data and index are kept in disk. We propose an in-memory storage organization for high insert performance. We also introduce a novel spatio-temporal index that maintains partial temporal indexes in a versionedgrid structure. The partial temporal indexes are organized as compressed bitmaps. With extensive evaluation, we demonstrate that our system supports high insert and query throughputs and it outperforms the leading LBS system by a significant margin.

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