Physical and thermal restrictions hinder commensurate performance gains from the ever increasing transistor density. While multi-core scaling helped alleviate dimmed or dark silicon for some time, future processors will need to become more heterogeneous. To this end, single instruction set architecture (ISA) heterogeneous processors are a particularly interesting solution that combines multiple cores with the same ISA but asymmetric performance and power characteristics. These processors, however, are no free lunch for database systems. Mapping jobs to the core that fits best is notoriously hard for the operating system or a compiler. To achieve optimal performance and energy efficiency, heterogeneity needs to be exposed to the database system.

Shipments of smartphones and tablets with wimpy CPUs are outpacing brawny PC and server shipments by an ever-increasing margin. While high performance database systems have traditionally been optimized for brawny systems, wimpy systems have received only little attention; leading to poor performance and energy inefficiency on such systems.

eScience and big data analytics applications are facing the challenge of efficiently evaluating complex queries over vast amounts of structured text data archived in network storage solutions. To analyze such data in traditional disk-based database systems, it needs to be bulk loaded, an operation whose performance largely depends on the wire speed of the data source and the speed of the data sink, i.e., the disk. As the speed of network adapters and disks has stagnated in the past, loading has become a major bottleneck. The delays it is causing are now ubiquitous as text formats are a preferred storage format for reasons of portability. But the game has changed: Ever increasing main memory capacities have fostered the development of in-memory database systems and very fast network infrastructures are on the verge of becoming economical. While hardware limitations for fast loading have disappeared, current approaches for main memory databases fail to saturate the now available wire speeds of tens of Gbit/s. With Instant Loading, we contribute a novel CSV loading approach that allows scalable bulk loading at wire speed. This is achieved by optimizing all phases of loading for modern super-scalar multi-core CPUs. Large main memory capacities and Instant Loading thereby facilitate a very efficient data staging processing model consisting of instantaneous load-work-unload cycles across data archives on a single node. Once data is loaded, updates and queries are efficiently processed with the flexibility, security, and high performance of relational main memory databases.