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Auflistung nach Schlagwort "Column stores"

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  • Zeitschriftenartikel
    Evaluating the Vector Supercomputer SX-Aurora TSUBASA as a Co-Processor for In-Memory Database Systems
    (Datenbank-Spektrum: Vol. 19, No. 3, 2019) Pietrzyk, Johannes; Habich, Dirk; Damme, Patrick; Focht, Erich; Lehner, Wolfgang
    In-memory column-store database systems are state of the art for the efficient processing of analytical workloads. In these systems, data compression as well as vectorization play an important role. Currently, the vectorized processing is done using regular SIMD (Single Instruction Multiple Data) extensions of modern processors. For example, Intel’s latest SIMD extension supports 512-bit vector registers which allows the parallel processing of 8× 64-bit values. From a database system perspective, this vectorization technique is not only very interesting for compression and decompression to reduce the computational overhead, but also for all database operators like joins, scan, as well as groupings. In contrast to these SIMD extensions, NEC Corporation has recently introduced a novel pure vector engine (supercomputer) as a co-processor called SX-Aurora TSUBASA. This vector engine features a vector length of 16.384 bits with the world’s highest bandwidth of up to 1.2 TB/s, which perfectly fits to data-intensive applications like in-memory database systems. Therefore, we describe the unique architecture and properties of this novel vector engine in this paper. Moreover, we present selected in-memory column-store-specific evaluation results to show the benefits of this vector engine compared to regular SIMD extensions. Finally, we conclude the paper with an outlook on our ongoing research activities in this direction.
  • Zeitschriftenartikel
    Sorting in Column Stores
    (Datenbank-Spektrum: Vol. 11, No. 2, 2011) Bößwetter, Daniel
    In recent years, we have seen a number of new database architectures based on the idea of vertical fragmentation of relations. These architectures target the analysis of huge amounts of relational data, because vertical fragmentation facilitates column scans which are common in analytic applications at the expense of single-tuple operations. Although sorting is a common operation for analytics, few is known about sorting vertically fragmented relations. This paper compares various possibilities to apply (external) merge sort to vertically fragmented relations on different layers of the memory hierarchy and gives hints on when to apply which one. We propose a Greedy algorithm to find the optimum mixture of steps that leads to a sorted version of a given relation which is stored column-wise.