The performance of main memory column stores highly depends on the scan and lookup operations on the base column layouts. Existing column-stores adopt a homogeneous column layout, leading to sub-optimal performance on real workloads since different columns possess different data characteristics. In this paper, we propose ByteStore, a column store that uses different storage layouts for different columns. We first present a novel data-conscious column layout, PP-VBS (Prefix-Preserving Variable Byte Slice). PP-VBS exploits data skew to accelerate scans without sacrificing lookup performance. Then, we present an experiment-driven column layout advisor to select individual column layouts for a workload. Extensive experiments on real data show that ByteStore outperforms homogeneous storage engines by up to 5.2X.
翻译:主内存列存储的性能高度取决于基列布局上的扫描和查找操作。现有列储采用同质的列布局,导致实际工作量的亚最佳性能,因为不同的列具有不同的数据特性。在本文件中,我们提议ByteStore,这是一个对不同的列使用不同存储布局的专列存储库。我们首先提出一个新的数据意识专列布局,PP-VBS(Preix-Preservicing vor Byte Sice) 。PP-VBS在不牺牲外观性能的情况下利用数据Skew加速扫描。然后,我们提出一个实验驱动的专列布局顾问,为工作量选择单列布局。对真实数据的大规模实验显示,ByteStore在最大到5.2X之前超越了同质存储引擎。