论文标题
MorphStore:具有整体压缩处理模型的分析查询引擎
MorphStore: Analytical Query Engine with a Holistic Compression-Enabled Processing Model
论文作者
论文摘要
在本文中,我们提出了MorphStore,这是一种具有新型整体压缩处理模型的开源内存中柱分析查询引擎。基本上,使用轻质整数压缩算法的压缩已经在现有内存列店数据库系统中起重要作用,但主要用于基本数据。特别是,在查询处理过程中,这些系统仅保持数据压缩,直到操作员无法直接处理压缩数据,然后将数据解压缩,但不会重新压缩。因此,没有利用查询处理过程中压缩的全部潜力。为了克服这一点,我们开发了一种新型的启用压缩的处理模型,如本文所示。正如我们要显示的那样,所有基本数据和所有中间体的压缩连续使用对减少整体内存足迹以及提高查询性能非常有益。
In this paper, we present MorphStore, an open-source in-memory columnar analytical query engine with a novel holistic compression-enabled processing model. Basically, compression using lightweight integer compression algorithms already plays an important role in existing in-memory column-store database systems, but mainly for base data. In particular, during query processing, these systems only keep the data compressed until an operator cannot process the compressed data directly, whereupon the data is decompressed, but not recompressed. Thus, the full potential of compression during query processing is not exploited. To overcome that, we developed a novel compression-enabled processing model as presented in this paper. As we are going to show, the continuous usage of compression for all base data and all intermediates is very beneficial to reduce the overall memory footprint as well as to improve the query performance.