Revisiting Data Compression in Column-Stores

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование


Data compression is widely used in contemporary column-oriented DBMSes to lower space usage and to speed up query processing. Pioneering systems have introduced compression to tackle the disk bandwidth bottleneck by trading CPU processing power for it. The main issue of this is a trade-off between the compression ratio and the decompression CPU cost. Existing results state that light-weight compression with small decompression costs outperforms heavy-weight compression schemes in column-stores. However, since the time these results were obtained, CPU, RAM, and disk performance have advanced considerably. Moreover, novel compression algorithms have emerged. In this paper, we revisit the problem of compression in disk-based column-stores. More precisely, we study the I/O-RAM compression scheme which implies that there are two types of pages of different size: disk pages (compressed) and in-memory pages (uncompressed). In this scheme, the buffer manager is responsible for decompressing pages as soon as they arrive from disk. This scheme is rather popular as it is easy to implement: several modern column and row-stores use it. We pose and address the following research questions: 1) Are heavy-weight compression schemes still inappropriate for disk-based column-stores?, 2) Are new light-weight compression algorithms better than the old ones?, 3) Is there a need for SIMD-employing decompression algorithms in case of a disk-based system? We study these questions experimentally using a columnar query engine and Star Schema Benchmark.

Язык оригиналаанглийский
Название основной публикацииModel and Data Engineering - 10th International Conference, MEDI 2021, Proceedings
РедакторыChristian Attiogbé, Sadok Ben Yahia
ИздательSpringer Nature
Число страниц14
ISBN (печатное издание)9783030784270
СостояниеОпубликовано - 2021
Событие10th International Conference on Model and Data Engineering, MEDI 2021 - Virtual, Online
Продолжительность: 21 июн 202123 июн 2021

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том12732 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349


конференция10th International Conference on Model and Data Engineering, MEDI 2021
ГородVirtual, Online

Предметные области Scopus

  • Теоретические компьютерные науки
  • Компьютерные науки (все)


Подробные сведения о темах исследования «Revisiting Data Compression in Column-Stores». Вместе они формируют уникальный семантический отпечаток (fingerprint).