MOSS-DB: a hardware-aware OLAP database

  • Authors:

  • Yansong Zhang;Wei Hu;Shan Wang

  • Affiliations:

  • Key Lab. of the Ministry of Education for Data Eng. and Knowledge Eng., Renmin Univ. of China, Beijing, China and School of Inf., Renmin Univ. of China, Beijing, China and National Survey Research ...;Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, China and School of Information, Renmin University of China, Beijin ...;Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, China and School of Information, Renmin University of China, Beijin ...

  • Venue:
  • WAIM'10 Proceedings of the 11th international conference on Web-age information management
  • Year:
  • 2010

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Abstract

The data intensive analytical workload becomes heavy burden for OLAP engine with increasing data volume, user population and query complexity. Large capacity random access memory, multi-level cache and multi-core hardware are main streams of computer. We propose a hardware-aware OLAP model named MOSS-DB which optimizes storage model according to data access features of dimensional tables and fact tables. A hard disk & main memory two-level storage model is employed to support directly dimensional tuple accessing join operator(DDTA-JOIN), DDTA-JOIN simplifies OLAP query processing by replacing traditional join operation with directly accessing dimensional tuple with memory address. So the star schema can be seen as virtual de-normalized table, OLAP query is also simplified to table scan, select and project operations. Query processing on sequence data structure is more suitable for multi-core parallel processing. Our proposal allows massive data DRDB(Disk Resident Database) storage technique to cooperate with MMDB(Main-Memory Database) processing technique, which breaks the main memory capacity limitation. The DDTA-JOIN operation can save cost for index, hash table, etc. For multi-core era, MOSS-DB can flexibly use parallel processing capability of CPU by dynamically dividing fact table into multiple scan partitions and gain maximum cache profit for shared dimensional data. In experiments, we measure that MOSS-DB outperforms conventional DRDB system, and it also outperforms MMDB in SSB testing.