Efficient bulk deletes for multi dimensional clustered tables in DB2

  • Authors:

  • Bishwaranjan Bhattacharjee;Timothy Malkemus;Sherman Lau;Sean McKeough;Jo-anne Kirton;Robin Von Boeschoten;John P Kennedy

  • Affiliations:

  • IBM T.J. Watson Research Center, Hawthorne, NY;IBM T.J. Watson Research Center, Hawthorne, NY;IBM Toronto Laboratories, Markham, Ontario, Canada;IBM Toronto Laboratories, Markham, Ontario, Canada;IBM Toronto Laboratories, Markham, Ontario, Canada;IBM Toronto Laboratories, Markham, Ontario, Canada;IBM Toronto Laboratories, Markham, Ontario, Canada

  • Venue:
  • VLDB '07 Proceedings of the 33rd international conference on Very large data bases
  • Year:
  • 2007

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Abstract

In data warehousing applications, the ability to efficiently delete large chunks of data from a table is very important. This feature is also known as Rollout or Bulk Deletes. Rollout is generally carried out periodically and is often done on more than one dimension or attribute. The ability to efficiently handle the updates of RID indexes while doing Rollouts is a well known problem for database engines and its solution is very important for data warehousing applications. DB2 UDB V8.1 introduced a new physical clustering scheme called Multi Dimensional Clustering (MDC) which allows users to cluster data in a table on multiple attributes or dimensions. This is very useful for query processing and maintenance activities including deletes. Subsequently, an enhancement was incorporated in DB2 UDB Viper 2 which allows for very efficient online rollout of data on dimensional boundaries even when there are a lot of secondary RID indexes defined on the table. This is done by the asynchronous updates of these RID indexes in the background while allowing the delete to commit and the table to be accessed. This paper details the design of MDC Rollout and the challenges that were encountered. It discusses some performance results which show order of magnitude improvements using it and the lessons learnt.