Managing operational business intelligence workloads

  • Authors:

  • Umeshwar Dayal;Harumi Kuno;Janet L. Wiener;Kevin Wilkinson;Archana Ganapathi;Stefan Krompass

  • Affiliations:

  • HPL Hewlett-Packard Laboratories, Palo Alto, CA;HPL Hewlett-Packard Laboratories, Palo Alto, CA;HPL Hewlett-Packard Laboratories, Palo Alto, CA;HPL Hewlett-Packard Laboratories, Palo Alto, CA;UCB UC Berkeley, Berkeley, CA;TUM Technische Universität München,, Munich, Germany

  • Venue:
  • ACM SIGOPS Operating Systems Review
  • Year:
  • 2009

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Abstract

We explore how to manage database workloads that contain a mixture of OLTP-like queries that run for milliseconds as well as business intelligence queries and maintenance tasks that last for hours. As data warehouses grow in size to petabytes and complex analytic queries play a greater role in day-to-day business operations, factors such as inaccurate cardinality estimates, data skew, and resource contention all make it notoriously difficult to predict how such queries will behave before they start executing. However, traditional workload management assumes that accurate expectations for the resource requirements and performance characteristics of a workload are available at compile-time, and relies on such information in order to make critical workload management decisions. In this paper, we describe our approach to dealing with inaccurate predictions. First, we evaluate the ability of workload management algorithms to handle workloads that include unexpectedly long-running queries. Second, we describe a new and more accurate method for predicting the resource usage of queries before runtime. We have carried out an extensive set of experiments, and report on a few of our results.