Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1496909.1496927 | Operating Systems Review |
Keywords | Field | DocType |
workload management algorithm,data skew,database performance prediction,resource requirement,critical workload management decision,accurate method,business intelligence query,traditional workload management,resource usage,resource contention,accurate expectation,business intelligence databases,operational business intelligence workloads,database workload managment,data warehouse,business intelligence | Data warehouse,Competitive intelligence,Computer science,Workload,Petabyte,Load balancing (computing),Business operations,Cardinality,Business intelligence,Distributed computing | Journal |
Volume | Issue | Citations |
43 | 1 | 12 |
PageRank | References | Authors |
0.67 | 23 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umeshwar Dayal | 1 | 8452 | 2538.92 |
| Harumi Kuno | 2 | 297 | 21.62 |
| Janet L. Wiener | 3 | 3813 | 600.46 |
| Kevin Wilkinson | 4 | 12 | 0.67 |
| Archana Ganapathi | 5 | 860 | 54.96 |
| Stefan Krompass | 6 | 184 | 13.15 |