An approach to managing the execution of large SQL queries.
ABSTRACT We present an approach to managing the execution of large queries that involves the decomposition of the queries into an equivalent set of smaller queries. The smaller queries are then scheduled such that the work is accomplished with less impact on other, possibly more important queries.
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Conference Paper: Heterogeneous Database Query Optimization in DB2 Universal DataJoiner.[Show abstract] [Hide abstract]
ABSTRACT: DataJoiner (DJ) is a heterogeneous database system that provides a single database image of multiple databases. It provides transparent access to tables at remote databases through user defined aliases (nicknames) that can be accessed as if they were local tables. DJ is also a fully functional relational database system. A couple of salient features of the DataJoiner query optimizer are: (1) A query submitted to DataJoiner is optimized using a cost model that takes into account the remote optimizer's capabilities in addition to the remote query processing capabilities and (2) If a remote database system lacks some functionality (eg: sorting), DataJoiner compensates for it. In this paper, we present the design of the Data- joiner query optimizer.VLDB'98, Proceedings of 24rd International Conference on Very Large Data Bases, August 24-27, 1998, New York City, New York, USA; 01/1998
Conference Paper: Managing the Performance Impact of Administrative Utilities.[Show abstract] [Hide abstract]
ABSTRACT: Administrative utilities (e.g., filesystem and database backups, garbage collection in the Java Virtual Machines) are an essential part of the operation of production systems. Since production work can be severely degraded by the exe- cution of such utilities, it is desirable to have policies of the form "There should be no more than an x% degradation of production work due to utility execution." Two challenges arise in providing such policies: (1) providing an effective mech- anism for throttling the resource consumption of utilities and (2) continuously translating from policy expressions of "degradation units" into the appropriate settings for the throttling mechanism. We address (1) by using self-imposed sleep, a technique that forces utilities to slow down their processing by a configurable amount. We address (2) by employing an online estimation scheme in combina- tion with a feedback loop. This throttling system is autonomous and adaptive and allows the system to self-manage its utilities to limit their performance impact, with only high-level policy input from the administrator. We demonstrate the ef- fectiveness of these approaches in a prototype system that incorporates these ca- pabilities into IBM's DB2 Universal Database server.Self-Managing Distributed Systems, 14th IFIP/IEEE International Workshop on Distributed Systems: Operations and Management, DSOM 2003, Heidelberg, Germany, October 20-22, 2003, Proceedings; 01/2003
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ABSTRACT: Current database workloads often consist of a mixture of short online transaction processing (OLTP) queries and large complex queries such as those typical of online analytical processing (OLAP). OLAP queries usually involve multiple joins, arithmetic operations, nested sub-queries, and other system or user-defined functions and they typically operate on large data sets. These resource intensive queries can monopolize the database system resources and negatively impact the performance of smaller, possibly more important, queries. In this thesis, we present an approach to managing the execution of large queries that involves the decomposition of large queries into an equivalent set of smaller queries and then scheduling the smaller queries so that the work is accomplished with less impact on other queries. We describe a prototype implementation of our approach for IBM DB2 ™ and present a set of experiments to evaluate the effectiveness of the approach. ii Acknowledgments