Create a Highly Available Microsoft SQL Server 2008 Environment
Written by the technical director for SQL Server Magazine, this expert guide shows you how to implement clustering and database mirroring in SQL Server 2008. Learn proven techniques for ensuring zero database loss, avoiding system downtime, and providing instant data backups. Filled with detailed instructions, Microsoft SQL Server 2008 High Availability with Clustering & Database Mirroring takes you from planning to management of a robust high availability solution.
Configure Windows Failover Clustering
Set up the Microsoft Distributed Transaction Coordinator on a two node failover cluster
Install SQL Server 2008 on your cluster
Handle cluster management and backup
Configure and manage database mirroring for high availability
Implement Hyper-V virtualization and Live Migration
Manage a SQL Server virtual machine
Michael Otey is the technical director for SQL Server Magazine and Windows IT Pro and the bestselling author of Microsoft SQL Server 2005 Developer's Guide, Microsoft SQL Server 2008 New Features, and several other books. He is president of TECA, Inc., a software development and consulting company.
Learn to implement clustering and load balancing solutions with Windows 2000 and Windows Server 2003, and deliver nearly 100 percent uptime. With a focus on real world production-based problems, the author delivers detailed high availability solutions that will give you the tools to roll out and troubleshoot these technologies.
A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics
Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible
Includes illustrative material andwell-known experimentsto offer hands-on experience
This book deals with the methods of text comparison which are based on different techniques of converting the text into a distribution on a certain finite support, be it a genetic text or a text of some other type. Such distribution is usually referred to as “spectrum”. The measure of dissimilarity of two texts is formally expressed as a certain “distance” between the spectra of these texts. Such definition implies that the similarity of the texts results from the similarity of the random processes generating the texts.
