【2014】PostgreSQL 9 High Availability Cookbook

Book 图书名称： PostgreSQL 9 High Availability Cookbook
Author 作者： Shaun M. Thomas
Publisher 出版社： Packt Publishing - ebooks Account
Page 页数： 403
Publishing Date 出版时间： Jul 17, 2014                        
Language 语言： English
Size 大小： 3 MB
Format 格式： pdf 文字版
ISBN: 1849516960, 9781849516969
Edition: 第1版 搜索过论坛，没有该文档


Over 100 recipes to design and implement a highly available server with the advanced features of PostgreSQLAbout This Book Create a PostgreSQL cluster that stays online even when disaster strikes Avoid costly downtime and data loss that can ruin your business Perform data replication and monitor your data with hands-on industry-driven recipes and detailed step-by-step explanations Who This Book Is For
If you are a PostgreSQL DBA working on Linux systems who want a database that never gives up, this book is for you. If you've ever experienced a database outage, restored from a backup, spent hours trying to repair a malfunctioning cluster, or simply want to guarantee system stability, this book is definitely for you.
What You Will Learn Protect your data with PostgreSQL replication and management tools such as Slony, Bucardo, and Londiste Choose the correct hardware for redundancy and scale Prepare for catastrophes and prevent them before they happen Reduce database resource contention with connection pooling Automate monitoring and alerts to visualize cluster activity using Nagios and collectd Construct a robust software stack that can detect and fix outages Design a scalable schema architecture to handle billions of queries In Detail
PostgreSQL, often known as simply "Postgres", is an object-relational database management system (ORDBMS) with an emphasis on extensibility and standards-compliance.

From hardware selection to software stacks and horizontal scalability, this book will help you build a versatile PostgreSQL cluster that will survive crashes, resist data corruption, and grow smoothly with customer demand. We start with selecting the necessary hardware to handle multiple failure scenarios with redundancy. Then, we discuss how to automate and visualize these checks with Nagios, check_mk, and Graphite. We'll finally round off by tackling the complex problem of data scalability.

== Table of contents ==
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