PostgreSQL 9 High Availability Cookbook

PostgreSQL 9 High Availability Cookbook

Posted by jack_miller | Published 7 months ago

With 7 ratings

By: Shaun M. Thomas

Purchased At: $44.99

Over 100 recipes to design and implement a highly available server with the advanced features of PostgreSQL

About 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.

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