- PostgreSQL 9 High Availability Cookbook
- Shaun M. Thomas
- 298字
- 2021-12-08 12:31:56
What this book covers
Chapter 1, Hardware Planning, sets the tone by covering the part that the appropriate hardware selection plays in a successful PostgreSQL cluster of any size.
Chapter 2, Handling and Avoiding Downtime, provides safe settings and defaults for a stable cluster and explains the basic techniques for responding to mishaps.
Chapter 3, Pooling Resources, presents PgBouncer and pgpool, two tools geared toward controlling PostgreSQL connections. Together, these can provide an abstraction layer to reduce the effect of outages and increase system performance.
Chapter 4, Troubleshooting, introduces a battery of common Unix and Linux tools and resources that can collect valuable diagnostic information. It also includes a couple of PostgreSQL views that can assist in finding database problems.
Chapter 5, Monitoring, further increases availability by adding Nagios, check_mk, collectd, and Graphite to watch active PostgreSQL clusters. This chapter helps us stay informed, and find potential problems before they happen.
Chapter 6, Replication, discusses several PostgreSQL replication scenarios and techniques for more durable data. This includes logical replication tools such as Slony, Bucardo, and Londiste.
Chapter 7, Replication Management Tools, brings WAL management to the forefront. It talks about integrating Barman, OmniPITR, repmgr, or walctl into PostgreSQL to further prevent data loss and control complicated multiserver clusters.
Chapter 8, Advanced Stack, explains how to use LVM, DRBD, and XFS to build a solid foundation and keep data on two servers simultaneously to prevent costly outages.
Chapter 9, Cluster Control, incorporates Pacemaker into the advanced stack. Fully automate PostgreSQL server migrations in case of impending maintenance or hardware failure.
Chapter 10, Data Distribution, shows how PostgreSQL features such as foreign data wrappers and materialized views can produce a scalable cluster. Included with this chapter is a simple data-sharding API technique to reduce dependency on a single PostgreSQL server.
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