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PostgreSQL 12 High Availability Cookbook

PostgreSQL 12 High Availability Cookbook

By : Shaun Thomas
4.5 (2)
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PostgreSQL 12 High Availability Cookbook

PostgreSQL 12 High Availability Cookbook

4.5 (2)
By: Shaun Thomas

Overview of this book

Databases are nothing without the data they store. In the event of an outage or technical catastrophe, immediate recovery is essential. This updated edition ensures that you will learn the important concepts related to node architecture design, as well as techniques such as using repmgr for failover automation. From cluster layout and hardware selection to software stacks and horizontal scalability, this PostgreSQL cookbook will help you build a PostgreSQL cluster that will survive crashes, resist data corruption, and grow smoothly with customer demand. You’ll start by understanding how to plan a PostgreSQL database architecture that is resistant to outages and scalable, as it is the scaffolding on which everything rests. With the bedrock established, you'll cover the topics that PostgreSQL database administrators need to know to manage a highly available cluster. This includes configuration, troubleshooting, monitoring and alerting, backups through proxies, failover automation, and other considerations that are essential for a healthy PostgreSQL cluster. Later, you’ll learn to use multi-master replication to maximize server availability. Later chapters will guide you through managing major version upgrades without downtime. By the end of this book, you’ll have learned how to build an efficient and adaptive PostgreSQL 12 database cluster.
Table of Contents (17 chapters)
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Testing DDL replication on each node

One of the potential roadblocks of multi-master replication is the fact that schemas must be compatible on all nodes. Any node that does not have the same columns and types on tables risks being unable to consume data from the upstream node that generated the incompatible tuple.

As a result, a hallmark feature of BDR (and likely any other multi-master system that emerges in the future) is that DDL is transparently replicated. This ensures that the schemas are always compatible with all nodes, and prevents costly delays in replication caused by stuck data.

This recipe will be a short proof of concept to prove that DDL is transparently replicated and serves as a good check to try on any new BDR cluster to ensure that it's working correctly.

Getting...

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