Book Image

PostgreSQL 11 Administration Cookbook

By : Simon Riggs, Gianni Ciolli, Sudheer Kumar Meesala
Book Image

PostgreSQL 11 Administration Cookbook

By: Simon Riggs, Gianni Ciolli, Sudheer Kumar Meesala

Overview of this book

PostgreSQL is a powerful, open source database management system with an enviable reputation for high performance and stability. With many new features in its arsenal, PostgreSQL 11 allows you to scale up your PostgreSQL infrastructure. This book takes a step-by-step, recipe-based approach to effective PostgreSQL administration. The book will introduce you to new features such as logical replication, native table partitioning, additional query parallelism, and much more to help you to understand and control, crash recovery and plan backups. You will learn how to tackle a variety of problems and pain points for any database administrator such as creating tables, managing views, improving performance, and securing your database. As you make steady progress, the book will draw attention to important topics such as monitoring roles, backup, and recovery of your PostgreSQL 11 database to help you understand roles and produce a summary of log files, ensuring high availability, concurrency, and replication. By the end of this book, you will have the necessary knowledge to manage your PostgreSQL 11 database efficiently.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Enforcing the same name and definition for columns

Sensibly designed databases have smooth, easy-to-understand definitions. This allows all users to understand the meaning of data in each table. It is an important way of removing data quality issues.

Getting ready

If you want to run the queries in this recipe as a test, then use the following examples. Alternatively, you can just check for problems in your own database:

CREATE TABLE s1.X(col1 smallint,col2 TEXT); 
CREATE TABLE s2.X(col1 integer,col3 NUMERIC);

How to do it...

First, we will show you how to identify columns that are defined in different ways in different tables, using a query against the catalog. We use an information_schema query, as follows:

  ||coalesce(' ' || text(character_maximum_length), '')
  ||coalesce(' ' || text(numeric_precision), '')
  ||coalesce(',' || text(numeric_scale), '')
  as data_type
FROM information_schema.columns...