Book Image

PostgreSQL High Performance Cookbook

By : Chitij Chauhan, Dinesh Kumar
Book Image

PostgreSQL High Performance Cookbook

By: Chitij Chauhan, Dinesh Kumar

Overview of this book

PostgreSQL is one of the most powerful and easy to use database management systems. It has strong support from the community and is being actively developed with a new release every year. PostgreSQL supports the most advanced features included in SQL standards. It also provides NoSQL capabilities and very rich data types and extensions. All of this makes PostgreSQL a very attractive solution in software systems. If you run a database, you want it to perform well and you want to be able to secure it. As the world’s most advanced open source database, PostgreSQL has unique built-in ways to achieve these goals. This book will show you a multitude of ways to enhance your database’s performance and give you insights into measuring and optimizing a PostgreSQL database to achieve better performance. This book is your one-stop guide to elevate your PostgreSQL knowledge to the next level. First, you’ll get familiarized with essential developer/administrator concepts such as load balancing, connection pooling, and distributing connections to multiple nodes. Next, you will explore memory optimization techniques before exploring the security controls offered by PostgreSQL. Then, you will move on to the essential database/server monitoring and replication strategies with PostgreSQL. Finally, you will learn about query processing algorithms.
Table of Contents (19 chapters)
PostgreSQL High Performance Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Partial indexes


In this recipe, we will be discussing how to create an index for the required data sample set.

Getting ready

Using partial indexes, we can reduce the size of an index by adding a predicate in the index definition. That is, only the entries that match the predicate will only be indexed instead of all of them. This partial index will be utilized when the index predicate satisfies the submitted SQL predicate.

How to do it...

For example, let's say that our application does a frequent query on bmsql_item as to list all the items that have a price between $5 to $10, then it is a candidate predicate to create a partial index as follows:

benchmarksql=# CREATE INDEX CONCURRENTLY part_idx ON bmsql_item(i_price) WHERE i_price BETWEEN 5 AND 10; 
CREATE INDEX

Let's query the table so as to return the items that have a cost between 5 and 10:

benchmarksql=# EXPLAIN SELECT * FROM bmsql_item WHERE i_price BETWEEN 5 AND 10; 
                                   QUERY PLAN                      ...