Book Image

Mastering PostgreSQL 11 - Second Edition

By : Hans-Jürgen Schönig
Book Image

Mastering PostgreSQL 11 - Second Edition

By: Hans-Jürgen Schönig

Overview of this book

This second edition of Mastering PostgreSQL 11 helps you build dynamic database solutions for enterprise applications using the latest release of PostgreSQL, which enables database analysts to design both the physical and technical aspects of the system architecture with ease. This book begins with an introduction to the newly released features in PostgreSQL 11 to help you build efficient and fault-tolerant PostgreSQL applications. You’ll examine all of the advanced aspects of PostgreSQL in detail, including logical replication, database clusters, performance tuning, monitoring, and user management. You will also work with the PostgreSQL optimizer, configuring PostgreSQL for high speed, and see how to move from Oracle to PostgreSQL. As you progress through the chapters, you will cover transactions, locking, indexes, and optimizing queries to improve performance. Additionally, you’ll learn to manage network security and explore backups and replications, while understanding the useful extensions of PostgreSQL so that you can optimize the speed and performance of large databases. By the end of this book, you will be able to use your database to its utmost capacity by implementing advanced administrative tasks with ease.
Table of Contents (15 chapters)
Free Chapter
1
PostgreSQL Overview

Improving speed using clustered tables

In this section, you will learn about the power of correlation and the power of clustered tables. What is this about? Consider that you want to read a whole area of data. This might be a certain time range, some block, IDs, and so on.

The runtime of such queries will vary, depending on the amount of data and the physical arrangement of data on the disk. So, even if you are running queries that return the same number of rows, two systems might not provide the answer within the same time span, as the physical disk layout might make a difference.

Here is an example:

test=# EXPLAIN (analyze true, buffers true, timing true)    
SELECT *
FROM t_test WHERE id < 10000;

QUERY PLAN
----------------------------------------------------------
Index Scan using idx_id on t_test
(cost=0.43..370.87 rows=10768 width...