Book Image

Mastering PostgreSQL 13 - Fourth Edition

By : Hans-Jürgen Schönig
Book Image

Mastering PostgreSQL 13 - Fourth Edition

By: Hans-Jürgen Schönig

Overview of this book

Thanks to its reliability, robustness, and high performance, PostgreSQL has become one of the most advanced open source databases on the market. This updated fourth edition will help you understand PostgreSQL administration and how to build dynamic database solutions for enterprise apps with the latest release of PostgreSQL, including designing both physical and technical aspects of the system architecture with ease. Starting with an introduction to the new features in PostgreSQL 13, this book will guide you in building efficient and fault-tolerant PostgreSQL apps. You’ll explore advanced PostgreSQL features, such as logical replication, database clusters, performance tuning, advanced indexing, monitoring, and user management, to manage and maintain your database. You’ll then work with the PostgreSQL optimizer, configure PostgreSQL for high speed, and move from Oracle to PostgreSQL. The book also covers transactions, locking, and indexes, and shows you how to improve performance with query optimization. You’ll also focus on how to manage network security and work with backups and replication while exploring useful PostgreSQL extensions that optimize the performance of large databases. By the end of this PostgreSQL book, you’ll be able to get the most out of your database by executing advanced administrative tasks.
Table of Contents (15 chapters)

Improving speed using clustered tables

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

The runtime of these 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..322.63 rows=9383 width=9)
(actual time=0.469..4.476 rows=9999 loops=1)
Index...