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Mastering PostgreSQL 12

Mastering PostgreSQL 12

By : Hans-Jürgen Schönig
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Mastering PostgreSQL 12

Mastering PostgreSQL 12

4 (5)
By: Hans-Jürgen Schönig

Overview of this book

Thanks to its reliability, robustness, and high performance, PostgreSQL has become the most advanced open source database on the market. This third edition of Mastering PostgreSQL helps you build dynamic database solutions for enterprise applications using the latest release of PostgreSQL, which enables database analysts to design both physical and technical aspects of system architecture with ease. Starting with an introduction to the newly released features in PostgreSQL 12, this book will help you build efficient and fault-tolerant PostgreSQL applications. You’ll thoroughly examine the advanced features of PostgreSQL, including logical replication, database clusters, performance tuning, monitoring, and user management. You’ll also work with the PostgreSQL optimizer, configure PostgreSQL for high speed, and understand how to move from Oracle to PostgreSQL. As you progress through the chapters, you’ll cover transactions, locking, indexes, and how to optimize queries for improved performance. Additionally, you’ll learn how to manage network security and explore backups and replications while understanding useful PostgreSQL extensions to help you in optimizing 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 implementing advanced administrative tasks effortlessly.
Table of Contents (18 chapters)
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1
Section 1: Basic Overview
4
Section 2: Advanced Concepts

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? Imagine 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 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..370.87 rows=10768 width...

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