Chapter 01, Relational Databases, introduces relational database system concepts, including relational database properties, relational algebra, and database modeling. Also, it describes different database management systems such as graph, document, key value, and columnar databases.

Chapter 02, PostgreSQL in Action, provides first-hand experience of installing the PostgreSQL server and client tools on different platforms. This chapter also introduces PostgreSQL capabilities, such as out-of-the-box replication support and its very rich data types.

Chapter 03, PostgreSQL Basic Building Blocks, provides some coding best practices, such as coding conventions of PostgreSQL and identifier names. This chapter describes the basic building blocks and the interaction between these blocks, mainly template databases, user databases, tablespaces, roles, and settings. Also, it describes basic data types and tables.

Chapter 04, PostgreSQL Advanced Building Blocks, introduces several building blocks, including views, indexes, functions, user-defined data types, triggers, and rules. This chapter provides use cases of these building blocks and compares building blocks that can be used for the same case, such as rules and triggers.

Chapter 05, SQL Language, introduces structured query language (SQL), which is used to interact with a database, create and maintain data structures, and enter data into databases as well as, change it, retrieve it, and delete it. SQL has commands related to data definition language (DDL), data manipulation language (DML), and data control language (DCL). Four SQL statements form the basis of DML and are described in this chapter. The SELECT statement is examined in detail to explain SQL concepts such as grouping and filtering to show what SQL expressions and conditions are and how to use subqueries. Some relational algebra topics are also covered in application to joining tables.

Chapter 06, Advanced Query Writing, describes advanced SQL concepts and features such as common table expressions and window functions. This helps you implement a logic that would not be possible without them, such as recursive queries. Other techniques explained here, such as the DISTINCT ON clause, the FILTER clause, and lateral subqueries, are not irreplaceable. However, they can help make a query smaller, easier, and faster.

Chapter 07, Server-Side Programming with PL/pgSQL, covers function parameters, such as the number of returned rows and function cost, which is mainly used by the query planner. Also, it presents control statements such as conditionals and iteration. Finally, it explains the concept of dynamic SQL and some recommended practices when using dynamic SQL.

Chapter 08, OLAP and Data Warehousing, introduces several concepts regarding the usage of recreational databases in the realm of analytical processing. It discusses the deference between OLTP load and OLAP loads; furthermore, it discusses the modeling aspect of OLAP applications. In addition to that, it discusses some technical methods to perform ETL (extract, transform, and load) operations such as the COPY command. Also, it discusses some features of PostgreSQL which increasing data retrieval performance such as index-only scans and table partitioning.

Chapter 09, Beyond Conventional Data types, discusses several rich data types, including arrays, hash stores, JSON documents, and full-text search. It presents operations and functions for each data type to initialize, insert, update, access, and delete these data types. Finally, it shows how PostgreSQL can be combined with Nginx to serve read-only restful requests.

Chapter 10, Transactions and Concurrency Control, discusses in detail the ACID properties and the relation between these properties and concurrency controls. This chapter also discusses concepts such as isolation levels and their side-effects and it shows these side-effects using SQL examples. Finally, the chapter discusses different locking methods, including pessimistic locking strategies such as row locking and advisory locks. 

Chapter 11, PostgreSQL Security, covers concepts of authentication and authorization. It describes PostgreSQL authentication methods and explains the structure of a PostgreSQL host-based authentication configuration file. It also discusses the permissions that can be granted to database building objects such as schemas, tables, views, indexes, and columns. Finally, it shows how sensitive data, such as passwords, can be protected using different techniques, including one-way and two-way encryption.

Chapter 12, The PostgreSQL Catalog, provides several recipes to maintain a database cluster, including cleaning up data, maintaining user processes, cleaning up indexes and unused databases objects, discovering and adding indexes to foreign keys, and so on.

Chapter 13, Optimizing Database Performance, discusses several approaches to optimize performance. It presents PostgreSQL cluster configuration settings, which are used in tuning the whole cluster's performance. Also, it presents common mistakes in writing queries and discusses several approaches to increase performance, such as using indexes or table partitioning and constraint exclusion.

Chapter 14, Testing, covers some aspects of the software testing process and how it can be applied to databases. Unit tests for databases can be written as SQL scripts or stored functions in a database. There are several frameworks that help us write unit tests and process the results of testing.

Chapter 15, Using PostgreSQL in Python Applications, discusses several advanced concepts, such as connection pooling, asynchronous access, and object relational mappers (ORMs). The chapter shows by example how to connect to database, query it, and perform updates using Python. Finally, it introduces different technologies that interact with PostgreSQL, and this gives the developer a broad overview of the state-of-the-art technologies.

Chapter 16, Scalability, discusses the problem of scalability and the CAP theorem in detail. Also, it covers data replication in PostgreSQL, including physical replication and logical replication. Finally, it shows different scaling scenarios and their implementation in PostgreSQL. 

In general, PostgreSQL server and client tools do not need exceptional hardware. PostgreSQL can be installed on almost all modern platforms, including Linux, Windows, and Mac. Also, in the book, when a certain library is needed, the installation instructions are given.

You need PostgreSQL version 10; however, most of the examples can be executed on earlier versions as well. In order to execute the sample code, scripts, and examples provided in the book, you need to have at least a PostgreSQL client tool installed on your machine—preferably psql—and access to a remote server running the PostgreSQL server.

In a Windows environment, the cmd.exe command prompt is not very convenient; thus, the user might consider using Cygwin (http://www.cygwin.com/) or another alternative, such as PowerShell.

Some chapters might requires additional software. For example, in Chapter 15, Using PostgreSQL in Python application, you need to install Python and the required Python libraries to interact with PostgreSQL. In Chapter 16, Scalability, Docker is used to give the reader a better user experience. 

For other chapters, such as Chapter 09, Beyond Conventional Data types, and Chapter 11, PostgreSQL Security, it is better to use Linux because some of the software used is not very convenient on Windows such as Nginx and GnuPG. To run a Linux distribution on your Windows machine, you can use Virtual Box (https://www.virtualbox.org/).

If you're interested in learning more about PostgreSQL--one of the most popular relational databases in the world--then this book is for you. Those looking to build solid database or data warehousing applications with PostgreSQL 10 will also find this book a useful resource. No prior knowledge of database programming or administration is required to get started with this book.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The next lines of code read the link and assign it to the open function."

A block of code is set as follows:

fin = open('data/fake_weather_data.csv','r',newline='')
reader = csv.reader(fin)
for row in reader:
    myData.append(row)

Any command-line input or output is written as follows:

$ mongoimport --file fake_weather_data.csv

New terms and important words are shown in bold.

Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."

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