Book Image

PostgreSQL Development Essentials

By : Baji Shaik
Book Image

PostgreSQL Development Essentials

By: Baji Shaik

Overview of this book

PostgreSQL is the most advanced open source database in the world. It is easy to install, configure, and maintain by following the documentation; however, it’s difficult to develop applications using programming languages and design databases accordingly. This book is what you need to get the most out of PostgreSQL You will begin with advanced SQL topics such as views, materialized views, and cursors, and learn about performing data type conversions. You will then perform trigger operations and use trigger functions in PostgreSQL. Next we walk through data modeling, normalization concepts, and the effect of transactions and locking on the database. The next half of the book covers the types of indexes, constrains, and the concepts of table partitioning, as well as the different mechanisms and approaches available to write efficient queries or code. Later, we explore PostgreSQL Extensions and Large Object Support in PostgreSQL. Finally, you will perform database operations in PostgreSQL using PHP and Java. By the end of this book, you will have mastered all the aspects of PostgreSQL development. You will be able to build efficient enterprise-grade applications with PostgreSQL by making use of these concepts

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PostgreSQL Development Essentials
Baji Shaik
Sep, 2016 | 210 pages
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Table of Contents (17 chapters)
PostgreSQL Development Essentials
PostgreSQL Development Essentials
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Advanced SQL
Advanced SQL
Creating views
Materialized views
Creating cursors
Using the GROUP BY clause
Using the HAVING clause
Using the UPDATE operation clauses
Using the LIMIT clause
Using subqueries
Subqueries that return multiple rows
Using the Union join
Using the Self join
Using the Outer join
Summary
2
Data Manipulation
Data Manipulation
Conversion between datatypes
Introduction to arrays
Summary
3
Triggers
Triggers
Introduction to triggers
Creating a trigger function
Summary
4
Understanding Database Design Concepts
Understanding Database Design Concepts
Basic design rules
Normalization
Anomalies in DBMS
Summary
5
Transactions and Locking
Transactions and Locking
Defining transactions
Summary
6
Indexes and Constraints
Indexes and Constraints
Introduction to indexes and constraints
Clustering on an index
Exclusion constraints
Summary
7
Table Partitioning
Table Partitioning
Table partitioning
Summary
8
Query Tuning and Optimization
Query Tuning and Optimization
Query tuning
Optimizer settings for cached data
Multiple ways to implement a query
Bad query performance with stale statistics
Explain Plan
Query operators
Summary
9
PostgreSQL Extensions and Large Object Support
PostgreSQL Extensions and Large Object Support
Creating an extension
Using binary large objects
Summary
10
Using PHP in PostgreSQL
Using PHP in PostgreSQL
Postgres with PHP
PHP-to-PostgreSQL connections
Dealing with DDLs
DML operations
Data retrieval
Helper functions to deal with data fetching
Summary
11
Using Java in PostgreSQL
Using Java in PostgreSQL
Making database connections to PostgreSQL using Java
Using prepared statements
Connection properties
Summary

Query tuning

Before discussing query tuning, first we should know how to write a query, and then we can tune it later on. Queries can be written in multiple ways, as per the requirement; however, we have to prefer an optimal way to write queries.

For example, say that your requirement is to get the row count from a static table. You can achieve this through the following three methods:

Method 1:

postgres=# SELECT SUM(1) FROM customers;
sum
------
1000
(1 row)
Time: 4.308 ms

Method 2:

postgres=# SELECT COUNT(*) FROM customers;
count
-------
1000
(1 row)
Time: 3.128 ms

Method 3:

postgres=# SELECT reltuples FROM pg_class WHERE relname =  'customers';
reltuples 
-----------
1000
(1 row)
Time: 0.576 ms

From the preceding three possible methods, we see that Method 3 gives the best result, and Method 2 is optimal when compared to Method 1. Method 1 takes a bit more time, when compared with Method 2, since it needs to SUM all the records at once, whereas the COUNT aggregate follows a different...

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