Efficiency

Learn how to measure efficiency and look at the concerns related to efficiency.

We'll cover the following

Efficiency in SQL
Efficiency in application code
How the application code works
SQL vs. application code

Efficiency can be measured in a number of ways, including a static and dynamic analysis of the code written.

Static analysis: Static analysis includes the time it takes a developer to come up with the solution and the maintenance burden it then represents (such as the likelihood of bug fixes, the complexity of fixing those bugs, how easy it is to review the code, and so on).

Dynamic analysis: This is concerned with what happens at runtime in terms of the resources we need to run the code, basically revolving around the processor, memory, network, and disk.

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Part I: Preface

Introduction to PostgreSQL

Part II: Introduction to Structured Query Language

Part III: Writing SQL Queries

Business Logic

A Small Application

The SQL REPL: An Interactive Setup

Indexing in PostgreSQL

Part IV: SQL Toolbox

SQL Tools

Managing Output in SQL

Understanding Nulls

Window Functions

Relations and Joins

Part V: Data Types

Serialization and Deserialization

PostgreSQL Data Types 101

Denormalized Data Types

Part VI: Data Modeling

Object Relational Mapping and Tooling for Database Modeling

Normalization

Practical Use Case: Geonames

Modelization Anti-Patterns

Denormalization

Not Only SQL

Part VII: Data Manipulation and Concurrency Control

Another Small Application

Isolation and Locking

Computing and Caching in SQL

Triggers

Listen and Notify

Batch Update, MoMA Collection

Part VIII: PostgreSQL Extensions

What’s a PostgreSQL Extension?

Auditing Changes with hstore

Million Song Dataset

Using Trigrams for Typos

Denormalizing Tags with intarray

The Most Popular Pub Names

Geolocation with PostgreSQL

Counting Distinct Users with HyperLogLog

Appendix

Efficiency