Welcome to this comprehensive guide on Regular Expressions (Regex). This tutorial is designed to equip you with the skills to craft powerful, time-saving regular expressions from scratch. We'll begin with foundational concepts, ensuring you can follow along even if you're new to the world of regex. However, this isn't just a basic guide; we'll delve deeper into how regex engines operate internally, giving you insights that will help you troubleshoot and optimize your patterns effectively.

What Are Regular Expressions? — Understanding the Basics

At its core, a regular expression is a pattern used to match sequences of text. The term originates from formal language theory, but for practical purposes, it refers to text-matching rules you can use across various applications and programming languages.

You'll often encounter abbreviations like regex or regexp. In this guide, we'll use "regex" as it flows naturally when pluralized as "regexes." Throughout this manual, regex patterns will be displayed within guillemets: «pattern». This notation clearly differentiates the regex from surrounding text or punctuation.

For example, the simple pattern «regex» is a valid regex that matches the literal text "regex." The term match refers to the segment of text that the regex engine identifies as conforming to the specified pattern. Matches will be highlighted using double quotation marks, such as "match."

A First Look at a Practical Regex Example

Let's consider a more complex pattern:

\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,4}\b

This regex describes an email address pattern. Breaking it down:

• \b: Denotes a word boundary to ensure the match starts at a distinct word.
• [A-Z0-9._%+-]+: Matches one or more letters, digits, dots, underscores, percentage signs, plus signs, or hyphens.
• @: The literal at-sign.
• [A-Z0-9.-]+: Matches the domain name.
• .: A literal dot.
• [A-Z]{2,4}: Matches the top-level domain (TLD) consisting of 2 to 4 letters.
• \b: Ensures the match ends at a word boundary.

With this pattern, you can:

• Search text files to identify email addresses.
• Validate whether a given string resembles a legitimate email address format.

In this tutorial, we'll refer to the text being processed as a string. This term is commonly used by programmers to describe a sequence of characters. Strings will be denoted using regular double quotes, such as "example string."

Regex patterns can be applied to any data that a programming language or software application can access, making them an incredibly versatile tool in text processing and data validation tasks.

Next, we'll explore how to construct regex patterns step by step, starting from simple character matches to more advanced techniques like capturing groups and lookaheads. Let's dive in!