A Beginner's Guide to Regular Expressions (Regex)
Published June 30, 2026
Prepared by Innealan Editorial
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Regular expressions describe patterns in text. They are useful for validation, search-and-replace, and extraction when the pattern is genuinely regular.
The essential building blocks
| Pattern | Meaning |
|---|---|
. | Any character except newline |
\d | Any digit (0-9) |
\w | Any “word” character (letters, digits, underscore) |
\s | Any whitespace character |
* | Zero or more of the previous token |
+ | One or more of the previous token |
? | Zero or one of the previous token (also makes quantifiers “lazy” when doubled, e.g. *?) |
{n,m} | Between n and m repetitions |
^ / $ | Start / end of string (or line, with the m flag) |
[abc] | Any one of the characters a, b, or c |
(...) | A capturing group |
(?:...) | A non-capturing group |
| | Alternation (“or”) |
Worked examples
Match a simple email address:
^[\w.+-]+@[\w-]+\.[a-zA-Z]{2,}$Extract all hashtags from a string:
#\w+Match a US-style phone number in a few common formats:
\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}Flags that change behavior
g(global): find all matches, not just the first.i(case-insensitive): ignores letter case.m(multiline): makes^/$match the start/end of each line, not just the whole string.s(dot-all): makes.match newlines too.
A word of caution
Regex is suitable for finding a line such as ERROR 503 in a log. It is a poor choice for parsing nested or context-sensitive formats such as HTML or JSON, where a real parser understands the structure. Keep a test string beside a pattern so a later edit does not quietly broaden what it matches.
Test your own patterns against sample text, with matches highlighted live, using our Regex Tester.