Regex Tester

What is a Regular Expression?

A regular expression (regex or regexp) is a sequence of characters that defines a search pattern for matching text. Originally formalized by mathematician Stephen Kleene in 1956, regex has become an indispensable tool in software development for tasks like input validation, text extraction, and find-and-replace operations. Every major programming language supports regular expressions natively or through standard libraries -- JavaScript uses the RegExp object, Python provides the re module, Java includes java.util.regex, and Go offers the regexp package. Despite syntactic differences between flavors (PCRE, POSIX, JavaScript), the core concepts remain the same. This tool runs entirely in your browser using the JavaScript regex engine, meaning your test data is never transmitted to any server. As you type a pattern above, the engine compiles it into a finite automaton and executes it against your test string in real time, highlighting every match and displaying captured groups with their character positions.

Common Use Cases for Regular Expressions

Email validation is one of the most frequent regex tasks. A basic pattern like [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} catches the majority of valid addresses, though the full RFC 5322 spec is far more permissive. Phone number parsing handles varying formats --\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4} matches "(555) 123-4567", "555.123.4567", and "555-123-4567" in a single pattern. URL extraction from unstructured text uses patterns anchored on the protocol (https?://) followed by allowed URI characters. Beyond validation, developers use regex for log file analysis -- extracting timestamps, error codes, and IP addresses from lines like2024-01-15 14:30:00 ERROR [192.168.1.1] Connection refused. In data cleaning, regex powers CSV normalization (stripping unwanted whitespace, standardizing date formats) and search-and-replace across codebases (renaming variables, migrating API call signatures). Form input validation in web applications relies on patterns for postal codes, credit card numbers (Luhn check aside), and username constraints like ^[a-zA-Z0-9_]{3,20}$. For scraping, regex extracts structured data from semi-structured HTML when a full parser is overkill -- though for complex DOM traversal, a proper parser is always preferred.

Regex Syntax Quick Reference

Character classes define sets of characters to match.[a-z] matches any lowercase letter, \d is shorthand for [0-9], \w matches word characters ([a-zA-Z0-9_]), and \s matches whitespace (spaces, tabs, newlines). Negate any class with a capital letter: \D means "not a digit." Quantifiers control repetition: * matches zero or more, + matches one or more, ? makes something optional, and {n,m} specifies an exact range. By default, quantifiers are greedy -- they consume as much text as possible. Appending? makes them lazy, so .*? matches the shortest possible string instead of the longest. Anchors like ^and $ assert position rather than matching characters; with the multiline flag (m), they match the start and end of each line rather than the entire string. Groups created with parentheses ()capture matched text for back-references or extraction -- use (?:) for non-capturing groups when you only need grouping without capture overhead.Lookahead ((?=...)) and lookbehind((?<=...)) assert that text exists before or after your match without including it in the result, which is useful for patterns like "match a number only if followed by a dollar sign": \d+(?=\$).