# Regex Sandbox

> Live regular expression playground with real-time match highlighting.

**Category:** Dev
**Keywords:** regex, regexp, patterns, match, developer, test, sandbox, grep, search, replace, logic, syntax
**URL:** https://complete.tools/regex-tester

## How it works

The Regex Tester processes inputs by taking a user-defined regular expression and a target string. It uses a regex engine that interprets the pattern according to its syntax rules. This engine scans through the target string and applies the regex pattern to identify matches. It utilizes algorithms like backtracking to explore possible matches, capturing groups when specified. The matching process results in a report detailing the positions of matches, any captured groups, and any substitutions or replacements if specified. The tool may also highlight errors in the regex syntax, providing debugging assistance.

## Who should use this

Web developers validating input fields for forms, data scientists parsing log files for analysis, and software testers checking string patterns in application outputs are specific use cases for the Regex Tester. Additionally, system administrators can use it to filter and extract relevant information from configuration files, while content managers might employ it to search for patterns in large datasets of text content.

## Worked examples

Example 1: A web developer wants to validate email addresses. They input the regex pattern `^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$` and test it against the email `test@example.com`. The tool shows that the email matches because it conforms to the structure defined by the regex. 

Example 2: A data analyst needs to extract dates from a text string. They enter the regex pattern `\b\d{2}/\d{2}/\d{4}\b` and the sample text `The event is on 12/05/2023 and 15/06/2023`. The tool indicates two matches: `12/05/2023` and `15/06/2023`, demonstrating how the regex finds specific date formats in the text.

## Limitations

The Regex Tester has specific limitations, including performance issues with very complex regex patterns that may lead to excessive backtracking, causing slow execution times. It may also not support certain advanced features found in specific programming languages, leading to discrepancies in expected results. Additionally, regex patterns can sometimes produce false positives, matching unintended text due to overlapping patterns. Lastly, the tester may have a character limit for input strings, which can restrict testing large datasets.

## FAQs

**Q:** What is the difference between greedy and lazy matching in regex?


**A:** Greedy matching attempts to match as much text as possible, while lazy matching captures the least amount of text necessary to satisfy the regex conditions. For example, in the pattern `\d+`, greedy matching on `12345` returns `12345`, while lazy matching with the pattern `\d+?` returns `1` first, then proceeds to match more digits.


**Q:** How can I use capturing groups in regex?


**A:** Capturing groups are defined by parentheses in a regex pattern. For instance, in the pattern `(\d{3})-(\d{2})-(\d{4})`, the groups capture the area code, prefix, and line number of a phone number format like `123-45-6789`. You can reference these groups in replacements or extract them for further processing.


**Q:** Why might my regex not match what I expect?


**A:** Common reasons include not accounting for case sensitivity, which can be addressed with modifiers, or incorrect pattern syntax. Additionally, special characters may require escaping to function properly within the regex context, such as using `\.` to match a literal period.

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*Generated from [complete.tools/regex-tester](https://complete.tools/regex-tester)*