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Python# Email Extraction Regex Patterns
# Copy-paste ready patterns for any language
# Pattern 1: Simple (Loose) - Fast extraction
SIMPLE = r'[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}'
# Pattern 2: Moderate (Recommended) - Balanced
MODERATE = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,7}\b'
# Pattern 3: Plus Addressing Support
PLUS_ADDR = r'[A-Za-z0-9._%+-]+\+?[A-Za-z0-9._%-]*@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}'
# Pattern 4: International Domains
INTERNATIONAL = r'[\w.%+-]+@[\w.-]+\.[\w]{2,}'
# Python usage example
import re
text = "Contact us at support@example.com"
emails = re.findall(MODERATE, text)
print(emails) # ['support@example.com']
# JavaScript usage
# const regex = /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,7}\b/gi;
# const emails = text.match(regex);
info About This Tool
The Email Extraction Regex Patterns Library provides battle-tested regular expressions for finding and extracting email addresses from any text source. Each pattern is optimized for different use cases and validation levels.
Available Patterns
- Simple (Loose) - Maximum recall, fastest performance. Best for logs, scraping
- Moderate - Balanced accuracy and speed. Recommended for most use cases
- Plus Addressing - Handles Gmail-style user+tag@domain.com format
- International - Unicode support for non-Latin domains
- RFC 5322 Strict - Full compliance for strict validation
Language Support
- Python - re.findall(), re.match()
- JavaScript - String.match(), RegExp.test()
- PHP - preg_match_all()
- Java - Pattern.compile(), Matcher
- SQL - MySQL REGEXP, PostgreSQL ~
- Command Line - grep -E, awk, sed
Performance Notes
- Simple patterns - O(n), 1M+ chars/sec
- Moderate patterns - O(n), 500K chars/sec
- RFC 5322 strict - O(n), 50K chars/sec (10x slower)
Pro Tip: For database queries use moderate pattern. For log analysis use simple pattern. For user input validation consider RFC 5322 strict.
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