Mohammad Waseem

Posted on Jan 31

Streamlining Email Flow Validation with Python and Open Source Tools in DevOps

#programming #devops

Ensuring reliable email delivery and validation is a critical component of modern DevOps workflows, especially when managing transactional or bulk email systems. In this blog post, we'll explore how a DevOps specialist can effectively validate email flows using Python and open source tools, enhancing the robustness and trustworthiness of email-based communications.

Why Validate Email Flows?

Email validation is essential to prevent issues such as delivery failures, spam classification, or security vulnerabilities. It involves checking email syntax, verifying domain existence, and simulating actual delivery to identify potential bottlenecks or misconfigurations in the email flow pipeline.

Tools and Libraries

For this task, we will leverage several open source libraries and tools:

Python: The core scripting language for automation and validation.
py3dns: To perform DNS lookups for MX records.
smtplib: Standard Python library for SMTP communication.
dnspython: For comprehensive DNS queries.
mailvalidator: To validate email syntax.

Step 1: Syntax Validation

First, we validate that email addresses conform to standard syntax rules. Using the mailvalidator library simplifies this process.

from mailvalidator import validate_email

def is_valid_syntax(email):
    try:
        validate_email(email)
        return True
    except Exception as e:
        return False

# Example usage
emails = ["test@example.com", "invalid-email", "user@domain"]
syntax_results = {email: is_valid_syntax(email) for email in emails}
print(\"Syntax validation results:\", syntax_results)

This step filters out malformed email addresses before proceeding to domain validation.

Step 2: Domain Validation via DNS MX Lookup

Next, we verify that the email's domain has valid MX records, indicating readiness to handle email traffic.

import dns.resolver

def has_mx_record(domain):
    try:
        answers = dns.resolver.resolve(domain, 'MX')
        return len(answers) > 0
    except (dns.resolver.NoAnswer, dns.resolver.NXDOMAIN, dns.resolver.NoNameservers):
        return False

# Checking domains
domains = [email.split('@')[1] for email, valid in syntax_results.items() if valid]
domain_checks = {domain: has_mx_record(domain) for domain in domains}
print(\"Domain MX record validation:\", domain_checks)

This ensures that the recipient's domain is capable of receiving emails.

Step 3: Simulating SMTP Connection

The final step involves attempting a connection via SMTP, which mimics real email flow and helps identify deliverability issues.

import smtplib

def test_smtp_connection(domain):
    try:
        server = smtplib.SMTP(f\"{domain}:25\", timeout=10)
        server.quit()
        return True
    except Exception:
        return False

smtp_results = {domain: test_smtp_connection(domain) for domain in domains}
print(\"SMTP connection tests:\", smtp_results)

Note: Some servers require specific configurations or might block open endpoint connections; this step should be contextualized with appropriate permissions.

Automating the Validation Pipeline

To ensure continuous validation, embed these scripts into your CI/CD pipeline, running at each deployment or update cycle. Integrating with monitoring tools provides real-time insight into email flow health.

Conclusion

By combining Python's capabilities with open source tools, a DevOps engineer can build a comprehensive email validation workflow that minimizes disruptions, enhances trust in email communications, and supports scalable systems. Regularly validating email flows helps prevent issues before they impact end-users, ensuring a smooth, reliable messaging experience.

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

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