Mendix Data Validation Techniques

When building applications in Mendix, ensuring that user inputs are accurate, consistent, and secure is critical. This is where data validation techniques come into play. Mendix, as a low-code platform, offers both built-in and customizable ways to validate data at various stages of your app—form submission, microflows, and domain model constraints.

In this blog, we’ll walk through the most effective data validation techniques in Mendix, helping you ensure your application maintains data integrity and delivers a smooth user experience.

🧱 1. Attribute-Level Validation

The most basic form of validation in Mendix is defining attribute constraints in the Domain Model. This allows you to ensure that the data conforms to expected formats or values before it's stored in the database.

Examples:

String length: Limit a name field to 50 characters.

Required fields: Mark attributes as required to prevent empty submissions.

Decimal precision: Ensure currency values have no more than two decimal places.

You can also add default values, set allowed ranges, or mark attributes as unique to prevent duplicates.

How to set it:

Go to Domain Model.

Open the entity attribute.

Set Required, Length, or Validation Rules.

✍️ 2. Using Validation Rules in Pages

When creating input forms or pages, Mendix allows you to attach input validation rules at the UI level.

Examples:

Show error messages if email format is invalid.

Restrict age input to numbers only.

Prevent form submission if a mandatory field is blank.

These validations are triggered immediately when a user interacts with the input fields, providing real-time feedback.

Steps:

Open a page with a data form.

Select a text box or field.

In Properties, set Required and define custom validation messages.

⚙️ 3. Custom Validation in Microflows

For more complex or conditional validation logic, Mendix allows you to implement validations using microflows. You can check one or more conditions and display error messages using the Validation Feedback activity.

Example Scenario:

If a user enters a birthdate that makes them under 18, show a validation error.

Steps:

Create a Microflow triggered by a Save button.

Add a decision node to check conditions.

Use Validation Feedback to show an error message tied to a specific attribute.

This is especially useful when multiple fields must be validated together or when logic depends on business rules.

🧩 4. Using Regular Expressions

You can use regex expressions in microflows or expression widgets to validate specific input formats such as:

Email addresses

Postal codes

Phone numbers

Example:

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matches($User/EmailAddress, '[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}')

If the input doesn’t match the pattern, trigger a Validation Feedback message.

🧪 5. On-Change Event Handlers

For dynamic form behavior, use on-change event handlers to validate and update data as the user types.

Example:

Automatically verify if a username already exists in the database.

Disable/enable fields based on previous inputs.

This improves UX by catching errors early—before the user even submits the form.

✅ Best Practices

Combine multiple validation layers: domain model, UI, and microflows.

Display clear, actionable error messages.

Reuse microflows for common validation logic across entities.

Validate at the earliest point possible to improve UX and prevent errors from persisting.

🧾 Conclusion

Mendix provides a comprehensive set of data validation techniques—from simple attribute rules to complex microflow logic. By leveraging these methods, you can ensure clean, accurate, and consistent data in your applications while offering a smoother user experience.

Whether you’re building a form-heavy internal tool or a customer-facing mobile app, applying the right combination of validation strategies is essential to building trust and functionality into your Mendix solutions.

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