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Table of Contents
1. Verify using Laravel's Eloquent model
2. Set field constraints in Django model
3. Define rules using JSON Schema or other structured ways
4. Handling suggestions when verification fails
Home PHP Framework YII How do I define validation rules for model attributes?

How do I define validation rules for model attributes?

Jul 06, 2025 am 12:43 AM

In web development, the verification rules for defining model attributes are to ensure data integrity and business logic are correctly executed. The core methods include: 1. Use form requests, validate() method or model $rules attribute in Laravel for verification; 2. Use field constraints and clean() methods to achieve verification in Django model; 3. Use JSON Schema to define structured verification rules; 4. Return clear error information when verification fails, avoid leaking sensitive content, and record logs to troubleshoot problems.

How do I define validation rules for model attributes?

In web development, verification rules for defining model attributes are to ensure the integrity of data and the correct execution of business logic. Different frameworks have different implementation methods, but the core idea is the same: check before data is saved, and if the conditions are not met, the operation will be rejected and an error message will be returned .

The following is a few common scenarios and tools as examples to introduce how to define verification rules for model attributes.


1. Verify using Laravel's Eloquent model

In Laravel, verification rules are usually defined in two ways:

  • Use form requests in the controller or call validate() method directly
  • Automatic verification in the model in combination with $rules attribute or Observer

For example, manually verify in the controller:

 $request->validate([
    'name' => 'required|string|max:255',
    'email' => 'required|email|unique:users',
]);

If you want to centrally manage rules, you can define a static method in your model:

 public static $rules = [
    'name' => 'required|string|max:255',
    'email' => 'required|email|unique:users',
];

Then refer these rules in the controller to keep the code neat.


2. Set field constraints in Django model

Django's model system itself supports field-level verification, such as:

 from django.db import models

class User(models.Model):
    name = models.CharField(max_length=255, blank=False)
    email = models.EmailField(unique=True)

Here blank=False and unique=True are the basic verification rules. You can also add more complex logic by overriding clean() method:

 def clean(self):
    if not self.email.endswith('@example.com'):
        raise ValidationError({'email': 'Must use @example.com email'})

This method is suitable for uniformly verifying the relationship between multiple fields before saving.


3. Define rules using JSON Schema or other structured ways

If you are not using a traditional ORM, but an API interface layer or a NoSQL database, you can consider using JSON Schema to describe the verification rules. For example:

 {
  "type": "object",
  "properties": {
    "username": { "type": "string", "minLength": 3 },
    "age": { "type": "number", "minimum": 0 }
  },
  "required": ["username"]
}

This structure is clear and reusable, and it also facilitates the front-end consistency verification.


4. Handling suggestions when verification fails

No matter what framework you are in, the following should be done after verification failure:

  • Returns clear error information, preferably classified by field
  • Do not expose sensitive information, such as database structure
  • Logs to troubleshoot abnormal inputs (especially offensive attempts)

For example, Laravel will throw ValidationException by default, and you can customize the response format:

 return redirect('back')->withErrors($validator)->withInput();

In Django, similar effects can be achieved by capturing ValidationError and constructing JSON responses.


Basically that's it. Although verification rules seem simple, they are easily overlooked in actual development, such as whether the field length limit is reasonable, how to deal with unique conflicts, etc. As long as you choose the right method according to project needs and maintain consistency, the robustness of the system can be effectively improved.

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