Consider this example:. There are two ways you can do this: by defining custom regular expressions, or by creating custom validation methods. If the validation technique you need to use can be completed by using regular expression matching, you can define a custom expression as a field validation rule:.
The example above checks if the login contains only letters and integers, with a minimum of three characters. The regular expression in the rule must be delimited by slashes. Sometimes checking data with regular expression patterns is not enough. For example, if you want to ensure that a promotional code can only be used 25 times, you need to add your own validation function, as shown below:.
The current field to be validated is passed into the function as first parameter as an associated array with field name as key and posted data as value. Your validation function can be in the model as in the example above , or in a behavior that the model implements. This includes mapped methods. This means that you can override existing validation methods such as alphaNumeric at an application level by adding the method to AppModel , or at model level.
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Your own validation methods must have public visibility. Validation methods that are protected and private are not supported. The method should return true if the value is valid. If the validation failed, return false.
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The other valid return value are strings which will be shown as the error message. Returning a string means the validation failed. Nevertheless, there are cases when you want to dynamically add, change or remove validation rules from the predefined set. All validation rules are stored in a ModelValidator object, which holds every rule set for each field in your model.
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Defining new validation rules is as easy as telling this object to store new validation methods for the fields you want to. The ModelValidator objects allows several ways for adding new fields to the set.
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The first one is using the add method:. This will add a single rule to the password field in the model. You can chain multiple calls to add to create as many rules as you like:. Alternatively, you can use the validator object to set rules directly to fields using the array interface:. Modifying current validation rules is also possible using the validator object, there are several ways in which you can alter current rules, append methods to a field or completely remove a rule from a field rule set:.
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If you wish to just modify a single property in a rule you can set properties directly into the CakeValidationRule object:. As with adding new rule to the set, it is also possible to modify existing rules using the array interface:. The Validation class in CakePHP contains many validation rules that can make model data validation much easier.
The length of the data for the field must fall within the specified numeric range. Both minimum and maximum values must be supplied. The data is checked by number of characters, not number of bytes. This rule is used to make sure that the field is left blank or only white space characters are present in its value. White space characters include space, tab, carriage return, and newline. The data for the field must be a boolean value. This rule is used to check whether the data is a valid credit card number.
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It defaults to false. Comparison is used to compare numeric values. Some examples are shown below:. This rule ensures that data is submitted in valid date formats. A single parameter which can be an array can be passed that will be used to check the format of the supplied date. The value of the parameter can be one of the following:. While many data stores require a certain date format, you might consider doing the heavy lifting by accepting a wide-array of date formats and trying to convert them, rather than forcing users to supply a given format.
The more work you can do for your users, the better. This rule ensures that the data is a valid datetime format. A parameter which can be an array can be passed to specify the format of the date. The value of the parameter can be one or more of the following:. Also a second parameter can be passed to specify a custom regular expression.
If this parameter is used, this will be the only validation that will occur. This rule ensures that the data is a valid decimal number. A parameter can be passed to specify the number of digits required after the decimal point. If no parameter is passed, the data will be validated as a scientific float, which will cause validation to fail if no digits are found after the decimal point:.
This checks whether the data is a valid email address. Passing a boolean true as the second parameter for this rule will also attempt to verify that the host for the address is valid:. This rule checks for valid file extensions like. Allow multiple extensions by passing them in array form. This rule allows you to check filesizes. All the operators supported by comparison are supported here as well. This rule will ensure that the value is in a given set. It needs an array of values. Comparison is case sensitive by default. This rule will ensure that a valid IPv4 or IPv6 address has been submitted.
Make sure to include the original field in the list of fields when making a unique rule across multiple fields. You may consider marking the listed fields as required. The Luhn algorithm: A checksum formula to validate a variety of identification numbers. Since 2. Autoren: Jan Goyvaerts , Steven Levithan , 2nd, re.
Home Regular Expressions Cookbook. Regular Expressions Cookbook. Autoren: Jan Goyvaerts , Steven Levithan. Keine Kommentare vorhanden Jetzt bewerten. Kommentar verfassen. Produkt empfehlen. Take the guesswork out of using regular expressions.