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📛 NAME

Data::FormValidator::Filters — Basic set of filters available in an Data::FormValidator profile.

🚀 Quick Reference

Use Case	Command	Description
🧹 Remove leading/trailing whitespace	filters => 'trim'	Trim whitespace from all fields
✂️ Split field into multiple values	FV_split(qr/\s*,\s*/)	Split field by pattern, validate each value individually
🔁 Find and replace in field	FV_replace(qr/Mark/,'Don')	Simple regex find-and-replace filter
🔤 Convert to lowercase	filters => 'lc'	Lowercase all input
🔠 Convert to uppercase	filters => 'uc'	Uppercase all input
🔢 Keep only digits	filters => 'digit'	Remove non-digit characters
🔤 Keep only alphanumeric	filters => 'alphanum'	Remove non-alphanumeric characters
🔢 Extract integer	filters => 'integer'	Extract a valid integer number
💰 Extract currency amount	filters => 'dollars'	Extract valid dollar amount
📞 Clean phone number	filters => 'phone'	Keep only valid phone characters
🗄️ Convert shell glob to SQL LIKE	filters => 'sql_wildcard'	Transform * to % for SQL wildcards
📝 Quote metacharacters	filters => 'quotemeta'	Escape all non-alphanumeric characters

📋 SYNOPSIS

use Data::FormValidator;

%profile = (
    filters => 'trim',
    ...
);

my $results = Data::FormValidator->check(  \%data, \%profile );

📖 DESCRIPTION

These are the builtin filters which may be specified as a name in the filters, field_filters, and field_filter_regexp_map parameters of the input profile.

Filters are applied as the first step of validation, possibly modifying a copy of the validation before any constraints are checked.

💡 RECOMMENDED USE

As a long time maintainer and user of Data::FormValidator, I recommend that filters be used with caution. They are immediately modifying the input provided, so the original data is lost. The few I recommend include trim, which removes leading and trailing whitespace. I have this turned on by default by using CGI::Application::Plugin::ValidateRM. It's also generally safe to use the lc and uc filters if you need that kind of data transformation.

Beyond simple filters, I recommend transforming the valid hash returned from validation if further changes are needed.

🔧 PROCEDURAL INTERFACE

You may also call these functions directly through the procedural interface by either importing them directly or importing the whole :filters group. For example, if you want to access the trim function directly, you could either do:

use Data::FormValidator::Filters (qw/filter_trim/);
# or
use Data::FormValidator::Filters (qw/:filters/);

$string = filter_trim($string);

Notice that when you call filters directly, you'll need to prefix the filter name with filter_.

🎛️ THE FILTERS

✂️ FV_split

use Data::FormValidator::Filters qw(FV_split);

# Validate every e-mail in a comma separated list

field_filters => {
   several_emails  => FV_split(qr/\s*,\s*/),

   # Any pattern that can be used by the 'split' builtin works.
   tab_sep_field   => FV_split('\t'),
},
constraint_methods => {
  several_emails => email(),
},

With this filter, you can split a field into multiple values. The constraint for the field will then be applied to every value.

This filter has a different naming convention because it is a higher-order function. Rather than returning a value directly, it returns a code reference to a standard Data::FormValidator filter.

After successfully being validated the values will appear as an arrayref.

🔁 FV_replace

use Data::FormValidator::Filters qw(FV_replace);

field_filters => {
   first_name   => FV_replace(qr/Mark/,'Don'),
},

FV_replace is a shorthand for writing simple find-and-replace filters. The above filter would be translated to this:

sub { my $v = shift; $v =~ s/Mark/Don/; $v }

For more complex filters, just write your own.

🧹 trim

Remove white space at the front and end of the fields.

✨ strip

Runs of white space are replaced by a single space.

🔢 digit

Remove non digits characters from the input.

🔤 alphanum

Remove non alphanumeric characters from the input.

🔢 integer

Extract from its input a valid integer number.

➕ pos_integer

Extract from its input a valid positive integer number.

⚠️ Bugs: This filter won't extract "9" from "a9+", it will instead extract "9+"

➖ neg_integer

Extract from its input a valid negative integer number.

⚠️ Bugs: This filter will currently filter the case of "a9-" to become "9-", which it should leave it alone.

🔢 decimal

Extract from its input a valid decimal number.

⚠️ Bugs: Given "1,000.23", it will currently return "1.000.23"

➕ pos_decimal

Extract from its input a valid positive decimal number.

⚠️ Bugs: Given "1,000.23", it will currently return "1.000.23"

➖ neg_decimal

Extract from its input a valid negative decimal number.

⚠️ Bugs: Given "1,000.23", it will currently return "1.000.23"

💰 dollars

Extract from its input a valid number to express dollars like currency.

⚠️ Bugs: This filter won't currently remove trailing numbers like "1.234".

📞 phone

Filters out characters which aren't valid for a phone number. (Only accept digits [0-9], space, comma, minus, parenthesis, period and pound [#].)

🗄️ sql_wildcard

Transforms shell glob wildcard (*) to the SQL like wildcard (%).

📝 quotemeta

Calls the quotemeta (quote non alphanumeric character) builtin on its input.

🔡 lc

Calls the lc (convert to lowercase) builtin on its input.

🔠 uc

Calls the uc (convert to uppercase) builtin on its input.

🔤 ucfirst

Calls the ucfirst (Uppercase first letter) builtin on its input.

📚 SEE ALSO

• Data::FormValidator
• Data::FormValidator::Constraints
• Data::FormValidator::Filters::Image — shrink incoming image uploads

👤 AUTHOR

Author: Francis J. Lacoste <francis.lacoste AT iNsu.COM>
Maintainer: Mark Stosberg <mark AT summersault.com>

©️ COPYRIGHT

Copyright (c) 1999,2000 iNsu Innovations Inc. All rights reserved.

This program is free software; you can redistribute it and/or modify it under the terms as perl itself.

📛 NAME 🚀 Quick Reference 📋 SYNOPSIS 📖 DESCRIPTION 💡 RECOMMENDED USE 🔧 PROCEDURAL INTERFACE 🎛️ THE FILTERS 📚 SEE ALSO 👤 AUTHOR ©️ COPYRIGHT