NAME
DBIx::Class::Valiant - Glue Valiant validations into DBIx::Class
SYNOPSIS
You need to add the components DBIx::Class::Valiant::Result and DBIx::Class::Valiant::ResultSet to your result and result classes:
package Example::Schema::Result::Person;
use base 'DBIx::Class::Core';
__PACKAGE__->load_components('Valiant::Result');
package Example::Schema::ResultSet::Person;
use base 'DBIx::Class::ResultSet';
__PACKAGE__->load_components('Valiant::ResultSet');Alternatively (and likely easier if you wish to use this across your entire DBIC schema) you can set these on your base result / resultset classes:
package Example::Schema::Result;
use strict;
use warnings;
use base 'DBIx::Class';
__PACKAGE__->load_components(qw/
Valiant::Result
Core
/);
package Example::Schema::ResultSet;
use strict;
use warnings;
use base 'DBIx::Class::ResultSet';
__PACKAGE__->load_components(qw/
Valiant::ResultSet
/);There's an example schema in the /example directory of the distribution to give you some hints.
NOTE If you are using more than one component, you need to add these first. Hopefully that is a restriction we can figure out how to remove (patches welcomed).
DESCRIPTION
NOTEThis works as in 'it passed my existing tests'. Feel free to use it if you are willing to get into the code, review / submit test cases, etc. Also at some point this will be pulled into its own distribution so please keep in mind. I will feel totally free to break backward compatibility on this until it seems stable. That being said support for validations at the single result level is pretty firm and I can't imagine significant changes. Support for validating nested results (results that have has_many or similar relationships) is likely to evolve as bugs are reported.
NOTE Update: I consider this code to be beta stage and will now only break things if its absolutely necessary to fix critical bugs or security matters.
This provides a base result component and resultset component that when added to your classes glue Valiant into DBIx::Class. You can set filters and validations on your result source classes very similarily to how you would use Valiant with Moo or Moose. Validations then run when you try to persist a change to the database; if validations fail then we will not complete persisting the change (typically via insert or update SQL). Errors can be read via the errors method just as on Moo or Moose based validations.
Additionally we support nested creates and updates; validations follow any nested changes and errors can be aggregated. Errors at any point in the nested create or update (or as is often the cases a mixed situation) will cancel the entire changeset (issuing a rollback if necessary). Please note that as stated above nested support is still considered a beta feature and bug reports / test cases (or patches) welcomed.
Documentation in this package only covers how Valiant is glued into your result sources and any local differences in behavior. If you need a comprehensive overview of how Valiant works you should refer to that package. Additionally if you are looking for how to generate HTML forms you should refer to Valiant::HTML::Form, Valiant::HTML::FormTags and Valiant::HTML::FormBuilder (or refer to the example application under directory '/example' for this distribution.)
Adding validations and filters
Assuming a base result and resultset as described above, you can add Valiant style validations to the result source columns or on the object as a whole. The helpers described in the Valiant core documentations all work the same on your DBIC result source fields; the main difference is that you must call these helpers as class/package methods:
package Example::Schema::Result::Profile;
use warnings;
use strict;
use base 'Example::Schema::Result';
__PACKAGE__->table("profile");
__PACKAGE__->add_columns(
id => { data_type => 'bigint', is_nullable => 0, is_auto_increment => 1 },
person_id => { data_type => 'integer', is_nullable => 0, is_foreign_key => 1 },
state_id => { data_type => 'integer', is_nullable => 0, is_foreign_key => 1 },
address => { data_type => 'varchar', is_nullable => 0, size => 48 },
city => { data_type => 'varchar', is_nullable => 0, size => 32 },
zip => { data_type => 'varchar', is_nullable => 0, size => 5 },
birthday => { data_type => 'date', is_nullable => 1, datetime_undef_if_invalid => 1 },
phone_number => { data_type => 'varchar', is_nullable => 1, size => 32 },
);
__PACKAGE__->set_primary_key("id");
__PACKAGE__->add_unique_constraint(['id','person_id']);
__PACKAGE__->belongs_to(
state =>
'Example::Schema::Result::State',
{ 'foreign.id' => 'self.state_id' }
);
__PACKAGE__->belongs_to(
person =>
'Example::Schema::Result::Person',
{ 'foreign.id' => 'self.person_id' }
);
__PACKAGE__->validates(address => (presence=>1, length=>[2,48]));
__PACKAGE__->validates(city => (presence=>1, length=>[2,32]));
__PACKAGE__->validates(zip => (presence=>1, format=>'zip'));
__PACKAGE__->validates(phone_number => (presence=>1, length=>[10,32]));
__PACKAGE__->validates(state_id => (presence=>1));
__PACKAGE__->validates(birthday => (
date => {
max => sub { pop->now->subtract(days=>1) }, # can't be born yesterday
min => sub { pop->years_ago(30) }, # Don't trust anyone over 30
}
)
);
1;Besides the requirement of calling validates, validates_with and filters as class methods there are a few other changes from the core Valiant behavior.
First validate is automatically run for you when you attempt to create or insert a record (or use related methods such as find_or_create). If validation fails the database operation will not occur but you will get a DBIC result with the records in cache along with any validation results. This makes it easier to round trip things like form validation since you can use the database result in your form response logic. For example:
my $person = $schema->resultset('Person')->first;
my $state = $schema->resultset('State')->first;
my $profile = $person->create_related('profile', +{
zip => "78621",
city => 'E',
address => '15604 Harry Lind Road',
birthday => DateTime->now->subtract(years=>55)->ymd,
phone_number => '2123879509',
state_id => $state->id,
});
$profile->invalid; # TRUE, so the record was NOT created!But the record does have the attempted values cached as expected, even if its not in the storage DB:
print $profile->zip; # 78621
print $profile->city; # EAnd you can query for errors:
Dumper $person_profile->errors->to_hash(full_messages=>1);Returns:
city => [
"city is too short (minimum is 2 characters)",
],
birthday => [
"Birthday chosen date can't be before @{[ DateTime->now->subtract(years=>30)->ymd ]}",
],It might seem strange to have a $profile object with invalid data, but it greatly simplifies your UI layer since you can use the same model for both valid and invalid data (and to query for errors.
validation context
By default when creating a new record we add a validation context 'create', and for updating we add a 'update' context. This way you can specify different validation rules for create and update very easily. If you want to add a custom context youc an do so with the context method which is part of your result API.
$person->context('registration')->update(\%records);Or you can add the context directly to your update or create call:
$person->update(+{ __context=>'registration', %records});See the core Valiant documentation for more on validation contexts.
Combining validations into column definitions
If you are hand writing your table source definitions you can add validations directly onto a column definition. You might prefer this if you think it looks neater and adds fewer lines of code.
package Example::Schema::Result::CreditCard;
use strict;
use warnings;
use base 'Example::Schema::Result';
__PACKAGE__->table("credit_card");
__PACKAGE__->load_components(qw/Valiant::Result/);
__PACKAGE__->set_primary_key("id");
__PACKAGE__->add_columns(
id => { data_type => 'integer', is_nullable => 0, is_auto_increment => 1 },
person_id => { data_type => 'integer', is_nullable => 0, is_foreign_key => 1 },
card_number => {
data_type => 'varchar',
is_nullable => 0,
size => '20',
validates => [ presence=>1, length=>[13,20], with=>'looks_like_a_cc' ],
filters => [trim => 1],
},
expiration => {
data_type => 'date',
is_nullable => 0,
validates => [ presence=>1, with=>'looks_like_a_datetime', with=>'is_future' ],
},
);As in the example above you can define filters this way as well.
DBIC Candy
This has DBIx::Class::Candy integration if you use that and prefer it:
package Schema::Create::Result::Person;
use DBIx::Class::Candy -base => 'Schema::Result';
table "person";
column id => { data_type => 'bigint', is_nullable => 0, is_auto_increment => 1 };
column username => { data_type => 'varchar', is_nullable => 0, size => 48 };
column first_name => { data_type => 'varchar', is_nullable => 0, size => 24 };
column last_name => { data_type => 'varchar', is_nullable => 0, size => 48 };
column password => { data_type => 'varchar', is_nullable => 0, size => 64 };
primary_key "id";
unique_constraint ['username'];
might_have profile => (
'Schema::Create::Result::Profile',
{ 'foreign.person_id' => 'self.id' }
);
filters username => (trim => 1);
validates profile => (result=>+{validations=>1} );
validates username => (presence=>1, length=>[3,24], format=>'alpha_numeric', unique=>1);
validates first_name => (presence=>1, length=>[2,24]);
validates last_name => (presence=>1, length=>[2,48]);
validates password => (presence=>1, length=>[8,24]);
validates password => (confirmation => { on=>'create' } );
validates password => (confirmation => {
on => 'update',
if => 'is_column_changed', # This method defined by DBIx::Class::Row
});
accept_nested_for 'profile', {update_only=>1};
1;When using this with DBIx::Class::Candy the following class methods are available as exports:
'filters', 'validates', 'filters_with', 'validates_with', 'accept_nested_for',
'auto_validation'EXAMPLE
Assuming you have a base result class Example::Schema::Result which uses the DBIx::Class::Valiant::Result component and a default resultset class which uses the Example::Schema::ResultSet component:
package Example::Schema::Result::Person;
use warnings;
use strict;
use base 'Example::Schema::Result';
__PACKAGE__->table("person");
__PACKAGE__->add_columns(
id => { data_type => 'bigint', is_nullable => 0, is_auto_increment => 1 },
username => { data_type => 'varchar', is_nullable => 0, size => 48 },
first_name => { data_type => 'varchar', is_nullable => 0, size => 24 },
last_name => { data_type => 'varchar', is_nullable => 0, size => 48 },
);
__PACKAGE__->validates(username => presence=>1, length=>[3,24], format=>'alpha_numeric', unique=>1);
__PACKAGE__->validates(first_name => (presence=>1, length=>[2,24]));
__PACKAGE__->validates(last_name => (presence=>1, length=>[2,48]));We now have Valiant validations wrapping any method which mutates the database.
my $person = $schema->resultset('Person')
->create({
username => 'jjn', # for this example we'll say this username is taken in the DB
first_name => 'j', # too short
last_name => 'n', # too short
});In this case since we tried to create a record that is not valid the create will be aborted (not saved to the DB and an object will be returned with errors:
$person->invalid; # true;
$person->errors->size; # 3
person->errors->full_messages_for('username'); # ['Username is not unique']
person->errors->full_messages_for('first_name'); # ['First Name is too short']
person->errors->full_messages_for('last_name'); # ['Last Name is too short']The object will have the invalid values properly populated:
$person->get_column('username'); # jjnYou might find this useful for building error message responses in for example html forms or other types or error responses.
See example directory for web application using Catalyst that uses this for more details.
NESTED VALIDATIONS
With a properly setup schema you can propagate creates/updates down into related result sources. Because this can be a security issue you must configure your result source classes to allow it. For example, let's say you have a Person result source that has a Profile (via 'might_have'), some Roles via a 'has_many' bridge result class called PersonRoles and finally a list of CreditCards directly via a 'has_many'. For the purposes of this example assume the base result and result set classes are setup to consume the Valiant components as in the "SYNOPSIS".
package Example::Schema::Result::Person;
use warnings;
use strict;
use base 'Example::Schema::Result';
# This first part is just the normal DBIC class data you assign to a result class
# in order to read and update tables as well as follow relationships:
__PACKAGE__->table("person");
__PACKAGE__->add_columns(
id => { data_type => 'bigint', is_nullable => 0, is_auto_increment => 1 },
username => { data_type => 'varchar', is_nullable => 0, size => 48 },
first_name => { data_type => 'varchar', is_nullable => 0, size => 24 },
last_name => { data_type => 'varchar', is_nullable => 0, size => 48 },
);
__PACKAGE__->set_primary_key("id");
__PACKAGE__->add_unique_constraint(['username']);
__PACKAGE__->might_have(
profile =>
'Example::Schema::Result::Profile',
{ 'foreign.person_id' => 'self.id' }
);
__PACKAGE__->has_many(
credit_cards =>
'Example::Schema::Result::CreditCard',
{ 'foreign.person_id' => 'self.id' }
);
__PACKAGE__->has_many(
person_roles =>
'Example::Schema::Result::PersonRole',
{ 'foreign.person_id' => 'self.id' }
);
__PACKAGE__->many_to_many('roles' => 'person_roles', 'role');
# In this next part we annotate this class with additional meta data which Valiant will
# use to performance validations as well as allow you to follow relationships at create / update
# time:
__PACKAGE__->validates(username => presence=>1, length=>[3,24], format=>'alpha_numeric', unique=>1);
__PACKAGE__->validates(first_name => (presence=>1, length=>[2,24]));
__PACKAGE__->validates(last_name => (presence=>1, length=>[2,48]));
__PACKAGE__->validates(credit_cards => (set_size=>+{min=>2, max=>4}));
__PACKAGE__->accept_nested_for('credit_cards', +{allow_destroy=>1});
__PACKAGE__->validates(person_roles => (set_size=>+{min=>1}));
__PACKAGE__->accept_nested_for('person_roles', {allow_destroy=>1});
__PACKAGE__->validates(profile => (presence=>1));
__PACKAGE__->accept_nested_for('profile');So in brief we add some simple validations on fields in the Person result class to validate things like the length of text fields and we added things like requiring a person to have at least one person_role and two credit_cards; we also specify that we want to follow those nested relationships and aggregate errors into the Persons result class (we'll see the validation definitions for those classes below). Also we allow that we can delete credit_cards as well as person_roles.
Here's how we setup these related classes. Please note that we in general avoid using the 'many_to_many' pseudo relationship. DBIC does not define enough internal meta data for m2m to work reliably and we had other issues around caching. As a result I recommend avoiding m2m for using with Valiant although it seems to work ok for simple use cases. Test cases and patches to improve this are welcomed (you can see there's quite a few m2m test cases already but I was never able to get the type of consistency I felt needed for a data storage layer where integrity is most important).
package Example::Schema::Result::Profile;
use warnings;
use strict;
use base 'Example::Schema::Result';
__PACKAGE__->table("profile");
__PACKAGE__->load_components(qw/Valiant::Result/);
__PACKAGE__->add_columns(
id => { data_type => 'bigint', is_nullable => 0, is_auto_increment => 1 },
person_id => { data_type => 'integer', is_nullable => 0, is_foreign_key => 1 },
address => { data_type => 'varchar', is_nullable => 0, size => 48 },
city => { data_type => 'varchar', is_nullable => 0, size => 32 },
zip => { data_type => 'varchar', is_nullable => 0, size => 5 },
birthday => { data_type => 'date', is_nullable => 1 },
phone_number => { data_type => 'varchar', is_nullable => 1, size => 32 },
);
__PACKAGE__->validates(address => (presence=>1, length=>[2,48]));
__PACKAGE__->validates(city => (presence=>1, length=>[2,32]));
__PACKAGE__->validates(zip => (presence=>1, format=>'zip'));
__PACKAGE__->validates(phone_number => (presence=>1, length=>[10,32]));
__PACKAGE__->validates(birthday => (
date => {
max => sub { pop->now->subtract(days=>1) }, # Can't be born yesterday :)
min => sub { pop->years_ago(30) }, # Don't trust anyone over 30
}
)
);
__PACKAGE__->set_primary_key("id");
__PACKAGE__->add_unique_constraint(['id','person_id']);
__PACKAGE__->belongs_to(
person =>
'Example::Schema::Result::Person',
{ 'foreign.id' => 'self.person_id' }
);
package Example::Schema::Result::CreditCard;
use strict;
use warnings;
use DateTime;
use DateTime::Format::Strptime;
use base 'Example::Schema::Result';
__PACKAGE__->table("credit_card");
__PACKAGE__->load_components(qw/Valiant::Result/);
__PACKAGE__->add_columns(
id => { data_type => 'integer', is_nullable => 0, is_auto_increment => 1 },
person_id => { data_type => 'integer', is_nullable => 0, is_foreign_key => 1 },
card_number => { data_type => 'varchar', is_nullable => 0, size => '20' },
expiration => { data_type => 'date', is_nullable => 0 },
);
__PACKAGE__->set_primary_key("id");
__PACKAGE__->validates(card_number => (presence=>1, length=>[13,20], with=>'looks_like_a_cc' ));
__PACKAGE__->validates(expiration => (presence=>1, with=>'looks_like_a_datetime', with=>'is_future' ));
__PACKAGE__->belongs_to(
person =>
'Example::Schema::Result::Person',
{ 'foreign.id' => 'self.person_id' }
);
sub looks_like_a_cc {
my ($self, $attribute_name, $value) = @_;
return if $value =~/^\d{13,20}$/;
$self->errors->add($attribute_name, 'does not look like a credit card');
}
my $strp = DateTime::Format::Strptime->new(pattern => '%Y-%m-%d');
sub looks_like_a_datetime {
my ($self, $attribute_name, $value) = @_;
my $dt = $strp->parse_datetime($value);
$self->errors->add($attribute_name, 'does not look like a datetime value') unless $dt;
}
sub is_future {
my ($self, $attribute_name, $value) = @_;
my $dt = $strp->parse_datetime($value);
return unless $dt; # Skip this validation if the user didn't give us a date time
# format (that's caught by ->looks_like_a_datetime).
$self->errors->add($attribute_name, 'must be in the future') unless $dt > DateTime->now);
}
package Example::Schema::Result::PersonRole;
use strict;
use warnings;
use base 'Example::Schema::Result';
__PACKAGE__->table("person_role");
__PACKAGE__->load_components(qw/Valiant::Result/);
__PACKAGE__->add_columns(
person_id => { data_type => 'integer', is_nullable => 0, is_foreign_key => 1 },
role_id => { data_type => 'integer', is_nullable => 0, is_foreign_key => 1 },
);
__PACKAGE__->set_primary_key("person_id", "role_id");
__PACKAGE__->belongs_to(
person =>
'Example::Schema::Result::Person',
{ 'foreign.id' => 'self.person_id' }
);
__PACKAGE__->belongs_to(
role =>
'Example::Schema::Result::Role',
{ 'foreign.id' => 'self.role_id' }
);
package Example::Schema::Result::Role;
use strict;
use warnings;
use base 'Example::Schema::Result';
__PACKAGE__->table("role");
__PACKAGE__->load_components(qw/Valiant::Result/);
__PACKAGE__->add_columns(
id => { data_type => 'integer', is_nullable => 0, is_auto_increment => 1 },
label => { data_type => 'varchar', is_nullable => 0, size => '24' },
);
__PACKAGE__->set_primary_key("id");
__PACKAGE__->add_unique_constraint(['label']);
__PACKAGE__->has_many(
person_roles =>
'Example::Schema::Result::PersonRole',
{ 'foreign.role_id' => 'self.id' }
);With this setup you could deeply validate / create a Person and its permitted relationships:
my $person = $schema
->resultset('Person')
->create({
first_name => "john",
last_name => "nap",
username => "jjn1",
profile => {
address => "15604 Harry Lind Road",
birthday => "2000-02-13",
city => "Elgin",
phone_number => "16467081837",
zip => '10000'
},
person_roles => [
{ role_id => 1 },
{ role_id => 2 },
{ role_id => 4 },
],
credit_cards => [
{
card_number => "3423423423423423",
expiration => "2222-02-02",
},
{
card_number => "1111222233334444",
expiration => "2333-02-02",
},
],
});If the proposed data fails validation then you won't create any records, but the errors can be viewed via the errors collection.
For doing validation on nested updates you need to do a bit more work to make sure you caches are properly populated from the database. You need to use 'prefetch' to locally cache all the results you are trying to validate:
my $person = Schema->resultset('Person')->find(
{ 'me.id'=>$pid },
{ prefetch => ['profile', 'credit_cards', {person_roles => 'role' }] }
);
$person->build_related_if_empty('profile');Then you can do an update:
$person->update({
username => 'jjn2',
profile => {
city => 'NYC'
},
});As before if there's a valiation issue the update won't happen.
NOTE Recursion warning! You cannot currently accept_nested_for for a relationship whose target result source is setting accept_nested_for into yourself. This is probably fixable, patches and use cases welcomed!
Deleting
If a nested relationship permits deleting (via the 'allow_destroy' flag) you can mark a row for deletion directly using the _delete flag:
$person->update({
username => 'jjn2',
person_roles => [
{ role_id => 1 },
{ role_id => 2, _delete => 1 },
{ role_id => 4, _delete => 1 },
],
});Inplicit Deletion: When deletion is permitted we automatically mark nested object that are fetched from 'prefetch' as deleted if they do not appear in the update statement. NOTE: This behavior is still under review and might change in the future so if you rely on it please be sure to follow update notes on newer versions of this code.
This example is far from complete, for now see example in example directory of the distribution and more examples in the tests directory. In particular this example doesn't really cover all the ins and outs of deleting. An overall tutorial is in the works but example submission or questions (that could eventually lead to a FAQ) are very welcomed.
WARNINGS & GOTCHAs
You must be careful with how you expose a deeply nested interface. If you simply pass fields from a web form you are potentially opening yourself to SQL injection and similar types of attacks. I recommend being very careful to sanitize incoming parameters, especially any related keys.
NOTEBe careful when reusing a result that has deeply nested values after you create or update. In order to keep data consistent, DBIC will blow away any prefetch caches after create or update. So that means if you try to reuse a result you may encounter errors that cause following creates or inserts to fails, especially if you have constraints on the related data, such as SetSize.
Many to Many
Many to Many type relationships are supported to the best of my ability but since these types of fake relationships have lots of known issues you are more likely to run into edge cases. In particular its a bad idea to have validations on both a m2m relations and also on the one to many relation that it bridges. This is likely to result in false positive validation errors due to the way the resultset cache works (and doesn't work) for m2m.
Happy to take patches for improvements to anyone that feels strongly about it. FWIW the core DBIC development team recommends staying away fron using the many to many code.
SEE ALSO
Valiant, DBIx::Class::Valiant::Result, DBIx::Class::Valiant::ResultSet, DBIx::Class
AUTHOR
See Valiant
COPYRIGHT & LICENSE
See Valiant