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Laurent Dami
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DBIx::DataModel - UML-based Object-Relational Mapping (ORM) framework


Version 2 of DBIx::DataModel is a major refactoring from versions 1.*, with a number of incompatible changes in the API (classes renamed, arguments renamed or reorganized, etc. -- see DBIx::DataModel::Doc::Delta_v2).

Initial subversions of the 2.* family included a layer of compatibility with version 1.*, so that old applications would continue to work (see DBIx::DataModel::Compatibility::V1). Since version 2.20, this compatibility layer is no longer loaded automatically; however, it can still be added on demand by writing

  use DBIx::DataModel -compatibility => 1.0;


in file "My/Schema.pm"


Load DBIx::DataModel.

  use DBIx::DataModel;

Declare the schema, either in shorthand notation :


or in verbose form :

    class => 'My::Schema',

This automatically creates a Perl class named My::Schema.

Various parameters may be specified within %options, like for example special columns to be filled automatically or to be ignored in every table :

  my $last_modif_generator = sub {$ENV{REMOTE_USER}.", ".scalar(localtime)};
  my %options = (
    auto_update_columns => {last_modif => $last_modif_generator},
    no_update_columns   => [qw/date_modif time_modif/],


Declare a "column type" with some handlers, either in shorthand notation :

  My::Schema->Type(Date => 
     from_DB  => sub {$_[0] =~ s/(\d\d\d\d)-(\d\d)-(\d\d)/$3.$2.$1/},
     to_DB    => sub {$_[0] =~ s/(\d\d)\.(\d\d)\.(\d\d\d\d)/$3-$2-$1/},
     validate => sub {$_[0] =~ m/(\d\d)\.(\d\d)\.(\d\d\d\d)/},

or in verbose form :

    name     => 'Date',
    handlers => {
      from_DB  => sub {$_[0] =~ s/(\d\d\d\d)-(\d\d)-(\d\d)/$3.$2.$1/},
      to_DB    => sub {$_[0] =~ s/(\d\d)\.(\d\d)\.(\d\d\d\d)/$3-$2-$1/},
      validate => sub {$_[0] =~ m/(\d\d)\.(\d\d)\.(\d\d\d\d)/},

This does not create a Perl class; it just defines an internal datastructure that will be attached to some columns in some tables. Here are some other examples of column types :

  # 'percent' conversion between database (0.8) and user (80)
    name     => 'Percent',
    handlers => {
      from_DB  => sub {$_[0] *= 100 if $_[0]},
      to_DB    => sub {$_[0] /= 100 if $_[0]},
      validate => sub {$_[0] =~ /1?\d?\d/}),
  # lists of values, stored as scalars with a ';' separator
    name     => 'Multivalue',
    handlers => {
     from_DB  => sub {$_[0] = [split /;/, $_[0] || ""]     },
     to_DB    => sub {$_[0] = join ";", @$_[0] if ref $_[0]},


Declare the tables, either in shorthand notation :

  MySchema->Table(qw/Employee   T_Employee   emp_id/)
          ->Table(qw/Department T_Department dpt_id/)
          ->Table(qw/Activity   T_Activity   act_id/);

or in verbose form :

    class       => 'Employee',
    db_name     => 'T_Employee',
    primary_key => 'emp_id',
    class       => 'Department',
    db_name     => 'T_Department',
    primary_key => 'dpt_id',
    class       => 'Activity',
    db_name     => 'T_Activity',
    primary_key => 'act_id',

Each table then becomes a Perl class (prefixed with the Schema name, i.e. My::Schema::Employee, etc.).

Column types within tables

Declare column types within these tables :

  #                                          type name  => applied_to_columns
  #                                          =========     ==================
  My::Schema::Employee->metadm->set_column_type(Date    => qw/d_birth/);
  My::Schema::Activity->metadm->set_column_type(Date    => qw/d_begin d_end/);
  My::Schema::Activity->metadm->set_column_type(Percent => qw/activity_rate/);


Declare associations or compositions in UML style, either in shorthand notation :

  #                           class      role     multiplicity  join
  #                           =====      ====     ============  ====
  My::Schema->Composition([qw/Employee   employee   1           emp_id /],
                          [qw/Activity   activities *           emp_id /])
            ->Association([qw/Department department 1           /],
                          [qw/Activity   activities *           /]);

or in verbose form :

    kind => 'Composition',
    A    => {
      table        => My::Schema::Employee->metadm,
      role         => 'employee',
      multiplicity => 1,
      join_cols    => [qw/emp_id/],
    B    => {
      table        => My::Schema::Activity->metadm,
      role         => 'activities',
      multiplicity => '*',
      join_cols    => [qw/emp_id/],
    kind => 'Association',
    A    => {
      table        => My::Schema::Department->metadm,
      role         => 'department',
      multiplicity => 1,
    B    => {
      table        => My::Schema::Activity->metadm,
      role         => 'activities',
      multiplicity => '*',

Declare a n-to-n association, on top of the linking table

  My::Schema->Association([qw/Department departments * activities department/],
                          [qw/Employee   employees   * activities employee/]);
  # or
    kind => 'Association',
    A    => {
      table        => My::Schema::Department->metadm,
      role         => 'departments',
      multiplicity => '*',
      join_cols    => [qw/activities department/],
    B    => {
      table        => My::Schema::Employee->metadm,
      role         => 'employees',
      multiplicity => '*',
      join_cols    => [qw/activities employee/],

Additional methods

For details that could not be expressed in a declarative way, just add a new method into the table class :

  package My::Schema::Activity; 
  sub active_period {
    my $self = shift;
    $self->{d_begin} or croak "activity has no d_begin";
    $self->{d_end} ? "from $self->{d_begin} to $self->{d_end}"
                   : "since $self->{d_begin}";

Data tree expansion

Declare how to automatically expand objects into data trees

  My::Schema::Activity->metadm->set_auto_expand(qw/employee department/);

Automatic schema generation

  perl -MDBIx::DataModel::Schema::Generator      \
       -e "fromDBI('dbi:connection:string')" --  \
       -schema My::New::Schema > My/New/Schema.pm

See DBIx::DataModel::Schema::Generator.

in file "myClient.pl"

Database connection

  use My::Schema;
  use DBI;
  my $dbh = DBI->connect($dsn, ...);
  My::Schema->dbh($dbh);                     # single-schema mode
  # or
  my $schema = My::Schema->new(dbh => $dbh); # multi-schema mode

Simple data retrieval

Search employees whose name starts with 'D' (select API is taken from SQL::Abstract)

  my $empl_D = My::Schema->table('Employee')->select(
    -where => {lastname => {-like => 'D%'}}

idem, but we just want a subset of the columns, and order by age.

  my $empl_F = My::Schema->table('Employee')->select(
    -columns  => [qw/firstname lastname d_birth/],
    -where    => {lastname => {-like => 'F%'}},
    -order_by => 'd_birth'

Print some info from employees. Because of the 'from_DB' handler associated with column type 'date', column 'd_birth' has been automatically converted to display format.

  foreach my $emp (@$empl_D) {
    print "$emp->{firstname} $emp->{lastname}, born $emp->{d_birth}\n";

Methods to follow joins

Follow the joins through role methods

  foreach my $act (@{$emp->activities}) {
    printf "working for %s from $act->{d_begin} to $act->{d_end}", 

Role methods can take arguments too, like select()

  my $recent_activities
    = $dpt->activities(-where => {d_begin => {'>=' => '2005-01-01'}});
  my @recent_employees
    = map {$_->employee(-columns => [qw/firstname lastname/])}

Data export : just regular hashrefs

Export the data : get related records and insert them into a data tree in memory; then remove all class information and export that tree.

  $_->expand('activities') foreach @$empl_D;
  my $export = My::Schema->unbless({employees => $empl_D});
  use Data::Dumper; print Dumper ($export); # export as PerlDump
  use XML::Simple;  print XMLout ($export); # export as XML
  use JSON;         print to_json($export); # export as Javascript
  use YAML;         print Dump   ($export); # export as YAML

Note: the unbless step is optional; it is proposed here because some exporter modules will not work if they encounter a blessed reference.

Database join

Select associated tables directly from a database join, in one single SQL statement (instead of iterating through role methods).

  my $lst = My::Schema->join(qw/Employee activities department/)
                      ->select(-columns => [qw/lastname dept_name d_begin/],
                               -where   => {d_begin => {'>=' => '2000-01-01'}});

Same thing, but forcing INNER joins

  my $lst = My::Schema->join(qw/Employee <=> activities <=> department/)

Statements and pagination

Instead of retrieving directly a list or records, get a statement :

  my $statement 
    = My::Schema->join(qw/Employee activities department/)
                ->select(-columns   => [qw/lastname dept_name d_begin/],
                         -where     => {d_begin => {'>=' => '2000-01-01'}},
                         -result_as => 'statement');

Retrieve a single row from the statement

  my $single_row = $statement->next or die "no more records";

Retrieve several rows at once

  my $rows = $statement->next(10); # arrayref

Go to a specific page and retrieve the corresponding rows

  my $statement 
    = My::Schema->join(qw/Employee activities department/)
                ->select(-columns   => [qw/lastname dept_name d_begin/],
                         -result_as => 'statement',
                         -page_size => 10);
  $statement->goto_page(3);    # absolute page positioning
  $statement->shift_pages(-2); # relative page positioning
  my ($first, $last) = $statement->page_boundaries;
  print "displaying rows $first to $last:";
  some_print_row_method($_) foreach @{$statement->page_rows};

Efficient use of statements

For fetching related rows : prepare a statement before the loop, execute it at each iteration.

  my $statement = $schema->table($name)->join(qw/role1 role2/);
  $statement->prepare(-columns => ...,
                      -where   => ...);
  my $list = $schema->table($name)->select(...);
  foreach my $obj (@$list) {
    my $related_rows = $statement->execute($obj)->all;
    # or
    my $related_rows = $statement->bind($obj)->select;

Fast statement : each data row is retrieved into the same memory location (avoids the overhead of allocating a hashref for each row). Faster, but such rows cannot be accumulated into an array (they must be used immediately) :

  my $fast_stmt = ..->select(..., -result_as => "fast_statement");
  while (my $row = $fast_stmt->next) {


  # update on a set of fields, primary key included
  my $table = $schema->table($table_name);
  $table->update({pk_field => $pk, field1 => $val1, field2 => $val2, ...});

  # update on a set of fields, primary key passed separately
  $table->update(@primary_key, {field1 => $val1, field2 => $val2, ...});

  # bulk update
  $table->update(-set   => {field1 => $val1, field2 => $val2, ...}
                 -where => \%condition);

  # invoking instances instead of table classes
  $obj->update({field1 => $val1, ...}); # updates specified fields
  $obj->update;                         # updates all fields stored in memory


  # invoking a table class
  my $table = $schema->table($table_name);

  # invoking an instance



DBIx::DataModel is a framework for building Perl abstractions (classes, objects and methods) that interact with relational database management systems (RDBMS). Of course the ubiquitous DBI module is used as a basic layer for communicating with databases; on top of that, DBIx::DataModel provides facilities for generating SQL queries, joining tables automatically, navigating through the results, converting values, and building complex datastructures so that other modules can conveniently exploit the data.

Perl ORMs

There are many other CPAN modules offering somewhat similar features, like Class::DBI, DBIx::Class, Tangram, Rose::DB::Object, Jifty::DBI, Fey::ORM, just to name a few well-known alternatives. Frameworks in this family are called object-relational mappings (ORMs) -- see http://en.wikipedia.org/wiki/Object-relational_mapping. The mere fact that Perl ORMs are so numerous demonstrates that there is more than one way to do it!

For various reasons, none of these did fit nicely in my context, so I decided to write DBIx:DataModel. Of course there might be also some reasons why DBIx:DataModel will not fit in your context, so just do your own shopping. Comparing various ORMs is complex and time-consuming, because of the many issues and design dimensions involved; as far as I know, there is no thorough comparison summary, but here are some pointers :

Strengths of DBIx::DataModel

The DESIGN chapter of this documentation will help you understand the philosophy of DBIx::DataModel. Just as a summary, here are some of its strong points :

  • UML-style declaration of relationships (instead of 'has_many', 'belongs_to', etc.)

  • efficiency through fine control of collaboration with the DBI layer (prepare/execute, fetch into reusable memory location, etc.)

  • uses SQL::Abstract::More for an improved API over SQL::Abstract (named parameters, additional clauses, simplified 'order_by', etc.)

  • clear conceptual distinction between

    • data sources (tables and joins),

    • database statements (stateful objects representing stepwise building of an SQL query and stepwise retrieval of results),

    • data rows (lightweight hashrefs containing nothing but column names and values)

  • joins with simple syntax and possible override of default INNER JOIN/LEFT JOIN properties; instances of joins multiply inherit from their member tables.

  • named placeholders

  • nested, cross-database transactions

  • choice between 'single-schema' mode (default, more economical) and 'multi-schema' mode (optional, more flexible, but a little more costly in memory)

DBIx::DataModel is used in production within a mission-critical application with several hundred users, for managing Geneva courts of law.


Here are some current limitations of DBIx::DataModel :

no schema versioning

DBIx::DataModel knows very little about the database schema (only tables, primary and foreign keys, and possibly some columns, if they need special 'Types'); therefore it provides no support for schema changes (and seldom needs to know about them).

no object caching nor 'dirty columns'

DBIx::DataModel does not keep track of data mutations in memory, and therefore provides no support for automatically propagating changes into the database; the client code has explicitly manage insert and update operations.

no 'cascaded update' nor 'insert or update'

Cascaded inserts and deletes are supported, but not cascaded updates. This would need 'insert or update', which at the moment is not supported either.

Backwards compatibility

Major version 2.0 and 1.0 introduced some incompatible changes in the architecture (see DBIx::DataModel::Doc::Delta_v2 and DBIx::DataModel::Doc::Delta_v1).

Compatibility layers can be loaded on demand by supplying the desired version number upon loading DBIx::DataModel :

  use DBIx::DataModel 2.0 -compatibility => 0.8;


Although the basic principles are quite simple, there are many details to discuss, so the documentation is quite long. In an attempt to accomodate for different needs of readers, it has been structured as follows :

  • The DESIGN chapter covers the architecture of DBIx::DataModel, its main distinctive features and the motivation for such features; it is of interest if you are comparing various ORMs, or if you want to globally understand how DBIx::DataModel works, and what it can or cannot do. This chapter also details the concept of statements, which underlies all SELECT requests to the database.

  • The QUICKSTART chapter is a guided tour that summarizes the main steps to get started with the framework.

  • The REFERENCE chapter is a complete reference to all methods, structured along usage steps : creating a schema, populating it with table and associations, parameterizing the framework, and finally data retrieval and manipulation methods.

  • The MISC chapter discusses how this framework interacts with its context (Perl namespaces, DBI layer, etc.), and how to work with self-referential associations.

  • The INTERNALS chapter documents the internal structure of the framework, for programmers who might be interested in extending it.

  • The GLOSSARY defines terms used in this documentation, and points to the software constructs that implement these terms.

  • The DELTA_v2 and DELTA_v1 chapters summarize the differences with previous versions.

  • The DBIx::DataModel::Schema::Generator documentation explains how to automatically generate a schema from a DBI connection, from a SQL::Translator description or from an existing DBIx::Class|DBIx::Class schema.

  • The DBIx::DataModel::Statement documentation documents the methods of statements (not included in the general REFERENCE chapter).

Presentation slides are also available at http://www.slideshare.net/ldami/dbix-datamodel-endetail


Upon loading, DBIx::DataModel::Join adds a coderef into global @INC (see "require" in perlfunc), so that it can take control and generate a class on the fly when retrieving frozen objects from "thaw" in Storable. This should be totally harmless unless you do some very special things with @INC.


Bugs should be reported via the CPAN bug tracker at http://rt.cpan.org/NoAuth/ReportBug.html?Queue=DBIx-DataModel.

There is a discussion group at http://groups.google.com/group/dbix-datamodel.

Sources are stored in an open repository at http://github.com/damil/DBIx-DataModel.


Laurent Dami, <laurent.dami AT etat ge ch>


Thanks to Cedric Bouvier for some bug fixes and improvements, and to Terrence Brannon for many fixes in the documentation.


Copyright 2006-2012 by Laurent Dami.

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.