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KEEDI SHARYANTO PERLANCAR AWNCORP

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Laurent Dami
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NAME

DBIx::DataModel::Doc::Cookbook - Helpful recipes

DOCUMENTATION CONTEXT

This chapter is part of the DBIx::DataModel manual.

DESCRIPTION

This chapter provides some recipes for common ORM tasks.

SCHEMA DECLARATION

Automatically generate a schema

A schema skeleton can be produced automatically from the following external sources : a DBI connection, a SQL::Translator parser, or a DBIx::Class schema. See DBIx::DataModel::Schema::Generator.

Add custom methods into a generated table class

Defining methods in any Perl class does not require to have a file corresponding to that class; it suffices to define the method within the appropriate package. So the easiest way to add methods into tables is to first let DBIx::DataModel create the schema and table classes, and then switch to those packages, all in the same file :

  # define schema, tables, associations (current package doesn't matter)
  DBIx::DataModel->Schema('Some::Schema')
    ->Table(qw/Foo foo foo_id/)
    ->Table(...)
    ->Association(...)
    ->...;

  # add a method into table 'Foo'
  package Some::Schema::Foo;
  sub my_added_method {
    my $self = shift;
    ...
  }

  # go back to main package
  package main;
  ...
   

Another way to achieve the same result is to use DBIx::DataModel's internal utility method for injecting methods into classes :

   use DBIx::DataModel::Meta::Utils;
   DBIx::DataModel::Meta::Utils->define_method(
      class          => 'Some::Schema::Foo',
      name           => 'my_added_method,
      body           => sub {my $self = shift; ...},
    );
  

Object inflation/deflation

Here is an example of inflating/deflating a scalar value from the database into a Perl object :

  # declare column type
  use Date::Simple;
  $schema->Type(Date_simple => 
    from_DB => sub {Date::Simple->new($_[0]) if $_[0] },
    to_DB   => sub {$_[0] = $_[0]->as_str    if $_[0] },
  );
  
  # apply column type to columns
  My::Table1->metadm->define_column_type(Date_simple => qw/d_start d_end/);
  My::Table2->metadm->define_column_type(Date_simple => qw/d_birth/);

Caveat: the from_DB / to_DB functions do not apply automatically within -where conditions. So the following would not work :

  use Date::Simple qw/today/;
  my $rows = $schema->table($name)->select(
    -where => {d_end => {'<' => today()}},
  );

because today() returns a Date::Simple object that will not be understood by SQL::Abstract when generating the SQL query. DBIx::DataModel is not clever enough to inspect the -where conditions and decide which column types to apply, so you have to do it yourself :

  my $today = today()->as_str;
  my $rows = $schema->table($name)->select(
    -where => {d_end => {'<' => $today}},
  );

Schema versioning

Currently DBIx::DataModel has no specific support for schema versioning. See CPAN module DBIx::VersionedSchema, or switch to the DBIx::Class ORM, that has good support for schema versioning.

DATA RETRIEVAL

Aggregator functions

Use normal SQL syntax for aggregators, and give them column aliases (with a vertical bar |) in order to retrieve the results.

  my $row = $source->select(-columns   => [qw/MAX(col1)|max_col1
                                              AVG(col2)|foo
                                              COUNT(DISTINCT(col3))|bar/],
                            -where     => ...,
                            -result_as => 'firstrow');
  print "max is : $row->{max_col1}, average is $row->{foo}";

Or you can dispense with column aliases, and retrieve the results directly into an arrayref, using -result_as => 'flat_arrayref' :

  my $array_ref = $source->select(-columns   => [qw/MAX(col1)
                                                   AVG(col2)
                                                   COUNT(DISTINCT(col3))/],
                                  -where     => ...,
                                  -result_as => 'flat_arrayref');
  my ($max_col1, $avg_col2, $count_col3) = @$array_ref;

Caveat: currently, from_DB handlers do not apply to aggregator functions. So if the aggregated result needs any transformation, you have to specify a column type for it :

  my $row = $source->select(
    -columns      => [qw/MAX(d_begin)|max_d_begin MIN(d_end)|min_d_end .../],
    -where        => ...,
    -column_types => {Date_simple => [qw/max_d_begin min_d_end/],
    -result_as    => 'firstrow'
  );

Database functions or stored procedures

Like above: normal SQL syntax and column aliases.

  my $rows = $source->select(-columns => [qw/FUNC(col1,col2)|func
                                            (col3+99)|big_col3/],
                             -where    => ...,
                             );
  print "$_->{func} and $_->{big_col3}" foreach @$rows;

Nested queries

  my $subquery = $source1->select(..., -result_as => 'subquery');
  my $rows     = $source2->select(
      -columns => ...,
      -where   => {foo => 123, bar => {-not_in => $subquery}}
   );

Hashref inflation

There is no need for a hashref inflator: rows returned by a select() can be used directly as hashrefs. For example here is a loop that prints a hash slice from each row :

  my $rows       = $schema->table($name)->select(...);
  my @print_cols = qw/col3 col6 col7/;
  foreach my $row (@$rows) {
    print @{$row}{@print_cols};
  }

In fact, each row is a blessed hashref. This can be a problem with some external modules like JSON that croak when encoding a blessed reference. In that case you can use the unbless function

  foreach my $row (@$rows) {
    $schema->unbless($row);
    print to_json($row);
  }

Custom SQL

Create a 'Perl view' to encapsulate your SQL, i.e. a DBIx::DataModel::Source::Table, possibly with a where clause :

  $meta_schema->define_table(
    name     => 'MyView',
    db_table => 'TABLE1 EXOTIC JOIN TABLE2 ON ...',
    where    => {col1 => $filter1, col2 => $filter2}
    parents  => [map {meta_schema->table($_)} qw/Table1 Table2/],
  );
  

DATA UPDATE

Transaction

  # anonymous sub containing the work to do
  my $to_do = sub {
    $table1->insert(...);
    $table2->delete(...);
  };
  # so far nothing has happened in the database
  
  # now do the transaction
  $schema->do_transaction($to_do);

Nested transaction

  $schema->do_transaction(sub {
    do_something();
    $schema->do_transaction(sub { some_nested_code();       });
    $schema->do_transaction(sub { some_other_nested_code(); });
  });

Nested transaction involving another database

  $schema->dbh($initial_dbh);
  $schema->do_transaction(sub {

    # start working in $initial_dbh
    do_something();

    # now some work in $other_dbh
    $schema->do_transaction(sub { some_nested_code();       }, $other_dbh);

    # here, implicitly we are back in $initial_dbh
    $schema->do_transaction(sub { some_other_nested_code(); });
  });
  # commits in both $initial_dbh and $other_dbh are performed here

Generating random keys

Override the _singleInsert() method

  package MySchema::SomeTable;

  sub _singleInsert {
    my ($self) = @_;
    my $class = ref $self;

    my ($key_column) = $class->primKey;

    for (1..$MAX_ATTEMPTS) {
      my $random_key = int(rand($MAX_RANDOM));

        $self->{$key_column} = $random_key;
        eval {$self->_rawInsert; 1} 
          and return $random_key;   # SUCCESS

        # if duplication error, try again; otherwise die
        last unless $DBI::errstr =~ $DUPLICATE_ERROR;
     }
     croak "cannot generate a random key for $class: $@";
  }

Cascaded insert

First insert an arrayref of subrecords within the main record hashref; then call insert on that main record. See example in insert(). This only works if the two classes are associated through a Composition. A datastructure containing the keys of all generated records can be retrieved by using the option

  my $tree_of_keys = $table->insert(..., -returning => {});

Cascaded delete

  # first gather information tree from the database
  my $author = My::DB::Author->fetch($author_id);
  my $distribs = $author->expand('distributions');
  $_->expand('modules') foreach @$distribs;
  
  # then delete the whole tree from the database
  $author->delete;

This only works if the two classes are associated through a Composition. The expand operations retrieve related records and add them into a tree in memory. Then delete removes from the database all records found in the tree; therefore this is not a "true" cascaded delete, because the client code is responsible for fetching the related records.

True cascaded delete is best implemented directly in the database, rather than at the ORM layer.

Timestamp validation

Goal : make sure that the record was not touched between the time it was presented to the user (display form) and the time the user wants to update or delete that record.

In order to do this, we will suppose that every record in every table has a timestamp field TS_MODIF, updated automatically by a trigger within the database. Below is a callback function that checks if the timestamp is still valid :

  sub _check_time_stamp {
    my ($record, $table, $where) = @_;
    if ($where) { # this is an update, not an insert

      my $displayed_timestamp = delete $record->{TS_MODIF};
      my $db_record  = $record->schema->table($table)->select(
        -columns   => 'TS_MODIF',
        -where     => $where,
        -for       => 'update', # optional, depends on your RDBMS
        -result_as => 'firstrow',
      )
        or croak "fetch timestamp: could not find record "
               . join(" / ", %$where);
     my $db_timestamp = $db_record->{TS_MODIF};
     $db_timestamp == $displayed_timestamp
       or croak "record in $table was modified by somebody else; please "
              . "refresh your screen and try again";
     }
  }

This callback function can then be registered as an auto_update_column when defining the schema :

  DBIx::DataModel->define_schema(
   class               => 'My::Schema',
   auto_update_columns => {TS_MODIF => \&_check_time_stamp},
  );

DATA CONVERSION

JSON

  use JSON;
  my $json_converter = JSON->new->convert_blessed(1);
  my $json_text      = $json_converter->encode($data_row);

By default, the JSON module refuses to convert any object into JSON; however, the "convert_blessed" in JSON option will accept to convert objects provided they possess a TO_JSON method. Such a method is implemented in the "DBIx::DataModel::Source" in DBIx::DataModel::Source class, so any data row can be converted into JSON.