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File Content: `Cookbook.pod`

=head1 NAME

DBD::SQLite::Cookbook - The DBD::SQLite Cookbook

=head1 DESCRIPTION

This is the L<DBD::SQLite> cookbook.

It is intended to provide a place to keep a variety of functions and
formals for use in callback APIs in L<DBD::SQLite>.

=head1 AGGREGATE FUNCTIONS

=head2 Variance

This is a simple aggregate function which returns a variance. It is
adapted from an example implementation in pysqlite.

  package variance;
  
  sub new { bless [], shift; }
  
  sub step {
      my ( $self, $value ) = @_;
  
      push @$self, $value;
  }
  
  sub finalize {
      my $self = $_[0];
  
      my $n = @$self;
  
      # Variance is NULL unless there is more than one row
      return undef unless $n || $n == 1;
  
      my $mu = 0;
      foreach my $v ( @$self ) {
          $mu += $v;
      }
      $mu /= $n;
  
      my $sigma = 0;
      foreach my $v ( @$self ) {
          $sigma += ($v - $mu)**2;
      }
      $sigma = $sigma / ($n - 1);
  
      return $sigma;
  }
  
  # NOTE: If you use an older DBI (< 1.608),
  # use $dbh->func(..., "create_aggregate") instead.
  $dbh->sqlite_create_aggregate( "variance", 1, 'variance' );

The function can then be used as:

  SELECT group_name, variance(score)
  FROM results
  GROUP BY group_name;

=head2 Variance (Memory Efficient)

A more efficient variance function, optimized for memory usage at the
expense of precision:

  package variance2;
  
  sub new { bless {sum => 0, count=>0, hash=> {} }, shift; }
  
  sub step {
      my ( $self, $value ) = @_;
      my $hash = $self->{hash};
  
      # by truncating and hashing, we can comsume many more data points
      $value = int($value); # change depending on need for precision
                            # use sprintf for arbitrary fp precision
      if (exists $hash->{$value}) {
          $hash->{$value}++;
      } else {
          $hash->{$value} = 1;
      }
      $self->{sum} += $value;
      $self->{count}++;
  }
  
  sub finalize {
      my $self = $_[0];
  
      # Variance is NULL unless there is more than one row
      return undef unless $self->{count} > 1;
  
      # calculate avg
      my $mu = $self->{sum} / $self->{count};
  
      my $sigma = 0;
      while (my ($h, $v) = each %{$self->{hash}}) {
          $sigma += (($h - $mu)**2) * $v;
      }
      $sigma = $sigma / ($self->{count} - 1);
  
      return $sigma;
  }

The function can then be used as:

  SELECT group_name, variance2(score)
  FROM results
  GROUP BY group_name;

=head2 Variance (Highly Scalable)

A third variable implementation, designed for arbitrarily large data sets:

  package variance3;
  
  sub new { bless {mu=>0, count=>0, S=>0}, shift; }
  
  sub step {
      my ( $self, $value ) = @_;
      $self->{count}++;
      my $delta = $value - $self->{mu};
      $self->{mu} += $delta/$self->{count};
      $self->{S} += $delta*($value - $self->{mu});
  }
  
  sub finalize {
      my $self = $_[0];
      return $self->{S} / ($self->{count} - 1);
  }

The function can then be used as:

  SELECT group_name, variance3(score)
  FROM results
  GROUP BY group_name;

=head1 FTS fulltext indexing

=head2 Sparing database disk space

As explained in L<http://www.sqlite.org/fts3.html#fts4_options>,
several options are available to specify how SQLite should store
indexed documents.

One strategy is to use SQLite only for the fulltext index and
metadata, and keep the full documents outside of SQLite; to do so, use
the C<content=""> option. For example, the following SQL creates
an FTS4 table with three columns - "a", "b", and "c":

   CREATE VIRTUAL TABLE t1 USING fts4(content="", a, b, c);
 
Data can be inserted into such an FTS4 table using an INSERT
statements. However, unlike ordinary FTS4 tables, the user must supply
an explicit integer docid value. For example:

  -- This statement is Ok:
  INSERT INTO t1(docid, a, b, c) VALUES(1, 'a b c', 'd e f', 'g h i');

  -- This statement causes an error, as no docid value has been provided:
  INSERT INTO t1(a, b, c) VALUES('j k l', 'm n o', 'p q r');
 
Of course your application will need an algorithm for finding
the external resource corresponding to any I<docid> stored within
SQLite. Furthermore, SQLite C<offsets()> and C<snippet()> functions
cannot be used, so if such functionality is needed, it has to be
directly programmed within the Perl application.


=head1 SUPPORT

Bugs should be reported via the CPAN bug tracker at

L<http://rt.cpan.org/NoAuth/ReportBug.html?Queue=DBD-SQLite>

=head1 TO DO

=over

=item * 

Add more and varied cookbook recipes, until we have enough to
turn them into a separate CPAN distribution.

=item * 

Create a series of tests scripts that validate the cookbook recipes.

=back

=head1 AUTHOR

Adam Kennedy E<lt>adamk@cpan.orgE<gt>

Laurent Dami E<lt>dami@cpan.orgE<gt>

=head1 COPYRIGHT

Copyright 2009 - 2012 Adam Kennedy.

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

The full text of the license can be found in the
LICENSE file included with this module.
File Content: Cookbook.pod

File Content: `Cookbook.pod`
