DBD::SQLite::Cookbook(3)

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

DESCRIPTION
This is the DBD::SQLite cookbook.

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

AGGREGATE FUNCTIONS

  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;

  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;

  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;

SUPPORT
Bugs should be reported via the CPAN bug tracker at

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

TO DO

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

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

AUTHOR
Adam Kennedy <adamk@cpan.org>

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.