=head1 NAME

SQL::Translator::Manual - sqlfairy user manual


SQL::Translator (AKA "SQLFairy") is a collection of modules for
transforming (mainly) SQL DDL files into a variety of other formats,
including other SQL dialects, documentation, images, and code.  In
this manual, we will attempt to address how to use SQLFairy for common
tasks.  For a lower-level discussion of how the code works, please
read the documentation for L<SQL::Translator>.

It may prove helpful to have a general understanding of the SQLFairy
code before continuing.  The code can be broken into three conceptual

=over 4

=item * Parsers

The parsers are responsible for reading the input files and describing
them to the Schema object middleware.

=item * Producers

The producers create the output as described by the Schema middleware.

=item * Schema objects

The Schema objects bridge the communication between the Parsers and
Producers by representing any parsed file through a standard set of
generic objects to represent concepts like Tables, Fields (columns),
Indices, Constraints, etc.


It's not necessary to understand how to write or manipulate any
of these for most common tasks, but you should aware of the concepts
as they will be referenced later in this document.


Most common tasks can be accomplished through the use of the script
interfaces to the SQL::Translator code.  All SQLFairy scripts begin
with "sqlt."  Here are the scripts and a description of what they each

=over 4

=item * sqlt

This is the main interface for text-to-text translations, e.g.,
converting a MySQL schema to Oracle.

=item * sqlt-diagram

This is a tailored interface for the Diagram producer and its many
myriad options.

=item * sqlt-diff

This script will examine two schemas and report the SQL commands
(ALTER, CREATE) needed to turn the first schema into the second.

=item * sqlt-dumper

This script generates a Perl script that can be used to connect to a
database and dump the data in each table in different formats, similar
to the "mysqldump" program.

=item * sqlt-graph

This is an interface to the GraphViz visualization tool and its myriad

=item * sqlt.cgi

This is a CGI script that presents an HTML form for uploading or
pasting a schema and choosing an output and the output options.


To read the full documentation for each script, use "perldoc" (or
execute any of the command-line scripts with the "--help" flag).


Probably the most common task SQLFairy is used for is to convert one
dialect of SQL to another.  If you have a text description of an SQL
database (AKA a "DDL" -- "Data Definition Language"), then you should
use the "sqlt" script with switches to indicate the parser and
producer and the name of the text file as the final argument.  For
example, to convert the "foo.sql" MySQL schema to a version suitable
for PostgreSQL, you would do the following:

  $ sqlt -f MySQL -t PostgreSQL foo.sql > foo-pg.sql

The "from" and "to" options are case-sensitive and must match exactly
the names of the Parser and Producer classes in SQL::Translator.  For
a complete listing of your options, execute "sqlt" with the "--list"


It is possible to extract some schemas directly from the database
without parsing a text file (the "foo.sql" in the above example).
This can prove significantly faster than parsing a text file.  To
do this, use the "DBI" parser and provide the necessary arguments to
connect to the database and indicate the producer class, like so:

  $ sqlt -f DBI --dsn dbi:mysql:FOO --db-user guest \
    --db-password p4ssw0rd -t PostgreSQL > foo

The "--list" option to "sqlt" will show the databases supported by
DBI parsers.


Certain structured document formats can be easily thought of as
tables.  SQLFairy can parse Microsoft Excel spreadsheets and
arbitrarily delimited text files just as if they were schemas which
contained only one table definition.  The column names are normalized
to something sane for most databases (whitespace is converted to
underscores and non-word characters are removed), and the data in each
field is scanned to determine the appropriate data type (character,
integer, or float) and size.  For instance, to convert a
comma-separated file to an SQLite database, do the following:

  $ sqlt -f xSV --fs ',' -t SQLite foo.csv > foo-sqlite.sql

Additionally, there is a non-SQL representation of relational schemas namely
XML.  Additionally, the only XML supported is our own version;  however, it
would be fairly easy to add an XML parser for something like the TorqueDB
(http://db.apache.org/torque/) project.  The actual parsing of XML should be
trivial given the number of XML parsers available, so all that would be left
would be to map the specific concepts in the source file to the Schema objects
in SQLFairy.

To convert a schema in SQLFairy's XML dialect to Oracle, do the following:

  $ sqlt -f XML-SQLFairy -t Oracle foo.xml > foo-oracle.sql


Parsing a schema is generally the most computationally expensive
operation performed by SQLFairy, so it may behoove you to serialize a
parsed schema if you need to perform repeated conversions.  For
example, as part of a build process the author converts a MySQL schema
first to YAML, then to PostgreSQL, Oracle, SQLite and Sybase.
Additionally, a variety of documentation in HTML and images is produced.
This can be accomplished like so:

  $ sqlt -f MySQL -t YAML schema-mysql.sql > schema.yaml
  $ sqlt -f YAML -t Oracle schema.yaml > schema-oracle.sql
  $ sqlt -f YAML -t PostgreSQL schema.yaml > schema-postgresql.sql
  $ ...

SQLFairy has three serialization producers, none of which is superior
to the other in their description of a schema.

=over 4

=item * XML-SQLFairy

This is the aforementioned XML format.  It is essentially a direct
mapping of the Schema objects into XML.  This can also provide a very
convenient bridge to describing a schema to a non-Perl application.
Providing a producer argument to "sqlt" of just "XML" will default to
using "XML-SQLFairy."

=item * Storable

This producer stores the Schema object using Perl's Storable.pm module
available on CPAN.

=item * YAML

This producer serialized the Schema object with the very readable
structured data format of YAML (http://www.yaml.org/).  Earlier
examples show serializing to YAML.



The visualization tools in SQLFairy can graphically represent the
tables, fields, datatypes and sizes, constraints, and foreign key
relationships in a very compact and intuitive format.  This can be
very beneficial in understanding and document large or small schemas.
Two producers in SQLFairy will create pseudo-E/R (entity-relationship)

=over 4

=item * Diagram

The first visualization tool in SQLFairy, this producer uses libgd to
draw a picture of the schema.  The tables are evenly distributed in
definition order running in columns (i.e., no graphing algorithms are
used), so the many of the lines showing the foreign key relationships
may cross over each other and the table boxes.  Please read the
documentation of the "sqlt-diagram" script for all the options
available to this producer.

=item * GraphViz

The layout of the GraphViz producer is far superior to the Diagram
producer as it uses the Graphviz binary from Bell Labs to create very
professional-looking graphs.  There are several different layout
algorithms and node shapes available.  Please see the documentation of
the "sqlt-graph" script for more information.



Given that so many applications interact with SQL databases, it's no
wonder that people have automated code to deal with this interaction.
Class::DBI from CPAN is one such module that allows a developer to
describe the relationships between tables and fields in class
declarations and then generates all the SQL to interact (SELECT,
UPDATE, DELETE, INSERT statements) at runtime.  Obviously, the schema
already describes itself, so it only makes sense that you should be
able to generate this kind of code directly from the schema.  The
"ClassDBI" producer in SQLFairy does just this, creating a Perl module
that inherits from Class::DBI and sets up most of the code needed to
interact with the database.  Here is an example of how to do this:

  $ sqlt -f MySQL -t ClassDBI foo.sql > Foo.pm

Then simply edit Foo.pm as needed and include it in your code.


The Dumper producer creates a Perl script that can select the fields
in each table and then create "INSERT" statements for each record in
the database similar to the output generated by MySQL's "mysqldump"

  $ sqlt -f YAML -t Dumper --dumper-db-user guest \
  > --dumper-db-pass p4ssw0rd --dumper-dsn dbi:mysql:FOO \
  > foo.yaml > foo-dumper.pl

And then execute the resulting script to dump the data:

  $ chmod +x foo-dumper.pl
  $ ./foo-dumper.pl > foo-data.sql

The dumper script also has a number of options available.  Execute the
script with the "--help" flag to read about them.


SQLFairy offers two producers to help document schemas:

=over 4

=item * HTML

This producer creates a single HTML document which uses HTML
formatting to describe the Schema objects and to create hyperlinks on
foreign key relationships.  This can be a surprisingly useful
documentation aid as it creates a very readable format that allows one
to jump easily to specific tables and fields.  It's also possible to
plugin your own CSS to further control the presentation of the HTML.

=item * POD

This is arguably not that useful of a producer by itself, but the
number of POD-conversion tools could be used to further transform the
POD into something more interesting.  The schema is basically
represented in POD sections where tables are broken down into fields,
indices, constraints, foreign keys, etc.



All of the producers which create text output could have been coded
using a templating system to mix in the dynamic output with static
text.  CPAN offers several diverse templating systems, but few are as
powerful as Template Toolkit (http://www.template-toolkit.org/).  You
can easily create your own producer without writing any Perl code at
all simply by writing a template using Template Toolkit's syntax.  The
template will be passed a reference to the Schema object briefly
described at the beginning of this document and mentioned many times
throughout.  For example, you could create a template that simply
prints the name of each table and field that looks like this:

  # file: schema.tt
  [% FOREACH table IN schema.get_tables %]
  Table: [% table.name %]
  [% FOREACH field IN table.get_fields -%]
    [% field.name %]
  [% END -%]
  [% END %]

And then process it like so:

  $ sqlt -f YAML -t TTSchema --template schema.tt foo.yaml

To create output like this:

  Table: foo

For more information on Template Toolkit, please install the
"Template" module and read the POD.


As mentioned above, the "sqlt-diff" schema examines two schemas and
creates SQL schema modification statements that can be used to
transform the first schema into the second.  The flag syntax is
somewhat quirky:

  $ sqlt-diff foo-v1.sql=MySQL foo-v2.sql=Oracle > diff.sql

As demonstrated, the schemas need not even be from the same vendor,
though this is likely to produce some spurious results as
datatypes are not currently viewed equivalent unless they match
exactly, even if they would be converted to the same.  For example,
MySQL's "integer" data type would be converted to Oracle's "number,"
but the differ isn't quite smart enough yet to figure this out.  Also,
as the SQL to ALTER a field definition varies from database vendor to
vendor, these statements are made using just the keyword "CHANGE" and
will likely need to be corrected for the target database.


Seeing all the above options and scripts, you may be pining for a
single, graphical interface to handle all these transformations and
choices.  This is exactly what the "sqlt.cgi" script provides.  Simply
drop this script into your web server's CGI directory and enable the
execute bit and you can point your web browser to an HTML form which
provides a simple interface to all the SQLFairy parsers and producers.


Now that you have seen how the parsers and producers interact via the
Schema objects, you may wish to create your own versions to plugin.

Producers are probably the easier concept to grok, so let's cover that
first.  By far the easiest way to create custom output is to use the
TTSchema producer in conjunction with a Template Toolkit template as
described earlier.  However, you can also easily pass a reference to a
subroutine that SQL::Translator can call for the production of the
output.  This subroutine will be passed a single argument of the
SQL::Translator object which you can use to access the Schema objects.
Please read the POD for SQL::Translator and SQL::Translator::Schema to
learn the methods you can call.  Here is a very simple example:


  use strict;
  use SQL::Translator;

  my $input = q[
      create table foo (
          foo_id int not null default '0' primary key,
          foo_name varchar(30) not null default ''

      create table bar (
          bar_id int not null default '0' primary key,
          bar_value varchar(100) not null default ''

  my $t = SQL::Translator->new;
  $t->parser('MySQL') or die $t->error;
  $t->producer( \&produce ) or die $t->error;
  my $output = $t->translate( \$input ) or die $t->error;
  print $output;

  sub produce {
      my $tr     = shift;
      my $schema = $tr->schema;
      my $output = '';
      for my $t ( $schema->get_tables ) {
          $output .= join('', "Table = ", $t->name, "\n");
      return $output;

Executing this script produces the following:

  $ ./my-producer.pl
  Table = foo
  Table = bar

A custom parser will be passed two arguments:  the SQL::Translator
object and the data to be parsed.  In this example, the schema will be
represented in a simple text format.  Each line is a table definition
where the fields are separated by colons.  The first field is the
table name and the following fields are column definitions where the
column name, data type and size are separated by spaces.  The
specifics of the example are unimportant -- what is being demonstrated
is that you have to decide how to parse the incoming data and then
map the concepts in the data to the Schema object.


  use strict;
  use SQL::Translator;

  my $input =
      "foo:foo_id int 11:foo_name varchar 30\n" .
      "bar:bar_id int 11:bar_value varchar 30"

  my $t = SQL::Translator->new;
  $t->parser( \&parser ) or die $t->error;
  $t->producer('Oracle') or die $t->error;
  my $output = $t->translate( \$input ) or die $t->error;
  print $output;

  sub parser {
      my ( $tr, $data ) = @_;
      my $schema = $tr->schema;

      for my $line ( split( /\n/, $data ) ) {
          my ( $table_name, @fields ) = split( /:/, $line );
          my $table = $schema->add_table( name => $table_name )
              or die $schema->error;
          for ( @fields ) {
              my ( $f_name, $type, $size ) = split;
                  name      => $f_name,
                  data_type => $type,
                  size      => $size,
              ) or die $table->error;

      return 1;

And here is the output produced by this script:

  -- Created by SQL::Translator::Producer::Oracle
  -- Created on Wed Mar 31 15:43:30 2004
  -- Table: foo

    foo_id number(11),
    foo_name varchar2(30)

  -- Table: bar

    bar_id number(11),
    bar_value varchar2(30)

If you create a useful parser or producer, you are encouraged to
submit your work to the SQLFairy project!


You may find that the TTSchema producer doesn't give you enough control over
templating and you want to play with the Template config or add you own
variables. Or maybe you just have a really good template you want to submit to
SQLFairy :) If so, the SQL::Translator::Producer::TT::Base producer may be
just for you! Instead of working like a normal producer it provides a base
class so you can cheaply build new producer modules based on templates.

It's simplest use is when we just want to put a single template in its own
module. So to create a Foo producer we create a F<Custom/Foo.pm> file as
follows, putting our template in the __DATA__ section.

 package Custom::Foo.pm;
 use base qw/SQL::Translator::Producer::TT::Base/;
 # Use our new class as the producer
 sub produce { return __PACKAGE__->new( translator => shift )->run; };

 [% FOREACH table IN schema.get_tables %]
 Table: [% table.name %]
 [% FOREACH field IN table.get_fields -%]
   [% field.name %]
 [% END -%]
 [% END %]

For that we get a producer called Custom::Foo that we can now call like a
normal producer (as long as the directory with F<Custom/Foo.pm> is in our @INC

 $ sqlt -f YAML -t Custom-Foo foo.yaml

The template gets variables of C<schema> and C<translator> to use in building
its output. You also get a number of methods you can override to hook into the
template generation.

B<tt_config> Allows you to set the config options used by the Template object.
The Template Toolkit provides a huge number of options which allow you to do all
sorts of magic (See L<Template::Manual::Config> for details). This method
provides a hook into them by returning a hash of options for the Template. e.g.
Say you want to use the INTERPOLATE option to save some typing in your template;

 sub tt_config { ( INTERPOLATE => 1 ); }

Another common use for this is to add you own filters to the template:

 sub tt_config {(
    FILTERS => { foo_filter => \&foo_filter, }

Another common extension is adding your own template variables. This is done
with B<tt_vars>:

 sub tt_vars { ( foo => "bar" ); }

What about using template files instead of DATA sections? You can already - if
you give a template on the command line your new producer will use that instead
of reading the DATA section:

 $ sqlt -f YAML -t Custom-Foo --template foo.tt foo.yaml

This is useful as you can set up a producer that adds a set of filters and
variables that you can then use in templates given on the command line. (There
is also a tt_schema method to over ride if you need even finer control over the
source of your template). Note that if you leave out the DATA section all
together then your producer will require a template file name to be given.

See L<SQL::Translator::Producer::TT::Base> for more details.

=head1 AUTHOR

Ken Y. Clark E<lt>kclark@cpan.orgE<gt>.