***************************
**SQL**: Database interface
***************************

.. highlight:: ada

A lot of applications need to provide **persistence** for their data
(or a part of it). This means the data needs to be somehow saved on the
disk, to be read and manipulated later, possibly after the application
has been terminated and restarted. Although Ada provides various solutions
for this (including the use of the streams as declared in the Ada Reference
Manual), the common technics is through the use of relational database
management systems (**RDBMS**. The term database is in fact overloaded in
this context, and has come to mean different things:

* The software system that implements file and query management.
  This is generally provided by a third-party. The common abbreviation for
  these is **DBMS**. Queries are generally written in a language called
  **SQL**. One of the issues is that each DBMS tends to make minor changes
  to this language. Another issue is that the way to send these SQL
  commands to the DBMS is vendor-specific. GNATColl tries to
  abstract this communication through its own API. It currently supports
  **PostgreSQL** and **sqlite**, and makes it relatively easy to change
  between these two systems. For instance, development could be done
  using a local sqlite DBMS, and then deployed (after testing, of course!)
  on a PostgreSQL system.

  The code in GNATColl is such that adding support for a new DBMS
  should be relatively easy.

* A place where an application stores its data. The term
  **database** in this document refers to this meaning. In a relational
  database, this place is organized into tables, each of which contains
  a number of fields. A row in a table represents one object. The set of
  tables and their fields is called the **schema** of the database.

Traditionally, writting the SQL queries is done inline: special markers
are inserted into your code to delimit sections that contain SQL code (as
opposed to Ada code), and these are then preprocessed to generate actual
code. This isn't the approach chosen in GNATColl: there are
several drawbacks, in particular your code is no longer Ada and various
tools will choke on it.

The other usual approach is to write the queries as strings, which are
passed, via a DBMS-specific API, to the DBMS server. This approach is
very fragile:

* The string might not contain **well-formed** SQL. This will
  unfortunately only be detected at run time when the DBMS complains.

* This is not **type safe**. You might be comparing a text field
  with an integer, for instance. In some cases, the DBMS will accept that
  (sqlite for instance), but in some other cases it won't (PostgreSQL). The
  result might then either raise an error, or return an empty list.

* There is a risk of **SQL injection**. Assuming the string is
  constructed dynamically (using Ada's `&` operator), it might be easy
  for a user to pass a string that breaks the query, and even destroys
  things in the database.

* As discussed previously, the SQL code might not be **portable**
  across DBMS. For instance, creating an automatically increment integer
  primary key in a table is DBMS specific.

* The string is fragile if the database **schema changes**. Finding
  whether a schema change impacts any of the queries requires looking at
  all the strings in your application.

* **performance** might be an issue. Whenever you execute a query,
  the DBMS will analyze it, decide how to execute it (for instance, whether
  it should traverse all the rows of a table, or whether it can do a faster
  lookup), and then retrieve the results. The analysis pass is typically
  slow (relatively the overall execution time), and queries can in fact
  be **prepared** on the server: they are then analyzed only once, and it
  is possible to run them several times without paying the price of the
  analysis every time. Such a query can also be **parameterized**, in that
  some values can be derefed until the query is actually executed.
  All the above is made easy and portable in GNATColl, instead of
  requering DBMS-specific technics.

* This might require **large amount of code** to setup the query,
  bind the parameters, execute it, and traverse the list of results.

GNATColl attempts to solve all these issues. It also
provides further performance improvements, for instance
by keeping connections to the DBMS open and reusing them when possible.
A paper was published at the Ada-Europe conference in 2008 which describes
the various steps we went through in the design of this library.

.. _Database_abstraction_layers:

Database Abstraction Layers
===========================

GNATColl organizes the API into several layers, each written on
top of the previous one and providing more abstraction. All layers are
compatible with each other and can be mixed inside a given application.

* **low-level binding**.

  This API is DBMS-specific, and is basically a mapping of the C API provided
  by the DBMS vendors into Ada. If you are porting C code, or working with an
  existing application, as a way to start using GNATColl before moving
  to higher levels of abstraction.

  The code is found in :file:`gnatcoll-sql-sqlite-gnade.ads` and
  :file:`gnatcoll-sql-postgres-gnade.ads`. The *gnade* part in the file names
  indicate that this code was initially inspired by the **GNADE** library
  that used to be available on the internet. Part of the code might in fact
  come from that library.

  Using this API requires writting the SQL queries as strings, with all the
  disadvantages that were highlighted at the beginning of this chapter.

* **GNATCOLL.SQL** and **GNATCOLL.SQL.Exec**

  The first of these packages makes it possible to write type-safe queries
  strongly linked to the database schema (thus with a guarantee that the
  query is up-to-date with regards to the schema). To accomplish this, it
  also relies on code that is generated automatically from a description of
  your database schema, using the tool `gnatcoll_db2ada`. To simplify
  memory management, the queries are automatically referenced counted and
  freed when they are no longer needed.

  The second of these packages provides communication with the DBMS. It
  provides a vendor-neutral API. You can send your queries either as strings,
  or preferably as written with `GNATCOLL.SQL`. It also provides a simple
  way to prepare parameterized statements on the server for maximum efficiency,
  as well as the reuse of existing DBMS connections. It provides a simple
  API to retrieve and manipulate the results from a query.

* **GNATCOLL.SQL.ORM** and **GNATCOLL.SQL.Sessions**

  This is an Object-Relational Mapping (ORM).

  The first of these packages makes it possible to manipulate a database
  without writting SQL. Instead, you manipulate Ada objects (tagged types),
  whose primitive operations might transparently execute SQL queries. This
  API provides caching for maximum efficiency. It relies on code automatically
  generated by `gnatcoll_db2ada` from the schema of your database. The
  generated objects can then be extended in your own code if needed.

  The second package encapsulates DBMS connections into higher-level objects
  which provide their own caching and work best with the ORM objects. A
  session is automatically released to a pool when no longer needed and will
  be reused later on.


The following sections will ignore the lower layer, and concentrate on the
other layers. They share a number of types and, again, are fully compatible
with each other. You could connect to the database, and then write some queries
using **GNATCOLL.SQL** and some using **GNATCOLL.SQL.ORM**.

.. _Database_example:

Database example
================

This section describes an example that will be extended throughout this
chapter. We will build an application that represents a library. Such
a library contains various media (books and dvds for instance), and
customers. A customer can borrow multiple media at the same time, but a
media is either at a customer's, or still in the library.

The GNATColl distribution includes an example directory which
contains all the code and data for this example.

.. _Database_schema:

Database schema
===============

As was mentioned earlier (:ref:`Database_abstraction_layers`),
GNATColl relies on automatic code generation to provide a type
safe interface to your database. This code is generated by an external
tool called `gnatcoll_db2ada`. In some cases, this tool requires an
installation of python (`www.python.org <www.python.org>`_) on your machine, since part
of the code is written in that language.

This tool is able to output various kind of information, and will be fully
described later (:ref:`The_gnatcoll_db2ada_tool`). However, the input
is always the same: this is the schema of your database, that is the list
of tables and fields that make up your database. There exist two ways to
provide that information:

* From a running database

  If you pass the DBMS vendor (postgresql, sqlite,...) and the connection
  parameters to `gnatcoll_db2ada`, it is able to query the schema on
  its own. However, this should not be the preferred method: this is similar
  to revert engineering assembly code into the original high-level code, and
  some semantic information will be missing. For instance, in SQL
  we have to create tables just to represent the many-to-many relationships.
  These extra tables are part of the implementation of the schema, but are
  just noise when it comes to the semantics of the schema. For this reason,
  it is better to use the second solution below:

* From a textual description

  Using the `-dbmodel` switch to `gnatcoll_db2ada`, you can pass
  a file that describes the schema. We do not use SQL as the syntax in this,
  because as explained above this is too low-level. This text file also
  provides additional capabilities that do not exist when reverse-engineering
  an existing database, for instance the ability to use name to represent
  reverse relationships for foreign keys (see below and the ORM).

  The most convenient editor for this file is Emacs, using the `org-mode`
  which provides convenient key shortcuts for editing the contents of ASCII
  tables. But any text editor will do, and you do not need to align the columns
  in this file.

  All lines starting with a hash sign ('#') will be ignored.

  This file is a collection of ASCII tables, each of which relates to one table
  or one SQL view in your database. The paragraphs start with a line
  containing::

      table ::=
         '|' ('ABSTRACT')? ('TABLE'|'VIEW') ['(' supertable ')']
         '|' <name> '|' <name_row> 

  "name" is the name of the table. The third pipe and third column are optional,
  and should be used to specify the name for the element represented by a single
  row. For instance, if the table is called "books", the third column could
  contain "book". This is used when generating objects for use with
  `GNATCOLL.SQL.ORM`.

  If the first line starts with the keyword `ABSTRACT`, then no instance
  of that table actually exists in the database. This is used in the context
  of table inheritance, so define shared fields only once among multiple tables.

  The keyword `TABLE` can be followed by the name of a table from which it
  inherits the fields. Currently, that supertable must be abstract, and the
  fields declared in that table are simply duplicated in the new table.

  Following the declaration of the table, the file then describe their fields,
  each on a separate line. Each of these lines must start with a pipe
  character ("|"), and contain a number of pipe-separated fields. The order of
  the fields is always given by the following grammar::

      fields ::=
         '|' <name> '|' <type>
         '|' ('PK'|''|'NULL'|'NOT NULL'|'INDEX'|'NOCASE') 
         '|' [default] '|' [doc] '|'
    
  The type of the field is the SQL type ("INTEGER", "TEXT", "TIMESTAMP", "DATE",
  "DOUBLE PRECISION", "MONEY", "BOOLEAN", "TIME", "CHARACTER(1)"). Any maximal
  length can be specified for strings, not just 1 as in this example.
  The tool will automatically convert these to
  Ada when generating Ada code. A special type ("AUTOINCREMENT") is an integer
  that is automatically incremented according to available ids in the table.
  The exact type used will depend on the specific DBMS.

  The property 'NOCASE' indicates that comparison should be case insensitive
  for this field.

  If the field is a foreign key (that is a value that must correspond to a row
  in another table), you can use the special syntax for its type::

      fk_type ::= 'FK' <table_name> [ '(' <reverse_name> ')' ]

  As you can see, the type of the field is not specified explicitly, but will
  always be that of the foreign table's primary key. With this syntax, the
  foreign table must have a single field for its primary key. GNATColl
  does not force a specific order for the declaration of tables: if is valid to
  have a foreign key to a table that hasn't been declared yet. There is however
  a restriction if you use the model to create a sqlite database (through the
  `-createdb` switch of `gnatcoll_db2ada`): in this case, a reference
  to a table that hasn't been defined yet may not be not through a field marked
  as NOT NULL. This is a limitation of the sqlite backend itself. The solution
  in this case is to reorder the declaration of tables, or drop the NOT NULL
  constraint.

  Another restriction is that a foreign key that is also a primary key must
  reference a table that has already been defined. You need to reorder the
  declaration of your tables to ensure this is the case.

  "reverse_name" is the optional name that will be generated in the Ada code for the
  reverse relationship, in the context of `GNATCOLL.SQL.ORM`.
  If the "reverse_name" is empty (the parenthesis are shown), no reverse
  relationship is generated. If the parenthesis and the reverse_name are both
  omitted, a default name is generated based on the name of the field.

  The third column in the fields definition indicates the constraints of the
  type. Multiple keywords can be used if they are separated by commas. Thus,
  "NOT NULL, INDEX" indicates a column that must be set by the user, and for
  which an index is created to speed up look ups.

  * A primary key ("PK")
  * The value must be defined ("NOT NULL")
  * The value can be left undefined ("NULL")
  * An index should be created for that column ("INDEX") to speed up
    the lookups.

  * The automatic index created for a Foreign Key should not be created
    ("NOINDEX"). Every time a field references another table, GNATColl will by
    default create an index for it, so that the ORM can more efficiently do a
    reverse query (from the target table's row find all the rows in the current
    table that reference that target row). This will in general provide more
    efficiency, but in some cases you never intend to do the reverse query and
    thus can spare the extra index.

  The fourth column gives the default value for the field, and is given in SQL
  syntax. Strings must be quoted with single quotes.

  The fifth column contains documentation for the field (if any). This
  documentation will be included in the generated code, so that IDEs can
  provide useful tooltips when navigating your application's code.

  After all the fields have been defined, you can specify extract constraints
  on the table. In particular, if you have a foreign key to a table that uses a
  tuple as its primary key, you can define that foreign key on a new line, as::

      FK ::= '|' "FK:" '|' <table> '|' <field_names>* 
         '|' <field_names>* '|'

  For instance::

    | TABLE | tableA |
    | FK: | tableB | fieldA1, fieldA2 | fieldB1, fieldB2 |
    
  It is also possible to crate multi-column indexes, as in the following
  example.  In this case, the third column contains the name of the index to
  create. If left blank, a default name will be computed by GNATColl::

    | TABLE | tableA |
    | INDEX: | field1,field2,field3 | name | 
    
  Going back to the example we described earlier (:ref:`Database_example`),
  let's describe the tables that are involved.

  The first table contains the customers. Here is its definition::
    
    | TABLE | customers     | customer        || The customer for the library |
    | id    | AUTOINCREMENT | PK              || Auto-generated id            |
    | first | TEXT          | NOT NULL        || Customers' first name        |
    | last  | TEXT          | NOT NULL, INDEX || Customers' last name         |
    
  We highly recommend to set a primary key on all tables.
  This is a field whose value is
  unique in the table, and thus that can act as an identifier for a specific
  row in the table (in this case for a specific customer). We recommand using
  integers for these ids for efficiency reasons. It is possible that the
  primary key will be made of several fields, in which case they should all
  have the "PK" constraint in the third column.

  A table with no primary key is still usable. The difference is in the
  code generated for the ORM (:ref:`The_Object_Relational_Mapping_layer`),
  since the `Delete` operation for this table will raise a
  `Program_Error` instead of doing the actual deletion (that's because there
  is no guaranteed unique identifier for the element, so the ORM does not know
  which one to delete -- we do not depend on having unique internal ids on the
  table, like some DBMS have). Likewise, the elements extracted from such a
  primary key-less table will not be cached locally in the session, and cannot
  be updated (only new elements can be created in the table).

  As we mentioned, the library contains two types of media, books and DVDs.
  Each of those has a title, an author. However, a book also has a number of
  pages and a DVD has a region where it can be viewed. There are various ways
  to represent this in a database. For illustration purposes, we will use
  table inheritance here: we will declare one abstract table (media) which
  contains the common fields, and two tables to represent the types of media.

  As we mentioned, a media can be borrowed by at most one customer, but a
  customer can have multiple media at any point in time. This is called a
  **one-to-many** relationship. In SQL, this is in general described through
  the use of a foreign key that goes from the table on the "many" side. In
  this example, we therefore have a foreign key from media to customers. We
  also provide a name for the reverse relationship, which will become clearer
  when we describe the ORM interface.

  Here are the declarations::

    | ABSTRACT TABLE | media               | media || The contents of the library |
    | id             | AUTOINCREMENT       | PK    || Auto-generated id           |
    | title          | TEXT                |       || The title of the media      |
    | author         | TEXT                |       || The author                  |
    | published      | DATE                |       || Publication date            |
    | borrowed_by    | FK customers(items) | NULL  || Who borrowed the media      |

    | TABLE (media) | books   | book |     | The books in the library |
    | pages         | INTEGER |      | 100 |                          |

    | TABLE (media) | dvds    | dvd |   | The dvds in the library |
    | region        | INTEGER |     | 1 |                         |
    
  For this example, all this description is put in a file called
  :file:`dbschema.txt`.

.. _The_gnatcoll_db2ada_tool:

The gnatcoll_db2ada tool
========================

As stated in the introduction, one of the goals of this library is to
make sure the application's code follows changes in the schema of your
database.

To reach this goal, an external tool, :file:`gnatcoll_db2ada` is provided
with GNATColl, and should be spawned as the first step of the
build process, or at least whenever the database schema changes. It
generates an Ada package (`Database` by default) which reflects the
current schema of the database.

This tool supports a number of command line parameters (the complete list
of which is available through the :file:`-h` switch). The most important of
those switches are:


*-dbhost host*, *-dbname name*, *-dbuser user*, *-dbpasswd passwd*, *-dbtype type*
  These parameters specify the connection parameters for the database. To
  find out the schema, :file:`gnatcoll_db2ada` can connect to an existing
  database (:ref:`Database_schema`). The user does not need to have
  write permission on the database, since all queries are read-only.

*-dbmodel file*
  This parameter can replace the above `-dbname`,... It specifies the
  name of a text file that contains the description of the database, therefore
  avoiding the need for already having a database up-and-running to generate
  the Ada interface.

  The format of this text file was described in the previous section.

  This switch is not compatible with `-enum` and `-vars` that
  really need an access to the database.

*-api PKG*
  This is the default behavior if you do not specify `-text` or
  `-createdb`. This will generate several files (:file:`PKG.ads`,
  :file:`PKG.adb` and :file:`PKG_names.ads`, where PKG is the argument given
  on the command line). These package represent your database schema, that
  is the list of all tables and their fields, as typed values. This is the
  building block for using `GNATCOLL.SQL` and write type-safe queries.

*-api-enums PKG*
  This is very similar to `-api`, except it will only extract values
  from an existing database as per the `-enum` and `-var` switches.
  The generated package does not include the description of the database
  schema. The goal is that the values are extracted once from an existing
  database, and then `-api` can be used to dump the schema from a
  textual description of the database.

*-adacreate*
  This should be used in combination with `-api`. In addition to the usual
  output of `-api`, it will also generate an Ada subprogram called
  `Create_Database` that can be used to recreate the database and its
  initial data (if `-load` was specified) from your application, without
  requiring access to the external files that define the schema and the
  initial data.

*-enum table,id,name,prefix,base*
  This parameter can be repeated several times if needed. It identifies
  one of the special tables of the database that acts as an enumeration
  type. It is indeed often the case that one or more tables in the
  database have a role similar to Ada's enumeration types, ie contains
  a list of values for information like the list of possible priorities,
  a list of countries,... Such lists are only manipulated by the
  maintainer of the database, not interactively, and some of their
  values have impact on the application's code (for instance, if a
  ticket has an urgent priority, we need to send a reminder every day --
  but the application needs to know what an urgent priority is).
  In such a case, it is convenient to generate these values as
  constants in the generated package. The output will be similar to::

    subtype Priority_Id is Integer;
    Priority_High   : constant Priority_Id := 3;
    Priority_Medium : constant Priority_Id := 2;
    Priority_Low    : constant Priority_Id := 1;
    Priority_High_Internal : constant Priority_Id := 4;

  This code would be extracted from a database table called, for
  instance, `ticket_priorities`, which contains the following::

    table ticket_priorities:
    name		| priority	| category
    high		| 3		| customer
    medium	| 2		| customer
    low		| 1		| customer
    high_internal	| 4		| internal

  To generate the above Ada code, you need to pass the following
  parameter to :file:`gnatcoll_db2ada`::

    -enum ticket_priorities,Priority,Priority,Integer

  where the second parameter is the name of the field in the table, and
  the first is the prefix to add in front of the name to generate the
  Ada constant's name. The last parameter should be either `Integer`
  or `String`, which influences the way the value of the Ada constant
  is generated (surrounded or not by quotes).

*-var name,table,field,criteria,comment*
  This is similar to the `-enum` switch, but extracts a single value
  from the database. Although applications should try and depend as little
  as possible on such specific values, it is sometimes unavoidable.

  For instance, if we have a table in the table with the following
  contents::

    table staff
    staff_id	| login
    0		| unassigned
    1		| user1

  We could extract the id that helps detect unassigned tickets with the
  following command line::

    -var no_assign_id,staff,staff_id,"login='unassigned'","help"

  which generates::

    No_Assigne_Id : constant := 0;
    --  help

  The application should use this constant rather than some hard-coded
  string `"unassigned"` or a named constant with the same value.
  The reason is that presumably the login will be made visible somewhere
  to the user, and we could decide to change it (or translate it to
  another language). In such a case, the application would break. On the
  other hand, using the constant `0` which we just extracted will
  remain valid, whatever the actual text we display for the user.

*-orm PKG*
  This will generate two files (:file:`PKG.ads` and :file:`PKG.adb`) that
  support `GNATCOLL.SQL.ORM` to write queries without writing actual
  SQL. This is often used in conjunction with :file:`-api`, as in::

    gnatcoll_db2ada -api Database -orm ORM -dbmodel dbschema.txt
    
  To use this switch, you need to have a version of `python
  <http://python.org>`_ installed on your development machine, since the code
  generation is currently implemented in python. The generated code can then be
  compiled on any machine, so it is enough to generate the code once and then
  possibly check it in your version control system.

*-ormtables LIST*
  Restrict the output of `-orm` to a subset of the tables. List is a
  comma-separated of table names.

*-dot*
  When this switch is specified, `gnatcoll_db2ada` generates a file
  called :file:`schema.dot` in the current directory. This file can be
  processed by the `dot` utility found in the `graphviz` suite,
  to produce a graphicaly representation of your database schema. Each table
  is represented as a rectangle showing the list of all attributes, and the
  foreign keys between the tables are represented as links.

  To produce this output, you need a python installation on your machine. If
  you also have `dot` installed, the file is processed automatically to
  generate a postscript document :file:`schema.ps`.

*-text*
  Instead of creating Ada files to represent the database schema, this switch
  will ask `gnatcoll_db2ada` to dump the schema as text. This is in a
  form hopefully easy to parse automatically, in case you have tools that
  need the schema information from your database in a DBMS-independent
  manner. This is the same format used for `-dbmodel`, so the switch
  `-text` can also be used to bootstrap the development if you already
  have an existing database.

*-createdb*
  Instead of the usual default output, `gnatcoll_db2ada` will output a
  set of SQL commands that can be used to re-create the set of all tables in
  your schema. This does not create the database itself (which might require
  special rights depending on your DBMS), only the tables.

  In most cases, this creation needs to be done by a system administrator
  with the appropriate rights, and thus will be done as part of the
  deployement of your application, not the application itself. This is
  particularly true for client-server databases like `PostgreSQL`.

  But in some simpler cases where the database is only manipulated by your
  application, and potentially only needs to exist while your application
  is running (often the case for `sqlite`), your application could be
  responsible for creating the database.


Default output of gnatcoll_db2ada
---------------------------------

From the command line arguments, `gnatcoll_db2ada` will generate
an Ada package, which contains one type per table in the database.
Each of these types has a similar structure. The implementation
details are not shown here, since they are mostly irrelevant and might
change. Currently, a lot of this code are types with discriminants. The
latter are `access-to-string`, to avoid duplicating strings in
memory and allocating and freeing memory for these. This provides
better performances::

  package Database is
     type T_Ticket_Priorities (...) is new SQL_Table (...) with record
         Priority : SQL_Field_Integer;
         Name     : SQL_Field_Text;
     end record;

     overriding function FK (Self : T_Ticket_Priorities; Foreign : SQL_Table'Class)
        return SQL_Criteria;

     Ticket_Priorities : constant T_Ticket_Priorities (...);
  end Database;

It provides a default instance of that type, which can be used to
write queries (see the next section). This type overrides one primitive
operation which is used to compute the foreign keys between that table
and any other table in the database (:ref:`Writing_queries`).

Note that the fields which are generated for the table (our example reuses
the previously seen table `ticket_priorities`) are typed, which as we
will see provides a simple additional type safety for our SQL queries.

database introspection in Ada
-----------------------------

As described above, the `-createdb` switch makes it possible to
create a database (or at least its schema). This operation can also be
performed directly from your Ada code by using the services provided in the
`GNATCOLL.SQL.Inspect` package. In particular, there are services for
reading the schema of a database either from a file or from a live
database, just as `gnatcoll_db2ada` does.

This results in a structure in memory that you can use to find out which
are the tables, what are their fields, their primary keys,...

It is also possible to dump this schema to a text file (with the same
format as expected by `-dbmodel`), or more interestingly to output
the SQL statements that are needed to create the tables in a database. In
the case of Sqlite, creating a table will also create the database file
if it doesn't exist yet, so no special rights are needed.

This input/output mechanism is implemented through an abstract
`Schema_IO` tagged type, with various concrete implementations (either
`File_Schema_IO` to read or write from/to a file, or
`DB_Schema_IO` to read or write from/to a database).

See the specs for more detail on these subprograms.

Back to the library example...
------------------------------

In the previous section, we have described our database schema in a text
file. We will now perform two operations:

.. highlight:: sql
    
* Create an empty database

  This should of course only be done once, not every time you run your
  application::

    gnatcolldbada -dbtype=sqlite -dbname=library.db -dbmodel=dbschema.txt -createdb

  In the case of this example, the sql commands that are executed for sqlite
  are::

    CREATE TABLE books (
       pages Integer DEFAULT '100',
       id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
       title Text,
       author Text,
       published Date,
       borrowed_by Integer);
    CREATE TABLE customers (
       id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
       first Text NOT NULL,
       last Text NOT NULL);
    CREATE TABLE dvds (
       region Integer DEFAULT '1',
       id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
       title Text,
       author Text,
       published Date,
       borrowed_by Integer);
    CREATE INDEX "customers_last" ON "customers" ("last");

.. highlight:: ada
    
* Generate the Ada code

  The details of the code will be described later. For now, our application
  will not use the ORM, so we do not generate code for it::

    gnatcoll_db2ada -api=Database -dbmodel=dbschema.txt

.. _Connecting_to_the_database:
    
Connecting to the database
==========================

This library abstracts the specifics of the various database engines
it supports. Ideally, code written for one database could be ported
almost transparently to another engine. This is not completely doable
in practice, since each system has its own SQL specifics, and unless
you are writing things very carefully, the interpretation of your queries
might be different from one system to the next.

However, the Ada code should remain untouched if you change the engine.
Various engines are supported out of the box (PostgreSQL and Sqlite),
although new ones can be added by overriding the appropriate SQL type
(`Database_Connection`). When you compile GNATColl, the
build scripts will try and detect what systems are installed on your
machine, and only build support for those. It is possible, if no
database was installed on your machine at that time, that the database
interface API is available (and your application compiles), but no
connection can be done to database at run time.

To connect to a DBMS, you need to specify the various connection parameters.
This is done via a `GNATCOLL.SQL.Exec.Database_Description` object.
The creation of this object depends on the specific DBMS you are connecting
to (and this is the only part of your code that needs to know about the
specific system). The packages `GNATCOLL.SQL.Postgres` and
`GNATCOLL.SQL.Sqlite` contain a `Setup` function, whose parameters
depend on the DBMS. They provide full documentation for their parameters.
Let's take a simple example from sqlite::

  with GNATCOLL.SQL.Sqlite;   -- or Postgres
  declare
     DB_Descr : GNATCOLL.SQL.Exec.Database_Description;
  begin
     DB_Descr := GNATCOLL.SQL.Sqlite.Setup ("dbname.db");
  end
  

At this point, no connection to the DBMS has been done, and no information
was exchanged.

To communicate with the database, however, we need to create another
object, a **GNATCOLL.SQL.Exec.Database_Connection**. Your application can
create any number of these. Typically, one would create one such connection
per task in the application, although other strategies are possible (like
a pool of reusable connections, where a task might be using two connections and
another task none at any point in time).

If you do not plan on using the ORM interface from **GNATCOLL.SQL.ORM**,
GNATColl provides a simple way to create a task-specific connection.
While in this task, the same connection will always be returned (thus you
do not have to pass it around in parameter, although the latter might be
more efficient)::

  declare
     DB : GNATCOLL.SQL.Exec.Database_Connection;
  begin
     DB := GNATCOLL.SQL.Exec.Get_Task_Connection
          (Description  => DB_Descr);
  end;

If your application is not multi-tasking, or you wish to implement your
own strategy for a connection pool, you can also use the following code
(using Ada 2005 dotted notation when calling the primitive operation). This
code will always create a new connection, not reuse an existing one, as
opposed to the code above::

  declare
     DB : GNATCOLL.SQL.Exec.Database_Connection;
  begin
     DB := DB_Descr.Build_Connection;
  end;

A note on concurrency: if you implement your own pool, you might sometimes
end up with dead locks when using sqlite. If a task uses two or more
connections to sqlite, and you setup GNATCOLL to create SQL
transactions even for `SELECT` statements (see
`GNATCOLL.SQL.Sqlite.Always_Use_Transactions`), the following scenario
will result in a deadlock::

     DB1 := ... new connection to sqlite
        ... execute a SELECT through DB1. The latter then holds a shared
        ... lock, preventing other connections from writing (but not from
        ... reading).
     DB2 := ... another connection in the same thread
        ... execute an INSERT through DB2. This tries to get a lock, which
        ... will fail while DB1 holds the shared lock. Since these are in
        ... the same thread, this will deadlock.
  
By default, GNATCOLL will not create SQL transactions for select statements
to avoid this case, which occurs frequently in code.

If you wish to reuse an existing connection later on, you must reset it. This
terminates any on-going SQL transaction, and resets various internal fields
that describe the state of the connection::

     Reset_Connection (DB);

In all three cases, the resulting database connection needs to be freed when
you no longer needed (which might be when your program terminates if you are
using pools) to avoid memory leaks. Nothing critical will appear if you do
not close, though, because the transactions to the DBMS server are saved
every time you call `Commit` in any case. So the code would end with::

     Free (DB);  --  for all connections you have opened
     Free (DB_Descr);

At this point, there still hasn't been any connection to the DBMS. This will
be done the first time a query is executed. If for some reason the connection
to the DBMS server is lost, GNATColl will automatically attempt to
reconnect a number of times before it gives up. This might break if there
was an ongoing SQL transaction, but simplifies your code since you do not
have to handle reconnection when there was a network failure, for instance.

As we saw before, the database interface can be used in multi-tasking
applications. In such a case, it is recommended that each thread has its
own connection to the database, since that is more efficient and you do
not have to handle locking.
However, this assumes that the database server itself is thread safe,
which most often is the case, but not for `sqlite` for instance.
In such a case, you can only connect one per application to the database,
and you will have to manage a queue of queries somehow.

If you want to use **GNATCOLL.SQL.Sessions** along with the Object-Relational
Mapping API, you will need to initialize the connection pool with the
**Database_Description**, but the session will then take care automatically
of creating the **Database_Connection**. See later sections for more details.

Loading initial data in the database
====================================

We have now created an empty database. To make the queries we will write
later more interesting, we are going to load initial data.

There are various ways to do it:

* Manually or with an external tool

  One can connect to the database with an external tool (a web interface
  when the DBMS provides one for instance), or via a command line tool
  (`psql` for PostgreSQL or `sqlite3` for Sqlite), and start
  inserting data manually. This shows one of the nice aspects of using a
  standard DBMS for your application: you can alter the database (for instance
  to do minor fixes in the data) with a lot of external tools that were
  developed specifically for that purpose and that provide a nice interface.
  However, this is also tedious and error prone, and can't be repeat easily
  every time we recreate the database (for instance before running automatic
  tests).

* Using `GNATCOLL.SQL.EXEC`

  As we will describe later, GNATColl contains all the required
  machinery for altering the contents of the database and creating new
  objects. Using `GNATCOLL.SQL.ORM` this can also be done at a high-level
  and completly hide SQL.

* Loading a data file

  A lot of frameworks call such a file that contains initial data a "fixture".
  We will use this technics as an example. At the Ada level, this is a simple
  call to `GNATCOLL.SQL.Inspect.Load_Data`. The package contains a lot
  more than just this subprogram (:ref:`The_gnatcoll_db2ada_tool`)::

    declare
       File : GNATCOLL.VFS.Virtual_File := Create ("fixture.txt");
       DB : Database_Connection;  --  created earlier
    begin
       GNATCOLL.SQL.Inspect.Load_Data (DB, File);
       DB.Commit;
    end;
    
  The format of this file is described just below.

As we mentioned, GNATColl can load data from a file. The format
of this file is similar to the one that describes the database schema. It
is a set of ASCII tables, each of which describes the data that should go
in a table (it is valid to duplicate tables). Each block starts with two
lines: The first one has two mandatory columns, the first of which contains
the text "TABLE", and the second contains the name of the table you want to
fill. The second line should contain as many columns as there are fields you
want to set. Not all the fields of the table need to have a corresponding
column if you want to set their contents to NULL (provided, of course,
that your schema allows it). For instance, we could add data for our
library example as such::

  | TABLE | customers |        |
  |    id | first     | last   |
  |-------+-----------+--------|
  |     1 | John      | Smith  |
  |     2 | Alain     | Dupont |

  | TABLE      | books   |       |            |             |
  | title      | author  | pages |  published | borrowed_by |
  |------------+---------+-------+------------+-------------|
  | Art of War | Sun Tzu |    90 | 01-01-2000 |           1 |
  | Ada RM     | WRG     |   250 | 01-07-2005 |             |
  
A few comments on the above: the `id` for `books` is not specified,
although the column is the primary key and therefore cannot be NULL. In fact,
since the type of the `id` was set to AUTOINCREMENT, GNATColl will
automatically assign valid values. We did not use this approach for the
id of `customers`, because we need to know this id to set the
`borrowed_by` field in the `books` table.

There is another approach to setting the `borrowed_by` field, which
is to give the value of another field of the `customers` table. This
of course only work if you know this value is unique, but that will often
be the case in your initial fixtures. Here is an example::

  | TABLE        | dvds     |        |                    |
  | title        | author   | region | borrowed_by(&last) |
  |--------------+----------+--------+--------------------|
  | The Birds    | Hitchcok |      1 | &Smith             |
  | The Dictator | Chaplin  |      3 | &Dupont            |
  

Here, the title of the column indicates that any value in this column might
be a reference to the `customers.last` value. Values which start
with an ampersand ("&") will therefore be looked up in `customers.last`,
and the `id` of the corresponding customer will be inserted in the
`dvds` table. It would still be valid to use directly customer ids
instead of references, this is just an extra flexibility that the references
give you to make your fixtures more readable.

However, if we are using such references we need to provide the database
schema to `Load_Data` so that it can write the proper queries. This
is done by using other services of the `GNATCOLL.SQL.Inspect` package.

The code for our example would be::

     Load_Data
        (DB, Create ("fixture.txt"),
         New_Schema_IO (Create ("dbschema.txt")).Read_Schema);

.. _Writing_queries:

Writing queries
===============

The second part of the database support in GNATColl is a set
of Ada subprograms which help write SQL queries. Traditional ways to
write such queries have been through embedded SQL (which requires a
pre-processing phase and complicate the editing of source files in
Ada-aware editors), or through simple strings that are passed as is
to the server. In the latter case, the compiler can not do any
verification on the string, and errors such a missing parenthesis or
misspelled table or field names will not be detected until the code
executes the query.

GNATColl tries to make sure that code that compiles contains
syntactically correct SQL queries and only reference existing tables
and fields. This of course does not ensure that the query is
semantically correct, but helps detect trivial errors as early as
possible.

Such queries are thus written via calls to Ada subprograms, as in the
following example::

  with GNATCOLL.SQL;  use GNATCOLL.SQL;
  with Database; use Database;
  declare
     Q : SQL_Query;
  begin
     Q := SQL_Select
       (Fields => Max (Ticket_Priorities.Priority)
           & Ticket_Priorities.Category,
        From   => Ticket_Priorities,
        Where  => Ticket_Priorities.Name /= "low",
        Group_By => Ticket_Priorities.Category);
  end;

The above example will return, for each type of priority (internal or
customer) the highest possible value. The interest of this query is
left to the user...

This is very similar to an actual SQL query. Field and table names come
from the package that was automatically generated by the
`gnatcoll_db2ada` tool, and therefore we know that our query is
only referencing existing fields. The syntactic correctness is ensured by
standard Ada rules. The `SQL_Select` accepts several parameters
corresponding to the usual SQL attributes like `GROUP BY`,
`HAVING`, `ORDER BY` and `LIMIT`.

The `From` parameter could be a list of tables if we need to join
them in some ways. Such a list is created with the overridden `"&"`
operator, just as for fields which you can see in the above example.
GNATColl also provides a `Left_Join` function to join two
tables when the second might have no matching field (see the SQL
documentation).

Similar functions exist for `SQL_Insert`, `SQL_Update` and
`SQL_Delete`. Each of those is extensively documented in the
:file:`gnatcoll-sql.ads` file.

It is worth noting that we do not have to write the query all at once.
In fact, we could build it depending on some other criteria. For
instance, imagine we have a procedure that does the query above, and
omits the priority specified as a parameter, or shows all priorities
if the empty string is passed. Such a procedure could be written as::

  procedure List_Priorities (Omit : String := "") is
    Q : SQL_Query;
    C : SQL_Criteria := No_Criteria;
  begin
    if Omit /= "" then
       C := Ticket_Priorities.Name /= Omit;
    end if;
    Q := SQL_Select
      (Fields => ..., -- as before
       Where  => C);
  end;

With such a code, it becomes easier to create queries on the fly
than it would be with directly writing strings.

The above call has not sent anything to the database yet, only created
a data structure in memory (more precisely a tree). In fact, we could
be somewhat lazy when writing the query and rely on auto-completion,
as in the following example::

  Q := SQL_Select
   (Fields => Max (Ticket_Priorities.Priority)
       & Ticket_Priorities.Category,
    Where  => Ticket_Priorities.Name /= "low");

  Auto_Complete (Q);

This query is exactly the same as before. However, we did not have to
specify the list of tables (which GNATColl can compute on its
own by looking at all the fields referenced in the query), nor the list
of fields in the `GROUP BY` clause, which once again can be computed
automatically by looking at those fields that are not used in a SQL
aggregate function. This auto-completion helps the maintenance of those
queries.

There is another case where GNATColl makes it somewhat easier
to write the queries, and that is to handle joins between tables. If your
schema was build with foreign keys, GNATColl can take advantage
of those.

Going back to our library example, let's assume we want to find out all
the books that were borrowed by the user "Smith". We need to involve two
tables (`Books` and `Customers`), and provide a join between them
so that the DBMS knows how to associate the rows from one with the rows from
the other. Here is a first example for such a query::

     Q := SQL_Select
        (Fields => Books.Title & Books.Pages,
         From   => Books & Customers,
         Where  => Books.Borrowed_By = Customers.Id
            and Customers.Last = "Smith");
  
In fact, we could also use auto-completion, and let GNATColl find
out the involved tables on its own. We thus write the simpler::

     Q := SQL_Select
        (Fields => Books.Title & Books.Pages,
         Where  => Books.Borrowed_By = Customers.Id
            and Customers.Last = "Smith");

There is one more things we can do to simplify the query and make it more
solid if the schema of the database changes. For instance, when a table
has a primary key made up of several fields, we need to make sure we always
have an "=" statement in the WHERE clause for all these fields between the
two tables. In our example above, we could at some point modify the schema
so that the primary key for `customers` is multiple (this is unlikely
in this example of course). To avoid this potential problems and make the
query somewhat easier to read, we can take advantage of the `FK`
subprograms generated by `gnatcoll_db2ada`. Using the Ada05 dotted
notation for the call, we can thus write::

     Q := SQL_Select
        (Fields => Books.Title & Books.Pages,
         Where  => Books.FK (Customers)
            and Customers.Last = "Smith");
  
Regarding memory management, there is no need for explicitly freeing
memory in the above code. GNATColl will automatically do this when
the query is no longer needed.

Executing queries
=================

Once we have our query in memory, we need to pass it on to the database
server itself, and retrieve the results.

Executing is done through the `GNATCOLL.SQL.Exec` package, as in the
following example::

  declare
     R : Forward_Cursor;
  begin
     R.Fetch (Connection => DB, Query => Q);
  end;

This reuses the connection we have established previously (`DB`)
(although now we are indeed connecting to the DBMS for the first time)
and sends it the query. The result of that query is then stored in
`R`, to be used later.

Some SQL commands execute code on the DBMS, but do not return a result.
In this case, you can use `Execute` instead of `Fetch`. This
is the case when you execute an `INSERT` or `UPDATE` statement
for instance. Using `Execute` avoids the need to declare the local
variable `R`.

If for some reason the connection to the database is no longer valid
(a transient network problem for instance), GNATColl will
attempt to reconnect and re-execute your query transparently, so that
your application does not need to handle this case.

We'll describe later (:ref:`Getting_results`) how to analyze the result
of the query.

Some versions of `Fetch` have an extra parameter `Use_Cache`,
set to `False` by default. If this parameter is true, and the exact same
query has already been executed before, its result will be reused
without even contacting the database server. The cache is automatically
invalidated every hour in any case. This cache is mostly useful for
tables that act like enumeration types, as we have seen before when
discussing the `-enum` parameter to :file:`gnatcoll_db2ada`. In this
case, the contents of the table changes very rarely, and the cache can
provide important speedups, whether the server is local or distant.
However, we recommend that you do actual measurements to know whether
this is indeed beneficial for you. You can always invalidate the
current cache with a call to `Invalidate_Cache` to force the
query to be done on the database server.

If your query produces an error (whether it is invalid, or any other
reason), a flag is toggled in the `Connection` parameter, which
you can query through the `Success` subprogram. As a result,
a possible continuation of the above code is::

  if Success (DB) then
     ...
  else
     ...  --  an error occurred
  end if

GNATColl also tries to be helpful in the way it handles SQL
transactions. Such transactions are a way to execute your query in a
sandbox, ie without affecting the database itself until you decide to
`COMMIT` the query. Should you decide to abort it (or
`ROLLBACK` as they say for SQL), then it is just as if nothing
happened. As a result, it is in general recommended to do all your changes
to the database from within a transaction. If one of the queries fail
because of invalid parameters, you just rollback and report the error
to the user. The database is still left in a consistent state. As an
additional benefit, executing within a transaction is sometimes faster,
as is the case for PostgreSQL for instance.

To help with this, GNATColl will automatically start a
transaction the first time you edit the database. It is then your
responsibility to either commit or rollback the transaction when you
are done modifying. A lot of database engines (among which PostgreSQL)
will not accept any further change to the database if one command in
the transaction has failed. To take advantage of this, GNATColl
will therefore not even send the command to the server if it is in a
failure state.

Here is code sample that modifies the database::

  Execute (DB, SQL_Insert (...));
  --  Executed in the same transaction

  Commit_Or_Rollback (DB);
  --  Commit if both insertion succeeded, rollback otherwise
  --  You can still check Success(DB) afterward if needed

.. _Prepared_queries:

Prepared queries
================

The previous section showed how to execute queries and statements. But
these were in fact relatively inefficient.

With most DBMS servers, it is possible to compile the query once on the
server, and then reuse that prepared query to significantly speed up
later searches when you reuse that prepared statement.

.. highlight:: sql

It is of course pretty rare to run exactly the same query or statement
multiple times with the same values. For instance, the following query
would not give much benefit if it was prepared, since you are unlikely
to reuse it exactly as is later on::

  SELECT * FROM data WHERE id=1
  
SQL (and GNATColl) provide a way to parameterize queries. Instead
of hard-coding the value `1` in the example above, you would in fact
use a special character (unfortunately specific to the DBMS you are
interfacing to) to indicate that the value will be provided when the
query is actually executed. For instance, `sqlite` would use::

  SELECT * FROM data WHERE id=?

.. highlight:: ada
  
You can write such a query in a DBMS-agnostic way by using GNATColl.
Assuming you have automatically generated :file:`database.ads` by using
`gnatcoll_db2ada`, here is the corresponding Ada code::

     with Database;  use Database;

     Q : constant SQL_Query :=
         SQL_Select
            (Fields => Data.Id & Data.Name
             From   => Data,
             Where  => Data.Id = Integer_Param (1));

GNATColl provides a number of functions (one per type of
field) to indicate that the value is currently unbound. `Integer_Param`,
`Text_Param`, `Boolean_Param`,... All take a single argument,
which is the index of the corresponding parameter. A query might need
several parameters, and each should have a different index. On the other
hand, the same parameter could be used in several places in the query.

Although the query above could be executed as is by providing the values
for the parameters, it is more efficient, as we mentioned at the beginning,
to compile it on the server. In theory, this preparation is done within the
context of a database connection (thus cannot be done for a global variable,
where we do not have connections yet, and where the query might be executed
by any connection later on).

GNATColl will let you indicate that the query should be prepared.
This basically sets up some internal data, but does not immediately compile
it on the server. The first time the query is executed in a given
connection, though, it will first be compiled. The result of this compilation
will be reused for that connection from then on. If you are using a
second connection, it will do its own compilation of the query.

So in our example we would add the following global variable::

     P : constant Prepared_Statement :=
        Prepare (Q, On_Server => True);

Two comments about this code:

* You do not have to use global variables. You can prepare the
  statement locally in a subprogram. A `Prepared_Statement` is a
  reference counted type, that will automatically free the memory on the
  server when it goes out of scope.

* Here, we prepared the statement on the server. If we had specified
  `On_Server => False`, we would still have sped things up, since Q
  would be converted to a string that can be sent to the DBMS, and from
  then on reused that string (note that this conversion is specific to
  each DBMS, since they don't always represent things the same way, in
  particular parameters, as we have seen above). Thus every time you use
  P you save the time of converting from the GNATColl tree
  representation of the query to a string for the DBMS.

Now that we have a prepared statement, we can simply execute it.
If the statement does not require parameters, the usual `Fetch`
and `Execute` subprograms have versions that work exactly the same
with prepared statements. They also accept a `Params` parameter that
contains the parameter to pass to the server. A number of `"+"`
operators are provided to create those parameters::

     declare
        F : Forward_Cursor;
     begin
        F.Fetch (DB, P, Params => (1 => +2));
        F.Fetch (DB, P, Params => (1 => +3));
     end;

Note that for string parameters, the `"+"` operator takes an
access to a string. This is for efficiency, to avoid allocating memory
and copying the string, and is safe because the parameters are only needed
while `Fetch` executes (even for a `Forward_Cursor`.

Back to our library example. We showed earlier how to write a query that
retrieves the books borrowed by customer "Smith". We will now make this
query more general: given a customer name, return all the books he has
borrowed. Since we expect to use this often, we will prepare it on the
server (in real life, this query is of little interest since the customer
name is not unique, we would instead use a query that takes the id of the
customer). In general we would create a global variable with::

     Borrowed : constant Prepared_Statement := Prepare
       (SQL_Select
          (Fields => Books.Title & Books.Pages,
           Where  => Books.FK (Customers)
             and Customers.Last = Text_Param (1));
        Auto_Complete => True,
        On_Server => True);
  
Then when we need to execute this query, we would do::

    declare
       Name : aliased String := "Smith";
    begin
       R.Fetch (DB, Borrowed, Params => (1 => +Smith'Access));
    end;

There is one last property on `Prepared_Statement`s: when you
prepare them, you can pass a `Use_Cache => True` parameter. When this
is used, the result of the query will be cached by GNATColl, and
reuse when the query is executed again later. This is the fastest way
to get the query, but should be used with care, since it will not detect
changes in the database. The local cache is automatically invalidated
every hour, so the query will be performed again at most one hour later.
Local caching is disabled when you execute a query with parameters. In
this case, prepare the query on the server which will still be reasonably
fast.

Finally, here are some examples of timings. The exact timing are
irrelevant, but it is interesting to look at the different between the
various scenarios. Each of them performs 100_000 simple queries similar
to the one used in this section::

  Not preparing the query, using `Direct_Cursor`:
     4.05s

  Not preparing the query, using `Forward_Cursor`, and only
  retrieving the first row:
     3.69s

  Preparing the query on the client (`On_Server => False`),
  with a `Direct_Cursor`. This saves the whole `GNATCOLL.SQL`
  manipulations and allocations:
     2.50s

  Preparing the query on the server, using `Direct_Cursor`:
     0.55s

  Caching the query locally (`Use_Cache => True`):
     0.13s

.. _Getting_results:
  
Getting results
===============

Once you have executed a `SELECT` query, you generally need to
examine the rows that were returned by the database server. This is done
in a loop, as in::

  while Has_Row (R) loop
      Put_Line ("Max priority=" & Integer_Value (R, 0)'Img
               & " for category=" & Value (R, 1));
      Next (R);
  end loop;

You can only read one row at a time, and as soon as you have moved to the
next row, there is no way to access a previously fetched row. This is the
greatest common denominator between the various database systems. In
particular, it proves efficient, since only one row needs to be kept in
memory at any point in time.

For each row, we then call one of the `Value` or `*Value`
functions which return the value in a specific row and a specific
column.

We mentioned earlier there was no way to go back to a row you fetched
previously except by executing the query again. This is in fact only
true if you use a `Forward_Cursor` to fetch the results.

But GNATColl provides another notion, a `Direct_Cursor`. In
this case, it fetches all the rows in memory when the query executes (thus
it needs to allocate more memory to save every thing, which can be costly
if the query is big). This behavior is supported natively by `PostgreSQL`,
but doesn't exist with `sqlite`, so GNATColl will simulate it
as efficiently as possible. But it will almost always be faster to use
a `Forward_Cursor`.

In exchange for this extra memory overhead, you can now traverse the list
of results in both directions, as well as access a specific row directly.
It is also possible to know the number of rows that matched (something hard
to do with a `Forward_Cursor` since you would need to traverse the
list once to count, and then execute the query again if you need the rows
themselves).

In general, the low-level DBMS C API use totally different approaches for
the two types of cursors (when they even provide them). By contrast,
GNATColl makes it very easy to change from one to the other just
by changing the type of a the result variable. So you would in general
start with a `Forward_Cursor`, and if you discover you in fact need
more advanced behavior you can pay the extra memory cost and use a
`Direct_Cursor`.

For both types of cursors, GNATColl automatically manages memory
(both on the client and on the DBMS), thus providing major simplification of
the code compared to using the low-level APIs.

Creating your own SQL types
===========================

GNATColl comes with a number of predefined types that you can use in
your queries. :file:`gnatcoll_db2ada` will generate a file using any of these
predefined types, based on what is defined in your actual database.

But sometimes, it is convenient to define your own SQL types to better
represent the logic of your application. For instance, you might want to
define a type that would be for a `Character` field, rather than use
the general `SQL_Field_Text`, just so that you can write statements
like::

    declare
       C : Character := 'A';
       Q : SQL_Query;
    begin
       Q := SQL_Select (.., Where => Table.Field = C);
    end

This is fortunately easily achieved by instantiating one generic package,
as such::

    with GNATCOLL.SQL_Impl; use GNATCOLL.SQL_Impl;

    function To_SQL (C : Character) return String is
    begin
       return "'" & C & "'";
    end To_SQL;

    package Character_Fields is new Field_Types (Character, To_SQL);
    type SQL_Field_Character is new Character_Fields.Field
       with null record;
  

This automatically makes available both the field type (which you can use in
your database description, as :file:`gnatcoll_db2ada` would do, but also
all comparison operators like `<`, `>`, `=`, and so on, both
to compare with another character field, or with `Character` Ada
variable. Likewise, this makes available the assignment operator `=`
so that you can create `INSERT` statements in the database.

Finally, the package `Character_Fields` contain other generic
packages which you can instantiate to bind SQL operators and functions that
are either predefined in SQL and have no equivalent in GNATColl yet,
or that are functions that you have created yourself on your DBMS server.

See the specs of `GNATCOLL.SQL_Impl` for more details. This package
is only really useful when writing your own types, since otherwise you
just have to use `GNATCOLL.SQL` to write the actual queries.

Query logs
==========

In :ref:`Logging_information` we discovered the logging module of
GNATColl. The database interface uses this module to log the
queries that are sent to the server.

If you activate traces in your application, the user can then activate
one of the following trace handles to get more information on the
exchange that exists between the database and the application. As we saw
before, the output of these traces can be sent to the standard output, a
file, the system logs,...

The following handles are provided:

* SQL.ERROR
  This stream is activated by default. Any error returned by the database
  (connection issues, failed transactions,...) will be logged on this stream

* SQL
  This stream logs all queries that are not SELECT queries, ie mostly all
  queries that actually modify the database

* SQL.SELECT
  This stream logs all select queries. It is separated from SQL because
  very often you will be mostly interested in the queries that impact the
  database, and logging all selects can generate a lot of output.

In our library example, we would add the following code to see all SQL
statements executed on the server::

  with GNATCOLL.Traces;  use GNATCOLL.Traces;
  procedure Main is
  begin
     GNATCOLL.Traces.Parse_Config_File (".gnatdebug");
     ... --  code as before
     GNATCOLL.Traces.Finalize;  --  reclaim memory

and then create a .gnatdebug in the directory from which we launch our
executable. This file would contain a single line containing "+" to
activate all log streams, or the following to activate only the subset of
fields related to SQL::

  SQL=yes
  SQL.SELECT=yes
  SQL.LITE=yes

.. _Writing_your_own_cursors:
  
Writing your own cursors
========================

The cursor interface we just saw is low-level, in that you get access to
each of the fields one by one. Often, when you design your own application,
it is better to abstract the database interface layer as much as possible.
As a result, it is often better to create record or other Ada types to
represent the contents of a row.

Fortunately, this can be done very easily based on the API provided by
`GNATCOLL.SQL`. Note that `GNATCOLL.SQL.ORM` provides a similar
approach based on automatically generated code, so might be even better.
But it is still useful to understand the basics of providing your own
objects.

Here is a code example that shows how this can be done::

    type Customer is record
       Id   : Integer;
       First, Last : Unbounded_String;
    end record;

    type My_Cursor is new Forward_Cursor with null record;
    function Element (Self : My_Cursor) return My_Row;
    function Do_Query (DB, ...) return My_Cursor;

The idea is that you create a function that does the query for you (based
on some parameters that are not shown here), and then returns a cursor over
the resulting set of rows. For each row, you can use the `Element`
function to get an Ada record for easier manipulation.

Let's first see how these types would be used in practice::

    declare
      C : My_Cursor := Do_Query (DB, ...);
    begin
      while Has_Row (C) loop
         Put_Line ("Id = " & Element (C).Id);
         Next (C);
      end loop;
    end;

So the loop itself is the same as before, except we no longer access each of
the individual fields directly. This means that if the query changes to
return more fields (or the same fields in a differente order for instance),
the code in your application does not need to change.

The specific implementation of the subprograms could be similar to the
following subprograms (we do not detail the writing of the SQL query itself,
which of course is specific to your application)::

    function Do_Query return My_Cursor is
       Q : constant SQL_Query := ....;
       R : My_Cursor;
    begin
       R.Fetch (DB, Q);
       return R;
    end Do_Query;

    function Element (Self : My_Cursor) return My_Row is
    begin
      return Customer'
         (Id    => Integer_Value (Self, 0),
          First => To_Unbounded_String (Value (Self, 1)),
          Last  => To_Unbounded_String (Value (Self, 2)));
    end Element;
  

There is one more complex case though. It might happen that an element
needs access to several rows to fill the Ada record. For instance, if we
are writing a CRM application and query the contacts and the companies they
work for, it is possible that a contact works for several companies. The
result of the SQL query would then look like this::
  
     contact_id | company_id
         1      |    100    
         1      |    101    
         2      |    100    
  

The sample code shown above will not work in this case, since Element is
not allowed to modify the cursor. In such a case, we need to take a slightly
different approach::

      type My_Cursor is new Forward_Cursor with null record;
      function Do_Query return My_Cursor; --  as before
      procedure Element_And_Next
         (Self : in out My_Cursor; Value : out My_Row);
  
where `Element_And_Next` will fill Value and call Next as many times
as needed. On exit, the cursor is left on the next row to be processed. The
usage then becomes::

     while Has_Row (R) loop
        Element_And_Next (R, Value);
     end loop;
  
To prevent the user from using Next incorrectly, you should probably override
`Next` with a procedure that does nothing (or raises a Program_Error
maybe). Make sure that in `Element_And_Next` you are calling the
inherited function, not the one you have overridden, though.

There is still one more catch. The user might depend on the two subprograms
`Rows_Count` and `Processed_Rows` to find out how many rows there
were in the query. In practice, he will likely be interested in the number
of distinct contacts in the tables (2 in our example) rather than the number
of rows in the result (3 in the example). You thus need to also override
those two subprograms to return correct values.

.. _The_object_relational_mapping_layer:

The Object-Relational Mapping layer (ORM)
=========================================

GNATColl provides a high-level interface to manipulate persistent
objects stored in a database, using a common paradigm called an
object-relational mapping. Such mappings exist for most programming
languages. In the design of GNATColl, we were especially inspired
by the python interface in `django` and `sqlalchemy`, although the
last two rely on dynamic run time introspection and GNATColl relies
on code generation instead.

This API is still compatible with `GNATCOLL.SQL`. In fact, we'll
show below cases where the two are mixed. It can also be mixed with
`GNATCOLL.SQL.Exec`, although this might be more risky. Communication
with the DBMS is mostly transparent in the ORM, and it uses various caches
to optimize things and make sure that if you modify an element the next
querie(s) will also return it. If you use `GNATCOLL.SQL.Exec` directly
you are bypassing this cache so you risk getting inconsistent results in
some cases.

In ORM, a table is not manipulated directly. Instead, you manipulate objects
that are read or written to a table. When we defined our database schema
(:ref:`Database_schema`), we gave two names on the first line of a table
definition. There was the name of the table in the database, and the name
of the object that each row represent. So for our library example we have
defined `Customer`, `Book` and `Dvd` objects. These objects
are declared in a package generated automatically by `gnatcoll_db2ada`.

There is first one minor change we need to do to our library example. The
ORM currently does not handle properly cases where an abstract class has
foreign keys to other tables. So we remove the `borrowed_by` field
from the `Media` table, and change the `books` table to be::


  | TABLE (media) | books                        | book |     | The books in the library |
  | pages         | INTEGER                      |      | 100 |                          |
  | borrowed_by   | FK customers(borrowed_books) | NULL |     | Who borrowed the media   |
  

Let's thus start by generating this code. We can replace the command we
ran earlier (with the `-api` switch) with one that will also generate
the ORM API::

     gnatcoll_db2ada -dbmode dbschema.txt -api Database -orm ORM

The ORM provides a pool of database connections through the package
`GNATCOLL.SQL.Sessions`. A session therefore acts as a wrapper around
a connection, and provides a lot more advanced features that will be
described later. The first thing to do in the code is to configure the
session pool. The `Setup` procedure takes a lot of parameters to
make sessions highly configurable. Some of these parameters will be
described and used in this documentation, others are for special usage and
are only documented in :file:`gnatcoll-sql-sessions.ads`. Here will we
use only specify the mandatory parameters and leave the default value for
the other parameters::

    GNATCOLL.SQL.Sessions.Setup
       (Descr  => GNATCOLL.SQL.Sqlite.Setup ("library.db"),
        Max_Sessions => 2);
  

The first parameter is the same `Database_Description` we saw
earlier (:ref:`Connecting_to_the_database`), but it will be freed
automatically by the sessions package, so you should not free it
yourself.

Once configure, we can now request a session. Through a session, we can
perform queries on the database, make objects persistent, write the
changes back to the database,.... We configured the session pool
to have at most 2 sessions. The first time we call `Get_New_Session`,
a new session will be created in the pool and marked as busy. While you
have a reference to it in your code (generally as a local variable), the
session belongs to this part of the code. When the session is no longer
in scope, it is automatically released to the pool to be reused for the
next call to `Get_New_Session`. If you call `Get_New_Session`
a second time while some part of your code holds a session (for instance
in a different task), a new session will be created. But if you do that
a third time while the other two are busy, the call to `Get_New_Session`
is blocking until one of the two sessions is released to the pool.

This technics ensures optimal use of the resources: we avoid creating
a new session every time (with the performance cost of connecting to the
database), but also avoid creating an unlimited number of sessions which
could saturate the server. Since the sessions are created lazily the first
time they are needed, you can also configure the package with a large
number of sessions with a limited cost.

Let's then take a new session in our code::

     Session : constant Session_Type := Get_New_Session;

and let's immediately write our first simple query. A customer comes at
the library, handles his card and we see his id (1). We need to look up
in the database to find out who he is. Fortunately, there is no SQL to
write for this::

    C : ORM.Detached_Customer'Class := Get_Customer (Session, Id => 1);

The call to `Get_Customer` performs a SQL query transparently, using
prepared statements for maximum efficiency. This results in a
`Customer` object.

`ORM` is the package that was generated automatically by
`gnatcoll_db2ada`. For each table in the database, it generates a
number of types:

* `Customer`

  This type represents a row of the `Customers` table. It comes with
  a number of primitive operations, in particular one for each of the
  fields in the table. Such an object is returned by a cursor, similarly
  to what was described in the previous section (:ref:`Writing_your_own_cursors`).
  This object is no longer valid as soon as the cursor moves to
  the next row (in the currently implementation, the object will describe
  the next row, but it is best not to rely on this). As a benefit, this
  object is light weight and does not make a copy of the value of the
  fields, only reference the memory that is already allocated for the cursor.

  This object redefines the equality operator ("=") to compare the
  primary key fields to get expected results.

* `Detached_Customer`

  A detached object is very similar to the `Customer` object, but it
  will remain valid even if the cursor moves or is destroyed. In fact, the
  object has made a copy of the value for all of its fields. This object
  is heavier than a `Customer`, but sometimes easier to manager. If
  you want to store an object in a data structure, you must always store
  a detached object.

  A detached object also embeds a cache for its foreign keys. In the
  context of our demo for instance, a `Book` object was borrowed by
  a customer. When returning from a query, the book knows the id of that
  customer. But if call `B.Borrowed_By` this returns a
  `Detached_Customer` object which is cached (the first time, a query
  is made to the DBMS to find the customer given his id, but the second
  time this value is already cached).

  One cache create a `Detached_Customer` from a `Customer` by
  calling the `Detach` primitive operation.

* `Customer_List`

  This type extends a `Forward_Cursor` (:ref:`Getting_results`). In
  addition to the usual `Has_Row` and `Next` operations, it also
  provides an `Element` operation that returns a `Customer` for
  easy manipulation of the results.

* `Direct_Customer_List`

  This type extends a `Direct_Cursor`. It also adds a `Element`
  operation that returns a `Customer` element.

* `Customers_Managers`

  This type is the base type to perform queries on the DBMS. A manager
  provides a number of primitive operations which end up creating a SQL
  query operation in the background, without making that explicit.

  Let's first write a query that returns all books in the database::

    declare
       M : Books_Managers := All_Books;
       BL : Book_List := M.Get (Session);
       B : Book;
    begin
       while BL.Has_Row loop
          B := BL.Element;
          Put_Line ("Book: " & B.Title);
          Put_Line ("   Borrowed by: " & B.Borrowed_By.Last);
          BL.Next;
       end loop;
    end;

  The manager `M` corresponds to a query that returns all the books
  in the database. The second line then executes the query on the database,
  and returns a list of books. We then traverse the list. Note how we access
  the book's title by calling a function, rather than by the index of a
  field as we did with `GNATCOLL.SQL.Exec` with Value(B, 0). The code
  is much less fragile this way.

  The line that calls `Borrowed_By` will execute an additional SQL
  query for each book. This might be inefficient if there is a large number
  of books. We will show later how this can be optimized.

  The manager however has a lot more primitive operations that can be used
  to alter the result. Each of these primitive operations returns a modified
  copy of the manager, so that you can easily chain calls to those primitive
  operations. Those operations are all declared in the package
  `GNATCOLL.SQL.ORM.Impl` if you want to look at the documentation.
  Here are those operations:

  * `Get` and `Get_Direct`

    As seen in the example above, these are the two functions that execute the
    query on the database, and returns a list of objects (respectively a
    `Customer_List` and a `Direct_Customer_List`).

  * `Distinct`

    Returns a copy of the manager that does not return twice a row with the
    same data (in SQL, this is the "DISTINCT" operator)

  * `Limit` (Count : Natural; From : Natural := 0)

    Returns a copy of the manager that returns a subset of the results, for
    instance the first `Count` ones.

  * `Order_By` (By : SQL_Field_List)

    Returns a copy of the manager that sorts the results according to a criteria.
    The criteria is a list of field as was defined in `GNATCOLL.SQL`.
    We can for instance returns the list of books sorted by title, and only the
    first 5 books, by replacing `M` with the following::

         M : Books_Managers := All_Books.Limit (5).Order_By (Books.Title);
      

  * `Filter`

    Returns a subset of the result matching a criteria. There are currently
    two versions of Filter: one is specialized for the table, and has one
    parameter for each field in the table. We can for instance return all the
    books by Alexandre Dumas by using::

         M : Books_Managers := All_Books.Filter (Author => "Dumas");
      

    This version only provides the equality operator for the fields of the
    table itself. If for instance we wanted all books with less than 50 pages,
    we would use the second version of filter. This version takes a
    `GNATCOLL.SQL.SQL_Criteria` similar to what was explained in previous
    sections, and we would write::

         M : Books_Managers := All_Books.Filter (Condition => Books.Pages < 50);

    More complex conditions are possible, involving other tables. Currently,
    the ORM does not have a very user-friendly interface for those, but you
    can always do this by falling back partially to SQL. For instance, if we
    want to retrieve all the books borrowed by user "Smith", we need to
    involve the `Customers` table, and thus make a join with the
    `Books` table. In the future, we intend to make this join automatic,
    but for now you will need to write::

         M : Books_Managers := All_Books.Filter
           (Books.FK (Customers)
            and Customers.Last = "Smith");

         -- SQL query: SELECT books.pages, books.borrowed_by, books.id,
         --    books.title, books.author, books.published
         --    FROM books, customers
         --    WHERE books.borrowed_by=customers.id AND customers.last='Smith'

    This is still simpler code than we were writting with `GNATCOLL.SQL`
    because we do not have to specify the fields or tables, and the results
    are objects rather than fields with specific indexes.

  * `Select_Related` (Depth : Integer; Follow_Left_Join : Boolean)

    This function returns a new manager that will retrieve all related
    objects. In the example we gave above, we mentioned that every time
    `B.Borrowed_By` was called, this resulted in a call to the DBMS.
    We can optimize this by making sure the manager will retrieve that
    information. As a result, there will be a single query rather than lots.
    Be careful however, since the query will return more data, so it might
    sometimes be more efficient to perform multiple smaller queries.

    `Depth` indicates on how many levels the objects should be retrieved.
    For instance, assume we change the schema such that a Book references
    a Customer which references an Address. If we pass 1 for `Depth`,
    the data for the book and the customer will be retrieved. If however you
    then call `B.Borrowed_By.Address` this will result in a query. So
    if you pass 2 for `Depth` the data for book, customers and addresses
    will be retrieved.

    The second parameter related to efficiency. When a foreign key was mentioned
    as `NOT NULL` in the schema, we know it is always pointing to an
    existing object in another table. `Select_Related` will always
    retrieve such objects. If, however, the foreign key can be null, ie there
    isn't necessarily a corresponding object in the other table, the SQL
    query needs to use a `LEFT JOIN`, which is less efficient.  By default,
    GNATColl will not retrieve such fields unless `Follow_Left_Join`
    was set to True.

    In our example, a book is not necessarily borrowed by a customer, so we need
    to follow the left joins::

         M : Books_Managers := All_Books.Filter
           (Books.FK (Customers)
            and Customers.Last = "Smith")
           .Select_Related (1, Follow_Left_Join => True);

         -- SQL query:  SELECT books.pages, books.borrowed_by, books.id,
         --    books.title, books.author, books.published,
         --    customers.id, customers.first, customers.last
         --    FROM (books LEFT JOIN customers ON books.borrowed_by=customers.id)
         --    WHERE books.borrowed_by=customers.id AND customers.last='Smith'
      
reverse relationships
--------------------

In fact, the query we wrote above could be written differently. Remember
we have already queries the `Customer` object for id 1 through a
call to `Get_Customer`. Since our schema specified a `reverse_name`
for the foreign key `borrowed_by` in the table `books`, we can
in fact simply use::

     BL := C.Borrowed_Books.Get (Session);

     -- SQL: SELECT books.pages, books.borrowed_by, books.id, books.title,
     --    books.author, books.published FROM books
     --    WHERE books.borrowed_by=1
  

`Borrowed_Books` is a function that was generated because there was
a `reverse_name`. It returns a `Books_Managers`, so we could
in fact further filter the list of borrowed books with the same primitive
operations we just saw. As you can see, the resulting SQL is optimital.

Let's optimize further the initial query. We have hard-coded the
customer name, but in fact we could be using the same subprograms we
were using for prepared statements (:ref:`Prepared_queries`), and even
prepare the query on the server for maximum efficiency. Since our application
is likely to use this query a lot, let's create a global variable::

     M : constant Books_Managers := All_Books.Filter
       (Books.FK (Customers)
        and Customers.Id = Integer_Param (1))
       .Select_Related (1, Follow_Left_Join => True);

     MP : constant ORM_Prepared_Statement :=
       M.Prepare (On_Server => True);

     ... later in the code

     Smith_Id : constant Natural := 1;
     BL : Book_List := MP.Get (Session, Params => (1 => Smith_Id));
  

The last call to `Get` is very efficient, with timing improvements
similar to the ones we discussed on the session about prepared statements
(:ref:`Prepared_queries`).

Modifying objects in the ORM
============================

The ORM is much more than writing queries. Once the objects are persistent,
they can also be simplify modified, and they will be saved in the database
transparently.

Let's start with a simple example. In the previous section, we retrieve an
object `C` representing a customer. Let's change his name, and make
sure the change is in the database::

     C := Get_Customer (Session, 1);
     C.Set_Last ("Smit");
     C.Set_First ("Andrew");
     Session.Commit;

A reasonable way to modify the database. However, this opens a can of
complex issues that need to be dealt with.

When we called `Set_Last`, this modify the objects in memory. At this
point, printing the value of `C.Last` would indeed print the new value
as expected. The object was also marked as modified. But no change was
made in the database.

Such a change in the database might in fact be rejected, depending on
whether there are constraints on the field. For instance, say there existed
a constraint that `Last` must be the same `First` (bear with me,
this is just an example). If we call `Set_Last`, the constraint is
not satisfied until we also call `Set_First`. But if the former resulted
in an immediate change in the database, it would be rejected and we would not
even get a change to call `Set_First`.

.. highlight:: sql

Instead, the session keeps a pointer to all the objects that have been
modified. When it is committed, it traverses this list of objects, and
commits their changes into the database. In the example we gave above, the
call to `Commit` will thus commit the changes to `C` in the
database. For efficiency, it uses a single SQL statement for that, which also
ensures the constraint remains valid::

  UPDATE customers SET first='Andrew', last='Smit' WHERE customers.id=1;
  

.. highlight:: ada

We can create a new customer by using similar code::

     C := New_Customer;
     C.Set_First ("John");
     C.Set_Last ("Lee");
     Session.Persist (C);

     Session.Commit;

`New_Customer` allocates a new object in memory. However, this object
is not persistent. You can call all the `Set_*` subprograms, but the
object will not be saved in the database until you add it explicitly to
a session with a call to `Persist`, and then `Commit` the
session as usual.

Another issue can occur when objects can be modified in memory. Imagine
we retrieve a customer, modify it in memory but do not commit to the
database yet because there are other changes we want to do in the same
SQL transaction. We then retrieve the list of all customers. Of course,
the customer we just modified is part of this list, but the DBMS does not
know about the change which currently only exists in memory.

Thankfully,
GNATColl takes care of this issue automatically: as we mentioned
before, all modified objects are stored in the session. When traversing
the list of results, the cursors will check whether the session already
contains an element with the same id that it sees in the result, and if
yes will return the existing (i.e. modified) element. For instance::

     C := Get_Customer (Session, Id => 1);
     C.Set_Last ("Lee");

     CL : Customer_List := All_Customers.Get (Session);
     while CL.Has_Row loop
        Put_Line (CL.Element.Last);
        CL.Next;
     end loop;
  
.. index:: Flush_Before_Query

The above example uses `CL.Element`, which is a light-weight
`Customer` object. Such objects will only see the in-memory changes
if you have set `Flush_Before_Query` to true when you configured
the sessions in the call to `GNATCOLL.SQL.Sessions.Setup`. Otherwise,
it will always return what's really in the database.

If the example was using `Detached_Customer` object (by calling
`CL.Element.Detach` for instance) then GNATColl looks up in
its internal cache and returns the cached element when possible. This is
a subtetly, but this is because an `Customer` only exists as long as
its cursor, and therefore cannot be cached in the session. In practive, the
`Flush_Before_Query` should almost always be true and there will be
not surprising results.

Object factories in ORM
=======================

Often, a database table is used to contain objects that are semantically
of a different kind. In this section, we will take a slightly different
example from the library. We no longer store the books and the dvds in
separate tables. Instead, we have one single `media` table which
contains the title and the author, as well as a new field `kind`
which is either 0 for a book or 1 for a dvd.

Let's now look at all the media borrowed by a customer::

     C : constant Customer'Class := Get_Customer (Session, Id => 1);
     ML : Media_List := C.Borrowed_Media.Get (Session);

     while ML.Has_Row loop
        case ML.Element.Kind is
           when 0 =>
              Put_Line ("A book " & ML.Element.Title);
           when 1 =>
              Put_Line ("A dvd " & ML.Element.Title);
        end case;
        ML.Next;
     end loop;

This code works, but requires a case statement. Now, let's imagine
the check out procedure is different for a book and a DVD (for the latter
we need to check that the disk is indeed in the box). We would have two
subprograms `Checkout_Book` and `Checkout_DVD` and call them
from the case. This isn't object-oriented programming.

Instead, we will declare two new types::

     type My_Media is abstract new ORM.Detached_Media with private;
     procedure Checkout (Self : My_Media) is abstract;

     type Detached_Book is new My_Media with private;
     overriding Checkout (Self : Detached_Book);

     type Detached_DVD is new My_Media with private;
     overriding Checkout (Self : Detached_DVD);

We could manually declare a new Media_List and override `Element` so
that it returns either of the two types instead of a `Media`.
But then we would also need to override `Get` so that it returns our
new list. This is tedious.

We will instead use an element factory in the session. This is a function
that gets a row of a table (in the form of a `Customer`), and returns
the appropriate type to use when the element is detached (by default,
the detached type corresponding to a `Customer` is a
`Detached_Customer`, and that's what we want to change).

So let's create such a factory::

  function Media_Factory
     (From    : Base_Element'Class;
      Default : Detached_Element'Class) return Detached_Element'Class
  is
  begin
     if From in Media'Class then
        case Media (From).Kind is
           when 0 => 
              return R : Detached_Book do null; end return;
           when 1 =>
              return R : Detached_DVD do null; end return;
           when others =>
              return Default;
        end case;
     end if;
     return Default;
  end Media_Factory;

  Session.Set_Factory (Media_Factory'Access);
  
This function is a bit tricky. It is associated with a given session (although
we can also register a default factory that will be associated with all
sessions by default). For all queries done through this session (and for
all tables) it will be called. So we must first check whether we are dealing
with a row from the `Media` table. If not, we simply return the
suggested `Default` value (which has the right `Detached_*` kind
corresponding to the type of `From`).

If we have a row from the `Media` table, we then retrieve its kind
(through the usual automatically generated function) to return an
instance of `Detached_Book` or `Detached_DVD`. We use the
Ada05 notation for extended return statements, but we could also use a
declare block with a local variable and return that variable. The returned
value does not need to be further initialized (the session will take care
of the rest of the initialization).

We can now write our code as such::

     C : constant Customer'Class := Get_Customer (Session, Id => 1);
     ML : Media_List := C.Borrowed_Media.Get (Session);

     while ML.Has_Row loop
        Checkout (ML.Element.Detach);   --  Dispatching
        ML.Next;
     end loop;

The loop is cleaner. Of course, we still have the case statement, but it
now only exists in the factory, no matter how many loops we have or how
many primitive operations of the media we want to define.

