.. -*- coding: utf-8 -*-

Yams *schema*

-------------

The **schema** is the core piece of a *CubicWeb* instance as it

defines and handles the data model. It is based on entity types that

are either already defined in `Yams`_ and the *CubicWeb* standard

library; or more specific types defined in cubes. The schema for a

cube is defined in a `schema` python module or package.

.. _`Yams`: http://www.logilab.org/project/yams

Overview

~~~~~~~~

The core idea of the yams schema is not far from the classical

`Entity-relationship`_ model. But while an E/R model (or `logical

model`) traditionally has to be manually translated to a lower-level

data description language (such as the SQL `create table`

sublanguage), also often described as the `physical model`, no such

step is required with |yams| and |cubicweb|.

.. _`Entity-relationship`: http://en.wikipedia.org/wiki/Entity-relationship_model

This is because in addition to high-level, logical |yams| models, one

uses the |rql| data manipulation language to query, insert, update and

delete data. |rql| abstracts as much of the underlying SQL database as

a |yams| schema abstracts from the physical layout. The vagaries of

SQL are avoided.

As a bonus point, such abstraction make it quite comfortable to build

or use different backends to which |rql| queries apply.

So, as in the E/R formalism, the building blocks are ``entities``

(:ref:`EntityType`), ``relationships`` (:ref:`RelationType`,

:ref:`RelationDefinition`) and ``attributes`` (handled like relation

with |yams|).

Let us detail a little the divergences between E/R and |yams|:

* all relationship are binary which means that to represent a

  non-binary relationship, one has to use an entity,

* relationships do not support attributes (yet, see:

  http://www.cubicweb.org/ticket/341318), hence the need to reify it

  as an entity if need arises,

* all entities have an `eid` attribute (an integer) that is its

  primary key (but it is possible to declare uniqueness on other

  attributes)

Also |yams| supports the notions of:

* relation type: that is, relationships can be established over a set

  of couple of entity types (henre the distinction made between

  `RelationType` and `RelationDefinition` below)

Finally |yams| has a few concepts of its own:

* relationships being oriented and binary, we call the left hand

  entity type the `subject` and the right hand entity type the

  `object`

.. note::

   The |yams| schema is available at run time through the .schema

   attribute of the `vregistry`.  It's an instance of

   :class:`cubicweb.schema.Schema`, which extends

   :class:`yams.schema.Schema`.

.. _EntityType:

Entity type

~~~~~~~~~~~

An entity type is an instance of :class:`yams.schema.EntitySchema`. Each entity type has

a set of attributes and relations, and some permissions which define who can add, read,

update or delete entities of this type.

The following built-in types are available: ``String``, ``Int``,

``Float``, ``Decimal``, ``Boolean``, ``Date``, ``Datetime``, ``Time``,

``Interval``, ``Byte`` and ``Password``. They can only be used as

attributes of an other entity type.

You can find more base entity types in

:ref:`pre_defined_entity_types`.

.. XXX yams inheritance

.. _RelationType:

Relation type

~~~~~~~~~~~~~

A relation type is an instance of

:class:`yams.schema.RelationSchema`. A relation type is simply a

semantic definition of a kind of relationship that may occur in an

application.

It may be referenced by zero, one or more relation definitions.

It is important to choose a good name, at least to avoid conflicts

with some semantically different relation defined in other cubes

(since there's only a shared name space for these names).

A relation type holds the following properties (which are hence shared

between all relation definitions of that type):

* `inlined`: boolean handling the physical optimization for archiving

  the relation in the subject entity table, instead of creating a specific

  table for the relation. This applies to relations where cardinality

  of subject->relation->object is 0..1 (`?`) or 1..1 (`1`) for *all* its relation

  definitions.

* `symmetric`: boolean indicating that the relation is symmetrical, which

  means that `X relation Y` implies `Y relation X`.

.. _RelationDefinition:

Relation definition

~~~~~~~~~~~~~~~~~~~

A relation definition is an instance of

:class:`yams.schema.RelationDefinition`. It is a complete triplet

"<subject entity type> <relation type> <object entity type>".

When creating a new instance of that class, the corresponding

:class:`RelationType` instance is created on the fly if necessary.

Properties

``````````

The available properties for relation definitions are enumerated

here. There are several kind of properties, as some relation

definitions are actually attribute definitions, and other are not.

Some properties may be completely optional, other may have a default

value.

Common properties for attributes and relations:

* `description`: an unicode string describing an attribute or a

  relation. By default this string will be used in the editing form of

  the entity, which means that it is supposed to help the end-user and

  should be flagged by the function `_` to be properly

  internationalized.

* `constraints`: a list of conditions/constraints that the relation has to

  satisfy (c.f. `Constraints`_)

* `cardinality`: a two character string specifying the cardinality of

  the relation. The first character defines the cardinality of the

  relation on the subject, and the second on the object. When a

  relation can have multiple subjects or objects, the cardinality

  applies to all, not on a one-to-one basis (so it must be

  consistent...). Default value is '**'. The possible values are

  inspired from regular expression syntax:

    * `1`: 1..1

    * `?`: 0..1

    * `+`: 1..n

    * `*`: 0..n

Attributes properties:

* `unique`: boolean indicating if the value of the attribute has to be

  unique or not within all entities of the same type (false by

  default)

* `indexed`: boolean indicating if an index needs to be created for

  this attribute in the database (false by default). This is useful

  only if you know that you will have to run numerous searches on the

  value of this attribute.

* `default`: default value of the attribute. In case of date types, the values

  which could be used correspond to the RQL keywords `TODAY` and `NOW`.

Properties for `String` attributes:

* `fulltextindexed`: boolean indicating if the attribute is part of

  the full text index (false by default) (*applicable on the type

  `Byte` as well*)

* `internationalizable`: boolean indicating if the value of the

  attribute is internationalizable (false by default)

Relation properties:

* `composite`: string indicating that the subject (composite ==

  'subject') is composed of the objects of the relations. For the

  opposite case (when the object is composed of the subjects of the

  relation), we just set 'object' as value. The composition implies

  that when the relation is deleted (so when the composite is deleted,

  at least), the composed are also deleted.

* `fulltext_container`: string indicating if the value if the full

  text indexation of the entity on one end of the relation should be

  used to find the entity on the other end. The possible values are

  'subject' or 'object'. For instance the use_email relation has that

  property set to 'subject', since when performing a full text search

  people want to find the entity using an email address, and not the

  entity representing the email address.

Constraints

```````````

By default, the available constraint types are:

General Constraints

......................

* `SizeConstraint`: allows to specify a minimum and/or maximum size on

  string (generic case of `maxsize`)

* `BoundConstraint`: allows to specify a minimum and/or maximum value

  on numeric types and date

.. sourcecode:: python

   from yams.constraints import BoundConstraint, TODAY

   BoundConstraint('<=', TODAY())

* `IntervalBoundConstraint`: allows to specify an interval with

  included values

.. sourcecode:: python

     class Node(EntityType):

         latitude = Float(constraints=[IntervalBoundConstraint(-90, +90)])

* `UniqueConstraint`: identical to "unique=True"

* `StaticVocabularyConstraint`: identical to "vocabulary=(...)"

.. XXX Attribute, NOW

RQL Based Constraints

......................

RQL based constraints may take three arguments. The first one is the ``WHERE``

clause of a RQL query used by the constraint. The second argument ``mainvars``

is the ``Any`` clause of the query. By default this include `S` reserved for the

subject of the relation and `O` for the object. Additional variables could be

specified using ``mainvars``. The argument expects a single string with all

variable's name separated by spaces. The last one, ``msg``, is the error message

displayed when the constraint fails. As RQLVocabularyConstraint never fails the

third argument is not available.

* `RQLConstraint`: allows to specify a RQL query that has to be satisfied

  by the subject and/or the object of relation. In this query the variables

  `S` and `O` are reserved for the relation subject and object entities.

* `RQLVocabularyConstraint`: similar to the previous type of constraint except

  that it does not express a "strong" constraint, which means it is only used to

  restrict the values listed in the drop-down menu of editing form, but it does

  not prevent another entity to be selected.

* `RQLUniqueConstraint`: allows to the specify a RQL query that ensure that an

  attribute is unique in a specific context. The Query must **never** return more

  than a single result to be satisfied. In this query the variables `S` is

  reserved for the relation subject entity. The other variables should be

  specified with the second constructor argument (mainvars). This constraints

  should be used when UniqueConstraint doesn't fit. Here is a simple example.

.. sourcecode:: python

    # Check that in the same Workflow each state's name is unique.  Using

    # UniqueConstraint (or unique=True) here would prevent states in different

    # workflows to have the same name.

    # With: State S, Workflow W, String N ; S state_of W, S name N

    RQLUniqueConstraint('S name N, S state_of WF, Y state_of WF, Y name N',

                        mainvars='Y',

                        msg=_('workflow already has a state of that name'))

.. XXX note about how to add new constraint

.. _securitymodel:

The security model

~~~~~~~~~~~~~~~~~~

The security model of `CubicWeb` is based on `Access Control List`.

The main principles are:

* users and groups of users

* a user belongs to at least one group of user

* permissions (read, update, create, delete)

* permissions are assigned to groups (and not to users)

For *CubicWeb* in particular:

* we associate rights at the entities/relations schema level

* for each entity, we distinguish four kinds of permissions: `read`,

  `add`, `update` and `delete`

* for each relation, we distinguish three kinds of permissions: `read`,

  `add` and `delete` (it is not possible to `modify` a relation)

* the default groups are: `administrators`, `users` and `guests`

* by default, users belong to the `users` group

* there is a virtual group called `owners` to which we

  can associate only `delete` and `update` permissions

  * we can not add users to the `Owners` group, they are

    implicitly added to it according to the context of the objects

    they own

  * the permissions of this group are only checked on `update`/`delete`

    actions if all the other groups the user belongs to do not provide

    those permissions

Setting permissions is done with the attribute `__permissions__` of entities and

relation types. The value of this attribute is a dictionary where the keys are the access types

(action), and the values are the authorized groups or expressions.

For an entity type, the possible actions are `read`, `add`, `update` and

`delete`.

For a relation type, the possible actions are `read`, `add`, and `delete`.

For each access type, a tuple indicates the name of the authorized groups and/or

one or multiple RQL expressions to satisfy to grant access. The access is

provided if the user is in one of the listed groups or if one of the RQL condition

is satisfied.

The standard user groups

````````````````````````

* `guests`

* `users`

* `managers`

* `owners`: virtual group corresponding to the entity's owner.

  This can only be used for the actions `update` and `delete` of an entity

  type.

It is also possible to use specific groups if they are defined in the

precreate script of the cube (``migration/precreate.py``). Defining groups in

postcreate script or later makes them unavailable for security

purposes (in this case, an `sync_schema_props_perms` command has to

be issued in a CubicWeb shell).

Use of RQL expression for write permissions

```````````````````````````````````````````

It is possible to define RQL expression to provide update permission

(`add`, `delete` and `update`) on relation and entity types.

RQL expression for entity type permission:

* you have to use the class `ERQLExpression`

* the used expression corresponds to the WHERE statement of an RQL query

* in this expression, the variables `X` and `U` are pre-defined references

  respectively on the current entity (on which the action is verified) and

  on the user who send the request

* it is possible to use, in this expression, a special relation

  "has_<ACTION>_permission" where the subject is the user and the

  object is any variable, meaning that the user needs to have

  permission to execute the action <ACTION> on the entities related

  to this variable

For RQL expressions on a relation type, the principles are the same except

for the following:

* you have to use the class `RRQLExpression` in the case of a non-final relation

* in the expression, the variables `S`, `O` and `U` are pre-defined references

  to respectively the subject and the object of the current relation (on

  which the action is being verified) and the user who executed the query

* we can also define rights over attributes of an entity (non-final relation),

  knowing that:

  - to define RQL expression, we have to use the class `ERQLExpression`

    in which `X` represents the entity the attribute belongs to

  - the permissions `add` and `delete` are equivalent. Only `add`/`read`

    are actually taken in consideration.

.. note::

  Potentially, the `use of an RQL expression to add an entity or a

  relation` can cause problems for the user interface, because if the

  expression uses the entity or the relation to create, then we are

  not able to verify the permissions before we actually add the entity

  (please note that this is not a problem for the RQL server at all,

  because the permissions checks are done after the creation). In such

  case, the permission check methods (CubicWebEntitySchema.check_perm

  and has_perm) can indicate that the user is not allowed to create

  this entity but can obtain the permission.  To compensate this

  problem, it is usually necessary, for such case, to use an action

  that reflects the schema permissions but which enables to check

  properly the permissions so that it would show up if necessary.

Use of RQL expression for reading rights

````````````````````````````````````````

The principles are the same but with the following restrictions:

* we can not use `RRQLExpression` on relation types for reading

* special relations "has_<ACTION>_permission" can not be used

Defining your schema using yams

-------------------------------

Entity type definition

~~~~~~~~~~~~~~~~~~~~~~

An entity type is defined by a Python class which inherits from

:class:`yams.buildobjs.EntityType`.  The class definition contains the

description of attributes and relations for the defined entity type.

The class name corresponds to the entity type name. It is expected to

be defined in the module ``mycube.schema``.

:Note on schema definition:

 The code in ``mycube.schema`` is not meant to be executed. The class

 EntityType mentioned above is different from the EntitySchema class

 described in the previous chapter. EntityType is a helper class to

 make Entity definition easier. Yams will process EntityType classes

 and create EntitySchema instances from these class definitions. Similar

 manipulation happen for relations.

When defining a schema using python files, you may use the following shortcuts:

- `required`: boolean indicating if the attribute is required, ed subject cardinality is '1'

- `vocabulary`: specify static possible values of an attribute

- `maxsize`: integer providing the maximum size of a string (no limit by default)

For example:

.. sourcecode:: python

  class Person(EntityType):

    """A person with the properties and the relations necessary for my

    application"""

    last_name = String(required=True, fulltextindexed=True)

    first_name = String(required=True, fulltextindexed=True)

    title = String(vocabulary=('Mr', 'Mrs', 'Miss'))

    date_of_birth = Date()

    works_for = SubjectRelation('Company', cardinality='?*')

The entity described above defines three attributes of type String,

last_name, first_name and title, an attribute of type Date for the date of

birth and a relation that connects a `Person` to another entity of type

`Company` through the semantic `works_for`.

:Naming convention:

 Entity class names must start with an uppercase letter. The common

 usage is to use ``CamelCase`` names.

 Attribute and relation names must start with a lowercase letter. The

 common usage is to use ``underscore_separated_words``. Attribute and

 relation names starting with a single underscore are permitted, to