The Database Abstraction Layer

– Ok, what if I need to use a database in my application?
you can use the included DAL

weppy integrates pyDAL as the preferred database abstraction layer, which gives you the ability to use a database in your application, writing the same code and using the same syntax independently on which of the available adapters you want to use for deploy your app (you just need to install one of the supported drivers):

Supported DBMS python driver(s)
SQLite sqlite3, pysqlite2, zxjdbc
PostgreSQL psycopg2, pg8000, zxjdbc
MySQL pymysql, mysqldb
Oracle cxoracle
MSSQL pyodbc
FireBird kinterbasdb, fdb, pyodbc
DB2 pyodbc
Informix informixdb
Ingres ingresdbi
Cubrid cubridb
Sybase Sybase
Teradata pyodbc
SAPDB sapdb
MongoDB pymongo

But how do you use it? Let's see it with an example:

from weppy import App
from weppy.dal import DAL, Model, Field

app = App(__name__)
app.config.db.uri = "sqlite://storage.sqlite"

class Post(Model):
    author = Field()
    title = Field()
    body = Field('text')

db = DAL(app)

app.common_handlers = [db.handler]

def post_by(author):
    posts = db( == author).select()
    return dict(posts=posts)

The above code is quite simple: the post_by() function list posts from a specific author. Let's reconstruct what we done in those simple lines:

  • we added an sqlite database to our application, stored on file storage.sqlite
  • we defined the Post model and its properties, which will create a posts table
  • we registered the database handler to our application so that it will be available during requests
  • we did a select on the posts table querying the author column

As you noticed, the fields defined for the table are available for queries as attributes, and calling db with a query as argument provides you a set on which you can do operations like the select().

Since pyDAL is well documented in the web2py reference manual, we wouldn't re-propose the complete documentation here, but will focus on the particular implementations you will find in weppy.
The main differences between using web2py or pyDAL directly, in comparison with weppy, differences that you should remember when looking at their documentations, are:

  • DAL class needs your app object as first parameter to work, and you store the configuration for the database in your app.config attribute
  • Field class in weppy doesn't accept the name for the field as the first parameter. This is because when you define your fields the name is captured by the attribute itself.
  • in weppy you can access tables both with table names and model names, so db.Post and db.posts will give the same object

Let's go further and inspect the models layer provided by weppy.


So, how a weppy model looks like? Using the upper example for the post inside a blog, and adding some features, an example model would be like this:

from markdown2 import markdown
from weppy.dal import Field, Model, computation

class Post(Model):
    author = Field()
    title = Field()
    body = Field('text')
    summary = Field('text')

    repr_values = {
        "body": lambda row, value: markdown(value)

    validation = {
        "title": {'presence': True},
        "body": {'presence': True}

    def make_summary(self, row):
        # custom code to create the summary from the text

As you can see, we added some validation rules, a representation rule to parse the markdown text of the post and produce html in the templates and a computation on the summary field. To use this model in your application you can use the define_models() method of the DAL class of weppy, as we seen in the example above:


Tables naming

Under default behavior, weppy will create the table using the name of the class and making it plural, so that the class Post will create the table posts, Comment will create table comments and so on.
If you want to customize the name of the table, you can use the tablename attribute inside your model:

Class Post(Model):
    tablename = "myposts"

just ensure the name is valid for the DBMS you're using.

weppy doesn't have a real pluralization system to evaluate names, so in case the name you've chosen for your model doesn't have a regular plural in english, you should write down the correct plural with the tablename attribute. Just as an example, a model named Mouse will be translated in the horrible "mouses" tablename, so you should assign:
tablename = "mice"


Field objects define your entity's properties, and will create the appropriate columns inside your tables, so in general you would write the name of the property and its type:

started = Field('datetime')

Available types for Field definition are:

Field type mapped to python object
string str
text str
blob str
bool bool
int int
float float
decimal(n,m) decimal.Decimal
time datetime.time
datetime datetime.datetime
password str
upload str
reference tablename int or pydal.objects.Row (depends on context)
list:string list of str
list:int list of int
list:reference tablename list of int or pydal.objects.Rows
json json

Using the right field type ensure the right columns types inside your tables, and allows you to benefit from the default validation implemented by weppy.


To implement a validation mechanism for your fields, you can use the validation parameter of the Field class, or the mapping dict with the name of the fields at the validation attribute inside your Model. Both method will produce the same result, just pick the one you prefer:

title = Field(validation={'presence': True})
validation = {
    'title': {'presence': True}

The validation rules you define will be used to validate the forms created from the models on the user input and inserts.
While you can find the complete list of available validators in the appropriate chapter of the documentation, here we list the default validation implemented by weppy on fields:

Field type default validation allow blank value
string {'len': {'lt': 255}} yes
text {'len': {'lt': 65536}} yes
bool {'in': (False, True)} no
int {'is': 'int'} no
float {'is': 'float'} no
decimal {'is': 'decimal'} no
date {'is': 'date'} no
time {'is': 'time'} no
datetime {'is': 'datetime'} no
reference tablename {'presence': True} no
list:int {'is': 'list:int'} no
list:reference tablename {'presence': True} no
json {'is': 'json'} no

When you want to allow your fields been empty, you can use the allow validator:
{'allow': 'blank'} or {'allow': 'empty'}

Disable default validation

Sometimes you may want to disable the default validation implemented by weppy. Depending on your needs, you have two different ways.
When you need to disable the default validation on a single Field, you can use the auto_validation parameter:

a = Field(auto_validation=False)

Otherwise, if you want to disable the default validation on every field of your Model, the auto_validation attribute is handy:

class MyModel(Model):
    auto_validation = False


As you've seen from the Field paragraph, weppy provides the reference field type to create relationships between tables. So, how should we use it?
Let's say we want to create a membership system of users in groups. We probably end up writing something like this:

class User(Model):
    name = Field()
    age = Field('int')

class Group(Model):
    name = Field()

class Membership(Model):
    user = Field('reference users', unique=True)
    group = Field('reference groups')

Now we have 1:N relationship between Group and Membership and 1:1 relationship between User and Membership. To select from the database the rows that match some of the relationships, we should write the queries using the referenced attributes and the id's of the record involved.
Or we can use the included helpers to avoid that.

Defining relations using belongs_to, has_one, has_many

New in version 0.4

weppy provides these three helpers to simplify operations with related entities. So how do they works? Let's see it with the above example, rewritten:

class User(Model):
    name = Field()
    age = Field('int')

class Group(Model):
    has_many('memberships', {'users': {'via': 'memberships'}})
    name = Field()

class Membership(Model):
    belongs_to('user', 'group')

    validation = {
        'user': {'unique': True}

– Dude, wait.. This is not more compact. I see more lines to do the same thing.

Right, we wrote more lines to do the same thing as above, but we have some advantages over the first method. In fact, if we want to get all users of a certain group, in the first scenario we should write:

group = db.Group(name="admins")
memberships = db( ==
users = []
for membership in membership:

while the has_many helper implements the memberships and the users methods on the Group model:

admins = db.Group(name="admins").users()

In the same way, if you want to get the group of a certain user, with the first method you have to write:

user = db.User(name="mario")
group = db.Membership(

while with the has_one helper:

group = db.User(name="mario")

So, if you use relationships quite often in your code, you will end with less lines of code.

Technical note:
has_one and has_many don't create columns inside your tables. While belongs_to adds a reference Field inside your model, and you will have a column for the id of the referenced record, has_one and has_many will create a Field.Virtual object that will be included in the rows on selects.

Obviously, you can use reference fields and write down your own Model methods as we will se in the next paragraphs; so finally, you can choose whatever way fits good for your project.

Specify models in relations

As per default behavior, belongs_to, has_one and has_many use the passed argument both for the attribute naming and the other model you're referencing to, so:

  • belongs_to('user') will add a user field to your model referenced to User model
  • has_one('user') will add a virtual user attribute to your rows referenced to User model
  • has_many('things') will add a virtual things attribute to your rows referenced to Thing model

Sometimes, you want to use a different name for the attribute, let's say, as an example, you need an owner attribute for the relation with the User model. You can reach this just writing:

belongs_to({'owner': 'User'})
has_one({'owner': 'User'})

The same works with has_many helper, and you will use it also in scenarios where your model names are not regular plurals in english, so for example, if you have a Mouse model, you will specify the relation:

has_many({'mice': 'Mouse'})

– Ok dude, what if I have a custom name for the foreign key? How do I specify that?
- You don't have to. weppy will handle it automatically

In fact, let's say you have a model named Thing which has a N:1 relation with User and you have the foreign key referred to User named user_id instead of user:

class User(Model):

class Thing(Model):
    belongs_to({'user_id': 'User'})

then your relation will work out of the box, since weppy will map things with the user_id foreign key in the Thing model.

has_many 'via'

As you've seen from the example above, the has_many helper also has a via option which you can use to export relationships trough other models.

The first use-case of the via option is the same of the example, and is useful when you need to access all the records accessible with the belongs_to of a membership table:

class User(Model):
    has_many('memberships', {'things': {'via': 'memberships'}})
    name = Field()

class Thing(Model):
    has_many('memberships', {'users': {'via': 'memberships'}})
    name = Field()

class Membership(Model):
    belongs_to('user', 'thing')

so you can access directly user.things() and thing.users().

The via can be useful also when you have something like this:

class University(Model):
    has_many('courses', {'attendants': {'via': 'courses'}})

class Course(Model):

class Attendand(Model):

in this case, you can access all the attendants of a university simply with university.attendants() (obviously you can access the university from the attendant using

has_many methods

Every time you use the has_many helper, weppy add an attribute of type Set (pydal's class) with the specified name on the Row object you've selected. Let's see it with the above example of users and things:

>>> u = db.User(id=1)
>>> u.memberships
<Set (memberships.user = 1)>
>>> u.things
<Set ((memberships.user = 1) AND (memberships.thing =>

Since the object is a specific set of your database responding to a query, you have all the standard methods to run operations on in:

method description
count count the records in the set
select get the records of the set
update update all the records in the set
validate_and_update perform a validation and update the records
delete delete all the records in the set
where return a subset given additional queries
add add a row to the set

As you observed, until now we used a shortcut for the select method just calling the set:

<Rows (1)>
>>> u.things()
<Rows (1)>

While all the methods described are quite intuitive, and works in the same way of running operations on tables, the add option can be quite useful when you need to add a relation to an existing object:

>>> cube = db.Thing(name="cube")
>>> user = db.User(id=1)
>>> user.things.add(cube)

which is just another way of doing:

>>> db.Membership.insert(user=user, thing=thing)

Model helpers and fields options

As you've seen for validations, weppy models have some reserved attributes which define some options for the fields inside your models. All the options listed in the next sections are available also as parameters of the Field class, and you can choose how to organize your code depending on your needs.

Forms read-writes

form_rw attribute of Model class helps you to hide some attributes to users when you create forms:

form_rw = {
    'started': False,
    'open': (True, False)

Any item of the dictionary can be a tuple, where the first value define if the field should be readable by the user and the second value define if the field should be writable, or bool that will set both values to the one given. By default, all fields are defined with rw at True.

You may prefer to explicit passing read-writes values to the fields, using rw parameter:

started = Field('datetime', rw=False)

Form labels

Labels are useful to produce good titles for your fields in forms:

form_labels = {
    'started': T("Opening date:")

Labels will decorate the input fields in your forms. In this example we used the weppy translator object to automatically translate the string in the correct language.

You can also use the label parameter of Field class:

started = Field('datetime', label=T("Opening date:"))

Form info

As for the labels, form_info attribute is useful to produce hints or helping blocks for your fields in forms:

form_info = {
    'started': T("Insert the desired opening date for your event in YYYY-MM-DD format.")

You can also use the info parameter of Field class:

started = Field('datetime', info=T("some description here"))

Default values

Helps you to set the default value for the field on record insertions:

default_values = {
    'started': lambda:

Which is the same of the default parameter of Field class:

started = Field('datetime', default=lambda:

Update values

As for the default_values attribute we've seen before, update_values helps you to set the default value for the field on record updates:

update_values = {
    'started': lambda:

Or you can use the update parameter of Field class:

started = Field('datetime', update=lambda:


Sometimes you need to give a better representation for the value of your entity:

repr_values = {
    'started': lambda row, value: prettydate(value)

and you can render it using:

MyModel.started.represent(record, record.started)

You may prefer to explicit passing representation rules to the signle fields, using representation parameter:

started = Field('datetime', representation=lambda row, value: prettydate(value))


Widgets are used to produce the relevant input part in the form produced from your model. Every Field object has a default widget depending on the type you defined, for example the datetime has an <input> html tag of type text. When you need to customize the look of your input blocks in the form, you can use your own widgets and pass them to the model with the appropriate attribute:

form_widgets = {
    'started': my_custom_widget

where my_custom_widget usually look like this:

def my_custom_widget(field, value):
    # some processing
    return myhtmlinput

And you can also use the widget parameter of Field class:

started = Field('datetime', widget=my_custom_widget)

The Model's setup helper

Sometimes you need to access your model attributes when defining other features, but, until now, we couldn't access the class or the instance itself. To avoid this problem, you can use the setup method of the model:

def setup(self):
    # you can access the database, the table and its fields
    db = self.db
    table = self.table
    field = self.table.fieldname


Sometimes you need some field values to be computed using other fields. For example:

from weppy.dal import Model, Field, computation

class Item(Model):
    price = Field('float')
    quantity = Field('int')
    total = Field('float')

    def total(self, row):
        return row.price*row.quantity

The function that does computation has to accept the row as parameter, and the computed value will be evaluated on both insert and updates.


When you need to perform certain computations on specific conditions, weppy helps you with the callbacks decorators, which will be invoked automatically. Here is the complete list of available decorators, with the parameters that will be passed to your decorated function:

decorator parameters
before_insert fields
after_insert fields, id
before_update set, fields
after_update set, fields
before_delete set
after_delete set

where fields is a dictionary containing fields and values passed to insert or update operations, id is the id of the newly inserted record, and set is the object used for the update or delete operation.

An example of usage can be a thumbnail function:

def update_avatar_thumb(self, s, fields):
    # process the image in fields['image']
    fields['image'] = thumbnail

Virtual fields

An alternative option to computed fields are the virtual ones. Considering the same example for the computations we can instead write:

from weppy.dal import Model, Field, virtualfield

class Item(Model):
    price = Field('float')
    quantity = Field('int')

    def total(self, row):
        return row.price*row.quantity

The difference between computation is that virtual fields are computed only when the record is selected, and they are not stored into the database. You can access the values as the common fields:

items = db(db.Item.price >= 2).select()
for item in items:

Field methods

Another option for computed fields is to use the fieldmethod decorator:

from weppy.dal import Model, Field, fieldmethod

class Item(Model):
    price = Field('float')
    quantity = Field('int')

    def total(self, row):
        return row.price*row.quantity

Field methods are evaluated on demand which means you have to invoke them when you want to access the values:

item = db(db.Item.price > 2).select().first()

Field methods can be useful also to create query shortcuts on other tables. Let's say we have defined another model called Warehouse for the quantity of items available in the warehouse, and we want to check the availability directly when we have the selected item:

from weppy.dal import Model, fieldmethod

class Item(Model):
    price = Field('float')
    quantity = Field('int')

    def total(self, row):
        return row.price*row.quantity

    def avail(self, row):
        w = self.db(self.db.Warehouse.item ==
        return w.in_store

and we can access the value simply doing:

item = db(db.Item.price > 2).select().first()
print item.availability()

Model methods

You can also define methods that will be available on the Model class itself. For instance, every weppy model comes with some pre-defined methods, for example:


will create the form for the entity defined in your model.

Other methods pre-defined in weppy are:

method description
validate process the values passed (field=value) and return None if they pass the validation defined in the model or a dictionary of errors
create insert a new record with the values passed (field=value) if they pass the validation

But how you can define your methods?
Let's say, for example that you want a shortcut for querying record in your model Show with the same basic condition, as in the case you need to call in several parts of your code only shows going to air today. Assuming you have a air_on field of type date in your model, you can write down something like this:

def onair_today(cls, query=None):
    _query = (cls.air_on == datetime.utcnow().date())
    if query:
        _query = _query & query
    return cls.db(_query).select()

now you can do:

today_shows = Show.onair_today()

and you're done.

As you observed, you can just use the standard classmethod decorator of the python language.

Accessing Model.method() refers to the model itself, while db.Model.attribute refers to the table instance you created with your model.