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Colander Basics

Basics of using colander include defining a colander schema, deserializing a data structure using a schema, serializing a data structure using a schema, and dealing with colander.Invalid exceptions.

Defining A Colander Schema

Imagine you want to deserialize and validate a serialization of data you’ve obtained by reading a YAML document. An example of such a data serialization might look something like this:

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{
 'name':'keith',
 'age':'20',
 'friends':[('1', 'jim'),('2', 'bob'), ('3', 'joe'), ('4', 'fred')],
 'phones':[{'location':'home', 'number':'555-1212'},
           {'location':'work', 'number':'555-8989'},],
}

Let’s further imagine you’d like to make sure, on demand, that a particular serialization of this type read from this YAML document or another YAML document is “valid”.

Notice that all the innermost values in the serialization are strings, even though some of them (such as age and the position of each friend) are more naturally integer-like. Let’s define a schema which will attempt to convert a serialization to a data structure that has different types.

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import colander

class Friend(colander.TupleSchema):
    rank = colander.SchemaNode(colander.Int(),
                              validator=colander.Range(0, 9999))
    name = colander.SchemaNode(colander.String())

class Phone(colander.MappingSchema):
    location = colander.SchemaNode(colander.String(),
                                  validator=colander.OneOf(['home', 'work']))
    number = colander.SchemaNode(colander.String())

class Friends(colander.SequenceSchema):
    friend = Friend()

class Phones(colander.SequenceSchema):
    phone = Phone()

class Person(colander.MappingSchema):
    name = colander.SchemaNode(colander.String())
    age = colander.SchemaNode(colander.Int(),
                             validator=colander.Range(0, 200))
    friends = Friends()
    phones = Phones()

For ease of reading, we’ve actually defined five schemas above, but we coalesce them all into a single Person schema. As the result of our definitions, a Person represents:

  • A name, which must be a string.
  • An age, which must be deserializable to an integer; after deserialization happens, a validator ensures that the integer is between 0 and 200 inclusive.
  • A sequence of friend structures. Each friend structure is a two-element tuple. The first element represents an integer rank; it must be between 0 and 9999 inclusive. The second element represents a string name.
  • A sequence of phone structures. Each phone structure is a mapping. Each phone mapping has two keys: location and number. The location must be one of work or home. The number must be a string.

Schema Node Objects

A schema is composed of one or more schema node objects, each typically of the class colander.SchemaNode, usually in a nested arrangement. Each schema node object has a required type, an optional preparer for adjusting data after deserialization, an optional validator for deserialized prepared data, an optional default, an optional missing, an optional title, an optional description, and a slightly less optional name. It also accepts arbitrary keyword arguments, which are attached directly as attributes to the node instance.

The type of a schema node indicates its data type (such as colander.Int or colander.String).

The preparer of a schema node is called after deserialization but before validation; it prepares a deserialized value for validation. Examples would be to prepend schemes that may be missing on url values or to filter html provided by a rich text editor. A preparer is not called during serialization, only during deserialization. You can also pass a schema node a list of preparers.

The validator of a schema node is called after deserialization and preparation ; it makes sure the value matches a constraint. An example of such a validator is provided in the schema above: validator=colander.Range(0, 200). A validator is not called after schema node serialization, only after node deserialization.

The default of a schema node indicates the value to be serialized if a value for the schema node is not found in the input data during serialization. It should be the deserialized representation. If a schema node does not have a default, it is considered “serialization required”.

The missing of a schema node indicates the value if a value for the schema node is not found in the input data during deserialization. It should be the deserialized representation. If a schema node does not have a missing, it is considered “deserialization required”. This value is never validated; it is considered pre-validated.

The name of a schema node appears in error reports.

The title of a schema node is metadata about a schema node that can be used by higher-level systems. By default, it is a capitalization of the name.

The description of a schema node is metadata about a schema node that can be used by higher-level systems. By default, it is empty.

The insert_before of a schema node is a string: if supplied, it names a sibling defined by a superclass for its parent node; the current node will be inserted before the named node. It is not useful unless a mapping schema is inherited from another mapping schema, and you need to control the ordering of the resulting nodes.

Any other keyword arguments to a schema node constructor will be attached to the node unmolested (e.g. when foo=1 is passed, the resulting schema node will have an attribute named foo with the value 1).

Note

You may see some higher-level systems (such as Deform) pass a widget argument to a SchemaNode constructor. Such systems make use of the fact that a SchemaNode can be passed arbitrary keyword arguments for extension purposes. widget and other keyword arguments not enumerated here but which are passed during schema node construction by someone constructing a schema for a particular purpose are not used internally by Colander; they are instead only meaningful to higher-level systems which consume Colander schemas. Abitrary keyword arguments are allowed to a schema node constructor in Colander 0.9+. Prior version disallow them.

Subclassing SchemaNode

As of Colander 1.0a1+, it is possible and advisable to subclass colander.SchemaNode in order to create a bundle of default node behavior. The subclass can define the following methods and attributes: preparer, validator, default, missing, name, title, description, widget, and after_bind.

The imperative style that looks like this still works, of course:

ranged_int = colander.SchemaNode(
    schema_type=colander.Int,
    validator=colander.Range(0, 10),
    default=10,
    title='Ranged Int'
    )

But in 1.0a1+, you can alternately now do something like this:

class RangedInt(colander.SchemaNode):
    schema_type = colander.Int
    validator = colander.Range(0, 10)
    default = 10
    title = 'Ranged Int'

ranged_int = RangedInt()

Values that are expected to be callables can now alternately be methods of the schemanode subclass instead of plain attributes:

class RangedInt(colander.SchemaNode):
    schema_type = colander.Int
    default = 10
    title = 'Ranged Int'

    def validator(self, node, cstruct):
       if not 0 < cstruct < 10:
           raise colander.Invalid(node, 'Must be between 0 and 10')

ranged_int = RangedInt()

Note that when implementing a method value such as validator that expects to receive a node argument, node must be provided in the call signature, even though node will almost always be the same as self. This is because Colander simply treats the method as another kind of callable, be it a method, or a function, or an instance that has a __call__ method. It doesn’t care that it happens to be a method of self, and it needs to support callables that are not methods, so it sends node in regardless.

You can’t use method definitions as colander.deferred callables. For example this will not work:

class RangedInt(colander.SchemaNode):
    schema_type = colander.Int
    default = 10
    title = 'Ranged Int'

    @colander.deferred
    def validator(self, node, kw):
       request = kw['request']
       def avalidator(node, cstruct):
           if not 0 < cstruct < 10:
               if request.user != 'admin':
                   raise colander.Invalid(node, 'Must be between 0 and 10')
       return avalidator

ranged_int = RangedInt()
bound_ranged_int = ranged_int.bind(request=request)

This will result in:

TypeError: avalidator() takes exactly 3 arguments (2 given)

However, if you treat the thing being decorated as a function instead of a method (remove the self argument from the argument list), it will indeed work):

class RangedInt(colander.SchemaNode):
    schema_type = colander.Int
    default = 10
    title = 'Ranged Int'

    @colander.deferred
    def validator(node, kw):
       request = kw['request']
       def avalidator(node, cstruct):
           if not 0 < cstruct < 10:
               if request.user != 'admin':
                   raise colander.Invalid(node, 'Must be between 0 and 10')
       return avalidator

ranged_int = RangedInt()
bound_ranged_int = ranged_int.bind(request=request)

In releases of Colander before 1.0a1+, the only way to defer the computation of values was via the colander.deferred decorator. In this release, however, you can instead use the bindings attribute of self to obtain access to the bind parameters within values that are plain old methods:

class RangedInt(colander.SchemaNode):
    schema_type = colander.Int
    default = 10
    title = 'Ranged Int'

    def validator(self, node, cstruct):
       request = self.bindings['request']
       if not 0 < cstruct < 10:
           if request.user != 'admin':
               raise colander.Invalid(node, 'Must be between 0 and 10')

ranged_int = RangedInt()
bound_range_int = ranged_int.bind(request=request)

If the things you’re trying to defer aren’t callables like validator, but they’re instead just plain attributes like missing or default, instead of using a colander.deferred, you can use after_bind to set attributes of the schemanode that rely on binding variables:

class UserIdSchemaNode(colander.SchemaNode):
    schema_type = colander.String
    title = 'User Id'

    def after_bind(self, node, kw):
        self.default = kw['request'].user.id

You can override the default values of a schemanode subclass in its constructor:

class RangedInt(colander.SchemaNode):
    schema_type = colander.Int
    default = 10
    title = 'Ranged Int'
    validator = colander.Range(0, 10)

ranged_int = RangedInt(validator=colander.Range(0, 20))

In the above example, the validation will be done on 0-20, not 0-10.

Normal inheritance rules apply to class attributes and methods defined in a schemanode subclass. If your schemanode subclass inherits from another schemanode class, your schemanode subclass’ methods and class attributes will override the superclass’ methods and class attributes.

Schema Objects

In the examples above, if you’ve been paying attention, you’ll have noticed that we’re defining classes which subclass from colander.MappingSchema, colander.TupleSchema and colander.SequenceSchema.

It’s turtles all the way down: the result of creating an instance of any of colander.MappingSchema, colander.TupleSchema or colander.SequenceSchema object is also a colander.SchemaNode object.

Instantiating a colander.MappingSchema creates a schema node which has a type value of colander.Mapping.

Instantiating a colander.TupleSchema creates a schema node which has a type value of colander.Tuple.

Instantiating a colander.SequenceSchema creates a schema node which has a type value of colander.Sequence.

The name of a schema node that is introduced as a class-level attribute of a colander.MappingSchema, colander.TupleSchema or a colander.SequenceSchema is its class attribute name. For example:

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import colander

class Phone(colander.MappingSchema):
    location = colander.SchemaNode(colander.String(),
                                  validator=colander.OneOf(['home', 'work']))
    number = colander.SchemaNode(colander.String())

The name of the schema node defined via location = colander.SchemaNode(..) within the schema above is location. The title of the same schema node is Location.

Deserialization

Earlier we defined a schema:

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import colander

class Friend(colander.TupleSchema):
    rank = colander.SchemaNode(colander.Int(),
                               validator=colander.Range(0, 9999))
    name = colander.SchemaNode(colander.String())

class Phone(colander.MappingSchema):
    location = colander.SchemaNode(colander.String(),
                                  validator=colander.OneOf(['home', 'work']))
    number = colander.SchemaNode(colander.String())

class Friends(colander.SequenceSchema):
    friend = Friend()

class Phones(colander.SequenceSchema):
    phone = Phone()

class Person(colander.MappingSchema):
    name = colander.SchemaNode(colander.String())
    age = colander.SchemaNode(colander.Int(),
                             validator=colander.Range(0, 200))
    friends = Friends()
    phones = Phones()

Let’s now use this schema to try to deserialize some concrete data structures.

Each of thse concrete data structures is called a cstruct. “cstruct” is an abbreviation of “colander structure”: you can think of a cstruct as a serialized representation of some application data. A “cstruct” is usually generated by the colander.SchemaNode.serialize() method, and is converted back into an application structure (aka appstruct) via colander.SchemaNode.deserialize().

Deserializing A Valid Serialization

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  cstruct = {
         'name':'keith',
         'age':'20',
         'friends':[('1', 'jim'),('2', 'bob'), ('3', 'joe'), ('4', 'fred')],
         'phones':[{'location':'home', 'number':'555-1212'},
                   {'location':'work', 'number':'555-8989'},],
         }
  schema = Person()
  deserialized = schema.deserialize(cstruct)

When schema.deserialize(cstruct) is called, because all the data in the schema is valid, and the structure represented by cstruct conforms to the schema, deserialized will be the following:

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  {
  'name':'keith',
  'age':20,
  'friends':[(1, 'jim'),(2, 'bob'), (3, 'joe'), (4, 'fred')],
  'phones':[{'location':'home', 'number':'555-1212'},
            {'location':'work', 'number':'555-8989'},],
  }

Note that all the friend rankings have been converted to integers, likewise for the age.

Deserializing An Invalid Serialization

Below, the cstruct structure has some problems. The age is a negative number. The rank for bob is t which is not a valid integer. The location of the first phone is bar, which is not a valid location (it is not one of “work” or “home”). What happens when a cstruct cannot be deserialized due to a data type error or a validation error?

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  import colander

  cstruct = {
         'name':'keith',
         'age':'-1',
         'friends':[('1', 'jim'),('t', 'bob'), ('3', 'joe'), ('4', 'fred')],
         'phones':[{'location':'bar', 'number':'555-1212'},
                   {'location':'work', 'number':'555-8989'},],
         }
  schema = Person()
  schema.deserialize(cstruct)

The deserialize method will raise an exception, and the except clause above will be invoked, causing an error message to be printed. It will print something like:

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Invalid: {'age':'-1 is less than minimum value 0',
         'friends.1.0':'"t" is not a number',
         'phones.0.location:'"bar" is not one of "home", "work"'}

The above error is telling us that:

  • The top-level age variable failed validation.
  • Bob’s rank (the Friend tuple name bob‘s zeroth element) is not a valid number.
  • The zeroth phone number has a bad location: it should be one of “home” or “work”.

We can optionally catch the exception raised and obtain the raw error dictionary:

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  import colander

  cstruct = {
         'name':'keith',
         'age':'-1',
         'friends':[('1', 'jim'),('t', 'bob'), ('3', 'joe'), ('4', 'fred')],
         'phones':[{'location':'bar', 'number':'555-1212'},
                   {'location':'work', 'number':'555-8989'},],
         }
  schema = Person()
  try:
      schema.deserialize(cstruct)
  except colander.Invalid, e:
      errors = e.asdict()
      print errors

This will print something like:

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{'age':'-1 is less than minimum value 0',
 'friends.1.0':'"t" is not a number',
 'phones.0.location:'"bar" is not one of "home", "work"'}

colander.Invalid Exceptions

The exceptions raised by Colander during deserialization are instances of the colander.Invalid exception class. We saw previously that instances of this exception class have a colander.Invalid.asdict() method which returns a dictionary of error messages. This dictionary is composed by Colander by walking the exception tree. The exception tree is composed entirely of colander.Invalid exceptions.

While the colander.Invalid.asdict() method is useful for simple error reporting, a more complex application, such as a form library that uses Colander as an underlying schema system, may need to do error reporting in a different way. In particular, such a system may need to present the errors next to a field in a form. It may need to translate error messages to another language. To do these things effectively, it will almost certainly need to walk and introspect the exception graph manually.

The colander.Invalid exceptions raised by Colander validation are very rich. They contain detailed information about the circumstances of an error. If you write a system based on Colander that needs to display and format Colander exceptions specially, you will need to get comfy with the Invalid exception API.

When a validation-related error occurs during deserialization, each node in the schema that had an error (and any of its parents) will be represented by a corresponding colander.Invalid exception. To support this behavior, each colander.Invalid exception has a children attribute which is a list. Each element in this list (if any) will also be an colander.Invalid exception, recursively, representing the error circumstances for a particular schema deserialization.

Each exception in the graph has a msg attribute, which will either be the value None, a str or unicode object, or a translation string instance representing a freeform error value set by a particular type during an unsuccessful deserialization. Exceptions that exist purely for structure will have a msg attribute with the value None. Each exception instance will also have an attribute named node, representing the schema node to which the exception is related.

Note

Translation strings are objects which behave like Unicode objects but have extra metadata associated with them for use in translation systems. See http://docs.repoze.org/projects/translationstring/dev/ for documentation about translation strings. All error messages used by Colander internally are translation strings, which means they can be translated to other languages. In particular, they are suitable for use as gettext message ids.

See the colander.Invalid API documentation for more information.

Preparing deserialized data for validation

In certain circumstances, it is necessary to modify the deserialized value before validating it.

For example, a String node may be required to contain content, but that content may come from a rich text editor. Such an editor may return <b></b> which may appear to be valid but doesn’t contain content, or <a href="javascript:alert('evil'')">good</a> which is valid, but only after some processing.

The following schema uses htmllaundry and a Preparer to do the correct thing in both cases:

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import colander
import htmllaundry

class Page(colander.MappingSchema):
    title = colander.SchemaNode(colander.String())
    content = colander.SchemaNode(colander.String(),
                                  preparer=htmllaundry.sanitize,
                                  validator=colander.Length(1))

You can even specify multiple preparers to be run in order, by passing a list of functions to the preparer kwarg, like so:

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import colander
# removes whitespace, newlines, and tabs from the beginning/end of a string
strip_whitespace = lambda v: v.strip(' \t\n\r') if v is not None else v
# replaces multiple spaces with a single space
remove_multiple_spaces = lambda v: re.sub(' +', ' ', v)

class Page(colander.MappingSchema):
    title = colander.SchemaNode(colander.String())
    content = colander.SchemaNode(colander.String(),
                                  preparer=[strip_whitespace, remove_multiple_spaces],
                                  validator=colander.Length(1))

Serialization

Serializing a data structure is obviously the inverse operation from deserializing a data structure. The colander.SchemaNode.serialize() method of a schema performs serialization of application data (aka an appstruct). If you pass the colander.SchemaNode.serialize() method data that can be understood by the schema types in the schema you’re calling it against, you will be returned a data structure of serialized values.

For example, given the following schema:

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import colander

class Person(colander.MappingSchema):
    name = colander.SchemaNode(colander.String())
    age = colander.SchemaNode(colander.Int(),
                              validator=colander.Range(0, 200))

We can serialize a matching data structure:

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  appstruct = {'age':20, 'name':'Bob'}
  schema = Person()
  serialized = schema.serialize(appstruct)

The value for serialized above will be {'age':'20', 'name':'Bob'}. Note that the age integer has become a string.

Serialization and deserialization are not completely symmetric, however. Although schema-driven data conversion happens during serialization, and default values are injected as necessary, colander types are defined in such a way that structural validation and validation of values does not happen as it does during deserialization. For example, the colander.null value is substituted into the cstruct for every missing subvalue in an appstruct, and none of the validators associated with the schema or any of is nodes is invoked.

This usually means you may “partially” serialize an appstruct where some of the values are missing. If we try to serialize partial data using the serialize method of the schema:

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  appstruct = {'age':20}
  schema = Person()
  serialized = schema.serialize(appstruct)

The value for serialized above will be {'age':'20', 'name':colander.null}. Note the age integer has become a string, and the missing name attribute has been replaced with colander.null. Above, even though we did not include the name attribute in the appstruct we fed to serialize, an error is not raised. For more information about colander.null substitution during serialization, see Serializing The Null Value.

The corollary: it is the responsibility of the developer to ensure he serializes “the right” data; colander will not raise an error when asked to serialize something that is partially nonsense.

Inheriting Schemas

Note

This feature is new as of Colander 0.9.9.

One class-based schema can be inherited from another. For example:

import colander
import pprint

class Friend(colander.MappingSchema):
    rank = colander.SchemaNode(
        colander.Int(),
        )
    name = colander.SchemaNode(
        colander.String(),
        )

class SpecialFriend(Friend):
    iwannacomefirst = colander.SchemaNode(
        colander.String(),
        insert_before='rank',
        )
    another = colander.SchemaNode(
        colander.String(),
        )

class SuperSpecialFriend(SpecialFriend):
    iwannacomefirst = colander.SchemaNode(
        colander.Int(),
        )

friend = SuperSpecialFriend()
pprint.pprint([(x, x.typ) for x in friend.children])

Here’s what’s printed when the above is run:

[(<colander.SchemaNode object at 38407568 (named iwannacomefirst)>,
  <colander.Integer object at 0x24a0d10>),
 (<colander.SchemaNode object at 37016144 (named rank)>,
  <colander.Integer object at 0x7f17c5606710>),
 (<colander.SchemaNode object at 37017424 (named name)>,
  <colander.String object at 0x234d610>),
 (<colander.SchemaNode object at 38407184 (named another)>,
  <colander.String object at 0x2359250>)]

Multiple inheritance also works:

import colander
import pprint

class One(colander.MappingSchema):
    a = colander.SchemaNode(
        colander.Int(),
        )
    b = colander.SchemaNode(
        colander.Int(),
        )

class Two(colander.MappingSchema):
    a = colander.SchemaNode(
        colander.String(),
        )
    c = colander.SchemaNode(
        colander.String(),
        )

class Three(One, Two):
    b = colander.SchemaNode(
        colander.Bool(),
        )
    d = colander.SchemaNode(
        colander.Bool(),
        )

s = Three()
pprint.pprint([(x, x.typ) for x in s.children])

Here’s what’s printed when the above is run:

[(<colander.SchemaNode object at 14868560 (named a)>,
  <colander.String object at 0xe25f90>),
 (<colander.SchemaNode object at 14868816 (named b)>,
  <colander.Boolean object at 0xe2e110>),
 (<colander.SchemaNode object at 14868688 (named c)>,
  <colander.String object at 0xe2e090>),
 (<colander.SchemaNode object at 14868944 (named d)>,
  <colander.Boolean object at 0xe2e190>)]

This feature only works with mapping schemas. A “mapping schema” is schema defined as a class which inherits from colander.Schema or colander.MappingSchema.

Ordering of child schema nodes when inheritance is used works like this: the “deepest” SchemaNode class in the MRO of the inheritance chain is consulted first for nodes, then the next deepest, then the next, and so on. So the deepest class’ nodes come first in the relative ordering of schema nodes, then the next deepest, and so on. For example:

class One(colander.MappingSchema):
    a = colander.SchemaNode(
        colander.String(),
        id='a1',
        )
    b = colander.SchemaNode(
        colander.String(),
        id='b1',
        )
    d = colander.SchemaNode(
        colander.String(),
        id='d1',
        )

class Two(One):
    a = colander.SchemaNode(
        colander.String(),
        id='a2',
        )
    c = colander.SchemaNode(
        colander.String(),
        id='c2',
        )
    e = colander.SchemaNode(
        colander.String(),
        id='e2',
        )

class Three(Two):
    b = colander.SchemaNode(
        colander.String(),
        id='b3',
        )
    d = colander.SchemaNode(
        colander.String(),
        id='d3',
        )
    f = colander.SchemaNode(
        colander.String(),
        id='f3',
        )

three = Three()

The ordering of child nodes computed in the schema node three will be ['a2', 'b3', 'd3', 'c2', 'e2', 'f3']. The ordering starts a1, b1, d1 because that’s the ordering of nodes in One, and One is the deepest SchemaNode in the inheritance hierarchy. Then it processes the nodes attached to Two, the next deepest, which causes a1 to be replaced by a2, and c2 and e2 to be appended to the node list. Then finally it processes the nodes attached to Three, which causes b1 to be replaced by b3, and d1 to be replaced by d3, then finally f is appended.

Multiple inheritance works the same way:

class One(colander.MappingSchema):
    a = colander.SchemaNode(
        colander.String(),
        id='a1',
        )
    b = colander.SchemaNode(
        colander.String(),
        id='b1',
        )
    d = colander.SchemaNode(
        colander.String(),
        id='d1',
        )

class Two(colander.MappingSchema):
    a = colander.SchemaNode(
        colander.String(),
        id='a2',
        )
    c = colander.SchemaNode(
        colander.String(),
        id='c2',
        )
    e = colander.SchemaNode(
        colander.String(),
        id='e2',
        )

class Three(Two, One):
    b = colander.SchemaNode(
        colander.String(),
        id='b3',
        )
    d = colander.SchemaNode(
        colander.String(),
        id='d3',
        )
    f = colander.SchemaNode(
        colander.String(),
        id='f3',
        )

three = Three()

The resulting node ordering of three is the same as the single inheritance example: ['a2', 'b3', 'd3', 'c2', 'e2', 'f3'] due to the MRO deepest-first ordering (One, then Two, then Three).

The behavior of subclassing one mapping schema using another is as follows:

  • A node declared in a subclass of a mapping schema overrides any node with the same name inherited from any superclass. The node remains at the child order of the superclass node unless the subclass node defines an insert_before value.
  • A node declared in a subclass of a mapping schema with a name that doesn’t override any node in a superclass will be placed after all nodes defined in all superclasses unless the subclass node defines an insert_before value. You can think of it like this: nodes added in subclasses will follow nodes added in superclasses unless the node is already defined in any of those superclasses.

An insert_before keyword argument may be passed to the SchemaNode constructor of mapping schema child nodes. This is a string which influences the node’s position in its mapping schema. The node will be inserted into the mapping schema before the node named by insert_before. An insert_before value must match the name of a schema node in a superclass or it must match the name of a schema node already defined in the class; it cannot name a schema node in a subclass, and it cannot name a schema node in the same class that hasn’t already been defined. If an insert_before is provided that doesn’t match any existing node name, a KeyError is raised.

If a schema node name conflicts with a schema value attribute name on the same class in a colander.MappingSchema, colander.TupleSchema or colander.SequenceSchema definition, you can work around this by giving the schema node a bogus name in the class definition but providing a correct name argument to the schema node constructor:

from colander import SchemaNode, MappingSchema

class SomeSchema(MappingSchema):
    title = 'Some Schema'
    thisnamewillbeignored = colander.SchemaNode(
                                        colander.String(),
                                        name='title'
                                        )

Note that such a workaround is only required if the conflicting names are attached to the exact same class definition. Colander scrapes off schema node definitions at each class’ construction time, so it’s not an issue for inherited values. For example:

from colander import SchemaNode, MappingSchema

class SomeSchema(MappingSchema):
    title = colander.SchemaNode(colander.String())

class AnotherSchema(SomeSchema):
    title = 'Some Schema'

schema = AnotherSchema()

In the above example, even though the title = 'Some Schema' appears to override the superclass’ title SchemaNode, a title SchemaNode will indeed be present in the child list of the schema instance (schema['title'] will return the title SchemaNode) and the schema’s title attribute will be Some Schema (schema.title will return Some Schema).

Defining A Schema Declaratively

Previously, we defined the schema in such a way that the individual sequences and mappings within the schema could be re-used in different schemas. If all nodes within a schema are only likely to be used in that schema, then the schema definition can be made more succinct using the instantiate class decorator as shown below:

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import colander

class Person(colander.MappingSchema):
    name = colander.SchemaNode(colander.String())
    age = colander.SchemaNode(colander.Int(),
                              validator=colander.Range(0, 200))

    @colander.instantiate()
    class friends(colander.SequenceSchema):

        @colander.instantiate()
        class friend(colander.TupleSchema):
            rank = colander.SchemaNode(colander.Int(),
                                       validator=colander.Range(0, 9999))
            name = colander.SchemaNode(colander.String())

    @colander.instantiate()
    class phones(colander.SequenceSchema):

        @colander.instantiate()
        class phone(colander.MappingSchema):
            location = colander.SchemaNode(colander.String(),
                                           validator=colander.OneOf(['home', 'work']))
            number = colander.SchemaNode(colander.String())

If you need to pass parameters when using this style of schema definition, such as a missing value to a SchemaNode during instantiation, you can pass these as parameters to instantiate. For example, if we wanted to limit the number of friends a person can have, and cater for people who have no friends, we could adjust the schema as shown below:

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class Person(colander.MappingSchema):

    @colander.instantiate(missing=(),
                          validator=colander.Length(max=5))
    class friends(colander.SequenceSchema):

        @colander.instantiate()
        class friend(colander.TupleSchema):
            name = colander.SchemaNode(colander.String())

Defining A Schema Imperatively

The above schema we defined was defined declaratively via a set of class statements. It’s often useful to create schemas more dynamically. For this reason, Colander offers an “imperative” mode of schema configuration. Here’s our previous declarative schema:

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import colander

class Friend(colander.TupleSchema):
    rank = colander.SchemaNode(colander.Int(),
                              validator=colander.Range(0, 9999))
    name = colander.SchemaNode(colander.String())

class Phone(colander.MappingSchema):
    location = colander.SchemaNode(colander.String(),
                                  validator=colander.OneOf(['home', 'work']))
    number = colander.SchemaNode(colander.String())

class Friends(colander.SequenceSchema):
    friend = Friend()

class Phones(colander.SequenceSchema):
    phone = Phone()

class Person(colander.MappingSchema):
    name = colander.SchemaNode(colander.String())
    age = colander.SchemaNode(colander.Int(),
                             validator=colander.Range(0, 200))
    friends = Friends()
    phones = Phones()

We can imperatively construct a completely equivalent schema like so:

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import colander

friend = colander.SchemaNode(colander.Tuple())
friend.add(colander.SchemaNode(colander.Int(),
                              validator=colander.Range(0, 9999),
           name='rank'))
friend.add(colander.SchemaNode(colander.String(), name='name'))

phone = colander.SchemaNode(colander.Mapping())
phone.add(colander.SchemaNode(colander.String(),
                             validator=colander.OneOf(['home', 'work']),
                             name='location'))
phone.add(colander.SchemaNode(colander.String(), name='number'))

schema = colander.SchemaNode(colander.Mapping())
schema.add(colander.SchemaNode(colander.String(), name='name'))
schema.add(colander.SchemaNode(colander.Int(), name='age',
                              validator=colander.Range(0, 200)))
schema.add(colander.SchemaNode(colander.Sequence(), friend, name='friends'))
schema.add(colander.SchemaNode(colander.Sequence(), phone, name='phones'))

Defining a schema imperatively is a lot uglier than defining a schema declaratively, but it’s often more useful when you need to define a schema dynamically. Perhaps in the body of a function or method you may need to disinclude a particular schema field based on a business condition; when you define a schema imperatively, you have more opportunity to control the schema composition.

Serializing and deserializing using a schema created imperatively is done exactly the same way as you would serialize or deserialize using a schema created declaratively:

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  data = {
         'name':'keith',
         'age':'20',
         'friends':[('1', 'jim'),('2', 'bob'), ('3', 'joe'), ('4', 'fred')],
         'phones':[{'location':'home', 'number':'555-1212'},
                   {'location':'work', 'number':'555-8989'},],
         }
  deserialized = schema.deserialize(data)

Gotchas

You may be using a module scope schema definition with the expectation that calling a colander.SchemaNode constructor will clone all of its subnodes. This is not the case.

For example, in a Python module, you might have code that looks like this:

from colander import SchemaNode, MappingSchema
from colander import Int

class MySchema1(MappingSchema):
    a = SchemaNode(Int())
class MySchema2(MappingSchema):
    b = MySchema1()

def afunction():
    s = MySchema2()
    s['a'].add(SchemaNode(Int(), name='c'))

Because you’re mutating a (by appending a child node to it via the colander.SchemaNode.add() method) you are probably expecting that you are working with a copy of a. This is incorrect: you’re mutating the module-scope copy of the a instance defined within the MySchema1 class. This is almost certainly not what you mean to do. The symptom of making such a mistake might be that multiple c nodes are added as children of a over the course of the Python process lifetime.

To get around this, use the colander.SchemaNode.clone() method to create a deep copy of an instance of a schema otherwise defined at module scope before mutating any of its subnodes:

def afunction():
    s = MySchema2().clone()
    s['a'].add(SchemaNode(Int(), name='c'))

colander.SchemaNode.clone() clones all the nodes in the schema, so you can work with a “deep copy” of the schema without disturbing the “template” schema nodes defined at a higher scope.