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.
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:
{
'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.
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
andnumber
. Thelocation
must be one ofwork
orhome
. The number must be a string.
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 default, 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 schema_order of a schema node is an integer which defines its ultimate order position within its parent 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.
As of Colander 0.9.9.1+, 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:
from colander import SchemaNode
ranged_int = colander.SchemaNode(
validator=colander.Range(0, 10),
default = 10,
title='Ranged Int'
)
But in 0.9.9.1+, you can alternately now do something like this:
from colander import SchemaNode
class RangedIntSchemaNode(SchemaNode):
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:
from colander import SchemaNode
class RangedIntSchemaNode(SchemaNode):
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:
from colander import SchemaNode
class RangedIntSchemaNode(SchemaNode):
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):
from colander import SchemaNode
class RangedIntSchemaNode(SchemaNode):
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 0.9.9.1+, 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:
from colander import SchemaNode
class RangedIntSchemaNode(SchemaNode):
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:
from colander import SchemaNode
class UserIdSchemaNode(SchemaNode):
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:
from colander import SchemaNode
class RangedIntSchemaNode(SchemaNode):
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.
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:
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
.
Earlier we defined a schema:
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
.
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:
{
'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.
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?
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:
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:
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:
{'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 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.
In certain circumstances, it is necessary to modify the deserialized value before validating it.
For example, a ~colander.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 ~colander.interfaces.Preparer
to do the correct thing in both cases:
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:
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))
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:
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:
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:
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_null
.
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.
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
).
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:
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:
import colander
friend = colander.SchemaNode(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(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(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:
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)
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 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.