Input Validation¶

Any primitive type¶

Certain generic restrictions can be applied to any type. They are listed below, along with their default values

• default = None - default value if the input is None.

• nillable = True - if True, the item can be null when provided. Note that

  this constraint only applies when the variable is actually provided in the input document and is ignored if it’s not. You should set min_occurs=1 if you want to force this variable to be present in incoming documents.

• min_occurs = 0 - set this to 1 to make the type mandatory. Can be set to

  any positive integer. Note that if nillable=False, the validator will still accept null values.

• max_occurs = 1 - can be set to any strictly positive integer. Values

  greater than 1 will imply an iterable of objects as native Python type. It can be set to unbounded or decimal.Decimal('inf') to denote an array with infinitely many elements.

These rules can be combined, the example below illustrates how to create a mandatory string:

String(min_occurs=1, min_len=1, nillable=False)

Numbers¶

Integers and other countable numerical data types (i.e. except Float or Double) can be compared with specific values, using the following keywords: ge, gt, le, lt (they correspond to >=, >, <=, <)

Integer(ge=1, le=12) #an integer between 1 and 12, i.e. 1 <= x <= 12

Strings¶

Strings can be validated against a regular expression:

Unicode(pattern = "[0-9]+") #must contain at least one digit, digits only

Length checks can be enforced as well:

Unicode(min_len = 5, max_len = 10)

If you want to keep an incoming bytestream as a str with a known encoding, that’s also possible with the String type. You can specify:

• Which encoding the strings must be in

• How to handle the situations in which a string cannot be decoded properly (to understand how this works, consult Python’s documentation)

  String(encoding = 'win-1251')
  String(unicode_errors = 'strict') #could be 'replace' or 'ignore'
  

These restrictions can be combined:

String(encoding='win-1251', max_len=20)
String(min_len=5, max_len=20, pattern='[a-z]')

Possible values¶

Sometimes you may want to allow only a finite set of values, or values which can be difficult to describe in terms of an interval. If this is the case, you can explicitly indicate the set:

Integer(values=[1984, 13, 45, 42])
Unicode(values=[u"alpha", u"bravo", u"charlie"]) # note the 'u' prefix

Advanced validation¶

Spyne offers several primitives for this purpose, they are defined in the ModelBase class, from which all the types are derived: https://github.com/arskom/spyne/blob/master/src/spyne/model/_base.py

These primitives are:

• validate_string - invoked when the variable is extracted from the input XML data.
• validate_native - invoked after the string is converted to a specific Python value.

Since all data comes in as a byte stream, when you read it you get a string. So the validate_string hook is your first line of defense against invalid data.

At a later stage, the data can be converted to its native type. Once that conversion occurs, you can apply some additional checks. Validation in this stage is handled by the validate_native hook.

>>> stringNumber = '123'
>>> stringNumber
'123'        # note the quotes, it is a string.
>>> number = int(stringNumber)
>>> number
123          # note the absence of quotes, it is a number.
>>> stringNumber == 123
False        # makes sense.
>>> number == 123
True

In the example above, number is an actual number and can be validated with validate_native, whereas stringNumber is a byte stream and should be validated by validate_string.

A practical example¶

A custom string type that can not contain the colon symbol ‘:’.

We’ll have to declare our own class, derived from Unicode (which, in turn, is derived from SimpleModel, which inherits from ModelBase).

class SpecialString(Unicode):
    """Custom string type that prohibits the use of colons"""

    @staticmethod
    def validate_string(cls, value):
        if value is not None and ":" in value:
            return False
        return True

A slightly more complicated example¶

A custom numerical type that verifies if the number is prime.

This time both flavours of validation are combined: validate_string to see if it is a number, and then validate_native to see if it is prime.

import re

class Prime(Integer):
    """Custom integer type that only accepts primes."""

    @staticmethod
    def validate_string(cls, value):
        """See if it is an integer."""

        return re.match("-?[0-9]+", value) is not None

    @staticmethod
    def validate_native(cls, value):
        """See if it is prime."""

        #calling a hypothetical function that checks if it is prime
        return is_prime(value)

Summary¶

• Simple checks can be applied at the XML schema level, you can control: - The length of a string, - The pattern with which a string must comply, - A numeric interval, etc.
• Spyne can apply arbitrary rules for the validation of input data: - validate_string is the first applied filter. - validate_native is the applied at the second phase. - Override these functions in your derived class to add new validation rules. - The validation functions must return a boolean value. - These rules are not shown in the XML schema.

What’s next?¶

Now that you’ve also learned how to tame incoming data, you can have a look at the SQLAlchemy Integration document where we explain how to easily integrate with SQLAlchemy by showing how to map Spyne objects to table definitions and rows returned by database queries.

You could also have a look at the Working with RPC Metadata section where service metadata management apis are introduced.

Otherwise, please refer to the rest of the documentation or the mailing list if you have further questions.