Set of tools that makes input data validation easier
Project description
Schematec
=========
.. image:: https://travis-ci.org/mylokin/redisext.svg?branch=master
:target: https://travis-ci.org/mylokin/redisext
Schematec is a set of tools that makes input data validation easier.
The purpose of this code is attempt to bring simplicity to applications
logics using separation of data validation and actual data processing.
Quickstart
----------
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
name=s.string,
tags=s.array(s.string),
)
.. code:: python
>>> data = {
... 'id': '1',
... 'name': 'Red Hot Chili Peppers',
... 'tags': ['funk', 'rock'],
... 'rank': '1',
... }
>>> schema(data)
{'id': 1, 'name': u'Red Hot Chili Peppers', 'tags': [u'funk', u'rock']}
Concepts
--------
Schematec module is based on three basic concepts:
* Schema
* Validator
* Converter
Schema
^^^^^^
Term "schema" is used to describe complex data struct such as dictionary(hashmap)
or array(list). Schemas has two different types of validation (it is not related to
array schemas):
* Default - Only values with required validator are required, other values are optional
* Weak - All values are optional
`schematec.exc.SchemaError` is raised in case provided data is incorrect.
Order of schema validations:
#. Unbound Validators
#. Schemas(inner)
#. Converters
#. Bound Validators
Validator
^^^^^^^^^
Term "validator" describes callable objects that perform different types of checks.
There are two types of validators in schematec:
* Bound - type related, for example "max length" validator is bound to sized type.
* Unbound - universal, for example "required" validator.
Raises `schematec.exc.ValidationError`.
Schematec provides following validators:
required
check if value is provided
length
check iterable for max length
regex
check if given value is valid
Converter
^^^^^^^^^
Term "converter" is used to describe cast functions. Schematec supports subset of JSON
data types.
Basic types:
- integer(int)
- string(str)
- boolean(bool)
Containers:
- array(list)
- dictionary(dict)
Raises `schematec.exc.ConvertationError`.
Convertation rules
=================
integer
-------
#. Any int or long value
#. Any suitable string/unicode
#. Boolean value
number
-------
#. Any float or int or long value
#. Any suitable string/unicode
#. Boolean value
string
------
#. Any suitable string/unicode
#. Any int or long value
boolean
-------
#. Boolean value
#. 0 or 1
#. '0' or '1'
#. u'0' or u'1'
dictionary
----------
#. Any mapping value(collections.Mapping)
array
-----
#. Any iterable value(collections.Iterable), but not a mapping
Complex Descriptors
===================
"Schema", "validator" and "converter" are internally referenced as "descriptors". Common task is
creation of complex validation rules for a field(or "complex descriptors"). To do this use bitwise
"and" operator on descriptors:
.. code:: python
>>> import schematec
>>> schematec.integer & schematec.required
<schematec.abc.ComplexDescriptor object at 0x10b05a0d0>
Sugar Schema
============
Schematec supports additional "magic" way to define your schemas. You can use simple dicts and lists
to describe your data. For example:
.. code:: python
>>> import schematec as s
>>> schema = {
... 'a': [{
... 'b': s.integer,
... }]
... }
>>> data = {
... 'a': [{'b': 1}, {'b': '1'}, {}]
... }
>>> s.process(schema, data)
{'a': [{'b': 1}, {'b': 1}, {}]}
Examples
========
Recursive schema
----------------
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
entity=s.dictionary(
name=s.string & s.required,
value=s.string,
)
)
.. code:: python
>>> data = {
... 'id': 1,
... 'entity': {
... 'name': 'song',
... 'value': 'californication',
... }
... }
>>> schema(data)
{'id': 1, 'entity': {'name': u'song', 'value': u'californication'}}
Errors handling
---------------
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
entity=s.dictionary(
name=s.string & s.required,
value=s.string,
)
)
.. code:: python
>>> data = {
... 'id': 1,
... 'entity': {
... 'value': 'californication',
... }
... }
>>> schema(data)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "schematec/schema.py", line 44, in __call__
value = schema(value, weak=weak)
File "schematec/schema.py", line 32, in __call__
validator(name, data)
File "schematec/validators.py", line 12, in __call__
raise exc.ValidationError(name)
schematec.exc.ValidationError: name
=========
.. image:: https://travis-ci.org/mylokin/redisext.svg?branch=master
:target: https://travis-ci.org/mylokin/redisext
Schematec is a set of tools that makes input data validation easier.
The purpose of this code is attempt to bring simplicity to applications
logics using separation of data validation and actual data processing.
Quickstart
----------
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
name=s.string,
tags=s.array(s.string),
)
.. code:: python
>>> data = {
... 'id': '1',
... 'name': 'Red Hot Chili Peppers',
... 'tags': ['funk', 'rock'],
... 'rank': '1',
... }
>>> schema(data)
{'id': 1, 'name': u'Red Hot Chili Peppers', 'tags': [u'funk', u'rock']}
Concepts
--------
Schematec module is based on three basic concepts:
* Schema
* Validator
* Converter
Schema
^^^^^^
Term "schema" is used to describe complex data struct such as dictionary(hashmap)
or array(list). Schemas has two different types of validation (it is not related to
array schemas):
* Default - Only values with required validator are required, other values are optional
* Weak - All values are optional
`schematec.exc.SchemaError` is raised in case provided data is incorrect.
Order of schema validations:
#. Unbound Validators
#. Schemas(inner)
#. Converters
#. Bound Validators
Validator
^^^^^^^^^
Term "validator" describes callable objects that perform different types of checks.
There are two types of validators in schematec:
* Bound - type related, for example "max length" validator is bound to sized type.
* Unbound - universal, for example "required" validator.
Raises `schematec.exc.ValidationError`.
Schematec provides following validators:
required
check if value is provided
length
check iterable for max length
regex
check if given value is valid
Converter
^^^^^^^^^
Term "converter" is used to describe cast functions. Schematec supports subset of JSON
data types.
Basic types:
- integer(int)
- string(str)
- boolean(bool)
Containers:
- array(list)
- dictionary(dict)
Raises `schematec.exc.ConvertationError`.
Convertation rules
=================
integer
-------
#. Any int or long value
#. Any suitable string/unicode
#. Boolean value
number
-------
#. Any float or int or long value
#. Any suitable string/unicode
#. Boolean value
string
------
#. Any suitable string/unicode
#. Any int or long value
boolean
-------
#. Boolean value
#. 0 or 1
#. '0' or '1'
#. u'0' or u'1'
dictionary
----------
#. Any mapping value(collections.Mapping)
array
-----
#. Any iterable value(collections.Iterable), but not a mapping
Complex Descriptors
===================
"Schema", "validator" and "converter" are internally referenced as "descriptors". Common task is
creation of complex validation rules for a field(or "complex descriptors"). To do this use bitwise
"and" operator on descriptors:
.. code:: python
>>> import schematec
>>> schematec.integer & schematec.required
<schematec.abc.ComplexDescriptor object at 0x10b05a0d0>
Sugar Schema
============
Schematec supports additional "magic" way to define your schemas. You can use simple dicts and lists
to describe your data. For example:
.. code:: python
>>> import schematec as s
>>> schema = {
... 'a': [{
... 'b': s.integer,
... }]
... }
>>> data = {
... 'a': [{'b': 1}, {'b': '1'}, {}]
... }
>>> s.process(schema, data)
{'a': [{'b': 1}, {'b': 1}, {}]}
Examples
========
Recursive schema
----------------
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
entity=s.dictionary(
name=s.string & s.required,
value=s.string,
)
)
.. code:: python
>>> data = {
... 'id': 1,
... 'entity': {
... 'name': 'song',
... 'value': 'californication',
... }
... }
>>> schema(data)
{'id': 1, 'entity': {'name': u'song', 'value': u'californication'}}
Errors handling
---------------
.. code:: python
import schematec as s
schema = s.dictionary(
id=s.integer & s.required,
entity=s.dictionary(
name=s.string & s.required,
value=s.string,
)
)
.. code:: python
>>> data = {
... 'id': 1,
... 'entity': {
... 'value': 'californication',
... }
... }
>>> schema(data)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "schematec/schema.py", line 44, in __call__
value = schema(value, weak=weak)
File "schematec/schema.py", line 32, in __call__
validator(name, data)
File "schematec/validators.py", line 12, in __call__
raise exc.ValidationError(name)
schematec.exc.ValidationError: name
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