Tissuebox is a pure Pythonic schema validator which takes advantage of Python's functional style programming to provide simple yet powerful validation framework. The standard usage would be validating incoming JSON objects upon http requests or to validate any Python dict in other common scenarios.
Use pip
to install Tissuebox
pip install tissuebox
Tissuebox requires Python 3.7 however we are considering to add support for earlier versions of Python3
Assume the incoming JSON object or a python dict which contains hotel details and we will build upon this example.
payload = {
"name": "Park Shereton",
"available": True,
"price_per_night": 270,
"email": "contact@shereton.com",
"web": "www.shereton.com",
}
You can use tissuebox
to define a schema to validate the payload against basic data types and validate using validate
method.
from tissuebox import validate
from tissuebox.basic import boolean, integer, string
schema = {
'name': string,
'available': boolean,
'price_per_night': integer
}
validate(schema, payload)
will return
(True, [])
A tissuebox
schema is simply a dict where keys are payload keys and values are type_functions to which the payload value would be passed. A type_function simply accepts a single parameter and returns a tuple with two items (boolean, msg)
.
Tissuebox aims to amass a collection of commonly used types to it's library. For now common data types like email
, url
, rfc_datetime
, geolocation
are part of tissuebox
's standard collections. You can contribute more via Github.
from tissuebox import validate
from tissuebox.basic import email, integer, string, url
schema = {
'name': string,
'price_per_night': integer,
"email": email,
"web": url
}
validate(schema, payload)
will return
(True, [])
One of the ways tissuebox
stands our from other alternatives is, the type_functions are stored and passed around as Python variables which is helpful in identifying the schema definition errors ahead of time as most IDEs will display squiggly lines if the variables aren't resolved, while other frameworks like JsonSchema and Cerebrus pass types within strings which is hard for IDEs to detect errors in the schema.
Defining a schema in a nested fashion is very straight forward which enables re-use schemas around. Consider if the payload has an address
field. We can define a separate schema as address_schema
and pass it to the main schema as below.
from tissuebox import validate
from tissuebox.basic import email, integer, string, url
payload = {
"name": "Park Shereton",
"available": True,
"price_per_night": 270,
"email": "contact@shereton.com",
"web": "www.shereton.com",
"address": {
"street": "128 George St",
"city": "Sydney",
"state": "NSW",
"zip": 2000
}
}
address = {
"street": string,
"city": string,
"state": string,
"zip": integer
}
schema = {
'name': string,
'price_per_night': integer,
"email": email,
"web": url,
"address": address
}
validate(schema, payload)
would return
(True, [])
The prefered method of defining nested schema is by using .
dot as delimiter to represent nested fields of the payload hierarchy. Apparently this comes up with the downside wherein if .
dot itself is part of keys which would be an unfortunate scenario. But it can improve the readability to a tremendous level. See it yourself how elegantly we can express the schema once we introduce the address
field to our payload.
schema = {
'name': string,
'price_per_night': integer,
"email": email,
"web": url,
"address.street": string,
"address.city": string,
"address.state": string,
"address.zip": integer
}
The primary reason why we suggest the later method is we can quickly define a nested field with any depth without creating unnecessary schema objects in the middle.
Let us try enforcing that the field address.state
must be one of 8 Australian states. Tissuebox let's you define an enum using the {}
i.e set()
syntax. Look at the example below.
schema = {
'name': string,
'price_per_night': integer,
"email": email,
"web": url,
"address.state": {'ACT', 'NSW', 'NT', 'QLD', 'SA', 'TAS', 'VIC', 'WA'},
"address.zip": integer
}
To have a feel how Tissuebox responds when we pass something which is not an Australian state
payload = {
"name": "Park Shereton",
"available": True,
"price_per_night": 270,
"email": "contact@shereton.com",
"web": "www.shereton.com",
"address": {
"street": "128 George St",
"city": "Sydney",
"state": "TX",
"zip": 2000
}
}
validate(schema, hotel)
would return
(False, ['["address"]["state"] is failing to be enum of `{\'SA\', \'QLD\', \'NT\', \'TAS\', \'VIC\', \'WA\', \'ACT\', \'NSW\'}`'])
Let us assume the payload has staffs
which is array of staff names.
payload = {
"name": "Park Shereton",
"email": "contact@shereton.com",
"web": "www.shereton.com",
"staffs" ["John Doe", "Jane Smith"],
}
Now the schema simple looks as below
schema = {
'name': string,
"email": email,
"web": url,
"staffs": [string]
}
So in order to declare an element as array simply use []
syntax, if it's array of string simply say [string]
. If it's array of cats simply say [cat]
. Array syntax can be either empty or single length where the element means a type_function or another nested schema.
There are two scenarios where Tissuebox implicitly handles the array.
- The incoming payload is simply list of dicts then Tissuebox knows that the given schema must be validated against all the items in the array.
- While declaring
.
dot separated nested attribute, and any of the middle element is array, Tissuebox is aware of such fact and will iterate the validation automatically.
These two cases are implemented to make Tissuebox as intuitive as possible,
By now you would have observed that tissuebox
schema is simply a collection of key:value
pairs where value
contains the data type verified against. tissuebox
defines them in the style of type_function
which is simply a boolean function that takes one or more parameters.
Let us assume you want to validate the zip code as a valid Australian one. Since tissuebox
does't have a built-in type function, for that purpose you can come up with your own type function as below. For brevity I've removed few fields in the payload & schema.
>>> def australian_zip(x):
... # https://www.etl-tools.com/regular-expressions/is-australian-post-code.html
... x = str(x)
... import re
... return re.match(r'^(0[289][0-9]{2})|([1345689][0-9]{3})|(2[0-8][0-9]{2})|(290[0-9])|(291[0-4])|(7[0-4][0-9]{2})|(7[8-9][0-9]{2})$', x), "must be a valida Australian zip"
...
>>> hotel = {
... "address": {
... "zip": 200
... }
... }
>>>
>>> schema = {
... "address.zip": australian_zip
... }
>>>
>>> validate(schema, hotel)
(False, ['["address"]["zip"] must be a valida Australian zip"])
In tissuebox
type_functions always accept one argument which is the payload value. There are times for a type_function it makes sense to accepts multiple parameters. To achieve that they are declared as Python's higher order functions.
Let us try validating where the price_per_night
must be multiple of 50. Also let us declare the Yelp review rating of a hotel must be between 1-5.
>>> from tissuebox import validate
>>> from tissuebox.basic import between, divisible, string
>>> schema = {
... "name": string,
... "rating": between(1, 5),
... "price_per_night": divisible(50)
... }
>>>
>>> hotel = {
... "name": "Park Shereton",
... "price_per_night": 370,
... "rating": 5.1
... }
>>>
>>> validate(schema, hotel)
(False, [
'["price_per_night"] is failing to be `divisible(50)`',
'["rating"] is failing to be `between(1, 5)`'
])
For curiosity here is the implementation of divisible
from Tissuebox library. It has been defined as a higher order function which returns another function which always accepts single parameter. While writing custom validators you are encouraged to use the same pattern.
def divisible(n):
def divisible(x):
return numeric(x) and numeric(n) and x % n == 0, "multiple of {}".format(n)
return divisible
As we have observed tissuebox
schema is a dict with key:value
format. In Python keys in dicts are unique. It's a terrible idea to redeclare same key since the data will be overridden.
Assume that you are attempting to do something like this
from tissuebox.basic import divisible, integer, positive, string
schema = {
'name': string,
'price_per_night': integer,
'price_per_night': positive,
'price_per_night': divisible(50),
"address.zip": integer
}
Here price_per_night
will be overridden by the latest declaration which must be avoided. This can be solved with another special syntax which yet Pythonic
Simply use ()
to chain type_functions.
```python
from tissuebox.basic import divisible, integer, positive, string
schema = {
'name': string,
'price_per_night': (integer, positive, divisible(50)),
"address.zip": integer
}
```
Now Tissuebox will iterate all these conditions against price_per_night
While Tissuebox validates the values with type_functions, it only does so only for the values are found in the payload. Otherwise they were simply ignored silently.
In a situation where a specific value is expected in payload declared them as required
function. And it's a common scenario to combine them under ()
operator as described in the above.
from tissuebox.basic import integer, required, string
schema = {
'name': (required, string),
"address.city": (required, string),
"address.zip": integer
}
- Tissuebox has lots of advantages than the current alternatives like jsonschema, cerebrus etc.
- Truly Pythonic and heavily relies on short & static methods. The schema definition itself takes full advantages of Python's built-in syntax like
{}
for enum,()
for parameterized function,[]
chaining multiple rules etc - Highly readable with concise schema definition.
- Highly extensible with ability to insert your own custom methods without complicated class inheritance.
- Ability to provide all the error messages upfront upon validation.