isitbullshit 0.2.0

Library to help with matching between JSON and simple schema

isitbullshit is small and funny library which is intended to be used like lightweight schema verification for JSONs but basically it could be used as a schema validator for every generic Python structure: dict, list, tuple etc. It is written to be pretty much Pythonic in a good sense: easy to use and very clean syntax but powerful enough to clean your needs. But mostly for verification of incoming JSONs. Actually it is really stable and I am using it in a several production projects, this is an excerpt because I really got tired from wheel reinventions.

Yes, this is a wheel reinvention also but probably you will like it. Let me show the code.

An example

Okay, let’s say you are doing some backend for the library and you have to process JSONs like this:

{
    "model": "book_collection",
    "pk": 318,
    "fields": {
        "books": [
            {
                "model": "book",
                "pk": 18,
                "fields": {
                    "title": "Jane Eyre",
                    "author": "Charlotte Brontë",
                    "isbn": {
                        "10": "0142437204",
                        "13": "978-0142437209"
                    },
                    "rate": null,
                    "language": "English",
                    "type": "paperback",
                    "tags": [
                        "Penguin Classics",
                        "Classics",
                        "Favorites"
                    ],
                    "published": {
                        "publisher": "Penguin Books",
                        "date": {
                            "day": 24,
                            "month": 4,
                            "year": 2003
                        }
                    }
                }
            },
            {
                "model": "book",
                "pk": 18,
                "fields": {
                    "title": "The Great Gatsby",
                    "author": "F.Scott Fitzgerald",
                    "isbn": {
                        "10": "185326041X",
                        "13": "978-1853260414"
                    },
                    "language": "English",
                    "type": "paperback",
                    "finished": true,
                    "rate": 4,
                    "tags": [
                        "Wordsworth Classics",
                        "Classics",
                        "Favorites"
                    ],
                    "published": {
                        "publisher": "Wordsworth Editions Ltd",
                        "date": {
                            "day": 1,
                            "month": 5,
                            "year": 1992
                        }
                    }
                }
            }
        ]
    }
}

You’ve got an idea, right? Pretty common and rather simple. Let’s compose a schema and verify it.

from json import loads
from isitbullshit import isitbullshit, OrSkipped

def rate_validator(value):
    if not (1 <= int(value) <= 5):
        raise ValueError(
            "Value {} has to be from 1 till 5".format(value)
        )

data = loads(request)
schema = {
    "model": str,
    "pk": int,
    "fields": {
        "books": [
            {
                "model": str,
                "pk": int,
                "fields": {
                    "title": str,
                    "author": str,
                    "isbn": {
                        "10": str,
                        "13": str
                    },
                    "language": str,
                    "type": ("paperback", "kindle"),
                    "finished": OrSkipped(True),
                    "rate": (rate_validator, None),
                    "tags": [str],
                    "published": {
                        "publisher": str,
                        "date": OrSkipped(
                            {
                                "day": int,
                                "month": int,
                                "year": int
                            }
                        )
                    }
                }
            }
        ]
    }
}

if isitbullshit(data, schema):
    raise Error400("Incoming request is not valid")
process(data)

Pretty straightforward. Let me explain what is going on here.

Basic concepts

isitbullshit was created to be used with JSONs and actively uses this fact that JSON perfectly matches to Python internal data structures. Basic rule here: if elements are equal then they should be validated without any problems.

So if you have a code like

>>> suspicious = {
...     "foo": 1,
...     "bar": 2
... }

then

>>> print isitbullshit(suspicious, suspicious)
False

Keep this in mind.

If elements are equal then no additional validation steps have to be used. Otherwise it tries to match types and do some explicit assertions.

So there are some rules.

Value validation

Value validation is pretty straighforward: if values are the sames or they are equal to each other (operation =) then validation has to be passed. So the rule is: if is or = works, then matching is successful.

>>> print isitbullshit(1, 1)
False
>>> print isitbullshit(1.0, 1.0)
False
>>> print isitbullshit(1.0, decimal.Decimal("1.0"))
False
>>> print isitbullshit(None, None)
False
>>> obj = object()
>>> print isitbullshit(obj, obj)
False

Type validation

If value validation is not passed then type validation is performed. The idea is: 1 is 1, right? But you will be satisfied if you know that 1 is int as well, right?

So

>>> print isitbullshit(1, int)
False
>>> print isitbullshit(1.0, float)
False
>>> print isitbullshit(decimal.Decimal("1.0"), decimal.Decimal)
False
>>> obj = object()
>>> print isitbullshit(obj, object)
False

Custom validation

Let’s back to an example. Have you mentioned that we have rate_validator function there? It is custom validator.

It works pretty simple: you define custom callable (function, lambda, class, etc) and isitbullshit gives it your value. If no exception is raised than we consider the value as successfully validated. So in our example if a rate field is not in (1, 5) interval or not integer then exception will be raised.

Custom validators are used mostly in cases if you have to check a content or do not so shallow verifications.

Another example is MongoDB. Do you use ObjectId?

>>> print isitbullshit(1, bson.ObjectId)
True
>>> print isitbullshit("507c7f79bcf86cd7994f6c0e", bson.ObjectId)
False

I hope you’ve got an idea.

OrSkipped validator

Sometimes we live in a real world which sucks. Sometimes we have schemaless data (and it sucks of course) so some fields from your requests are missed. Or you do not care. isitbullshit has 2 different fixes for that: OrSkipped and WHATEVER.

If you wrap a part of your validator in OrSkipped than you mark that it is ok if this field would be absent. Argument is a validator of course. And if field is in place, it will be validated as expected.

>>> schema = {
...     "foo": 1,
...     "bar": OrSkipped(int),
...     "baz": OrSkipped(str)
>>> }
>>> print isitbullshit({"foo": 1, "bar": 1}, schema)
False
>>> print isitbullshit({"foo": 1, "bar": "str"}, schema)
True
>>> print isitbullshit({"foo": 1, "bar": 1, "baz": 1}, schema)
True
>>> print isitbullshit({"foo": 1, "bar": 1, "baz": "str"}, schema)
False

So if we miss any field, it is ok. Unless it is presented and validator-argument point us to a bullshit.

OrSkipped has to be used only with dictionary field validation. You can put it anywhere but then it has no special meaning, just an object.

By the way, type validation rule is still here: itisbullshit(something, something) == False anyway so the following code is valid (and it is reasonable, right?)

>>> schema = {
...     "foo": 1,
...     "bar": OrSkipped(int),
...     "baz": OrSkipped(str)
>>> }
>>> isitbullshit(schema, schema)
False
>>> stripped_schema = dict((k, v) for k, v in schema.iteritems() if k != "baz")
>>> isitbullshit(stripped_schema, schema)
False
>>> isitbullshit(schema, stripped_schema)
False

Guess why.

WHATEVER validator

WHATEVER is a mark that you do not care what value is. It could be anything, none cares.

>>> schema = {
...     "foo": 1,
...     "bar": WHATEVER
>>> }
>>> print isitbullshit({"foo": 1, "bar": 1}, schema)
False
>>> print isitbullshit({"foo": 1, "bar": "str"}, schema)
False
>>> print isitbullshit({"foo": 1, "bar": object()}, schema)
False
>>> print isitbullshit({"foo": 1, "bar": os.path}, schema)
False
>>> print isitbullshit({"foo": 1, "bar": [1, 2, 3]}, schema)
False

See? We do not care about a value of a bar.

WHATEVER could be used with any type.

Dict validation

You’ve already saw a dict validation so let me repeat your assumptions: yes, we match values with the same keys. But there is only one pitfall: if suspicious element has more fields than schema, then validation is ok also.

It has it’s own meaning: we can put only those keys and fields we actually care about. Our software later will work only with this subset so why should we care about the rest of rubbish?

So, an example again:

>>> schema = {
...     "foo": 1,
...     "bar": str
>>> }
>>> print isitbullshit({"foo": 1, "bar": "st"}, schema)
False
>>> print isitbullshit({"foo": 1, "bar": "str", "baz": 1}, schema)
False
>>> print isitbullshit({"foo": 1, "bar": "str", "baz": object()}, schema)
False

As you can see, we did not mention any baz in an element but validation still passed.

List validation

List validation is pretty simple: we define one validator and it will be matched to any list element.

>>> print isitbullshit([1, 2, 3], [int])
False
>>> print isitbullshit([1, 2, 3], [str])
True
>>> print isitbullshit([1, 2, "3"], [int])
True

In the last example, "3" is not an integer so fail.

How could we manage situations when we have heterogeneous elements? We have to use tuples.

And please remember that isitbullshit(something, something) == False.

Tuple validation

Tuple validation is pretty easy to understand if you consider it as a OR condition. We define several validators and value has to match at least one. So

>>> print isitbullshit(1, (str, dict))
True
>>> print isitbullshit(1, (str, int))
False

1 is not str but it is int.

Now let’s try to fix an example in the previous chapter.

>>> print isitbullshit([1, 2, "3"], [int])
True
>>> print isitbullshit([1, 2, "3"], [(int, str)])
False

And again, do not forget about a rule of thumb: isitbullshit(something, something) == False.

raise_for_problem function

This package also provides you with another method to validate, raise_for_problem actually this is a core method which raises an exception on a problem. isitbullshit allows you to get an idea what is happening in both Python2 and Python3, let’s check an example.

>>> try:
...     raise_for_problem({"foo": "1", "bar": {"baz": 2}}, {"foo": "1", "bar": {"baz": str}})
... except ItIsBullshitError as err:
...     print err
{'foo': '1', 'bar': {'baz': 2}}:
    {'baz': 2}:
        2: Scheme mismatch <type 'str'>

Quite clear and nice.

IsItBullshitMixin mixin

isitbullshit also supplied with IsItBullshitMixin which is intended to be mixed with unittest.TestCase. It allows you to use 2 additional methods:

• assertBullshit

• assertNotBullshit

Guess what do they do.

from unittest import TestCase
from isitbullshit import IsItBullshitMixin

class BullshitTestCase(IsIsBullshitMixin, TestCase):

    def test_bullshit(self):
        self.assertBullshit(1, None)

    def test_not_bullshit(self):
        self.assertNotBullshit(1, int)