vtjson 1.3.5

A lightweight package for validating JSON like Python objects

vtjson

A lightweight package for validating JSON like Python objects.

Schemas

Validation of JSON like Python objects is done according to a "schema" which is somewhat inspired by a typescript type. The format of a schema is more or less self explanatory as the following example shows.

Example

Below is the schema of the run object in the mongodb database underlying the Fishtest web application https://tests.stockfishchess.org/tests

import math
from datetime import datetime
from bson.objectid import ObjectId
from vtjson import ip_address, number, regex, url

net_name = regex("nn-[a-z0-9]{12}.nnue", name="net_name")
tc = regex(r"([1-9]\d*/)?\d+(\.\d+)?(\+\d+(\.\d+)?)?", name="tc")
str_int = regex(r"[1-9]\d*", name="str_int")
sha = regex(r"[a-f0-9]{40}", name="sha")
country_code = regex(r"[A-Z][A-Z]", name="country_code")
run_id = regex(r"[a-f0-9]{24}", name="run_id")

worker_info_schema = {
    "uname": str,
    "architecture": [str, str],
    "concurrency": int,
    "max_memory": int,
    "min_threads": int,
    "username": str,
    "version": int,
    "python_version": [int, int, int],
    "gcc_version": [int, int, int],
    "compiler": {"clang++", "g++"},
    "unique_key": str,
    "modified": bool,
    "ARCH": str,
    "nps": number,
    "near_github_api_limit": bool,
    "remote_addr": ip_address,
    "country_code": {country_code, "?"},
}

results_schema = {
    "wins": int,
    "losses": int,
    "draws": int,
    "crashes": int,
    "time_losses": int,
    "pentanomial": [int, int, int, int, int],
}

schema = {
    "_id?": ObjectId,
    "start_time": datetime,
    "last_updated": datetime,
    "tc_base": number,
    "base_same_as_master": bool,
    "results_stale?": bool,
    "rescheduled_from?": run_id,
    "approved": bool,
    "approver": str,
    "finished": bool,
    "deleted": bool,
    "failed": bool,
    "is_green": bool,
    "is_yellow": bool,
    "workers?": int,
    "cores?": int,
    "results": results_schema,
    "results_info?": {
        "style": str,
        "info": [str, ...],
    },
    "args": {
        "base_tag": str,
        "new_tag": str,
        "base_net": net_name,
        "new_net": net_name,
        "num_games": int,
        "tc": tc,
        "new_tc": tc,
        "book": str,
        "book_depth": str_int,
        "threads": int,
        "resolved_base": sha,
        "resolved_new": sha,
        "msg_base": str,
        "msg_new": str,
        "base_options": str,
        "new_options": str,
        "info": str,
        "base_signature": str_int,
        "new_signature": str_int,
        "username": str,
        "tests_repo": url,
        "auto_purge": bool,
        "throughput": number,
        "itp": number,
        "priority": number,
        "adjudication": bool,
        "sprt?": {
            "alpha": 0.05,
            "beta": 0.05,
            "elo0": number,
            "elo1": number,
            "elo_model": "normalized",
            "state": {"", "accepted", "rejected"},
            "llr": number,
            "batch_size": int,
            "lower_bound": -math.log(19),
            "upper_bound": math.log(19),
            "lost_samples?": int,
            "illegal_update?": int,
            "overshoot?": {
                "last_update": int,
                "skipped_updates": int,
                "ref0": number,
                "m0": number,
                "sq0": number,
                "ref1": number,
                "m1": number,
                "sq1": number,
            },
        },
        "spsa?": {
            "A": number,
            "alpha": number,
            "gamma": number,
            "raw_params": str,
            "iter": int,
            "num_iter": int,
            "params": [
                {
                    "name": str,
                    "start": number,
                    "min": number,
                    "max": number,
                    "c_end": number,
                    "r_end": number,
                    "c": number,
                    "a_end": number,
                    "a": number,
                    "theta": number,
                },
                ...,
            ],
            "param_history?": [
                [{"theta": number, "R": number, "c": number}, ...],
                ...,
            ],
        },
    },
    "tasks": [
        {
            "num_games": int,
            "active": bool,
            "last_updated": datetime,
            "start": int,
            "residual?": number,
            "residual_color?": str,
            "bad?": True,
            "stats": results_schema,
            "worker_info": worker_info_schema,
        },
        ...,
    ],
    "bad_tasks?": [
        {
            "num_games": int,
            "active": False,
            "last_updated": datetime,
            "start": int,
            "residual": number,
            "residual_color": str,
            "bad": True,
            "task_id": int,
            "stats": results_schema,
            "worker_info": worker_info_schema,
        },
        ...,
    ],
}

Conventions

• As in typescript, a (string) key ending in "?" represents an optional key. The corresponding schema (the item the key points to) will only be used for validation when the key is present in the object that should be validated. A key can also be made optional by wrapping it as optional_key(key).
• If in a list/tuple the last entry is ... (ellipsis) it means that the next to last entry will be repeated zero or more times. In this way generic types can be created. For example the schema [str, ...] represents a list of strings.

Usage

To validate an object against a schema one can simply do

validate(schema, object)

If the validation fails this will throw a ValidationError and the exception contains an explanation about what went wrong. The full signature of validate is

validate(schema, object, name="object", strict=True)

• The optional name argument is used to refer to the object being validated in the returned message.
• The optional argument strict indicates whether or not the object being validated is allowed to have keys/entries which are not in the schema.

Wrappers

A wrapper takes one or more schemas as arguments and produces a new schema.

• An object matches the schema union(schema1, ..., schemaN) if it matches one of the schemas schema1, ..., schemaN. This is almost the same as {schema1, ..., schemaN}, or equivalently set((schema1, ..., schemaN)) if schema1, ..., schemaN are hashable.
• An object matches the schema intersect(schema1, ..., schemaN) if it matches all the schemas schema1, ..., schemaN.
• An object matches the schema complement(schema) if it does not match schema.
• An object matches the schema lax(schema) when it matches schema with strict=False, see below.
• An object matches the schema strict(schema) when it matches schema with strict=True, see below.
• An object matches the schema quote(schema) if it is equal to schema. For example the schema {"cats", "dogs"} matches the strings "cats" and "dogs" but the schema quote({"cats", "dogs"}) matches the set {"cats", "dogs"}.
• An object matches the schema set_name(schema, name) if it matches schema. But the name argument will be used in non-validation messages.

Built-ins

Some built-ins take arguments. If no arguments are given then the () can be omitted. So email is equivalent to email().

• regex(pattern, name=None, fullmatch=True, flags=0). This matches the strings which match the given pattern. The optional name argument may be used to give the regular expression a descriptive name. By default the entire string is matched, but this can be overruled via the fullmatch argument. The flags argument has the usual meaning.
• interval(lowerbound, upperbound). This checks if lowerbound <= object <= upperbound, provided the comparisons make sense. An upper/lowerbound ... (ellipsis) means that the corresponding inequality is not checked.
• number. Matches int and float.
• email. Checks if the object is a valid email address. This uses the package email_validator. The email schema accepts the same options as validate_email in loc. cit.
• ip_address and url. These are similar to email.
• domain_name(ascii_only=True, resolve=False). Checks if the object is a valid domain name. If ascii_only=False then allow IDNA domain names. If resolve=True check if the domain name resolves.
• date_time(format=None). Without argument this represents an ISO 8601 date-time. The format argument represents a format string for strftime.
• date and time. These represents an ISO 8601 date and an ISO 8601 time.

Format

A schema can be, in order of precedence:

• An object having a __validate__ attribute with signature

  __validate__(object, name, strict)
  

  This is for example how the wrapper schemas are implemented internally. The parameters of __validate__() have the same semantics as those of validate(). The return value of __validate__() should be the empty string if validation succeeds, and otherwise it should be an explanation about what went wrong.
• A Python type. In that case validation is done by checking membership.
• A callable. Validation is done by applying the callable to the object. If applying the callable throws an exception then the corresponding message will be part of the non-validation message.
• A list or a tuple. Validation is done by first checking membership of the corresponding types, and then performing validation for each of the entries of the object being validated against the corresponding entries of the schema.
• A dictionary. Validation is done by first checking membership of the dict type, and then performing validation for each of the items of the object being validated against the corresponding items of the schema.
• A set. A set validates an object, if one of its members does.
• An arbitrary Python object. Validation is done by checking equality of the schema and the object, except when the schema is of type float, in which case math.isclose is used.

Creating types

A cool feature of vtjson is that one can transform a schema into a genuine Python type via

t = make_type(schema)

so that validation can be done via

isinstance(object, t)

The drawback, compared to using validate directly, is that there is no feedback when validation fails. You can get it back as a console debug message via the optional debug argument to make_type. The full signature of make_type is

make_type(schema, name=None, strict=True, debug=False)

The optional name argument is used to set the __name__ attribute of the type. If it is not supplied then vtjson tries to make an educated guess.

Examples

>>> from vtjson import set_name, validate
>>> schema = {"fruit" : {"apple", "pear", "strawberry"}, "price" : float}
>>> object = {"fruit" : "dog", "price": 1.0 }
>>> validate(schema, object)
...
vtjson.ValidationError: object['fruit'] (value:dog) is not equal to 'pear' and object['fruit'] (value:dog) is not equal to 'strawberry' and object['fruit'] (value:dog) is not equal to 'apple'
>>> fruit = set_name({"apple", "pear", "strawberry"}, "fruit")
>>> schema = {"fruit" : fruit, "price" : float}
>>> validate(schema, object)
...
vtjson.ValidationError: object['fruit'] (value:dog) is not of type 'fruit'
>>> object = {"fruit" : "apple"}
>>> validate(schema, object)
...
vtjson.ValidationError: object['price'] is missing

For many more examples see the file test_validate.py in the source distribution.

FAQ

Q: Why not just use the Python implementation of JSON schema (see https://pypi.org/project/jsonschema/)?

A: Various reasons.

• A vtjson schema is much more concise than a JSON schema!
• vtjson can validate objects which are more general than strictly JSON. See the introductory example above.
• More fundamentally, the design philosophy of vtsjon is different. A JSON schema is language independent and fully declarative. These are very nice properties but, this being said, declarative languages have a tendency to suffer from feature creep as they try to deal with more and more exotic use cases (e.g. css). A vtjson schema on the other hand leverages the versatility of the Python language. It is generally declarative, with a limited, but easily extendable set of primitives. But if more functionality is needed then it can be extended by using appropriate bits of Python code (as the ordered_pair example below illustrates). In practice this is what you will need in any case since a purely declarative language will never be able to deal with every possible validation scenario.

Q: How is this different from https://pypi.org/project/json-checker/ ?

A: Good question! I discovered json-checker after I had written vtjson. Although the details are different json-checker and vtjson share many of the same principles.

Q: How to combine validations?

A: Use intersect. For example the following schema validates positive integers but reject positive floats.

schema = intersect(int, interval(0, ...))

More generally one may use the pattern intersect(schema, more_validations) where the first argument makes sure that the object to be validated has the required layout to be an acceptable input for the later arguments. For example an ordered pair of integers can be validated using the schema

def ordered_pair(o):
    return o[0] <= o[1]
schema = intersect((int, int), ordered_pair)

Or in a one liner

schema = intersect((int, int), set_name(lambda o: o[0] <= o[1], "ordered_pair"))

The following also works if you are content with less nice output on validation failure (try it)

schema = intersect((int, int), lambda o: o[0] <= o[1])