vtjson 1.8.2

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 a simplified version of 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 glob, 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")
uuid = regex(r"[0-9a-zA-Z]{2,}(-[a-f0-9]{4}){3}-[a-f0-9]{12}", name="uuid")
epd_file = glob("*.epd", name="epd_file")
pgn_file = glob("*.pgn", name="pgn_file")

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": union("clang++", "g++"),
    "unique_key": uuid,
    "modified": bool,
    "ARCH": str,
    "nps": number,
    "near_github_api_limit": bool,
    "remote_addr": ip_address,
    "country_code": union(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,
    "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_nets": [net_name, ...],
        "new_nets": [net_name, ...],
        "num_games": int,
        "tc": tc,
        "new_tc": tc,
        "book": union(epd_file, pgn_file),
        "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": union("", "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) if it matches schema when validated with strict=False.
• An object matches the schema strict(schema) if it matches schema when validated with strict=True.
• An object matches the schema set_name(schema, name) if it matches schema. But the name argument will be used in non-validation messages.
• 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"}.

Built-ins

Some built-ins take arguments. If no arguments are given then the parentheses 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.
• glob(pattern, name=None). Unix style filename matching. This is implemented using pathlib.PurePath().match().
• div(divisor, remainder=0, name=None). This matches the integers x such that (x - remainder) % divisor == 0.
• 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 represent an ISO 8601 date and an ISO 8601 time.
• anything. Matches anything. This is functionally the same as just object.
• nothing. Matches nothing.

Mixins

Mixins are built-ins that are usually combined with other schemas using intersect.

• one_of(key1, ..., keyN). This represents a dictionary with exactly one key among key1, ..., keyN.
• at_least_one_of(key1, ..., keyN). This represents a dictionary with a least one key among key1, ..., keyN.
• at_most_one_of(key1, ..., keyN). This represents an dictionary with at most one key among key1, ..., keyN.
• keys(key1, ..., keyN). This represents a dictionary containing all the keys in key1, ..., keyN.
• interval(lb, ub, strict_lb=False, strict_ub=False). This checks if lb <= object <= ub, provided the comparisons make sense. An upper/lowerbound ... (ellipsis) means that the corresponding inequality is not checked. The optional arguments strict_lb, strict_ub indicate whether the corresponding inequalities should be strict.
• gt(lb). This checks if object > lb.
• ge(lb). This checks if object >= lb.
• lt(ub). This checks if object < ub.
• le(ub). This checks if object <= ub.
• size(lowerbound, upperbound=None). Matches the objects (which support len() such as strings or lists) whose length is in the interval [upperbound, lowerbound]. The value of upperbound can be ... (ellipsis). If upperbound=None then upperbound is set to lowerbound.
• fields({field1: schema1, field2: schema2, ..., fieldN: schemaN}). Matches Python objects with attributes field1, field2, ..., fieldN whose corresponding values should validate against schema1, schema2, ..., schemaN respectively.
• magic(mime_type, name=None). Checks if a buffer (for example a string or a byte array) has the given mime type. This is implemented using the python-magic package.
• filter(callable, schema, filter_name=None). Applies callable to the object and validates the result with schema. The optional argument filter_name is used in non-validation messages.

Conditional schemas

• ifthen(if_schema, then_schema, else_schema=None). It the object matches the if_schema then it should also match the then_schema. If the object does not match the if_schema then it should match the else_schema, if present.
• cond((if_schema1, then_schema1), ... , (if_schemaN, then_schemaN)). An object is successively validated against if_schema1, if_schema2, ... until a validation succeeds. When this happens the object should match the corresponding then_schema. If no if_schema succeeds then the object is considered to have been validated. If one sets if_schemaN equal to anything then this serves as a catch all.

Pre-compiling a schema

An object matches the schema compile(schema) if it matches schema. vtjson compiles a schema before performing a validation against it, so pre-compiling is not necessary but it gains a bit of performance as it needs to be done only once. Compiling is an idempotent operation. It does nothing for an already compiled schema.

The full signature of compile() is

compile(schema, _deferred_compiles=None)

but the optional argument _deferred_compiles should not be set by the user.

Format

A schema can be, in order of precedence:

• A class with the following properties:
  • it has a no-argument constructor;
  • the instances have a __validate__ method with signature

  __validate__(self, object, name, strict)
  

  • 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.
• An object having a __validate__ attribute with signature

  __validate__(object, name, strict)
  

  as above.
• An object having a __compile__ attribute with signature

  __compile__(_deferred_compiles=None)
  

  This is an advanced feature which is used for the implementation of wrapper schemas. __compile__(), which is invoked by compile(), should produce an object with a __validate__ attribute as described above. The optional argument _deferred_compiles is an opaque data structure for handling recursive schemas. It should be passed unmodified to any internal invokations of compile(). Please consult the source code of vtjson for more details.
• 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. Keys can themselves be schemas. E.g. {str: str} represents a dictionary whose keys and values are both strings. If keys are not basic values such as instances of bool, int, float, str, etc... then they are automatically optional.
• 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

A good source of more advanced examples is the file schemas.py in the source distribution of Fishtest. Another source of examples is the file test_validate.py in the source distribution of vtjson.

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: Why yet another Python validation framework?

A: Good question! Initially vtjson consisted of home grown code for validating api calls and database accesses in the Fishtest framework. However the clear and concise schema format seemed to be of independent interest and so the code was refactored into the current self-contained package.

Q: Why are there no variables in vtjson (see https://opis.io/json-schema/2.x/variables.html)?

A: They did not seem to be essential yet. In our use cases conditional schemas were sufficient to achieve the required functionality. See for example the action_schema in schemas.py. More importantly vtjson has a strict separation between the definition of a schema and its subsequent use for validation. By allowing a schema to refer directly to the object being validated this separation would become blurred. This being said, I am still thinking about a good way to introduce variables.

Q: Does vtjson support recursive schemas?

A: Yes. But it requires a bit of Python gymnastics to create them. Here is an example

person={}
person["mother"]=union(person, None)
person["father"]=union(person, None)

which matches e.g.

{"father": {"father": None, "mother": None}, "mother": {"father": None, "mother": None}}

Note that you can create an infinite recursion by validating a recursive object against a recursive schema.

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])