vtjson 1.1.7

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, union, 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": union("clang++", "g++"),
    "unique_key": str,
    "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,
    "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": 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.
• The schema may contain tuples, even though these are not valid JSON. In fact any Python object is a valid schema (see below).

Usage

To validate an object against a schema one can simply do

explanation = validate(schema, object)

If the validation is succesful then the return value is the empty string. Otherwise it contains an explanation 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.

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.

Wrappers

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

• An object matches the schema union(schema1, schema2) if it matches schema1 or schema2. Unions of more than two schemas are also valid.
• An object matches the schema intersect(schema1, schema2) if it matches schema1 and schema2. Intersections of more than two schemas are also valid.
• 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 set_name(schema, name) when it matches schema. But the name argument will be used in non-validation messages.

Built-ins

• 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, ip_address and url. These match strings with the implied format.

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 and the return value of __validate__() have the same semantics as those of validate(), as discussed above.
• A Python type. In that case validation is done by checking membership.
• A callable. Validation is done by applying the callable to the object.
• 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.
• 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.

Examples

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

FAQ

Q: Why not just use json-schema?

A: vtjson can validate objects which are more general than strictly JSON. See the example above. But the main reason for the existence of vtjson is that it is easily extensible in a Pythonic way.

Q: Shouldn't validate throw an exception instead of returning a string when validation fails?

A: Perhaps. That would be more Pythonic. On the other hand the current approach seems easier to use. I am thinking about it.

Q: How to combine validations?

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

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

(but rejects positive floats). More generally one can 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 following 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])