jsoncompat

Python bindings for checking compatibility of evolving JSON Schemas and generating example values.

Installation

Install from PyPI:

pip install jsoncompat==0.3.1

Quick start

import jsoncompat as jsc

old_schema = '{"type": "string"}'
new_schema = '{"type": "number"}'

is_compatible = jsc.check_compat(old_schema, new_schema, jsc.Role.BOTH)
print(is_compatible)

# Generate example values for a schema
generator = jsc.generator_for(old_schema)
example = generator.generate_value(depth=5)
print(example)

API

check_compat(old_schema_json: str, new_schema_json: str, role: str = "both") -> bool
- role must be "serializer", "deserializer", or "both".
- Raises ValueError for invalid schemas or hard unsupported compatibility features such as non-integral number.multipleOf.
generator_for(schema_json: str) -> Generator
- Parses the schema once and returns a reusable generator.
- Generator.generate_value(depth: int = 5) -> str returns a JSON string for one generated value accepted by the schema.
- Raises ValueError when the schema is invalid, known to be unsatisfiable, or generation exhausts its retry budget.
validator_for(schema_json: str) -> Validator
- Parses the schema once and returns a reusable validator.
- Validator.is_valid_json(instance_json: str) -> bool validates JSON strings against the parsed schema.
- Validator.is_valid_value(instance: JsonValue) -> bool validates Python JSON-compatible values: None, bool, finite int/float, str, list, tuple, and dict[str, ...].
generate_value(schema_json: str, depth: int = 5) -> str
- Deprecated. Use generator_for(schema_json).generate_value(depth) instead.
is_valid(schema_json: str, instance_json: str) -> bool
- Deprecated. Use validator_for(schema_json).is_valid_json(instance_json) instead.
jsoncompat.codegen.dataclasses runtime helpers for generated dataclass models
Role.SERIALIZER, Role.DESERIALIZER, and Role.BOTH are string constants accepted by check_compat.

Generated dataclasses use from_value(...) / to_value(...) for Python JSON values and deserialize(...) / serialize(...) for encoded JSON, YAML, and MessagePack. JSON is the default format. Install optional codecs with jsoncompat[yaml] or jsoncompat[msgpack]. All direct constructors and conversion methods accept keyword-only skip_validation=True when the caller already guarantees schema validity. It skips only the attached JSON Schema check; wire-format parsing and JSON-value normalization, runtime type conversion, and reader/writer direction guards still apply.

Only classes emitted by jsoncompat codegen --target dataclasses implement this runtime contract. Generated modules contain only dataclass declarations; the first constructor or conversion call derives and caches one shared native plan from their field metadata. Checked use then compiles the relevant JSON Schema validator once, while skip_validation=True leaves it uncompiled. There is no custom-subclass or Python-constructor fallback. Custom construction hooks and Python defaults/default factories are therefore intentionally outside the model-definition surface.

Generated array and object fields accept ordinary lists and dictionaries at construction boundaries, then store them as deeply immutable values exposed as Sequence[...] and Mapping[...]. Use to_value() when a mutable JSON-value tree is required by another API.

See the canonical plain-schema example for an ordinary generated model that both serializes and deserializes. The canonical stamped-schema example covers versioned writer/reader envelopes and historical schemas.

Schemas are passed as JSON strings. check_compat returns a boolean verdict and raises ValueError for invalid JSON, invalid schemas, or hard unsupported compatibility cases.

More detail

Benchmarks

Run the generated dataclass runtime microbenchmark from the repository root:

just python-bench

The benchmark pins Pydantic v2 and compares the same valid nested payload using strict, frozen Pydantic models. Pydantic's dump methods do not revalidate the model, so their closest jsoncompat comparison is the skip_validation=True serialization path.

To measure fresh-interpreter startup separately from steady-state conversion, including generated-module import, first trusted use, first checked use, and an equivalent Pydantic v2 graph:

just python-bench-startup

Each sample runs in a new Python process and reports both absolute time and the increment over an empty process. Results are written under target/python-benchmark alongside the other repeatable benchmark profiles.

For a larger workload, benchmark a balanced recursive graph containing discriminated unions, lists, mappings, and omitted fields:

just python-bench-scale

The default depth and fanout produce 1,365 model nodes. Override them, along with the iteration and repeat counts, as positional recipe arguments. The underlying script also accepts --profile to print cumulative Python profiles for checked and trusted JSON deserialization.

To benchmark JSON -> generated dataclass -> JSON across the complete checked-in JSON Schema fixture corpus, and compare Pydantic v2 wherever its generated peer passes the semantic-equivalence screen:

just python-bench-fixtures

This includes every embedded fuzz schema and both sides of every backcompat fixture. Jsoncompat timings cover every fixture with a representable schema and valid sample independently of Pydantic coverage. Generated Pydantic modules and the detailed JSON report are written to target/python-fixture-benchmark. The report retains explicit entries for jsoncompat or Pydantic generation failures, semantic mismatches, and schemas without a shared valid value; those cases are never silently removed from the denominator. Before timing, generated Pydantic validators are screened against the jsoncompat schema validator with every fixture test plus deterministic type and property mutations. This prevents ignored JSON Schema keywords from looking like performance wins. Pydantic uses one precompiled TypeAdapter per generated type, as recommended for repeated validation. Shared generated values are checked in separately from the generated model artifacts so fresh clones benchmark the same values.

License

MIT License. See:

https://github.com/ostrowr/jsoncompat/blob/main/LICENSE

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jsoncompat 0.4.2

jsoncompat

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MD5	`34c6d2c97543792f37ff26187f5c833c`
BLAKE2b-256	`6bdadb079706dbf8cc37beb4b805a048a283befa4b74ccba605f336030809d92`

Algorithm	Hash digest
SHA256	`9596d835c619e563ea75878672def11681c7ab37254cdabf0a5ed6dbf254b122`
MD5	`0301828015c8ba42f174488d76c26d64`
BLAKE2b-256	`e8be2b31a98205c1ea98191ceeb93b3281354d4758b78a485d0a178c9220973e`

Algorithm	Hash digest
SHA256	`36b4f277678e3a26d62f10cb281e5077f073fbd680a813dc6ed09730f521c259`
MD5	`843b2023076772fe7f8565d42f7ccb45`
BLAKE2b-256	`fd507db531743ac326b6f27a874c1f19f37a73e7a25d6940fe4aa3486b2a8f34`

Algorithm	Hash digest
SHA256	`495f19cb12a32aef3aad21b3069116319e3e7a76a6c0674c94733d8569b17a68`
MD5	`4a92005fa2001dd4b69f21eece625769`
BLAKE2b-256	`e3f6b7d782162d802e2baef31f7351b092454dc14f271f062dd80d4bbcd8e1f4`