Skip to main content

Type-safe JSON (de)serialization

Project description

typjson

Type-safe JSON (de)serialization for Python. Compatible with mypy type hints.

Requirements

  • Python 3.7 or newer

Features

  • Type safety in runtime and mypy compatibility
  • Support for types out of the box:
    • Primitive types:
      • str, int, float, bool, Decimal, None
      • date as "%Y-%m-%d", datetime as "%Y-%m-%dT%H:%M:%S%z", time as "%H:%M:%S"
      • UUID as str in format "8-4-4-4-12"
      • char type as str of length 1
    • Union[] and therefore Optional[]
    • Structure types: List[], Tuple[], Dict[str, T], Set[]
    • Enum classes
    • Data classes
  • Support for custom encoders and decoders
  • API similar to standard json module

Simple Usage

from typ import json
from typing import *
from datetime import date
from dataclasses import dataclass


@dataclass
class Address:
    street: str
    house: int
    apt: Optional[str]


@dataclass
class Person:
    first_name: str
    last_name: str
    languages: List[str]
    address: Address
    birth_date: date


person = Person(
    "John",
    "Smith",
    ["English", "Russian"],
    Address("Main", 1, "2A"),
    date(year=1984, month=8, day=1)
)

json_str = json.dumps(person, indent=2)
loaded_person = json.loads(Person, json_str)

assert person == loaded_person

Value of json_str that is dumped and loaded in the code example above looks like:

{
  "first_name": "John",
  "last_name": "Smith",
  "languages": [
    "English",
    "Russian"
  ],
  "address": {
    "street": "Main",
    "house": 1,
    "apt": "2A"
  },
  "birth_date": "1984-08-01"
}

Type Safety

Runtime

What is type safety in Python? Since Python is dynamically typed language it's hard to provide any types guarantees before runtime. However types could be checked in run time. This is exactly what typjson library is doing. Consider following example for Address type defined above:

from typ import json
from typing import *
from dataclasses import dataclass


@dataclass
class Address:
    street: str
    house: int
    apt: Optional[str]

json_str = """{"street": "Main", "house": 1, "apt": 2}"""
loaded_address = json.loads(Address, json_str)

The apt field has type defined as Optional[str] however value provided in JSON is 2 which is number type in JSON and it's obviously not compatible with Optional[str]. Respectively json.loads call will raise JsonError:

typ.encoding.JsonError: Value 2 can not be deserialized as typing.Union[str, NoneType]

Call json.loads will either return instance of requested type with all nested types checked or raise a error. This is runtime type safety of typjson.

Compile Time (mypy)

Functions in typ.json module dumps, loads, dump, load have proper type hints. Therefore types could be validated with mypy tool:

json_str = """{"street": "Main", "house": 1, "apt": 2}"""
loaded_address = json.loads(Address, json_str)
loaded_address = "some other address"

This will produce error in mypy as type of loaded_address is inferred as Address:

error: Incompatible types in assignment (expression has type "str", variable has type "Address")

This provides type safety in compile time.

API Overview

typjson API is similar to json module API. Main functions are defined in typ.json module. Most useful functions are typ.json.loads and typ.json.dumps. If something went wrong during JSON encoding/decoding then JsonError is raised. In fact typ.json functions are using json module under the hood for final conversion between python structures and JSON.

List of supported types if provided here. Custom Encoding section describes how any type could be supported in addition to types that are supported out of the box.

Supported Types

Primitive Types

Python type JSON type Notes
int number
float number
decimal.Decimal number
boolean boolean
typ.typing.char string string with length 1
str string
uuid.UUID string lower case hex symbols with hyphens as 8-4-4-4-12
datetime.date string ISO 8601 yyyy-mm-dd
datetime.datetime string ISO 8601 yyyy-mm-ddThh:mm:ss.ffffff
datetime.time string ISO 8601 hh:mm:ss.ffffff
typ.typing.NoneType
type(None)
null

Non Primitive Types

Python type JSON type Notes
List[T] array homogeneous, items encoded
Dict[str, T] object fields values of T encoded
Set[T] array homogeneous, items of T encoded
Tuple[T, K, ...] array heterogeneous, items of T, K, ... encoded
Union[T, K, ...] look for T, K, ... T, K, ... encoded
list array heterogeneous, items are encoded
dict object
tuple array heterogeneous, items are encoded
Enum classes look for member type enum members are encoded according to their types
class decorated with
@dataclass
object field types are respected
class decorated with
@union
object object with single field
Any any type anything

Null-safety

All types can not have None value besides NoneType aka type(None). Optional[T] allows None value. So if nullable str is needed Optional[str] would be a good fit. Optional[T] type is in fact Union[T, NoneType] therefore in typjson it's supported via Union[] support. Because of this Optional[T] is not listed above since it's just a Union.

Custom Encoding

In fact all types that are supported out of the box are supported via encoders and decoders. Examples of custom encoder and decoder are provided just for basic understanding. For deeper insight the one might be interested to look at source code of typ.encoding module.

Custom Encoder

typ.json.dump and typ.json.dumps functions take list of encoders as a parameter. Those encoders are custom encoders that are used in addition to standard built-in encoders. Let's implement custom encoder that will code all integers as strings in JSON:

from typ.encoding import Unsupported, check_type

def encode_int_custom(encoder, typ, value):
    if typ != int:
        # if this encoder is not applicable to the typ it should return Unsupported
        return Unsupported
    # there's a helper function checking that value is instance of specified type - int
    check_type(int, value)
    # return encoded value
    return str(value)

from typ import json
assert json.dumps([3, 4, 5], encoders=[encode_int_custom]) == '["3", "4", "5"]'

In the code above encode_int_custom is provided into typ.json.dumps call and it's used prior standard built-in int encoding. As it's deemostrated in the assert it successfully encoded integers as strings. Please never do this in real life - this code is provided only for demonstration purposes.

Encoder function is defined as: Callable[['Encoder', Type[K], K], Union[Any, UnsupportedType]] There's an encoder parameter of every custom encoder which holds instance of Encoder. It is useful for encoding nested types, like lists or classes, etc.

Custom Decoder

typ.json.load and typ.json.loads functions take list of decoders as a parameter. Similarly to encoding it's useful for custom decoding logic. Here's a mirror example for decoding int type from strings in JSON:

from typ.encoding import Unsupported, check_type

def decode_int_custom(decoder, typ, json_value):
    if typ != int:
        # if this encoder is not applicable to the typ it should return Unsupported
        return Unsupported
    # check that JSON has string in the json_value
    check_type(str, json_value)
    # return decoded value
    return int(json_value)

from typ import json
assert loads(List[int], '["3", "4", "5"]', decoders=[decode_int_custom]) == [3, 4, 5]

Decoder function is defined as: Callable[['Decoder', Type[K], Any], Union[K, UnsupportedType]].

API Reference

typ.json.dumps

typ.json.dumps(value: T, typ: Optional[Type[T]] = None, case: CaseConverter = None, encoders: List[EncodeFunc] = [], indent: Optional[int] = None) -> str

Serialize value to a JSON formatted str using specified type.

value Python object to be serialized to JSON.

typ type information for value. If None is provided then actual type of value is used otherwise value is checked to be valid instance of typ.

case case converter, see fields case.

encoders list of custom encoders, see custom encoding.

indent optional non-negative indent level for JSON. If None is provided then JSON is represented as single line without indentation.

Returns JSON string or raises JsonError.

typ.json.dump

typ.json.dump(fp: IO[str], value: T, typ: Optional[Type[T]] = None, case: CaseConverter = None, encoders: List[EncodeFunc] = [], indent: Optional[int] = None) -> None

Serialize value as a JSON formatted stream.

fp stream to write JSON to.

Other arguments have the same meaning as in typ.json.dumps.

typ.json.loads

typ.json.loads(typ: Type[T], json_str: str, case: CaseConverter = None, decoders: List[DecodeFunc] = []) -> T

Deserialize json_str to a Python object of specified type.

typ type to deserialize JSON into.

json_str string containing JSON.

case case converter, see fields case.

decoders list of custom decoders, see custom encoding.

Returns instance of M or raises JsonError.

typ.json.load

typ.json.load(fp: IO[str], typ: Type[T], case: CaseConverter = None, decoders: List[DecodeFunc] = []) -> T

Deserialize stream to a Python object of specified type.

fp stream to read JSON from.

Other arguments have the same meaning as in typ.json.loads

typ.json.JsonError (defined as typ.encoding.JsonError)

JsonError raised in case of any issue during encoding/decoding JSON data according to type information provided.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

typjson-0.0.32.tar.gz (12.8 kB view hashes)

Uploaded source

Built Distribution

typjson-0.0.32-py3-none-any.whl (9.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page