# dacite
[](https://travis-ci.org/konradhalas/dacite)
[](https://coveralls.io/github/konradhalas/dacite?branch=master)
[](https://pypi.python.org/pypi/dacite/)
[](https://pypi.python.org/pypi/dacite/)
[](https://pypi.python.org/pypi/dacite/)
[](https://github.com/ambv/black)
This module simplifies creation of data classes ([PEP 557][pep-557])
from dictionaries.
## Installation
To install dacite, simply use `pip`:
```
$ pip install dacite
```
## Requirements
Minimum Python version supported by `dacite` is 3.6.
## Quick start
```python
from dataclasses import dataclass
from dacite import from_dict
@dataclass
class User:
name: str
age: int
is_active: bool
data = {
'name': 'John',
'age': 30,
'is_active': True,
}
user = from_dict(data_class=User, data=data)
assert user == User(name='John', age=30, is_active=True)
```
## Features
Dacite supports following features:
- nested structures
- (basic) types checking
- optional fields (i.e. `typing.Optional`)
- unions
- forward references
- collections
- custom type hooks
## Motivation
Passing plain dictionaries as a data container between your functions or
methods isn't a good practice. Of course you can always create your
custom class instead, but this solution is an overkill if you only want
to merge a few fields within a single object.
Fortunately Python has a good solution to this problem - data classes.
Thanks to `@dataclass` decorator you can easily create a new custom
type with a list of given fields in a declarative manner. Data classes
support type hints by design.
However, even if you are using data classes, you have to create their
instances somehow. In many such cases, your input is a dictionary - it
can be a payload from a HTTP request or a raw data from a database. If
you want to convert those dictionaries into data classes, `dacite` is
your best friend.
This library was originally created to simplify creation of type hinted
data transfer objects (DTO) which can cross the boundaries in the
application architecture.
It's important to mention that `dacite` is not a data validation library.
There are dozens of awesome data validation projects and it doesn't make
sense to duplicate this functionality within `dacite`. If you want to
validate your data first, you should combine `dacite` with one of data
validation library.
Please check [Use Case](#use-case) section for a real-life example.
## Usage
Dacite is based on a single function - `dacite.from_dict`. This function
takes 3 parameters:
- `data_class` - data class type
- `data` - dictionary of input data
- `config` (optional) - configuration of the creation process, instance
of `dacite.Config` class
Configuration is a (data) class with following fields:
- `type_hooks`
- `cast`
- `forward_references`
- `check_types`
- `strict`
- `strict_unions_match`
The examples below show all features of `from_dict` function and usage
of all `Config` parameters.
### Nested structures
You can pass a data with nested dictionaries and it will create a proper
result.
```python
@dataclass
class A:
x: str
y: int
@dataclass
class B:
a: A
data = {
'a': {
'x': 'test',
'y': 1,
}
}
result = from_dict(data_class=B, data=data)
assert result == B(a=A(x='test', y=1))
```
### Optional fields
Whenever your data class has a `Optional` field and you will not provide
input data for this field, it will take the `None` value.
```python
from typing import Optional
@dataclass
class A:
x: str
y: Optional[int]
data = {
'x': 'test',
}
result = from_dict(data_class=A, data=data)
assert result == A(x='test', y=None)
```
### Unions
If your field can accept multiple types, you should use `Union`. Dacite
will try to match data with provided types one by one. If none will
match, it will raise `UnionMatchError` exception.
```python
from typing import Union
@dataclass
class A:
x: str
@dataclass
class B:
y: int
@dataclass
class C:
u: Union[A, B]
data = {
'u': {
'y': 1,
},
}
result = from_dict(data_class=C, data=data)
assert result == C(u=B(y=1))
```
### Collections
Dacite supports fields defined as collections. It works for both - basic
types and data classes.
```python
@dataclass
class A:
x: str
y: int
@dataclass
class B:
a_list: List[A]
data = {
'a_list': [
{
'x': 'test1',
'y': 1,
},
{
'x': 'test2',
'y': 2,
}
],
}
result = from_dict(data_class=B, data=data)
assert result == B(a_list=[A(x='test1', y=1), A(x='test2', y=2)])
```
### Type hooks
You can use `Config.type_hooks` argument if you want to transform the input
data of a data class field with given type into the new value. You have to
pass a following mapping: `{Type: callable}`, where `callable` is a
`Callable[[Any], Any]`.
```python
@dataclass
class A:
x: str
data = {
'x': 'TEST',
}
result = from_dict(data_class=A, data=data, config=Config(type_hooks={str: str.lower}))
assert result == A(x='test')
```
If a data class field type is a `Optional[T]` you can pass both -
`Optional[T]` or just `T` - as a key in `type_hooks`. The same with generic
collections, e.g. when a field has type `List[T]` you can use `List[T]` to
transform whole collection or `T` to transform each item.
### Casting
It's a very common case that you want to create an instance of a field type
from the input data with just calling your type with the input value. Of
course you can use `type_hooks={T: T}` to achieve this goal but `cast=[T]` is
an easier and more expressive way. It also works with base classes - if `T`
is a base class of type `S`, all fields of type `S` will be also "casted".
```python
from enum import Enum
class E(Enum):
X = 'x'
Y = 'y'
Z = 'z'
@dataclass
class A:
e: E
data = {
'e': 'x',
}
result = from_dict(data_class=A, data=data, config=Config(cast=[E]))
# or
result = from_dict(data_class=A, data=data, config=Config(cast=[Enum]))
assert result == A(e=E.X)
```
### Forward References
Definition of forward references can be passed as a `{'name': Type}` mapping to
`Config.forward_references`. This dict is passed to `typing.get_type_hints()` as the
`globalns` param when evaluating each field's type.
```python
@dataclass
class X:
y: "Y"
@dataclass
class Y:
s: str
data = from_dict(X, {"y": {"s": "text"}}, Config(forward_references={"Y": Y}))
assert data == X(Y("text"))
```
### Types checking
There are rare cases when `dacite` built-in type checker can not validate
your types (e.g. custom generic class) or you have such functionality
covered by other library and you don't want to validate your types twice.
In such case you can disable type checking with `Config(check_types=False)`.
By default types checking is enabled.
```python
T = TypeVar('T')
class X(Generic[T]):
pass
@dataclass
class A:
x: X[str]
x = X[str]()
assert from_dict(A, {'x': x}, config=Config(check_types=False)) == A(x=x)
```
### Strict mode
By default `from_dict` ignores additional keys (not matching data class field)
in the input data. If you want change this behaviour set `Config.strict` to
`True`. In case of unexpected key `from_dict` will raise `UnexpectedDataError`
exception.
### Strict unions match
`Union` allows to define multiple possible types for a given field. By default
`dacite` is trying to find the first matching type for a provided data and it
returns instance of this type. It means that it's possible that there are other
matching types further on the `Union` types list. With `strict_unions_match`
only a single match is allowed, otherwise `dacite` raises `StrictUnionMatchError`.
## Exceptions
Whenever something goes wrong, `from_dict` will raise adequate
exception. There are a few of them:
- `WrongTypeError` - raised when a type of a input value does not match
with a type of a data class field
- `MissingValueError` - raised when you don't provide a value for a
required field
- `UnionMatchError` - raised when provided data does not match any type
of `Union`
- `ForwardReferenceError` - raised when undefined forward reference encountered in
dataclass
- `UnexpectedDataError` - raised when `strict` mode is enabled and the input
data has not matching keys
- `StrictUnionMatchError` - raised when `strict_unions_match` mode is enabled
and the input data has ambiguous `Union` match
## Development
First of all - if you want to submit your pull request, thank you very much!
I really appreciate your support.
Please remember that every new feature, bug fix or improvement should be tested.
100% code coverage is a must-have.
We are using a few static code analysis tools to increase the code quality
(`black`, `mypy`, `pylint`). Please make sure that you are not generating any
errors/warnings before you submit your PR. You can find current configuration
in `.github/*` directory.
Last but not least, if you want to introduce new feature, please discuss it
first within an issue.
### How to start
Clone `dacite` repository:
```
$ git clone git@github.com:konradhalas/dacite.git
```
Create and activate virtualenv in the way you like:
```
$ python3 -m venv dacite-env
$ source dacite-env/bin/activate
```
Install all `dacite` dependencies:
```
$ pip install -e .[dev]
```
And, optionally but recommended, install pre-commit hook for black:
```
$ pre-commit install
```
To run tests you just have to fire:
```
$ pytest
```
### Performance testing
`dacite` is a small library, but its use is potentially very extensive. Thus, it is crucial
to ensure good performance of the library.
We achieve that with the help of `pytest-benchmark` library, and a suite of dedicated performance tests
which can be found in the `tests/performance` directory. The CI process runs these tests automatically,
but they can also be helpful locally, while developing the library.
Whenever you run `pytest` command, a new benchmark report is saved to `/.benchmarks` directory.
You can easily compare these reports by running: `pytest-benchmark compare`, which will load all the runs
and display them in a table, where you can compare the performance of each run.
You can even specify which particular runs you want to compare, e.g. `pytest-benchmark compare 0001 0003 0005`.
## Use case
There are many cases when we receive "raw" data (Python dicts) as a input to
our system. HTTP request payload is a very common use case. In most web
frameworks we receive request data as a simple dictionary. Instead of
passing this dict down to your "business" code, it's a good idea to create
something more "robust".
Following example is a simple `flask` app - it has single `/products` endpoint.
You can use this endpoint to "create" product in your system. Our core
`create_product` function expects data class as a parameter. Thanks to `dacite`
we can easily build such data class from `POST` request payload.
```python
from dataclasses import dataclass
from typing import List
from flask import Flask, request, Response
import dacite
app = Flask(__name__)
@dataclass
class ProductVariantData:
code: str
description: str = ''
stock: int = 0
@dataclass
class ProductData:
name: str
price: float
variants: List[ProductVariantData]
def create_product(product_data: ProductData) -> None:
pass # your business logic here
@app.route("/products", methods=['POST'])
def products():
product_data = dacite.from_dict(
data_class=ProductData,
data=request.get_json(),
)
create_product(product_data=product_data)
return Response(status=201)
```
What if we want to validate our data (e.g. check if `code` has 6 characters)?
Such features are out of scope of `dacite` but we can easily combine it with
one of data validation library. Let's try with
[marshmallow](https://marshmallow.readthedocs.io).
First of all we have to define our data validation schemas:
```python
from marshmallow import Schema, fields, ValidationError
def validate_code(code):
if len(code) != 6:
raise ValidationError('Code must have 6 characters.')
class ProductVariantDataSchema(Schema):
code = fields.Str(required=True, validate=validate_code)
description = fields.Str(required=False)
stock = fields.Int(required=False)
class ProductDataSchema(Schema):
name = fields.Str(required=True)
price = fields.Decimal(required=True)
variants = fields.Nested(ProductVariantDataSchema(many=True))
```
And use them within our endpoint:
```python
@app.route("/products", methods=['POST'])
def products():
schema = ProductDataSchema()
result, errors = schema.load(request.get_json())
if errors:
return Response(
response=json.dumps(errors),
status=400,
mimetype='application/json',
)
product_data = dacite.from_dict(
data_class=ProductData,
data=result,
)
create_product(product_data=product_data)
return Response(status=201)
```
Still `dacite` helps us to create data class from "raw" dict with validated data.
### Cache
`dacite` uses some LRU caching to improve its performance where possible. To use the caching utility:
```python
from dacite import set_cache_size, get_cache_size, clear_cache
get_cache_size() # outputs the current LRU max_size, default is 2048
set_cache_size(4096) # set LRU max_size to 4096
set_cache_size(None) # set LRU max_size to None
clear_cache() # Clear the cache
```
The caching is completely transparent from the interface perspective.
## Changelog
Follow `dacite` updates in [CHANGELOG][changelog].
## Authors
Created by [Konrad Hałas][halas-homepage].
[pep-557]: https://www.python.org/dev/peps/pep-0557/
[halas-homepage]: https://konradhalas.pl
[changelog]: https://github.com/konradhalas/dacite/blob/master/CHANGELOG.md
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"description": "\n\n# dacite\n\n[](https://travis-ci.org/konradhalas/dacite)\n[](https://coveralls.io/github/konradhalas/dacite?branch=master)\n[](https://pypi.python.org/pypi/dacite/)\n[](https://pypi.python.org/pypi/dacite/)\n[](https://pypi.python.org/pypi/dacite/)\n[](https://github.com/ambv/black)\n\nThis module simplifies creation of data classes ([PEP 557][pep-557])\nfrom dictionaries.\n\n## Installation\n\nTo install dacite, simply use `pip`:\n\n```\n$ pip install dacite\n```\n\n## Requirements\n\nMinimum Python version supported by `dacite` is 3.6.\n\n## Quick start\n\n```python\nfrom dataclasses import dataclass\nfrom dacite import from_dict\n\n\n@dataclass\nclass User:\n name: str\n age: int\n is_active: bool\n\n\ndata = {\n 'name': 'John',\n 'age': 30,\n 'is_active': True,\n}\n\nuser = from_dict(data_class=User, data=data)\n\nassert user == User(name='John', age=30, is_active=True)\n```\n\n## Features\n\nDacite supports following features:\n\n- nested structures\n- (basic) types checking\n- optional fields (i.e. `typing.Optional`)\n- unions\n- forward references\n- collections\n- custom type hooks\n\n## Motivation\n\nPassing plain dictionaries as a data container between your functions or\nmethods isn't a good practice. Of course you can always create your\ncustom class instead, but this solution is an overkill if you only want\nto merge a few fields within a single object.\n\nFortunately Python has a good solution to this problem - data classes.\nThanks to `@dataclass` decorator you can easily create a new custom\ntype with a list of given fields in a declarative manner. Data classes\nsupport type hints by design.\n\nHowever, even if you are using data classes, you have to create their\ninstances somehow. In many such cases, your input is a dictionary - it\ncan be a payload from a HTTP request or a raw data from a database. If\nyou want to convert those dictionaries into data classes, `dacite` is\nyour best friend.\n\nThis library was originally created to simplify creation of type hinted\ndata transfer objects (DTO) which can cross the boundaries in the\napplication architecture.\n\nIt's important to mention that `dacite` is not a data validation library.\nThere are dozens of awesome data validation projects and it doesn't make\nsense to duplicate this functionality within `dacite`. If you want to \nvalidate your data first, you should combine `dacite` with one of data \nvalidation library.\n\nPlease check [Use Case](#use-case) section for a real-life example.\n\n## Usage\n\nDacite is based on a single function - `dacite.from_dict`. This function\ntakes 3 parameters:\n\n- `data_class` - data class type\n- `data` - dictionary of input data\n- `config` (optional) - configuration of the creation process, instance\nof `dacite.Config` class\n\nConfiguration is a (data) class with following fields:\n\n- `type_hooks`\n- `cast`\n- `forward_references`\n- `check_types`\n- `strict`\n- `strict_unions_match`\n\nThe examples below show all features of `from_dict` function and usage\nof all `Config` parameters.\n\n### Nested structures\n\nYou can pass a data with nested dictionaries and it will create a proper\nresult.\n\n```python\n@dataclass\nclass A:\n x: str\n y: int\n\n\n@dataclass\nclass B:\n a: A\n\n\ndata = {\n 'a': {\n 'x': 'test',\n 'y': 1,\n }\n}\n\nresult = from_dict(data_class=B, data=data)\n\nassert result == B(a=A(x='test', y=1))\n```\n\n### Optional fields\n\nWhenever your data class has a `Optional` field and you will not provide\ninput data for this field, it will take the `None` value.\n\n```python\nfrom typing import Optional\n\n@dataclass\nclass A:\n x: str\n y: Optional[int]\n\n\ndata = {\n 'x': 'test',\n}\n\nresult = from_dict(data_class=A, data=data)\n\nassert result == A(x='test', y=None)\n```\n\n### Unions\n\nIf your field can accept multiple types, you should use `Union`. Dacite\nwill try to match data with provided types one by one. If none will\nmatch, it will raise `UnionMatchError` exception.\n\n```python\nfrom typing import Union\n\n@dataclass\nclass A:\n x: str\n\n@dataclass\nclass B:\n y: int\n\n@dataclass\nclass C:\n u: Union[A, B]\n\n\ndata = {\n 'u': {\n 'y': 1,\n },\n}\n\nresult = from_dict(data_class=C, data=data)\n\nassert result == C(u=B(y=1))\n```\n\n### Collections\n\nDacite supports fields defined as collections. It works for both - basic\ntypes and data classes.\n\n```python\n@dataclass\nclass A:\n x: str\n y: int\n\n\n@dataclass\nclass B:\n a_list: List[A]\n\n\ndata = {\n 'a_list': [\n {\n 'x': 'test1',\n 'y': 1,\n },\n {\n 'x': 'test2',\n 'y': 2,\n }\n ],\n}\n\nresult = from_dict(data_class=B, data=data)\n\nassert result == B(a_list=[A(x='test1', y=1), A(x='test2', y=2)])\n```\n\n### Type hooks\n\nYou can use `Config.type_hooks` argument if you want to transform the input \ndata of a data class field with given type into the new value. You have to \npass a following mapping: `{Type: callable}`, where `callable` is a \n`Callable[[Any], Any]`.\n\n```python\n@dataclass\nclass A:\n x: str\n\n\ndata = {\n 'x': 'TEST',\n}\n\nresult = from_dict(data_class=A, data=data, config=Config(type_hooks={str: str.lower}))\n\nassert result == A(x='test')\n```\n\nIf a data class field type is a `Optional[T]` you can pass both - \n`Optional[T]` or just `T` - as a key in `type_hooks`. The same with generic \ncollections, e.g. when a field has type `List[T]` you can use `List[T]` to \ntransform whole collection or `T` to transform each item. \n\n### Casting\n\nIt's a very common case that you want to create an instance of a field type \nfrom the input data with just calling your type with the input value. Of \ncourse you can use `type_hooks={T: T}` to achieve this goal but `cast=[T]` is \nan easier and more expressive way. It also works with base classes - if `T` \nis a base class of type `S`, all fields of type `S` will be also \"casted\".\n\n```python\nfrom enum import Enum\n\nclass E(Enum):\n X = 'x'\n Y = 'y'\n Z = 'z'\n\n@dataclass\nclass A:\n e: E\n\n\ndata = {\n 'e': 'x',\n}\n\nresult = from_dict(data_class=A, data=data, config=Config(cast=[E]))\n\n# or\n\nresult = from_dict(data_class=A, data=data, config=Config(cast=[Enum]))\n\nassert result == A(e=E.X)\n```\n\n### Forward References\n\nDefinition of forward references can be passed as a `{'name': Type}` mapping to \n`Config.forward_references`. This dict is passed to `typing.get_type_hints()` as the \n`globalns` param when evaluating each field's type.\n\n```python\n@dataclass\nclass X:\n y: \"Y\"\n\n@dataclass\nclass Y:\n s: str\n\ndata = from_dict(X, {\"y\": {\"s\": \"text\"}}, Config(forward_references={\"Y\": Y}))\nassert data == X(Y(\"text\"))\n```\n\n### Types checking\n\nThere are rare cases when `dacite` built-in type checker can not validate \nyour types (e.g. custom generic class) or you have such functionality \ncovered by other library and you don't want to validate your types twice. \nIn such case you can disable type checking with `Config(check_types=False)`.\nBy default types checking is enabled.\n\n```python\nT = TypeVar('T')\n\n\nclass X(Generic[T]):\n pass\n\n\n@dataclass\nclass A:\n x: X[str]\n\n\nx = X[str]()\n\nassert from_dict(A, {'x': x}, config=Config(check_types=False)) == A(x=x)\n```\n\n### Strict mode\n\nBy default `from_dict` ignores additional keys (not matching data class field) \nin the input data. If you want change this behaviour set `Config.strict` to \n`True`. In case of unexpected key `from_dict` will raise `UnexpectedDataError` \nexception.\n\n### Strict unions match\n\n`Union` allows to define multiple possible types for a given field. By default \n`dacite` is trying to find the first matching type for a provided data and it \nreturns instance of this type. It means that it's possible that there are other \nmatching types further on the `Union` types list. With `strict_unions_match` \nonly a single match is allowed, otherwise `dacite` raises `StrictUnionMatchError`.\n\n## Exceptions\n\nWhenever something goes wrong, `from_dict` will raise adequate\nexception. There are a few of them:\n\n- `WrongTypeError` - raised when a type of a input value does not match\nwith a type of a data class field\n- `MissingValueError` - raised when you don't provide a value for a\nrequired field\n- `UnionMatchError` - raised when provided data does not match any type\nof `Union`\n- `ForwardReferenceError` - raised when undefined forward reference encountered in\ndataclass\n- `UnexpectedDataError` - raised when `strict` mode is enabled and the input \ndata has not matching keys\n- `StrictUnionMatchError` - raised when `strict_unions_match` mode is enabled \nand the input data has ambiguous `Union` match\n\n## Development\n\nFirst of all - if you want to submit your pull request, thank you very much! \nI really appreciate your support.\n\nPlease remember that every new feature, bug fix or improvement should be tested. \n100% code coverage is a must-have. \n\nWe are using a few static code analysis tools to increase the code quality \n(`black`, `mypy`, `pylint`). Please make sure that you are not generating any \nerrors/warnings before you submit your PR. You can find current configuration\nin `.github/*` directory.\n\nLast but not least, if you want to introduce new feature, please discuss it \nfirst within an issue.\n\n### How to start\n\nClone `dacite` repository:\n\n```\n$ git clone git@github.com:konradhalas/dacite.git\n```\n\nCreate and activate virtualenv in the way you like:\n\n```\n$ python3 -m venv dacite-env\n$ source dacite-env/bin/activate\n```\n\nInstall all `dacite` dependencies:\n\n```\n$ pip install -e .[dev]\n```\n\nAnd, optionally but recommended, install pre-commit hook for black:\n\n```\n$ pre-commit install\n```\n\nTo run tests you just have to fire:\n\n```\n$ pytest\n```\n\n### Performance testing\n\n`dacite` is a small library, but its use is potentially very extensive. Thus, it is crucial\nto ensure good performance of the library.\n\nWe achieve that with the help of `pytest-benchmark` library, and a suite of dedicated performance tests\nwhich can be found in the `tests/performance` directory. The CI process runs these tests automatically,\nbut they can also be helpful locally, while developing the library.\n\nWhenever you run `pytest` command, a new benchmark report is saved to `/.benchmarks` directory.\nYou can easily compare these reports by running: `pytest-benchmark compare`, which will load all the runs\nand display them in a table, where you can compare the performance of each run.\n\nYou can even specify which particular runs you want to compare, e.g. `pytest-benchmark compare 0001 0003 0005`.\n \n## Use case\n\nThere are many cases when we receive \"raw\" data (Python dicts) as a input to \nour system. HTTP request payload is a very common use case. In most web \nframeworks we receive request data as a simple dictionary. Instead of \npassing this dict down to your \"business\" code, it's a good idea to create \nsomething more \"robust\".\n\nFollowing example is a simple `flask` app - it has single `/products` endpoint.\nYou can use this endpoint to \"create\" product in your system. Our core \n`create_product` function expects data class as a parameter. Thanks to `dacite` \nwe can easily build such data class from `POST` request payload.\n\n\n```python\nfrom dataclasses import dataclass\nfrom typing import List\n\nfrom flask import Flask, request, Response\n\nimport dacite\n\napp = Flask(__name__)\n\n\n@dataclass\nclass ProductVariantData:\n code: str\n description: str = ''\n stock: int = 0\n\n\n@dataclass\nclass ProductData:\n name: str\n price: float\n variants: List[ProductVariantData]\n\n\ndef create_product(product_data: ProductData) -> None:\n pass # your business logic here\n\n\n@app.route(\"/products\", methods=['POST'])\ndef products():\n product_data = dacite.from_dict(\n data_class=ProductData,\n data=request.get_json(),\n )\n create_product(product_data=product_data)\n return Response(status=201)\n\n```\n\nWhat if we want to validate our data (e.g. check if `code` has 6 characters)? \nSuch features are out of scope of `dacite` but we can easily combine it with \none of data validation library. Let's try with \n[marshmallow](https://marshmallow.readthedocs.io).\n\nFirst of all we have to define our data validation schemas:\n\n```python\nfrom marshmallow import Schema, fields, ValidationError\n\n\ndef validate_code(code):\n if len(code) != 6:\n raise ValidationError('Code must have 6 characters.')\n\n\nclass ProductVariantDataSchema(Schema):\n code = fields.Str(required=True, validate=validate_code)\n description = fields.Str(required=False)\n stock = fields.Int(required=False)\n\n\nclass ProductDataSchema(Schema):\n name = fields.Str(required=True)\n price = fields.Decimal(required=True)\n variants = fields.Nested(ProductVariantDataSchema(many=True))\n```\n\nAnd use them within our endpoint:\n\n```python\n@app.route(\"/products\", methods=['POST'])\ndef products():\n schema = ProductDataSchema()\n result, errors = schema.load(request.get_json())\n if errors:\n return Response(\n response=json.dumps(errors), \n status=400, \n mimetype='application/json',\n )\n product_data = dacite.from_dict(\n data_class=ProductData,\n data=result,\n )\n create_product(product_data=product_data)\n return Response(status=201)\n```\n\nStill `dacite` helps us to create data class from \"raw\" dict with validated data.\n\n### Cache\n\n`dacite` uses some LRU caching to improve its performance where possible. To use the caching utility:\n\n```python\nfrom dacite import set_cache_size, get_cache_size, clear_cache\n\nget_cache_size() # outputs the current LRU max_size, default is 2048\nset_cache_size(4096) # set LRU max_size to 4096\nset_cache_size(None) # set LRU max_size to None\nclear_cache() # Clear the cache\n```\n\nThe caching is completely transparent from the interface perspective.\n\n## Changelog\n\nFollow `dacite` updates in [CHANGELOG][changelog].\n\n## Authors\n\nCreated by [Konrad Ha\u0142as][halas-homepage].\n\n[pep-557]: https://www.python.org/dev/peps/pep-0557/\n[halas-homepage]: https://konradhalas.pl\n[changelog]: https://github.com/konradhalas/dacite/blob/master/CHANGELOG.md\n",
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