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django-graphene-framework-0.0.2
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مشاهده بیشترتوضیحات
Framework to streamline working with django and graphene
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | django-graphene-framework-0.0.2 |
| نام | django-graphene-framework |
| نسخه کتابخانه | 0.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Maciej Łyskawiński |
| ایمیل نویسنده | lyskawinski.maciej@gmail.com |
| آدرس صفحه اصلی | https://github.com/LonguCodes/DGF/ |
| آدرس اینترنتی | https://pypi.org/project/django-graphene-framework/ |
| مجوز | - |
# Introduction
DGF is a framework for streamlining the writting process using django and graphene.
<h2> Disclaimer </h2>
This package was initally created for personal project, so it may lack general features you need or have unnecesarry features that you don't need. If you find any of these feel free to leave a issue on [github](https://github.com/LonguCodes/DGF)
# Instalation
The package can be found on pypi
'pip install django-graphene-framework'
# Dependencies
This package requires you to install _graphene_ and _django_. If installing using pip the dependencies will be installed automatically
`pip install graphene django`
# Basics
DGF allows you to give easy acces to your models through graphql.
Let's assume we have a model `Profile`
```python
# models.py
from django.db.models import Model,CharField
class Profile(Model):
first_name = CharField(max_lenght=20)
last_name = CharField(max_lenght=20)
```
We can easily transform it to graphql-accessable model by creating a schema
```python
# schema.py
from DGF import Schema
from .models import Profile
class ProfileSchema(Schema):
class Meta:
model = Profile
```
We specify the information in `Meta` class. The `model` field determines of which model is the schema of.
After that we have to register the schema, so it can be transformed info graphene's schema
We create a combiner and the register the schema
```python
# schema.py
from DGF import Schema, Combiner
from .models import Profile
class ProfileSchema(Schema):
class Meta:
model = Profile
combiner = Combiner()
combiner.register(ProfileSchema)
schema = combiner.to_schema()
```
The `schema` can be used like any other graphene schema.
**!!! DGF has not built-in endpoint for graphql as of now !!!**
# Requests
The schema will generate `query` for the model and `add`, `change` and `delete` mutations.
**Filter parameters** are in form of `(lowercase) _<Name of the field>`. Relation fields are excluded.
**Value parameteres** are in form of `(lowercase) <Name of the field>`.
Autofields are excluded. Relations can be provided using ids.
**Return values** are in form of `(lowercase) <Name of the field>`.
## Query
The name of the query will the `<Name of your model>Query`. It returns chosen data about the adequate entries.
```graphql
{
ProfileQuery(_id: 1) {
id
first_name
last_name
}
}
# Will get information about every profile with id = 1
```
## Mutations
The name of mutation will be `<Name of your Model>{Add,Change,Delete}` depending of the type of mutation you want to use.
**Add** mutation can get `value parameters` for the new database entry. It returns chosed data about the added entry.
```graphql
mutation {
ProfileAdd(first_name: "Tom") {
id
first_name
}
}
# Will add new profile with first_name = "Tom" and return it's id and first_name
```
**Change** mutation can get `value parameters` for the changed database entry as well as `filter paramters` for which entries to change. It returns chosen data about the changed entries.
```
mutation {
ProfileChange(_first_name:"Tom", last_name:"Smith"){
first_name
last_name
}
}
# Will change last_name to "Smith" of every profile with name "Tom" and then return first_name and last_name of these profiles.
```
**Delete** mutation can get `filter paramteres` for which entries to delete. It returns ids of the deleted entries (will return chosen data in future versions).
```
mutation {
ProfileDelete(_last_name:"Smith")
}
# Will delete every entry with last_name = "Smith" and return the ids of these entries.
# Note the lack of {}
```
# Custom request handling
You can specify which requests should be possible by overriding `allowed_requests`.
```python
from DGF import Schema, QUERY, ADD
from .models import Profile
allowed_requests = [QUERY,ADD]
class ProfileSchema(Schema):
class Meta:
model = Profile
```
You can specify which fields should be accessable by adding `fields` field in `Meta` class of the schema.
```python
# schema.py
from DGF import Schema
from .models import Profile
class ProfileSchema(Schema):
class Meta:
model = Profile
fields = ['first_name','id']
```
If `fields` is not defined, all fields will be added.
You can also add your own fields to the schema.
```python
# schema.py
from graphene import Int
from DGF import Schema, Field
from .models import Profile
class ProfileSchema(Schema):
age = Field(name='age',field=Int()) # The name is required
class Meta:
model = Profile
fields = ['first_name','id','age']
```
If `fields` is not defined, all custom fields will be added. Custom fields will override fields from the model with the same name.
## Custom request logic
Custom fields have to be handled by custom request logic. To add custom request logic you can override a method in the schema.
The methods accepts parameters:
- `schema` - to which schema this method refers
- `model` - to which model this method refers
- `data` - data from the previous link in the chain (for more information read [Pipeline](#Pipeline))
- `raw_data` - the raw, unprocessed request
- `params` - additional data (like `schema`, `model`, `raw_data`,`type` and custom data added by the [Pipeline](#pipeline) modules)
### Fetch
Fetch method get the data from the database. Name of the fetch method is in form of `fetch_{query,add,change,delete}`. Fetch should return the model / list of models.
### Execute
Fetch fucntion execute the request on the provided data. Name of the execute method is in form of `execute_{query,add,change,delete}`. Execute should return the same type as the request's type (see [Query](#query) and [Mutations](#mutations))
# Pipeline
DGF has built-in pipeline that let's you customize the behaviour on every step. The default pipeline is composed of:
- `AuthLink` - tries to authenticate the user based on the provided credentials using authenticatos
- `RequestPermissionLink` - checks if the user has permission to make this request
- `FetchLink` - fetches the data from the database about the model
- `ObjectPermissionLink` - filters the data based on the permission the user has
- `ExecuteLink` - executes the request (applies only to `Change` and `Delete` mutations by default)
First link in the pipeline get's the data from graphene, every other gets the data from previous one.
You can change the pipeline by setting `DGF_PIPELINE` in the settings.
```python
# settings.py
DGF_PIPELINE=[
'DGF.builtins.FetchPipeline',
'DGF.builtins.ExecutePipeline'
]
# The bare minimum for everything to work out of the box
```
### Auth Link
Addes new param `user` that represents the authenticated user (uses the same model as django). The authentication is done using [Authenticators](#authenticators). If it's not possible to authenticate user, sets `user` to `AnonymousUser`.
### Request Permission Link
Uses [Permissions](#permissions) to check if the user is suppose to use this request. If not, throws `Unauthorized`.
### Fetch Link
Calls fetch method on the schema (see [Fetch](#fetch)).
### Object Permission Link
Uses [Permissions](#permissions) to filter the data.
### Execute Link
Calls execute method on the schema (see [Execute](#execute))
## Custom link
You can create your own link by overriding the `BaseLink` class and the `process` method inside.
```python
# links.py
from DGF import BaseLink
class CustomLink(BaseLink):
def process(self, data):
# Your custom logic
return data
```
You can get and set additional parameters by using `self.params` or `self.context`.
# Auth
## Authenticators
Authenticators allow you to authenticate the user without writting custom backend. Parameters for the authentication can be both in body or headers. By default [Model Authenticator](#model-authenticator) is used.
### Model Authenticator
Model authenticator is used for authenticating the user using username and password. It uses Django's default backend.
### Backend Authenticator
Base class for authenticator, that use existing backends. You need to define `args` field with names of all paramateres needed for authentication and it will call django's `authenticate` to try to authenticate the user
```python
class ModelAuthenticator(BackendAuthenticator):
args = ['username', 'password']
```
### Bearer Authenticator
Base class for authenticator, that user the Bearer token. You need to override the `authenticate_token` method, which should return the user.
```python
from django.conf import settings
from jwt import decode
from django.contrib.auth import get_user_model
from DGF.builtins.authenticators import BearerAuthenticator
class JWTAuthenticator(BearerAuthenticator):
@classmethod
def authenticate_token(cls, token):
if not token:
return None
try:
decoded = decode(token, settings.SECRET_KEY, algorithms=['HS256'])
return get_user_model().objects.get(pk=decoded['sub'])
except:
return None
```
### Custom Authenticator
You can write custom authenticator by overriding `BaseAuthenticator` and `authenticate` method inside as well as `args` field.
```python
# authenticators.py
from django.contrib.auth import get_user_model
import DGF.Authenticator
class CustomAuthenticator(BaseAuthenticator):
args = ['username']
@classmethod
def authenticate(cls,username, **kwargs):
try:
return get_user_model().objects.get(username=username)
except:
return None
```
## Permissions
Permissions allow you to limit the user im terms of requests and particular entries.
You can set permission for each schema by overriding the `permissions` field.
```python
# schema.py
from DGF import Schema
from DGF.builtins import ReadOnlyPermission
from .models import Profile
class ProfileSchema(Schema):
class Meta:
model = Profile
permissions = [ReadOnlyPermission]
```
### Read Only Permission
Allows only `Query` requests.
### Custom permissions
You can write your own permissions by overriding `BasePermissions` class as well as overriding one or more of the methods:
- `has_object_permission` - for [Object Permission Link](#object-permission-link)
- `has_request_permission` - for [Request Permission Link](#request-permission-link)
Both methods can accept `schema`, `model`, `data` and `params`.
```python
# permission.py
from DGF import BasePermission
class IsAuthenticated(BasePermission):
@staticmethod
def has_request_permission(params, **kwargs):
return params['user'].is_authenticated
class IsOwner(BasePermission):
@staticmethod
def has_object_permission(schema, data, params, **kwargs):
return getattr(data,'owner',None) == params['user']
```
نیازمندی
| مقدار | نام |
|---|---|
| - | django |
| - | graphene |
نحوه نصب
نصب پکیج whl django-graphene-framework-0.0.2:
pip install django-graphene-framework-0.0.2.whl
نصب پکیج tar.gz django-graphene-framework-0.0.2:
pip install django-graphene-framework-0.0.2.tar.gz