chocs 0.13.1

Lightweight and powerful wsgi rest framework for rapid building applications based on wsgi servers.

Chocs

Chocs is a modern HTTP framework for building AWS HTTP API/REST API and WSGI compatible applications. Chocs aims to be small, expressive, and robust. It provides an elegant API for writing fault-proof, extensible microservices.

Features

• AWS Serverless integration
• Open api integration
• Elegant and easy API
• No additional bloat like built-in template engines, session handlers, etc.
• Compatible with all WSGI servers
• Loosely coupled components which can be used separately
• Multipart body parsing
• Graceful error handling
• HTTP middleware support
• Fast routing

Installation

pip install chocs

Quick start

from chocs import Application
from chocs import HttpRequest
from chocs import HttpResponse
from chocs import serve


http = Application()

@http.get("/hello/{name}")
def hello(request: HttpRequest) -> HttpResponse:
    return HttpResponse(f"Hello {request.path_parameters.get('name')}!")

serve(http)

Keep in mind that the serve() function is using the bjoern package, so make sure you included it in your project dependencies before using it. You are able to use any WSGI compatible server.

Table of Contents

• Usage
  • Running application with Gunicorn (or any other WSGI server)
  • Running application in AWS Lambda (Http api or rest api)
  • Routing
    • Parametrized routes
    • Wildcard routes
    • Route groups
  • Middleware
  • Integration with openapi
  • Transforming request's payload to custom dataclasses
    • Strict mode
    • Non-strict mode, aka: auto hydration
    • Dataclass support library
  • Handling validation errors with custom middleware
  • Defining and using a custom middleware
  • Request
  • Response
    • chocs.HttpResponse.status_code: chocs.HttpStatus
  • Working with cookies
    • Reading client cookies
    • Setting cookies
• Contributing
  • Prerequisites
  • Installation
  • Running tests
  • Linting
  • PR

Usage

Running application with Gunicorn (or any other WSGI server)

# myapp.py
from chocs import Application
from chocs import HttpRequest
from chocs import HttpResponse
from chocs import create_wsgi_handler


http = Application()


@http.get("/hello/{name}*")
def hello(request: HttpRequest) -> HttpResponse:
    return HttpResponse(f"Hello {request.path_parameters.get('name')}!")

app = create_wsgi_handler(http, debug=False)

gunicorn -w 4 myapp:app

Running application in AWS Lambda (Http api or rest api)

handler.py

import logging

from chocs import HttpRequest
from chocs import HttpResponse
from chocs import Application

logger = logging.getLogger()
logger.setLevel(logging.INFO)


http = Application()


@http.get("/hello/{name}")
def hello_handler(request: HttpRequest) -> HttpResponse:
    logger.info("Hello AWS!")
    logger.info(request.attributes.get("aws_context"))
    logger.info(request.attributes.get("aws_event"))
    return HttpResponse(f"Hello {request.path_parameters.get('name')}")


__all__ = ["hello_handler"]

serverless.yml

service: aws-hello-name

provider:
  name: aws
  runtime: python3.8

plugins:
  - serverless-python-requirements

custom:
  pythonRequirements:
    dockerizePip: true

functions:
  hello_name:
    handler: handler.hello_handler
    events:
      - httpApi:
          method: GET
          path: /hello/{name}

serverless deploy

Routing

Chocs is shipped with a built-in routing module. The easiest way to utilise chocs' routing is to use chocs.router object. chocs.router is an instance of the module's internal class chocs.application.Application, which provides a simple API where each function is a decorator corresponding to an HTTP method.

from chocs import Application, HttpResponse, HttpRequest


http = Application()


@http.get("/hello")
def hello(req: HttpRequest) -> HttpResponse:
    ...

The above example will assign the hello function to handle a GET /hello request.

Available methods:

• delete
• get
• head
• options
• patch
• post
• put
• trace

Parametrized routes

Routes can contain parameterised parts. Parameters must be enclosed within { and }.

from chocs import Application

http = Application()


@http.get("/pet/{id}")
def hello():
    ...

Will match the following URIs:

• /pet/1
• /pet/abc
• /pet/abc1

Wildcard routes

Asterisks (*) can be used in the route's pattern to match any possible combination. Keep in mind that routes which do not contain wildcards are prioritised over routes with wildcards.

from chocs import Application

http = Application()


@http.get("/pet/*", id)
def hello():
    ...

The above example will match following URIs:

• /pet/a
• /pet/a/b/c
• /pet/12jd/fds

Route groups

Chocs supports route groups. Route groups is implemented through context lib interface. If you need to split your application in smaller chunks with standalone req/res handlers consider the following example:

from threading import Thread

from chocs.wsgi import serve 
from chocs import Application
from chocs import HttpRequest
from chocs import HttpResponse

main_app = Application()

with main_app.group("/users/{id}") as user_module:
    
    @user_module.post("/profile_picture")  # POST /users/{id}/profile_pictures
    def create_profile_picture(request: HttpRequest) -> HttpResponse:
        ...
    
    @user_module.get("/profile_picture")  # GET /users/{id}/profile_pictures
    def get_profile_picture(request: HttpRequest) -> HttpResponse:
        ...
    
    @user_module.get("/badges") # GET /users/{id}/badges
    def badges(request: HttpRequest) -> HttpResponse:
        ...

with main_app.group("/payments") as payment_module:

    @payment_module.get("/analytics") # GET /payments/analytics
    def get_analytics(request: HttpRequest) -> HttpResponse:
        ...

if __name__ == '__main__':
    def wsgi_user_module():
        serve(user_module, port=8081)
    def wsgi_payment_module():
        serve(payment_module, port=8082)

    Thread(target=wsgi_user_module).start()
    payment_module()

The above example shows how to run two different modules, which support their own routes on two different ports in the one process.

Middleware

Middleware are functions or classes that inherit chocs.Middleware. Middlewares have access to the request object and the next function which can be used to control middleware stack flow. Successful middleware execution should call the next function which accepts a chocs.HttpRequest instance and returns chocs.HttpReponse.

Middlewares can perform various tasks:

• Making changes in request/response objects ending
• Validating input data
• Authenticating users
• End request-response cycle
• Connecting to external data sources

Integration with openapi

To provide automatic validation for your request based on open api specification, use chocs.middleware.OpenApiMiddleware middleware bundled with chocs:

from chocs.middleware import OpenApiMiddleware
from chocs import Application, HttpRequest, HttpResponse
from os import path
from dataclasses import dataclass

# absolute path to file containing open api documentation; yaml and json files are supported
openapi_filename = path.join(path.dirname(__file__), "/openapi.yml")

# instantiating application and passing open api middleware
app = Application(OpenApiMiddleware(openapi_filename, validate_body=True, validate_query=True))

# defining our dataclass for better type support
@dataclass()
class Pet:
    id: str
    name: str

# the registered route must correspond to open api route within `path` section.
# if request body is invalid the registered function will not be executed
@app.post("/pets") # `parsed_body` parameter can be used to map request to certain type
def create_pet(request: HttpRequest) -> HttpResponse:
    try: 
        pet = Pet(**request.parsed_body)
    except TypeError:
        return HttpResponse(status=400)
    
    return HttpResponse(pet.name)

Complete integration example can be found here

Chocs automatically validates:

• request body, application/json header must be present for successful validation
• query string parameters
• request headers

Transforming request's payload to custom dataclasses

from chocs.middleware import ParsedBodyMiddleware
from chocs import Application, HttpRequest, HttpResponse
from chocs.dataclasses import asdict
from dataclasses import dataclass
import json

# You can define whether to use strict mode or not for all defined routes.
app = Application(ParsedBodyMiddleware(strict=False))

@dataclass
class Pet:
    id: str
    name: str

@app.post("/pets", parsed_body=Pet) # you can also override default strict mode
def create_pet(request: HttpRequest) -> HttpResponse:
    pet: Pet = request.parsed_body
    assert isinstance(pet, Pet)
    return HttpResponse(json.dumps(asdict(pet)))

Strict mode

Strict mode is using initialiser defined in dataclass. Which means the request data is simply unpacked and passed to your dataclass, so you have to manually transform nested data to dataclasses in order to conform your dataclass interface, for example:

from chocs.middleware import ParsedBodyMiddleware
from chocs import Application, HttpRequest, HttpResponse
from dataclasses import dataclass
from typing import List

app = Application(ParsedBodyMiddleware())

@dataclass
class Tag:
  name: str
  id: str

@dataclass
class Pet:
 id: str
 name: str
 age: int
 tags: List[Tag]
 
 def __post_init__(self):  # post init might be used to reformat your data
  self.age = int(self.age)
  tmp_tags = self.tags
  self.tags = []
  for tag in tmp_tags:
   self.tags.append(Tag(**tag))

@app.post("/pets", parsed_body=Pet) 
def create_pet(request: HttpRequest) -> HttpResponse:
 pet: Pet = request.parsed_body
 assert isinstance(pet.tags[0], Tag)
 assert isinstance(pet, Pet)
 return HttpResponse(pet.name)

Non-strict mode, aka: auto hydration

In non-strict mode chocs takes care of instantiating and hydrating your dataclasses. Complex and deeply nested structures are supported as long as used types are supported by chocs hydration mechanism. List of supported types can be found below in dataclass support library

Note: post_init method is not called as a part of hydration process.

Dataclass support library

Dataclass support library is composed of two functions to help with daily tasks while working with dataclasses.

chocs.dataclasses.init_dataclass(value: dict, type_name)

init_dataclass function is instantiating dataclass of specified type_name and will hydrate the instance with values passed in value dictionary. Each of the passed dictionary's keys must correspond to dataclass' attributes in order to be properly interpreted.

This function support complex and nested hydration, which means if your dataclass aggregates other dataclasses or defines complex typing, init_dataclass function will respect your type annotations and will cast values to match the defined types.

If attributes in your dataclass do not specify the type value will be hydrated in to a newly created instance as is.

chocs.dataclasses.asdict(value)

asdict is the opposite of init_dataclass function, it takes an instance of dataclass as argument, and extracts its members to a dictionary, so the returned data can be stored as json object orn easily serialised to any other format.

Supported data types

bool

Passed value is automatically hydrated to boolean with python's built-in bool on hydration and extraction.

dict

Passed value is automatically hydrated to dict with python's built-in dict on hydration and extraction.

float

Passed value is automatically hydrated to float with python's built-in float on hydration and extraction.

frozenset

Passed value is automatically hydrated to frozen set with python's built-in frozenset and extracted to list.

int

Passed value is automatically hydrated to int with python's built-in int on hydration and extraction.

list

Passed value is automatically hydrated to list with python's built-in list on hydration and extraction.

set

Passed value is automatically hydrated to set with python's built-in set and extracted to list.

str

Passed value is automatically hydrated to string with python's built-in str on hydration and extraction.

tuple

Passed value is automatically hydrated to tuple with python's built-in tuple and extracted to list.

collections.namedtuple

Passed value is automatically hydrated to named tuple and extracted to list.

collections.deque

Passed value is automatically hydrated to an instance of collections.deque and extracted to list.

collections.OrderedDict

Passed value is automatically hydrated to an instance of collections.OrderedDict and extracted to dict.

datetime.date

Passed value must be valid ISO-8601 date string, then it is automatically hydrated to an instance of datetime.date class and extracted to ISO-8601 format compatible string.

datetime.datetime

Passed value must be valid ISO-8601 date time string, then it is automatically hydrated to an instance of datetime.datetime class and extracted to ISO-8601 format compatible string.

datetime.time

Passed value must be valid ISO-8601 time string, then it is automatically hydrated to an instance of datetime.time class and extracted to ISO-8601 format compatible string.

datetime.timedelta

Passed value must be valid ISO-8601 duration string, then it is automatically hydrated to an instance of datetime.timedelta class and extracted to ISO-8601 format compatible string.

decimal.Decimal

Passed value must be a string containing valid decimal number representation, for more please read python's manual about decimal.Decimal, on extraction value is extracted back to string.

enum.Enum

Supports hydration of all instances of enum.Enum subclasses as long as value can be assigned to one of the members defined in the specified enum.Enum subclass. During extraction the value is extracted to value of the enum member.

enum.IntEnum

Same as enum.Enum.

typing.Any

Passed value is unchanged during hydration and extraction process.

typing.AnyStr

Same as str

typing.Deque

Same as collection.dequeue with one exception, if subtype is defined, eg typing.Deque[int] each item inside queue is hydrated accordingly to subtype.

typing.Dict

Same as dict with exception that keys and values are respectively hydrated and extracted to match annotated type.

typing.FrozenSet

Same as frozenset with exception that values of a frozen set are respectively hydrated and extracted to match annotated type.

typing.List

Same as list with exception that values of a list are respectively hydrated and extracted to match annotated type.

typing.NamedTuple

Same as namedtuple.

typing.Optional

Optional types can carry additional None value which chocs' hydration process will respect, so for example if your type is typing.Optional[int] None value is not hydrated to int.

typing.Set

Same as set with exception that values of a set are respectively hydrated and extracted to match annotated type.

typing.Tuple

Same as tuple with exception that values of a set are respectively hydrated and extracted to match annotated types. Ellipsis operator (...) is also supported.

typing.TypedDict

Same as dict but values of a dict are respectively hydrated and extracted to match annotated types.

Handling validation errors with custom middleware

By default, if validation fails users will see 500 response. This behavior can be changed if custom middleware that catches validation errors is defined and used in application.

Defining and using a custom middleware

The following code defines simple function middleware to catch validation errors when they appear and notifies users:

from chocs.middleware import OpenApiMiddleware
from chocs.json_schema.errors import ValidationError
from chocs import Application, HttpRequest, HttpResponse
from dataclasses import dataclass
import json
from typing import Callable
from os import path

openapi_filename = path.join(path.dirname(__file__), "/openapi.yml")


# middleware must always accept two parameters; HttpRequest and Callable and return HttpResponse
def handle_errors(request: HttpRequest, next: Callable) -> HttpResponse:
    try:
        return next(request) # we pass request further to middleware pipeline
    except ValidationError as error: # if exception is thrown it is caught here and new response is generated instead
        json_response = {
            "code": error.code,
            "message": str(error),
        }
        return HttpResponse(json.dumps(json_response), status=422)
    
# error handling middleware must go before open api one to catch errors thrown inside open api middleware
app = Application(handle_errors, OpenApiMiddleware(openapi_filename))

@dataclass()
class Pet:
  id: str
  name: str

@app.post("/pets", parsed_body=Pet)
def create_pet(request: HttpRequest) -> HttpResponse:
  assert isinstance(request.parsed_body, Pet)

  pet = request.parsed_body
  return HttpResponse(pet.name)

Full working example can be found inside examples directory

Request

Moved to wiki

Response

chocs.Response object is a part of request-response flow, and it is required to be returned by all functions decorated with router.* method. Instance of the response class is recognised by chocs.Application and used to generate real response served to your clients.

chocs.HttpResponse.body: io.BytesIO

Body served to server's clients.

chocs.HttpResponse.status_code: chocs.HttpStatus

Valid response code, instance of chocs.HttpStatus enum can be used or just a status code's number.

chocs.HttpResponse.cookies:chocs.HttpCookieJar

Response's cookies

chocs.HttpResponse.write(body: Union[bytes, str, bytearray])

Write bytes to response body

chocs.HttpResponse.close()

Makes body non-writable.

chocs.HttpResponse.writable: bool

Indicates whether response's body is writable.

chocs.HttpResponse.parsed_body:chocs.HttpMessage

Depending on the content type it could be one of the following:

• chocs.FormHttpMessage
• chocs.JsonHttpMessage
• chocs.MultipartHttpMessage
• chocs.YamlHttpMessage

chocs.HttpResponse.as_dict(): dict

Tries to convert response body to a dict and returns it.

Note this will only work with json and yaml content types.

chocs.HttpResponse.as_str(): str

Returns response content as a string.

Working with cookies

chocs.HttpCookieJar object takes care of cookie handling. It can be accessed in dict-like manner, when item is requested, instance of chocs.HttpCookie is returned to user.

Cookies can be set either by passing string value to the chocs.CookieJar's key, or by calling chocs.CookieJar.append method which accepts instance of chocs.Cookie.

Reading client cookies

Cookies can be easily accessed from chocs.Request.cookies object which is injected as a parameter to each function registered as route handler. Consider the following example:

from chocs import HttpRequest
from chocs import HttpResponse
from chocs import Application
from chocs import serve

http = Application()


@http.get("/cookies")
def read_cookies(request: HttpRequest) -> HttpResponse:

    message = "Hello"
    if "user_name" in request.cookies:
        message += f", {str(request.cookies['user_name'])}"
    message += "!"

    return HttpResponse(body=message)

serve(http)

Setting cookies

from datetime import datetime

from chocs import HttpCookie
from chocs import HttpRequest
from chocs import HttpResponse
from chocs import Application
from chocs import serve

http = Application()


@http.get("/cookies")
def read_cookies(request: HttpRequest) -> HttpResponse:
    response = HttpResponse(body="Hi! I have baked some cookies for ya!")
    response.cookies['simple-cookie'] = "Simple cookie for simple people"
    response.cookies.append(HttpCookie("advanced-cookie", "This cookie will expire in 2021-01-01", expires=datetime(2021, 1, 1)))
    return response

serve(http)

Contributing

Prerequisites

• libev
• python 3.8
• docker

Installation

poetry install

Running tests

poetry run pytest

Linting

poetry run black .
poetry run isort .
poetry run mypy .

PR