async-http-requests 0.7.1

Asynchronous HTTP requests

Async Requests

Python library to handle asynchronous http requests, built on top of the requests library

• Installation
• Usage

Installation

PyPI page

pip install async-http-requests

Update

Added support for python free-threading builds, using concurrent.futures.ThreadPoolExecutor

Usage

The library provide support for asynchronous http requests, using the consumer-producer pattern leveraging the built-in python modu.

Instantiate the class AsyncHTTP specifying a list of RequestObject, (you can specify N_PRODUCERS and N_CONSUMERS: default values are 10 for both)

The RequestObject supports all keyword arguments of the requests methods (headers,params,data, ...). It allows you to specify different keyword arguments across different requests

from AysncRequests import AsyncHTPP, RequestObject, RequestType,

# Public API endpoint, it retrieves all public APIs listed under params specification
# Refer to this if you want to know more https://api.publicapis.org/
api = 'https://api.publicapis.org/entries'

# default N_PRODUCERS and N_CONSUMERS to 10

endpoints = [
    RequestObject(url = api, params = {"title":"cat"}),
    RequestObject(url = api, params = {"title":"dog"})
]

requests = AsyncHTTP(
    url = endpoints
) 


# specify number of producers and consumers

requests = AsyncHTTP(
    url = endpoints,
    multithreding = True, # bool
    workers = 4, # number of threads, default is os.cpu_count()
    N_PRODUCERS = 10,
    N_CONSUMERS = 10
)

To get the responses you can either call the generic async_request method explicitly specifying the http request type by passing to the request_type argument a RequestType Enum (GET, POST, PUT, PATCH, DELETE, HEAD), or you can use the async_get, async_post, async_delete, async_put, async_patch, async_head method without having to spepcify the request type.

requests.async_request(
    request_type=RequestType.GET,
)

requests.async_get()

All methods support additional fixed keyword arguments such as headers, auth etc. as per the usual requests module, in case you need certain arguments to stay fixed across requests

For the additional paramaters refer to the requests module documentation Requests docs

headers = {'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36'} # Keyword arguments FIXED for all requests

# Specify the RequestType

requests.async_request(
    request_type=RequestType.GET,
    haeders = headers # using the same header across all the requests
)

# Using async_get

requests.async_get(headers = headers)  # using the same header across all the requests

All methods support the use of callback functions, to be used by the consumers on the request.Response object generated by the producers, as they become available in the asyncio.Queue.

In case of unsuccessfull requests the parameter max_retries can be specified (default is 0). When max retries is set larger than zero, any http request that has returned an error will be sent again up to the specified number of times if not successfull. If the given requests still generates problems after all the attempts it will be pushed to the requests.error_response and won't be found in requests.response

The max_retries paramater can be useful when you are sending a big number of requests to the same server, in which case you can get an error just momentarily even if nothing is really wrong.

def example_callaback(response: request.Response):
    return response.status_code 


requests.async_request(
    request_type=RequestType.GET,
    max_retries = 5,  # retry sendign the requests 5 times for all the unsuccessfull ones
    callback = example_callback,
    headers = headers
)


# no retries specified -> defaults to no further attempts in case of errors
requests.async_get(
            headers = headers,
            callback = lambda x: x.json() # lambda as callback
)

• The results will be stored in a list object, where you'll find either the requests.Response objects, or the output of the callback function.
• Requests that return an error code will be saved in requests.error_response
• A summary of all the requests that were not successfull (if any) can be displayed summarized in a pandas.DataFrame object through the requests.error_data attribute

requests.response
requests.error_response
requests.error_data

Check the test.py script for an example.