treqvscrul
treq is a Python library for making HTTP requests that provides a simple, convenient API for interacting with web services. It is inspired byt the popular requests library, but powered by Twisted asynchronous engine which allows promise based concurrency.
treq provides a simple, high-level API for making HTTP requests, including methods for GET, POST, PUT, DELETE, etc. It also allows for easy handling of JSON data, automatic decompression of gzipped responses, and connection pooling.
treq is a lightweight library and it's easy to use, it's a good choice for small to medium-sized projects where ease of use is more important than performance.
In web scraping treq isn't commonly used as it doesn't support HTTP2 but it's the only Twisted based HTTP client. treq is also based on callback/errback promises (like Scrapy) which can be easier to understand and maintain compared to asyncio's corountines.
crul is a R library for sending HTTP requests and web scraping. It is designed to be simple and easy to use, while still providing powerful functionality for working with HTTP requests and scraping web pages.
One of the main features of crul is its intuitive and easy-to-use syntax for sending HTTP requests. It allows you to easily specify the HTTP method, headers, and body of a request, and also provides a simple way to handle the response.
crul also has the ability to handle different types of requests and responses, including GET, POST, PUT, DELETE, and PATCH. It also support for handling redirects, cookies, and authentication.
Another feature of crul is its support for web scraping. The library provides a simple and efficient way to extract data from web pages, using a syntax similar to that of the XML and httr libraries. It also allows to easily filter the extracted data based on a specific criteria.
crul also supports parallel scraping, which allows to make multiple requests at the same time, thus speeding up the scraping process.
In addition to these features, crul has a good compatibility with other R packages such as tidyverse and purrr which facilitates the manipulation of the data obtained after scraping.
Highlights
Example Use
from twisted.internet import reactor
from twisted.internet.task import react
from twisted.internet.defer import ensureDeferred
import treq
# treq can be used with twisted's reactor with callbacks
response_deferred = treq.get(
"http://httpbin.org/get"
)
# or POST
response_deferred = treq.post(
"http://httpbin.org/post",
json={"key": "value"}, # JSON
data={"key": "value"}, # Form Data
)
# add callback or errback
def handle_response(response):
print(response.code)
response.text().addCallback(lambda body: print(body))
def handle_error(failure):
print(failure)
# this callback will be called when request completes:
response_deferred.addCallback(handle_response)
# this errback will be called if request fails
response_deferred.addErrback(handle_error)
# this will be called if request completes or fails:
response_deferred.addBoth(lambda _: reactor.stop()) # close twisted once finished
if __name__ == '__main__':
reactor.run()
#Note that treq can also be used with async/await:
async def main():
# content reads response data and get sends a get request:
print(await treq.content(await treq.get("https://example.com/")))
if __name__ == '__main__':
react(lambda reactor: ensureDeferred(main()))
</div>
<div class="lib-example" markdown>
```r
library(crul)
# Sending a GET request to a website
response <- HttpClient$new("https://www.example.com")$get()
# Sending a POST request to a website
request_body <- list(param1 = "value1", param2 = "value2")
response <- HttpClient$new("https://www.example.com")$post(body = request_body)
# Extracting the status code and body of the response
status_code <- response$status_code()
body <- response$body()
# crul also allows easy asynchronous requests:
urls <- c("https://www.example1.com", "https://www.example2.com", "https://www.example3.com")
# Creating a list of request objects from urls
requests <- lapply(urls, function(url) {
HttpClient$new(url)$get()
})
# Sending the requests asynchronously
responses <- async(requests)
# Extracting the status code and body of the responses
status_codes <- lapply(responses, function(response) response$status_code())
bodies <- lapply(responses, function(response) response$body())