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crulvspycurl

MIT 16 1 101
59.0 thousand (month) Nov 09 2016 1.4.0(1 year, 5 months ago)
1,038 9 8 LGPL/MIT
7.45.3(10 days ago) Feb 25 2003 1.1 million (month)

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.

PycURL is a Python interface to libcurl, a multi-protocol file transfer library written in C. PycURL allows developers to use a variety of network protocols in their Python programs, including HTTP, FTP, SMTP, POP3, and many more.

PycURL is often used in web scraping, data analysis, and automation tasks, as it allows developers to send and receive data over the internet. It can be used to perform various types of requests, such as GET, POST, PUT, and DELETE, and can also handle file uploads and downloads, cookies, and redirects.

One of the key features of PycURL is its support for SSL and proxy servers, which allows developers to securely transfer data over the internet and work around any network restrictions. PycURL also supports a wide range of authentication methods, such as Basic, Digest, and NTLM, and allows developers to easily set custom headers and query parameters.

Just like cURL itself, PycURL is also highly configurable and allows for fine-grained control over various aspects of the transfer, such as timeouts, retries, buffer sizes, and verbosity levels. Additionally, PycURL also provides easy access to the underlying libcurl library, which allows developers to access advanced functionality that is not exposed by the PycURL API.

It's important to note that PycURL is a wrapper around the libcurl library and therefore provides the same functionality and performance as libcurl.

Main strengths of PycURL is that it uses cURL which is one of the most feature rich low-level http clients. The downside is that it's a very low-level client (see the examples below) with complex API making use in web scraping very difficult and niche.

Highlights


http2uses-curlasync
uses-curlhttp2multi-partresponse-streaminghttp-proxy

Example Use


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())
import pycurl
from io import BytesIO

buf = BytesIO()
headers = BytesIO()

c = pycurl.Curl()
c.setopt(c.HTTP_VERSION, c.CURL_HTTP_VERSION_2_0)  # set to use http2
# set proxy
c.setopt(c.PROXY, 'http://proxy.example.com:8080') 
c.setopt(c.PROXYUSERNAME, 'username')
c.setopt(c.PROXYPASSWORD, 'password')

# make a request
c.setopt(c.URL, 'https://httpbin.org/get')
c.setopt(c.WRITEFUNCTION, buf.write)  # where to save response body
c.setopt(c.HEADERFUNCTION, headers.write)  # where to save response headers
# to make post request enable POST option:
# c.setopt(c.POST, 1)
# c.setopt(c.POSTFIELDS, 'key1=value1&key2=value2')
c.perform()  # send request

# read response
data = buf.getvalue().decode()
headers = headers.getvalue().decode()  # headers as a string
headers = dict([h.split(': ') for h in headers.splitlines() if ': ' in h])  # headers as a dict
c.close()

# multiple concurrent requests can be made using CurlMulti object:
# Create a CurlMulti object
multi = pycurl.CurlMulti()
# Set the number of maximum connections
multi.setopt(pycurl.MAXCONNECTS, 5)

# Create a list to store the Curl objects
curls = []

# Add the first request
c1 = pycurl.Curl()
c1.setopt(c1.URL, 'https://httpbin.org/get')
c1.setopt(c1.WRITEFUNCTION, BytesIO().write)
multi.add_handle(c1)
curls.append(c1)

# Add the second request
c2 = pycurl.Curl()
c2.setopt(c2.URL, 'https://httpbin.org/')
c2.setopt(c2.WRITEFUNCTION, BytesIO().write)
multi.add_handle(c2)
curls.append(c2)

# Start the requests
while True:
    ret, _ = multi.perform()
    if ret != pycurl.E_CALL_MULTI_PERFORM:
        break

# Close the connections
for c in curls:
    multi.remove_handle(c)
    c.close()

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