UK TV Proxy Server

A proxy server acts as an intermediary between client and server parts of distributed network applications. The role of a transit node provides a logical break in the direct connection between the server and the client. A proxy server can also act as a firewall if the traffic it controls does not go through a workaround.

To speed up scraping by leveraging asynchronous programming in Python, you can use the asyncio library along with asynchronous HTTP requests. The aiohttp library is commonly used for asynchronous HTTP requests. Here's a basic example to help you get started:

Install Required Packages:

pip install aiohttp

Asynchronous Scraping Script:

import asyncio
import aiohttp

async def scrape_url(session, url):
    try:
        async with session.get(url) as response:
            if response.status == 200:
                content = await response.text()
                # Process the content as needed
                print(f"Scraped {url}: {len(content)} characters")
            else:
                print(f"Failed to scrape {url}. Status code: {response.status}")
    except Exception as e:
        print(f"Error scraping {url}: {str(e)}")

async def main():
    urls_to_scrape = [
        'https://example.com/page1',
        'https://example.com/page2',
        # Add more URLs as needed
    ]

    async with aiohttp.ClientSession() as session:
        tasks = [scrape_url(session, url) for url in urls_to_scrape]
        await asyncio.gather(*tasks)

if __name__ == "__main__":
    asyncio.run(main())

• • This script defines an asynchronous function scrape_url to perform the scraping for a given URL.
  • The main function creates an asynchronous HTTP session using aiohttp.ClientSession and gathers the scraping tasks.
  • The asyncio.run(main()) line runs the main asynchronous function.

• Running the Script:

python your_scraper_script.py

This example demonstrates the basics of asynchronous scraping. Asynchronous programming can significantly speed up scraping tasks, especially when making multiple concurrent HTTP requests.

Keep in mind that not all websites support asynchronous scraping, and some may have restrictions or rate limiting. Always adhere to the website's terms of service, and consider adding delays between requests to avoid overloading the server.

To wait for a button to be clickable using Selenium, you can use the WebDriverWait class along with the expected_conditions module. Here's an example using Python:

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

# Set the path to the ChromeDriver executable
chrome_driver_path = "path/to/chromedriver"

# Initialize the Chrome WebDriver
driver = webdriver.Chrome(executable_path=chrome_driver_path)

# Your Selenium code goes here

# Wait for the button to be clickable
button = WebDriverWait(driver, 10).until(
    EC.element_to_be_clickable((By.ID, "button-id"))
)

# Click the button
button.click()

# Your code after clicking the button

# Close the browser
driver.quit()

Replace path/to/chromedriver with the appropriate path to your ChromeDriver executable and "button-id" with the ID of the button you want to wait for.

In this example, WebDriverWait will wait for up to 10 seconds for the button with the specified ID to become clickable. If the button is not clickable within the specified time, a TimeoutException will be raised.

You can also use other expected_conditions such as visibility_of_element_located, presence_of_element_located, or staleness_of depending on your specific use case.

To optimize the performance of Selenium with Chrome and Chromedriver, you can consider several strategies:

Latest Versions:
Ensure that you are using the latest version of Chrome and Chromedriver. They are frequently updated to include performance improvements and bug fixes.

Chromedriver Version Compatibility:
Make sure that the version of Chromedriver you are using is compatible with the version of Chrome installed on your machine. Mismatched versions may lead to unexpected behavior.

Headless Mode:
If you don't need to see the browser window during automation, consider running Chrome in headless mode. Headless mode can significantly improve the speed of browser automation.

chrome_options.add_argument('--headless')

Chrome Options:
Experiment with different Chrome options to see how they affect performance. For example, you can set options related to GPU usage, image loading, and more.

chrome_options.add_argument('--disable-gpu')
chrome_options.add_argument('--blink-settings=imagesEnabled=false')

Page Loading Strategy:
Adjust the page loading strategy. For example, you can set pageLoadStrategy to 'eager' or 'none' if it fits your use case.

chrome_options.add_argument('--pageLoadStrategy=eager')

Timeouts:
Adjust timeouts appropriately. For example, setting script timeouts or implicit waits can help to avoid unnecessary waiting times.

driver.set_script_timeout(10)
driver.implicitly_wait(5)

Parallel Execution:
Consider parallel execution of tests. Running tests in parallel can significantly reduce overall execution time.

Browser Window Size:
Set a specific window size to avoid unnecessary rendering.

chrome_options.add_argument('window-size=1920x1080')

Disable Extensions:
Disable unnecessary Chrome extensions during testing.

chrome_options.add_argument('--disable-extensions')

Logging:
Enable logging to identify any issues or bottlenecks.

service_args = ['--verbose', '--log-path=/path/to/chromedriver.log']
service = ChromeService(executable_path='/path/to/chromedriver', service_args=service_args)

To keep only unique external links while scraping with Scrapy, you can use a set to track the visited external links and filter out duplicates. Here's an example spider that demonstrates how to achieve this:

import scrapy
from urllib.parse import urlparse, urljoin

class UniqueLinksSpider(scrapy.Spider):
    name = 'unique_links'
    start_urls = ['http://example.com']  # Replace with the starting URL of your choice
    visited_external_links = set()

    def parse(self, response):
        # Extract all links from the current page
        all_links = response.css('a::attr(href)').extract()

        for link in all_links:
            full_url = urljoin(response.url, link)

            # Check if the link is external
            if urlparse(full_url).netloc != urlparse(response.url).netloc:
                # Check if it's a unique external link
                if full_url not in self.visited_external_links:
                    # Add the link to the set of visited external links
                    self.visited_external_links.add(full_url)

                    # Yield the link or process it further
                    yield {
                        'external_link': full_url
                    }

        # Follow links to other pages
        for next_page_url in response.css('a::attr(href)').extract():
            yield scrapy.Request(url=urljoin(response.url, next_page_url), callback=self.parse)

- visited_external_links is a class variable that keeps track of the unique external links across all instances of the spider.
- The parse method extracts all links from the current page.
- For each link, it checks if it is an external link by comparing the netloc (domain) of the current page and the link.
- If the link is external, it checks if it is unique by looking at the visited_external_links set.
- If the link is unique, it is added to the set, and the spider yields the link or processes it further.
- The spider then follows links to other pages, recursively calling the parse method.

Remember to replace the start_urls with the URL from which you want to start scraping.