Static Residential Proxy SOCKS5

Technically, a proxy is an ordinary computer or server connected to a network (local or Internet). It accepts traffic from the user, redirects it to the address that was specified in the request. And then receives the response from the server and transmits it to the user's equipment. That is, it is actually an intermediary.

Scraping data from a community wall on VK (Vkontakte) using the VK API requires authentication and making requests to the API endpoints. VK provides an official API that you can use to access various data, including posts from community walls.

Here's a general guide on how to scrape posts from a community wall using the VK API:

1. Create a VK App:

   • Go to the VK Developers website and create a VK app.
   • Obtain the "App ID" and "Secure Key" for your app.

2. Authentication:

   • Get an access token to authenticate your requests. You can use the Implicit Flow for user authentication or the Client Credentials Flow for app authentication.

3. Make API Requests:

   • Use the obtained access token to make requests to the VK API. The endpoint for getting wall posts is wall.get.
   • You'll need the community's ID or screen name to get its wall posts.

Here's an example using Python and the requests library:

import requests

# Replace with your VK app details and access token
app_id = 'your_app_id'
secure_key = 'your_secure_key'
access_token = 'your_access_token'

# Replace with the community ID or screen name
community_id = 'your_community_id_or_screen_name'

# API endpoint for getting wall posts
api_url = f'https://api.vk.com/method/wall.get?owner_id=-{community_id}&count=10&access_token={access_token}&v=5.131'

# Make the API request
response = requests.get(api_url)
data = response.json()

# Extract and print the posts
if 'response' in data and 'items' in data['response']:
    posts = data['response']['items']
    for post in posts:
        print(post['text'])
else:
    print('Error fetching wall posts')

Note: Make sure to handle errors and check the VK API documentation for more details on available parameters and responses.

To reduce the resource consumption of Selenium with Google Chrome, you can try the following methods:

1. Use ChromeOptions:

You can use the ChromeOptions class to configure ChromeDriver settings that can help reduce resource consumption. For example, you can set the window size to a smaller value or disable certain features like animations and extensions.

from selenium import webdriver
from selenium.webdriver.chrome.options import Options

chrome_options = Options()
chrome_options.add_argument("--start-maximized")
chrome_options.add_argument("--disable-extensions")
chrome_options.add_argument("--disable-gpu")
chrome_options.add_argument("--headless")

driver = webdriver.Chrome(options=chrome_options)

driver.get('your_url')

# Rest of your code

driver.quit()

2. Use a headless browser:

A headless browser is a browser that runs without a graphical user interface (GUI). Running a headless browser can reduce resource consumption, as it doesn't require rendering a visual interface. You can enable headless mode by adding the --headless argument to the ChromeOptions.

3. Limit the number of concurrent instances:

If you're running multiple instances of Selenium with ChromeDriver, consider limiting the number of concurrent instances to avoid overloading your system resources.

4. Use a lighter browser:

Consider using a lighter browser like Firefox or Edge instead of Google Chrome. These browsers generally consume fewer resources than Chrome, and you can still use Selenium with them.

5. Close unnecessary browser tabs:

Close any unnecessary browser tabs or windows to free up system resources.

6. Optimize your code:

Review your Selenium code to identify and remove any unnecessary or inefficient operations that may be consuming resources. For example, avoid using excessive loops, and use explicit waits instead of implicit waits.

Remember that the specific resource consumption of Selenium with Google Chrome depends on various factors, including the complexity of the web pages you're testing, the number of elements on the page, and the performance of your system. Experiment with the above methods to find the best combination for your needs.

Extreme RAM consumption in Firefox Selenium can be caused by a variety of factors. Here are some steps you can take to troubleshoot and resolve the issue:

1. Update Firefox and Selenium: Ensure you are using the latest versions of Firefox and Selenium, as updates often include performance improvements and bug fixes.

2. Use Firefox Options: When initializing the Firefox WebDriver, pass the -marionette option to use the Marionette protocol, which can help reduce memory usage.

from selenium import webdriver

driver = webdriver.Firefox(executable_path, options=["-marionette"])

3. Use Firefox Profile: Create a custom Firefox profile and use it with Selenium to limit memory usage.

from selenium import webdriver
from selenium.webdriver.firefox.options import Options
from selenium.webdriver.firefox.firefox_profile import FirefoxProfile

profile = FirefoxProfile()
profile.set_preference("browser.sessionstore.max_tabs_undoc", 0)
profile.set_preference("browser.sessionstore.max_windows_undoc", 0)
profile.set_preference("browser.sessionstore.max_windows", 0)
profile.set_preference("browser.sessionstore.max_tabs", 0)

options = Options()
options.profile = profile

driver = webdriver.Firefox(executable_path, options=options)

4. Limit Browser Tabs: If you are using multiple tabs, try to limit the number of tabs open at the same time, as each tab consumes additional memory.

5. Disable Extensions: Disable any unnecessary browser extensions, as they can consume memory and slow down the browser.

6. Close Unused Windows: Close any unnecessary browser windows to free up memory.

7. Adjust Timeouts: Increase the implicit and explicit wait timeouts to reduce the frequency of operations that might cause memory leaks.

driver.implicitly_wait(10)
driver.set_page_load_timeout(10)

8. Use Headless Mode: Run Firefox in headless mode to reduce memory usage by not rendering the UI.

options.add_argument("--headless")

9. Monitor Memory Usage: Use tools like Task Manager (Windows) or Activity Monitor (macOS) to monitor memory usage and identify any specific tests or operations that are causing high memory consumption.

10. Profile Memory Usage: Use Firefox's built-in performance profiling tools to identify memory leaks and optimize your code.

If none of these steps resolve the issue, consider using a different browser or WebDriver, such as Chrome or Edge, which may have better memory management.

Download MarketApp, log in to your account and download the extension. Then go to the settings, find the item "Basic" and click on "Get your key". In the box provided to get your key, type Localhost, and then an IP key will appear, allowing you to trade freely on the marketplace.