Gishath Proxy

In the upper right corner of the browser, click "Settings and Other", and then select the "Options" tab in the window that appears. Once the "General" window opens, locate the "Advanced" tab and click "Open proxy settings" in the menu that appears. Here, in the line "Use a proxy server", select "On". In the "Address" field, you must specify the IP address of the proxy, and in the "Port" field - the port of the proxy. The last thing to do is to click "Save".

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.

Parsing huge XML files can be challenging due to their size. Here are some tips for efficient XML parsing:

1. Use Streaming Parsers:

   • Streaming parsers, like SAX (Simple API for XML) or StAX (Streaming API for XML), process XML data sequentially. They don't load the entire XML document into memory, making them memory-efficient for large files.

2. XPath for Selective Parsing:

   • XPath is a query language for XML that allows you to navigate through elements and attributes. Use XPath to selectively parse only the parts of the XML document that you need, rather than parsing the entire file.

3. Incremental Parsing:

   • Break the XML document into smaller chunks and parse them incrementally. This can be useful when dealing with extremely large files. Process one chunk at a time instead of loading the entire file into memory.

4. Memory Management:

   • If using a DOM (Document Object Model) parser, be mindful of memory usage. Large XML files can consume a significant amount of memory when loaded into a DOM. Consider using a streaming approach instead.

5. Parallel Processing:

   • If the XML document is partitioned into multiple sections, you can leverage parallel processing to parse different sections concurrently. Be cautious with synchronization to avoid conflicts.

6. Compression:

   • If the XML files are too large, consider compressing them before parsing. This can reduce the file size and speed up the parsing process. Remember to decompress the file before parsing.

7. Optimize Code and Libraries:

   • Ensure that you are using an efficient XML parsing library. Some libraries are better optimized for large files. Additionally, optimize your code for performance by minimizing unnecessary operations.

8. Use Memory-Mapped Files:

   • Memory-mapped files allow parts of a file to be mapped directly into memory. This can be beneficial for random access to different parts of a large XML file.

9. Consider External Tools:

   • In some cases, using external tools specifically designed for processing large XML files might be a good option. These tools are often optimized for performance and memory usage.

Remember that the optimal approach may vary depending on the specific requirements of your application and the characteristics of the XML files you are dealing with.

An "open" proxy means one that is publicly available. It can be used by many network users at the same time. But because of this its bandwidth is also quite low, because the server simultaneously handles all requests through a single port.

Chromium does not support proxies in-house. There is a corresponding item in the menu, but clicking on it will open the regular proxy server settings in Windows or MacOS.