uml – Use case diagram for a web scraper

I am creating a news application as a project in which data ( the articles ) ,will be gathered and displayed on site using a web scraper.

In this project I am scraping data from different sites, storing it into database and making it view on site.
The extracted data is stored on a data base that is being connected to a back end server that feeds end points to the front end application.

My questions are:

should i have only one use case for the user since he is the only one interacting with the application ?

( there are no CRUD operations the user can only see the diffrent articles and save them or add a comment and see the diffrent comments on the article)
if not what will the diffrent uses cases be ?

Is data collection a use case?

here is a display of the architecture am using for this project

enter image description here

xss: possible attack vectors for a website scraper

I've written a little utility that, given a website address, goes and fetches some metadata from the site. My ultimate goal here is to use this within a website that allows users to enter a site, and then this utility goes and gets information: title, URL, and description.

I'm specifically looking at certain tags within the HTML, and I'm encoding the return data, so I think I'll be safe from XSS attacks. However, I wonder if there are other attack vectors that this leaves me open to.

architecture – Scraper in repository separate from display component?

Let me explain my thoughts on the architecture of the project I'm working on.
The project code repository consists of:

  • Scrapy Component: Of course, it is used to scrape data, process it and calculate relationships between data. MySQL database is populated.
  • Django display component – simply displays the data stored in the database using many filters.

Right now they are implemented as two separate docked containers that work fine.
The idea of ​​the old colleagues was to go further with the divided and divided repositories as well.

I can see the potential ability to create CI / CDs per repository so it will only run tests / lintes / controls and eventually just implement the container that was actually modified. It won't run everything for another container that is fine (logical separation).

But since they are actually working on the same tables in the database (Scrapy fills them in, Django reads them) it seems like overkill to me. You would need to keep two separate database model specifications in sync across both repositories. Scrapy currently uses Django ORM to interact with DB.

What you think? Do you think it's worth splitting the code repository into two separate ones and keeping the models in sync on both? Or maybe not? Is there a way to trigger / run the Gitlab CI / CD process only for the affected container in a single repository?

Thank you

ProxyOwl – Automated Proxy Scraper website for $ 29

ProxyOwl – Automated Proxy Scraper Website

Have you ever wanted to have your own Proxy Scraper site? You are in the right place, ProxyOwl is a powerful proxy scraping site created in Bootstrap 4, HTML and JS fully automated.

Characteristics

  • Built with Bootstrap 4 / HTML and JS
  • Fully Automated Proxy Scraper
  • Scrape proxies from a secure source
  • Easy to customize
  • Can be installed in free or paid hosting
  • Install on unlimited domains
  • One time purchase
  • It runs online or locally directly in your browser

Frequently asked questions

Q: What is this exactly?

A: It is a proxy scraper site.

Q: Do I receive the script after purchase?

A: Once you order, you get the complete script in a zip file.

Q: How to install it?

A: All you have to do is upload it to your host, you can also use it locally in your browser, it is up to you.

Q: Do I need to have coding skills to customize it?

A: You don't need coding skills to customize, HTML / JS files can be edited very easily even if you don't know anything about coding.

Q: How many proxies does he scratch?

A: This is the first public release (1.0.0). You can scratch + 1K proxies per scratch. More proxy sources will be added in future updates.

Q: Can I earn money with this script?

So can. You can turn this script into a money machine.

. (tagsToTranslate) Proxy (t) Scraper (t) Html (t) Website

ProxyOwl – $ 29 Automated Proxy Scraper Site

ProxyOwl – Automated Proxy Scraper Site

Have you ever wanted to have your own Proxy Scraper site? You are in the right place, ProxyOwl is a powerful proxy scraping site created in Bootstrap 4, HTML and JS fully automated.

Characteristics

  • Built with Bootstrap 4 / HTML and JS
  • Fully Automated Proxy Scraper
  • Scrape proxies from a secure source
  • Easy to customize
  • Can be installed in free or paid hosting
  • Install on unlimited domains
  • One time purchase
  • It runs online or locally directly in your browser

Frequently asked questions

Q: What is this exactly?

A: It is a proxy scraper site.

Q: Do I receive the script after purchase?

A: Once you order, you get the complete script in a zip file.

Q: How to install it?

A: All you have to do is upload it to your host, you can also use it locally in your browser, it is up to you.

Q: Do I need to have coding skills to customize it?

A: You don't need coding skills to customize, HTML / JS files can be edited very easily even if you don't know anything about coding.

Q: How many proxies does he scratch?

A: This is the first public release (1.0.0). You can scratch + 1K proxies per scratch. More proxy sources will be added in future updates.

Q: Can I earn money with this script?

So can. You can turn this script into a money machine.

. (tagsToTranslate) Proxy (t) Scraper (t) Html (t) Website

PHP web scraper

I am trying to take the prices of the products that are within the link that I am trying to run. The problem is that the website does not allow me to enter to take the information. The code I have right now is:


Residential proxies in front of data centers. GSA Proxy Scraper.

It would be great if you could add a filter that allowed separation or filtering of residential proxies in front of data centers in the GSA proxy scraper. It would also be great if we could dig a little deeper into the location. I know the country is a filter option now, but if I could also offer city / state (for the US), that would be very useful. Are these requests something that could be added?

"Loaded Tracking List" (Email Scraper Premium) no longer works

Hi,
Email Scraper (Loaded Tracking List) no longer works.
This happens: the threads of work in progress decrease from 100 to 1. Queued sites vary between 40 and 370.
I have a list of URLs to process of 99 (depth level 2).
What could be the reason?
Thank you !

Are you using proxies? Because if you run out of proxy servers, that will happen.

No. There are no loaded proxies. Why do the "work threads" go down?

When you drag, you can only drag as many URLs as there are. So, if it is at level 2 and there are only 14 internal links to track and you have it configured in 100 threads, you will only use 14 of them because those are all the links there are. Process through 1 complete domain at a time.

I don't think this is the problem. It looks like a firewall, but there is none.
What could I try to solve the problem?
Thanks in advance

Could be. Security software / malware verifiers and firewalls can eliminate connections.

So just add an exception for the entire scrapebox folder in all security / malware software verifiers

If you use Windows 10 and no other security software, then Windows Defender is enabled. By that I mean you can turn it off and turn it on again, Microsoft thinks we are idiots.

You have to hack the registry to remain off, so be sure to add an exception in Windows Defender.

python: Instagram scraper is slowing

You probably won't be able to do much to accelerate get_hashtag_posts since it is an API call; If you try to force it by executing multiple queries in parallel, a well-designed API will interpret it as a DDOS attack and limit your speed.

However, when it comes to your code, you must use a set instead of a list, since the sets are optimized for exactly what you are doing:

users: Set(str) = set()
for post in loader.get_hashtag_posts(HASHTAG):
    users.add(post.owner_username)

This should be a constant time operation, while a list will take longer to find the longer (linear time).

python – Simple Nagios Scraper – version 2

Version 1 – Beginner web scraper for Nagios

Changes to the current version:

  • Moved NAGIOS_DATA dictionary to separate the file (and added to .gitignore)
  • Functions used with DOCSTRINGS
  • Eliminated multiple redundants print() statements
  • Really read PEP8 standards and renamed variables to match the requirements

Again, beginner Python programmer. I appreciate the comments!

import requests
from scraper import NAGIOS_DATA
from bs4 import BeautifulSoup
from requests.auth import HTTPBasicAuth, HTTPDigestAuth


def get_url_response(url, user, password, auth_type):
    """Get the response from a URL.

    Args:
        url (str): Nagios base URL
        user (str): Nagios username
        password (str): Nagios password
        auth_type (str): Nagios auth_type - Basic or Digest

    Returns: Response object
    """

    if auth_type == "Basic":
        return requests.get(url, auth=HTTPBasicAuth(user, password))
    return requests.get(url, auth=HTTPDigestAuth(user, password))


def main():
    """
    Main entry to the program
    """

    # for nagios_entry in ALL_NAGIOS_INFO:
    for url, auth_data in NAGIOS_DATA.items():
        user, password, auth_type = auth_data("user"), auth_data("password"), 
            auth_data("auth_type")
        full_url = "{}/cgi-bin/status.cgi?host=all".format(url)
        response = get_url_response(full_url, user, password, auth_type)
        if response.status_code == 200:
            html = BeautifulSoup(response.text, "html.parser")
            for i, items in enumerate(html.select('td')):
                if i == 3:
                    hostsAll = items.text.split('n')
                    hosts_up = hostsAll(12)
                    hosts_down = hostsAll(13)
                    hosts_unreachable = hostsAll(14)
                    hosts_pending = hostsAll(15)
                    hosts_problems = hostsAll(24)
                    hosts_types = hostsAll(25)
                if i == 12:
                    serviceAll = items.text.split('n')
                    service_ok = serviceAll(13)
                    service_warning = serviceAll(14)
                    service_unknown = serviceAll(15)
                    service_critical = serviceAll(16)
                    service_problems = serviceAll(26)
                    service_types = serviceAll(27)
                # print(i, items.text) ## To get the index and text
            print_stats(
                user, url, hosts_up, hosts_down, hosts_unreachable,
                hosts_pending, hosts_problems, hosts_types, service_ok,
                service_warning, service_unknown, service_critical,
                service_problems, service_types)

    # print("Request returned:nn{}".format(html.text))
    # To get the full request


def print_stats(
        user, url, hosts_up, hosts_down, hosts_unreachable, hosts_pending,
        hosts_problems, hosts_types, service_ok, service_warning,
        service_unknown, service_critical, service_problems, service_types):
    print("""{}@{}:
                Hosts
    UptDowntUnreachabletPendingtProblemstTypes
    {}t{}t{}tt{}t{}tt{}
                Services
    OKtWarningtUnknowntCriticaltProblemstTypes
    {}t{}t{}t{}tt{}tt{}""".format(
        user, url, hosts_up, hosts_down, hosts_unreachable, hosts_pending,
        hosts_problems, hosts_types, service_ok, service_warning,
        service_unknown, service_critical, service_problems, service_types))

if __name__ == '__main__':
    main()

scraper.py source:

NAGIOS_DATA = {
    'http://192.168.0.5/nagios': {
        'user': 'nagiosadmin',
        'password': 'PasswordHere1',
        'auth_type': 'Basic'
    },
    'https://www.example.com/nagios': {
        'user': 'exampleuser',
        'password': 'P@ssw0rd2',
        'auth_type': 'Digest'
    },
}