Web Scraping with Worker Systems: A Guide

IronWorker: Taking Web Scraping to the Next Level

IronWorker is a scalable, serverless worker system that offers a robust platform for web scraping. By combining IronWorker with your scraping strategies, you can harness the full power of concurrent processing, fault tolerance, load balancing, and scalability.

How to Set up IronWorker for Web Scraping?

Implementing IronWorker for your web scraping project is straightforward. Here's a step-by-step guide using the weather forecasting use case:

1. Set Up Your IronWorker Account: Sign up here to get started. Then install and configure the Iron command line tool (IronCLI) following this guide.
2. Create Your Worker: Write the code for your worker in the language of your choice. Here's a Python snippet that scrapes a test website (example.com). In this example we’re scrapping the “h1” header:

   from bs4 import BeautifulSoup
   import requests
   
   def fetch_header(url):
       response = requests.get(url)
       soup = BeautifulSoup(response.text, 'html.parser')
       header = soup.find('h1').text
       return header
   
   url = "<http://example.com>"
   header = fetch_header(url)
   print(header)
   

3. Install dependencies. Create the requirements.txt file with the following content:

   bs4
   requests
   

   Run this command to install dependencies into “packages” directory:

   docker run --rm -v "$PWD":/worker -w /worker iron/python:3.6-dev pip3 install -t packages -r requirements.txt
   

4. Zip and Upload Your Worker. Run this command:

   zip -r web_scrapper.zip .
   iron worker upload --name web_scrapper --zip web_scrapper.zip iron/python:3.6 python web_scrapper.py
   

5. Schedule or Run Your Worker: Set a schedule for your worker to run on a regular basis or manually trigger it as per your requirements:

   iron worker queue web_scrapper